CN116318365A - Space-time service big data platform with multiple elements - Google Patents
Space-time service big data platform with multiple elements Download PDFInfo
- Publication number
- CN116318365A CN116318365A CN202310320242.8A CN202310320242A CN116318365A CN 116318365 A CN116318365 A CN 116318365A CN 202310320242 A CN202310320242 A CN 202310320242A CN 116318365 A CN116318365 A CN 116318365A
- Authority
- CN
- China
- Prior art keywords
- data
- space
- big data
- time
- platform
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012545 processing Methods 0.000 claims abstract description 18
- 238000013523 data management Methods 0.000 claims abstract description 9
- 230000010354 integration Effects 0.000 claims abstract description 4
- 238000007689 inspection Methods 0.000 claims description 95
- 238000012544 monitoring process Methods 0.000 claims description 16
- 238000007405 data analysis Methods 0.000 claims description 14
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 claims description 11
- 230000001276 controlling effect Effects 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 238000007726 management method Methods 0.000 abstract description 10
- 230000005540 biological transmission Effects 0.000 abstract description 8
- 238000001514 detection method Methods 0.000 abstract description 2
- 230000006855 networking Effects 0.000 abstract 1
- 238000005406 washing Methods 0.000 abstract 1
- 230000006870 function Effects 0.000 description 20
- 238000000034 method Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 4
- QVFWZNCVPCJQOP-UHFFFAOYSA-N chloralodol Chemical compound CC(O)(C)CC(C)OC(O)C(Cl)(Cl)Cl QVFWZNCVPCJQOP-UHFFFAOYSA-N 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 230000004927 fusion Effects 0.000 description 4
- 239000003129 oil well Substances 0.000 description 4
- 230000002265 prevention Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000013507 mapping Methods 0.000 description 3
- 238000005538 encapsulation Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000009966 trimming Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/1851—Systems using a satellite or space-based relay
- H04B7/18513—Transmission in a satellite or space-based system
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/1851—Systems using a satellite or space-based relay
- H04B7/18519—Operations control, administration or maintenance
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/55—Push-based network services
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Astronomy & Astrophysics (AREA)
- Aviation & Aerospace Engineering (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the application provides a space-time service big data platform of space-time multiple integration including infrastructure layer, platform layer and software layer, carry out the operation of patrolling and examining, obtain including unmanned aerial vehicle carries the load and acquires the data of area, the space-time big data of thing networking data waiting detection area, utilize the satellite network to realize the transmission of data, thereby realize space-time big data in time passback, then the platform layer carries out data management to the space-time big data through the cluster server, handle the space-time big data such as washing, fuse, rule, acquire high value data, thereby acquire the information of patrolling and examining in time accurate follow space-time big data, then carry out the propelling movement with the information of patrolling and examining through the software layer, make relevant personnel in time solve the problem of finding, thereby the difficult, the low processing efficiency's of space-time service big data passback has been solved, thereby the overall management of the operation of patrolling and examining the area of treating and examining has been realized through space-time service big data platform.
Description
Technical Field
The embodiment of the application relates to the technical field of data processing, in particular to a space-time service big data platform with multiple elements.
Background
For fields such as forest fire prevention, border monitoring, pipeline inspection, oil well monitoring, emergency mapping, aviation remote sensing, electric power inspection, emergency search and rescue, island monitoring and the like, the safety or normal operation of the areas is ensured, and therefore the areas need to be inspected.
At present, most of the inspection operations for these areas are relatively simple modes, for example, the unmanned aerial vehicle periodically cruises, data are transmitted back to the ground, and inspection personnel on the ground judge through videos or pictures, or rely on manual inspection operations, and inspection personnel hike to perform inspection operations according to inspection routes.
Therefore, the current inspection operation mode still needs to rely on inspection staff to a great extent, so that it is difficult to perform overall management on the inspection operation of the area to be inspected, current development requirements are not met any more, for example, it is difficult to perform timely and effective data management on the acquired inspection data, and thus, hysteresis and inaccuracy exist on the detection of potential safety hazards and faults of the area to be inspected.
Disclosure of Invention
The embodiment of the application provides a space-time service big data platform with multiple elements, which solves the problems that accurate inspection data are difficult to obtain and the transmission of the inspection data is difficult, so that the inspection discovery problem is difficult to solve in time.
The embodiment of the application provides a space-time service big data platform of many integration of world, its characterized in that, space-time service big data platform's system architecture includes: an infrastructure layer, a platform layer, and a software layer;
the infrastructure layer includes: the system comprises a physical layer and a network layer, wherein the physical layer comprises a cluster server, a switch and/or a router;
the network layer includes: the system comprises a satellite network and the Internet, wherein the satellite network is used for transmitting space-time big data of a region to be detected on an unmanned aerial vehicle to a cluster server of the space-time service big data platform, and the space-time big data comprises: unmanned aerial vehicle flight data, flight control data, video stream data, image data and GPS geographic information data;
the platform layer is used for carrying out data management on the space-time big data based on a big data processing algorithm integrated on the cluster server, and acquiring the processed space-time big data and the inspection information of the area to be detected;
the software layer is used for pushing the processed space-time big data and/or the routing inspection information.
Optionally, the network layer further includes: the internet of things is used for transmitting internet of things data corresponding to the internet of things terminal equipment arranged in the to-be-detected area to the unmanned aerial vehicle, so that space-time big data including the internet of things data are stored on the unmanned aerial vehicle.
Optionally, the satellite network includes: broadband satellite networks and narrowband satellite networks.
Optionally, the network layer further includes: and the VPN network is used for reading and accessing the processed space-time big data and/or the patrol information in the space-time service big data platform by a user and remotely controlling and monitoring the unmanned aerial vehicle.
Optionally, the cluster server includes: streaming media server, file server, computing server cluster based on Mapreduce algorithm;
the streaming media server is used for acquiring the video stream data, analyzing and/or converting the video stream data, and sending the analyzed and/or converted video stream data to the computation server cluster based on the Mapreduce algorithm;
the computation server cluster based on the Mapreduce algorithm is provided with an algorithm and a processing program for preprocessing the space-time big data of the area to be detected, so as to obtain the inspection information of the area to be detected, and the analyzed and/or converted video stream data and the inspection information are pushed through the software layer;
the file server is used for storing the processed space-time big data, the patrol information and daily data documents of the space-time service big data platform.
Optionally, the computing server cluster based on the Mapreduce algorithm includes: the system comprises a data analysis server, a data forwarding server and a database server;
the data analysis server is used for cleaning and regulating the space-time big data of the area to be detected and identifying fault points, acquiring the inspection information and sending the inspection information to the data forwarding server;
the data forwarding server is used for forwarding received unmanned aerial vehicle flight data, flight control data, the parsed and/or converted video stream data and the recognition result in a directional manner;
and the database server is used for managing the user information of the space-time service big data platform and storing the space-time big data so as to manage the space-time big data and enable a user to access the space-time service big data platform.
Optionally, the infrastructure layer further includes: a display terminal;
the display terminal is used for displaying the processed space-time big data, the unmanned aerial vehicle flight route and the patrol information.
Optionally, the method further comprises: an individual soldier device;
the individual soldier equipment is used for interacting the patrol personnel with the space-time service big data platform.
Optionally, the method further comprises: a comprehensive intelligent task vehicle;
the comprehensive intelligent task vehicle is used for interacting with the unmanned aerial vehicle through the satellite network, controlling the unmanned aerial vehicle and acquiring space-time big data of the area to be detected.
Optionally, the method further comprises: and the firewall is arranged between the cluster server and the switch and/or the router.
The embodiment of the application is based on cloud computing, the architecture comprises an infrastructure layer, a platform layer and a software layer, a space-time service large data platform is integrated with the space and the earth, and is used for carrying out inspection operations in the fields of forest fire prevention, border monitoring, pipeline inspection, oil well monitoring, emergency mapping, aviation remote sensing, electric power inspection, emergency search and rescue, sea island monitoring and the like, wherein the infrastructure layer comprises a cluster server, a switch and/or a router, the space-time large data of an area to be detected can be acquired through an unmanned aerial vehicle, when the operation environment of the inspection operation is hard and severe, the inspection operation is difficult to carry out by an inspection personnel, the problem that accurate inspection data is difficult to acquire is solved, in addition, the data is transmitted by a satellite network, then the data is converted by a gateway station, so that the space-time large data of the area to be detected on the unmanned aerial vehicle is timely transmitted back to a space-time service large data platform control center, the platform layer can timely and accurately acquire inspection information from the space-time large data through analysis processing of the cluster server, and then the inspection information is pushed through the software layer, the problem that the relevant personnel is difficult to acquire the real-time service large data of the area to be inspected, and the problem that the relevant inspection operation is difficult to be timely solved is solved, and the problem of the real-time service large data is timely found to be timely and the inspection area is difficult to be timely solved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the prior art descriptions, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a system architecture diagram of a space-time service big data platform according to an embodiment of the present application;
FIG. 2 is a network topology provided in an embodiment of the present application;
FIG. 3 is a system architecture diagram of a space-time service big data platform according to another embodiment of the present application;
fig. 4 is a schematic data flow diagram according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
To fields such as forest fire prevention, border monitoring, pipeline inspection, oil well monitoring, emergent survey and drawing, aviation remote sensing, marine vessel monitoring, electric power inspection, emergent search and rescue, island monitoring need to carry out inspection operation so that can in time discover the problem, solve the problem, avoid serious loss.
At present, the inspection operation in these fields is simpler in inspection operation mode, for example, by controlling an unmanned aerial vehicle to cruise and shoot videos and pictures above an area to be inspected, then, the videos and the pictures are transmitted to equipment of an on-ground inspection personnel, the inspection personnel can carry out the inspection operation by watching the videos and the pictures, or the inspection personnel periodically carry out the inspection operation on foot according to an inspection route, if the inspection personnel inspect a fault, the inspection personnel capable of solving on site can solve the fault, the inspection personnel cannot send fault information to corresponding staff, and the staff processes the fault after receiving the fault information.
Therefore, the current inspection operation still depends on manual inspection operation to a great extent, and the inspection mode is difficult to realize overall management of the area to be inspected, so that the inspection efficiency is low, the inspection result is inaccurate, and the like.
Therefore, in order to solve the problems existing in the routing inspection operation in the prior art, the application provides a space-time service big data platform with multiple elements, which comprises an infrastructure layer, a platform layer and a software layer, wherein the space-time service big data platform can acquire space-time big data of an area to be inspected, the space-time big data are acquired when equipment such as a load carried on an unmanned plane performs routing inspection operation on the area to be inspected, then a built satellite network and other networks are used for real-time acquisition of routing inspection information transmitted to a cluster server of the space-time service big data platform, and then a big data algorithm for processing the space-time big data is carried in the cluster server, so that the space-time service big data are subjected to data management, cleaning and trimming and other processing through the cluster server, routing inspection information of the area to be inspected can be efficiently and accurately extracted, and then the routing inspection information of the area to be inspected is sent to the software layer, so that a user can acquire the routing inspection information of the area to be inspected, and the problems found during the routing inspection operation are timely solved. That is, the space-time service big data platform combines various modes such as satellite remote sensing, aviation remote sensing, big data processing, cloud computing and the like, realizes multiple functions such as acquisition, analysis, distribution, reporting, early warning and the like of space-time big data, and realizes overall management of inspection operation of an inspection area.
Fig. 1 is a system architecture diagram of a spatio-temporal service big data platform according to an embodiment of the present application, where, as shown in fig. 1, the system architecture of the spatio-temporal service big data platform includes: an infrastructure layer, a platform layer, and a software layer.
The infrastructure layer, i.e. the IaaS layer, comprises a physical layer and a network layer, the physical layer comprises a cluster server, the cluster server is used for carrying out data management on space-time big data, and the platform layer realizes the function of carrying out data management on the space-time big data based on the cluster server.
The switch and/or the router are equipment for realizing network interconnection and data transmission, are arranged in the unmanned plane platform and the control center of the space-time service big data platform, and the network layer realizes the functions based on the switch and/or the router.
The network layer comprises a satellite network and the Internet, wherein the satellite network is used for transmitting space-time big data of an area to be detected on the unmanned aerial vehicle to the Internet through the gateway station, and the Internet is used for transmitting the space-time big data to the cluster server through the switch.
The platform layer, namely the PaaS layer, is used for carrying out data management on the space-time big data of the area to be detected based on the cluster server, obtaining the processed space-time big data and extracting the inspection information of the area to be detected from the space-time big data of the area to be detected.
The software layer, namely the SaaS layer, is used for pushing the processed space-time big data and/or the patrol information of the area to be detected.
The data transmission direction of the space-time big data of the area to be detected is described in detail as follows:
when the unmanned aerial vehicle performs flight inspection operation in the area to be inspected, video stream data and image data of the area to be inspected are obtained through carried loads, wherein the loads can comprise three-light photoelectric pods, inclined cameras, orthographic cameras and shouting phones, and therefore videos and images of the area to be inspected can be shot.
Then, the unmanned aerial vehicle transmits space-time big data of the area to be inspected, namely video stream data, image data and platform equipment parameters (including unmanned aerial vehicle flight data, flight control data and load parameters) to a gateway station on the ground through a satellite network. Alternatively, the satellite network may be a broadband satellite network in order to improve data transmission efficiency. Further, the satellite network may be a redundant arrangement of a broadband satellite network and a narrowband satellite network, such that when the broadband satellite network fails, data transmission is performed through the narrowband satellite network.
The gateway station analyzes, converts and encapsulates the received space-time big data, and converts the space-time big data into data which can be transmitted through the Internet.
And then, the space-time big data is sent to a switch of the space-time service big data platform through the Internet, and the switch distributes the space-time big data to a cluster server of the space-time service big data platform.
The cluster server collects, denoises, cleans, transforms, regularizes, pushes and the like the received space-time big data according to the embedded big data processing algorithm and processing program, acquires the processed space-time big data, and extracts the inspection information from the space-time big data of the area to be detected, wherein the inspection information can be, for example, the position of a fault point. In the processed space-time big data, the data have relevance, such as space-time relevance, position relevance, visual data, and easy understanding, thereby facilitating the access and viewing of users.
The software layer pushes the processed space-time big data and/or the routing inspection information, specifically, for example, the processed space-time big data and/or the routing inspection information can be displayed on a web interface, and the processed space-time big data and/or the routing inspection information are sent through a mobile phone short message and pushed through an APP, so that a user can know the routing inspection information so as to process the routing inspection information in time.
Optionally, the network layer may further include: the VPN network is used for accessing and/or controlling the space-time service big data platform by a user, reading the processed space-time big data and the patrol information so as to know the condition of the area to be detected at any time and remotely control and monitor the unmanned aerial vehicle, so that the user can control the flight route of the unmanned aerial vehicle according to the self requirement, and the needed space-time big data is obtained.
According to the embodiment, based on cloud computing, the architecture comprises an space-time service big data platform with multiple integration of an infrastructure layer, a platform layer and a software layer, and the space-time service big data platform is used for carrying out inspection operations in the fields of forest fire prevention, border monitoring, pipeline inspection, oil well monitoring, emergency mapping, aviation remote sensing, electric power inspection, emergency search and rescue, sea island monitoring and the like, wherein the infrastructure layer comprises a cluster server, a switch and/or a router, the space-time big data of an area to be detected can be acquired through an unmanned aerial vehicle, the problem that when the operation environment of the inspection operation is hard and severe, inspection personnel are difficult to carry out inspection operation, accurate inspection data are difficult to acquire is solved, in addition, the satellite network is utilized to realize data transmission, then the data conversion is realized by utilizing a gateway station, so that the space-time big data of the area to be detected on the unmanned aerial vehicle are timely transmitted back to a space-time service big data platform control center, the platform layer can timely and accurately acquire inspection information from the space-time big data through the cluster server, then the software layer can push relevant personnel to realize the inspection operation of the area, the problem that the relevant inspection information is difficult to be timely and the relevant inspection information can be timely solved, and the problem of the inspection platform is timely solved, and the problem of the inspection of the area to be timely and the inspection data can be timely solved.
Fig. 2 is a network topology diagram provided by an embodiment of the present application, fig. 3 is a system architecture diagram of a space-time service big data platform provided by another embodiment of the present application, and fig. 4 is a data flow diagram provided by an embodiment of the present application.
As shown in fig. 2, the apparatus required for implementing the space-time service big data platform with multiple elements integrated in the world includes: the intelligent task vehicle comprises Internet of things terminal equipment, an unmanned aerial vehicle platform, a cluster server, a switch, satellites, gateway stations, firewalls, individual equipment, mobile equipment, user terminals and comprehensive intelligent task vehicles which are deployed in multiple places.
The Internet of things terminal equipment deployed in multiple places is provided with a LoRa monitoring device and is used for collecting state data of the Internet of things terminal equipment, namely Internet of things data, so that data quantity and data sources of space-time big data are enriched, all the Internet of things terminal equipment is connected through a LoRa gateway, the Internet of things data are transmitted to an unmanned aerial vehicle platform through the LoRa gateway, collection of the Internet of things data is completed, and meanwhile, the Internet of things terminal equipment can be reversely controlled through the LoRa gateway.
The unmanned aerial vehicle platform is composed of an unmanned aerial vehicle, a satellite antenna, other redundant network equipment, loads and the like, the unmanned aerial vehicle carries equipment to fly to the site of the area to be inspected according to the instructions of the ground control center, the ground equipment on the area to be inspected and the area to be inspected are inspected, videos and images are shot, video stream data and image data are obtained, and the video stream data, the image data, the internet of things data and the like are used as space-time big data of the area to be inspected and are transmitted back to the space-time service big data platform in real time.
Wherein, space-time big data further includes: platform equipment parameters, infrared data, radar data and GPS geographic information data, wherein the platform equipment parameters comprise unmanned aerial vehicle flight data and flight control data.
The cluster server comprises a streaming media server, a file server and a computation server cluster based on a Mapreduce algorithm, and the computation server cluster based on the Mapreduce algorithm comprises a data analysis server, a data forwarding server and a database server.
The satellite generally selects a broadband satellite, and achieves the functions of real-time feedback of space-time big data on the unmanned aerial vehicle, IP communication, broadband Internet surfing and the like through the broadband satellite.
And the gateway station is used for carrying out analysis, conversion and encapsulation on the received space-time big data of the area to be detected and converting the space-time big data into data which can be transmitted through the Internet.
As shown in fig. 4, the core switch, which is one of the switch and/or the router, is disposed in a control center of the space-time service big data platform, and is configured to distribute the space-time big data to a corresponding server according to a destination address of the space-time big data. The video stream data are transmitted to the streaming media server and the data analysis server; the parameters of the platform equipment are UDP data packets, and the UDP data packets are sent to a data forwarding server; and forwarding the data of the Internet of things and the image data to a data analysis server.
A firewall is disposed between the cluster server and the core switch.
The individual equipment can monitor outdoors when the unmanned aerial vehicle inspection is carried out on the area to be inspected, and the individual equipment is used for communication with a control center of the space-time service big data platform through the Internet and the base station.
The mobile device is an ultra-converged notebook computer, and can temporarily take over the control rights of the unmanned aerial vehicle and the load through the VPN network under the condition that the space-time service big data platform is authorized.
And the user terminal can remotely read the data of the time space service big data platform through the VPN network by a user of the time space service big data platform, wherein the data is in the authority range of the user and is inaccessible in the non-authority range.
Synthesize wisdom mission car, realize unmanned aerial vehicle's transportation, wait to patrol and examine the region with unmanned aerial vehicle transportation, guarantee unmanned aerial vehicle's normal flight, and possess the control authority who takes over space-time service center, carry out remote control to unmanned aerial vehicle and load, the function of the collection data to unmanned aerial vehicle and load. The data of the comprehensive intelligent task vehicle are also transmitted through the satellite network.
The network topology shown in fig. 2 shows that the network chain of the space-time service big data platform comprises a satellite network, an internet of things, a VPN private network and the internet.
The satellite network is used for realizing communication between the unmanned aerial vehicle platform and a server of the space-time big data platform control center, and the space-time big data of the area to be patrolled and examined, which is acquired by the unmanned aerial vehicle platform, is transmitted to the Internet through the gateway station, so that the server of the space-time big data platform is acquired.
The internet of things is used for realizing communication between the internet of things terminal equipment and the unmanned aerial vehicle platform and transmitting internet of things data acquired by the internet of things terminal equipment to the unmanned aerial vehicle platform.
The user can remotely read and access the data of the time service big data platform through the VPN private network, and can remotely control and monitor the unmanned aerial vehicle and the load through the notebook computer deployed with the control software environment.
The ground mobile equipment and individual soldier operators can complete information interaction with the space-time service big data platform control center through the base station and the Internet, such as voice call, video call and other functions.
The space-time service big data platform control center obtains and transmits data by the exchanger, and all data flow in the space-time service big data platform control center needs to pass through the firewall and then interact with the internal server.
The gateway station realizes the mutual analysis, conversion and encapsulation between satellite data and internet data transmitted by a satellite network, and the gateway station is connected with a space-time service big data platform control center through a private network, so that the stability and high speed of a data link are ensured.
As shown in fig. 3, the space-time service big data platform is based on a cloud architecture, and includes an infrastructure layer (IaaS layer), a platform layer (PaaS layer) and a software layer (SaaS layer), specifically:
the IaaS layer includes: a physical layer and a network layer.
Wherein the physical layer comprises: streaming media server, file server, computation server cluster based on Mapreduce algorithm, switch and/or router, display terminal.
The streaming media server is used for processing the video streaming data, analyzing/converting the video streaming data, and pushing the video streaming data to interface software such as a Web browser interface, a mobile terminal APP, a WeChat applet, user terminal software and the like in different formats as shown in FIG. 4.
And the file server is used for storing daily data documents, image data, platform equipment parameters and other information of the space-time service big data platform, has an information sharing function, and can be used for reading and writing access to the data by a user through the VPN.
The computation server cluster based on the Mapreduce algorithm comprises: the system comprises a data analysis server, a data forwarding server and a database server. The data analysis server analyzes the data of the Internet of things and deletes the frame header of the datagram, the data content is recombined and converted into data of JSON format according to the display format of the Web end and sent to the data forwarding server, the data forwarding server is distributed to the Web end and the mobile phone APP end, the Web end and the APP end always subscribe the data of the Internet of things, video stream data and image data are subjected to image recognition, fault points are identified, and accordingly the fault points are marked on images or video screenshot images, and then the data are pushed to the Web end, the APP end and user end software by the data forwarding server, and fault tasks which need to be processed by workers are notified in a telephone/short message mode.
The database server is used for the user to access the data of the space-time service big data platform and has a database management function, and comprises: system configuration and management, data access and update management of space-time service big data platform, data integrity management and data security management. Query and manipulation functions of the database, including database retrieval and modification. Database maintenance functions, including data import/export management, database structure maintenance, data recovery functions, and performance monitoring. Wherein the database needs to run in parallel, the database server must support a parallel running mechanism to handle the simultaneous occurrence of multiple events, since more than one user accesses the database at the same time.
The display terminal is an intelligent display terminal, can realize split-screen display, is not limited by stations and the size of a visual area, and has the functions of N pairs N, N to 1, randomly adjusting the display area, randomly arranging the display area and the like.
The network layer is used for guaranteeing smoothness of data links of the space-time service big data platform and completing interconnection and interworking among different networks.
Wherein, the network layer includes: satellite networks, VPN networks, internet of things, and the internet.
The satellite network comprises a broadband satellite network and a narrowband satellite network, wherein the broadband satellite network is used for transmitting platform equipment data such as unmanned plane platform data, comprehensive intelligent task vehicle platform data and the like, and is interconnected with the Internet through a ground gateway station; the narrow-band satellite network is used as a redundant network of the broadband satellite network, and transmits platform equipment data such as unmanned aerial vehicle platform data, comprehensive intelligent task vehicle data and the like under the condition of broadband satellite network faults and is interconnected with the Internet through a ground gateway station.
VPN network realizes the function of mobile equipment temporary control unmanned plane platform, and remote user equipment accesses and reads the data of the time space service big data platform.
The internet of things transmits the internet of things data acquired by the internet of things equipment in the area to be detected to the unmanned aerial vehicle, so that the unmanned aerial vehicle stores space-time big data of the area to be detected including the internet of things data of the area to be detected, and the control of the internet of things equipment can be realized through the internet of things.
The Internet is connected with ground equipment, and the connection between the space-time service big data platform and the user side, the gateway station, the individual equipment, the mobile equipment and the like is opened.
The PaaS layer mainly comprises a data layer, a data fusion layer and a service layer, combines the functions of data collection, extraction, cleaning, fusion, normalization, pushing, release and the like of a big data algorithm, and realizes the functions based on a cluster server.
The data layer data comprise platform equipment data such as unmanned aerial vehicle flight data, flight control data and the like, video stream data, infrared data, radar data, image data, internet of things data and GPS geographic information data.
The fusion layer mainly realizes the data management work of the data received by the data layer and mainly comprises the functions of removing redundant data, converting the format of video stream data, deeply learning and identifying image data, formatting the data packet JSON of the internet of things, cleaning and regulating the image data, grabbing data generated by different projects, preprocessing and denoising, and the like.
The service layer provides the following services for the data after the fusion processing of the fusion layer:
and (3) data forwarding: forwarding UDP, TCP, MQTT data to a corresponding user terminal to realize a data stream directional service function;
and (3) fault identification: intelligent identification is carried out on video stream data and image data, potential hidden danger and fault points are accurately positioned, and a fault area is locked at the first time;
streaming media service: the method has the service functions of pushing, pulling, collecting, caching, scheduling, transmitting and playing, converting video protocols and the like, and performs streaming protocol conversion on video stream data, supports the pushing of the video stream protocols such as RSTP, RMTP, RTP, HLS and the like to a Web interface, user side software, mobile phone mobile terminal APP and the like.
Subscription/publication: the Web terminal subscribes to the Internet of things data according to the JSON format, and the Internet of things terminal equipment periodically publishes the Internet of things data to the Web terminal.
Map service: the space-time service big data platform integrates map information such as a sky map, a Gold, a hundred degrees, tengxun, google and the like through a map API interface, and provides GIS service for flight service.
The SaaS software layer mainly realizes the function of an application layer, and the space-time service big data platform pushes the routing inspection information obtained through processing through mobile phone mobile terminal APP, web interface, mobile phone short message, mobile phone, user software and the like.
Wherein, the Web interface: the unmanned aerial vehicle intelligent control system is characterized by integrally displaying real-time operation pictures of the unmanned aerial vehicle, flight control work data of the unmanned aerial vehicle, nacelle operation data, internet of things data and individual operation data, remotely controlling unmanned aerial vehicle, nacelle and Internet of things equipment, and realizing remote voice and video call with a comprehensive intelligent mission car and an individual.
Mobile terminal APP and WeChat applet: according to the login identity, the data of the space-time service big data platform are obtained remotely, the information comprises video pictures, platform equipment data, alarm information and the like, and the mobile phone APP is integrated with the fault point alarm pushing service, so that the fault position can be clarified, a fault elimination course is pushed, and a user is assisted in solving the fault.
Short message of mobile phone: the fault identification service discovers a fault point by carrying out AI identification on image information such as video stream data, image data and the like, and pushes the fault point to a corresponding user through a short message, so that the fault point reaches the site for solving the fault at the first time.
Cell phone: and if the fault point does not check the response within the specified time, the space-time service big data platform calls corresponding staff to carry out task reminding.
User side software: the unmanned aerial vehicle platform flight control system comprises load control software and flight control software, and can remotely monitor and control flight operation tasks of the unmanned aerial vehicle platform.
Taking the data transmission direction as an example for explanation:
the method comprises the steps of collecting state data of terminal equipment of the Internet of things, namely, the data of the Internet of things, transmitting the data of the Internet of things to an unmanned aerial vehicle platform through the Internet of things, enabling an unmanned aerial vehicle to receive the data of the Internet of things, shooting and acquiring videos and images of a region to be inspected by the unmanned aerial vehicle, forming video stream data and image data, and then transmitting the data of the Internet of things, the video stream data, the image data, unmanned aerial vehicle flight data, flight control data and comprehensive intelligent task vehicle data through a satellite network by the unmanned aerial vehicle so that a server of a space-time service big data platform acquires space-time service big data. The spatio-temporal service big data includes: platform equipment parameters (including unmanned aerial vehicle flight data and flight control data, comprehensive intelligent mission car data and the like), video stream data, image data and Internet of things data, wherein a server of the space-time service big data platform also acquires map information through a map API interface, and provides GIS service for unmanned aerial vehicle flight.
The gateway station processes the space-time big data sent by the satellite network and converts the space-time big data into internet private line data so that the data can be transmitted through the internet.
The space-time service big data platform obtains space-time big data through the switch and sends a remote control instruction to the terminal equipment of the Internet of things and the like. And the switch distributes the space-time big data to the corresponding server according to the destination address of the space-time big data.
The video stream data is distributed to a stream media server and a data analysis server, wherein the stream media server analyzes/converts the video stream data, then pushes the video stream data to interface software such as a web server, a mobile terminal APP, a WeChat applet, user side software and the like in different formats, and sends the processed video stream data to a file server for storage.
The platform equipment data are sent to the data forwarding server to be sent to user side software and web side servers corresponding to relevant equipment, for example, a control terminal of the unmanned aerial vehicle.
And the data of the Internet of things and the image data are sent to a data analysis server.
The data analysis server is provided with image recognition and video recognition deep learning software and processing programs, so that images and videos are recognized, inspection information is obtained, the inspection information is pushed to a Web end server, a mobile phone APP end and user end software, and related personnel are informed of tasks to be processed in a telephone/short message mode.
Specifically, when the image and the video are identified, the deep learning software and the processing program comprise a neural network and an automatic identification algorithm combined with YOLO in genetics to realize rapid and accurate image data identification. The image and video identification process comprises the following steps:
the first step: performing image model gridding on video stream data and image data, and cutting the video stream data and the image data into 8 x 8 small grids;
and a second step of: processing the gridded image by using a convolutional neural network;
and a third step of: applying a convolution repetitive training grid;
fourth step: adjusting the coordinate value and the confidence coefficient of the image;
fifth step: converting the image by using a digital matrix transformation;
sixth step: and generating a population by using a genetic algorithm, analyzing fitness values of each individual, and calculating the optimal individual.
The present application is not limited to the image processing algorithm used when image recognition is performed on video stream data and image data.
The file server stores received time-space data, patrol information obtained after processing and analyzing the time-space big data, and daily data documents of the time-space service big data platform.
The functions of cleaning, analyzing, reorganizing and the like of messy, multivariable and strongly-coupled space-time big data are realized through the server cluster comprising the streaming media server, the file server, the data analysis server, the data forwarding server and the database server, and the data and processing algorithms and processing programs carried in the streaming media server, the file server, the data analysis server, the data forwarding server and the database server, and the directional pushing of the inspection information is realized, the efficiency of inspection operation and the processing speed of fault points and the like are improved, so that the value of the space-time big data is improved.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A space-time service big data platform of a space-time multiple integration, which is characterized in that the system architecture of the space-time service big data platform comprises: an infrastructure layer, a platform layer, and a software layer;
the infrastructure layer includes: the system comprises a physical layer and a network layer, wherein the physical layer comprises a cluster server, a switch and/or a router;
the network layer includes: the system comprises a satellite network and the Internet, wherein the satellite network is used for transmitting space-time big data of a region to be detected on an unmanned aerial vehicle to a cluster server of the space-time service big data platform, and the space-time big data comprises: unmanned aerial vehicle flight data, flight control data, video stream data, image data and GPS geographic information data;
the platform layer is used for carrying out data management on the space-time big data based on a big data processing algorithm integrated on the cluster server, and acquiring the processed space-time big data and the inspection information of the area to be detected;
the software layer is used for pushing the processed space-time big data and/or the routing inspection information.
2. The spatio-temporal services big data platform of claim 1, wherein the network layer further comprises: the internet of things is used for transmitting internet of things data corresponding to the internet of things terminal equipment arranged in the to-be-detected area to the unmanned aerial vehicle, so that space-time big data including the internet of things data are stored on the unmanned aerial vehicle.
3. The space-time service big data platform of claim 1, wherein the satellite network comprises: broadband satellite networks and narrowband satellite networks.
4. The spatio-temporal services big data platform of claim 1, wherein the network layer further comprises: and the VPN network is used for reading and accessing the processed space-time big data and/or the patrol information in the space-time service big data platform by a user and remotely controlling and monitoring the unmanned aerial vehicle.
5. The spatio-temporal service big data platform of claim 1, wherein the cluster server comprises: streaming media server, file server, computing server cluster based on Mapreduce algorithm;
the streaming media server is used for acquiring the video stream data, analyzing and/or converting the video stream data, and sending the analyzed and/or converted video stream data to the computation server cluster based on the Mapreduce algorithm;
the computation server cluster based on the Mapreduce algorithm is provided with an algorithm and a processing program for preprocessing the space-time big data of the area to be detected, so as to obtain the inspection information of the area to be detected, and the analyzed and/or converted video stream data and the inspection information are pushed through the software layer;
the file server is used for storing the processed space-time big data, the patrol information and daily data documents of the space-time service big data platform.
6. The spatio-temporal service big data platform of claim 5, wherein said Mapreduce algorithm-based computing server cluster comprises: the system comprises a data analysis server, a data forwarding server and a database server;
the data analysis server is used for cleaning and regulating the space-time big data of the area to be detected and identifying fault points, acquiring the inspection information and sending the inspection information to the data forwarding server;
the data forwarding server is used for forwarding received unmanned aerial vehicle flight data, flight control data, the parsed and/or converted video stream data and the recognition result in a directional manner;
and the database server is used for managing the user information of the space-time service big data platform and storing the space-time big data so as to manage the space-time big data and enable a user to access the space-time service big data platform.
7. The spatio-temporal service big data platform of any of claims 1-6, wherein the infrastructure layer further comprises: a display terminal;
the display terminal is used for displaying the processed space-time big data, the unmanned aerial vehicle flight route and the patrol information.
8. The spatio-temporal service big data platform of any of claims 1-6, further comprising: an individual soldier device;
the individual soldier equipment is used for interacting the patrol personnel with the space-time service big data platform.
9. The spatio-temporal service big data platform of any of claims 1-6, further comprising: a comprehensive intelligent task vehicle;
the comprehensive intelligent task vehicle is used for interacting with the unmanned aerial vehicle through the satellite network, controlling the unmanned aerial vehicle and acquiring space-time big data of the area to be detected.
10. The spatio-temporal service big data platform of any of claims 1-6, further comprising: and the firewall is arranged between the cluster server and the switch and/or the router.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310320242.8A CN116318365A (en) | 2023-03-28 | 2023-03-28 | Space-time service big data platform with multiple elements |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310320242.8A CN116318365A (en) | 2023-03-28 | 2023-03-28 | Space-time service big data platform with multiple elements |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116318365A true CN116318365A (en) | 2023-06-23 |
Family
ID=86795842
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310320242.8A Pending CN116318365A (en) | 2023-03-28 | 2023-03-28 | Space-time service big data platform with multiple elements |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116318365A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116938984A (en) * | 2023-09-14 | 2023-10-24 | 四川泓宝润业工程技术有限公司 | Pipeline inspection method based on unmanned aerial vehicle and automatic hangar |
-
2023
- 2023-03-28 CN CN202310320242.8A patent/CN116318365A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116938984A (en) * | 2023-09-14 | 2023-10-24 | 四川泓宝润业工程技术有限公司 | Pipeline inspection method based on unmanned aerial vehicle and automatic hangar |
| CN116938984B (en) * | 2023-09-14 | 2023-11-21 | 四川泓宝润业工程技术有限公司 | Pipeline inspection method based on unmanned aerial vehicle and automatic hangar |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109447048B (en) | Artificial intelligence early warning system | |
| US20210389293A1 (en) | Methods and Systems for Water Area Pollution Intelligent Monitoring and Analysis | |
| Geraldes et al. | UAV-based situational awareness system using deep learning | |
| CN202058215U (en) | On-line safety inspection system | |
| CN111444014A (en) | Ocean observation network system and method based on EC-REST system structure | |
| CN102594620B (en) | Linkable distributed network intrusion detection method based on behavior description | |
| CN114362367A (en) | Cloud edge cooperation-oriented power transmission line monitoring system and method, and cloud edge cooperation-oriented power transmission line identification system and method | |
| CN106547814A (en) | A kind of power transmission line unmanned machine patrols and examines the structuring automatic archiving method of image | |
| CN109376660B (en) | Target monitoring method, device and system | |
| CN116318365A (en) | Space-time service big data platform with multiple elements | |
| Alabdulkarim et al. | Urban analytics in crowd management in the context of Hajj | |
| CN107172191A (en) | Multi-rotor unmanned aerial vehicle remote monitoring method based on multi-priority services | |
| CN108600372B (en) | Information interaction system based on multiple power transmission equipment inspection devices and control terminal | |
| CN114360197A (en) | Intelligent cooperative alarm system for power transmission line | |
| CN109038813A (en) | Power transmission line intelligent managing and control system | |
| CN114494916A (en) | Black-neck crane monitoring and tracking method based on YOLO and DeepsORT | |
| CN110176085A (en) | Power station equipment cruising inspection system based on two dimensional code | |
| CN111988397B (en) | Earthquake-proof disaster-reduction disaster-relief method and system based on edge calculation | |
| CN112016380B (en) | Wild animal monitoring method and system | |
| CN116580362B (en) | Transmission operation cross-system fusion data acquisition method and digital asset processing system | |
| CN112764427A (en) | Relay unmanned aerial vehicle inspection system | |
| CN115542951B (en) | Unmanned aerial vehicle centralized management and control method, system, equipment and medium based on 5G network | |
| CN112202789A (en) | Method and system for processing panoramic sensing data of transformer substation | |
| CN114979214A (en) | Intelligent cooperative alarm system, method and device for power transmission line | |
| CN115841762A (en) | Unmanned vehicle control method, unmanned vehicle control system and unmanned vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |