CN111624929A - High-voltage distribution room safety intelligent monitoring regulation and control system based on big data - Google Patents
High-voltage distribution room safety intelligent monitoring regulation and control system based on big data Download PDFInfo
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- CN111624929A CN111624929A CN202010548905.8A CN202010548905A CN111624929A CN 111624929 A CN111624929 A CN 111624929A CN 202010548905 A CN202010548905 A CN 202010548905A CN 111624929 A CN111624929 A CN 111624929A
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
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- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
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Abstract
The invention discloses a big data-based high-voltage distribution room safety intelligent monitoring and control system which comprises a monitoring area division module, a temperature acquisition and processing module, a humidity acquisition and processing module, an area image acquisition module, a management database, an area image comparison and classification module, a ventilation coefficient analysis module, a ventilation execution terminal, a central server, a dispelling alarm module and a remote control center. The invention collects the temperature and the humidity in each monitoring subarea of the high-voltage distribution room in real time and collects images through the temperature collection processing module, the humidity collection processing module and the image collection module, counts the ventilation coefficient by combining the ventilation coefficient analysis module, visually displays the environmental condition of the high-voltage distribution room, and simultaneously discovers, identifies and classifies the mouse animal and spark phenomena in each monitoring subarea according to the regional image comparison classification module, thereby realizing the automatic monitoring and potential safety hazard troubleshooting of the high-voltage distribution room and ensuring the normal operation of a power grid.
Description
Technical Field
The invention relates to the technical field of safety monitoring of high-voltage distribution rooms, in particular to a safety intelligent monitoring and regulating system of a high-voltage distribution room based on big data.
Background
The distribution room is an indoor distribution place with low-voltage load, is used as an important component element in an electric power supply system, is responsible for the step-down and transmission of electric energy, mainly distributes the electric energy for low-voltage users, and is provided with a medium-voltage incoming line (a small amount of outgoing lines can be arranged), a distribution transformer and a low-voltage distribution device. Facilities of equipment with voltage class of 10kV or below are divided into a high-voltage distribution room and a low-voltage distribution room.
Present power transmission system mainly adopts high voltage transmission, then supply power after the step-down, mainly accomplish through high-voltage distribution room when the step-down, high-voltage distribution room, because of its inside installation multiple distribution equipment, under the safe not-passing circumstances, short circuit electric shock accident easily takes place, burn out electrical equipment, influence electric wire netting safe operation and power supply reliability, for effectively improving user's safe power consumption, reduce the power supply line tripping operation because of equipment trouble causes, need monitor high-voltage distribution room environment.
The traditional high-voltage distribution room monitoring is in a mode of regularly and manually inspecting, the working efficiency is low, accurate detection on the temperature and the humidity of the environment of the high-voltage distribution room cannot be realized due to the limitation of manual inspection, meanwhile, due to the particularity of the activity of small murine animals, the artificial inspection is difficult to find the murine animals appearing in a certain corner of the high-voltage distribution room, so that potential safety hazards exist in the high-voltage distribution room, short-circuit fire disasters appearing in the high-voltage distribution room cannot be timely found and processed in non-inspection time, and the normal operation of a power grid is influenced.
Disclosure of Invention
The invention aims to provide a high-voltage distribution room safety intelligent monitoring and control system based on big data, which divides a monitoring area of a high-voltage distribution room through a monitoring area dividing module, collects the temperature and the humidity in the monitoring area of the high-voltage distribution room in real time and collects images by combining a temperature collecting and processing module, a humidity collecting and processing module and an area image collecting module, counts the ventilation coefficient by combining a ventilation coefficient analyzing module, and simultaneously identifies the phenomena of rodents and sparks in the monitoring area of the high-voltage distribution room according to the area image comparison and classification module and performs classification processing, thereby solving the problems in the background technology.
The purpose of the invention can be realized by the following technical scheme:
a big data-based high-voltage distribution room safety intelligent monitoring and control system comprises a monitoring area dividing module, a temperature acquisition and processing module, a humidity acquisition and processing module, an area image acquisition module, a management database, an area image comparison and classification module, a ventilation coefficient analysis module, a ventilation execution terminal, a central server, a dispelling alarm module and a remote control center;
the monitoring area dividing module is used for uniformly dividing the length, the width and the height of the high-voltage distribution room, dividing the high-voltage distribution room to be monitored into a plurality of monitoring sub-areas which are identical in volume and are mutually connected according to a dividing mode of spatial grid distribution, numbering the monitoring sub-areas according to a preset sequence, and sequentially marking the monitoring sub-areas as 1,2.
The temperature acquisition processing module is connected with the monitoring area division module, comprises a plurality of temperature sensors which are all arranged in each monitoring subarea and are used for acquiring the temperature of each monitoring subarea in real time and dividing the temperature according to the acquisition time period to obtain a time period temperature set R of each monitoring subarea every dayi(ri1,ri2,...,rit,...,ri6)，rit represents the temperature value of the ith collection time period of the ith monitoring sub-region, t represents the collection time period, and is 1,2,3,4,5 and 6, and the temperature collection processing module sends the time period temperature set of each monitoring sub-region to the ventilation coefficient analysis module;
the humidity acquisition processing module is connected with the monitoring area division module, comprises a plurality of humidity sensors which are all installed in each monitoring subarea and used for acquiring the humidity of each monitoring subarea in real time and dividing the humidity according to the acquisition time period to obtain a time period humidity set G of each monitoring subarea every dayi(gi1,gi2,...,git,...,gi6)，git is a humidity value of the ith acquisition time period of the ith monitoring subarea, t is an acquisition time period, and is 1,2,3,4,5 and 6, and the humidity acquisition processing module sends the time period humidity set of each monitoring subarea to the ventilation coefficient analysis module;
the regional image acquisition module and the monitoring region division module comprise a plurality of cameras which are respectively arranged in each monitoring sub-region and are used for acquiring images of each monitoring sub-region and sending the images to the image preprocessing module;
the image preprocessing module is connected with the regional image acquisition module and used for receiving the images of the monitoring subregions sent by the regional image acquisition module, carrying out contrast improvement, noise filtering and high-definition filtering on the received images of the monitoring subregions to obtain preprocessed images and sending the preprocessed images to the regional image comparison classification module;
the management database is used for storing safe temperature and safe humidity values of the high-voltage distribution room, storing standard still images of all monitoring sub-areas, storing characteristic vectors corresponding to all abnormal point types, wherein the abnormal point types comprise rodent and sparks, and storing a safe ventilation coefficient range, a temperature influence coefficient and a humidity influence coefficient;
the regional image comparison and classification module is connected with the image preprocessing module and used for receiving the preprocessed images of the monitoring subregions sent by the image preprocessing module, dividing the preprocessed images of the monitoring subregions into a plurality of local images, roughly comparing the local images with standard still images of the monitoring subregions in a management database correspondingly, judging whether abnormal points exist or not, marking the monitoring subregions as abnormal monitoring subregions if the abnormal points exist, dividing the monitoring subregions into the abnormal monitoring subregions and normal monitoring subregions according to whether the abnormal points exist or not in the images of the monitoring subregions, focusing and amplifying the local images where the abnormal points exist in the images of the abnormal monitoring subregions, extracting the characteristic points identified by the abnormal points, wherein the characteristic points comprise contour shapes, colors and dynamics and are compared with the characteristic vectors corresponding to the abnormal points stored in the management database one by one, counting the similarity of the extracted feature points and feature vectors corresponding to the abnormal points, screening the abnormal point category with the highest similarity, outputting the abnormal point category with the highest similarity when the highest similarity is greater than a set similarity threshold, dividing the abnormal monitoring sub-regions into abnormal monitoring sub-regions with murine animals and abnormal monitoring sub-regions with sparks according to the output abnormal point category in the abnormal monitoring sub-regions, and sending the abnormal monitoring sub-region set with murine animals and the abnormal monitoring sub-region set with sparks to a central server by a regional image contrast classification module;
coefficient of ventilationThe analysis module is respectively connected with the temperature acquisition processing module and the humidity acquisition processing module, receives the time period temperature set of each monitoring subarea every day sent by the temperature acquisition processing module and the time period humidity set of each monitoring subarea every day sent by the humidity acquisition processing module, extracts the safe temperature and the safe humidity value of the high-voltage distribution room in the management database, compares the received time period temperature set of each monitoring subarea every day with the safe temperature value stored in the management database, and obtains a time period temperature comparison set delta R of each monitoring subarea every dayi(Δri1,Δri2,...,Δrit,...,Δri6)，Δrit is the difference value between the temperature of the t-th collection time period of the ith monitoring sub-region and the safe temperature value, and meanwhile, the received time period humidity set of each monitoring sub-region is compared with the safe humidity value stored in the management database to obtain the time period humidity comparison set delta G of each monitoring sub-regioni(Δgi1,Δgi2,...,Δgit,...,Δgi6)，Δgit is a difference value between the humidity value and the safety humidity value of the tth collection time period of the ith monitoring sub-region, and the ventilation coefficient of the high-voltage distribution room is counted according to the temperature comparison set and the humidity comparison set of each monitoring sub-region in each day time period and is sent to the central server;
the central server is respectively connected with the ventilation coefficient analysis module and the regional image comparison and classification module, receives the ventilation coefficient sent by the ventilation coefficient analysis module, extracts a safe ventilation coefficient range stored in the management database, does not send a control command to the ventilation execution terminal if the received ventilation coefficient is smaller than the lower limit value of the safe ventilation coefficient range, sends a primary ventilation control command to the ventilation execution terminal if the received ventilation coefficient is within the safe ventilation coefficient range, and sends a secondary ventilation control command to the ventilation execution terminal if the received ventilation coefficient is larger than the upper limit value of the safe ventilation coefficient range;
meanwhile, the central server receives the abnormal monitoring subarea set with the murine and the abnormal monitoring subarea set with the spark sent by the regional image comparison classification module, and sends a repelling instruction to the repelling alarm module for the received abnormal monitoring subarea set with the murine; for the received abnormal monitoring subarea set with sparks, the serial number of the abnormal monitoring subarea with sparks is sent to a remote control center, meanwhile, a fire alarm signal is sent to the remote control center, and a main power switch of the high-voltage distribution room is closed;
the ventilation execution terminal is connected with the central server and used for receiving the ventilation grades sent by the central server and carrying out ventilation of different grades;
the driving alarm module is connected with the central server and used for receiving a driving alarm instruction sent by the central server and driving the central server in a voice alarm mode;
and the remote control center is connected with the central server and is used for receiving the abnormal monitoring sub-region number with sparks sent by the central server and dispatching related operators on duty for processing.
Preferably, the camera is a high-definition camera.
Further, the calculation formula of the ventilation coefficient is
Δrit is expressed as the difference between the temperature of the ith acquisition time period of the ith monitoring sub-region and the safe temperature value, delta git is expressed as the difference between the humidity value of the ith acquisition time period of the ith monitoring sub-area and the safety humidity value, rit、git is respectively expressed as the temperature and humidity values of t acquisition time periods of the ith monitoring subarea, r0Expressed as the safety temperature value, g0Expressed as safe humidity value, ξrExpressed as the temperature coefficient of influence, ξgExpressed as the humidity influence coefficient.
Further, the specific method for dispelling the effects of the sound alarm comprises the following steps:
s1: the repelling alarm module sends out a sound alarm signal and checks whether the murine in the monitoring subarea is driven away or not, if so, the step S2 is executed, and if not, the step S3 is executed;
s2: checking images of other monitoring subregions, searching whether the murine animal escapes to other monitoring subregions, and executing the step S3 if the murine animal is found in other monitoring subregions;
s3: the driving-away alarm module increases the volume and frequency of the sound alarm and checks whether the murine in the monitoring sub-area is driven away or not, if so, the step S2 is executed, and if not, the step S4 is executed;
s4: the warning module is used for sending a warning signal to the remote control center, and the remote control center dispatches relevant operators on duty to process the warning signal.
Has the advantages that:
(1) according to the invention, the monitoring area of the high-voltage distribution room is divided through the monitoring area dividing module, the temperature acquisition and processing module, the humidity acquisition and processing module and the image acquisition module are combined to acquire the temperature and the humidity in each monitoring subarea in real time and acquire images, the ventilation coefficient analysis module is combined to count the ventilation coefficient, the environmental condition of the high-voltage distribution room is visually displayed, and simultaneously, the mouse animal and spark phenomena appearing in each monitoring subarea are discovered and identified according to the regional image comparison and classification module and are classified, so that the automatic monitoring and potential safety hazard investigation of the high-voltage distribution room are realized, the serious safety accident caused by the undiscovered potential safety hazard is avoided, and the normal operation of a power grid is ensured.
(2) According to the invention, the high-voltage distribution room is divided into the monitoring sub-areas by adopting a space gridding dividing mode, and the cameras are arranged in the monitoring sub-areas to carry out all-dimensional image acquisition, so that the monitoring strength is improved, the condition that a certain area is missed to be inspected due to manual inspection is avoided, and the occurrence rate of potential safety hazards is reduced.
(3) According to the invention, the alarm module is used for expelling the rodent in the abnormal monitoring subarea with the rodent, the remote service center is used for dispatching the relevant on-duty personnel to process the abnormal monitoring subarea with the spark, the rodent and spark phenomena can be reasonably utilized to carry out different safety hazard degrees on the high-voltage distribution room, the classification processing is carried out intelligently, the workload of the on-duty personnel is reduced, the resources can be utilized maximally, and the intelligence and the flexibility of the system are embodied.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the intelligent monitoring and control system for high-voltage distribution room safety based on big data comprises a monitoring area division module, a temperature acquisition and processing module, a humidity acquisition and processing module, an area image acquisition module, a management database, an area image comparison and classification module, a ventilation coefficient analysis module, a ventilation execution terminal, a central server, a dispelling alarm module and a remote control center.
The monitoring area dividing module is used for uniformly dividing the length, the width and the height of the high-voltage distribution room, dividing the high-voltage distribution room to be monitored into a plurality of monitoring sub-areas which are the same in volume and are connected with one another according to a dividing mode of space grid distribution, numbering the monitoring sub-areas according to a preset sequence, and sequentially marking the monitoring sub-areas as 1,2.
The temperature acquisition processing module is connected with the monitoring area division module and comprises a plurality of temperature sensors which are all installedIn each monitoring sub-area, the temperature acquisition module is used for acquiring the temperature of each monitoring sub-area in real time and dividing the temperature according to the acquisition time period to obtain a time period temperature set R of each monitoring sub-area every dayi(ri1,ri2,...,rit,...,ri6)，rit is a temperature value of the ith collection time period of the ith monitoring sub-area, t is a collection time period, t is 1,2,3,4,5 and 6, wherein the collection time corresponding to each collection time period is 24: 00-4: 00, 4:00-8:00, 8:00-12:00, 12:00-16:00, 16:00-20:00 and 20:00-24:00 respectively, and the temperature collection processing module sends the time period temperature set of each monitoring sub-area to the ventilation coefficient analysis module.
The humidity acquisition processing module is connected with the monitoring area division module, comprises a plurality of humidity sensors which are all installed in each monitoring subarea and used for acquiring the humidity of each monitoring subarea in real time and dividing the humidity according to the acquisition time period to obtain a time period humidity set G of each monitoring subarea every dayi(gi1,gi2,...,git,...,gi6)，git is the humidity value of the ith acquisition time period of the ith monitoring subarea, t is the acquisition time period, t is 1,2,3,4,5,6, the acquisition time corresponding to each acquisition time period is 24: 00-4: 00, 4:00-8:00, 8:00-12:00, 12:00-16:00, 16:00-20:00, 20:00-24:00, and the humidity acquisition processing module sends the time period humidity set of each monitoring subarea to the ventilation coefficient analysis module.
Regional image acquisition module divides the module with the monitoring area, including a plurality of cameras, camera position high definition digtal camera, it is installed respectively in each monitoring subregion for gather the image of each monitoring subregion, realized high voltage distribution room's all-round image acquisition, improved the monitoring dynamics, avoid artifical the patrolling and examining and lead to certain region leak to examine, reduced the incidence of potential safety hazard, regional image acquisition module sends the image of each monitoring subregion of gathering to image preprocessing module.
The image preprocessing module is connected with the regional image acquisition module and used for receiving the images of the monitoring subregions sent by the regional image acquisition module, improving the contrast, filtering noise and performing high-definition filtering processing on the received images of the monitoring subregions, reducing the influence of brightness change on the images, obtaining preprocessed images and sending the preprocessed images to the regional image comparison classification module.
The management database stores safe temperature and safe humidity values of the high-voltage distribution room, stores standard still images of all monitoring sub-areas, and stores characteristic vectors corresponding to all abnormal point types, wherein the abnormal point types comprise rodents and sparks, the characteristic vectors of the rodents comprise contour shapes, colors, dynamics and the like, the characteristic vectors of the sparks comprise shapes, brightness, colors and the like, and meanwhile, the safety ventilation coefficient range, the temperature influence coefficient and the humidity influence coefficient are stored.
The regional image comparison and classification module is connected with the image preprocessing module and used for receiving the preprocessed images of the monitoring subregions sent by the image preprocessing module, dividing the preprocessed images of the monitoring subregions into a plurality of local images, roughly comparing the local images of the monitoring subregions with standard still images of the monitoring subregions in a management database correspondingly, judging whether abnormal points exist or not, if the abnormal points exist, marking the monitoring subregions as abnormal monitoring subregions, dividing the monitoring subregions into the abnormal monitoring subregions and normal monitoring subregions according to whether the abnormal points exist or not in the images of the monitoring subregions, focusing and amplifying the local images where the abnormal points in the images of the abnormal monitoring subregions exist, extracting the characteristic points identified by the abnormal points, wherein the characteristic points comprise contour shapes, colors and dynamic states, comparing the abnormal monitoring sub-regions with the sparks, and sending the abnormal monitoring sub-region set with the rodents and the abnormal monitoring sub-region set with the sparks to a central server by a regional image comparison classification module.
A ventilation coefficient analysis module which is respectively connected with the temperature acquisition processing module and the humidity acquisition processing module, receives the time period temperature set of each monitoring subarea every day sent by the temperature acquisition processing module and the time period humidity set of each monitoring subarea every day sent by the humidity acquisition processing module, extracts the safe temperature and the safe humidity value of the high-voltage distribution room in the management database, compares the received time period temperature set of each monitoring subarea every day with the safe temperature value stored in the management database, and obtains a time period temperature comparison set delta R of each monitoring subarea every dayi(Δri1,Δri2,...,Δrit,...,Δri6)，Δrit is the difference value between the temperature of the t-th collection time period of the ith monitoring sub-region and the safe temperature value, and meanwhile, the received time period humidity set of each monitoring sub-region is compared with the safe humidity value stored in the management database to obtain the time period humidity comparison set delta G of each monitoring sub-regioni(Δgi1,Δgi2,...,Δgit,...,Δgi6)，Δgit is the difference between the humidity value and the safety humidity value of the tth collection time period of the ith monitoring sub-region, and the ventilation coefficient of the high-voltage distribution room is counted according to the temperature comparison set and the humidity comparison set of each monitoring sub-region in each day time period
Δrit is expressed as the difference between the temperature of the ith acquisition time period of the ith monitoring sub-region and the safe temperature value, delta git is expressed as the difference between the humidity value of the ith acquisition time period of the ith monitoring sub-area and the safety humidity value, rit、git is respectively expressed as the temperature and humidity values of t acquisition time periods of the ith monitoring subarea, r0Expressed as the safety temperature value, g0Expressed as safe humidity value, ξrExpressed as the temperature coefficient of influence, ξgExpressed as the humidity influence coefficient, the greater the ventilation coefficient, the tableThe higher the temperature and the higher the humidity in the high-voltage distribution room, the higher the ventilation coefficient analysis module sends the statistical ventilation coefficient to the central server.
The central server is respectively connected with the ventilation coefficient analysis module and the regional image comparison and classification module, receives the ventilation coefficient sent by the ventilation coefficient analysis module, extracts a safe ventilation coefficient range stored in the management database, does not send a control command to the ventilation execution terminal if the received ventilation coefficient is smaller than the lower limit value of the safe ventilation coefficient range, sends a primary ventilation control command to the ventilation execution terminal if the received ventilation coefficient is within the safe ventilation coefficient range, and sends a secondary ventilation control command to the ventilation execution terminal if the received ventilation coefficient is larger than the upper limit value of the safe ventilation coefficient range;
meanwhile, the central server receives the abnormal monitoring subarea set with the murine and the abnormal monitoring subarea set with the spark sent by the regional image comparison classification module, and sends a repelling instruction to the repelling alarm module for the received abnormal monitoring subarea set with the murine; the received abnormal monitoring subarea set with sparks sends the serial number of the abnormal monitoring subarea with sparks to a remote control center, sends a fire alarm signal to the remote control center, closes a main power switch of the high-voltage distribution room, and classifies the rodent and the sparks appearing in the abnormal monitoring subarea, so that the safety of the high-voltage distribution room can be harmed to different degrees reasonably by utilizing the rodent and spark phenomena, the classification treatment is carried out intelligently, the workload of operators on duty is reduced, the resources can be utilized to the maximum extent, and the intelligence and the flexibility of the system are reflected.
And the ventilation execution terminal is connected with the central server and is used for receiving the ventilation grades sent by the central server and carrying out ventilation of different grades.
The dispelling alarm module is connected with the central server and used for receiving a dispelling alarm instruction sent by the central server and dispelling the voice alarm instruction, and the specific method for dispelling the voice alarm comprises the following steps:
s1: the repelling alarm module sends out a sound alarm signal and checks whether the murine in the monitoring subarea is driven away or not, if so, the step S2 is executed, and if not, the step S3 is executed;
s2: checking images of other monitoring subregions, searching whether the murine animal escapes to other monitoring subregions, and executing the step S3 if the murine animal is found in other monitoring subregions;
s3: the driving-away alarm module increases the volume and frequency of the sound alarm and checks whether the murine in the monitoring sub-area is driven away or not, if so, the step S2 is executed, and if not, the step S4 is executed;
s4: the warning module is used for sending a warning signal to the remote control center, and the remote control center dispatches relevant operators on duty to process the warning signal.
And the remote control center is connected with the central server and is used for receiving the abnormal monitoring sub-region number with sparks sent by the central server and dispatching related operators on duty for processing.
According to the invention, the monitoring area of the high-voltage distribution room is divided through the monitoring area dividing module, the temperature acquisition and processing module, the humidity acquisition and processing module and the image acquisition module are combined to acquire the temperature and the humidity in each monitoring subarea in real time and acquire images, the ventilation coefficient analysis module is combined to count the ventilation coefficient, the environmental condition of the high-voltage distribution room is visually displayed, meanwhile, the mouse animal and spark phenomena appearing in each monitoring subarea are discovered and identified according to the regional image comparison and classification module, and classification processing is carried out, the monitoring strength is improved, the automatic monitoring and potential safety hazard investigation of the high-voltage distribution room are realized, the serious safety accident caused by the undiscovered potential safety hazard is avoided, and the normal operation of a power grid is ensured.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (4)
1. The utility model provides a high voltage electricity distribution room safety intelligent monitoring regulation and control system based on big data which characterized in that: the system comprises a monitoring area division module, a temperature acquisition and processing module, a humidity acquisition and processing module, an area image acquisition module, a management database, an area image comparison and classification module, a ventilation coefficient analysis module, a ventilation execution terminal, a central server, a dispelling alarm module and a remote control center;
the monitoring area dividing module is used for uniformly dividing the length, the width and the height of the high-voltage distribution room, dividing the high-voltage distribution room to be monitored into a plurality of monitoring sub-areas which are identical in volume and are mutually connected according to a dividing mode of spatial grid distribution, numbering the monitoring sub-areas according to a preset sequence, and sequentially marking the monitoring sub-areas as 1,2.
The temperature acquisition processing module is connected with the monitoring area division module, comprises a plurality of temperature sensors which are all arranged in each monitoring subarea and are used for acquiring the temperature of each monitoring subarea in real time and dividing the temperature according to the acquisition time period to obtain the time period temperature set R of each monitoring subarea every dayi(ri1,ri2,...,rit,...,ri6)，rit represents the temperature value of the ith collection time period of the ith monitoring sub-region, t represents the collection time period, and is 1,2,3,4,5 and 6, and the temperature collection processing module sends the time period temperature set of each monitoring sub-region to the ventilation coefficient analysis module;
the humidity acquisition processing module is connected with the monitoring area division module, comprises a plurality of humidity sensors which are all installed in each monitoring subarea and used for acquiring the humidity of each monitoring subarea in real time and dividing according to the acquisition time period to obtain a time period humidity set G of each monitoring subarea every dayi(gi1,gi2,...,git,...,gi6)，git is expressed as the humidity value of the ith acquisition time period of the ith monitoring subarea, t is expressed as the acquisition time period, t is 1,2,3,4,5 and 6, and a humidity acquisition processing moduleThe block sends the daily time period humidity set of each monitoring subarea to a ventilation coefficient analysis module;
the area image acquisition module and the monitoring area division module comprise a plurality of cameras which are respectively arranged in each monitoring subarea and are used for acquiring images of each monitoring subarea and sending the images to the image preprocessing module;
the image preprocessing module is connected with the regional image acquisition module and used for receiving the images of the monitoring subregions sent by the regional image acquisition module, carrying out contrast improvement, noise filtering and high-definition filtering on the received images of the monitoring subregions to obtain preprocessed images and sending the preprocessed images to the regional image comparison classification module;
the management database stores safe temperature and safe humidity values of the high-voltage distribution room, stores standard still images of all monitoring sub-areas, stores characteristic vectors corresponding to all abnormal point types, the abnormal point types comprise rodent and spark, and simultaneously stores a safe ventilation coefficient range, a temperature influence coefficient and a humidity influence coefficient;
the regional image comparison and classification module is connected with the image preprocessing module and is used for receiving the preprocessed images of the monitoring subregions sent by the image preprocessing module, dividing the preprocessed images of the monitoring subregions into a plurality of local images, roughly comparing the local images with standard still images of the monitoring subregions in a management database correspondingly, judging whether abnormal points exist or not, if the abnormal points exist, marking the monitoring subregions as abnormal monitoring subregions, dividing the monitoring subregions into the abnormal monitoring subregions and normal monitoring subregions according to whether the abnormal points exist in the images of the monitoring subregions, focusing and amplifying the local images where the abnormal points exist in the images of the abnormal monitoring subregions, extracting the characteristic points identified by the abnormal points, wherein the characteristic points comprise contour shapes, colors and dynamic states, and comparing the characteristic vectors corresponding to the abnormal points stored in the management database one by one, counting the similarity of the extracted feature points and feature vectors corresponding to the abnormal points, screening the abnormal point category with the highest similarity, outputting the abnormal point category with the highest similarity when the highest similarity is greater than a set similarity threshold, dividing the abnormal monitoring sub-regions into abnormal monitoring sub-regions with murine animals and abnormal monitoring sub-regions with sparks according to the output abnormal point category in the abnormal monitoring sub-regions, and sending the abnormal monitoring sub-region set with murine animals and the abnormal monitoring sub-region set with sparks to a central server by a regional image contrast classification module;
the ventilation coefficient analysis module is respectively connected with the temperature acquisition processing module and the humidity acquisition processing module, receives the time period temperature set of each monitoring subarea every day sent by the temperature acquisition processing module and the time period humidity set of each monitoring subarea every day sent by the humidity acquisition processing module, extracts the safe temperature and the safe humidity value of the high-voltage distribution room in the management database, compares the received time period temperature set of each monitoring subarea every day with the safe temperature value stored in the management database, and obtains a time period temperature comparison set delta R of each monitoring subarea every dayi(Δri1,Δri2,...,Δrit,...,Δri6)，Δrit is the difference value between the temperature of the ith collection time period of the ith monitoring sub-region and the safe temperature value, and meanwhile, the received time period humidity set of each monitoring sub-region is compared with the safe humidity value stored in the management database to obtain the temperature and humidity comparison set delta G of each time period of each monitoring sub-regioni(Δgi1,Δgi2,...,Δgit,...,Δgi6)，Δgit is a difference value between the humidity value and the safety humidity value of the tth collection time period of the ith monitoring sub-region, and the ventilation coefficient of the high-voltage distribution room is counted according to the temperature comparison set and the humidity comparison set of each monitoring sub-region in each day time period and is sent to the central server;
the central server is respectively connected with the ventilation coefficient analysis module and the regional image comparison classification module, receives the ventilation coefficient sent by the ventilation coefficient analysis module, extracts a safe ventilation coefficient range stored in the management database, does not send a control command to the ventilation execution terminal if the received ventilation coefficient is smaller than the lower limit value of the safe ventilation coefficient range, sends a primary ventilation control command to the ventilation execution terminal if the received ventilation coefficient is within the safe ventilation coefficient range, and sends a secondary ventilation control command to the ventilation execution terminal if the received ventilation coefficient is larger than the upper limit value of the safe ventilation coefficient range;
meanwhile, the central server receives the abnormal monitoring subarea set with the murine and the abnormal monitoring subarea set with the spark sent by the regional image comparison classification module, and sends a repelling instruction to the repelling alarm module for the received abnormal monitoring subarea set with the murine; for the received abnormal monitoring subarea set with sparks, the serial number of the abnormal monitoring subarea with sparks is sent to a remote control center, meanwhile, a fire alarm signal is sent to the remote control center, and a main power switch of the high-voltage distribution room is closed;
the ventilation execution terminal is connected with the central server and used for receiving the ventilation grades sent by the central server and carrying out ventilation of different grades;
the driving alarm module is connected with the central server and used for receiving a driving alarm instruction sent by the central server and driving the driving alarm instruction in a voice alarm mode;
and the remote control center is connected with the central server and is used for receiving the abnormal monitoring sub-region number with sparks sent by the central server and dispatching related on-duty personnel for processing.
2. The intelligent monitoring and control system for the safety of the high-voltage distribution room based on the big data according to claim 1, characterized in that: the camera is a high-definition camera.
3. The intelligent monitoring and control system for the safety of the high-voltage distribution room based on the big data according to claim 1, characterized in that: the ventilation coefficient is calculated by the formula
Δrit representsIs the difference between the temperature of the ith acquisition time period of the ith monitoring sub-region and the safety temperature value, delta git is expressed as the difference between the humidity value of the ith acquisition time period of the ith monitoring sub-area and the safety humidity value, rit、git is respectively expressed as the temperature and humidity values of t acquisition time periods of the ith monitoring subarea, r0Expressed as the safety temperature value, g0Expressed as safe humidity value, ξrExpressed as the temperature coefficient of influence, ξgExpressed as the humidity influence coefficient.
4. The intelligent monitoring and control system for the safety of the high-voltage distribution room based on the big data according to claim 1, characterized in that: the specific method for dispelling the voice alarm comprises the following steps:
s1: the repelling alarm module sends out a sound alarm signal and checks whether the murine in the monitoring subarea is driven away or not, if so, the step S2 is executed, and if not, the step S3 is executed;
s2: checking images of other monitoring subregions, searching whether the murine animal escapes to other monitoring subregions, and executing the step S3 if the murine animal is found in other monitoring subregions;
s3: the driving-away alarm module increases the volume and frequency of the sound alarm and checks whether the murine in the monitoring sub-area is driven away or not, if so, the step S2 is executed, and if not, the step S4 is executed;
s4: the warning module is used for sending a warning signal to the remote control center, and the remote control center dispatches relevant operators on duty to process the warning signal.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112269812A (en) * | 2020-10-16 | 2021-01-26 | 苏宇航 | Intelligent power distribution network safety monitoring management system based on big data |
| CN112903026A (en) * | 2021-02-25 | 2021-06-04 | 南京翰氜信息科技有限公司 | Engineering safety remote online monitoring system based on machine vision and artificial intelligence |
| CN112964735A (en) * | 2021-02-02 | 2021-06-15 | 南京柏王智能装备科技有限公司 | Rail transit safety intelligent monitoring method based on big data analysis and cloud monitoring platform |
| CN115150552A (en) * | 2022-06-23 | 2022-10-04 | 中国华能集团清洁能源技术研究院有限公司 | Constructor safety monitoring method, system and device based on deep learning self-adaption |
-
2020
- 2020-06-16 CN CN202010548905.8A patent/CN111624929A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112269812A (en) * | 2020-10-16 | 2021-01-26 | 苏宇航 | Intelligent power distribution network safety monitoring management system based on big data |
| CN112964735A (en) * | 2021-02-02 | 2021-06-15 | 南京柏王智能装备科技有限公司 | Rail transit safety intelligent monitoring method based on big data analysis and cloud monitoring platform |
| CN112903026A (en) * | 2021-02-25 | 2021-06-04 | 南京翰氜信息科技有限公司 | Engineering safety remote online monitoring system based on machine vision and artificial intelligence |
| CN115150552A (en) * | 2022-06-23 | 2022-10-04 | 中国华能集团清洁能源技术研究院有限公司 | Constructor safety monitoring method, system and device based on deep learning self-adaption |
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