CN113359829B - Unmanned aerial vehicle power plant intelligent inspection method based on big data - Google Patents
Unmanned aerial vehicle power plant intelligent inspection method based on big data Download PDFInfo
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- CN113359829B CN113359829B CN202110650069.9A CN202110650069A CN113359829B CN 113359829 B CN113359829 B CN 113359829B CN 202110650069 A CN202110650069 A CN 202110650069A CN 113359829 B CN113359829 B CN 113359829B
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- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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Abstract
The invention discloses an unmanned aerial vehicle power plant intelligent inspection method based on big data, which belongs to the field of artificial intelligence and is used for solving the problems of how to quickly locate an area needing inspection in a power plant area through an AI auxiliary technology, and the problem of carrying out inspection with a target property and increasing the inspection efficiency.
Description
Technical Field
The invention belongs to the field of artificial intelligence, relates to an intelligent inspection technology of a power plant, and particularly relates to an unmanned aerial vehicle power plant intelligent inspection method based on big data.
Background
The intelligent patrol of a power plant is usually based on a patrol inspector and an unmanned aerial vehicle, and the unmanned aerial vehicle is a general name of an unmanned aerial vehicle which is controlled by a wireless signal or a set program. Along with the rapid development of electron and material technology, the quality is light, efficient unmanned aerial vehicle equipment constantly emerges, because unmanned aerial vehicle can carry out comparatively high-efficient convenient work under high altitude long voyage, consequently it is military, survey and drawing, shoot, monitor etc. and obtained extensive application in the field, unmanned aerial vehicle's characteristic and its function make it have fine agreeable nature with power plant planning and patrol and examine work, power plant planning patrols and examines the accurate earth's surface structure in-process needs initiative planning region, geographic data such as power plant facility position, this can utilize to carry on remote sensing monitoring equipment such as camera on unmanned aerial vehicle and acquire, later obtain usable geographic data after mode analysis processes such as image recognition. With the improvement of corresponding data processing and equipment, the main problem that actually influences the application of the unmanned aerial vehicle in the fields of power inspection and the like is the continuous working capacity of the unmanned aerial vehicle, and the unmanned aerial vehicle is different from a large high-altitude detection robot in size and strong in cruising ability, and in the power inspection process, the unmanned aerial vehicle generally comprises a power transmission line from a power transmission network infrastructure. In order to ensure the picture quality, the flying height is relatively low, and better controllability is needed to avoid vegetation and buildings along the transmission line, so a small or medium-sized unmanned aerial vehicle with smaller volume and more flexible control is generally adopted, but the load capacity of the small or medium-sized unmanned aerial vehicle is limited, after remote sensing detection equipment meeting the operation requirements is additionally arranged, the space available for equipping batteries in the unmanned aerial vehicle is insufficient, and the continuous working capacity of the unmanned aerial vehicle is limited.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle power plant intelligent inspection method based on big data, which is used for solving the problems of rapidly positioning an area needing inspection in a power plant area through an AI auxiliary technology, performing targeted inspection, reducing the inspection intensity of an inspector and an inspection machine and increasing the inspection efficiency.
The purpose of the invention can be realized by the following technical scheme:
the utility model provides an unmanned aerial vehicle power plant intelligence inspection method based on big data, includes and acquires a plurality of regions of patrolling and examining, discerns a plurality of regional interior equipment of patrolling and examining and dispatch and patrol and examine unmanned aerial vehicle and patrol and examine, still includes:
establishing an unmanned aerial vehicle station, wherein a plurality of inspection unmanned aerial vehicles are parked in the unmanned aerial vehicle station, and any inspection unmanned aerial vehicle is controlled by a control end;
establishing inspection height layer data of inspection equipment in a plurality of inspection areas;
associating the data of the inspection height layer to any inspection unmanned aerial vehicle;
obtaining a daily inspection strategy through big data analysis, and associating the daily inspection strategy into any inspection unmanned aerial vehicle;
and optionally, a shunting selection layer is arranged in each inspection unmanned aerial vehicle and is used for obtaining an inspection path by combining inspection height layer data, an inspection strategy every day and the data of the unmanned aerial vehicle.
Further, before obtaining the daily patrol strategy through big data analysis, the method further comprises:
collecting fault information of inspection equipment in a plurality of inspection areas, wherein the fault information comprises weather information, temperature information, fault image information and equipment height layer information which take fault occurrence time as a node;
and extracting unique information numbers of all equipment in the fault information, summarizing the fault information with the same unique information number, marking data with the same weather information and temperature information in the same fault occurrence time as the same information, and summarizing the weather information, the temperature information and the fault occurrence time corresponding to the same information when the same information is greater than a preset threshold value to obtain a fault summary table.
Further, collecting the fault information of the inspection equipment in a plurality of inspection areas comprises:
and collecting the fault information of the inspection equipment in the only inspection area or/and collecting the fault information of the inspection equipment in any inspection area.
Further, the obtaining of the daily patrol strategy through big data analysis includes:
acquiring weather information and temperature information corresponding to the installation position of inspection equipment in an inspection area by taking hours as a unit, and correspondingly associating the weather information and the temperature information acquired by taking hours as a unit to the inspection equipment to generate an external inspection condition table;
acquiring a unique inspection number of inspection equipment, wherein the unique inspection number corresponds to a unique information number of the inspection equipment;
acquiring fault information in a fault summary table corresponding to the unique information number with the same unique inspection number;
comparing the external polling condition table with the fault information,
when the weather information and the temperature information corresponding to the same time in the external inspection condition table are the same as the weather information and the temperature information corresponding to any one of the nodes in the fault information at the same time, marking the same time corresponding to the weather information and the temperature information in the external inspection condition table as inspection time;
when the weather information or the temperature information corresponding to the same time in the external polling condition table is the same as the weather information or the temperature information corresponding to any one of the nodes in the fault information at the same time, marking the same time corresponding to the weather information or the temperature information in the external polling condition table as auxiliary polling time;
and summarizing the inspection time and the auxiliary inspection time to obtain a daily inspection strategy corresponding to the inspection equipment.
Further, establishing patrol inspection height layer data of patrol inspection equipment in a plurality of patrol inspection areas comprises:
collecting high-definition satellite pictures of a plurality of routing inspection areas, wherein the resolution of the high-definition satellite pictures comprises 2 x 2 or 8 x 8;
presetting a high-definition satellite picture corresponding to the inspection equipment;
inputting the high-definition satellite picture of the inspection area, the high-definition satellite picture corresponding to the inspection equipment and the installation height of the inspection equipment into a deep neural network for learning to obtain an equipment inspection model;
the high-definition satellite picture corresponding to the inspection equipment comprises an outline image and a shadow image of the inspection equipment;
inputting the high-definition satellite pictures of the polling areas updated every day into a device polling model to obtain the height of polling equipment;
a plurality of height layering intervals are preset, and when the height of the inspection equipment meets the height layering intervals, data of the inspection height layer are generated.
Further, the shunt selection layer includes:
the device comprises an electric energy consumption calculation module, a residual electric energy calculation module and a charging electric energy calculation module;
the electric energy consumption calculation module is used for calculating the electric energy which is required to be consumed when the inspection unmanned aerial vehicle arrives at the inspection equipment within the inspection time;
the residual electric energy calculation module is used for acquiring residual electric energy of the unmanned aerial vehicle;
the charging electric energy calculation module is used for acquiring charging electric energy of the unmanned aerial vehicle which is patrolled and examined before the patrolling and examining time.
Further, the method also comprises the following steps:
the judging module is used for judging whether the remaining electric quantity meets the polling requirement of the polling equipment corresponding to the auxiliary polling time after the polling unmanned aerial vehicle reaches the polling equipment within the polling time and the polling task is finished;
in particular, the time interval between the auxiliary inspection time and the inspection time is obtained,
if the time interval is larger than the time threshold, the inspection unmanned aerial vehicle returns to the unmanned aerial vehicle station;
the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection-assisting equipment corresponding to the inspection time is obtained through the electric energy consumption calculation module;
obtaining the residual electric energy of the inspection unmanned aerial vehicle through a residual electric energy calculation module;
acquiring an arrival time node when the inspection unmanned aerial vehicle arrives at the unmanned aerial vehicle station, and calculating charging electric energy between the arrival time node and the auxiliary inspection time through the charging electric energy calculation module;
if the sum of the residual electric energy and the charging electric energy of the inspection unmanned aerial vehicle is larger than the electric energy required when the inspection unmanned aerial vehicle flies to inspection-assisting equipment corresponding to the inspection time, performing inspection;
if the sum of the residual electric energy and the charging electric energy of the inspection unmanned aerial vehicle is less than the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection-assisting equipment corresponding to the inspection time, performing inspection;
if the time interval is smaller than the time threshold, the inspection unmanned aerial vehicle obtains the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection equipment corresponding to the inspection assisting time through the electric energy consumption calculation module;
if the residual electric energy of the inspection unmanned aerial vehicle is larger than the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection-assisting equipment corresponding to the inspection time, performing inspection;
if the remaining electric energy of the inspection unmanned aerial vehicle is less than the electric energy required when the inspection unmanned aerial vehicle flies to the inspection equipment corresponding to the inspection assisting time, the inspection is not carried out, and the unmanned aerial vehicle station is returned.
Further, the inspection comprises the step of shooting an inspection photo of the inspection equipment, and the height layer data of the inspection equipment is obtained through the equipment inspection model.
And further, comparing the fault image information with the inspection photo to obtain fault information.
Compared with the prior art, the invention has the beneficial effects that:
by setting the data of the inspection height layer, different inspection tasks can be selected according to different heights when the unmanned aerial vehicle is inspected, and the inspection accuracy and the safety of the unmanned aerial vehicle are ensured;
the daily inspection strategy is obtained through big data analysis, so that inspection is dynamic inspection, and meanwhile, through big data analysis, inspection analysis can be accurately performed, inspection effect is guaranteed, and inspection efficiency is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The embodiments of the present invention will be described below with reference to the drawings.
The terms "first," "second," and the like in the description and claims of the present application and in the foregoing drawings are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Traditionally, in the power plant inspection process, manual daily inspection is mostly adopted, but the inspection in place condition and the recorded data cannot be used for mastering the authenticity of the power plant inspection; a large amount of routing inspection information is inconvenient to store, easy to lose and labor-consuming; the information query is inconvenient, and the data cannot be effectively utilized to make auxiliary decisions on defect analysis, type selection and the like of the equipment. Few SCADA remote monitoring is adopted, but the working conditions of the existing monitoring terminal are harsh in SCADA remote monitoring; the communication system is complicated and changeable, and reliable transmission is not guaranteed; low coverage rate, high cost and the like. Because a power plant has the particularity that equipment for inspection is placed densely, safety factor requirements are high, and the like, and a general inspection system is poor in adaptation to the uniqueness, a technology for inspection by using an unmanned aerial vehicle is provided, but how to enable the unmanned aerial vehicle to conduct inspection in a wider range is provided.
To the technical problem, the application provides an unmanned aerial vehicle power plant intelligent inspection method based on big data:
including obtaining a plurality of regions of patrolling and examining, discerning a plurality of regional interior equipment of patrolling and examining of patrolling and dispatching and patrol and examine unmanned aerial vehicle and patrol and examine, still include:
establishing an unmanned aerial vehicle station, wherein a plurality of inspection unmanned aerial vehicles are parked in the unmanned aerial vehicle station, and any inspection unmanned aerial vehicle is controlled by a control end;
establishing inspection height layer data of inspection equipment in a plurality of inspection areas;
associating the data of the inspection height layer to any inspection unmanned aerial vehicle;
obtaining a daily inspection strategy through big data analysis, and associating the daily inspection strategy into any inspection unmanned aerial vehicle;
a shunting selection layer is arranged in any routing inspection unmanned aerial vehicle and used for combining data of a routing inspection height layer, a daily routing inspection strategy and data of the unmanned aerial vehicle to obtain a routing inspection path.
In a further embodiment, the power plant is comprised of power generation equipment and ancillary facilities such as boiler equipment, steam turbine equipment, cables, distribution transformers, switchgear, current limiting appliances, and the like.
The embodiment of the application can be applied to electronic equipment such as a personal computer, a smart phone (such as an Android mobile phone and an iOS mobile phone), a tablet computer, a palm computer or wearable equipment, and can also be applied to multimedia playing application (such as a QQ music player) or multimedia editing application (such as Au) operated by the electronic equipment.
Based on the above description, an embodiment of the present invention provides an unmanned aerial vehicle power plant intelligent inspection method based on big data as shown in fig. 1, where before obtaining a daily inspection policy through big data analysis, the method further includes:
collecting fault information of inspection equipment in a plurality of inspection areas, wherein the fault information comprises weather information, temperature information, fault image information and equipment height layer information which take fault occurrence time as a node;
when concrete implementation, collect a plurality of regional interior fault information of patrolling and examining equipment of patrolling and examining and include:
and collecting the fault information of the inspection equipment in the only inspection area or/and collecting the fault information of the inspection equipment in any inspection area.
The unique inspection area refers to the same inspection area and fault information of inspection equipment corresponding to different time periods, and any inspection area refers to different inspection areas and fault information of inspection equipment corresponding to the same or different time periods; the above can be selected according to the requirements of enterprises;
and extracting unique information numbers of all equipment in the fault information, summarizing the fault information with the same unique information number, marking data with the same weather information and temperature information in the same fault occurrence time as the same information, and summarizing the weather information, the temperature information and the fault occurrence time corresponding to the same information when the same information is greater than a preset threshold value to obtain a fault summary table.
By compiling the fault summary table, data support can be provided for big data analysis, the reliability of big data analysis is ensured, and meanwhile, fault analysis is provided for power plant inspection;
the method for establishing the data of the inspection height layers of the inspection equipment in the plurality of inspection areas comprises the following steps:
acquiring high-definition satellite pictures of a plurality of routing inspection areas, wherein the resolution of the high-definition satellite pictures comprises 2 x 2 or 8 x 8;
presetting a high-definition satellite picture corresponding to the inspection equipment;
inputting the high-definition satellite pictures of the inspection area, the high-definition satellite pictures corresponding to the inspection equipment and the installation height of the inspection equipment into a deep neural network for learning to obtain an equipment inspection model;
the high-definition satellite pictures corresponding to the inspection equipment comprise an outline image and a shadow image of the inspection equipment;
inputting the high-definition satellite pictures of the polling areas updated every day into a device polling model to obtain the height of polling equipment;
a plurality of height layering intervals are preset, and when the height of the inspection equipment meets the height layering intervals, data of the inspection height layer are generated.
When the unmanned aerial vehicle is implemented specifically, most of the inspection equipment is installed in the high air or at a position far away from the ground, the height of the inspection equipment cannot be inspected well by workers for inspection, the ground clearance of the inspection equipment can be quickly probed through a height sensor built in the unmanned aerial vehicle, and the maintenance requirement is determined by comparing the ground clearance with the initial installation position;
meanwhile, different routing inspection tasks can be selected according to the data of the routing inspection height layer when the unmanned aerial vehicle is in routing inspection, and the routing inspection accuracy and the safety of the unmanned aerial vehicle are ensured;
in specific implementation, the daily patrol strategy obtained through big data analysis comprises the following steps:
acquiring weather information and temperature information corresponding to the installation position of inspection equipment in an inspection area by taking hours as a unit, and correspondingly associating the weather information and the temperature information acquired by taking hours as a unit to the inspection equipment to generate an external inspection condition table;
acquiring a unique inspection number of inspection equipment, wherein the unique inspection number corresponds to a unique information number of the inspection equipment;
acquiring fault information in a fault summary table corresponding to a unique information number with the same unique inspection number;
comparing the external inspection condition table with the fault information,
when the weather information and the temperature information corresponding to the same time node in the fault information are the same as those corresponding to any same time node in the external polling condition table, marking the same time corresponding to the weather information and the temperature information in the external polling condition table as polling time;
when the weather information or the temperature information corresponding to the same time in the external patrol inspection condition table is the same as the weather information or the temperature information corresponding to any same time node in the fault information, marking the same time corresponding to the weather information or the temperature information in the external patrol inspection condition table as auxiliary patrol inspection time;
and summarizing the inspection time and the auxiliary inspection time to obtain a daily inspection strategy corresponding to the inspection equipment.
The strategy of patrolling and examining every day is obtained through big data analysis for patrol and examine for the developments and patrol and examine, through big data analysis, can be accurate patrol and examine the analysis, guarantee to patrol and examine the effect, promote the efficiency of patrolling and examining, avoid regularly patrolling and examining the equipment damage of patrolling and examining that leads to under the specific condition and not in time discover, cause economic loss.
In specific implementation, the shunt selection layer includes:
acquiring the residual electric energy of the unmanned aerial vehicle; the charging electric energy of the unmanned aerial vehicle is patrolled and examined before the patrol and examine time is obtained.
Calculating the electric energy consumed by the inspection unmanned aerial vehicle to reach the inspection equipment within the inspection time;
judging whether the remaining electric quantity meets the inspection of the inspection equipment corresponding to the auxiliary inspection time after the inspection unmanned aerial vehicle reaches the inspection equipment within the inspection time and the inspection task is finished;
in particular, the time interval between the auxiliary inspection time and the inspection time is obtained,
if the time interval is larger than the time threshold, the inspection unmanned aerial vehicle returns to the unmanned aerial vehicle station;
the electric energy required by the inspection equipment corresponding to the time from the flying of the inspection unmanned aerial vehicle to the auxiliary inspection is obtained through the electric energy consumption calculation module;
obtaining the residual electric energy of the inspection unmanned aerial vehicle through a residual electric energy calculation module;
acquiring an arrival time node when the inspection unmanned aerial vehicle arrives at an unmanned aerial vehicle station, and calculating charging electric energy between the arrival time node and auxiliary inspection time through a charging electric energy calculation module;
if the sum of the residual electric energy and the charging electric energy of the inspection unmanned aerial vehicle is larger than the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection-assisted equipment corresponding to the inspection time, performing inspection;
if the sum of the residual electric energy and the charging electric energy of the inspection unmanned aerial vehicle is smaller than the electric energy required by inspection equipment corresponding to the time from the flying of the inspection unmanned aerial vehicle to the inspection assisting time, performing inspection;
if the time interval is smaller than the time threshold, the inspection unmanned aerial vehicle obtains the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection equipment corresponding to the inspection assisting time through the electric energy consumption calculation module;
if the residual electric energy of the inspection unmanned aerial vehicle is larger than the electric energy required by the inspection equipment corresponding to the time from the flying of the inspection unmanned aerial vehicle to the inspection-assisting time, performing inspection;
if the remaining electric energy of the inspection unmanned aerial vehicle is less than the electric energy required when the inspection unmanned aerial vehicle flies to the inspection equipment corresponding to the inspection assisting time, the inspection is not carried out, and the unmanned aerial vehicle station is returned.
When the system is specifically implemented, the inspection comprises the steps of shooting an inspection photo of the inspection equipment, obtaining height layer data of the inspection equipment through an equipment inspection model, and comparing fault image information with the inspection photo to obtain fault information.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, indirect coupling or communication connection between devices or units, and may be in an electrical or other form.
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 units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several 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 above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (8)
1. The utility model provides an unmanned aerial vehicle power plant intelligence inspection method based on big data, includes that it patrols and examines the equipment and dispatch and patrol and examine unmanned aerial vehicle and patrol and examine to obtain a plurality of regions of patrolling and examining, discern in a plurality of regions of patrolling and examining, its characterized in that still includes:
establishing an unmanned aerial vehicle station, wherein a plurality of inspection unmanned aerial vehicles are parked in the unmanned aerial vehicle station, and any inspection unmanned aerial vehicle is controlled by a control end;
establishing inspection height layer data of inspection equipment in a plurality of inspection areas;
associating the data of the inspection height layer to any inspection unmanned aerial vehicle;
obtaining a daily inspection strategy through big data analysis, and associating the daily inspection strategy into any inspection unmanned aerial vehicle;
a shunting selection layer is arranged in any routing inspection unmanned aerial vehicle and is used for obtaining a routing inspection path by combining data of a routing inspection height layer, a daily routing inspection strategy and data of the unmanned aerial vehicle;
the daily inspection strategy obtained through big data analysis comprises the following steps:
acquiring weather information and temperature information corresponding to the installation position of the inspection equipment in the inspection area by taking hours as a unit, correspondingly associating the weather information and the temperature information acquired by taking hours as a unit to the inspection equipment, and generating an external inspection condition table;
acquiring a unique inspection number of inspection equipment, wherein the unique inspection number corresponds to a unique information number of the inspection equipment;
acquiring fault information in a fault summary table corresponding to the unique information number with the same unique inspection number;
comparing the external inspection condition table with the fault information,
when the weather information and the temperature information corresponding to the same time node in the fault information are the same as those corresponding to any one time node in the external polling condition table, marking the same time corresponding to the weather information and the temperature information in the external polling condition table as polling time;
when the weather information or the temperature information corresponding to the same time in the external inspection condition table is the same as the weather information or the temperature information corresponding to any one of the nodes in the fault information at the same time, marking the same time corresponding to the weather information or the temperature information in the external inspection condition table as auxiliary inspection time;
and summarizing the inspection time and the auxiliary inspection time to obtain a daily inspection strategy corresponding to the inspection equipment.
2. The intelligent unmanned aerial vehicle power plant inspection method according to claim 1, wherein the obtaining of the daily inspection strategy through big data analysis further comprises:
collecting fault information of inspection equipment in a plurality of inspection areas, wherein the fault information comprises weather information, temperature information, fault image information and equipment height layer information which take fault occurrence time as a node;
and extracting unique information numbers of all equipment in the fault information, summarizing the fault information with the same unique information number, marking data with the same weather information and temperature information in the same fault occurrence time as the same information, and summarizing the weather information, the temperature information and the fault occurrence time corresponding to the same information when the same information is greater than a preset threshold value to obtain a fault summary table.
3. The unmanned aerial vehicle power plant intelligent inspection method according to claim 2, wherein the collecting fault information of inspection equipment in a plurality of inspection areas comprises:
and collecting the fault information of the inspection equipment in the only inspection area or/and collecting the fault information of the inspection equipment in any inspection area.
4. The intelligent unmanned aerial vehicle power plant inspection method according to claim 3, wherein the establishing of inspection height layer data of inspection equipment in a plurality of inspection areas comprises:
acquiring high-definition satellite pictures of a plurality of routing inspection areas, wherein the resolution of the high-definition satellite pictures comprises 2 x 2 or 8 x 8;
presetting a high-definition satellite picture corresponding to the inspection equipment;
inputting the high-definition satellite picture of the inspection area, the high-definition satellite picture corresponding to the inspection equipment and the installation height of the inspection equipment into a deep neural network for learning to obtain an equipment inspection model;
the high-definition satellite picture corresponding to the inspection equipment comprises an outline image and a shadow image of the inspection equipment;
inputting the high-definition satellite pictures of the polling areas updated every day into a device polling model to obtain the height of polling devices;
a plurality of height layering intervals are preset, and when the height of the inspection equipment meets the height layering intervals, data of the inspection height layer are generated.
5. The unmanned aerial vehicle power plant intelligent inspection method based on big data according to claim 4, wherein the shunting selection layer comprises:
taking the residual electric energy of the unmanned aerial vehicle; acquiring charging electric energy for polling unmanned aerial vehicle before polling time
The electric energy that the unmanned aerial vehicle that patrols and examines arrived to patrol and examine equipment in the time of patrolling and examining is calculated needs to be consumed.
6. The unmanned aerial vehicle power plant intelligent inspection method based on big data according to claim 5, further comprising:
judging whether the remaining electric quantity meets the inspection of the inspection equipment corresponding to the auxiliary inspection time after the inspection unmanned aerial vehicle reaches the inspection equipment within the inspection time and the inspection task is finished;
specifically, the time interval between the auxiliary inspection time and the inspection time is obtained,
if the time interval is larger than the time threshold, the inspection unmanned aerial vehicle returns to the unmanned aerial vehicle station;
the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection-assisting equipment corresponding to the inspection time is obtained through the electric energy consumption calculation module;
obtaining the residual electric energy of the inspection unmanned aerial vehicle through a residual electric energy calculation module;
acquiring an arrival time node when the inspection unmanned aerial vehicle arrives at the unmanned aerial vehicle station, and calculating charging electric energy between the arrival time node and the auxiliary inspection time through the charging electric energy calculation module;
if the sum of the residual electric energy and the charging electric energy of the inspection unmanned aerial vehicle is larger than the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection-aided equipment corresponding to the inspection time, performing inspection;
if the sum of the residual electric energy and the charging electric energy of the inspection unmanned aerial vehicle is less than the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection-assisting equipment corresponding to the inspection time, performing inspection;
if the time interval is smaller than the time threshold, the inspection unmanned aerial vehicle obtains the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection equipment corresponding to the inspection assisting time through the electric energy consumption calculation module;
if the residual electric energy of the inspection unmanned aerial vehicle is larger than the electric energy required by the inspection unmanned aerial vehicle when the inspection unmanned aerial vehicle flies to the inspection-assisting equipment corresponding to the inspection time, performing inspection;
if the remaining electric energy of the inspection unmanned aerial vehicle is less than the electric energy required when the inspection unmanned aerial vehicle flies to the inspection equipment corresponding to the inspection assisting time, the inspection is not carried out, and the unmanned aerial vehicle station is returned.
7. The unmanned aerial vehicle power plant intelligent inspection method based on big data according to claim 6, wherein the inspection comprises taking inspection photos of inspection equipment, and height layer data of the inspection equipment is obtained through the equipment inspection model.
8. The unmanned aerial vehicle power plant intelligent inspection method based on big data according to claim 7, wherein the fault information is obtained by comparing the fault image information with the inspection photos.
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