CN114755949A - Rural wisdom remote sensing monitored control system based on human settlements environment - Google Patents
Rural wisdom remote sensing monitored control system based on human settlements environment Download PDFInfo
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- CN114755949A CN114755949A CN202210278556.1A CN202210278556A CN114755949A CN 114755949 A CN114755949 A CN 114755949A CN 202210278556 A CN202210278556 A CN 202210278556A CN 114755949 A CN114755949 A CN 114755949A
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Abstract
The invention discloses a rural intelligent remote sensing monitoring system based on human living environment, which comprises a remote sensing satellite, a ground monitoring base station and a remote sensing satellite, wherein the remote sensing satellite is used for acquiring remote sensing image information of a monitoring area and sending the remote sensing image information to the ground monitoring base station; the ground monitoring mechanisms are used for acquiring real-time image information and ecological environment information of a monitoring area and sending the real-time image information and the ecological environment information to the ground monitoring base station; the hydrological monitoring mechanisms are used for acquiring water environment information of a monitoring area and transmitting the water environment information to the ground monitoring base station; when any one of the information is analyzed and an abnormal condition occurs, the ground monitoring base station sends alarm information to the remote monitoring platform and the intelligent terminal, wherein the remote monitoring platform generates a scheduling command after receiving the alarm information and sends the scheduling command to patrol personnel; the rural natural environment monitoring system has the advantages that the safety of rural living environment can be improved, and rural natural environment can be monitored comprehensively.
Description
Technical Field
The invention relates to a remote sensing monitoring system, in particular to a rural intelligent remote sensing monitoring system based on a human living environment.
Background
The human environment is a complex system formed by natural, human, economic and social factors and the like, is an important mark for measuring the development level of regional social economy and the living standard of people, is a system engineering related to multiple aspects of economy, society and the like, and can provide an optimization strategy for the development of social economy through the human environment evaluation index.
When a safety event occurs in rural areas, the living position of each family is relatively far, so that rescue cannot be rapidly performed, and the monitoring of the environment has certain limitation.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a rural intelligent remote sensing monitoring system based on human living environment, which can improve the safety of rural living environment and monitor rural natural environment more comprehensively.
The technical scheme adopted by the invention for solving the technical problems is as follows: rural wisdom remote sensing monitored control system based on human settlements environment includes
The remote sensing satellite is used for acquiring remote sensing image information of a monitoring area and sending the remote sensing image information to the ground monitoring base station;
the ground monitoring mechanisms are used for acquiring real-time image information and ecological environment information of a monitoring area and sending the real-time image information and the ecological environment information to the ground monitoring base station;
the hydrological monitoring mechanisms are used for acquiring water environment information of a monitoring area and transmitting the water environment information to the ground monitoring base station;
the ground monitoring base station is used for receiving remote sensing image information, real-time image information, ecological environment information and water environment information, analyzing the information by the analysis module and transmitting the information to the remote monitoring platform and the intelligent terminal respectively, and when abnormal conditions occur after any information is analyzed, the ground monitoring base station sends alarm information to the remote monitoring platform and the intelligent terminal, wherein the remote monitoring platform generates a scheduling command after receiving the alarm information and sends the scheduling command to patrol personnel.
The ground monitoring base station processes the information of the remote sensing image as follows,
specifically, S1: training and constructing a fire risk prediction model;
s2: preprocessing remote sensing image information, wherein the preprocessing comprises image enhancement, smooth filtering, edge detection, atmospheric radiation correction and stripe filtering;
s3: inputting the preprocessed remote sensing image information into the trained fire risk prediction model, and executing S4 if the probability value output by the fire risk prediction model is greater than the set probability threshold value for fire, or executing S5 if the probability value output by the fire risk prediction model is not greater than the set probability threshold value for fire;
s4: storing the data, performing risk assessment through an analysis module, judging a risk level according to an assessment result, and sending the risk level to a remote monitoring platform and an intelligent terminal, wherein the risk level judgment comprises a general risk level, a moderate risk level and a serious risk level;
s5: and storing the data.
Specifically, the specific steps of S1 are as follows,
s11: acquiring a fire picture, constructing a fire image data set, and sending the fire image data set to a log file generation module;
s12: the log file generation module receives and stores the received pictures and uploads log folders to the data processing module periodically, and the data processing module receives the log folders and then puts information into a ResNet network pre-training model for training to build a fire risk prediction model;
S13: and detecting the fire image data set through the fire image detection model, analyzing and judging the detection result, and feeding back and correcting the fire risk prediction model until the prediction capability of the fire risk prediction model meets the set requirement.
Specifically, every ground monitoring mechanism includes the pole setting, be provided with image acquisition module and ecological environment collection module in the pole setting, image acquisition module is used for gathering real-time image information to send for through network transmission module ground control basic station, ecological environment collection module is used for gathering ecological environment information, and send for through network transmission module ground control basic station.
Specifically, the process of processing the real-time image information by the ground monitoring base station is as follows,
step 1: acquiring real-time image information, and segmenting the real-time image information to obtain a real-time image data set;
and 2, step: sending the real-time image data set to a trained deep learning judgment model for behavior analysis, if the analysis result is abnormal, executing the step 3, otherwise, executing the step 4;
and step 3: combining an avatar information base, wherein the avatar information base stores a child avatar and child-associated relative avatars, if the current behavior person is matched with the child-associated relative information, executing the step 4, otherwise, sending alarm information;
And 4, step 4: and storing the data.
Specifically, the ecological environment acquisition module comprises a wind speed and wind direction monitoring unit, a temperature and humidity monitoring unit, a PM2.5 monitoring unit, an ultraviolet monitoring unit, an air pressure monitoring unit and a noise monitoring unit, wherein the wind speed and wind direction monitoring unit is used for acquiring wind speed and wind direction information of a current monitoring area and sending the wind speed and wind direction information to a gathering unit, the temperature and humidity monitoring unit is used for acquiring temperature and humidity information of the current monitoring area and sending the temperature and humidity information to the gathering unit, the PM2.5 monitoring unit is used for acquiring PM2.5 information of the current monitoring area and sending the PM2.5 information to the gathering unit, the ultraviolet monitoring unit is used for acquiring ultraviolet information of the current monitoring area and sending the ultraviolet information to the gathering unit, the air pressure monitoring unit is used for acquiring atmospheric pressure information of the current monitoring area and sending the atmospheric pressure information to the gathering unit, and the noise monitoring unit is used for acquiring noise information of the current monitoring area, and the collecting unit collects wind speed and wind direction information, temperature and humidity information, PM2.5 information, ultraviolet information, atmospheric pressure information and noise information to form ecological environment information, and the ecological environment information is sent to the ground monitoring base station through a network transmission module.
Specifically, every hydrological monitoring mechanism is including fixing the carrier element, Doppler flowmeter, fluviograph and the water quality testing unit in aqueous, the Doppler flowmeter the fluviograph and the water quality testing unit is fixed respectively on the carrier element, just the Doppler flowmeter is used for detecting water flow rate information, the fluviograph is used for detecting water level information, the water quality testing unit is used for detecting water quality information, water flow rate information the water level information with form after the water quality information gathers water environment information.
Specifically, the water quality detection unit comprises any one or more of a Ph value sensor, a dissolved oxygen sensor, a conductivity sensor, a COD sensor, a turbidity sensor and a water temperature sensor.
Compared with the prior art, the invention has the advantages that:
1. by the combined use of the remote sensing satellite, the ground monitoring mechanism and the hydrological monitoring mechanism, rural environment can be monitored more comprehensively and all-weather;
2. the remote sensing satellite is mainly used for acquiring remote sensing image information of a monitoring area, the remote sensing image information can reflect landform changes of the area within a certain time period, the most important is that the remote sensing satellite is used for monitoring whether a fire disaster occurs or not, and once the fire disaster occurs, the remote sensing satellite can inform a ground monitoring base station in real time so as to quickly reflect the fire disaster;
3. The ground monitoring mechanism is used for acquiring real-time image information and ecological environment information of a monitored area, the acquisition of the real-time image information is mainly used for avoiding safety events such as the turning of children and the like, the monitoring area is subjected to 24-hour video recording, and the acquisition of the ecological environment information can be used for monitoring the air quality information of the current area in real time;
4. the hydrological monitoring mechanism is used for acquiring water environment information of a monitored area, the water environment information is acquired mainly by acquiring the water quality in real time, and the hydrological monitoring mechanism plays an important role in timely mastering the water quality condition of a water source area, early warning of major or sudden water pollution accidents, guaranteeing drinking water safety, controlling sewage to discharge after reaching standards and the like;
5. the ground monitoring base station can analyze various information, can prompt in time once abnormity occurs, can realize calculation and storage of large capacity and big data, and has high working efficiency.
Drawings
FIG. 1 is a schematic view of the structure of the present invention;
FIG. 2 is a block diagram of a process for processing remote sensing image information according to the present invention;
FIG. 3 is a schematic view of a ground monitoring mechanism according to the present invention;
fig. 4 is a schematic structural diagram of the hydrological monitoring mechanism of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, but the present invention is not limited thereto.
The embodiment is as follows: as shown in the figure, a rural intelligent remote sensing monitoring system based on human living environment comprises
The remote sensing satellite 1 is used for collecting remote sensing image information of a monitoring area and sending the remote sensing image information to the ground monitoring base station 3;
the ground monitoring mechanisms 2 are used for acquiring real-time image information and ecological environment information of a monitoring area and sending the real-time image information and the ecological environment information to the ground monitoring base station 3;
the hydrological monitoring mechanisms 3 are used for acquiring water environment information of a monitoring area and sending the water environment information to the ground monitoring base station 3;
the ground monitoring base station 4 is used for receiving remote sensing image information, real-time image information, ecological environment information and water environment information, analyzing the information by the analysis module 41 and transmitting the information to the remote monitoring platform 42 and the intelligent terminal 43 respectively, when abnormal conditions occur after any one of the information is analyzed, the ground monitoring base station 4 sends alarm information to the remote monitoring platform 42 and the intelligent terminal 43, wherein the remote monitoring platform 42 generates a scheduling command after receiving the alarm information and sends the scheduling command to patrol personnel.
The ground monitoring base station 4 processes the information of the remote sensing image as follows,
S1: training and constructing a fire risk prediction model;
s2: preprocessing remote sensing image information, wherein the preprocessing comprises image enhancement, smooth filtering, edge detection, atmospheric radiation correction and stripe filtering;
s3: inputting the preprocessed remote sensing image information into the trained fire risk prediction model, and executing S4 if the probability value output by the fire risk prediction model is greater than the set probability threshold value for fire, or executing S5 if the probability value output by the fire risk prediction model is not greater than the set probability threshold value for fire;
s4: storing the data, performing risk assessment through the analysis module 41, performing risk grade judgment according to an assessment result, and sending the risk grade to the remote monitoring platform 42 and the intelligent terminal 43, wherein the risk grade judgment comprises a general risk grade, a moderate risk grade and a serious risk grade;
s5: and storing the data.
The specific steps of S1 are as follows,
s11: acquiring a fire picture, constructing a fire image data set, and sending the fire image data set to a log file generation module;
s12: the log file generation module receives and stores the received pictures and uploads log folders to the data processing module periodically, and the data processing module receives the log folders and then puts information into a ResNet network pre-training model for training to build a fire risk prediction model;
S13: and detecting the fire image data set through the fire image detection model, analyzing and judging the detection result, and feeding back and correcting the fire risk prediction model until the prediction capability of the fire risk prediction model meets the set requirement.
Every ground monitoring mechanism 2 includes the pole setting, is provided with image acquisition module 21 and ecological environment collection module 22 in the pole setting, and image acquisition module 21 is used for gathering real-time image information to send for ground control basic station 4 through network transmission module, ecological environment collection module 22 is used for gathering ecological environment information, and sends for ground control basic station 4 through network transmission module.
The processing process adopts the pre-trained ResNet network model for detecting the fire images of the monitoring video, solves the problem of few fire images in the monitoring video, fully exerts the image feature extraction capability of deep learning, and is beneficial to accurately identifying the fire images in the monitoring video; in addition, a feedback correction mechanism is added to perform feedback training on the detection model, so that the diagnosis and identification capability of the model is further improved.
The real-time image information is processed by the ground monitoring base station 4 as follows,
step 1: acquiring real-time image information, and segmenting the real-time image information to obtain a real-time image data set;
And 2, step: sending the real-time image data set to a trained deep learning judgment model for behavior analysis, if the analysis result is abnormal, executing a step 3, otherwise, executing a step 4;
and 3, step 3: combining with a head portrait information base, wherein the head portrait information base stores a head portrait of a child and a head portrait of a relative related to the child, if the current behavior person is matched with the relative information related to the child, executing the step 4, otherwise, sending alarm information;
and 4, step 4: and storing the data.
The processing process adopts a pre-trained deep learning judgment model to perform behavior analysis, when judging whether abnormal behaviors exist in the current real-time image information, the abnormal behaviors comprise behaviors such as crying and screaming, curbing, forcing and the like of children, the next judging process is entered, the judging process mainly judges whether the current adult is a person related to the children, if the current adult is related to the children, the real-time image information data is stored, and if the current adult is not related to the children, the children are judged to have the risk of being abducted, and an alarm mechanism is triggered.
The ecological environment acquisition module 22 comprises a wind speed and direction monitoring unit 23, a temperature and humidity monitoring unit 24, a PM2.5 monitoring unit 25, an ultraviolet monitoring unit 26, an air pressure monitoring unit 27 and a noise monitoring unit 28, wherein the wind speed and direction monitoring unit 23 is used for acquiring wind speed and direction information of a current monitoring area and sending the wind speed and direction information to a summarizing unit 29, the temperature and humidity monitoring unit 24 is used for acquiring temperature and humidity information of the current monitoring area and sending the temperature and humidity information to the summarizing unit 29, the PM2.5 monitoring unit is used for acquiring PM2.5 information of the current monitoring area and sending the PM2.5 information to the summarizing unit 29, the ultraviolet monitoring unit 26 is used for acquiring ultraviolet information of the current monitoring area and sending the ultraviolet information to the summarizing unit 29, the air pressure monitoring unit 27 is used for acquiring atmospheric pressure information of the current monitoring area and sending the atmospheric pressure information to the summarizing unit 29, and the noise monitoring unit 28 is used for acquiring noise information of the current monitoring area, and the wind speed and wind direction information, the temperature and humidity information, the PM2.5 information, the ultraviolet information, the atmospheric pressure information and the noise information are summarized by the summarizing unit 29 to form ecological environment information, and the ecological environment information is transmitted to the ground monitoring base station 4 through the network transmission module.
Each hydrological monitoring mechanism 3 comprises a bearing unit fixed in water, a Doppler flow meter 31, a water level meter 32 and a water quality detection unit 33, wherein the Doppler flow meter 31, the water level meter 32 and the water quality detection unit 33 are respectively fixed on the bearing unit, the Doppler flow meter 31 is used for detecting water flow rate information, the water level meter 32 is used for detecting water level information, the water quality detection unit 33 is used for detecting water quality information, and water flow rate information, water level information and water quality information form water environment information after being summarized.
The water quality detection unit 33 includes any one or more of a Ph sensor, a dissolved oxygen sensor, a conductivity sensor, a COD sensor, a turbidity sensor, and a water temperature sensor.
It should be noted that the above mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, and the present invention may be modified in materials and structures, or replaced with technical equivalents. Therefore, structural equivalents made by using the description and drawings of the present invention or by directly or indirectly applying to other related arts are also encompassed within the scope of the present invention.
Claims (8)
1. The utility model provides a rural wisdom remote sensing monitored control system based on human settlements environment which characterized in that: comprises that
The remote sensing satellite is used for acquiring remote sensing image information of a monitoring area and sending the remote sensing image information to the ground monitoring base station;
the ground monitoring mechanisms are used for acquiring real-time image information and ecological environment information of a monitoring area and sending the real-time image information and the ecological environment information to the ground monitoring base station;
the hydrological monitoring mechanisms are used for acquiring water environment information of a monitoring area and sending the water environment information to the ground monitoring base station;
the ground monitoring base station is used for receiving remote sensing image information, real-time image information, ecological environment information and water environment information, analyzing the information by the analysis module and transmitting the information to the remote monitoring platform and the intelligent terminal respectively, and when abnormal conditions occur after any information is analyzed, the ground monitoring base station sends alarm information to the remote monitoring platform and the intelligent terminal, wherein the remote monitoring platform generates a scheduling command after receiving the alarm information and sends the scheduling command to patrol personnel.
2. The rural intelligent remote sensing monitoring system based on human-living environment according to claim 1, characterized in that: the ground monitoring base station processes the information of the remote sensing image as follows,
S1: training and constructing a fire risk prediction model;
s2: preprocessing remote sensing image information, wherein the preprocessing comprises image enhancement, smoothing filtering, edge detection, atmospheric radiation correction and strip filtering;
s3: inputting the preprocessed remote sensing image information into the trained fire risk prediction model, and executing S4 if the probability value output by the fire risk prediction model is greater than the set probability threshold value of fire, otherwise executing S5;
s4: storing the data, performing risk evaluation through an analysis module, judging the risk level according to the evaluation result, and sending the risk level to a remote monitoring platform and an intelligent terminal, wherein the risk level judgment comprises a general risk level, a moderate risk level and a serious risk level;
s5: and storing the data.
3. The rural intelligent remote sensing monitoring system based on human-living environment according to claim 2, characterized in that: the specific steps of S1 are as follows,
s11: acquiring a fire picture, constructing a fire image data set, and sending the fire image data set to a log file generation module;
s12: the log file generation module receives and stores the received pictures and uploads log folders to the data processing module periodically, and the data processing module receives the log folders and then puts information into a ResNet network pre-training model for training to build a fire risk prediction model;
S13: and detecting the fire image data set through the fire image detection model, analyzing and judging the detection result, and feeding back and correcting the fire risk prediction model until the prediction capability of the fire risk prediction model meets the set requirement.
4. The rural intelligent remote sensing monitoring system based on human-living environment according to claim 1, characterized in that: every ground monitoring mechanism includes the pole setting, be provided with image acquisition module and ecological environment collection module in the pole setting, image acquisition module is used for gathering real-time image information to send for through network transmission module ground monitoring basic station, ecological environment collection module is used for gathering ecological environment information to send for through network transmission module ground monitoring basic station.
5. The rural intelligent remote sensing monitoring system based on human-living environment according to claim 4, characterized in that: the ground monitoring base station processes the real-time image information as follows,
step 1: acquiring real-time image information, and segmenting the real-time image information to obtain a real-time image data set;
step 2: sending the real-time image data set to a trained deep learning judgment model for behavior analysis, if the analysis result is abnormal, executing the step 3, otherwise, executing the step 4;
And 3, step 3: combining an avatar information base, wherein the avatar information base stores a child avatar and child-associated relative avatars, if the current behavior person is matched with the child-associated relative information, executing the step 4, otherwise, sending alarm information;
and 4, step 4: and storing the data.
6. The rural intelligent remote sensing monitoring system based on human-living environment according to claim 4, characterized in that: the ecological environment acquisition module comprises a wind speed and direction monitoring unit, a temperature and humidity monitoring unit, a PM2.5 monitoring unit, an ultraviolet monitoring unit, an air pressure monitoring unit and a noise monitoring unit, wherein the wind speed and direction monitoring unit is used for acquiring wind speed and direction information of a current monitoring area and sending the wind speed and direction information to a gathering unit, the temperature and humidity monitoring unit is used for acquiring temperature and humidity information of the current monitoring area and sending the temperature and humidity information to the gathering unit, the PM2.5 monitoring unit is used for acquiring PM2.5 information of the current monitoring area and sending the PM2.5 information to the gathering unit, the ultraviolet monitoring unit is used for acquiring ultraviolet information of the current monitoring area and sending the ultraviolet information to the gathering unit, the air pressure monitoring unit is used for acquiring atmospheric pressure information of the current monitoring area and sending the atmospheric pressure information to the gathering unit, the noise monitoring unit is used for acquiring noise information of the current monitoring area and sending the noise information to the gathering unit, the collecting unit collects wind speed and direction information, temperature and humidity information, PM2.5 information, ultraviolet information, atmospheric pressure information and noise information to form ecological environment information, and the ecological environment information is sent to the ground monitoring base station through a network transmission module.
7. The rural intelligent remote sensing monitoring system based on human-living environment according to claim 1, characterized in that: every hydrology monitoring mechanism is including fixing bearing unit, Doppler flowmeter, fluviograph and the water quality testing unit in aqueous, the Doppler flowmeter the fluviograph and the water quality testing unit is fixed respectively on the bearing unit, just the Doppler flowmeter is used for detecting water velocity information, the fluviograph is used for detecting water level information, the water quality testing unit is used for detecting water quality information, water velocity information the water level information with form after the water quality information gathers water environment information.
8. The rural intelligent remote sensing monitoring system based on human-living environment according to claim 7, characterized in that: the water quality detection unit comprises one or more of a Ph value sensor, a dissolved oxygen sensor, a conductivity sensor, a COD sensor, a turbidity sensor and a water temperature sensor.
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| CN117499424A (en) * | 2023-08-23 | 2024-02-02 | 云南云岭高速公路交通科技有限公司 | Tunnel water fire control data acquisition monitoring system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117499424A (en) * | 2023-08-23 | 2024-02-02 | 云南云岭高速公路交通科技有限公司 | Tunnel water fire control data acquisition monitoring system |
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