CN115578651A - Intelligent unmanned aerial vehicle inspection and dynamic inventory management system for distribution network line - Google Patents
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Abstract
The invention relates to the field of power distribution network line inspection, and discloses a distribution network line unmanned aerial vehicle intelligent inspection and dynamic inventory management system.
Description
Technical Field
The invention relates to the field of power distribution network line inspection, in particular to an unmanned aerial vehicle intelligent inspection and dynamic inventory management system for a power distribution network line.
Background
Because the network distribution line is routed in the severe and severe areas with severe natural conditions such as unmanned areas and mountain areas with the proportion of more than 20%, the transmission line equipment runs in complicated and changeable open environments for years, and after the conducting wires, the lightning conductors, the insulators and the hardware fittings of the distribution line run for a long time, the conditions of strand breakage, corrosion, overheating, flashover and the like can happen, and the defects and hidden dangers of safe operation of the line are seriously influenced. The existing distribution network line adopts intelligent inspection by an unmanned aerial vehicle, and identifies the state of each part on the distribution network line by identifying the image returned by the unmanned aerial vehicle. However, the existing inspection system usually detects along a preset path, failure easily-occurring points cannot be distinguished, and the stock of various parts is fixedly arranged, so that the parts are not allocated timely or the stock is excessive.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an intelligent inspection and dynamic inventory management system for an unmanned aerial vehicle on a distribution network line, which is used for overcoming the defects in the prior art and has the characteristics of convenience in determining key inspection areas and flexibly allocating the inventory quantity of parts.
In order to achieve the purpose, the invention provides the following technical scheme:
join in marriage net twine way unmanned aerial vehicle intelligence and patrol and dynamic inventory management system, include
The geography acquisition module is preset with topography conditions comprising topography height, topography fall and geomorphology conditions, acquires and identifies detection image information returned by the unmanned aerial vehicle and outputs the vegetation conditions comprising vegetation coverage data, shielding data and vegetation growth, the vegetation coverage data reflects the covering type and area of plants in the detection image, the shielding data reflects the shielding degree of distribution network lines, and the vegetation growth reflects the luxuriant degree of plant growth,
a weather acquisition module that acquires weather information defined as weather data,
a fault judgment module for acquiring the detection image information and outputting inspection results including normal operation data and abnormal data,
a maintenance recording module, wherein a maintenance database is preset in the maintenance recording module, the abnormal data is recorded in the maintenance database and historical fault data is generated, each historical fault data comprises fault time, fault position, fault parts and the number of replacement parts,
a statistical module configured with statistical strategies to calculate a predicted failure probability for each region, the predicted failure probability being related to the terrain conditions, vegetation conditions, meteorological data, historical failure data,
a planning module, which makes an inspection plan in a preset planning period according to the predicted fault probability, wherein the inspection plan comprises an inspection route and a key inspection area,
the inventory configuration module is divided into maintenance ranges according to the positions of the power supply stations, each maintenance range covers the inspection route, the inventory configuration module is used for configuring the inventory quantity according to the predicted failure probability of each part in each maintenance range, and the inventory is allocated among a plurality of maintenance ranges.
In the present invention, it is preferable that the geographic acquisition module is configured with a first processing policy, specifically,
the method comprises the following steps: a number of straight line features are obtained within the detected image information to define a predicted straight line,
step two: judging whether two parallel prediction straight lines exist or not, if so, defining the two prediction straight lines as a reference group,
step three: selecting a group with the minimum actual distance from the reference group as a prediction group, wherein the actual distance represents the distance between two prediction straight lines in the reference group, two parallel straight lines in the prediction group are defined as line side lines, the length of the line side lines is measured to define the exposure length,
step four: and extending each line edge to intersect with the edge of the detected image information to obtain an extended line segment, obtaining the length of the extended line segment to define as a predicted length, and calculating the occlusion data, wherein the occlusion data is expressed by the ratio of the difference between the predicted length and the exposure length to the predicted length.
In the present invention, preferably, the geographic acquisition module is configured with a plant database configured with texture data and corresponding vegetation information, the geographic acquisition module is configured with a second processing strategy, specifically,
the method comprises the following steps: performing binarization processing on the detection image information to divide the detection image information into a plurality of detection areas;
step two: acquiring color features and texture features of each detection area based on the RGB image of the detection image information, searching corresponding vegetation information by taking the texture features as indexes, and calculating the ratio of the size of the detection area corresponding to the vegetation information to the size of the detection image information area, namely the plant coverage data;
step three: dividing the detection region based on edge detection to obtain each blade region, and calculating an area mean value of each blade region, wherein the area mean value reflects the average size of each blade in the detection image information;
and step four, acquiring the topography situation as topography data, acquiring the flight height of the unmanned aerial vehicle to define as height data, and combining the area mean value to acquire the vegetation growth.
In the invention, preferably, a plurality of disaster-prone points can be extracted in the maintenance range of each power supply station according to the topography and vegetation conditions, the disaster-prone points specifically include lightning strike-prone points, impact-prone points, flood-prone points, landslide-prone points and plant conflict points, and the key routing inspection area includes the disaster-prone points.
In the present invention, preferably, the inventory configuration module calculates the predicted inventory amount according to the predicted failure probability, the inventory configuration module presets a minimum inventory amount, if the minimum value of the predicted inventory amount is smaller than the minimum inventory amount, the minimum inventory amount is used as an inventory lower limit threshold, otherwise, the minimum predicted inventory amount is used as an inventory lower limit threshold, and the maximum value of the predicted inventory amount is used as an inventory upper limit threshold.
In the present invention, preferably, the actual inventory is compared with the inventory lower limit threshold and the inventory upper limit threshold, the actual inventory reflects the actual quantity of each part, if the actual inventory is greater than the inventory upper limit threshold, the corresponding part is allocated to another maintenance area, and if the actual inventory is less than the inventory lower limit threshold, the corresponding part is allocated from another area.
In the present invention, preferably, the inventory configuration module is configured with an inventory scheduling policy, selects a plurality of scheduling policies such that the actual inventory of each maintenance area conforms to the inventory upper limit threshold and the inventory lower limit threshold, and the scheduling policies include the number of scheduling parts and scheduling routes, calculates and selects the scheduling policy with the minimum length of each scheduling route, and makes the scheduling policy.
In the present invention, it is preferable that the value of the predicted stock quantity is set to be a smallest positive integer not smaller than the calculated value.
In the present invention, preferably, the statistical module is configured with a time correction unit, the time correction unit corrects the weight parameter of the historical fault data according to a time span from the last maintenance, and the larger the time span is, the higher the weight parameter of the historical fault data is.
In the present invention, preferably, the weather information is queried by a weather bureau.
The invention has the beneficial effects that:
according to the method, the geography condition and the vegetation condition are obtained through geography, the weather information is obtained through the weather obtaining module and is combined with historical fault data, the statistical module is used for calculating the predicted fault probability of each area to obtain a key routing inspection area and the configured stock quantity, disasters possibly occurring in the area are predicted and marked through weather, actual geography conditions and the vegetation condition, so that the key routing inspection area can be mainly judged when the unmanned aerial vehicle is in routing inspection and fault judgment, the stock quantity of each part is predicted, the stock quantity is scheduled in advance, the stock utilization rate is improved, and the condition that the needed parts are insufficient when the fault occurs is reduced.
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FIG. 1 is a system architecture diagram of the present invention.
Reference numerals:
1. a geography acquisition module; 2. a weather acquisition module; 3. a fault judgment module; 4. a maintenance recording module; 5. a statistical module; 6. a planning module; 7. an inventory configuration module.
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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. 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 herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, the present embodiment provides a distribution network line unmanned aerial vehicle intelligent inspection and dynamic inventory management system, which includes a geography acquisition module 1, a weather acquisition module 2, a fault judgment module 3, a maintenance record module 4, a statistics module 5, a planning module 6 and an inventory configuration module 7. The disasters which may appear in the area are predicted and marked by utilizing weather, actual terrain conditions and vegetation conditions, so that the unmanned aerial vehicle can mainly judge the key patrol area when patrolling and judging faults, prejudge the stock quantity of each part, schedule the stock quantity in advance, improve the stock utilization rate and reduce the occurrence of the condition that required parts are insufficient when faults occur.
The geography acquisition module 1 presets the relief condition, and the relief condition includes relief height, relief fall and the landform condition, and the geography acquisition module 1 acquires the detection image information of discernment unmanned aerial vehicle passback and outputs the vegetation condition, and the vegetation condition includes vegetation cover data, shelters from data and vegetation growing trend, and vegetation cover data reflects plant cover kind and area in the detection image, shelters from the degree of sheltering from that the data reflection joins in marriage net twine way, and vegetation growing trend reflects vegetation growing's luxuriant degree. The geographical acquisition module 1 is configured with a first processing strategy, and the first processing strategy specifically includes the following steps: acquiring a plurality of straight line characteristics in the detected image information to define the straight line characteristics as a predicted straight line; step two: judging whether two parallel prediction straight lines exist or not, and if so, defining the two prediction straight lines as a reference group; step three: selecting a group with the minimum actual distance from the reference group as a prediction group, wherein the actual distance represents the distance between two prediction straight lines in the reference group, two parallel straight lines in the prediction group are defined as line side lines, and the length of the line side lines is measured to be defined as exposure length; step four: extending each line edge to intersect with the edge of the detected image information to obtain an extended line segment, obtaining the length of the extended line segment to define as a predicted length, and calculating occlusion data, the occlusion data being represented by the ratio of the difference between the predicted length and the exposure length to the predicted length. The shielding data can reflect the influence of the vegetation height on the distribution network line, for example, when the shielding degree of the vegetation is high, the vegetation topples, the branches are broken and the like, the distribution network line is damaged, and the influence degree of weather such as rainstorm and storm is larger.
The geographical acquisition module 1 is configured with a plant database, the plant database is configured with texture data and corresponding vegetation information, the geographical acquisition module 1 is configured with a second processing strategy, and the second processing strategy specifically comprises the following steps: performing binarization processing on the detection image information to divide the detection image information into a plurality of detection areas; step two: acquiring color features and texture features of each detection area based on an RGB image of the detection image information, searching corresponding vegetation information by taking the texture features as indexes, and calculating the ratio of the size of the detection area corresponding to the vegetation information to the size of the detection image information area, namely plant coverage data; step three: dividing the detection area based on edge detection to obtain each blade area, and calculating the area mean value of each blade area, wherein the area mean value reflects the average size of each blade in the detection image information; and step four, acquiring the terrain situation as terrain data, acquiring the flight height of the unmanned aerial vehicle to define as height data, and combining the area mean value to acquire vegetation growth. The maintenance scope of each power supply station can be according to relief situation and vegetation condition extraction a plurality of calamity easily-sent points, and calamity easily-sent points specifically include thunderbolt easily-sent points, and the easy-sent points of impact, flood easily-sent points, landslide easily-sent points and plant conflict point, and the key region of patrolling and examining includes calamity easily-sent points. The vegetation cover data can be used for prejudging the type of natural disasters, and the method is more intuitive and accurate when the type of the accident easy to occur is determined and the inventory quantity of accessories is determined.
The weather obtaining module 2 obtains weather information which is defined as weather data and is inquired by a weather bureau.
The fault judgment module 3 acquires the detection image information and outputs a polling result, wherein the polling result comprises normal operation data and abnormal data. The failure determination module 3 specifically utilizes the existing determination logic to operate, and therefore is not described in detail in this application. The picture information real-time transmission system of patrolling and examining that unmanned aerial vehicle shot, the risk hidden danger that the system of patrolling and examining probably exists through AI image recognition intelligent analysis appears the risk hidden danger very first time and reminds at APP bullet window, and accessible SMS, little letter applet etc. inform relevant responsible person simultaneously.
The maintenance recording module 4 is preset with a maintenance database, abnormal data are recorded in the maintenance database and historical fault data are generated, each historical fault data comprises fault time, fault position, fault parts and replacement part number, and the historical fault data are updated according to abnormal signals and after manual confirmation.
The statistical module 5 is configured with statistical strategies to calculate the predicted failure probabilities for the regions, which are related to the terrain conditions, vegetation conditions, meteorological data, and historical failure data. The statistical module 5 is configured with a time correction unit, the time correction unit corrects the weight parameter of the historical fault data according to the time span from the last maintenance, and the larger the time span is, the higher the weight parameter of the historical fault data is.
The planning module 6 formulates an inspection plan in a preset planning period according to the predicted fault probability, the inspection plan comprises an inspection route and a key inspection area, the key inspection area is related to the terrain condition and the vegetation condition, and besides the disaster easily-caused point, the part point position which easily generates loss also belongs to the key inspection area. The power supply station patrols and examines the APP through unmanned aerial vehicle intelligence and carries out daily unmanned aerial vehicle and patrols and examines work, before unmanned aerial vehicle takes off and patrols and examines the process dynamic adjustment and patrol and examine the key, patrol and examine key not have the synchronization of time to unmanned aerial vehicle of adjustment.
The inventory configuration module 7 divides maintenance ranges according to the positions of the power supply stations, each maintenance range covers the inspection route, the inventory configuration module 7 configures inventory quantity according to the predicted failure probability of each part in each maintenance range, and the inventory is allocated among a plurality of maintenance ranges. The inventory configuration module 7 calculates the predicted inventory according to the predicted failure probability, the inventory configuration module 7 presets a minimum inventory quantity, if the minimum value of the predicted inventory quantity is smaller than the minimum inventory quantity, the minimum inventory quantity is used as an inventory lower limit threshold, otherwise, the minimum predicted inventory quantity is used as an inventory lower limit threshold, and the maximum value of the predicted inventory quantity is used as an inventory upper limit threshold. And comparing the actual inventory with an inventory lower limit threshold and an inventory upper limit threshold, wherein the actual inventory reflects the actual quantity of each part, if the actual inventory is greater than the inventory upper limit threshold, the corresponding part is allocated to other maintenance areas, and if the actual inventory is less than the inventory lower limit threshold, the corresponding part is allocated from other areas. The inventory configuration module 7 is configured with an inventory scheduling strategy, selects a plurality of allocation strategies to enable the actual inventory of each maintenance area to accord with an inventory upper limit threshold and an inventory lower limit threshold, the allocation strategies comprise the number of scheduling parts and scheduling routes, calculates and selects the allocation strategy with the minimum length of each scheduling route and uses the allocation strategy as the inventory scheduling strategy. The value of the predicted stock quantity is set to be not less than the smallest positive integer of the calculated value.
The unmanned aerial vehicle intelligent inspection system dynamically adjusts the inventory upper limit threshold of the accessories of the power supply station according to big data analysis and by combining geographical position and climate characteristics, and the unmanned aerial vehicle intelligent inspection system performs intelligent analysis of big data and real-time adjustment of the inventory lower limit threshold of the accessories according to inspection logs and maintenance records, statistical information of all devices of distribution network lines governed by the power supply station, fault occurrence rate, climate, geographical position and the like. And accessories with the inventory lower than the inventory lower limit threshold are allocated in time to be warehoused, so that sufficient accessories are ensured to be maintained once the distribution network line fails, and the accessories with the inventory higher than the inventory upper limit threshold are timely stocked and transferred to the central warehouse, so that the most economical inventory allocation is ensured.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to those skilled in the art without departing from the principles of the present invention should also be considered as within the scope of the present invention.
Claims (10)
1. The utility model provides a join in marriage net twine way unmanned aerial vehicle intelligence and patrol and dynamic inventory management system which characterized in that: the unmanned aerial vehicle vegetation detection system comprises a geography acquisition module (1), wherein the geography acquisition module (1) is preset with geography conditions comprising geography height, geography fall and landform conditions, the geography acquisition module (1) acquires detection image information for identifying return of the unmanned aerial vehicle and outputs vegetation conditions comprising vegetation coverage data, shielding data and vegetation growth conditions, the vegetation coverage data reflect the type and area of plant coverage in the detection image, the shielding data reflect the shielding degree of a distribution network line, and the vegetation growth conditions reflect the luxuriant degree of plant growth,
a weather acquisition module (2), wherein the weather acquisition module (2) acquires weather information which is defined as weather data,
a fault judgment module (3), wherein the fault judgment module (3) acquires the detection image information and outputs a polling result, the polling result comprises normal operation data and abnormal data,
a maintenance recording module (4), wherein a maintenance database is preset in the maintenance recording module (4), the abnormal data is recorded in the maintenance database and historical fault data is generated, each historical fault data comprises fault time, fault position, fault parts and the number of replacement parts,
a statistical module (5), the statistical module (5) configured with a statistical strategy to calculate a predicted failure probability for each region, the predicted failure probability being related to the terrain conditions, vegetation conditions, meteorological data, historical failure data,
the planning module (6) makes an inspection plan in a preset planning period according to the predicted fault probability, the inspection plan comprises an inspection route and a key inspection area,
the inventory configuration module (7) is used for dividing maintenance ranges according to the positions of the power supply stations, each maintenance range covers the routing inspection route, the inventory configuration module (7) is used for configuring the inventory quantity according to the predicted fault probability of each part in each maintenance range, and the inventory is allocated among a plurality of maintenance ranges.
2. The intelligent routing inspection and dynamic inventory management system for the unmanned aerial vehicle with the distribution network lines, according to claim 1, is characterized in that: the geographic acquisition module (1) is configured with a first processing strategy, which is specifically,
the method comprises the following steps: a number of straight line features are acquired within the detected image information to define as predicted straight lines,
step two: judging whether two parallel prediction straight lines exist or not, if so, defining the two prediction straight lines as a reference group,
step three: selecting a group with the minimum actual distance from the reference group as a prediction group, wherein the actual distance represents the distance between two prediction straight lines in the reference group, two parallel straight lines in the prediction group are defined as line side lines, the length of the line side lines is measured to be defined as exposure length,
step four: and extending each line edge to intersect with the edge of the detected image information to obtain an extended line segment, obtaining the length of the extended line segment to define as a predicted length, and calculating the occlusion data, wherein the occlusion data is expressed by the ratio of the difference between the predicted length and the exposure length to the predicted length.
3. The intelligent routing inspection and dynamic inventory management system for the unmanned aerial vehicle with the distribution network lines, according to claim 1, is characterized in that: the geographical acquisition module (1) is provided with a plant database, the plant database is provided with texture data and corresponding vegetation information, the geographical acquisition module (1) is provided with a second processing strategy, the second processing strategy is specifically,
the method comprises the following steps: performing binarization processing on the detection image information to divide the detection image information into a plurality of detection areas;
step two: acquiring color features and texture features of each detection area based on the RGB image of the detection image information, searching the corresponding vegetation information by taking the texture features as indexes, and calculating the ratio of the size of the detection area corresponding to the vegetation information to the size of the detection image information area, namely the plant coverage data;
step three: dividing the detection region based on edge detection to obtain each leaf region, and calculating an area mean value of each leaf region, wherein the area mean value reflects the average size of each leaf in the detection image information;
and step four, acquiring the terrain situation as terrain data, acquiring the flight height of the unmanned aerial vehicle to define as height data, and combining the area mean value to acquire the vegetation growth.
4. The intelligent routing inspection and dynamic inventory management system for the unmanned aerial vehicle with the distribution network lines, according to claim 1, is characterized in that: the maintenance scope at each power supply station can be based on a plurality of calamity easily-occurring point positions are extracted to relief situation and vegetation condition, calamity easily-occurring point position specifically includes thunderbolt easily-occurring point, strikes easily-occurring point, and flood easily-occurring point, landslide easily-occurring point and plant conflict point, the key region of patrolling and examining includes calamity easily-occurring point.
5. The intelligent routing inspection and dynamic inventory management system for the unmanned aerial vehicle with the distribution network lines, according to claim 1, is characterized in that: the inventory configuration module (7) calculates a predicted inventory according to the predicted failure probability, the inventory configuration module (7) presets a minimum inventory quantity, if the minimum value of the predicted inventory quantity is smaller than the minimum inventory quantity, the minimum inventory quantity is used as an inventory lower limit threshold, otherwise, the minimum predicted inventory quantity is used as an inventory lower limit threshold, and the maximum value of the predicted inventory quantity is used as an inventory upper limit threshold.
6. The system of claim 5, wherein the system comprises: and comparing the actual inventory with the inventory lower limit threshold and the inventory upper limit threshold, wherein the actual inventory reflects the actual quantity of each part, if the actual inventory is greater than the inventory upper limit threshold, the corresponding part is allocated to other maintenance areas, and if the actual inventory is less than the inventory lower limit threshold, the corresponding part is allocated from other areas.
7. The system of claim 5, wherein the system comprises: the inventory configuration module (7) is configured with an inventory scheduling strategy, a plurality of allocation strategies are selected to enable the actual inventory of each maintenance area to accord with the inventory upper limit threshold and the inventory lower limit threshold, the allocation strategies comprise the number of scheduling parts and scheduling routes, and the allocation strategy with the minimum length of each scheduling route is calculated and selected and is used as the inventory scheduling strategy.
8. The system of claim 5, wherein the system comprises: the value of the predicted stock quantity is set to be not less than the smallest positive integer of the calculated value.
9. The intelligent routing inspection and dynamic inventory management system for the unmanned aerial vehicle with the distribution network lines, according to claim 1, is characterized in that: the statistical module (5) is provided with a time correction unit, the time correction unit corrects the weight parameters of the historical fault data according to the time span from the last maintenance, and the larger the time span is, the higher the weight parameters of the historical fault data are.
10. The intelligent routing inspection and dynamic inventory management system for the unmanned aerial vehicle with the distribution network lines, according to claim 1, is characterized in that: and the meteorological information is inquired by a meteorological bureau.
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