CN116994352A - Computer lab regularly patrol and examine monitoring system based on intelligence antistatic floor - Google Patents
Computer lab regularly patrol and examine monitoring system based on intelligence antistatic floor Download PDFInfo
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- CN116994352A CN116994352A CN202310963435.5A CN202310963435A CN116994352A CN 116994352 A CN116994352 A CN 116994352A CN 202310963435 A CN202310963435 A CN 202310963435A CN 116994352 A CN116994352 A CN 116994352A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 74
- 238000007689 inspection Methods 0.000 claims abstract description 111
- 238000000034 method Methods 0.000 claims abstract description 33
- 230000007613 environmental effect Effects 0.000 claims abstract description 22
- 230000000737 periodic effect Effects 0.000 claims abstract description 13
- 239000003550 marker Substances 0.000 claims description 59
- 230000001133 acceleration Effects 0.000 claims description 13
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- OSGAYBCDTDRGGQ-UHFFFAOYSA-L calcium sulfate Chemical compound [Ca+2].[O-]S([O-])(=O)=O OSGAYBCDTDRGGQ-UHFFFAOYSA-L 0.000 claims description 6
- 238000006073 displacement reaction Methods 0.000 claims description 6
- 238000005286 illumination Methods 0.000 claims description 4
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- 230000002159 abnormal effect Effects 0.000 description 2
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- 238000013500 data storage Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
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- G—PHYSICS
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/20—Checking timed patrols, e.g. of watchman
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
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Abstract
The application discloses a machine room periodic inspection monitoring system based on an intelligent antistatic floor, which comprises an inspection robot, a sensor network and a monitoring system, wherein the monitoring system is respectively in wireless connection with the inspection robot and the sensor network; the method comprises the steps of collecting image data and first environment data of a space above an intelligent antistatic floor through a patrol robot, and identifying the image data; acquiring second environmental data of the space under the intelligent antistatic floor through a sensor network; the monitoring system is used for receiving the identification result, the first environmental data and the second environmental data, judging the threshold value of the first environmental data and the second environmental data, and alarming according to the identification result and the judgment result.
Description
Technical Field
The application relates to the technical field of machine room monitoring, in particular to a machine room periodic inspection monitoring system based on an intelligent antistatic floor.
Background
Machine rooms are important components of many communications or networking industries as data centers for their monitoring. Because the modern computer lab is many, supporting equipment is many, traditional manual inspection can only realize daily inspection and periodic inspection, can increase staff's burden. When equipment fails, workers also need to check the equipment first, cannot accurately position the equipment and process the equipment in time. Traditional manual inspection can not satisfy the needs of modern computer lab, intelligent computer lab monitored control system appears at present stage, but current intelligent computer lab monitored control system generally adopts suspension type supervisory equipment, it has the control blind area in antistatic floor below, antistatic floor below exists wiring and vacant space, the environmental monitoring of floor below is equally important, above-mentioned supervisory equipment can't effectively monitor floor below space, the computer lab location must adopt the basic station location simultaneously, need a plurality of basic stations cooperation to realize the location, the location basic station deploys a lot of and the cost is higher.
Disclosure of Invention
In order to solve the problems of machine room monitoring in the prior art, the application provides a machine room periodic inspection monitoring system based on an intelligent antistatic floor, which can effectively monitor the upper space and the lower space of the intelligent antistatic floor.
In order to achieve the technical purpose, the application provides the following technical scheme: computer lab regularly patrol and examine monitoring system based on intelligence antistatic floor, include:
the system comprises a patrol robot, a sensor network and a monitoring system, wherein the monitoring system is respectively and wirelessly connected with the patrol robot and the sensor network;
the method comprises the steps of collecting image data and first environment data of a space above an intelligent antistatic floor through a patrol robot, and identifying the image data;
acquiring second environmental data of the space under the intelligent antistatic floor through a sensor network;
the monitoring system is used for receiving the identification result, the first environmental data and the second environmental data, judging the threshold value of the first environmental data and the second environmental data, and alarming according to the identification result and the judgment result.
Optionally, the inspection robot is provided with a temperature sensor and a humidity sensor, wherein the temperature sensor and the humidity sensor are used for acquiring temperature and humidity data of the space above the intelligent antistatic floor, namely first environmental data.
Optionally, a controller, a depth camera, an acceleration sensor and an inertial sensor are further arranged on the inspection robot;
the method comprises the steps of collecting a depth image containing a marker combination through a depth camera;
acquiring acceleration data and steering data of the inspection robot through the acceleration sensor and the inertial sensor;
the method comprises the steps that time domain integration is carried out on acceleration data through a controller to generate mobile displacement, a first real-time position is generated according to initial time, inspection real-time, mobile displacement and steering data, and the first real-time position is used as positioning information to be transmitted to a monitoring system;
identifying the marker combination in the depth image, judging the marker combination after the identification, identifying the number of corner points and the advancing direction of the marker combination according to the marker combination judging result, searching the corresponding calibration position of the marker combination according to the corner point data and the advancing direction, acquiring the depth information of the marker combination in the depth image, acquiring a second real-time position according to the depth information and the calibration position, controlling the inspection robot to advance at a constant speed and transmitting the second real-time position as positioning information to a monitoring system when the second real-time position is acquired, and acquiring a second moment according to the uniform-speed advancing speed and the depth information;
obtaining a third real-time position between a first moment and a second moment according to the position of the first moment, the difference value between the inspection real-time and the first moment and the uniform speed travelling speed, and transmitting the third real-time position as positioning information to a monitoring system; the first moment is the moment when the number of the markers in the marker combination is reduced, and the second moment is the moment when the inspection robot predicts that the marker combination reaches the corresponding calibration position;
when the inspection real-time reaches the second moment, the first real-time position is used as positioning information to be transmitted to the monitoring system so as to achieve positioning information acquisition of the inspection robot.
Optionally, the marker combination is formed by arranging a plurality of markers in a fixed direction, the markers are in a "+" shape, and meanwhile, the number of the markers and the advancing direction of the inspection robot correspond to the calibration positions.
Optionally, a first image sensor is arranged on the inspection robot, and the first image sensor is used for collecting first image data containing the floor;
the first image data is segmented and identified through the processor to generate a floor area, the first image data is segmented through a clustering algorithm to generate a floor damage area, and the floor damage area and corresponding positioning information are transmitted to the monitoring system.
Optionally, a second image sensor is further arranged on the inspection robot, and the second image sensor collects second image data including equipment in the machine room;
and identifying the second image data through the processor, judging the fault indicator lamp according to the identification result, and transmitting the fault indicator lamp judgment result and the corresponding positioning information to the monitoring system.
Optionally, the sensor network includes a current sensor, a temperature sensor, a moisture sensor and an illumination sensor, and the sensor network is wirelessly connected with the monitoring system through a gateway device.
Optionally, the monitoring system stores the received data through a tree-shaped storage structure.
Optionally, the intelligent antistatic floor adopts a calcium sulfate OA intelligent network floor.
The application has the following technical effects:
the intelligent anti-static floor upper space in the machine room is regularly inspected through the inspection robot, the related markers are arranged on the floor to be positioned in combination with the sensor positioning mode, meanwhile, damage to the floor, lighting of fault indication lamps and temperature and humidity information are identified and monitored, environmental information is collected below the intelligent anti-static floor through a sensor network and is transmitted to a monitoring system, the monitoring system alarms, related personnel are reminded to check, meanwhile, the monitoring system provides a related data storage scheme, fragmented information is ordered, and the inspection and tracing of related personnel are facilitated.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a system according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
As shown in fig. 1, the application provides a machine room periodic inspection monitoring system based on an intelligent antistatic floor, which comprises: the system comprises a patrol robot, a sensor network and a monitoring system;
in the machine room, an intelligent anti-static floor is used as a reference, the inspection robot is used for inspecting the upper part of the floor, a sensor network is arranged below the floor to monitor related data, inspection data of the inspection robot and monitoring data of the sensor network are wirelessly transmitted to a monitoring system, the monitoring system monitors the data, and different data are alarmed according to related thresholds, so that the environment of the machine room is monitored.
Regular inspection is performed through the inspection robot, the inspection robot adopts the machine room inspection robot, and the inspection time can be preset to be determined according to the actual inspection condition. The inspection robot is provided with an image sensor, a temperature sensor, a humidity sensor, a depth camera, an acceleration sensor and an inertial sensor, wherein the two image sensors are arranged, data collected by the first image sensor are used for monitoring whether damage occurs on a floor, data monitored by the second image sensor are used for judging whether a fault indicator lights up, the temperature sensor and the humidity sensor monitor temperature and moisture data in the inspection process, the depth camera, the acceleration sensor and the inertial sensor are used for realizing positioning of the inspection robot, a CPU processor is arranged inside the inspection robot, and the processor controls movement of the inspection robot and relevant data processing.
In the inspection process, the position information of the inspection robot needs to be determined in real time. The application selects a mode of combining sensor self-positioning with marker positioning aiming at the inspection robot, and the application determines the position of the inspection robot by arranging related markers on the floor of a machine room and carrying related sensors and depth cameras on the inspection robot, and the application can further solve the problems of non-universal frequency and inaccurate positioning of a base station identification positioning chip by measuring the sensor cameras of the inspection robot and using uniform markers;
in the inspection process, an inspection path is set in the three-dimensional simulation model through pre-storing the three-dimensional simulation model of an inspection machine room, the inspection path is preset, the speed and the steering of the robot are controlled through a processor to realize the inspection of the preset path, the moving displacement and the moving direction of the robot are detected in real time through an acceleration sensor and an inertial sensor, the moving displacement is obtained through a time domain integration mode of the acceleration sensor, the inertial sensor can directly conduct angle rotation measurement of the robot, the measured data of the two sensors are calculated, the real-time is combined, the moving path of the inspection robot can be monitored, the initial position and the direction of the inspection robot are combined, after the initial position and the direction are determined, the moving path of the inspection robot is determined in a certain time based on the initial position and the direction, the steering direction of the inspection robot is drawn, the relevant path of the inspection robot is drawn, the final node of the path is the real-time position of the robot, and the real-time position of the path of the inspection robot can be measured and generated according to the data; setting a marker to correct a driving path detected by a sensor; the problem of inaccurate long-time positioning caused by time domain integration due to small errors when the sensor is used for positioning is solved.
Before the marker is positioned, the marker is required to be preset on the intelligent antistatic floor, the marker is not covered by a server or other equipment, the marker is set to be in a "+" shape in the embodiment, the marker is not too large in size, meanwhile, the marker is required to be kept to be in a certain width in the transverse direction and the vertical direction, so that 12 corner points of a single marker shape can be detected, the marker size is set according to manual experience, the condition is met, the condition is omitted, the number of the corner points is increased by accumulating the number of the markers and arranging the markers in order to form marker combinations, the number of the corner points corresponds to the positions of the markers, and the marker combinations are set at fixed intervals on a planned path. The fixed interval is set with manual experience. In the process of setting the markers, the center position of the second marker in the marker combination is used as the calibration position in the actual machine room.
In order to reduce the use of the number of the markers, in the setting process, the transverse direction and the vertical direction of the machine room, namely the x-axis y-axis direction of the plane of the machine room, are used as references, and the running direction of the inspection robot on the inspection path is determined to be the transverse direction or the vertical direction, in the different transverse and vertical directions, the marker arrangement direction of the same direction can be adopted, when the inspection robot recognizes the markers on the intelligent antistatic floor, the arrangement direction of the markers in the images shot by the depth cameras is different due to the fact that the running directions of the markers are different, when the arrangement direction of the markers in the marker combination is the transverse direction, the x-axis y-axis direction of the markers shot by the depth cameras is the x-axis direction in the images, the running direction of the markers shot by the depth cameras is the y-axis direction in the images, and the marker combination can be respectively set according to the running direction on the inspection path, for example, when the first running direction is the corresponding transverse direction, the corresponding marker combination is arranged in the transverse direction, the corresponding to the second position is the vertical direction, the marker arrangement direction is also different, the marker arrangement direction in the corresponding to the vertical direction is arranged in the transverse direction, the same, the marker arrangement direction is arranged in the transverse direction is the transverse direction, the marker combination is arranged in the transverse direction is the transverse direction, the vertical direction is the positive direction is arranged, and the marker arrangement is the transverse direction is the positive and the vertical direction is arranged in the transverse direction is the positive direction, the calibration positions can be effectively distinguished, and the use of a certain number of markers can be reduced.
After the setting of the markers is completed, positioning the inspection robot in the travelling process, firstly using a combination of an acceleration sensor and an inertial sensor to position, identifying whether the marker combination exists in an image acquired by a depth camera through an identification model in the travelling process, wherein the model structure of the identification model can use a deep learning model, such as a CNN (computer numerical network) network and a DNN (digital network), performing marker training through the deep learning model to generate an identification model, and continuing travelling if the identification model cannot identify the marker combination, and simultaneously using a sensor combination mode to position;
marking a marker combination area as an interested area after the relevant marker combination is identified, eliminating the influences of floor gaps and machine room equipment through the interested area marking, carrying out corner identification on the interested area through a corner detection Fast algorithm, counting the number of corners, judging whether the number of the corners is a multiple of 12, continuing to travel according to a set route if the judgment result is not the multiple of 12, and simultaneously using sensor combination for positioning, namely, transmitting sensor resolving information as positioning information to a monitoring system;
and carrying out the related identification marking statistics judgment process in real time until the judgment result is a multiple of 12, at this time, according to the positions corresponding to the markers corresponding to the number of the angular points, under the condition that the arrangement directions of the markers in the marker combination are transverse in the machine room, counting the number of pixels in the x-axis and y-axis directions of the region of interest, when the number of pixels in the x-axis is greater than the number of pixels in the y-axis direction, considering transverse travelling, selecting the calibration positions corresponding to the number of the marker combination travelling transversely, when the number of pixels in the y-axis direction is greater than the number of pixels in the x-axis direction, considering vertical travelling, selecting the calibration positions corresponding to the number of the vertical marker combination, determining the pixels in the center of the second marker in the image shot by the depth camera after the calibration positions are determined, presetting the center positions of the inspection robot in the ground projection as the coordinate system of the depth camera, taking the positive direction and the vertical ground direction of the inspection robot as the x-axis and the z-axis of the coordinate system of the depth camera, carrying out the conversion of the coordinate system of the depth camera in the coordinate system in the x-axis and the depth camera in the x-axis direction, and the coordinate system of the depth camera in the coordinate system, and calculating the coordinate system in the world coordinate system of the second coordinate system is not in the world coordinate system.
Due to the arrangement of the origin of the world coordinate system, the depth information is equal to the azimuth and the distance between the center of the second marker and the inspection robot, the corresponding relation between the calibration position and the marker combination quantity traveling direction data can be obtained through searching keywords of the traveling direction and the quantity in the inspection robot through preset storage, the calibration position is taken as a basic point, the depth information is the azimuth and the distance difference value between the calibration position and the inspection robot, the two are combined, the position of the inspection robot is calculated reversely, the identification judgment and the calculation are carried out in real time, the position of the inspection robot can be calculated in real time, when the position of the inspection robot can be identified through the marker combination, the position information calculated by the replacement sensor is transmitted to the monitoring system as positioning information, the traveling speed of the inspection robot is kept unchanged, and the time for reaching the marker is calculated through the speed of uniform speed and the distance between the marker combination and the inspection robot.
Recording the time when the number of the markers in the marker combination identified by the region of interest is reduced as a first time, and recording the time when the predicted markers arrive as a second time. The method comprises the steps of effectively monitoring the position of the segment from the first moment to the second moment, calculating the difference value between the time corresponding to the current position of the inspection robot and the first moment in real time, calculating the travel distance of the inspection robot by combining the difference value with the speed of uniform travel, calculating the position of the current inspection robot according to the position of the inspection robot at the first moment, transmitting the position information calculated by the position information replacement sensor calculated at the current moment to a monitoring system as positioning information, recording the uniform travel speed by taking the mark target position as an initial position basis when the second moment is reached, determining the travel position by a combined positioning method by taking the initial position basis and the uniform travel speed as the basis, and transmitting the position determination results of different stages to the monitoring system as positioning information of the inspection robot.
The inspection robot performs real-time positioning by combining a sensor with a marker positioning mode, depth information of different pixels in an image is counted by a depth camera in the travelling process, a distance threshold is set, the depth information is judged in real time according to the distance threshold, when the numerical value of a certain direction in the depth information is smaller than the distance threshold, an alarm is given, travelling is stopped, the current position is recorded, the current position is taken as an initial position basis, and travelling is performed again after the current object moves, and real-time positioning is performed.
In the real-time positioning process, the floor damage is identified through the data acquired by the first image sensor. The shooting angle of the first image sensor is as much as possible, the first image sensor comprises a floor area, a processor in the inspection robot carries out relevant damage detection on an antistatic floor in the process of collecting relevant machine room environment images, the damage of the floor is mainly embodied as a crack, the damage of the antistatic floor is judged, the floor area of the machine room environment images is required to be extracted, different areas are segmented through a relevant clustering algorithm, the areas of a server and the floor are segmented, after the floor area is extracted, the damage of the floor is segmented again through the clustering algorithm, and finally the crack of the floor is identified;
in the process, the clustering algorithm adopts the k-means clustering algorithm to divide, in the dividing process, for a server and a floor, firstly, the number of related fixed clusters is set, the cluster data is set according to the number of server types through manual experience, in the embodiment, the number of the fixed clusters is set to be 2-4, after the setting is completed, the pixels in the image are clustered through the clustering algorithm, the clustering areas of the floor are selected in different clustering areas to be subjected to rough extraction, and after the extraction is completed, the floor area is determined;
after the floor area is determined, the damaged position in the floor area is segmented by using a clustering algorithm again, at this time, the floor area is segmented, the segmented area contains pixel points belonging to the damaged position and pixel points not belonging to the damaged position, if a fixed cluster number segmentation method is simply adopted, additional pixel points are introduced to increase the number of the segmented damaged position pixel points, at this time, the number of the damaged position pixel points is required to be reduced through correlation processing, in the process of increasing the number of the clusters, the additional pixel points can be transferred to other clusters, and then the number of the segmented damaged position pixel points is reduced, but if the number of the clusters is increased blindly, the actual damaged position pixel points are transferred to the other cluster areas, at this time, the optimal cluster number is required to be selected, so that the related damaged position pixel points are ensured to be reserved, and the additional pixel points are removed.
On the basis of the principle, the number of clusters of the clustering algorithm is also required to be preset, and the number of clusters in the current process is required to be set according to the image of the floor by an elbow method. In the process, the image sensor is used for collecting related floor images, after the floor images are collected, the floor images are segmented through a clustering algorithm, the number of pixel points belonging to damaged positions in the floor images when the number of clusters is 2 is recorded and is recorded as initial pixel point number, then the number of clusters is sequentially increased to iteratively segment the floor images, in the iteration process, the number of clusters is sequentially increased by one, the number of pixel points of damaged positions under different cluster numbers is recorded and is recorded as iterative pixel point number, after the number of iterative pixel points is obtained, the ratio of the initial pixel point number to the iterative pixel point number is calculated respectively, the inflection point of the ratio is determined through an elbow method, and determining the number of iterative pixel point numbers corresponding to inflection points as the number of optimal clusters, if a plurality of floor images are used, counting the number of the optimal clusters corresponding to the plurality of floor images, selecting the number of the optimal clusters with the largest occurrence number as the number of the optimal clusters used, replacing the number of the clusters segmented at the damaged position with the number of the optimal clusters, carrying out cluster recognition on a floor area acquired in real time by using a clustering algorithm of the number of the optimal clusters, generating a corresponding floor damage clustering result, segmenting damaged positions in the floor area after the floor damage clustering, and obtaining the pixel points corresponding to the damaged positions to determine the size and the shape of the damage.
The inspection robot transmits the damage information, namely the damage size and the damage shape, to the monitoring system, the monitoring system records the corresponding position of the current inspection robot, and when the inspection robot is searched and checked to the periphery by related personnel at the corresponding position of the inspection robot in the subsequent searching process, the damage position is searched and processed.
The second image sensor of the inspection robot searches fault indication lamps in a machine room, the shooting angle of the second image sensor is as large as possible, the machine room equipment is shot, images of the machine room equipment are acquired in real time in the advancing process of the inspection robot, the machine room equipment is identified through an identification model of the machine room equipment, the identification model adopts a deep learning model, the deep learning model can adopt a CNN network or a DNN network, the machine room equipment identification model is trained through relevant historical images of the machine room equipment, the machine room equipment is identified through the identification model of the machine room equipment, after the machine room equipment is identified, the machine room equipment is divided into regions of interest, after the machine room equipment is identified, as the fault indication lamps have uniform colors, such as red, RBG channel values of pixels are determined in the regions of interest, meanwhile, relevant thresholds such as R channel 200, B channel 20 and G channel 20 are set, when the R channel is higher than the threshold and other channels are lower than the threshold, the position of the inspection robot corresponding to the fault indication lamps is judged to appear in red, the position of the inspection robot, the corresponding image corresponding to the second image sensor and the corresponding judgment center are recorded, and the monitoring result is recorded to the monitoring center.
Temperature sensor and humidity sensor that patrol and examine and set up on the robot gather the temperature and the humidity information in real time in space above the floor to with temperature and humidity information transmission give monitoring center, monitoring center real-time supervision humiture information, when surpassing certain threshold value, early warning carries out, and humiture threshold value can confirm according to manual experience.
The inspection robot moves on the intelligent antistatic floor to inspect, a sensor network is arranged under the floor, the intelligent antistatic floor adopts a calcium sulfate OA intelligent network floor, the product can meet the comprehensive wiring requirement, and various pipelines can be laid and maintained only by slightly lifting the floor after the floor is laid. The wireless current sensor is arranged at an important position of a line, the important position can be determined according to manual experience, the temperature sensor, the moisture sensor and the illumination sensor are arranged in a space below the intelligent antistatic floor, the installation positions of the wireless sensors can be set according to manual experience, the sensors can be arranged at equal intervals in the space below the intelligent antistatic floor, the sensor network is formed by the sensors, a single gateway is responsible for communication of the sensor network and is communicated with a monitoring system through a plurality of gateways, machine room environmental data in the sensor network are transmitted to the monitoring system, the monitoring system is connected with a plurality of gateway devices, the single gateway device is connected with the sensor network, the expansibility of the sensor network is improved through the arrangement of the gateway devices, when the sensor network transmits data, the sensor network is labeled with different sensors, the gateway devices are labeled simultaneously, the monitoring system can correspondingly find out environmental data of corresponding positions through the labels when receiving the data of the sensor and the gateway devices, meanwhile, the intelligent antistatic floor is used, when the intelligent antistatic floor is damaged, the floor is not required to be maintained or the floor is convenient to maintain under a certain floor.
After receiving the data transmitted by the sensor network, the monitoring system sets early warning threshold values for the current, the temperature, the moisture and the illumination data respectively, wherein the early warning threshold values can be set according to manual experience, and after the early warning threshold values are set, when the data exceeds the threshold values, the monitoring system alarms in real time, wherein an audible and visual mode or a pop-up dialog box mode is adopted as an alarm mode, and the alarm is carried out through the mode after the alarm.
The monitoring system displays and stores the data of the inspection robot and the sensor network after acquiring the data, wherein the monitoring system adopts a server, in the storage process, the content is stored through a tree-shaped storage structure, the machine room information is used as the uppermost root element, the space information of the intelligent antistatic floor is used as the lower sub-element of the machine room information, the space information of the intelligent antistatic floor comprises upper information and lower information, the upper information corresponds to the inspection robot, the lower information corresponds to the sensor network, the inspection robot acquires data as detailed description of the upper information, the data acquired by the sensor network is used as detailed description of the lower information, meanwhile, the monitoring abnormal data is marked and extracted, the monitoring abnormal data, the corresponding time, position and the machine room information are extracted, the fragmentation information is ordered through the storage result, and related personnel can conveniently check and trace the source.
The intelligent anti-static floor upper space in the machine room is regularly inspected through the inspection robot, the related markers are arranged on the floor to be positioned in combination with the sensor positioning mode, meanwhile, damage to the floor, lighting of fault indication lamps and temperature and humidity information are identified and monitored, environmental information is collected below the intelligent anti-static floor through a sensor network and is transmitted to a monitoring system, the monitoring system alarms, related personnel are reminded to check, meanwhile, the monitoring system provides a related data storage scheme, fragmented information is ordered, and the inspection and tracing of related personnel are facilitated.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (9)
1. Computer lab regularly patrol and examine monitoring system based on intelligence antistatic floor, its characterized in that includes:
the system comprises a patrol robot, a sensor network and a monitoring system, wherein the monitoring system is respectively and wirelessly connected with the patrol robot and the sensor network;
the method comprises the steps of collecting image data and first environment data of a space above an intelligent antistatic floor through a patrol robot, and identifying the image data;
acquiring second environmental data of the space under the intelligent antistatic floor through a sensor network;
the monitoring system is used for receiving the identification result, the first environmental data and the second environmental data, judging the threshold value of the first environmental data and the second environmental data, and alarming according to the identification result and the judgment result.
2. The machine room periodic inspection monitoring system of claim 1, wherein:
the inspection robot is provided with a temperature sensor and a humidity sensor, wherein the temperature sensor and the humidity sensor are used for acquiring temperature and humidity data of a space above the intelligent antistatic floor, namely first environmental data.
3. The machine room periodic inspection monitoring system of claim 1, wherein:
the inspection robot is also provided with a controller, a depth camera, an acceleration sensor and an inertial sensor;
the method comprises the steps of collecting a depth image containing a marker combination through a depth camera;
acquiring acceleration data and steering data of the inspection robot through the acceleration sensor and the inertial sensor;
the method comprises the steps that time domain integration is carried out on acceleration data through a controller to generate mobile displacement, a first real-time position is generated according to initial time, inspection real-time, mobile displacement and steering data, and the first real-time position is used as positioning information to be transmitted to a monitoring system;
identifying the marker combination in the depth image, judging the marker combination after the identification, identifying the number of corner points and the advancing direction of the marker combination according to the marker combination judging result, searching the corresponding calibration position of the marker combination according to the corner point data and the advancing direction, acquiring the depth information of the marker combination in the depth image, acquiring a second real-time position according to the depth information and the calibration position, controlling the inspection robot to advance at a constant speed and transmitting the second real-time position as positioning information to a monitoring system when the second real-time position is acquired, and acquiring a second moment according to the uniform-speed advancing speed and the depth information;
obtaining a third real-time position between a first moment and a second moment according to the position of the first moment, the difference value between the inspection real-time and the first moment and the uniform speed travelling speed, and transmitting the third real-time position as positioning information to a monitoring system; the first moment is the moment when the number of the markers in the marker combination is reduced, and the second moment is the moment when the inspection robot predicts that the marker combination reaches the corresponding calibration position;
when the inspection real-time reaches the second moment, the first real-time position is used as positioning information to be transmitted to the monitoring system so as to achieve positioning information acquisition of the inspection robot.
4. A machine room periodic inspection monitoring system according to claim 3, characterized in that:
the marker combination is formed by arranging a plurality of markers in a fixed direction, the markers are in a "+" shape, and meanwhile, the number of the markers and the advancing direction of the inspection robot correspond to the calibration positions.
5. A machine room periodic inspection monitoring system according to claim 3, characterized in that:
the inspection robot is provided with a first image sensor, wherein first image data comprising the floor are collected through the first image sensor;
the first image data is segmented and identified through the processor to generate a floor area, the first image data is segmented through a clustering algorithm to generate a floor damage area, and the floor damage area and corresponding positioning information are transmitted to the monitoring system.
6. The machine room periodic inspection monitoring system of claim 1, wherein:
the inspection robot is further provided with a second image sensor, wherein the second image sensor collects second image data comprising equipment of the machine room;
and identifying the second image data through the processor, judging the fault indicator lamp according to the identification result, and transmitting the fault indicator lamp judgment result and the corresponding positioning information to the monitoring system.
7. The machine room periodic inspection monitoring system of claim 1, wherein:
the sensor network comprises a current sensor, a temperature sensor, a moisture sensor and an illumination sensor, and is in wireless connection with the monitoring system through gateway equipment.
8. The machine room periodic inspection monitoring system of claim 1, wherein:
the monitoring system stores the received data through a tree-shaped storage structure.
9. The machine room periodic inspection monitoring system of claim 1, wherein:
the intelligent antistatic floor adopts a calcium sulfate OA intelligent network floor.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105783915A (en) * | 2016-04-15 | 2016-07-20 | 深圳马路创新科技有限公司 | Robot global space positioning method based on graphical labels and camera |
| CN108388194A (en) * | 2018-03-27 | 2018-08-10 | 中铁第勘察设计院集团有限公司 | Railway machine room intelligent robot inspection system and its method |
| CN109500827A (en) * | 2018-11-23 | 2019-03-22 | 北京中大科慧科技发展有限公司 | Machine room intelligent crusing robot |
| CN114527752A (en) * | 2022-01-25 | 2022-05-24 | 浙江省交通投资集团有限公司智慧交通研究分公司 | Accurate positioning method for detection data of track inspection robot in low satellite signal environment |
-
2023
- 2023-08-02 CN CN202310963435.5A patent/CN116994352A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105783915A (en) * | 2016-04-15 | 2016-07-20 | 深圳马路创新科技有限公司 | Robot global space positioning method based on graphical labels and camera |
| CN108388194A (en) * | 2018-03-27 | 2018-08-10 | 中铁第勘察设计院集团有限公司 | Railway machine room intelligent robot inspection system and its method |
| CN109500827A (en) * | 2018-11-23 | 2019-03-22 | 北京中大科慧科技发展有限公司 | Machine room intelligent crusing robot |
| CN114527752A (en) * | 2022-01-25 | 2022-05-24 | 浙江省交通投资集团有限公司智慧交通研究分公司 | Accurate positioning method for detection data of track inspection robot in low satellite signal environment |
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