CN115017014A - Highway electromechanical monitoring system and method - Google Patents

Highway electromechanical monitoring system and method Download PDF

Info

Publication number
CN115017014A
CN115017014A CN202210807874.2A CN202210807874A CN115017014A CN 115017014 A CN115017014 A CN 115017014A CN 202210807874 A CN202210807874 A CN 202210807874A CN 115017014 A CN115017014 A CN 115017014A
Authority
CN
China
Prior art keywords
data
monitoring
monitoring system
structured
charging
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.)
Granted
Application number
CN202210807874.2A
Other languages
Chinese (zh)
Other versions
CN115017014B (en
Inventor
龙开红
周玲
甘洁之
黄伟彪
陈佳
陈洽尧
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Litong Technology Investment Co ltd
Original Assignee
Guangdong Litong Technology Investment Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Guangdong Litong Technology Investment Co ltd filed Critical Guangdong Litong Technology Investment Co ltd
Priority to CN202210807874.2A priority Critical patent/CN115017014B/en
Publication of CN115017014A publication Critical patent/CN115017014A/en
Application granted granted Critical
Publication of CN115017014B publication Critical patent/CN115017014B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425—Traffic logging, e.g. anomaly detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computing Systems (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Computer Security & Cryptography (AREA)
  • Mathematical Physics (AREA)
  • Computer Hardware Design (AREA)
  • Mathematical Analysis (AREA)
  • Algebra (AREA)
  • Mathematical Optimization (AREA)
  • Probability & Statistics with Applications (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Alarm Systems (AREA)

Abstract

The invention provides an electromechanical monitoring system for an expressway, which comprises: the system comprises a central monitoring server, a first monitoring system, a second monitoring system, a third monitoring system, a charging system, a monitoring system and a network safety system; the first monitoring system collects the original data of the charging system, processes the original data and sends the processed data to the central monitoring server; the second monitoring system collects the original data of the monitoring system, processes the original data and sends the processed data to the central monitoring server; the third monitoring system collects the original data of the network safety system, processes the original data and sends the processed data to the central monitoring server; and the central monitoring server determines the comprehensive health condition of the charging system, the monitoring system and the network security system based on the received and processed data of the charging system, the monitoring system and the network security system. The invention monitors the electromechanical system through a plurality of data dimensions, and is convenient for finding the fault of the system.

Description

Highway electromechanical monitoring system and method
Technical Field
The invention relates to the technical field of computer data processing, in particular to an electromechanical monitoring system and method for an expressway.
Background
At present, an electromechanical monitoring system of a highway generally realizes a basic monitoring and early warning function based on equipment running state and the like, charging service monitoring does not form a unified standard, monitoring indexes are not comprehensive, judgment logic is not rigorous, service development requirements cannot be supported, a charging network is isolated from a monitoring network, and the monitoring function also forms a service dispersion situation. The monitoring system also lacks the combination with operation and maintenance data and flow in function, and the service expansibility is poor.
In the prior art, a statistical interval estimation method is applied to perform early warning, and when an index value exceeds the following interval, the early warning is performed:
Figure 100002_DEST_PATH_IMAGE001
a forward indicator;
Figure DEST_PATH_IMAGE002
reverse direction index;
since the section is judged only statistically whether the index is abnormal, it is difficult to accurately judge whether the equipment is abnormal.
In addition, in the prior art, fault evaluation is also performed by using a factor analysis method, but in the prior art, a factor analysis model determines the factor weight depending on the dispersion degree of each factor, which may cause some factors with lower dispersion degrees but have larger influence on actual services, resulting in inaccurate analysis results.
Disclosure of Invention
The present invention proposes the following technical solutions to address one or more technical defects in the prior art.
A highway electromechanical monitoring system, the system comprising: the system comprises a central monitoring server, a first monitoring system, a second monitoring system, a third monitoring system, a charging system, a monitoring system and a network safety system;
the first monitoring system collects the original data of the charging system, processes the original data and sends the processed data to the central monitoring server;
the second monitoring system collects original data of the monitoring system, processes the original data and sends the processed data to the central monitoring server;
the third monitoring system collects the original data of the network security system, processes the original data and sends the processed data to the central monitoring server;
and the central monitoring server determines the comprehensive health condition of the charging system, the monitoring system and the network security system based on the received and processed data of the charging system, the monitoring system and the network security system.
Furthermore, the highway is divided into a plurality of sections according to the length, each section is provided with a first monitoring system, a second monitoring system and a third monitoring system, and the first monitoring system, the second monitoring system and the third monitoring system are respectively connected with the charging system, the monitoring system and the network safety system of the section.
Furthermore, the first monitoring system comprises a first edge computing device, a first structured data acquisition device and a first log analysis device, the original data of the charging system comprises transaction data, system performance data, device state data, system setting parameters and system log data, the first log analysis device carries out structured processing on the transaction data, the system performance data, the device state data, the system setting parameters and the system log data to obtain structured data of the charging system, and the first structured data acquisition device obtains the structured data of the charging system from the first log analysis device and then sends the structured data of the charging system to the first edge computing device through first heartbeat data to be subjected to preliminary screening, collection and analysis and then sends the data to the central monitoring server.
Further, the second monitoring system includes a second edge computing device, a second structured data acquisition device, and an image AI diagnostic system, the raw data of the monitoring system comprises video image data, equipment state data, system setting parameters and system log data, the image AI diagnostic system determines a sharpness value of a fixed line in an image using an AI algorithm, and judging whether the comprehensive sharpness value of the image reaches a threshold value, judging whether the video quality reaches the standard, generating the result of whether the video quality reaches the standard, equipment state data, system setting parameters and system log data into monitoring system structured data, and sending the monitoring system structured data acquired by the second structured data acquisition equipment from the image AI diagnosis system to the second edge computing equipment for preliminary screening, collection and analysis and then sending to a central monitoring server.
Furthermore, the third monitoring system includes a third edge computing device, a third structured data collection device, and a third log analysis device, where the original data of the network security system includes security threat event data, network state data, device state data, and system log data, the third log analysis device performs structured processing on the security threat event data, the network state data, the device state data, and the system log data to obtain structured data of the network security system, and the third structured data collection device obtains the structured data of the network security system from the third log analysis device, and then sends the structured data of the network security system to the third edge computing device through the third heartbeat data, performs preliminary screening, aggregation, and analysis, and then sends the structured data to the central monitoring server.
Furthermore, the first edge computing device performs preliminary screening, collection and analysis on the structured data of the charging system and the structured data of the network security system by using an interval estimation early warning method, and then only sends abnormal data to the central monitoring server, wherein the interval estimation early warning method comprises the following steps:
Figure DEST_PATH_IMAGE003
the forward index is failed, but the index value does not exceed the early warning value;
Figure 477795DEST_PATH_IMAGE004
forward index, no fault occurs, but the index value exceeds the early warning value;
Figure DEST_PATH_IMAGE005
the reverse index is adopted, and the index value does not exceed the early warning value when a fault occurs;
Figure 332618DEST_PATH_IMAGE006
the reverse index indicates that no fault occurs but the index value exceeds the early warning value;
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE007
is the index sample mean, a is the confidence, u is the quantile at a/2 confidence,
Figure 689519DEST_PATH_IMAGE008
is the sample standard deviation, and n is the number of data.
Further, the image AI diagnostic system performs the operations of: extracting the gray value of an image shot by a monitoring camera; calculating the average gray value of the image:
Figure DEST_PATH_IMAGE009
wherein x and y are coordinate values of pixel points in the picture, and f (x, y) is a gray value of the pixel point (x, y); the variance of the mean gray value of the image is calculated by using a variance method:
Figure 756832DEST_PATH_IMAGE010
(ii) a The video static environment is sampled, the influence of dynamic factors such as vehicles on the gray value is avoided, and the variance of the gray value under different external environments is collected to determine different external environmentsThe variance threshold t under the environment is automatically adapted and monitored when the illuminance data of the meteorological sensor is accessed; as the gray value variance can be increased due to vehicle dynamic factors, the system carries out snapshot for multiple times and takes the minimum value
Figure DEST_PATH_IMAGE011
When is coming into contact with
Figure 508888DEST_PATH_IMAGE012
Time, or when the environmental parameters are not changed obviously but the gray value variance-to-ring ratio is reduced obviously
Figure DEST_PATH_IMAGE013
The early warning is carried out, toll station and road surface monitoring video automatic monitoring are realized, wherein, k is dynamic environment influence parameter, confirms through the sampling, promptly: minimum gray variance in dynamic environment/minimum gray variance in static environment.
Furthermore, the manner of determining the comprehensive health condition of the charging system, the monitoring system and the network security system by the central monitoring server based on the received processed data of the charging system, the monitoring system and the network security system is as follows:
calculating the comprehensive health status value by adopting an optimized factor analysis method:
Figure 767568DEST_PATH_IMAGE014
；
wherein, w 1 、w 2 、w 2 Factor weights, f, for charging system, monitoring system and network security system, respectively 1 、f 2 、f 3 Actual failure rates of the charging system, the monitoring system and the network security system respectively, m is the total number of actual failures related to each factor, k 1 、k 2 、k 3 Are respectively and 1 、f 2 、f 3 the number of actual failures associated.
Further, f 1 、f 2 、f 3 By central monitoring server based on the connectionAnd calculating abnormal data in the received structured data of the charging system, the structured data of the monitoring system and the structured data of the network security system.
Furthermore, the central monitoring server is a cloud server.
The invention also provides an electromechanical monitoring method of the expressway electromechanical monitoring system based on any one of the above, which comprises the following steps:
a charging system acquisition step, wherein the first monitoring system acquires original data of the charging system, processes the original data and sends the processed data to the central monitoring server;
a monitoring system acquisition step, wherein the second monitoring system acquires original data of the monitoring system, processes the original data and sends the processed original data to the central monitoring server;
a network security system acquisition step, wherein the third monitoring system acquires original data of the network security system, processes the original data and sends the processed data to the central monitoring server;
and processing, namely determining the comprehensive health conditions of the charging system, the monitoring system and the network security system by the central monitoring server based on the received and processed data of the charging system, the monitoring system and the network security system.
Further, in the step of treating, the f 1 、f 2 、f 3 And the data are calculated by the central monitoring server based on the received abnormal data in the structured data of the charging system, the structured data of the monitoring system and the structured data of the network security system.
Furthermore, the central monitoring server is a cloud server.
The invention has the technical effects that: the invention relates to an electromechanical monitoring system for an expressway, which comprises: the system comprises a central monitoring server, a first monitoring system, a second monitoring system, a third monitoring system, a charging system, a monitoring system and a network safety system; the first monitoring system collects the original data of the charging system, processes the original data and sends the processed data to the central monitoring server; the second monitoring system collects the original data of the monitoring system, processes the original data and sends the processed data to the central monitoring server; the third monitoring system collects the original data of the network safety system, processes the original data and sends the processed data to the central monitoring server; and the central monitoring server determines the comprehensive health condition of the charging system, the monitoring system and the network security system based on the received and processed data of the charging system, the monitoring system and the network security system. In the invention, the terminal nodes such as charging/monitoring and the like perform self-monitoring based on-site service requirements, and edge calculation is performed according to service subsystem differentiation by taking road sections as sub-nodes, so that intelligent system monitoring at the road section level is realized; the central monitoring server carries out cloud deployment, extracts key monitoring and service data from each service subsystem of a road section, carries out three-dimensional monitoring on the whole-network full-service data, ensures monitoring accuracy and early warning timeliness, namely carries out monitoring on an electromechanical system through a plurality of data dimensions, and is convenient for finding out faults of the system; according to the invention, a charging system, a monitoring system and a network security system are respectively processed during data acquisition, a log analysis system is established, key information of service system operation in logs is extracted through strict logic analysis, preliminary statistics is carried out, unstructured data such as time and the like are structured, transmission and storage of original files are greatly reduced, efficient application of data is realized, an AI algorithm is used for judging the sharpness value of a fixed line in an image, and whether the comprehensive sharpness value of the image reaches a threshold value is judged, so that whether the video quality reaches the standard is judged, the data transmission quantity is reduced, and the system operation efficiency is improved; in the invention, the gray value of the video image is extracted, the AI algorithm is used for judging the sharpness value of the fixed line in the image, and whether the comprehensive sharpness value of the image reaches the threshold value is judged, so that whether the video quality reaches the standard is judged, and in the calculation process, the variance threshold value under the external environment is automatically adapted and monitored to perform early warning based on the real-time illumination data of the meteorological sensor, so that the automatic monitoring of the toll station and the road surface monitoring video is realized.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings.
Fig. 1 is a block diagram of a highway electromechanical monitoring system according to an embodiment of the present invention.
Fig. 2 is a flow chart of a highway electromechanical monitoring method according to an embodiment of the invention.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 shows a motorway electromechanical monitoring system of the invention, said system comprising: the system comprises a central monitoring server, a first monitoring system, a second monitoring system, a third monitoring system, a charging system, a monitoring system and a network safety system;
the first monitoring system collects the original data of the charging system, processes the original data and sends the processed data to the central monitoring server;
the second monitoring system collects the original data of the monitoring system, processes the original data and sends the processed data to the central monitoring server;
the third monitoring system collects the original data of the network safety system, processes the original data and sends the processed data to the central monitoring server;
and the central monitoring server determines the comprehensive health condition of the charging system, the monitoring system and the network security system based on the received and processed data of the charging system, the monitoring system and the network security system.
In one embodiment, the highway is divided into a plurality of sections according to the length, and each section is provided with a first monitoring system, a second monitoring system and a third monitoring system which are respectively connected with the charging system, the monitoring system and the network security system of the section.
In the invention, the terminal nodes such as charging/monitoring and the like carry out self-monitoring based on the field service requirement, and edge calculation is carried out according to the service subsystem differentiation by taking the road section as a sub-node, thereby realizing the system intelligent monitoring of the road section level; the central monitoring server performs cloud deployment, extracts key monitoring and service data from each service subsystem of a road section, performs whole-network full-service data three-dimensional monitoring, and ensures monitoring accuracy and early warning timeliness, namely, the electromechanical system is monitored through a plurality of data dimensions, so that system faults are conveniently found, which is an important invention point of the invention.
In one embodiment, the first monitoring system includes a first edge computing device, a first structured data collection device, and a first log analysis device, the raw data of the charging system includes transaction data, system performance data, device status data, system setting parameters, and system log data, the first log analysis device performs structured processing on the transaction data, the system performance data, the device status data, the system setting parameters, and the system log data to obtain structured data of the charging system, and the first structured data collection device obtains the structured data of the charging system from the first log analysis device, and then sends the structured data of the charging system to the first edge computing device through first heartbeat data to perform preliminary screening, collection, and analysis, and then sends the structured data to the central monitoring server.
In one embodiment, the second monitoring system includes a second edge computing device, a second structured data acquisition device, and an image AI diagnostic system, the raw data of the monitoring system comprises video image data, equipment state data, system setting parameters and system log data, the image AI diagnostic system determines a sharpness value of a fixed line in an image using an AI algorithm, and judging whether the comprehensive sharpness value of the image reaches a threshold value, judging whether the video quality reaches the standard, generating the result of whether the video quality reaches the standard, equipment state data, system setting parameters and system log data into monitoring system structured data, and sending the monitoring system structured data acquired by the second structured data acquisition equipment from the image AI diagnosis system to the second edge computing equipment for preliminary screening, collection and analysis and then sending to a central monitoring server.
In one embodiment, the third monitoring system includes a third edge computing device, a third structured data collection device, and a third log analysis device, where the raw data of the network security system includes security threat event data, network state data, device state data, and system log data, the third log analysis device performs structured processing on the security threat event data, the network state data, the device state data, and the system log data to obtain structured data of the network security system, and the third structured data collection device obtains the structured data of the network security system from the third log analysis device, and then sends the structured data of the network security system to the third edge computing device through a third heartbeat data collection device for preliminary screening, aggregation, analysis, and then sends the structured data to the central monitoring server.
In the invention, the charging system, the monitoring system and the network security system are respectively processed during data acquisition, and a set of log analysis system is established, extracting the key information of the operation of the service system in the log and carrying out preliminary statistics through strict logic analysis, and structuralizes unstructured data such as time and the like, greatly reduces the transmission and storage of original files, realizes the high-efficiency application of data, and uses AI algorithm to judge the sharpness value of the fixed line in the image and judge whether the integrated sharpness value of the image reaches the threshold value, thereby judging whether the video quality reaches the standard or not, reducing the data transmission quantity, improving the system operation efficiency, in addition, in the invention, the first heartbeat data, the second heartbeat data and the third heartbeat data can be set according to the size of the data volume to be transmitted, so that the data transmission efficiency is further improved, which is another important invention point of the invention.
In one embodiment, the first edge computing device performs preliminary screening, aggregation and analysis on the charging system structured data and the network security system structured data by using an interval estimation early warning method, and then sends only abnormal data to the central monitoring server, wherein the interval estimation early warning method is as follows:
Figure 237864DEST_PATH_IMAGE003
the forward index is failed, but the index value does not exceed the early warning value;
Figure 905606DEST_PATH_IMAGE004
forward index, no fault occurs, but the index value exceeds the early warning value;
Figure 43326DEST_PATH_IMAGE005
the reverse index is adopted, and the index value does not exceed the early warning value when a fault occurs;
Figure 771111DEST_PATH_IMAGE006
the reverse index indicates that no fault occurs but the index value exceeds the early warning value;
wherein the content of the first and second substances,
Figure 994282DEST_PATH_IMAGE007
is the index sample mean, a is the confidence, u is the quantile at a/2 confidence,
Figure 200135DEST_PATH_IMAGE008
is the sample standard deviation, and n is the number of data.
In the invention, the interval of the interval estimation method in the background technology is optimized by combining the production environment, and when the production environment fails and the related indexes do not exceed the interval range, the interval is contracted at a fixed ratio; when the production environment is not in fault but the related indexes exceed the range of the interval, the interval is expanded by a fixed proportion. And the model is continuously trained by combining actual service data, and the early warning value of the index is optimized. Thereby improving the accuracy of the fault prediction, which is another important invention point of the invention.
In one embodiment, the image AI diagnostic system performs the operations of: extracting the gray value of an image shot by a monitoring camera;calculating the average gray value of the image:
Figure 192362DEST_PATH_IMAGE009
wherein x and y are coordinate values of pixel points in the picture, and f (x, y) is a gray value of the pixel point (x, y); the variance of the mean gray value of the image is calculated by using a variance method:
Figure 583723DEST_PATH_IMAGE010
(ii) a Sampling a video static environment, avoiding influence of dynamic factors such as vehicles on a gray value, collecting gray value variances under different external environments to determine variance threshold values t under different external environments, and automatically adapting and monitoring the variance threshold values t under the external environments when the illuminance data of a meteorological sensor is accessed; as the gray value variance can be increased due to vehicle dynamic factors, the system carries out snapshot for multiple times and takes the minimum value
Figure 559770DEST_PATH_IMAGE011
When is coming into contact with
Figure 303735DEST_PATH_IMAGE012
Time, or when the environmental parameters are not changed obviously but the gray value variance-to-ring ratio is reduced obviously
Figure 416047DEST_PATH_IMAGE013
The early warning is carried out, toll station and road surface monitoring video automatic monitoring are realized, wherein, k is dynamic environment influence parameter, confirms through the sampling, promptly: minimum grayscale variance in dynamic environment/minimum grayscale variance in static environment.
In the invention, the gray value of the video image is extracted, the AI algorithm is used for judging the sharpness value of the fixed line in the image, and whether the comprehensive sharpness value of the image reaches the threshold value is judged, so that whether the video quality reaches the standard is judged, and in the calculation process, the variance threshold value under the external environment is automatically adapted and monitored to perform early warning based on the real-time illumination data of the meteorological sensor, so that the automatic monitoring of the toll station and the road surface monitoring video is realized, which is another important invention point of the invention.
In one embodiment, the central monitoring server determines the comprehensive health condition of the charging system, the monitoring system and the network security system based on the received processed data of the charging system, the monitoring system and the network security system by:
calculating the comprehensive health status value by adopting an optimized factor analysis method:
Figure 220055DEST_PATH_IMAGE014
；
wherein w 1 、w 2 、w 2 Factor weights for charging systems, monitoring systems and network security systems, f 1 、f 2 、f 3 Actual failure rates for charging systems, monitoring systems and network security systems, m being the total number of actual failures associated with each factor, k 1 、k 2 、k 3 Are respectively and 1 、f 2 、f 3 the number of actual faults associated.
In one embodiment, f 1 、f 2 、f 3 And the central monitoring server calculates the abnormal data based on the received structured data of the charging system, the structured data of the monitoring system and the structured data of the network security system. In one embodiment, the central monitoring server is a cloud server.
In the invention, because the traditional factor analysis model is dependent on the discrete degree of each factor when determining the factor weight, some factors with lower discrete degree but larger influence on actual service are probably caused, the invention combines the model with the actual fault occurrence rate and provides the optimized factor analysis method, namely, on the basis of the traditional factor analysis method, correction is carried out according to the number of various faults and the total number of the faults, so that the accuracy of the optimized factor analysis method is improved, which is another important invention point of the invention.
Fig. 2 shows an electromechanical monitoring method of an electromechanical highway monitoring system according to any one of the above embodiments, the method comprising:
a charging system acquisition step S201, wherein the first monitoring system acquires original data of the charging system, processes the original data and sends the processed data to the central monitoring server;
a monitoring system acquisition step S202, wherein the second monitoring system acquires original data of the monitoring system, processes the original data and sends the processed data to the central monitoring server;
a network security system acquisition step S203, wherein the third monitoring system acquires original data of the network security system, processes the original data and sends the processed data to the central monitoring server;
and a processing step S204, wherein the central monitoring server determines the comprehensive health condition of the charging system, the monitoring system and the network security system based on the received and processed data of the charging system, the monitoring system and the network security system.
In one embodiment, the highway is divided into a plurality of sections according to the length, and each section is provided with a first monitoring system, a second monitoring system and a third monitoring system which are respectively connected with the charging system, the monitoring system and the network security system of the section.
In the invention, the terminal nodes such as charging/monitoring and the like perform self-monitoring based on-site service requirements, and edge calculation is performed according to service subsystem differentiation by taking road sections as sub-nodes, so that intelligent system monitoring at the road section level is realized; the central monitoring server performs cloud deployment, extracts key monitoring and service data from each service subsystem of a road section, performs whole-network full-service data three-dimensional monitoring, and ensures monitoring accuracy and early warning timeliness, namely, the electromechanical system is monitored through a plurality of data dimensions, so that system faults are conveniently found, which is an important invention point of the invention.
In one embodiment, the first monitoring system includes a first edge computing device, a first structured data collection device, and a first log analysis device, the raw data of the charging system includes transaction data, system performance data, device status data, system setting parameters, and system log data, the first log analysis device performs structured processing on the transaction data, the system performance data, the device status data, the system setting parameters, and the system log data to obtain structured data of the charging system, and the first structured data collection device obtains the structured data of the charging system from the first log analysis device, and then sends the structured data of the charging system to the first edge computing device through first heartbeat data to perform preliminary screening, collection, and analysis, and then sends the structured data to the central monitoring server.
In one embodiment, the second monitoring system includes a second edge computing device, a second structured data acquisition device, and an image AI diagnostic system, the raw data of the monitoring system comprises video image data, equipment state data, system setting parameters and system log data, the image AI diagnostic system determines a sharpness value of a fixed line in an image using an AI algorithm, and judging whether the comprehensive sharpness value of the image reaches a threshold value, judging whether the video quality reaches the standard, generating the result of whether the video quality reaches the standard, equipment state data, system setting parameters and system log data into monitoring system structured data, and sending the monitoring system structured data acquired by the second structured data acquisition equipment from the image AI diagnosis system to the second edge computing equipment for preliminary screening, collection and analysis and then sending to a central monitoring server.
In one embodiment, the third monitoring system includes a third edge computing device, a third structured data collection device, and a third log analysis device, where the raw data of the network security system includes security threat event data, network state data, device state data, and system log data, the third log analysis device performs structured processing on the security threat event data, the network state data, the device state data, and the system log data to obtain structured data of the network security system, and the third structured data collection device obtains the structured data of the network security system from the third log analysis device, and then sends the structured data of the network security system to the third edge computing device through a third heartbeat data to perform preliminary screening, aggregation, and analysis, and then sends the structured data to the central monitoring server.
In the invention, the charging system, the monitoring system and the network security system are respectively processed during data acquisition, and a set of log analysis system is established, extracting the key information of the operation of the service system in the log and carrying out preliminary statistics through strict logic analysis, and structuralizes unstructured data such as time and the like, greatly reduces the transmission and storage of original files, realizes the high-efficiency application of data, and uses AI algorithm to judge the sharpness value of the fixed line in the image and judge whether the integrated sharpness value of the image reaches the threshold value, thereby judging whether the video quality reaches the standard or not, reducing the data transmission quantity, improving the system operation efficiency, in addition, in the invention, the first heartbeat data, the second heartbeat data and the third heartbeat data can be set according to the size of the data volume to be transmitted, so that the data transmission efficiency is further improved, which is another important invention point of the invention.
In one embodiment, the first edge computing device performs preliminary screening, aggregation and analysis on the charging system structured data and the network security system structured data by using an interval estimation early warning method, and then sends only abnormal data to the central monitoring server, wherein the interval estimation early warning method is as follows:
Figure 355501DEST_PATH_IMAGE003
the forward index is failed, but the index value does not exceed the early warning value;
Figure 401693DEST_PATH_IMAGE004
forward index, no fault occurs, but the index value exceeds the early warning value;
Figure 368512DEST_PATH_IMAGE005
the reverse index is adopted, and the index value does not exceed the early warning value when a fault occurs;
Figure 609000DEST_PATH_IMAGE006
the index value exceeds the early warning value when the fault does not occur;
wherein the content of the first and second substances,
Figure 28480DEST_PATH_IMAGE007
is an indexSample mean, a is confidence, u is quantile at a/2 confidence,
Figure 379827DEST_PATH_IMAGE008
is the sample standard deviation, and n is the number of data.
In the invention, the interval of the interval estimation method in the background technology is optimized by combining the production environment, and when the production environment fails and the related indexes do not exceed the interval range, the interval is shrunk by a fixed proportion; when the production environment is not in fault but the related indexes exceed the range of the interval, the interval is expanded by a fixed proportion. And the model is continuously trained by combining actual service data, and the early warning value of the index is optimized. Thereby improving the accuracy of the fault prediction, which is another important invention point of the invention.
In one embodiment, the image AI diagnostic system performs the operations of: extracting the gray value of an image shot by a monitoring camera; calculating the average gray value of the image:
Figure 201153DEST_PATH_IMAGE009
wherein x and y are coordinate values of pixel points in the picture, and f (x, y) is a gray value of the pixel point (x, y); the variance of the mean gray value of the image is calculated by using a variance method:
Figure 878121DEST_PATH_IMAGE010
(ii) a Sampling a video static environment, avoiding influence of dynamic factors such as vehicles on a gray value, collecting gray value variances under different external environments to determine variance threshold values t under different external environments, and automatically adapting and monitoring the variance threshold values t under the external environments when the illuminance data of a meteorological sensor is accessed; as the gray value variance can be increased due to vehicle dynamic factors, the system carries out snapshot for multiple times and takes the minimum value
Figure 784898DEST_PATH_IMAGE011
When is coming into contact with
Figure 674356DEST_PATH_IMAGE012
No significant change in time, or environmental parametersBut the gray value variance-to-ring ratio is significantly reduced
Figure 114303DEST_PATH_IMAGE013
The early warning is carried out, toll station and road surface monitoring video automatic monitoring are realized, wherein, k is dynamic environment influence parameter, confirms through the sampling, promptly: minimum gray variance in dynamic environment/minimum gray variance in static environment.
In the invention, the gray value of the video image is extracted, the AI algorithm is used for judging the sharpness value of the fixed line in the image, and whether the comprehensive sharpness value of the image reaches the threshold value is judged, so that whether the video quality reaches the standard is judged, and in the calculation process, the variance threshold value under the external environment is automatically adapted and monitored to perform early warning based on the real-time illumination data of the meteorological sensor, so that the automatic monitoring of the toll station and the road surface monitoring video is realized, which is another important invention point of the invention.
In one embodiment, the central monitoring server determines the comprehensive health condition of the charging system, the monitoring system and the network security system based on the received processed data of the charging system, the monitoring system and the network security system by:
calculating the comprehensive health status value by adopting an optimized factor analysis method:
Figure 696594DEST_PATH_IMAGE014
；
wherein, w 1 、w 2 、w 2 Factor weights for charging systems, monitoring systems and network security systems, f 1 、f 2 、f 3 Actual failure rates for charging systems, monitoring systems and network security systems, m being the total number of actual failures associated with each factor, k 1 、k 2 、k 3 Are respectively and 1 、f 2 、f 3 the number of actual failures associated.
In one embodiment, f 1 、f 2 、f 3 Receipt-based structured data of charging system and monitoring system by central monitoring serverAnd calculating abnormal data in the structured data and the network safety system structured data. In one embodiment, the central monitoring server is a cloud server.
In the invention, because the traditional factor analysis model is dependent on the discrete degree of each factor when determining the factor weight, some factors with lower discrete degree but larger influence on actual service are probably caused, the invention combines the model with the actual fault occurrence rate and provides the optimized factor analysis method, namely, on the basis of the traditional factor analysis method, correction is carried out according to the number of various faults and the total number of the faults, so that the accuracy of the optimized factor analysis method is improved, which is another important invention point of the invention.
An embodiment of the present invention provides a computer storage medium, on which a computer program is stored, and the computer program on the computer storage medium is executed by a processor to implement the method, and the computer storage medium can be a hard disk, a DVD, a CD, a flash memory, and the like.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application or portions thereof contributing to the prior art may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the apparatuses according to the embodiments or some parts of the embodiments of the present application.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made thereto without departing from the spirit and scope of the invention and it is intended to cover in the claims the invention as defined in the appended claims.

Claims (10)

1. An electromechanical highway monitoring system, comprising: the system comprises a central monitoring server, a first monitoring system, a second monitoring system, a third monitoring system, a charging system, a monitoring system and a network safety system;
the first monitoring system collects the original data of the charging system, processes the original data and sends the processed data to the central monitoring server;
the second monitoring system collects the original data of the monitoring system, processes the original data and sends the processed data to the central monitoring server;
the third monitoring system collects the original data of the network safety system, processes the original data and sends the processed data to the central monitoring server;
and the central monitoring server determines the comprehensive health condition of the charging system, the monitoring system and the network security system based on the received and processed data of the charging system, the monitoring system and the network security system.
2. The system of claim 1, wherein the highway is divided into a plurality of sections according to length, each section having a first monitoring system, a second monitoring system and a third monitoring system, the first monitoring system, the second monitoring system and the third monitoring system being adapted to be connected to the toll collection system, the monitoring system and the network security system of the section, respectively.
3. The system according to claim 2, wherein the first monitoring system comprises a first edge computing device, a first structured data collection device and a first log analysis device, the raw data of the charging system comprises transaction data, system performance data, device status data, system setting parameters and system log data, the first log analysis device performs structured processing on the transaction data, the system performance data, the device status data, the system setting parameters and the system log data to obtain structured data of the charging system, and the first structured data collection device obtains the structured data of the charging system from the first log analysis device, and then sends the structured data of the charging system to the first edge computing device for preliminary screening, aggregation and analysis, and then sends the data to the central monitoring server.
4. The system of claim 3, wherein the second monitoring system comprises a second edge computing device, a second structured data collecting device, and an image AI diagnostic system, the raw data of the monitoring system comprises video image data, device status data, system setting parameters, and system log data, the image AI diagnostic system uses AI algorithm to determine the sharpness value of the fixed line in the image, and determine whether the integrated sharpness value of the image reaches a threshold value, and determine whether the video quality reaches the standard, and generate the result of the standard, the device status data, the system setting parameters, and the system log data into structured data of the monitoring system, the second structured data collecting device obtains the structured data of the monitoring system from the image diagnostic system, and then sends the structured data of the monitoring system to the second edge computing device via a second heartbeat data for preliminary screening, And after being collected and analyzed, the data are sent to a central monitoring server.
5. The system according to claim 4, wherein the third monitoring system includes a third edge computing device, a third structured data collection device, and a third log analysis device, the raw data of the cybersecurity system includes security threat event data, network status data, device status data, and system log data, the third log analysis device performs a structured processing on the security threat event data, the network status data, the device status data, and the system log data to obtain the structured data of the cybersecurity system, and the third structured data collection device obtains the structured data of the cybersecurity system from the third log analysis device, and then sends the structured data of the cybersecurity system to the third edge computing device through a third heartbeat data to perform a preliminary screening, an aggregation, and an analysis, and then sends the structured data to the central monitoring server.
6. The system according to any of claims 1-5, wherein the central monitoring server determines the integrated health of the charging system, the monitoring system and the network security system based on the received processed data of the charging system, the monitoring system and the network security system by:
calculating the comprehensive health status value by adopting an optimized factor analysis method:
Figure DEST_PATH_IMAGE001
；
wherein w 1 、w 2 、w 2 Factor weights, f, for charging system, monitoring system and network security system, respectively 1 、f 2 、f 3 Actual failure rates of the charging system, the monitoring system and the network security system, respectively, m is the total number of actual failures associated with the factors, k 1 、k 2 、k 3 Are respectively and 1 、f 2 、f 3 the number of actual faults associated.
7. The system of claim 6, wherein the central monitoring server is a cloud server.
8. An electromechanical monitoring method based on the electromechanical monitoring system for the highway according to claim 6, characterized in that the method comprises the following steps:
a charging system acquisition step, wherein the first monitoring system acquires original data of the charging system, processes the original data and sends the processed data to the central monitoring server;
a monitoring system acquisition step, wherein the second monitoring system acquires original data of the monitoring system, processes the original data and sends the processed original data to the central monitoring server;
a network security system acquisition step, wherein the third monitoring system acquires original data of the network security system, processes the original data and sends the processed data to the central monitoring server;
and processing, namely determining the comprehensive health condition of the charging system, the monitoring system and the network security system by the central monitoring server based on the received processed data of the charging system, the monitoring system and the network security system.
9. The method of claim 8, wherein in the step of processing, the f 1 、f 2 、f 3 And the data are calculated by the central monitoring server based on the received abnormal data in the structured data of the charging system, the structured data of the monitoring system and the structured data of the network security system.
10. The method of claim 9, wherein the central monitoring server is a cloud server.
CN202210807874.2A 2022-07-11 2022-07-11 Highway electromechanical monitoring system and method Active CN115017014B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210807874.2A CN115017014B (en) 2022-07-11 2022-07-11 Highway electromechanical monitoring system and method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210807874.2A CN115017014B (en) 2022-07-11 2022-07-11 Highway electromechanical monitoring system and method

Publications (2)

Publication Number Publication Date
CN115017014A true CN115017014A (en) 2022-09-06
CN115017014B CN115017014B (en) 2023-03-24

Family

ID=83080412

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210807874.2A Active CN115017014B (en) 2022-07-11 2022-07-11 Highway electromechanical monitoring system and method

Country Status (1)

Country Link
CN (1) CN115017014B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111726252A (en) * 2020-06-17 2020-09-29 浪潮集团有限公司 Abnormal data reporting method and tool for industrial equipment
US20210096911A1 (en) * 2020-08-17 2021-04-01 Essence Information Technology Co., Ltd Fine granularity real-time supervision system based on edge computing
CN113409179A (en) * 2021-01-26 2021-09-17 陕西交通电子工程科技有限公司 ETC portal operation state monitoring system for expressway
CN114462897A (en) * 2022-04-12 2022-05-10 广东利通科技投资有限公司 Highway electromechanical system comprehensive performance evaluation method, device and storage medium

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111726252A (en) * 2020-06-17 2020-09-29 浪潮集团有限公司 Abnormal data reporting method and tool for industrial equipment
US20210096911A1 (en) * 2020-08-17 2021-04-01 Essence Information Technology Co., Ltd Fine granularity real-time supervision system based on edge computing
CN113409179A (en) * 2021-01-26 2021-09-17 陕西交通电子工程科技有限公司 ETC portal operation state monitoring system for expressway
CN114462897A (en) * 2022-04-12 2022-05-10 广东利通科技投资有限公司 Highway electromechanical system comprehensive performance evaluation method, device and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王艳艳等: "后高速时代机电系统后评估内容分析", 《科教导刊(下旬)》 *

Also Published As

Publication number Publication date
CN115017014B (en) 2023-03-24

Similar Documents

Publication Publication Date Title
CN110839016B (en) Abnormal flow monitoring method, device, equipment and storage medium
CN110912737A (en) Dynamic perception performance early warning method based on hybrid model
WO2019019749A1 (en) Method and apparatus for detecting memory abnormalities
CN114581856B (en) Agricultural unit motion state identification method and system based on Beidou system and cloud platform
CN113269042B (en) Intelligent traffic management method and system based on driving vehicle violation identification
CN114781510A (en) Fault positioning method, device, system and storage medium
CN110662024A (en) Video quality diagnosis method and device based on multiple frames and electronic equipment
CN116594857A (en) Office software intelligent interaction management platform based on artificial intelligence
CN114615086A (en) Vehicle-mounted CAN network intrusion detection method
CN115017014B (en) Highway electromechanical monitoring system and method
CN111555917A (en) Alarm information processing method and device based on cloud platform
CN110990223A (en) Monitoring alarm method and device based on system log
CN110543827B (en) Multi-class vehicle detection method based on Gaussian mixture model and deep learning
CN111258788B (en) Disk failure prediction method, device and computer readable storage medium
EP3627389B1 (en) Method and system for automatic analysis of video streams captured by a plurality of surveillance system cameras
CN115408182A (en) Service system fault positioning method and device
CN112287461A (en) Automobile driving simulator braking system modeling method based on Gaussian process regression
CN111209789A (en) Method for carrying out image acquisition and analysis on key item point occurrence moment
CN115830806B (en) Natural disaster condition analysis system based on high-resolution remote sensing
CN117437765B (en) Accident alarm method and system based on big data driving records
CN112346985B (en) ACC function failure determination method, system, device and storage medium
CN116823233B (en) User data processing method and system based on full-period operation and maintenance
CN115514620B (en) Anomaly detection method and cloud network platform
CN117097578B (en) Network traffic safety monitoring method, system, medium and electronic equipment
CN107947984B (en) Fault prediction processing method and system for railway passenger service

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
GR01 Patent grant
GR01 Patent grant