CN117523799A - Intelligent monitoring method and system for potential safety hazards - Google Patents
Intelligent monitoring method and system for potential safety hazards Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 104
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- 238000005984 hydrogenation reaction Methods 0.000 description 8
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- 206010000117 Abnormal behaviour Diseases 0.000 description 1
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/08—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using communication transmission lines
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B7/00—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00
- G08B7/06—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00 using electric transmission, e.g. involving audible and visible signalling through the use of sound and light sources
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/50—Safety; Security of things, users, data or systems
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Abstract
The invention relates to the technical field of equipment safety monitoring, and discloses an intelligent monitoring method and system for potential safety hazards, wherein the method comprises the following steps: s1, configuration: configuring a monitoring area and equipment; s2, collecting: collecting monitoring area information and equipment operation parameters; s3, analysis: analyzing equipment operation parameters in a monitoring area; s4, pushing: pushing alarm information; s5, treatment: and disposing the alarm information. The invention solves the problems of easy error occurrence, lower efficiency, low intelligent degree and the like in the prior art.
Description
Technical Field
The invention relates to the technical field of equipment safety monitoring, in particular to an intelligent monitoring method and system for potential safety hazards.
Background
The special equipment is equipment with high risk, complexity, specialty and technical performance, and comprises a boiler, a pressure vessel, a gas filling machine, a hydrogenation machine, a liquid filling machine, a lifting machine, an elevator and the like. Special equipment is applied to various industries, such as: CNG filling station, LNG filling station, hydrogenation station, gas station, mine, liquefaction mill etc.. According to the regulations of the safety supervision of special equipment issued by the national market supervision administration, the activities of design, manufacture, installation, transformation, maintenance, inspection and detection of special equipment must meet the national standard and industry standard, and the special equipment must be used after the special equipment is inspected and detected to be qualified by the safety supervision of the special equipment. In order to maintain the life and property safety of people, the supervision and control of safety are more and more challenging in the modern society, and the safety production is more and more challenging due to industrialization, city and other reasons.
At present, the safety supervision of special equipment mainly reduces personnel life and property loss, and adopts some safety supervision schemes, and at present, two measures are mainly adopted, namely: installing a monitoring module on the equipment, collecting key parameters of the monitoring equipment in real time, comparing and judging whether the equipment is abnormal or not through parameter early warning design, and informing patrol personnel for rechecking by colleague background early warning; and a second measure: and (3) arranging patrol personnel to periodically patrol special equipment and the field, patrol environmental conditions, whether operation parameters of key equipment are normal, whether a control system gives an early warning or not and the like, reporting the patrol conditions, and generating patrol records.
The prior art has mainly the following disadvantages:
the inadequacies of timing inspection may include: the skill level of the inspector is not enough, the operation and maintenance methods of the equipment are not familiar, errors are easy to occur, and the equipment cannot normally operate; filling in the inspection table manually is easy to make errors, so that potential safety hazards are caused, and inspection personnel cannot punch cards according to rules for inspection; the biggest problem of manual spot inspection is that management and standardization are difficult to achieve, spot inspection is guaranteed to be in place, and missed inspection, false inspection and the like are frequently caused. To increase the aggressiveness and initiative of inspectors, the challenges of the job need to be increased. The regular patrol of personnel has dead angles of time and space, and all-weather, omnibearing and real-time monitoring can not be realized.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the intelligent monitoring method and the intelligent monitoring system for the potential safety hazard, and solves the problems of easy error, low efficiency, low intelligent degree and the like in the prior art.
The invention solves the problems by adopting the following technical scheme:
a potential safety hazard intelligent monitoring method comprises the following steps:
s1, configuration: configuring a monitoring area and equipment;
s2, collecting: collecting monitoring area information and equipment operation parameters;
s3, analysis: analyzing equipment operation parameters in a monitoring area;
s4, pushing: pushing alarm information;
s5, treatment: and disposing the alarm information.
As a preferred technical solution, step S1 includes the following steps:
s11, setting a camera monitoring area;
s12, configuring an artificial intelligence algorithm and a flow of each area IA monitoring;
s13, configuring an Internet of things gateway to monitor and collect frequency parameters;
s14, configuring the effective range of the monitoring point parameter;
s15, configuring a risk grade value of a monitoring point;
s16, configuring an address and a port of a network controller of the audible and visual alarm;
s17, configuring physical parameters of an optical alarm;
the order of steps S11 to S17 may be arbitrarily changed.
As a preferred technical solution, step S11 includes the following steps:
s111, configuring a camera monitoring area name;
s112, configuring behavior identification content of a monitoring area;
s113, setting alarm behaviors, alarm grades and alarm contents of behavior identification;
s114, setting an artificial intelligence algorithm bound by a camera;
s115, setting an artificial intelligent recognition operation flow.
As a preferred technical solution, step S11 further includes the following steps:
s116, setting gateway monitoring equipment of the Internet of things;
s117, setting a name corresponding to each equipment acquisition point and a corresponding point table;
s118, setting a normal working operation parameter range corresponding to a point acquired by each device;
s119, setting the acquisition frequency of the gateway of the Internet of things;
s1110, setting an alarm level of the acquisition point equipment, setting an early warning value for the docking level, and setting alarm content;
s1111, setting physical parameters of the light alarm.
As a preferred technical solution, step S2 includes the following steps:
s21, a camera collects real-time video of a monitoring area;
s22, uploading the real-time picture to a video edge gateway through an ONVIF/RTSP protocol;
s23, uploading the real-time picture to an IA video server through a video edge gateway;
s24, the gateway of the Internet of things periodically collects operation parameters of the equipment and uploads the operation parameters to the security application supervision platform.
As a preferred technical solution, step S3 includes the following steps:
s31, after uploading the real-time picture data to an AI server, analyzing the real-time picture data through an artificial intelligence algorithm;
s32, the safety supervision platform analyzes the operation parameters of the equipment through data modeling.
As a preferred embodiment, step S32 includes the steps of:
s321, establishing a device real-time acquisition point data warehouse, performing data processing in a de-duplication, filtering and deviation rectifying mode on the data warehouse, establishing a data prediction model and a prediction algorithm aiming at various characteristic values, and performing associated prediction on real-time data of device operation parameters;
s322, starting an analysis program to perform Fourier transform, hilbert transform and wavelet denoising algorithm processing on the real-time data of the equipment operation parameters, and finally giving a pre-judgment result value and a classification result through setting a threshold range.
As a preferred technical solution, step S4 includes the following steps:
s41, the AI service component pushes the alarm information to the safety supervision application platform through an MQTT message, and simultaneously, the AI service component sends the alarm information to the video edge gateway;
s42, the safety supervision platform issues alarm levels and language alarm contents through the MQTT message, and simultaneously pushes alarm information or alarm pictures to a safety personnel WeChat applet terminal;
s43, the network controller subscribes and acquires alarm information content and alarm mode and duration through the MQTT message; s44, the network controller issues alarm information to the audible and visual alarm;
s45, the video edge gateway transmits the language alarm information to the appointed camera to carry out the language alarm.
As a preferred technical solution, step S5 includes the following steps:
s51, alarm grade analysis;
s52, alarm grade treatment pre-judgment;
s53, issuing an alarm grade treatment instruction to the gateway of the Internet of things;
s54, the gateway of the Internet of things changes equipment parameters, corrects equipment operation parameters or performs emergency shutdown.
The intelligent monitoring system for potential safety hazards is used for realizing the intelligent monitoring method for potential safety hazards, and comprises the following modules connected in sequence:
and (3) a configuration module: the method is used for configuring the monitoring area and the equipment;
and the acquisition module is used for: the monitoring system is used for collecting monitoring area information and equipment operation parameters;
and an analysis module: the device operation parameter analysis module is used for analyzing the device operation parameters in the monitoring area;
and the pushing module is used for: the alarm device is used for pushing alarm information;
a treatment module: and the alarm information is used for processing the alarm information.
Compared with the prior art, the invention has the following beneficial effects:
the intelligent monitoring for 24 hours continuously monitors and solves the problems of safety, real-time monitoring, efficiency and artificial intelligence and big data analysis and processing, and in addition, the mechanism of data acquisition and intelligent disposal of the Internet of things is adopted, so that the accuracy and efficiency of potential safety hazards existing in special equipment are improved, the response is precise, and the quick disposal is realized, thereby reducing the occurrence of operation safety accidents of the special equipment, reducing property loss and improving the life quality of people.
Drawings
FIG. 1 is a schematic diagram of steps of an intelligent monitoring method for potential safety hazards according to the invention;
fig. 2 is a hardware block diagram for implementing the intelligent monitoring method for potential safety hazards according to the invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
Example 1
As shown in fig. 1 to 2, the intelligent monitoring method based on the potential safety hazard of the special equipment comprises the following steps:
step 1, configuring a monitoring area and equipment, wherein the step further comprises the following substeps: 1-1, setting a camera monitoring area; 1-2, configuring an artificial intelligence algorithm and a flow of each area IA monitoring; 1-3, configuring an Internet of things gateway to monitor and collect frequency parameters; 1-4, configuring the effective range of the parameters of the monitoring points; 1-5, configuring a risk grade value of a monitoring point; 1-6, configuring an address and a port of a network controller of the audible and visual alarm; 1-7, configuring physical parameters of the optical alarm.
Step 2, monitoring area information acquisition and equipment operation parameter acquisition, wherein the steps further comprise the following sub-steps: 2-1, a camera collects real-time pictures of a monitoring area; 2-2, uploading to a video edge gateway through an ONVIF/RTSP protocol; 2-3, uploading to an IA video server through a video edge gateway; 2-4, uploading the operation parameters of the gateway timing acquisition equipment of the Internet of things to a security application supervision platform, and acquiring the frequency, wherein the sub-step 1-3 of the step 1 is required to be referred to, and the gateway monitoring and frequency parameter acquisition of the Internet of things are configured;
step 3, monitoring information analysis, the step further includes the sub-steps of: 3-1, the video real-time data uploading AI server analyzes through an artificial intelligence algorithm; 3-2, analyzing the equipment operation parameters by the safety supervision platform through data modeling;
the data modeling is to build a special device real-time acquisition point data warehouse, perform data processing such as de-duplication, filtering, deviation correction and the like on the data warehouse, cope with data mutation caused by sudden interference, improve the reliability and quality of the data, build a data prediction model and related prediction algorithm aiming at various characteristic values, and achieve the associated prediction on real-time data. If the immersed pump bearing type parameter information is established, namely bearing type, number of rolling bodies, diameter, pitch diameter and contact angle, a test template is established, namely test equipment is selected, a bearing is bound, frequency parameters are set, the acquisition device is informed to start an acquisition program and acquire data according to the template, and the acquisition device acquires the data to a platform and stores the data. And starting an analysis program to perform algorithm processing such as Fourier transformation, hilbert transformation, wavelet denoising and the like, and finally giving reasonable pre-judgment result values and classification results by setting a reasonable threshold range.
Step 4, pushing alarm information, wherein the step further comprises the following substeps: 4-1, the AI service component pushes the alarm information to the safety supervision application platform through the MQTT message and simultaneously sends the alarm information to the video edge gateway; 4-2, the safety supervision platform issues alarm levels and language alarm contents through the MQTT message, and simultaneously pushes alarm information or alarm pictures to a safety personnel WeChat applet end (such as personnel intrusion, personnel intrusion area, personnel intrusion picture and other information); 4-3, the network controller subscribes to acquire alarm information content and alarm mode and duration through the MQTT; 4-4, the network controller transmits the alarm information to the audible and visual alarm; 4-5, the video edge gateway issues a voice alarm to the appointed camera (the camera needs to support voice broadcasting, for example, personnel intrude into the voice alarm information is 'you have entered a dangerous area, please leave rapidly').
Step 5, intelligent handling, which further comprises the sub-steps of: 5-1, alarm grade analysis; 5-2, alarm grade treatment pre-judgment; 5-3, issuing an alarm grade treatment instruction to the gateway of the Internet of things; 5-4, the gateway of the Internet of things changes the parameters of special equipment, and can correct the operation parameters of the equipment or carry out emergency shutdown.
Preferably, a monitoring station and a camera monitoring area are configured, and each area is required to monitor behaviors; the gateway of the Internet of things is configured to monitor and collect frequency parameters, and the substep 1-1 specifically comprises the following steps:
a) Configuring a camera monitoring area name;
b) Configuring monitoring area behavior identification content, such as: person off duty, break in, make phone call, smoke and smoke inhalation, smoke and fire monitoring alarm, etc.;
c) Setting alarm levels of behavior recognition personnel off duty, rushing into, making a call, smoking smoke and fire monitoring, and establishing risk, analysis and alarm relations (such as: setting the non-staff intrusion into a system hydrogenation station unloading area as a risk of middle-risk, comparing and analyzing staff entering a monitoring area with staff, analyzing and confirming that the staff intrusion is the non-staff intrusion, and corresponding prompt language alarm ' you have entered a dangerous area, please leave quickly ' and pushing related alarms and intrusion pictures to a security patrol staff through message pushing ';
d) Setting an artificial intelligent algorithm, wherein the existing system application integrated algorithm comprises face recognition, license plate recognition, perimeter humanoid invasion, perimeter article electronic fence, flame alarm, call receiving, personnel falling, personnel loitering, personnel gathering, smoke alarm, personnel off duty and the like, and 1 camera binds a plurality of algorithms according to application scenes;
e) Setting an artificial intelligence recognition operation flow, such as: the method comprises the steps of driving in a vehicle, getting off a driver, opening a trunk, checking a vehicle by a filling person, checking a gas cylinder by the filling person, filling, finishing filling, and driving off the vehicle;
f) The method comprises the steps of setting important equipment for monitoring the gateway of the Internet of things, such as conventional equipment for monitoring the gateway of the Internet of things of an LNG filling station, wherein the equipment comprises the following steps: LNG filling station: LNG storage tank, immersed pump, unload liquid feed liquor valve, hydrogenation station: important equipment such as a compressor sledge, a hydrogen storage bottle group, a water chilling unit of a hydrogenation machine and the like;
g) Setting a corresponding name and a corresponding point table of each special equipment acquisition point, for example: LNG filling station: LNG storage tank (upper inlet pressure corresponding point table 40001, storage tank liquid level corresponding point table 40015), immersed pump (inlet temperature, inlet pressure corresponding point tables 40017, 40019 respectively), hydrogenation station: compressor sledge (inlet pressure and outlet pressure respectively correspond to the point tables 41001 and 41003), water chilling unit of hydrogenation machine (inlet pressure, inlet temperature, outlet pressure and outlet temperature respectively correspond to the point tables 41005, 41007, 41009 and 41011), and the like;
h) Setting a normal working operation parameter range corresponding to a point collected by each special device, for example: the inlet temperature of the immersed pump is in the normal range of-200 to 100 ℃, and the inlet pressure of the immersed pump is in the normal range of 0Mpa to 1.0Mpa (the inlet pressure early-warning value of the immersed pump is consistent with the safety pressure early-warning value of the storage tank);
i) Setting the acquisition frequency of the gateway of the Internet of things, such as: setting the acquisition frequency to be 100ms, namely acquiring real-time equipment operation parameters once every 100 ms;
j) Setting an alarm level of the acquisition point equipment, setting an early warning value for the docking level, and establishing risk, analysis and alarm relations (for example, the inlet pressure of an immersed pump of an LNG filling station is set to be greater than 1.0Mpa for risk control, real-time pressure comparison analysis of the inlet of the immersed pump is acquired through an Internet of things gateway, when the real-time pressure is greater than or equal to 1.0Mpa, an immersed pump stop command is required to be issued, and a safety officer is informed of on-site checking of the pressure of a storage tank and reasonable manual dispersion through message pushing; manual bleeding means that the pressure of the storage tank is reduced by manually opening the exhaust valve;
k) Setting physical parameters of the light alarm, such as: serial port number COM3, baud rate 115200bps, data bit 8, stop bit 1, check bit None.
Preferably, the intelligent monitoring area realizes all-weather, omnibearing and real-time monitoring, the key point position operation parameters of the equipment are collected according to the set collection frequency, the real-time monitoring is carried out, the safety is improved, and the step 2 is specifically as follows:
a) The monitoring area information is collected in real time, such as the filling area information collection of a filling station, and the vehicle entering station can be collected, the vehicle license plate recognition and collection can be carried out, whether a driver acts next time, whether a call is made on a filling site, the smoking behavior is carried out, and the filling operation flow of filling personnel is collected (gas cylinder checking, gun inserting and filling);
b) The camera information acquisition mainly supports ONVIF/RTSP protocol, and is compatible with most brands in the market at present, such as: the implementation cost is reduced by using the equipment operated by the station in Tiandi Wei industry, haikang Wei-V, dahua le orange and the like;
c) Uploading to a video AI server through a video edge gateway:
d) The video AI server pushes the alarm picture to the safety supervision application platform through the MQTT, the HTTP alarm information and the alarm picture;
e) The safety supervision application platform needs to review the field for real-time monitoring, and needs to establish an online channel with the edge gateway by the video A1 server for real-time monitoring;
f) The gateway of the internet of things acquires equipment operation parameters according to set acquisition frequency, the key parameters of special equipment need to refer to sub-steps 1-3 of the step 1, the gateway of the internet of things is configured to monitor and acquire the frequency parameters, the gateway of the internet of things is downloaded and synchronized to the gateway of the internet of things, the gateway of the internet of things periodically acquires a synchronous key parameter acquisition point table of the special equipment according to the acquisition frequency, the related operation data are acquired according to the frequency, the acquisition mode supports modbus485, modbus TCP and S7 protocols, and the operation parameters of the special equipment are determined according to the special equipment monitored by a station, such as: the LNG filling station acquires equipment parameters such as inlet pressure, LNG storage tank liquid level, immersed pump inlet temperature, immersed pump inlet pressure, immersed pump outlet temperature, immersed pump outlet pressure and the like on the LNG storage tank;
g) And uploading the acquired equipment operation parameters to a safety supervision application platform through the MQTT.
Preferably, monitoring the monitoring situation of the monitoring area to monitor and analyze the risk level, analyzing the running condition of the equipment by colleagues on the key running parameters of the special equipment, analyzing whether the equipment has risks, and judging the site risk level by monitoring the risks and the equipment risks, wherein the step 3 specifically comprises:
a) The video AI service component is used for monitoring whether safety compliance is monitored in real time by combining a pre-designed control flow with an artificial intelligent algorithm;
example one: the AI monitoring setting of the unloading area of the hydrogenation station prohibits non-staff from entering, prohibits the call from making and prohibits the abnormal behavior related to smoking from alarming and prompting, and the behavior of the interloper clothes and the person is analyzed through artificial intelligence to determine whether the behavior is prohibited.
Example two: the filling flow of the gas station is monitored, the safety monitoring is carried out on the flows of entering the station, stopping the station, getting off the vehicle by a driver, opening a back box, checking a gas cylinder, opening an engine cover, inserting a gun, starting filling, ending filling, leaving the station and the like, and the analysis is carried out on the object state, the behavior of a person and the safety operation flow of the combined filling check through an artificial intelligent algorithm.
b) Analyzing the monitoring behavior result according to the artificial intelligence big data, determining risk and risk level, presetting a weight formula by a safety supervision system, and calculating a risk value;
c) Analyzing key operation parameters, preset operation values and early warning values of the special equipment, determining risks and risk grades of the special equipment by using the f) data according to the substep 1-1 and applying a comparison algorithm, and calculating a risk value according to a preset weight formula of a safety supervision application system;
preferably, the step 4 of analyzing and confirming whether the risk needs to be alarmed or not and pushing relevant information to a safety officer and a patrol personnel is specifically as follows:
a) The video AI service component pushes information of analysis early warning, abnormal pictures, time and the like to the safety supervision application platform;
b) The safety supervision application platform analyzes whether to push the alarm level and the language alarm content according to the risk level value, and confirms that the alarm level and the language alarm content are audible and visual alarm, push patrol, safety, station length, and low risk audible and visual alarm according to the risk level value; general risk audible and visual alarm + push patrol; middle risk audible and visual alarm + push patrol and security personnel; high-risk audible and visual alarms and push patrol workers, safety workers and station stations, namely, the safety workers cannot control the disposal;
example one: low risk, such as non-staff intruding into the warning area, adopting sound-light alarm to drive off;
example two: and (3) low risk, such as gas filling when a vehicle enters a station, opening an engine cover driver without getting off after gas filling personnel check the gas cylinder, and prompting in a language that the risk in the vehicle is high during gas filling so as to ask the driver to get off the vehicle.
Preferably, the intelligent treatment can be performed according to the risk level, and the step 5 specifically includes:
a) Rapid pre-alarm treatment comprising the pushing of risk level data according to claim 5;
b) General and middle risk can be analyzed by correcting special equipment parameters, system modeling data are used for giving correction parameters, the correction parameters are sent to an internet of things gateway through TCP, the correction parameters are sent to equipment through the internet of things gateway, equipment operation risk is reduced through quick treatment, and a patrol man and a safety man are subjected to on-site analysis and verification;
c) And if the risk is high, the system can rapidly and accurately send out an emergency stop instruction, so that the huge risk brought by continuous operation is reduced, a patrol man, a security person and a station are grown to the site to continuously analyze the cause of the problem, and a subsequent treatment scheme is determined.
The invention discloses a method and a system for intelligently monitoring potential safety hazards, wherein the method comprises the following steps: configuring a monitoring station, configuring camera monitoring areas, configuring a point table, a collection frequency and the like corresponding to key collection parameters of special equipment for each area, continuously monitoring the special equipment for 24 hours through the video monitoring area, continuously collecting the key parameters of the special equipment by an Internet of things gateway according to the set frequency, analyzing by artificial intelligence, big data, a comparison algorithm and the like, confirming the risk level of the relevant monitoring points, determining an audible and visual alarm and personnel pushing information according to the analyzed risk level, and simultaneously rapidly responding to a decision-making treatment scheme according to the analyzed risk level.
The invention can monitor all-weather, all-round and real-time, combines artificial intelligence and big data analysis and processing, rapidly influences and disposes a mechanism and a pre-warning and message pushing mechanism, improves the accuracy and efficiency of potential safety hazards of monitoring points, reduces the occurrence of operation safety accidents of special equipment, reduces property loss and improves the life quality of people.
The intelligent monitoring for 24 hours continuously monitors and solves the problems of safety, real-time monitoring, efficiency and artificial intelligence and big data analysis and processing, and in addition, the mechanism of data acquisition and intelligent disposal of the Internet of things is adopted, so that the accuracy and efficiency of potential safety hazards existing in special equipment are improved, the response is precise, and the quick disposal is realized, thereby reducing the occurrence of operation safety accidents of the special equipment, reducing property loss and improving the life quality of people.
As described above, the present invention can be preferably implemented.
All of the features disclosed in all of the embodiments of this specification, or all of the steps in any method or process disclosed implicitly, except for the mutually exclusive features and/or steps, may be combined and/or expanded and substituted in any way.
The foregoing description of the preferred embodiment of the invention is not intended to limit the invention in any way, but rather to cover all modifications, equivalents, improvements and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The intelligent monitoring method for the potential safety hazards is characterized by comprising the following steps of:
s1, configuration: configuring a monitoring area and equipment;
s2, collecting: collecting monitoring area information and equipment operation parameters;
s3, analysis: analyzing equipment operation parameters in a monitoring area;
s4, pushing: pushing alarm information;
s5, treatment: and disposing the alarm information.
2. The intelligent monitoring method of potential safety hazards according to claim 1, wherein step S1 comprises the steps of:
s11, setting a camera monitoring area;
s12, configuring an artificial intelligence algorithm and a flow of each area IA monitoring;
s13, configuring an Internet of things gateway to monitor and collect frequency parameters;
s14, configuring the effective range of the monitoring point parameter;
s15, configuring a risk grade value of a monitoring point;
s16, configuring an address and a port of a network controller of the audible and visual alarm;
s17, configuring physical parameters of an optical alarm;
the order of steps S11 to S17 may be arbitrarily changed.
3. The intelligent monitoring method of potential safety hazards according to claim 2, wherein step S11 comprises the steps of:
s111, configuring a camera monitoring area name;
s112, configuring behavior identification content of a monitoring area;
s113, setting alarm behaviors, alarm grades and alarm contents of behavior identification;
s114, setting an artificial intelligence algorithm bound by a camera;
s115, setting an artificial intelligent recognition operation flow.
4. The intelligent monitoring method for potential safety hazards according to claim 3, wherein step S11 further comprises the steps of:
s116, setting gateway monitoring equipment of the Internet of things;
s117, setting a name corresponding to each equipment acquisition point and a corresponding point table;
s118, setting a normal working operation parameter range corresponding to a point acquired by each device;
s119, setting the acquisition frequency of the gateway of the Internet of things;
s1110, setting an alarm level of the acquisition point equipment, setting an early warning value for the docking level, and setting alarm content;
s1111, setting physical parameters of the light alarm.
5. The intelligent monitoring method for potential safety hazards according to any one of claims 1 to 4, wherein step S2 comprises the steps of:
s21, a camera collects real-time video of a monitoring area;
s22, uploading the real-time picture to a video edge gateway through an ONVIF/RTSP protocol;
s23, uploading the real-time picture to an IA video server through a video edge gateway;
s24, the gateway of the Internet of things periodically collects operation parameters of the equipment and uploads the operation parameters to the security application supervision platform.
6. The intelligent monitoring method for potential safety hazards according to claim 5, wherein step S3 comprises the following steps:
s31, after uploading the real-time picture data to an AI server, analyzing the real-time picture data through an artificial intelligence algorithm;
s32, the safety supervision platform analyzes the operation parameters of the equipment through data modeling.
7. The intelligent monitoring method of potential safety hazard according to claim 6, wherein the step S32 comprises the steps of:
s321, establishing a device real-time acquisition point data warehouse, performing data processing in a de-duplication, filtering and deviation rectifying mode on the data warehouse, establishing a data prediction model and a prediction algorithm aiming at various characteristic values, and performing associated prediction on real-time data of device operation parameters;
s322, starting an analysis program to perform Fourier transform, hilbert transform and wavelet denoising algorithm processing on the real-time data of the equipment operation parameters, and finally giving a pre-judgment result value and a classification result through setting a threshold range.
8. The intelligent monitoring method of potential safety hazards according to claim 7, wherein step S4 comprises the steps of:
s41, the AI service component pushes the alarm information to the safety supervision application platform through an MQTT message, and simultaneously, the AI service component sends the alarm information to the video edge gateway;
s42, the safety supervision platform issues alarm levels and language alarm contents through the MQTT message, and simultaneously pushes alarm information or alarm pictures to a safety personnel WeChat applet terminal;
s43, the network controller subscribes and acquires alarm information content and alarm mode and duration through the MQTT message;
s44, the network controller issues alarm information to the audible and visual alarm;
s45, the video edge gateway transmits the language alarm information to the appointed camera to carry out the language alarm.
9. The intelligent monitoring method for potential safety hazards according to claim 8, wherein step S5 comprises the following steps:
s51, alarm grade analysis;
s52, alarm grade treatment pre-judgment;
s53, issuing an alarm grade treatment instruction to the gateway of the Internet of things;
s54, the gateway of the Internet of things changes equipment parameters, corrects equipment operation parameters or performs emergency shutdown.
10. The intelligent monitoring system for potential safety hazards is characterized by being used for realizing the intelligent monitoring method for potential safety hazards according to any one of claims 1 to 9, and comprises the following modules connected in sequence:
and (3) a configuration module: the method is used for configuring the monitoring area and the equipment;
and the acquisition module is used for: the monitoring system is used for collecting monitoring area information and equipment operation parameters;
and an analysis module: the device operation parameter analysis module is used for analyzing the device operation parameters in the monitoring area;
and the pushing module is used for: the alarm device is used for pushing alarm information;
a treatment module: and the alarm information is used for processing the alarm information.
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