CN116952654A - Environment monitoring and early warning system for administrative supervision - Google Patents
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Abstract
The invention relates to the technical field of environmental monitoring and management, and discloses an environmental monitoring and early warning system for administrative supervision, which comprises the following components: the sampling module comprises a plurality of sampling units which are arranged in a designated area according to a preset arrangement mode; the anomaly analysis module and the spatial data processing module are used for acquiring the spatial distribution state of the corresponding detection item according to the anomaly point position of the similar detection item; the early warning analysis module is connected with the spatial data processing module and the abnormality analysis module and is used for carrying out environmental early warning according to the spatial distribution state; according to the invention, the process of monitoring the environmental data in the appointed area is realized by arranging the plurality of sampling units, different types of real-time environmental data are acquired by the sampling units of different types, the anomaly analysis is carried out by the anomaly analysis module, the spatial distribution state is obtained according to the obtained positions of the anomaly points, the overall judgment is carried out on the environmental state according to the spatial distribution state, and the judgment is more accurate and comprehensive compared with the judgment of a single position.
Description
Technical Field
The invention relates to the technical field of environmental monitoring management, in particular to an environmental monitoring and early warning system for administrative supervision.
Background
Along with economic development, it has been proposed in planning to strengthen government regulatory responsibilities in health fields such as medical sanitation, food, medicine, environment, sports and the like, establish a supervision and management system combining government supervision, business self-discipline and social supervision, and strengthen the construction of a health field supervision law enforcement system and capability.
In the process of reinforcing food and drug safety supervision, the requirements of the construction of a supervision law enforcement system are reinforced for strengthening comprehensive supervision law enforcement and food and drug safety supervision. In the process of enhancing environmental management and reducing pollution emission, a diversified online intelligent supervision platform and a video remote online command platform which can be used by administrative departments of all levels in the whole city and are responsible for supervision are required to be built. And constructing a data sharing interface between each department of the municipal administration and the municipal administration, and realizing the sharing application of administrative law enforcement data on an information sharing platform.
In administrative supervision, diversified monitoring management exists, which relates to off-site supervision of monitoring, water quality, noise, radiation and the like, and law enforcement scheduling collaboration is implemented, so that on-site conditions can be mastered in real time through a GIS map visualization technology; however, in the current administrative supervision and execution process, environmental supervision and early warning are mostly performed for a certain point, and comprehensive and integral environmental supervision and early warning cannot be performed for a specified certain area.
Disclosure of Invention
The invention aims to provide an environment monitoring and early warning system for administrative supervision, which solves the following technical problems:
how to perform comprehensive and accurate environmental monitoring and early warning aiming at a designated area.
The aim of the invention can be achieved by the following technical scheme:
an environmental monitoring and early warning system for administrative supervision, comprising:
the sampling module comprises a plurality of sampling units which are arranged in a specified area according to a preset arrangement mode, and the sampling units are used for real-time environment data of corresponding detection items in the real-time specified area;
the anomaly analysis module is connected with the sampling module and used for acquiring the positions of the anomaly points according to the real-time environment data;
the spatial data processing module is connected with the anomaly analysis module and is used for acquiring the spatial distribution state of the corresponding detection item according to the anomaly point position of the similar detection item;
and the early warning analysis module is connected with the spatial data processing module and the abnormality analysis module and is used for carrying out environmental early warning according to the spatial distribution state.
According to the invention, the process of monitoring the environmental data in the designated area is realized by arranging a plurality of sampling units, different types of real-time environmental data are obtained by different types of sampling units, for example, the special water quality monitoring and collecting equipment for river pollution is used, and the river water quality is effectively monitored by a data network; for example, the water quality acquisition equipment of the water plant monitors the PH value, the water turbidity, the residual chlorine, the pressure and the water temperature of the water quality of the water plant; for example, the collection feedback of the front-end radiation monitoring instrument can monitor the radiation leakage condition of large medical equipment of medical enterprises, record real-time radiation values, data fluctuation states and the like.
By taking radiation monitoring as an example, the front-end radiation monitoring instrument can be arranged in a factory according to a preset arrangement mode, the abnormality analysis module is used for carrying out abnormality analysis, the space distribution state is obtained according to the obtained abnormal point position, the environment state is judged integrally according to the space distribution state, and the judgment of the single position is more accurate and comprehensive.
As a further scheme of the invention: the anomaly analysis module includes:
the recording module is used for storing the preset arrangement mode, the real-time environment data, the detection item standard threshold value and the corresponding abnormal point position;
the comparison module is connected with the recording module, and is used for comparing the real-time environment data of the ith detection item with the standard threshold Thr of the corresponding detection item for the real-time environment data of each detection item i Comparing;
if all the real-time environmental data are lower than Thr i Judging that the environment monitoring is normal;
otherwise, acquiring the real-time environment data higher than Thr i Is a sampling unit X of (1) ij And the corresponding out-of-tolerance Z ij ，j∈[1，M]M is the i-th detection item, the real-time environment data is higher than Thr i Is used for the number of sampling units.
As a further scheme of the invention: the spatial data processing module comprises:
an abnormal point determining module for determining the sampling unit X according to the preset arrangement mode ij As the outlier position;
the abnormal region analysis module is used for generating a connecting line surrounding map of the corresponding detection item according to the abnormal point position;
the method for acquiring the link surrounding map comprises the following steps:
when M is more than or equal to 3, aiming at the real-time environment data of the ith detection item, all corresponding sampling units X ij And (5) performing linear connection.
As a further scheme of the invention: the early warning analysis module comprises:
the early warning analysis unit is used for evaluating the environmental risk value of the designated area according to a preset judgment rule;
the preset judging rule comprises the following steps:
when M is less than or equal to N, the corresponding environment risk value Ep is obtained through a formula I i ：
Wherein N is a preset value, mu is a preset coefficient, st Zi And the deviation reference value is a preset corresponding item deviation reference value.
As a further scheme of the invention: the early warning analysis module further comprises:
the surrounding data analysis module is used for acquiring a surrounding area S according to the connecting line surrounding map i ；
The tangent point module is used for scribing the map around the connecting line according to a preset scribing rule, and acquiring an intersection point diagram on the scribing path;
an identification module for acquiring risk score coefficient F of the specified region according to the intersection point diagram i ；
The preset judging rule further comprises:
when M is more than N, obtaining the corresponding environment risk value Ep through a formula II i ：
Wherein mu 1 、μ 2 Are all the preset coefficients, and the preset coefficients are all the same,the reference value is a preset corresponding surrounding area reference value; the identification module is obtained based on AI technology.
As a further scheme of the invention: the identification module comprises:
the intersection point recovery module is used for decrypting and reproducing the map around the connecting line according to the intersection point diagram and the preset scribing rule;
a judging and identifying unit for obtaining a risk scoring coefficient F of the specified area according to the reproduced link surrounding map i ；
The judging and identifying unit is a trained neural network model.
As a further scheme of the invention: the early warning analysis module further comprises:
an early warning unit, configured to, in the followingWhen the risk early warning signal is pushed, the risk early warning signal is pushed; wherein (1)>Is a preset risk value threshold.
The invention has the beneficial effects that: according to the invention, the process of monitoring the environmental data in the appointed area is realized by arranging the plurality of sampling units, different types of real-time environmental data are acquired by the sampling units of different types, the anomaly analysis is carried out by the anomaly analysis module, the spatial distribution state is obtained according to the obtained positions of the anomaly points, the overall judgment is carried out on the environmental state according to the spatial distribution state, and the judgment is more accurate and comprehensive compared with the judgment of a single position.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of the module connection of the environmental monitoring and early warning system of the present invention.
Detailed Description
The following description of the embodiments of the present invention 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 invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention is an environmental monitoring and early warning system for administrative supervision, comprising:
the sampling module comprises a plurality of sampling units which are arranged in a specified area according to a preset arrangement mode, and the sampling units are used for real-time environment data of corresponding detection items in the real-time specified area;
the anomaly analysis module is connected with the sampling module and used for acquiring the positions of the anomaly points according to the real-time environment data;
the spatial data processing module is connected with the anomaly analysis module and is used for acquiring the spatial distribution state of the corresponding detection item according to the anomaly point position of the similar detection item;
and the early warning analysis module is connected with the spatial data processing module and the abnormality analysis module and is used for carrying out environmental early warning according to the spatial distribution state.
In this embodiment, a plurality of sampling units may be provided to realize a process of monitoring environmental data in a designated area, different types of real-time environmental data may be obtained by different types of sampling units, for example, a water quality monitoring and collecting device dedicated to river pollution may be used to effectively monitor river water quality through a data network; for example, the water quality acquisition equipment of the water plant monitors the PH value, the water turbidity, the residual chlorine, the pressure and the water temperature of the water quality of the water plant; for example, the collection feedback of the front-end radiation monitoring instrument can monitor the radiation leakage condition of large medical equipment of medical enterprises, record real-time radiation values, data fluctuation states and the like.
By taking radiation monitoring as an example, the front-end radiation monitoring instrument can be arranged in a factory according to a preset arrangement mode, the abnormality analysis module is used for carrying out abnormality analysis, the space distribution state is obtained according to the obtained abnormal point position, the environment state is judged integrally according to the space distribution state, and the judgment of the single position is more accurate and comprehensive.
It should be noted that, the environmental parameter items in this embodiment may further include temperature, humidity, noise level, smoke concentration, harmful gas concentration, heavy metal concentration, and the like, and the sampling unit includes a sensor assembly for detecting the environmental parameter items, which are not further described herein.
As a further scheme of the invention: the anomaly analysis module includes:
the recording module is used for storing the preset arrangement mode, the real-time environment data, the detection item standard threshold value and the corresponding abnormal point position;
the comparison module is connected with the recording module, and is used for comparing the real-time environment data of the ith detection item with the standard threshold Thr of the corresponding detection item for the real-time environment data of each detection item i Comparing;
if all the real-time environmental data are lower than Thr i Judging that the environment monitoring is normal;
otherwise, acquiring the real-time environment data higher than Thr i Is a sampling unit X of (1) ij And the corresponding out-of-tolerance Z ij ，j∈[1，M]M is the i-th detection item, the real-time environment data is higher than Thr i Is used for the number of sampling units.
It should be noted that, the M values are determined according to the upper limit of the number of sampling units actually installed in the designated area and the detection items actually monitored, which are not described in detail herein; in addition, the analysis process in the present embodiment makes a judgment for the portion of the parameter item data exceeding the preset range, and the portion below the preset range is not within the consideration range of the present embodiment.
As a further scheme of the invention: the spatial data processing module comprises:
an abnormal point determining module for determining the sampling unit X according to the preset arrangement mode ij As the outlier position;
the abnormal region analysis module is used for generating a connecting line surrounding map of the corresponding detection item according to the abnormal point position;
the method for acquiring the link surrounding map comprises the following steps:
when M is more than or equal to 3, aiming at the real-time environment data of the ith detection item, all corresponding sampling units X ij And (5) performing linear connection.
Taking radiation quantity monitoring as an example, 10 sampling units are installed in advance in a target area according to a preset arrangement mode and used for acquiring radiation intensity data of a position, wherein 5 sampling units acquire 5 real-time environment data relative to a corresponding threshold value Thr when abnormal conditions occur in the sampling data i Is of out of tolerance Z ij The location of the location; the map around the connecting line is the 5 sampling units X ij And generating two-by-two wires.
As a further scheme of the invention: the early warning analysis module comprises:
the early warning analysis unit is used for evaluating the environmental risk value of the designated area according to a preset judgment rule;
the preset judging rule comprises the following steps:
when M is less than or equal to N, the corresponding environment risk value Ep is obtained through a formula I i ：
Wherein N is a preset value, mu is a preset coefficient, st Zi And the deviation reference value is a preset corresponding item deviation reference value.
As a further scheme of the invention: the early warning analysis module further comprises:
the surrounding data analysis module is used for acquiring a surrounding area S according to the connecting line surrounding map i ；
The tangent point module is used for scribing the map around the connecting line according to a preset scribing rule, and acquiring an intersection point diagram on the scribing path;
an identification module for acquiring risk score coefficient F of the specified region according to the intersection point diagram i ；
In the above technical solution, the surrounding area S i Can reflect the concentration degree and the severity degree of abnormal points to a certain extent, and the surrounding area S i The larger can indicate to a certain extent that the larger the abnormal range or the larger the number of abnormal points; in addition, the positions of the sampling units are fixed, the arrangement mode is preset, so that the obtained intersection point graphs are unique under the same scribing mode of the map surrounded by the same connecting line, the intersection point graphs can be used as passwords, and the scribing mode are used as keys for sending, so that the security and confidentiality of data in the transmission process are enhanced, and the application scene of a special organization is met. The change of the scribing mode is equivalent to the change of the secret key, so that the scribing mode has a quite bottom effect.
The preset judging rule further comprises:
when M is more than N, obtaining the corresponding environment risk value Ep through a formula II i ：
Wherein mu 1 、μ 2 Are all the preset coefficients, and the preset coefficients are all the same,the reference value is a preset corresponding surrounding area reference value; the identification module is obtained based on AI technology.
As a further scheme of the invention: the identification module comprises:
the intersection point recovery module is used for decrypting and reproducing the map around the connecting line according to the intersection point diagram and the preset scribing rule;
a judging and identifying unit for obtaining a risk scoring coefficient F of the specified area according to the reproduced link surrounding map i ；
The judging and identifying unit is a trained neural network model.
The link bounding map is analyzed using a Convolutional Neural Network (CNN) to determine risk scoring coefficients for anomalies depicted by the link bounding map. The training samples used for training the CNN are different from the images input with the trained CNN in that the training samples are manually marked, and the number of the sampling units in the designated area is limited, so that the number of the obtained link surrounding maps is limited, and the accuracy of the Convolutional Neural Network (CNN) identification judgment can be well ensured; and because Convolutional Neural Network (CNN) is the advantage that can be trained, can retrain and just can popularize and apply to the environmental data monitoring process in different appointed areas.
Convolutional Neural Networks (CNNs) include a number of functional components, each component typically having parameters associated with it. Without the application of any robust image processing system, the specific values of those parameters necessary for a successful and accurate image classification are not known a priori. Thus, through an iterative process, candidate architectures and candidate parameters for the CNN may be selected to construct, train, and optimize the CNN. For example, the iterative process may include: a candidate architecture is selected from the plurality of candidate architectures and a set of candidate parameters for the selected candidate architecture is validated. Candidate architectures may include a classifier type, several convolutional layers and sub-sampling (subsampling) layers. Candidate parameters may include a learning rate, a batch size, a maximum number of training epochs (training epochs), an input image size, a feature map (feature map) number at each layer of the CNN, a convolution filter size, a sub-sampling pool size, a number of hidden layers, a number of units in each hidden layer, a selected classifier algorithm, and a number of output categories. Additionally, a preprocessing protocol may also be selected to enhance the specifics in the image for the selected candidate architecture and the selected candidate parameters.
The iterative process may include: intermediate CNNs were constructed using training sets and the performance of the intermediate CNNs on the validation set was evaluated (estimated). For example, the evaluation determines whether the intermediate CNN meets a verification threshold (such as less than 20% error rate). This iterative process is repeated until a predetermined number (e.g., 25) of intermediate CNNs meet the validation threshold. According to an example, each intermediate CNN has a different value for the selected candidate parameter. Then, the most accurate set of intermediate CNNs is generated from the predetermined number of intermediate CNNs. For example, the set may be the first 5 most accurate intermediate CNNs. The next step may include: a set algorithm is selected to aggregate and/or combine the predictions for each intermediate CNN in the set to form a set prediction. The predictions for each intermediate CNN in the set can then be used to classify the image or objects in the image.
As a further scheme of the invention: the early warning analysis module further comprises:
an early warning unit, configured to, in the followingWhen the risk early warning signal is pushed, the risk early warning signal is pushed; wherein (1)>Is a preset risk value threshold.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (7)
1. An environmental monitoring and early warning system for administrative supervision, which is characterized by comprising:
the sampling module comprises a plurality of sampling units which are arranged in a specified area according to a preset arrangement mode, and the sampling units are used for real-time environment data of corresponding detection items in the real-time specified area;
the anomaly analysis module is connected with the sampling module and used for acquiring the positions of the anomaly points according to the real-time environment data;
the spatial data processing module is connected with the anomaly analysis module and is used for acquiring the spatial distribution state of the corresponding detection item according to the anomaly point position of the similar detection item;
and the early warning analysis module is connected with the spatial data processing module and the abnormality analysis module and is used for carrying out environmental early warning according to the spatial distribution state.
2. The environmental monitoring and warning system for administrative supervision according to claim 1, wherein the abnormality analysis module comprises:
the recording module is used for storing the preset arrangement mode, the real-time environment data, the detection item standard threshold value and the corresponding abnormal point position;
the comparison module is connected with the recording module, and is used for comparing the real-time environment data of the ith detection item with the standard threshold Thr of the corresponding detection item for the real-time environment data of each detection item i Comparing;
if all the real-time environmental data are lower than Thr i Judging that the environment monitoring is normal;
otherwise, acquiring the real-time environment data higher than Thr i Is a sampling unit X of (1) ij And the corresponding out-of-tolerance Z ij ，j∈[1，M]M is the i-th detection item, the real-time environment data is higher than Thr i Is used for the number of sampling units.
3. The environmental monitoring and warning system for administrative supervision according to claim 2, wherein the spatial data processing module includes:
an abnormal point determining module for determining the sampling unit X according to the preset arrangement mode ij As the outlier position;
the abnormal region analysis module is used for generating a connecting line surrounding map of the corresponding detection item according to the abnormal point position;
the method for acquiring the link surrounding map comprises the following steps:
when M is more than or equal to 3, aiming at the real-time environment data of the ith detection item, all corresponding sampling units X ij And (5) performing linear connection.
4. The environmental monitoring and warning system for administrative supervision according to claim 3, wherein the warning analysis module comprises:
the early warning analysis unit is used for evaluating the environmental risk value of the designated area according to a preset judgment rule;
the preset judging rule comprises the following steps:
when M is less than or equal to N, the corresponding environment risk value Ep is obtained through a formula I i ：
Wherein N is a preset value, mu is a preset coefficient, st Zi And the deviation reference value is a preset corresponding item deviation reference value.
5. The environmental monitoring and warning system for administrative supervision according to claim 4, wherein the warning analysis module further comprises:
surrounding data analysis module, forAcquiring an enclosing area S according to the connecting line enclosing map i ；
The tangent point module is used for scribing the map around the connecting line according to a preset scribing rule, and acquiring an intersection point diagram on the scribing path;
an identification module for acquiring risk score coefficient F of the specified region according to the intersection point diagram i ；
The preset judging rule further comprises:
when M is more than N, obtaining the corresponding environment risk value Ep through a formula II i ：
Wherein mu 1 、μ 2 Are all the preset coefficients, and the preset coefficients are all the same,the reference value is a preset corresponding surrounding area reference value; the identification module is obtained based on AI technology.
6. The environmental monitoring and warning system for administrative supervision according to claim 5, wherein the identification module comprises:
the intersection point recovery module is used for decrypting and reproducing the map around the connecting line according to the intersection point diagram and the preset scribing rule;
a judging and identifying unit for obtaining a risk scoring coefficient F of the specified area according to the reproduced link surrounding map i ；
The judging and identifying unit is a trained neural network model.
7. The environmental monitoring and warning system for administrative supervision according to claim 4, wherein the warning analysis module further comprises:
an early warning unit, configured to, in the followingWhen the risk early warning signal is pushed, the risk early warning signal is pushed; wherein (1)>Is a preset risk value threshold.
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Cited By (2)
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| CN117826693A (en) * | 2024-03-05 | 2024-04-05 | 山东港源管道物流有限公司 | Intelligent oil depot monitoring and early warning system and method |
| CN118520317A (en) * | 2024-07-23 | 2024-08-20 | 四川福莱宝生物科技有限公司 | Biological element data analysis method suitable for high-throughput analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200231466A1 (en) * | 2017-10-09 | 2020-07-23 | Zijun Xia | Intelligent systems and methods for process and asset health diagnosis, anomoly detection and control in wastewater treatment plants or drinking water plants |
| CN114485570A (en) * | 2022-02-07 | 2022-05-13 | 中交路桥科技有限公司 | Intelligent monitoring, measuring and early warning system and method for construction safety of tunnel under construction |
| CN115979349A (en) * | 2023-03-17 | 2023-04-18 | 广东南海电力设计院工程有限公司 | Power station space environment monitoring method and system |
| CN116105802A (en) * | 2023-04-12 | 2023-05-12 | 广东普蓝地理信息服务有限公司 | Underground facility safety monitoring and early warning method based on Internet of things |
| CN116227752A (en) * | 2023-05-09 | 2023-06-06 | 安徽必喀秋软件技术有限公司 | Park facility management system based on Internet of things |
-
2023
- 2023-07-11 CN CN202310846130.6A patent/CN116952654B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200231466A1 (en) * | 2017-10-09 | 2020-07-23 | Zijun Xia | Intelligent systems and methods for process and asset health diagnosis, anomoly detection and control in wastewater treatment plants or drinking water plants |
| CN114485570A (en) * | 2022-02-07 | 2022-05-13 | 中交路桥科技有限公司 | Intelligent monitoring, measuring and early warning system and method for construction safety of tunnel under construction |
| CN115979349A (en) * | 2023-03-17 | 2023-04-18 | 广东南海电力设计院工程有限公司 | Power station space environment monitoring method and system |
| CN116105802A (en) * | 2023-04-12 | 2023-05-12 | 广东普蓝地理信息服务有限公司 | Underground facility safety monitoring and early warning method based on Internet of things |
| CN116227752A (en) * | 2023-05-09 | 2023-06-06 | 安徽必喀秋软件技术有限公司 | Park facility management system based on Internet of things |
Non-Patent Citations (2)
| Title |
|---|
| JOOHWAN KIM: "Minimizing delay and maximizing lifetime for wireless sensor networks with anycast", 《.IEEE/ACM TRANSACTIONS ON NETWORKING》, 31 December 2010 (2010-12-31) * |
| 张启敏: "危险化学品生产企业危险源辨识方法研究", 《中国安全生产科学技术》, 31 December 2012 (2012-12-31) * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117826693A (en) * | 2024-03-05 | 2024-04-05 | 山东港源管道物流有限公司 | Intelligent oil depot monitoring and early warning system and method |
| CN117826693B (en) * | 2024-03-05 | 2024-05-17 | 山东港源管道物流有限公司 | Intelligent oil depot monitoring and early warning system and method |
| CN118520317A (en) * | 2024-07-23 | 2024-08-20 | 四川福莱宝生物科技有限公司 | Biological element data analysis method suitable for high-throughput analysis |
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