CN115310843A - Environment monitoring and early warning method, device and equipment for electric power operation safety - Google Patents
Environment monitoring and early warning method, device and equipment for electric power operation safety Download PDFInfo
- Publication number
- CN115310843A CN115310843A CN202210988266.6A CN202210988266A CN115310843A CN 115310843 A CN115310843 A CN 115310843A CN 202210988266 A CN202210988266 A CN 202210988266A CN 115310843 A CN115310843 A CN 115310843A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- noise
- data
- early warning
- fault
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 228
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000004364 calculation method Methods 0.000 claims abstract description 46
- 230000005672 electromagnetic field Effects 0.000 claims abstract description 33
- 230000005684 electric field Effects 0.000 claims abstract description 6
- 238000004458 analytical method Methods 0.000 claims description 56
- 239000007789 gas Substances 0.000 claims description 37
- 230000006870 function Effects 0.000 claims description 34
- 238000012545 processing Methods 0.000 claims description 31
- 230000007613 environmental effect Effects 0.000 claims description 30
- 238000004891 communication Methods 0.000 claims description 27
- 230000005540 biological transmission Effects 0.000 claims description 17
- 238000003745 diagnosis Methods 0.000 claims description 13
- 238000001228 spectrum Methods 0.000 claims description 13
- 238000010276 construction Methods 0.000 claims description 12
- 230000005670 electromagnetic radiation Effects 0.000 claims description 10
- 238000003860 storage Methods 0.000 claims description 8
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 7
- 238000009826 distribution Methods 0.000 claims description 7
- 238000012423 maintenance Methods 0.000 claims description 7
- 239000001301 oxygen Substances 0.000 claims description 7
- 229910052760 oxygen Inorganic materials 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000007084 catalytic combustion reaction Methods 0.000 claims description 6
- 239000002341 toxic gas Substances 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 6
- 238000003775 Density Functional Theory Methods 0.000 claims description 5
- 238000011161 development Methods 0.000 claims description 5
- 230000000737 periodic effect Effects 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 5
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 8
- 238000005070 sampling Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Water Supply & Treatment (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Power Engineering (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses an environment monitoring and early warning method, device and equipment for electric power operation safety, and belongs to the technical field of electric power operation safety. The harm of factors such as an electric field, noise and the like in an operation field to a human body is not considered in the conventional monitoring scheme, and the monitoring scheme is not comprehensive enough and is not beneficial to popularization and use. The invention relates to an environment monitoring and early warning method for electric power operation safety, which carries out omnibearing monitoring on vital sign information, gas data, noise data and electromagnetic field data, and processes related monitoring data by constructing an edge calculation model to obtain a trigger signal; constructing an early warning model, setting a monitoring threshold value, and judging a trigger signal; when the trigger signal is greater than the monitoring threshold value, alarm information pushing is carried out, so that safety of operating personnel and operating tasks can be guaranteed practically, meanwhile, the method can be applied to electric power digital intelligent safety management and control, and all-directional safety service is provided for operation environment risk monitoring early warning and field intelligent management and control.
Description
Technical Field
The invention relates to an environment monitoring and early warning method, device and equipment for electric power operation safety, and belongs to the technical field of electric power operation safety.
Background
With the increasing and complicating of the structure and construction scale of the power grid, the traditional safety management and control mode is difficult to meet the requirement of new state development. The combination of internet of things application and the introduction of artificial intelligence technology realizes effective linkage of a daily business management platform and a field control means, ensures safety of operators and operation tasks, and is a problem to be solved urgently by the existing electric power safety supervision technology.
Chinese patent (publication No. CN 113837657A) provides a live working risk identification early warning and emergency disposal method, which can quickly judge the current working environment, accurately adapt to various indoor and outdoor working scenes, select a proper communication mode according to different scenes, ensure that the effective transmission of communication data can be realized under different scenes, and ensure the high efficiency and smoothness of communication in the whole working process. Through the dynamic analysis algorithm, the risk level of the operation environment is analyzed, the level of the risk level of the operation environment can be effectively predicted, the danger degree of the operation environment of the operation personnel is timely reminded, and casualties can be effectively reduced. The remote monitoring center carries out comprehensive analysis to operation environment monitoring data, personnel vital sign data, carries out quick discernment and intelligent prejudgement to the risk of live working, in time issues early warning information, matches corresponding emergency treatment plan according to safe risk and early warning condition, in time carries out corresponding emergency treatment, provides safety guarantee and technical support for personnel's safety to guarantee live working personnel's construction safety effectively.
However, the above-mentioned environmental monitoring scheme only considers the influence of temperature and humidity and combustible explosive gas on the construction safety of live-wire workers, and does not consider factors such as electromagnetic field and noise in the operation site, which are also harmful to human body, so the above-mentioned monitoring scheme is not comprehensive enough, and is not beneficial to popularization and use.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for monitoring vital sign information, gas data, noise data and electromagnetic field data in an all-around manner by constructing a monitoring data acquisition model; processing related monitoring data by constructing an edge calculation model to obtain a trigger signal; constructing an early warning model, setting a monitoring threshold value, and judging a trigger signal; when the trigger signal is greater than the monitoring threshold value, the alarm information is pushed, the scheme is scientific, reasonable, simple and practical, the safety of operating personnel and operating tasks can be guaranteed practically, and the environment monitoring and early warning method for the electric power operation safety is convenient to popularize and use.
The invention also aims to provide an environment monitoring and early warning device for electric power operation safety, which can carry out all-dimensional monitoring on vital sign information, gas data, noise data and electromagnetic field data by arranging a monitoring terminal, a network communication module and an edge computing device, has a simple and practical structure, can practically ensure the safety of operators and operation tasks, and is convenient to popularize and use.
The invention aims to provide a system, a method and a device for electric power digital intelligent safety control, which can be applied to electric power digital intelligent safety control and provide all-round safety service for operation environmental risk monitoring and early warning and field digital intelligent control; the environmental monitoring and early warning method, the device and the equipment for electric power operation safety can realize effective linkage of a daily service management platform and a field control means and ensure safety of operating personnel and operation tasks by combining application of the Internet of things.
In order to achieve one of the above objects, a first technical solution of the present invention is:
an environmental monitoring and early warning method for electric power operation safety,
the method comprises the following steps:
firstly, constructing a monitoring data acquisition model and acquiring related monitoring data;
the relevant monitoring data includes vital sign information, gas data, noise data, and electromagnetic field data;
secondly, constructing an edge calculation model, and processing the related monitoring data in the first step to obtain a trigger signal;
thirdly, constructing an early warning model, setting a monitoring threshold value, and judging a trigger signal;
when the trigger signal is larger than the monitoring threshold value, alarm information is pushed;
and when the trigger signal is smaller than the monitoring threshold value, executing a first step.
Through continuous exploration and test, the vital sign information, the gas data, the noise data and the electromagnetic field data are monitored in all directions by constructing a monitoring data acquisition model, and the relevant monitoring data are processed by constructing an edge calculation model to obtain a trigger signal; constructing an early warning model, setting a monitoring threshold value, and judging a trigger signal; when the trigger signal is greater than the monitoring threshold value, the alarm information pushing is carried out, the scheme is scientific, reasonable, simple and practical, the safety of operating personnel and operating tasks can be guaranteed practically, and the device is convenient to popularize and use.
Furthermore, the invention can be applied to electric power digital intelligent safety control, and can provide all-round safety service for operation environment risk monitoring and early warning and on-site digital intelligent control; by combining the application of the Internet of things, the daily business management platform can be effectively linked with the field management and control means, and the safety of operators and operation tasks is guaranteed.
As a preferable technical measure:
in the first step, the construction method of the monitoring data acquisition model comprises the following steps:
monitoring vital signs through a heartbeat monitoring sensor;
monitoring oxygen and toxic gas in the gas by an electrochemical sensor;
monitoring combustible gas in the gas by a catalytic combustion type sensor;
monitoring field noise through sound acquisition monitoring equipment;
monitoring a power frequency and radio frequency electromagnetic field through an ultra-wideband electromagnetic radiation analysis terminal, wherein the frequency range of the ultra-wideband electromagnetic radiation analysis terminal covers a frequency range from low frequency to millimeter wave;
the power frequency and radio frequency electromagnetic field is used for measuring the electric field or/and the magnetic field intensity and the power density in an alternating current transmission and transformation project or/and a power distribution system or/and a rail transit power supply system or/and a charging pile.
As a preferable technical measure:
in the second step, the construction method of the edge calculation model is as follows:
judging whether an operator exists according to the vital sign information;
and when the judgment result is that the operator exists, analyzing the gas data or/and the noise data or/and the electromagnetic field data, and sending the analysis result to the early warning model as a trigger signal.
As a preferable technical measure:
the edge calculation model is provided with a noise processing unit and a fault analysis characteristic database;
the noise processing unit analyzes the noise data by adopting a Fourier transform method to obtain the amplitude and the frequency of the noise;
the method for constructing the fault analysis feature database comprises the following steps:
acquiring the frequency spectrum values of normal noise and fault signals to form a noise template with amplitude and frequency as characteristics;
and the noise processing unit transmits the analysis and calculation results to a fault analysis characteristic database, and counts, induces and analyzes the amplitude and frequency of the noise to obtain a fault diagnosis criterion.
As a preferable technical measure:
the analysis processing steps of the Fourier transform method are as follows:
step 21, acquiring original noise data;
step 22, converting the noise data in the step 21 into an N-point sequence through Fast Fourier Transform (FFT) so as to capture accidental signals or signals with short duration;
step 23, decomposing the N point sequence in the step 22 into a series of short sequences in sequence;
and step 24, according to the symmetry and periodicity of the exponential factors in the DFT calculation formula, calculating the DFT corresponding to the short sequence in the step 23 and combining the DFT appropriately to obtain the amplitude and the frequency of the noise.
As a preferable technical measure:
the fault diagnosis method comprises the following steps:
step 31, obtaining a noise feature vector according to the amplitude and frequency of the noise
Step 32, calculating the vector sequence of the noise characteristic vector in the step 31 according to a fuzzy recognition algorithm;
step 33, comparing the vector sequence in the step 32 with a noise template in a fault analysis feature database, and calculating the proximity membership of the vector sequence and the noise template;
and when the membership degree is more than or equal to 0.9, the same or similar templates are regarded as, and the recognition result is output.
As a preferable technical measure:
the processing analysis of the edge calculation model on the noise comprises the following contents:
s1: the real-time environmental noise monitoring data is transmitted to a noise monitoring intermediate database established in advance in an edge computing link edge computing server through a data transmission module after being marked by field artificial faults by sound acquisition monitoring equipment.
S2: judging whether the same noise monitoring data record exists in the noise monitoring intermediate database, and if the same noise monitoring data record does not exist, transmitting the environmental real-time noise monitoring data in the S1 into the noise monitoring intermediate database;
comparing fault labels, if similar noise data are collected, constructing a new fault model through noise AI learning, analyzing the amplitude and frequency of the noise by adopting Fast Fourier Transform (FFT), and inputting the analysis and calculation result into a fault analysis characteristic database established in advance in an edge calculation server;
s3: classifying the collected fault models in the S2, arranging the fault models in an edge calculation server by adopting a mathematical statistics method, and constructing a new fault analysis characteristic database for judging the fault type corresponding to the field noise;
s4: transmitting noise monitoring data monitored by the site environment to an edge computing server through a network communication medium, comparing the noise monitoring data with a model in a noise fault analysis characteristic database in the S3, and computing the proximity membership degree according to site operation contents and environment; and when the membership degree is more than or equal to 0.9, determining and identifying the fault type, and informing field operators of the fault type in time through a network communication medium to guide the development of safety maintenance operation.
In order to achieve one of the above objects, a second technical solution of the present invention is:
an environment monitoring and early warning device for the safety of electric power operation,
the environmental monitoring and early warning method for the electric power operation safety is adopted;
the system comprises a monitoring terminal, a network communication module and an edge computing device;
the monitoring terminal is provided with a gas monitoring function module, a noise monitoring function module and an electromagnetic field monitoring function module;
the network communication module is provided with a transmission medium;
the edge computing device is provided with a monitoring threshold value monitoring system and an early warning system;
the monitoring terminal network communication module sends related monitoring data to the edge computing device;
and obtaining an edge calculation result through a monitoring threshold value monitoring system, and pushing through an early warning system and carrying out on-site early warning display and processing.
Through continuous exploration and test, the monitoring terminal, the network communication module and the edge computing device are arranged, so that the vital sign information, the gas data, the noise data and the electromagnetic field data can be monitored in all directions, the structure is simple and practical, the safety of operators and operation tasks can be guaranteed practically, and the device is convenient to popularize and use.
Furthermore, the environment monitoring and early warning device based on edge calculation can be applied to electric power digital intelligent safety control, and can provide all-round safety service for operation environment risk monitoring and early warning and field digital intelligent control; by combining the application of the Internet of things, the daily business management platform can be effectively linked with the field management and control means, and the safety of operators and operation tasks is guaranteed.
As a preferable technical measure:
the gas monitoring function module is provided with an electrochemical sensor for detecting oxygen and toxic gas and a catalytic combustion sensor for detecting combustible gas;
the noise monitoring function module is assembled with on-site noise acquisition and detection equipment and is used for monitoring the noise of an on-site operation working surface;
the electromagnetic field monitoring function module is assembled with an ultra-wideband electromagnetic radiation analysis terminal for monitoring power frequency and radio frequency electromagnetic fields, and is used for measuring the electric field, the magnetic field intensity and the power density of alternating current transmission and transformation engineering, a power distribution system, a rail transit power supply system or a charging pile, and the frequency range of the ultra-wideband electromagnetic radiation analysis terminal covers a frequency range from low frequency to millimeter wave.
In order to achieve one of the above objects, a third technical solution of the present invention is:
a computer device, comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement an environmental monitoring and warning method for power operation safety employing one of the methods described above.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, through continuous exploration and test, vital sign information, gas data, noise data and electromagnetic field data are monitored in all directions, and relevant monitoring data are processed by constructing an edge calculation model to obtain a trigger signal; constructing an early warning model, setting a monitoring threshold value, and judging a trigger signal; when the trigger signal is greater than the monitoring threshold value, the alarm information is pushed, the scheme is scientific, reasonable, simple and practical, the safety of operating personnel and operating tasks can be guaranteed practically, and the method is convenient to popularize and use.
Furthermore, the invention can be applied to electric power digital intelligent safety control, and can provide all-round safety service for operation environment risk monitoring and early warning and on-site digital intelligent control; by combining the application of the Internet of things, the daily business management platform can be effectively linked with the field management and control means, and the safety of operators and operation tasks is guaranteed.
Furthermore, through continuous exploration and test, the monitoring terminal, the network communication module and the edge computing device are arranged, so that the vital sign information, the gas data, the noise data and the electromagnetic field data can be monitored in all directions, the structure is simple, the practicability is realized, the safety of operators and operation tasks can be guaranteed practically, and the popularization and the use are convenient.
Drawings
FIG. 1 is a structural diagram of an environmental monitoring and early warning device according to the present invention;
FIG. 2 is a flow chart of the present invention for implementing environmental monitoring and early warning;
FIG. 3 is a flowchart of an environmental monitoring and warning method according to the present invention;
fig. 4 is a flow chart of the fault diagnosis feature extraction and judgment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "or/and" includes any and all combinations of one or more of the associated listed items.
The invention relates to a specific embodiment of an environmental monitoring and early warning method for electric power operation safety, which comprises the following steps:
an environment monitoring and early warning method for electric power operation safety comprises the following steps:
firstly, constructing a monitoring data acquisition model and acquiring related monitoring data;
the relevant monitoring data includes vital sign information, gas data, noise data and electromagnetic field data;
secondly, constructing an edge calculation model, and processing the related monitoring data in the first step to obtain a trigger signal;
thirdly, constructing an early warning model, setting a monitoring threshold value, and judging a trigger signal;
when the trigger signal is greater than the monitoring threshold value, carrying out alarm information pushing;
and when the trigger signal is smaller than the monitoring threshold value, executing a first step.
One specific embodiment of the present invention for obtaining relevant monitoring data:
the method for acquiring the related monitoring data comprises the following steps:
monitoring vital signs through a heartbeat monitoring sensor;
monitoring oxygen and toxic gas in the gas by an electrochemical sensor;
monitoring combustible gas in the gas by a catalytic combustion type sensor;
monitoring field noise through sound acquisition monitoring equipment;
monitoring a power frequency and radio frequency electromagnetic field through an ultra-wideband electromagnetic radiation analysis terminal, wherein the frequency range of the ultra-wideband electromagnetic radiation analysis terminal covers a frequency range from low frequency to millimeter wave;
the power frequency and radio frequency electromagnetic field is used for measuring the electric field or/and the magnetic field intensity and the power density in an alternating current transmission and transformation project or/and a power distribution system or/and a rail transit power supply system or/and a charging pile.
The first embodiment of the edge calculation model of the present invention:
the construction method of the edge calculation model comprises the following steps:
judging whether an operator exists according to the vital sign information;
and when the judgment result is that the operator exists, analyzing the gas data or/and the noise data or/and the electromagnetic field data, and sending the analysis result to the early warning model as a trigger signal.
The second embodiment of the edge calculation model of the present invention:
the edge calculation model is provided with a noise processing unit and a fault analysis characteristic database;
the noise processing unit analyzes the noise data by adopting a Fourier transform method to obtain the amplitude and the frequency of the noise;
the method for constructing the fault analysis feature database comprises the following steps:
acquiring the frequency spectrum values of normal noise and fault signals to form a noise template with amplitude and frequency as characteristics;
and the noise processing unit transmits the analysis and calculation results to a fault analysis characteristic database, and counts, induces and analyzes the amplitude and frequency of the noise to obtain a fault diagnosis criterion.
The invention discloses a specific embodiment of a Fourier transform method, which comprises the following steps:
the analysis processing steps of the Fourier transform method are as follows:
step 21, acquiring original noise data;
step 22, converting the noise data in the step 21 into an N-point sequence through Fast Fourier Transform (FFT) so as to capture an accidental signal or a signal with short duration;
step 23, decomposing the N point sequence in the step 22 into a series of short sequences in sequence;
and step 24, according to the symmetry and periodicity of the exponential factors in the DFT calculation formula, calculating the DFT corresponding to the short sequence in the step 23 and combining the DFT appropriately to obtain the amplitude and the frequency of the noise.
One specific embodiment of the fault diagnosis of the present invention:
the fault diagnosis method comprises the following steps:
step 31, obtaining a noise feature vector according to the amplitude and frequency of the noise
Step 32, calculating the vector sequence of the noise characteristic vector in the step 31 according to a fuzzy recognition algorithm;
step 33, comparing the vector sequence in the step 32 with a noise template in a fault analysis feature database, and calculating the proximity membership of the vector sequence and the noise template;
and when the membership degree is more than or equal to 0.9, the same or similar templates are regarded as, and the recognition result is output.
One embodiment of the noise processing analysis of the present invention:
the processing analysis of the edge calculation model on the noise comprises the following contents:
s1: the real-time environmental noise monitoring data is transmitted to a noise monitoring intermediate database established in advance in an edge computing link edge computing server through a data transmission module after being marked by field artificial faults by sound acquisition monitoring equipment.
S2: judging whether the same noise monitoring data record exists in the noise monitoring intermediate database, and if the same noise monitoring data record does not exist, transmitting the environmental real-time noise monitoring data in the S1 into the noise monitoring intermediate database;
comparing the fault labels, if similar noise data are collected, constructing a new fault model through noise AI learning, analyzing the noise amplitude and frequency by adopting Fast Fourier Transform (FFT), and inputting the analysis and calculation result into a fault analysis characteristic database established in advance in an edge calculation server;
s3: classifying the collected fault models in the S2, arranging the fault models in an edge calculation server by adopting a mathematical statistics method, and constructing a new fault analysis characteristic database for judging the fault types corresponding to the field noises;
s4: transmitting noise monitoring data monitored by the site environment to an edge computing server through a network communication medium, comparing the noise monitoring data with a model in a noise fault analysis characteristic database in the S3, and computing the proximity membership degree according to site operation contents and environment; and when the membership degree is more than or equal to 0.9, determining and identifying the fault type, and informing field operators of the fault type in time through a network communication medium to guide the development of safety maintenance operation.
The invention relates to a first specific embodiment of an environment monitoring and early warning device for electric power operation safety, which comprises the following steps:
an environment monitoring and early warning device for the safety of electric power operation,
the environmental monitoring and early warning method for the electric power operation safety is adopted;
the system comprises a monitoring terminal, a network communication module and an edge computing device;
the monitoring terminal is provided with a gas monitoring function module, a noise monitoring function module and an electromagnetic field monitoring function module;
the network communication module is provided with a transmission medium;
the edge computing device is provided with a monitoring threshold value monitoring system and an early warning system;
the monitoring terminal network communication module sends related monitoring data to the edge computing device;
and obtaining an edge calculation result through a monitoring threshold value monitoring system, and pushing through an early warning system and carrying out on-site early warning display and processing.
The invention is used for a second specific embodiment of the environment monitoring and early warning device for electric power operation safety:
the utility model provides an environment monitoring early warning device for electric power operation safety, whole framework comprises monitoring terminal, network communication module and marginal computing device three layer construction.
In this embodiment, the monitoring terminal is composed of three monitoring function modules of gas, noise and electromagnetic field, and the data of the monitoring terminal is transmitted to the edge computing device through a network communication module formed by a transmission medium. The edge calculation device consists of a monitoring terminal monitoring threshold value monitoring system and an early warning system, the edge calculation result is pushed through an alarm information processing module, the site early warning display and processing are carried out, and the monitoring result or the acousto-optic early warning signal is transmitted by a communication layer. The system architecture is shown in fig. 1.
In the embodiment, the gas monitoring function module is provided with an oxygen sensor, a combustible gas sensor and a plurality of gas sensors. The oxygen and toxic gas detection adopts an electrochemical sensor, and the combustible gas detection adopts a catalytic combustion sensor. The detection of various gases is supported by monitoring of upper computer software.
In the embodiment, the noise monitoring function module is provided with field noise acquisition and detection equipment for monitoring the noise of a field operation working surface, and can assist fault analysis and diagnosis and load distribution rationality evaluation according to the normal/abnormal operation noise of different field power facilities.
In the embodiment, the electromagnetic field monitoring function module is provided with an ultra-wideband electromagnetic radiation analysis terminal for monitoring power frequency and radio frequency electromagnetic fields, and is used for measuring the electric and magnetic field intensity and power density of environments such as alternating current power transmission and transformation engineering, a power distribution system, a rail transit power supply system, a charging pile and the like, and the frequency range covers from low frequency to millimeter wave frequency bands.
When the intelligent monitoring and early warning device is used, the gas, noise and electromagnetic field monitoring function modules are arranged on the electric power operation construction site, real-time gas, noise and electromagnetic field environment monitoring data of the environment are sent to the edge computing device through the intelligent environment monitoring and early warning device, the edge computing device compares and judges a data threshold value through the logic control mathematical model, whether alarm processing is formed or not and pushes the construction site to display and process the data, and closed-loop monitoring is formed. The intelligent environment monitoring and early warning implementation process based on edge computing is shown in fig. 2:
in this embodiment, after each monitoring function module device is powered on, the monitoring function module device actively reports self-check heartbeat information of the detection function module to the edge computing device, the edge computing device obtains the self-check heartbeat information of the monitoring function module, and then sets the state of the monitoring function module device sending heartbeat information to the on-line state of the device, the edge computing device then sends a monitoring data information query command to the monitoring function module sending self-check heartbeat information at regular time, the monitoring function module then performs data acquisition and reports the acquired data information to the edge computing device, the edge computing device performs data analysis and comparison through the reported acquired data and then sends corresponding logic results to each relevant monitoring function module device end, each relevant monitoring function module makes corresponding logic actions according to the corresponding logic results, and the environment intelligent monitoring and early warning monitoring flow based on edge computing is shown in fig. 3:
aiming at the noise of the working face of the power operation field operation, the edge computing device adopts a fast Fourier transform (fast Fourier transform FFT) signal processing module to assist in analyzing the noise property (amplitude and frequency), and the analysis and calculation result is recorded into a fault analysis characteristic database which is established in advance in an edge computing server.
A fast Fourier transform (fast Fourier transform) spectrum analysis method, that is, an efficient and fast calculation method for calculating discrete Fourier transform (discrete Fourier transform DFT) by using a spectrum analyzer. By adopting the algorithm, the multiplication times required by calculating the Discrete Fourier Transform (DFT) are greatly reduced, and the more the number N of the transformed sampling points is, the more remarkable the saved calculation amount is. The original N-point sequence is decomposed into a series of short sequences in turn, and the symmetric and periodic properties of exponential factors in a Discrete Fourier Transform (DFT) calculation formula are utilized to calculate the Discrete Fourier Transform (DFT) corresponding to the short sequences and properly combine the sequences, so that repeated calculation can be eliminated, multiplication operation is reduced, the structure is simplified, and the operation speed is improved. The miniature fast fourier transform FFT spectrum analyzer digitizes the signal with an ADC converter and then fast fourier transform the stored values. The fast fourier transform FFT method has an advantage in that sporadic signals or signals of short duration can be captured. Key parameters of the miniature fast fourier transform FFT spectrum analyzer are sampling rate and dynamic range. According to the aroma law, the sampling rate must be higher than 40kHz if an audio bandwidth of 20kHz is to be measured. The spectrum analyzer is capable of displaying a spectrum up to half the maximum sampling rate.
Storing the frequency spectrum values of the normal noise and the fault signal in an edge computing server to form a characteristic frequency spectrum database by using the amplitude and the frequency. And counting, inducing and analyzing the change rule of the frequency spectrum to obtain a fault diagnosis criterion. Extracting a feature vector of input noise, calculating a noise feature vector sequence according to a fuzzy recognition algorithm, and calculating a feature frequency spectrum database to store the proximity membership between noise templates. When the membership degree is more than or equal to 0.9, the electric appliance is regarded as the same or similar template, and the identification result, namely the fault code of the electric appliance is output, so that the accuracy of monitoring the running noise of the electric appliance is improved. In the study of noise properties, the signal waveform is not directly processed, but is converted into a characteristic related to a frequency spectrum. The sound characteristics representing the operation condition of the electric appliance are mainly contained in the amplitude and frequency information, and the phase is not considered for the moment. The flow of extracting and judging the fault diagnosis characteristics of the field power utilization facility (taking a power transformer as an example) based on the noise environment monitoring and early warning device and the edge calculation prompt is shown in fig. 4:
and (3) assisting to analyze the reason of the abnormal operation noise according to the amplitude and the frequency of the noise signal of the field power facility, and constructing a fault diagnosis system based on the field noise. The method can take effect while learning, applying and researching, and provides fault diagnosis value-added service for field noise monitoring. The noise model base establishment work is implemented in three stages:
the first stage is as follows: the on-site noise acquisition terminal sends the environmental real-time noise monitoring data to a noise monitoring intermediate database which is established in advance in an edge computing link edge computing server through on-site artificial fault marking and a data transmission module.
And a second stage: the field noise collecting terminal sends the environment real-time noise monitoring data to the edge computing server in the edge computing link through the network communication medium after the field artificial fault marking. Judging whether similar noise data records exist in a noise monitoring intermediate database according to field artificial fault marks, and if the similar noise data records do not exist, transmitting the environmental real-time noise monitoring data into the noise monitoring intermediate database; and comparing and marking faults, if similar noise data are collected, further arranging a fault model through noise AI learning, analyzing the noise property (amplitude and frequency) by adopting a fast Fourier transform (fast Fourier transform FFT) signal processing module in an auxiliary manner, and recording the analysis and calculation result into a fault analysis characteristic database which is established in advance in an edge calculation server.
And a third stage: on the basis of collecting more fault models, classifying the fault models, sorting the fault models by adopting a mathematical statistics method in an edge calculation server, and constructing a fault analysis characteristic database for judging the fault type corresponding to the field noise. Transmitting the noise monitoring data monitored by the site environment to the edge computing server through a network communication medium, comparing the noise monitoring data with the model in the noise fault analysis characteristic database, referring to the site operation content and the environment, prompting whether the site extraction model is close to the corresponding data in the characteristic database according to the proximity membership degree being more than or equal to 0.9, determining and identifying the fault type if the site extraction model is close to the corresponding data in the characteristic database, and informing the site operation personnel of the fault type in time through the network communication medium to guide the development of safety maintenance operation. And meanwhile, continuously collecting the noise model which is not put in storage.
The implementation of the safety assessment mode of the embodiment takes the data transmission module as a transmission link, and embodies the reasonable structure of the heavy-edge calculation server of the light-field acquisition terminal. The safety management and maintenance of the operation construction site are promoted from the aspects of personnel, properties, machines, regulations, science and technology, environment and the like, the safety of the construction site is changed from post-treatment to pre-management and control, and the regular maintenance of the equipment is changed to the state maintenance of the equipment.
An embodiment of an apparatus to which the method of the invention is applied:
a computer apparatus, comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method for environmental monitoring and forewarning for power operation safety as described above.
An embodiment of a computer medium to which the method of the invention is applied is:
a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements an environmental monitoring and warning method for power operation safety as described above.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as methods, systems, computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
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 to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. An environment monitoring and early warning method for electric power operation safety is characterized in that,
the method comprises the following steps:
firstly, constructing a monitoring data acquisition model and acquiring related monitoring data;
the relevant monitoring data includes vital sign information, gas data, noise data and electromagnetic field data;
secondly, constructing an edge calculation model, and processing the related monitoring data in the first step to obtain a trigger signal;
thirdly, constructing an early warning model, setting a monitoring threshold value, and judging a trigger signal;
when the trigger signal is larger than the monitoring threshold value, alarm information is pushed;
and when the trigger signal is smaller than the monitoring threshold value, executing a first step.
2. The environmental monitoring and early warning method for electric power operation safety as recited in claim 1,
in the first step, the construction method of the monitoring data acquisition model comprises the following steps:
monitoring vital signs through a heartbeat monitoring sensor;
monitoring oxygen and toxic gases in the gas through an electrochemical sensor;
monitoring combustible gas in the gas by a catalytic combustion type sensor;
monitoring the field noise through sound acquisition monitoring equipment;
monitoring a power frequency and radio frequency electromagnetic field through an ultra-wideband electromagnetic radiation analysis terminal, wherein the frequency range of the ultra-wideband electromagnetic radiation analysis terminal covers a frequency range from low frequency to millimeter wave;
the power frequency and radio frequency electromagnetic field is used for measuring the electric field or/and the magnetic field intensity and the power density in an alternating current transmission and transformation project or/and a power distribution system or/and a rail transit power supply system or/and a charging pile.
3. The environmental monitoring and early warning method for electric power operation safety as recited in claim 1,
in the second step, the method for constructing the edge calculation model is as follows:
judging whether an operator exists or not according to the vital sign information;
and when the judgment result is that the operator exists, analyzing the gas data or/and the noise data or/and the electromagnetic field data, and sending the analysis result to the early warning model as a trigger signal.
4. The environmental monitoring and early warning method for electric power operation safety as recited in claim 3,
the edge calculation model is provided with a noise processing unit and a fault analysis characteristic database;
the noise processing unit analyzes the noise data by adopting a Fourier transform method to obtain the amplitude and the frequency of the noise;
the method for constructing the fault analysis feature database comprises the following steps:
acquiring the frequency spectrum values of normal noise and fault signals to form a noise template with amplitude and frequency as characteristics;
and the noise processing unit transmits the analysis and calculation results to a fault analysis characteristic database, and counts, induces and analyzes the amplitude and frequency of the noise to obtain a fault diagnosis criterion.
5. The environmental monitoring and early warning method for electric power operation safety as recited in claim 4,
the analysis processing steps of the Fourier transform method are as follows:
step 21, acquiring original noise data;
step 22, converting the noise data in the step 21 into an N-point sequence through Fast Fourier Transform (FFT) so as to capture an accidental signal or a signal with short duration;
step 23, sequentially decomposing the N point sequence in the step 22 into a series of short sequences;
and 24, according to the symmetry and the periodic properties of the exponential factors in the DFT calculation formula, calculating the DFT corresponding to the short sequence in the step 23 and properly combining the DFT to obtain the amplitude and the frequency of the noise.
6. The environmental monitoring and early warning method for electric power operation safety as recited in claim 4,
the fault diagnosis method comprises the following steps:
step 31, obtaining a noise feature vector according to the amplitude and frequency of the noise
Step 32, calculating a vector sequence of the noise characteristic vector in the step 31 according to a fuzzy recognition algorithm;
step 33, comparing the vector sequence in the step 32 with a noise template in a fault analysis feature database, and calculating the proximity membership of the vector sequence and the noise template;
and when the membership degree is greater than or equal to 0.9, the templates are regarded as the same or similar templates, and the identification result is output.
7. The environmental monitoring and early warning method for electric power operation safety as recited in claim 1,
the processing analysis of the edge calculation model on the noise comprises the following contents:
s1: real-time environmental noise monitoring data are transmitted to a noise monitoring intermediate database established in advance in an edge computing link edge computing server through a data transmission module after field artificial fault marking by sound acquisition monitoring equipment;
s2: judging whether the same noise monitoring data record exists in the noise monitoring intermediate database, and if the same noise monitoring data record does not exist, transmitting the environmental real-time noise monitoring data in the S1 into the noise monitoring intermediate database;
comparing the fault labels, if similar noise data are collected, constructing a new fault model through noise AI learning, analyzing the noise amplitude and frequency by adopting Fast Fourier Transform (FFT), and inputting the analysis and calculation result into a fault analysis characteristic database established in advance in an edge calculation server;
s3: classifying the collected fault models in the S2, arranging the fault models in an edge calculation server by adopting a mathematical statistics method, and constructing a new fault analysis characteristic database for judging the fault types corresponding to the field noises;
s4: transmitting noise monitoring data monitored by the site environment to an edge computing server through a network communication medium, comparing the noise monitoring data with a model in a noise fault analysis characteristic database in the S3, and computing the proximity membership degree according to site operation contents and environment; and when the membership degree is more than or equal to 0.9, determining and identifying the fault type, and informing field operators of the fault type in time through a network communication medium to guide the development of safety maintenance operation.
8. An environment monitoring and early warning device for electric power operation safety is characterized in that,
adopting the environmental monitoring and early warning method for electric power operation safety as claimed in any one of claims 1-7;
the system comprises a monitoring terminal, a network communication module and an edge computing device;
the monitoring terminal is provided with a gas monitoring function module, a noise monitoring function module and an electromagnetic field monitoring function module;
the network communication module is provided with a transmission medium;
the edge computing device is provided with a monitoring threshold value monitoring system and an early warning system;
the monitoring terminal network communication module sends related monitoring data to the edge computing device;
and obtaining an edge calculation result through a monitoring threshold value monitoring system, and pushing through an early warning system and carrying out on-site early warning display and processing.
9. The environmental monitoring and early warning device for electric power operation safety as recited in claim 8,
the gas monitoring function module is provided with an electrochemical sensor for detecting oxygen and toxic gas and a catalytic combustion sensor for detecting combustible gas;
the noise monitoring function module is assembled with on-site noise acquisition and detection equipment and is used for monitoring the noise of an on-site operation working surface;
the electromagnetic field monitoring function module is provided with an ultra-wideband electromagnetic radiation analysis terminal for monitoring power frequency and radio frequency electromagnetic fields, and is used for measuring the electric field, the magnetic field intensity and the power density of alternating current transmission and transformation engineering, a power distribution system, a rail transit power supply system or a charging pile, and the frequency range of the electromagnetic field monitoring function module covers the frequency range from low frequency to millimeter wave.
10. A computer device, characterized in that,
the method comprises the following steps:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method for environmental monitoring and forewarning for power operation safety as recited in any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210988266.6A CN115310843A (en) | 2022-08-17 | 2022-08-17 | Environment monitoring and early warning method, device and equipment for electric power operation safety |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210988266.6A CN115310843A (en) | 2022-08-17 | 2022-08-17 | Environment monitoring and early warning method, device and equipment for electric power operation safety |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115310843A true CN115310843A (en) | 2022-11-08 |
Family
ID=83863160
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210988266.6A Pending CN115310843A (en) | 2022-08-17 | 2022-08-17 | Environment monitoring and early warning method, device and equipment for electric power operation safety |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115310843A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115664551A (en) * | 2022-12-23 | 2023-01-31 | 深圳市傲立电子有限公司 | Safety monitoring and early warning system for radio frequency power amplifier |
| CN115983648A (en) * | 2023-03-22 | 2023-04-18 | 无锡广盈集团有限公司 | Cable accessory installation monitoring method and system |
| CN116359642A (en) * | 2023-03-10 | 2023-06-30 | 湖南金烽信息科技有限公司 | Transformer running state 5G intelligent monitoring system and method |
| CN116667203A (en) * | 2023-05-30 | 2023-08-29 | 国网湖北省电力有限公司超高压公司 | Electric power basic operation safety protection method and system based on gas detector |
| CN117130016A (en) * | 2023-10-26 | 2023-11-28 | 深圳市麦微智能电子有限公司 | Personal safety monitoring system, method, device and medium based on Beidou satellite |
| CN118061839A (en) * | 2024-04-19 | 2024-05-24 | 成都智邦科技有限公司 | Charging pile charging control method based on information monitoring |
-
2022
- 2022-08-17 CN CN202210988266.6A patent/CN115310843A/en active Pending
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115664551A (en) * | 2022-12-23 | 2023-01-31 | 深圳市傲立电子有限公司 | Safety monitoring and early warning system for radio frequency power amplifier |
| CN115664551B (en) * | 2022-12-23 | 2023-03-21 | 深圳市傲立电子有限公司 | Safety monitoring and early warning system for radio frequency power amplifier |
| CN116359642A (en) * | 2023-03-10 | 2023-06-30 | 湖南金烽信息科技有限公司 | Transformer running state 5G intelligent monitoring system and method |
| CN116359642B (en) * | 2023-03-10 | 2024-01-23 | 湖南金烽信息科技有限公司 | Transformer running state 5G intelligent monitoring system and method |
| CN115983648A (en) * | 2023-03-22 | 2023-04-18 | 无锡广盈集团有限公司 | Cable accessory installation monitoring method and system |
| CN116667203A (en) * | 2023-05-30 | 2023-08-29 | 国网湖北省电力有限公司超高压公司 | Electric power basic operation safety protection method and system based on gas detector |
| CN116667203B (en) * | 2023-05-30 | 2023-11-03 | 国网湖北省电力有限公司超高压公司 | Electric power basic operation safety protection method and system based on gas detector |
| CN117130016A (en) * | 2023-10-26 | 2023-11-28 | 深圳市麦微智能电子有限公司 | Personal safety monitoring system, method, device and medium based on Beidou satellite |
| CN117130016B (en) * | 2023-10-26 | 2024-02-06 | 深圳市麦微智能电子有限公司 | Personal safety monitoring system, method, device and medium based on Beidou satellite |
| CN118061839A (en) * | 2024-04-19 | 2024-05-24 | 成都智邦科技有限公司 | Charging pile charging control method based on information monitoring |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115310843A (en) | Environment monitoring and early warning method, device and equipment for electric power operation safety | |
| CN111445106B (en) | Safety control method and system for fault processing operation site of electricity acquisition equipment | |
| Daponte et al. | TransientMeter: A distributed measurement system for power quality monitoring | |
| CN113689013A (en) | Intelligent monitoring and operation and maintenance system for transformer | |
| CN112288021A (en) | Medical wastewater monitoring data quality control method, device and system | |
| CN112326583A (en) | Intelligent toxic gas detection system and method based on Internet of things | |
| CN115575584B (en) | Atmospheric environment cadmium content monitoring and early warning method and system | |
| CN116933044A (en) | Intelligent processing method and system for power supply data | |
| CN111855935A (en) | River course water quality monitoring and early warning system | |
| CN110728381A (en) | Intelligent power plant inspection method and system based on RFID and data processing | |
| CN111668927A (en) | Substation intelligent inspection system based on ubiquitous power Internet of things and control method thereof | |
| CN116976561A (en) | Full life cycle carbon footprint accounting method for high-voltage cable equipment | |
| CN113131378A (en) | Intelligent inspection system and method for secondary system of transformer substation based on image recognition | |
| CN113887749A (en) | Cloud edge cooperation-based multi-dimensional monitoring and disposal method, device and platform for power internet of things | |
| CN116561519A (en) | Electric carbon emission process monitoring method and system based on big data of power grid | |
| CN116595062A (en) | Park carbon emission monitoring and early warning method | |
| CN115019411A (en) | Routing inspection system and method based on voice interaction | |
| CN117390944A (en) | Substation operation condition simulation system | |
| CN114693010A (en) | Management and control system and method for production equipment | |
| CN117074852A (en) | Power distribution network electric energy monitoring and early warning management method and system | |
| CN110033527A (en) | A kind of electric operating security control intelligent robot and its implementation | |
| CN112113145A (en) | Intelligent online detection method for chemical pipeline safety | |
| CN111224468B (en) | Photovoltaic equipment safe operation and maintenance platform based on cloud computing | |
| CN111949125A (en) | Site operation panoramic perception management system based on knowledge graph | |
| Ren | Design and Implementation of Fault Diagnosis System for Power Internet of Things Equipment Based on Neural Network |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20230713 Address after: 310014 No. 1, Huadian lane, Zhaohui eighth District, Gongshu District, Hangzhou City, Zhejiang Province Applicant after: STATE GRID ZHEJIANG ELECTRIC POWER COMPANY LIMITED ELECTRIC POWER Research Institute Applicant after: STATE GRID ZHEJIANG ELECTRIC POWER Co.,Ltd. Address before: The eight district of Hangzhou city in Zhejiang province 310014 Huadian Zhaohui under No. 1 Lane Applicant before: STATE GRID ZHEJIANG ELECTRIC POWER COMPANY LIMITED ELECTRIC POWER Research Institute Applicant before: STATE GRID ZHEJIANG ELECTRIC POWER Co.,Ltd. Applicant before: ZHEJIANG HUAYUN CLEAN ENERGY CO.,LTD. |
|
| TA01 | Transfer of patent application right |