CN115988198A - Big data-based inspection method and system for front-end sensing equipment - Google Patents
Big data-based inspection method and system for front-end sensing equipment Download PDFInfo
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- CN115988198A CN115988198A CN202211655415.3A CN202211655415A CN115988198A CN 115988198 A CN115988198 A CN 115988198A CN 202211655415 A CN202211655415 A CN 202211655415A CN 115988198 A CN115988198 A CN 115988198A
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Abstract
The invention provides a big data-based inspection method and a big data-based inspection system for front-end sensing equipment, wherein the method comprises the following steps: responding to the acquisition of the stored front-end sensing equipment data information; and analyzing based on the acquired data information, and judging the running condition of the front-end sensing equipment so as to further finish the full-amount inspection and intelligent inspection of the front-end sensing equipment. According to the technical scheme, historical data are collected, mined, calculated and analyzed based on a big data platform, existing equipment is researched and judged, the problem that important front-end sensing equipment is damaged is solved, the problem can be found and processed in time, a work order is sent to maintenance personnel for maintenance in time, the front-end sensing equipment is recovered, the problem that equipment is found in time through intelligent recommendation and routing inspection is solved, maintenance is carried out immediately, and major events are avoided.
Description
Technical Field
The invention belongs to the technical field of big data processing, and particularly relates to a big data-based inspection method and system for front-end sensing equipment.
Background
Many existing front-end sensing devices have problems which cannot be found in time, and key devices need to be frequently patrolled and examined to find the problems in time and process the problems.
However, it takes a long time to complete the whole inspection of the front-end sensing device once and the problems cannot be found in time, when one device is in normal inspection, the problems can be found only after the next inspection is suddenly broken, and the middle period is one day; many front-end sensing devices have problems and cannot find the problems in time, and when major security problems occur, the devices are suddenly found to be damaged; when the case needs to be checked, the problems of equipment failure, no video storage, video screen blurring, video loss and the like are found; collected historical data are not mined and analyzed, and actual values are not exerted.
In view of this, it is very significant to provide a method and a system for routing inspection of front-end sensing devices based on big data.
Disclosure of Invention
In order to solve the problems that the abnormality of the front-end sensing equipment cannot be found in time, the working efficiency is low and the like due to insufficient inspection force of the front-end sensing equipment in the prior art, the invention provides a method and a system for inspecting the front-end sensing equipment based on big data, and aims to solve the technical defects.
In a first aspect, the invention provides a big data-based inspection method for front-end sensing equipment, which comprises the following steps:
responding to the acquisition of the stored front-end sensing equipment data information;
and analyzing based on the acquired data information, and judging the running condition of the front-end sensing equipment so as to further finish the full-amount inspection and intelligent inspection of the front-end sensing equipment.
Preferably, the method further comprises the following steps: and taking the stream from the front-end sensing equipment and storing the stream to the video platform.
Further preferably, the total inspection of the front-end sensing device specifically comprises:
collecting user behavior data log generated by a user using a video platform and service data generated by a portrait platform and a vehicle platform through a flume platform and an sqoop platform, then pushing the collected data to kafka, and storing the flume data collected by the kafka into Hdfs of hadoop of a big data platform for storage;
performing data layering statistical sorting on a data warehouse formed by hive on a big data platform through a video diagnosis platform to generate recommended data, and diagnosing recommended equipment ranking through the video quality diagnosis platform to generate a video quality diagnosis result;
monitoring a video through an IT monitoring platform to generate monitoring data;
and carrying out notification alarm by combining the video quality diagnosis data and the monitoring data of the IT monitoring platform.
Further preferably, the user behavior data log comprises the steps of viewing videos, previewing videos, downloading videos, collecting, grading and replaying by using a video platform client; the business data generated by the portrait platform and the vehicle platform comprise portrait data captured by the portrait cloud platform, vehicle data captured by the vehicle platform and case incident places of related departments.
Further preferably, the data hierarchical statistical sorting comprises ranking and sorting the MySQL report system according to the equipment scores;
the equipment scoring rules comprise video checking, video downloading, scoring, portrait snapshot and vehicle snapshot adding one point each time; scoring according to the degree of traffic congestion; the first-level point location of the incident place of the related department is fifth, the second-level point location is third and the third-level point location is first.
Further preferably, the method further comprises the following steps: and when the integrity of the routing inspection result video, the online and offline number of the equipment and the video quality are lower than preset thresholds, the unified platform sends short messages and voices to inform and alarm.
Further preferably, the method further comprises the following steps: and traffic situation information generated by a map development platform is added, gathered and pushed to kafka, and intelligent inspection is carried out on front-end sensing equipment of the counterweight positions.
In a second aspect, an embodiment of the present invention further provides an inspection system for front-end sensing devices based on big data, where the inspection system specifically includes:
the data storage module is used for storing the data information of the front-end sensing equipment;
the data acquisition module is used for responding to and acquiring the stored data information of the front-end sensing equipment;
and the analysis and judgment module is used for analyzing based on the acquired data information and judging the running condition of the front-end sensing equipment so as to further finish the full-amount inspection and intelligent inspection of the front-end sensing equipment.
In a third aspect, an embodiment of the present invention provides an electronic device, including: one or more processors; storage means for storing one or more programs which, when executed by one or more processors, cause the one or more processors to carry out a method as described in any one of the implementations of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
Compared with the prior art, the beneficial results of the invention are as follows:
according to the technical scheme, historical data are collected, mined, calculated and analyzed based on a big data platform, existing equipment is researched and judged, the problem that important front-end sensing equipment is damaged is solved, the problem can be found and processed in time, a work order is sent to maintenance personnel for maintenance in time, the front-end sensing equipment is recovered, the problem that equipment is found in time through intelligent recommendation and routing inspection is solved, maintenance is carried out immediately, and major events are avoided.
Drawings
The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and together with the description serve to explain the principles of the invention. Other embodiments and many of the intended advantages of embodiments will be readily appreciated as they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding similar parts.
FIG. 1 is an exemplary device architecture diagram in which an embodiment of the present invention may be employed;
fig. 2 is a schematic flowchart of a polling method of a front-end sensing device based on big data according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of full-scale routing inspection in a routing inspection method of front-end sensing equipment based on big data according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of intelligent routing inspection in a routing inspection method of front-end sensing equipment based on big data according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an architecture of an inspection system of a big-data-based front-end sensing device according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a computer apparatus suitable for use with an electronic device to implement an embodiment of the invention.
Detailed Description
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. In this regard, directional terminology, such as "top," "bottom," "left," "right," "up," "down," etc., is used with reference to the orientation of the figure(s) being described. Because components of embodiments can be positioned in a number of different orientations, the directional terminology is used for purposes of illustration and is in no way limiting. It is to be understood that other embodiments may be utilized and logical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 1 illustrates an exemplary system architecture 100 for a method for processing information or an apparatus for processing information to which embodiments of the present invention may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having communication functions, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a background information processing server that processes check request information transmitted by the terminal apparatuses 101, 102, 103. The background information processing server may analyze and perform other processing on the received verification request information, and obtain a processing result (e.g., verification success information used to represent that the verification request is a legal request).
It should be noted that the method for processing information provided by the embodiment of the present invention is generally executed by the server 105, and accordingly, the apparatus for processing information is generally disposed in the server 105. In addition, the method for sending information provided by the embodiment of the present invention is generally executed by the terminal equipment 101, 102, 103, and accordingly, the apparatus for sending information is generally disposed in the terminal equipment 101, 102, 103.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (for example, to provide distributed services), or may be implemented as a single piece of software or multiple pieces of software modules, which is not limited herein.
The technical scheme of the invention mainly solves the problem when important front-end sensing equipment is damaged, and can find and process in time, dispatch a work order to maintenance personnel, maintain in time and recover the front-end sensing equipment. The general inspection method is to send all the devices to the diagnosis service for diagnosis. A polling task can take several hours, even a day. When the equipment has problems, the problems cannot be timely discovered. The invention mainly provides two schemes of full-scale inspection and intelligent inspection, solves the problems, and recommends valuable data based on the flows of data acquisition, data analysis, data storage, data mining and the like of a big data frame.
In a first aspect, fig. 2 shows that an embodiment of the present invention discloses a method for polling front-end sensing devices based on big data, and as shown in fig. 2, the method includes the following steps:
s101, fetching a stream from a front-end sensing device and storing the stream to a video platform;
s102, responding to the acquired and stored data information of the front-end sensing equipment;
s103, analyzing based on the acquired data information, and judging the running condition of the front-end sensing equipment so as to further finish the full-amount inspection and intelligent inspection of the front-end sensing equipment.
Specifically, referring to fig. 3, the full-scale inspection specifically includes:
(1) A user collects user behavior data log generated by a video platform and service data generated by a portrait platform vehicle platform through flash and sqoop, and then pushes the collected data to kafka, and the flash stores the data to a big data platform hadoop through collecting the data of the kafka, and Hdfs stores the data;
a. user behavior data log:
viewing videos, previewing videos, downloading videos, collecting, grading and replaying in a video platform client;
b. service data:
the portrait cloud platform captures portrait data;
the vehicle platform captures vehicle data;
the public security investigation department case incident location;
(2) The video diagnosis platform performs statistical sorting on hierarchical data (ods, dwd, dws, ads) of a data warehouse formed by hive of the big data platform to generate recommended data, and the recommended equipment ranking generates a result through diagnosis of the video quality diagnosis platform;
the calculation rules mainly include:
and (4) viewing the video: each time is divided into 1 minute
Video playback: each time is divided into 1 minute
Downloading a video: each time is divided into 1 minute
And (3) scoring: each time is +1 min
And (3) snapshot of the portrait: each time is divided into 1 minute
Vehicle snapshot: each time is divided into 1 minute
And (3) traffic jam: scoring based on congestion level
Public security case incident location: the first-level point location is divided into 5 points, the second-level point location is divided into 3 points, and the third-level point location is divided into 1 point location;
(3) The IT monitoring platform generates monitoring data (CPU, memory, disk, network and bandwidth) for video monitoring;
(4) And (4) carrying out notification alarm by combining the video quality diagnosis data and the IT monitoring data.
Referring to fig. 4, the intelligence is patrolled and examined mainly and is patrolled and examined to the equipment of key point position, specifically includes:
(1) Log generated by using generated user behavior data by a user on a video platform is collected by a flash and pushed to kafka, service data generated by a portrait platform and a vehicle platform are stored to mysql, and data change monitored by a flash cdc is pushed to kafka; wherein, the first and the second end of the pipe are connected with each other,
a. user behavior data:
a video platform client (view video, video preview, download video, collect, score, playback);
b. service data:
portrait cloud platform snapshot portrait data
Vehicle platform snapshot vehicle data
Case incident place of public security investigation department
(2) The method comprises the steps that the Gord map development platform generates traffic situation information to be gathered and pushed to kafka;
(3) Pushing the data generated in the steps (1) and (2) to a big data platform flink for real-time acquisition, calculation, analysis and generation of equipment ranking, and diagnosing the equipment ranking recommended by the big data platform through a video diagnosis platform to generate a result;
(4) The IT monitoring platform generates monitoring data (CPU, memory, disk, network and bandwidth) for video monitoring;
(5) And carrying out notification alarm by combining the video quality diagnosis data and the IT monitoring data.
In a second aspect, an embodiment of the present invention further discloses an inspection system for front-end sensing devices based on big data, and with reference to fig. 5, the system specifically includes: a data storage module 51, a data acquisition module 52 and an analysis and judgment module 53.
In a specific embodiment, the data storage module 51 is configured to store data information of the front-end sensing device; a data acquisition module 52, configured to respond to acquiring stored front-end perceptual device data information; and the analysis and judgment module 53 is configured to analyze the acquired data information and judge the operation condition of the front-end sensing device, so as to further complete the full-amount inspection and the intelligent inspection of the front-end sensing device.
According to the technical scheme, historical data are collected, mined, calculated and analyzed on the basis of a large data platform, existing equipment is researched and judged, the problem that important front-end sensing equipment is damaged is solved, processing can be found in time, a work order is sent to maintenance personnel for maintenance in time, the front-end sensing equipment is recovered, the problem that equipment is found in time through intelligent recommendation and routing inspection is solved, maintenance is carried out immediately, and major events are avoided.
Referring now to FIG. 6, a block diagram of a computer apparatus 600 suitable for use with an electronic device (e.g., the server or terminal device shown in FIG. 1) to implement an embodiment of the invention is shown. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer apparatus 600 includes a Central Processing Unit (CPU) 601 and a Graphics Processing Unit (GPU) 602, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 603 or a program loaded from a storage section 609 into a Random Access Memory (RAM) 606. In the RAM 604, various programs and data necessary for the operation of the apparatus 600 are also stored. The CPU 601, GPU602, ROM 603, and RAM 604 are connected to each other via a bus 605. An input/output (I/O) interface 606 is also connected to bus 605.
The following components are connected to the I/O interface 606: an input portion 607 including a keyboard, a mouse, and the like; an output section 608 including a display such as a Liquid Crystal Display (LCD) and a speaker; a storage section 609 including a hard disk and the like; and a communication section 610 including a network interface card such as a LAN card, a modem, or the like. The communication section 610 performs communication processing via a network such as the internet. The drive 611 may also be connected to the I/O interface 606 as needed. A removable medium 612 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 611 as necessary, so that a computer program read out therefrom is mounted into the storage section 609 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communication section 610, and/or installed from the removable media 612. The computer programs, when executed by a Central Processing Unit (CPU) 601 and a Graphics Processor (GPU) 602, perform the above-described functions defined in the method of the present invention.
It should be noted that the computer readable medium of the present invention can be a computer readable signal medium or a computer readable medium or any combination of the two. The computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or any combination of the foregoing. More specific examples of the computer readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be provided in a processor.
As another aspect, the present invention also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: taking a stream from the front-end sensing equipment and storing the stream to a video platform; responding to the acquisition of the stored front-end sensing equipment data information; and analyzing based on the acquired data information, and judging the running condition of the front-end sensing equipment so as to further finish the full-amount inspection and intelligent inspection of the front-end sensing equipment.
The foregoing description is only exemplary of the preferred embodiments of the invention and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention according to the present invention is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the scope of the invention as defined by the appended claims. For example, the above features and (but not limited to) features having similar functions disclosed in the present invention are mutually replaced to form the technical solution.
Claims (10)
1. A big data-based inspection method for front-end sensing equipment is characterized by comprising the following steps:
responding to the acquisition of the stored front-end sensing equipment data information;
and analyzing based on the acquired data information, and judging the running condition of the front-end sensing equipment so as to further finish the full-amount inspection and intelligent inspection of the front-end sensing equipment.
2. The big data based inspection method for the front-end sensing equipment, according to claim 1, further comprising: and taking the stream from the front-end sensing equipment and storing the stream to the video platform.
3. The inspection method of the front-end sensing equipment based on the big data according to claim 2, wherein the full inspection of the front-end sensing equipment specifically comprises:
collecting user behavior data log generated by a user using a video platform and service data generated by a portrait platform and a vehicle platform through a flash and sqoop platform, then pushing the collected data to kafka, and storing the flash to Hdfs of hadoop of a big data platform through collecting data of the kafka for storage;
performing data layering statistical sorting on a data warehouse formed by hive on a big data platform through a video diagnosis platform to generate recommended data, and diagnosing recommended equipment ranking through the video quality diagnosis platform to generate a video quality diagnosis result;
monitoring a video through an IT monitoring platform to generate monitoring data;
and carrying out notification alarm by combining the video quality diagnosis data and the monitoring data of the IT monitoring platform.
4. The inspection method according to claim 3, wherein the user behavior data log comprises video viewing, video preview, video download, collection, scoring and playback by using a video platform client; the business data generated by the portrait platform and the vehicle platform comprise portrait data captured by the portrait cloud platform, vehicle data captured by the vehicle platform and case incident places of related departments.
5. The inspection method for the front-end sensing equipment based on the big data according to claim 4, wherein the data layering statistics and sorting comprises ranking and sorting according to equipment scores by a MySQL report system;
the equipment scoring rules comprise video checking, video downloading, scoring, portrait snapshot and vehicle snapshot adding one point each time; scoring according to the degree of traffic congestion; the first-level point location fifth, the second-level point location third and the third-level point location first of the incident location of the related department.
6. The big data based inspection method for the front-end sensing equipment, according to claim 5, further comprising: and when the integrity of the polling result video, the online and offline number of the equipment and the video quality are lower than a preset threshold value, sending a short message and sending voice through the unified platform to inform and alarm.
7. The big data based inspection method for the front-end sensing equipment, according to claim 6, further comprising: and traffic situation information generated by a map development platform is added, gathered and pushed to kafka, and intelligent inspection is carried out on front-end sensing equipment of the counterweight positions.
8. The utility model provides a system of patrolling and examining of front end perception equipment based on big data which characterized in that, this system specifically includes:
the data storage module is used for storing data information of the front-end sensing equipment;
the data acquisition module is used for responding to and acquiring the stored data information of the front-end sensing equipment;
and the analysis and judgment module is used for analyzing based on the acquired data information and judging the running condition of the front-end sensing equipment so as to further finish the full-amount inspection and intelligent inspection of the front-end sensing equipment.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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