CN114487283B - Remote intelligent diagnosis and operation and maintenance method and system for air quality monitoring system - Google Patents
Remote intelligent diagnosis and operation and maintenance method and system for air quality monitoring system Download PDFInfo
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Abstract
The invention provides a remote intelligent diagnosis and operation and maintenance method and system of an air quality monitoring system, wherein the method comprises the following steps: matching the plurality of sensors based on the first target area; based on a first monitoring scheme, performing air quality monitoring by using a plurality of sensors to obtain a first data set; transmitting the data to an air quality monitoring platform in a wireless communication mode to obtain a second data set; performing anomaly detection on the second data set, and uploading an anomaly detection result to a visualization component for rendering to obtain a first display result; and generating a first regulation and control instruction based on the first display result, and regulating and controlling the air quality monitoring system. The method solves the technical problems that in the prior art, because the display method of the air monitoring system is poor in intuitiveness, non-professional staff is difficult to participate in daily management, control, operation and maintenance work, system operation and maintenance staff are short, the operation and maintenance cost of a entrusted third-party mechanism is high, and the management and control efficiency and quality of a remote system are difficult to guarantee.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a remote intelligent diagnosis and operation and maintenance method and system of an air quality monitoring system.
Background
In order to monitor air quality conveniently, data support is carried out on air pollution treatment work, a large number of automatic air quality monitoring substation stations are added in cities, and real-time effective monitoring on various pollutants in the air is expected. With the increase of air quality detection stations, more and more air monitoring types and detection instruments are adopted, the shortage problem of operation and maintenance talents of an air quality detection system is more prominent, a large number of daily management and control works are carried out by a third party commission, the data quality and the management and control effect are difficult to guarantee, and the qualification of the third party commission is also difficult to evaluate. How to reduce the management and control cost, improve the efficiency and the quality of the monitoring result, and reduce the difficulty of daily operation and maintenance management is of great concern.
However, the prior art has at least the following technical problems:
the technical problems that the display method of the air monitoring system is poor in intuitiveness, so that non-professional staff are difficult to participate in daily management, control, operation and maintenance work, system operation and maintenance staff are short, the operation and maintenance cost of a entrusted third-party organization is high, and the management efficiency and quality of a remote system are difficult to guarantee exist.
Disclosure of Invention
The remote intelligent diagnosis and operation and maintenance method and system for the air quality monitoring system solve the technical problems that in the prior art, due to the fact that the display method of the air quality monitoring system is poor in intuitiveness, non-professional personnel are difficult to participate in daily management and control operation and maintenance work, system operation and maintenance personnel are short, operation and maintenance cost of a entrusted third-party mechanism is high, and efficiency and quality of remote system management and control are difficult to guarantee. The system has the advantages that the monitoring data analysis results are visually presented through the visualization of the data and the operation instructions, the monitoring work efficiency of non-professional staff is improved, the operation and maintenance cost is reduced, the remote intelligent control is realized, the timeliness of system abnormal data detection is improved, the air quality detection system can be pertinently controlled, and the monitoring data is more accurate and scientific.
In view of the above, the present application provides a remote intelligent diagnosis and operation and maintenance method and system for an air quality monitoring system.
In a first aspect, the present application provides a method for remote intelligent diagnosis and operation of an air quality monitoring system, wherein the method comprises: obtaining a first target area, matching the plurality of sensors based on the first target area; based on a first monitoring scheme, performing air quality monitoring by using the plurality of sensors to obtain a first data set; transmitting the first data set to an air quality monitoring platform in a wireless communication mode, wherein the air quality monitoring platform comprises a visualization component; performing data preprocessing on the first data set received by the air quality monitoring platform to obtain a second data set; checking the second data set based on a monitoring data abnormality detection model, and uploading an abnormality detection result to the visualization component for content rendering to obtain a first display result; and generating a first regulation and control instruction based on the first display result, and regulating and controlling the air quality monitoring system.
In another aspect, the present application provides a remote intelligent diagnostic and operation system for an air quality monitoring system, wherein the system comprises: a first execution unit for obtaining a first target area, based on which a plurality of sensors are matched; a first obtaining unit for performing air quality monitoring using the plurality of sensors based on a first monitoring scheme, obtaining a first data set; the second execution unit is used for transmitting the first data set to an air quality monitoring platform in a wireless communication mode, wherein the air quality monitoring platform comprises a visualization component; the second obtaining unit is used for carrying out data preprocessing on the first data set received by the air quality monitoring platform to obtain a second data set; the third obtaining unit is used for checking the second data set based on a monitoring data abnormality detection model, uploading an abnormality detection result to the visualization component for content rendering, and obtaining a first display result; and the third execution unit is used for generating a first regulation and control instruction based on the first display result to regulate and control the air quality monitoring system.
In a third aspect, the present application provides a remote intelligent diagnostic and operational maintenance system for an air quality monitoring system, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of the first aspects when the program is executed by the processor.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
matching the plurality of sensors based on the target area due to the adoption of the obtained target area; based on a first monitoring scheme, air quality monitoring is carried out through a plurality of sensors, and a first data set is obtained; transmitting the first data set to an air quality monitoring platform in a wireless communication mode, and performing data preprocessing after the air quality monitoring platform receives the first data set to obtain a second data set; checking the second data set based on the monitoring data anomaly detection model, and uploading the anomaly detection result to the visualization component for content rendering to obtain a first display result; based on the abnormal condition of display, generate first regulation and control instruction, carry out the technical scheme of remote regulation and control to air quality monitoring system according to first regulation and control instruction, this application is through providing a remote intelligent diagnosis and operation and maintenance method and system of air quality monitoring system, the visual presentation of data and operation instruction has been passed through, the efficiency of monitoring work is improved to non-professional, reduce operation and maintenance cost, realize remote intelligent management and control, improve the timeliness that system abnormal data detected, can carry out the pertinence regulation and control to air quality detecting system, make the monitoring data more accurate, scientific technological effect.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
FIG. 1 is a flow chart of a remote intelligent diagnosis and operation method for an air quality monitoring system according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a remote intelligent diagnosis and operation method for an air quality monitoring system to obtain a second monitoring scheme according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a remote intelligent diagnosis and operation method for air quality monitoring system according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a remote intelligent diagnosis and operation system of an air quality monitoring system according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Reference numerals illustrate: the device comprises a first execution unit 11, a first acquisition unit 12, a second execution unit 13, a second acquisition unit 14, a third acquisition unit 15, a third execution unit 16, an electronic device 300, a memory 301, a processor 302, a communication interface 303, and a bus architecture 304.
Detailed Description
The remote intelligent diagnosis and operation and maintenance method and system for the air quality monitoring system solve the technical problems that in the prior art, due to the fact that the display method of the air quality monitoring system is poor in intuitiveness, non-professional personnel are difficult to participate in daily management and control operation and maintenance work, system operation and maintenance personnel are short, operation and maintenance cost of a entrusted third-party mechanism is high, and efficiency and quality of remote system management and control are difficult to guarantee. The system has the advantages that the monitoring data analysis results are visually presented through the visualization of the data and the operation instructions, the monitoring work efficiency of non-professional staff is improved, the operation and maintenance cost is reduced, the remote intelligent control is realized, the timeliness of system abnormal data detection is improved, the air quality detection system can be pertinently controlled, and the monitoring data is more accurate and scientific.
With the increase of air quality detection stations, more and more air monitoring types and detection instruments are adopted, and the shortage problem of operation and maintenance talents of an air quality detection system is more remarkable. A large amount of daily operation and maintenance work is carried out by a third party entrusting organization, the data quality and the operation and maintenance quality are difficult to ensure, and the qualification of the third party entrusting organization is also difficult to evaluate. How to reduce operation and maintenance cost, improve efficiency and quality of monitoring results, and reduce difficulty of daily operation and maintenance management is of great concern. The method solves the technical problems that in the prior art, because the display method of the air monitoring system is poor in intuitiveness, non-professional staff is difficult to participate in daily management, control, operation and maintenance work, system operation and maintenance staff are short, the operation and maintenance cost of a entrusted third-party mechanism is high, and the management and control efficiency and quality of a remote system are difficult to guarantee.
Aiming at the technical problems, the technical scheme provided by the application has the following overall thought:
the application provides a remote intelligent diagnosis and operation and maintenance method of an air quality monitoring system, wherein the method comprises the following steps: obtaining a target area, and matching the plurality of sensors based on the target area; based on a first monitoring scheme, air quality monitoring is carried out through a plurality of sensors, and a first data set is obtained; transmitting the first data set to an air quality monitoring platform in a wireless communication mode, and performing data preprocessing after the air quality monitoring platform receives the first data set to obtain a second data set; checking the second data set based on the monitoring data anomaly detection model, and uploading the anomaly detection result to the visualization component for content rendering to obtain a first display result; based on the displayed abnormal condition, a first regulation and control instruction is generated, and the air quality monitoring system is remotely regulated and controlled according to the first regulation and control instruction.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides a remote intelligent diagnosis and operation method of an air quality monitoring system, wherein the method is applied to the air quality monitoring system, and the air quality monitoring system is communicatively connected with a plurality of sensors, and the method includes:
S100: obtaining a first target area, matching the plurality of sensors based on the first target area;
specifically, the air quality monitoring operation can be roughly classified into an indoor air monitoring operation and an atmospheric (outdoor) air monitoring operation. The first target area is any area to be monitored, and the plurality of sensors include but are not limited to a temperature and humidity sensor, NO 2 Sensor, PM2.5 sensor, PM10 sensor, CO sensor, SO 2 Sensor, O 3 A sensor. The plurality of sensors are matched according to different monitoring areas, namely the corresponding sensors are matched according to the air pollution indexes to be monitored.
S200: based on a first monitoring scheme, performing air quality monitoring by using the plurality of sensors to obtain a first data set;
s300: transmitting the first data set to an air quality monitoring platform in a wireless communication mode, wherein the air quality monitoring platform comprises a visualization component;
specifically, the first monitoring scheme is obtained based on the first target area and the matched multiple sensors, wherein the first monitoring scheme comprises, but is not limited to, information such as the type, the number, the monitoring precision, the monitoring frequency, the monitoring time period and the like of the used sensors. And acquiring air quality monitoring data by a plurality of sensors to obtain the first data set. And transmitting the acquired data to the air quality monitoring platform in real time in a wireless communication mode. The data can be compressed and then transmitted, so that the transmission data quantity is reduced, and the transmission speed is improved. The air quality monitoring platform includes a visualization component, including but not limited to a liquid crystal display, capable of presenting received data in different forms. Meanwhile, the data is uploaded to the air quality monitoring platform, so that a foundation can be laid for realizing remote intelligent regulation and control.
S400: performing data preprocessing on the first data set received by the air quality monitoring platform to obtain a second data set;
s500: checking the second data set based on a monitoring data abnormality detection model, and uploading an abnormality detection result to the visualization component for content rendering to obtain a first display result;
specifically, the received first data set includes data collected by a plurality of sensors, so that in order to ensure the monitoring accuracy, a plurality of measurements and a plurality of points of measurement are performed, so that the data volume in the first data set is large and abnormal data may exist. And carrying out repeated data, maximum value deletion, minimum value deletion and the like in the first data set through data preprocessing to obtain the second data set. And performing anomaly detection on the second data set by using the monitoring data anomaly detection model. The monitoring data anomaly detection model is obtained by training the anomaly detection model through a Gaussian distribution algorithm, and can be used for anomaly detection of the data of the second data set, so that accuracy of the monitoring data is detected. Uploading the abnormal detection result to the visualization component, displaying the abnormal detection result on the air quality monitoring platform as the first display result through content rendering, visually displaying the analysis result of the monitoring data through data visualization, improving the monitoring work efficiency of non-professional, reducing the operation and maintenance cost,
S600: and generating a first regulation and control instruction based on the first display result, and regulating and controlling the air quality monitoring system.
Specifically, the air quality monitoring platform generates a corresponding solution, namely the first regulation and control instruction, based on the first display result, so as to remotely regulate and control the air quality monitoring system. As an example, without limitation: if the PM2.5 concentration detection result in the first display result is obviously lower than the historical data, the detection result is detected as abnormal data, at the moment, PM2.5 index monitoring is required to be regulated and controlled, secondary monitoring is carried out, and if the result is still abnormal, the standby sensor can be replaced to continue monitoring, so that the remote regulation and control of the air quality monitoring system are realized. Through remote intelligent control, can in time detect system abnormal data to the air quality detecting system carries out the pertinence regulation and control, obtains more accurate, scientific monitoring data.
Further, the detecting the second data set based on the monitoring data anomaly detection model, uploading the anomaly detection result to the visualization component for content rendering, and obtaining a first display result, and step S500 in the embodiment of the present application further includes:
S510: obtaining a variance and an average value of each data in the second data set;
s520: training the anomaly detection model according to the variance and the average value of each data through a Gaussian distribution algorithm to obtain a monitored data anomaly detection model;
s530: based on the monitoring data anomaly detection model, acquiring an anomaly data set in the second data set;
s540: uploading the abnormal data set to the visualization component for content rendering to obtain the first display result.
Specifically, a certain data point is different from most global data points, so that the point forms a single point of abnormality, which is also called global abnormality, and abnormal points in the data can be identified through a data mining means. In data mining, anomaly detection can identify anomalous observations in a data set. Gaussian distribution, also known as normal distribution, is a probability distribution, and is also the most common distribution in nature. The gaussian distribution has two parameters μ and σ 2 Is a continuous random variable distribution. The first parameter μ is the mean value of a random variable following a normal distribution, and the second parameter σ 2 Is the variance of this random variable. Generally if we consider that the variable x corresponds to a gaussian distribution, then its probability density function can be obtained:
Wherein p is probability density, x is variable, μ is average value, σ 2 Variance, σ is standard deviation.
Further, using the second data set x (1), x (2), x (m) measured, the variance and average of each data is calculated as follows:
wherein x is a variable, μ is an average value, σ 2 Is the variance. And randomly extracting a group of historical data for testing to complete the construction of an abnormality detection model. And training the anomaly detection model according to the variance and the average value of each data through a Gaussian distribution algorithm to obtain a monitored data anomaly detection model. And detecting abnormal data of the second data set acquired and updated in real time by using the trained monitoring data abnormal detection model. The probability density function is used for calculating the abnormal probability P (x), and the judgment threshold epsilon value can be selected according to the ratio of the precision rate to the recall rate. When P (x) < ε, the abnormal data set is obtained for the abnormal data. Uploading the abnormal data set to the visualization component and completing content rendering to obtain the first display result. The first display result comprises normal data and abnormal data of each sensor acquired in real time. Abnormal data can be displayed in the form of dialog boxes, messages and the like through the visual component, so that monitoring personnel are reminded of abnormal data monitoring, and troubleshooting is needed.
Further, the step S400 of the embodiment of the present application further includes:
s410: performing data cleaning and data integration on the first data set received by the air quality monitoring platform;
s420: rendering the integrated data to a first radar chart, wherein the first radar chart comprises response values of the plurality of sensors;
s430: uploading the first radar map to a visualization component for content rendering to obtain a second display result;
s440: and taking the data cleaning and data integration result as the second data set.
Specifically, after the air quality monitoring platform receives the data signals, the data signals are converted into data through the existing signal conversion technology, and the first data set is obtained. Because various interferences in air monitoring often cause problems of repetition, low accuracy and the like of acquired data, data preprocessing is required. Data cleansing includes, but is not limited to, deleting duplicate values, maxima, minima, sub-collection of missing data, and the like. And integrating the data acquired for multiple times by the same sensor after data cleaning. And rendering the preprocessed data onto a radar map to obtain the first radar map. The response values of different sensors and the content differences of different air pollutants can be intuitively seen from the radar chart. And along with the extension of time, can obtain second radar map, third radar map … … N radar map, can help monitoring personnel to observe the air pollutant trend of change through the radar map to be favorable to treating air pollution. And uploading the first radar map to a visualization assembly, and after rendering, referring to the radar map in the air quality monitoring platform, so that the efficiency of data analysis and data analysis can be improved. And taking the data after data integration as the second data set, and improving the efficiency and accuracy of subsequent data processing through data cleaning and data integration.
Further, the step S530 further includes, based on the monitored data anomaly detection model, obtaining an anomaly data set in the second data set:
s531: obtaining a first proportional coefficient, wherein the first proportional coefficient is a proportional coefficient of an abnormal data set in the second data set to the second data set;
s532: when the first proportion coefficient exceeds a first abnormal threshold value, a first detection instruction is obtained;
s533: uploading the first detection instruction to the visualization component for content rendering to obtain a third display result;
s534: and executing the first detection instruction based on the third display result, and performing quality detection on the plurality of sensors.
Specifically, when the data in the second data set is subjected to anomaly detection, the anomaly data set is obtained, and a scaling factor of the anomaly data set to the second data set is taken as the first scaling factor. The first scale factor reflects the specific gravity of the anomaly data and also reflects the accuracy of the monitored data. A first abnormal threshold is preset, and the first abnormal threshold has a certain tolerance. When the first proportion coefficient is smaller than the first abnormal threshold value, the abnormal data in the abnormal data set is little, and the abnormal data is most probably caused by measurement errors and belongs to a normal fault tolerance range. And when the first proportion coefficient exceeds the first abnormality threshold, indicating that the air quality monitoring system is abnormal, and obtaining the first detection instruction. The first detection instruction is used for mainly detecting the abnormal data acquisition device, namely the sensor corresponding to the abnormal data. And uploading the first monitoring instruction to the visualization component for content rendering, obtaining the third display result, and displaying the sensor to be detected. And the staff of the air quality monitoring platform executes the first detection instruction according to the third display result to perform key detection on the sensor. The processing efficiency of abnormal data can be improved, and simultaneously, non-professional personnel can also carry out partial work of remote monitoring, so that the working efficiency of the air quality monitoring center is improved.
Further, the embodiment of the application further includes:
s541: obtaining the first display result, wherein the first display result comprises PM2.5 concentration, PM10 concentration, carbon monoxide concentration, ozone concentration, sulfur dioxide concentration and nitrogen dioxide concentration monitoring results;
s542: obtaining an air quality grade according to the air quality evaluation standard and the first display result;
s543: based on the air quality level, a second monitoring scheme is obtained, wherein the second monitoring scheme is used for monitoring the quality of air in the next monitoring period.
Specifically, the first display result includes data results of a plurality of sensor detection including, but not limited to, PM2.5 concentration, PM10 concentration, carbon monoxide concentration, ozone concentration, sulfur dioxide concentration, nitrogen dioxide concentration monitoring results, results of abnormality detection analysis, etc., and the detection item may be added by a method of amplifying the sensor. The reference standard for calculation of the known Air Quality Index (AQI) is the current environmental air quality rating standard. The air quality class can be classified according to Air Quality Index (AQI): first-order: the air quality index is less than or equal to 50 priority, and the second level is as follows: the air quality index is less than or equal to 100, and the three stages: air quality index is less than or equal to 150 and slightly pollutes, four stages: medium pollution with air quality index less than or equal to 200, five stages: the air quality index is less than or equal to 300 severe pollution, six stages: the air quality index >300 is severely contaminated. And calculating the pollutant concentration of a single item in the first display result by using an Air Quality Index (AQI) to obtain the actual air quality grade. And according to the obtained actual air quality grade, acquiring the second monitoring scheme matched and preset by the system for air quality monitoring work in the next monitoring period. The air quality grade can be accurately divided through the real-time monitoring result, the current air quality can be accurately mastered by staff, the air monitoring scheme can be flexibly adjusted, and the accuracy, the scientificity and the flexibility of air monitoring are improved.
Further, as shown in fig. 2, the obtaining the second monitoring scheme based on the air quality level, step S543 in the embodiment of the present application further includes:
s5431: acquiring a historical air monitoring data set and a historical monitoring scheme set;
s5432: marking the historical air monitoring data set by using the air quality grade to obtain a first marking data set;
s5433: performing deviation degree analysis on the first marked data set and the historical monitoring scheme set to obtain a first analysis result;
s5434: based on the first analysis result, constructing a mapping relation between the air quality grade and a monitoring scheme set;
s5435: and obtaining the second monitoring scheme based on the mapping relation and the air quality grade.
Specifically, the air quality monitoring system stores a large amount of historical data during use, and a preset monitoring scheme is reserved. And the historical data corresponds to a historical monitoring scheme. And marking the historical air monitoring data set by using the air quality grade, so as to obtain a historical data set with grade marks. And taking the marked data as the first marked data set. And carrying out deviation degree analysis on the first marked data set and the historical monitoring scheme set, wherein the deviation degree analysis is used for analyzing whether the monitoring scheme used by the data of each level is accurate and scientific. Examples are: summarizing the monitoring schemes corresponding to the same grade, and calculating the precision and accuracy of the historical data corresponding to different monitoring schemes in the unified grade. And obtaining the first analysis result through deviation degree analysis, taking the scheme with the lowest deviation degree as a monitoring scheme corresponding to each level, and constructing a mapping relation between the two schemes. Further, a second monitoring scheme is obtained according to the air quality level based on the mapping relationship and the air quality level. The technical effects of improving the accuracy and the scientificity of the decision of the detection scheme and improving the quality of the monitoring result are achieved.
Further, as shown in fig. 3, the step S600 of the embodiment of the present application further includes:
s610: based on the first display result, obtaining an association monitoring link, wherein the association monitoring link comprises association monitoring equipment and an association transmission path;
s620: performing fault searching on the associated monitoring equipment and the associated transmission path to obtain a fault searching result;
s630: generating a first regulation and control instruction based on the fault finding result;
s640: and regulating and controlling the air quality monitoring system based on the first regulating and controlling instruction.
Specifically, the abnormality detection data, for example, the monitoring data of the PM2.5 sensor, can be known based on the first display result, and the cause of the abnormality may be the abnormality detected by the sensor or the abnormality in the wireless transmission process. The association monitoring link is thus obtained based on the first display result. The associated monitoring link comprises the associated monitoring equipment and an associated transmission path, performs fault searching on data detection, data transmission and data reception, and obtains a fault searching result after the fault searching is completed through corresponding technical means. And generating the first regulation and control instruction based on the fault finding result, and repairing the system aiming at the fault through the first regulation and control instruction, so that the air quality monitoring system is remotely regulated and controlled, the system fault can be sharply found, and the system safety is ensured.
In summary, the remote intelligent diagnosis and operation and maintenance method and system for the air quality monitoring system provided by the embodiment of the application have the following technical effects:
1. matching the plurality of sensors based on the target area due to the adoption of the obtained target area; based on a first monitoring scheme, air quality monitoring is carried out through a plurality of sensors, and a first data set is obtained; transmitting the first data set to an air quality monitoring platform in a wireless communication mode, and performing data preprocessing after the air quality monitoring platform receives the first data set to obtain a second data set; checking the second data set based on the monitoring data anomaly detection model, and uploading the anomaly detection result to the visualization component for content rendering to obtain a first display result; based on the abnormal condition of display, generate first regulation and control instruction, according to the technical scheme that first regulation and control instruction carries out remote regulation and control to air quality monitoring system, this application embodiment is through providing a remote intelligent diagnosis and operation and maintenance method and system of air quality monitoring system, the visual presentation of through data and operation instruction has been reached, the efficiency of monitoring data analysis result is visual, improve non-professional monitoring work, reduce the operation and maintenance cost, realize remote intelligent management and control, improve the timeliness that system abnormal data detected, can carry out the pertinence regulation and control to air quality detecting system, make the monitoring data more accurate, scientific technological effect.
2. Due to the adoption of the method for accurately dividing the air quality level through the real-time monitoring result, the method is beneficial for workers to accurately grasp the current air quality, and the air monitoring scheme can be flexibly adjusted, so that the technical effects of accuracy, scientificity and flexibility of air monitoring are improved.
Example two
Based on the same inventive concept as the remote intelligent diagnosis and operation and maintenance method of the air quality monitoring system in the foregoing embodiments, as shown in fig. 4, an embodiment of the present application provides a remote intelligent diagnosis and operation and maintenance system of an air quality monitoring system, wherein the system includes:
a first execution unit 11, the first execution unit 11 being configured to obtain a first target area, and to match a plurality of sensors based on the first target area;
a first obtaining unit 12, where the first obtaining unit 12 is configured to perform air quality monitoring using the plurality of sensors based on a first monitoring scheme to obtain a first data set;
the second execution unit 13 is configured to transmit the first data set to an air quality monitoring platform in a wireless communication manner, where the air quality monitoring platform includes a visualization component;
The second obtaining unit 14 is configured to perform data preprocessing on the first data set received by the air quality monitoring platform, so as to obtain a second data set;
the third obtaining unit 15 is configured to verify the second data set based on a monitored data anomaly detection model, upload an anomaly detection result to the visualization component for content rendering, and obtain a first display result;
and the third execution unit 16 is configured to generate a first regulation instruction based on the first display result, and regulate the air quality monitoring system.
Further, the system includes:
a fourth obtaining unit configured to obtain a variance and an average value of each data in the second data set;
the fifth obtaining unit is used for training the anomaly detection model according to the variance and the average value of each data through a Gaussian distribution algorithm to obtain a monitored data anomaly detection model;
a sixth obtaining unit configured to obtain an abnormal data set in the second data set based on the monitoring data abnormality detection model;
And the seventh obtaining unit is used for uploading the abnormal data set to the visualization component for content rendering, and obtaining the first display result.
Further, the system includes:
the fourth execution unit is used for carrying out data cleaning and data integration on the first data set received by the air quality monitoring platform;
a fifth execution unit, configured to render the integrated data to a first radar map, where the first radar map includes response values of the plurality of sensors;
the eighth obtaining unit is used for uploading the first radar map to a visualization component for content rendering and obtaining a second display result;
and the sixth execution unit is used for taking the data cleaning and data integration result as the second data set.
Further, the system includes:
a ninth obtaining unit, configured to obtain a first scaling factor, where the first scaling factor is a scaling factor of an abnormal data set in the second data set to occupy the second data set;
A tenth obtaining unit configured to obtain a first detection instruction when the first scale coefficient exceeds a first abnormality threshold;
an eleventh obtaining unit, configured to upload the first detection instruction to the visualization component for content rendering, to obtain a third display result;
and a seventh execution unit, configured to execute the first detection instruction based on the third display result, and perform quality detection on the plurality of sensors.
Further, the system includes:
a twelfth obtaining unit for obtaining the first display result, wherein the first display result includes a PM2.5 concentration, a PM10 concentration, a carbon monoxide concentration, an ozone concentration, a sulfur dioxide concentration, and a nitrogen dioxide concentration monitoring result;
a thirteenth obtaining unit configured to obtain an air quality level based on the air quality evaluation criterion and the first display result;
a fourteenth obtaining unit for obtaining a second monitoring scheme for quality monitoring of air in a next monitoring period based on the air quality level.
Further, the system includes:
a fifteenth obtaining unit for obtaining a historical air monitoring data set and a historical monitoring scheme set;
a sixteenth obtaining unit for tagging the historical air monitoring data set with the air quality level to obtain a first tagged data set;
a seventeenth obtaining unit, configured to perform a deviation analysis on the first marker data set and the history monitoring scheme set, to obtain a first analysis result;
the first construction unit is used for constructing the mapping relation between the air quality grade and the monitoring scheme set based on the first analysis result;
an eighteenth obtaining unit for obtaining the second monitoring scheme based on the mapping relation and the air quality level.
Further, the system includes:
a nineteenth obtaining unit, configured to obtain an association monitoring link based on the first display result, where the association monitoring link includes an association monitoring device and an association transmission path;
The twentieth acquisition unit is used for carrying out fault finding on the associated monitoring equipment and the associated transmission path to obtain a fault finding result;
the eighth execution unit is used for generating a first regulation and control instruction based on the fault finding result;
and the ninth execution unit is used for regulating and controlling the air quality monitoring system based on the first regulation and control instruction.
Exemplary electronic device
An electronic device of an embodiment of the present application is described below with reference to fig. 5.
Based on the same inventive concept as the remote intelligent diagnosis and operation and maintenance method of the air quality monitoring system in the foregoing embodiments, the embodiments of the present application further provide a remote intelligent diagnosis and operation and maintenance system of the air quality monitoring system, including: a processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the method of any of the first aspects.
The electronic device 300 includes: a processor 302, a communication interface 303, a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein the communication interface 303, the processor 302 and the memory 301 may be interconnected by a bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry Standard architecture, EISA) bus, among others. The bus architecture 304 may be divided into address buses, data buses, control buses, and the like. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of the programs of the present application.
The communication interface 303 uses any transceiver-like system for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), wired access network, etc.
The memory 301 may be, but is not limited to, ROM or other type of static storage device that may store static information and instructions, RAM or other type of dynamic storage device that may store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through bus architecture 304. The memory may also be integrated with the processor.
The memory 301 is used for storing computer-executable instructions for executing the embodiments of the present application, and is controlled by the processor 302 to execute the instructions. The processor 302 is configured to execute computer-executable instructions stored in the memory 301, thereby implementing a remote intelligent diagnosis and operation and maintenance method for an air quality monitoring system according to the above-described embodiments of the present application.
Alternatively, the computer-executable instructions in the embodiments of the present application may be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
The embodiment of the application provides a remote intelligent diagnosis and operation and maintenance method of an air quality monitoring system, wherein the method comprises the following steps: obtaining a target area, and matching the plurality of sensors based on the target area; based on a first monitoring scheme, air quality monitoring is carried out through a plurality of sensors, and a first data set is obtained; transmitting the first data set to an air quality monitoring platform in a wireless communication mode, and performing data preprocessing after the air quality monitoring platform receives the first data set to obtain a second data set; checking the second data set based on the monitoring data anomaly detection model, and uploading the anomaly detection result to the visualization component for content rendering to obtain a first display result; based on the displayed abnormal condition, a first regulation and control instruction is generated, and the air quality monitoring system is remotely regulated and controlled according to the first regulation and control instruction.
Those of ordinary skill in the art will appreciate that: the various numbers of first, second, etc. referred to in this application are merely for convenience of description and are not intended to limit the scope of embodiments of the present application, nor to indicate a sequence. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any one," or the like, refers to any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one of a, b, or c (species ) may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable system. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more servers, data centers, etc. that can be integrated with the available medium. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
The various illustrative logical blocks and circuits described in the embodiments of the present application may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic system, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the general purpose processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing systems, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the embodiments of the present application may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software elements may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. In an example, a storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may reside in a terminal. In the alternative, the processor and the storage medium may reside in different components in a terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are merely exemplary illustrations of the application as defined herein and are deemed to cover any and all modifications, variations, combinations, or equivalents within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, the present application is intended to cover such modifications and adaptations, provided that such modifications and adaptations are within the scope of the present application and the equivalents thereof.
Claims (6)
1. A method of remote intelligent diagnosis and operation of an air quality monitoring system, the method being applied to an air quality monitoring system, the air quality monitoring system being communicatively coupled to a plurality of sensors, the method comprising:
obtaining a first target area, matching the plurality of sensors based on the first target area;
based on a first monitoring scheme, performing air quality monitoring by using the plurality of sensors to obtain a first data set;
Transmitting the first data set to an air quality monitoring platform in a wireless communication mode, and converting the data signal into data through the existing signal conversion technology after the air quality monitoring platform receives the data signal to obtain the first data set, wherein the air quality monitoring platform comprises a visualization component;
performing data preprocessing on the first data set received by the air quality monitoring platform to obtain a second data set;
checking the second data set based on a monitoring data anomaly detection model, uploading an anomaly detection result to the visualization component for content rendering, and obtaining a first display result, wherein the method comprises the following steps:
obtaining a variance and an average value of each data in the second data set;
training the anomaly detection model according to the variance and the average value of each data through a Gaussian distribution algorithm to obtain a monitored data anomaly detection model;
based on the monitoring data anomaly detection model, acquiring an anomaly data set in the second data set;
uploading the abnormal data set to the visualization component for content rendering to obtain the first display result;
Based on the first display result, generating a first regulation and control instruction to regulate and control the air quality monitoring system, including:
based on the first display result, obtaining an association monitoring link, wherein the association monitoring link comprises association monitoring equipment and an association transmission path;
performing fault searching on the associated monitoring equipment and the associated transmission path to obtain a fault searching result;
generating a first regulation and control instruction based on the fault finding result;
regulating and controlling the air quality monitoring system based on a first regulation and control instruction;
the method comprises the steps of performing data preprocessing on the first data set received by the air quality monitoring platform to obtain a second data set, and comprises the following steps:
performing data cleaning and data integration on the first data set received by the air quality monitoring platform;
rendering the integrated data to a first radar chart, wherein the first radar chart comprises response values of the plurality of sensors;
uploading the first radar map to a visualization component for content rendering to obtain a second display result;
and taking the data cleaning and data integration result as the second data set.
2. A method of remote intelligent diagnosis and operation of an air quality monitoring system according to claim 1, wherein said obtaining an anomaly data set in said second data set based on said monitored data anomaly detection model, further comprises:
obtaining a first proportional coefficient, wherein the first proportional coefficient is a proportional coefficient of an abnormal data set in the second data set to the second data set;
when the first proportion coefficient exceeds a first abnormal threshold value, a first detection instruction is obtained;
uploading the first detection instruction to the visualization component for content rendering to obtain a third display result;
and executing the first detection instruction based on the third display result, and performing quality detection on the plurality of sensors.
3. A method of remote intelligent diagnosis and operation of an air quality monitoring system according to claim 1, said method further comprising:
obtaining the first display result, wherein the first display result comprises PM2.5 concentration, PM10 concentration, carbon monoxide concentration, ozone concentration, sulfur dioxide concentration and nitrogen dioxide concentration monitoring results;
obtaining an air quality grade according to the air quality evaluation standard and the first display result;
Based on the air quality level, a second monitoring scheme is obtained, wherein the second monitoring scheme is used for monitoring the quality of air in the next monitoring period.
4. A method of remote intelligent diagnosis and operation of an air quality monitoring system according to claim 3, wherein said obtaining a second monitoring scheme based on said air quality level, said method further comprises:
acquiring a historical air monitoring data set and a historical monitoring scheme set;
marking the historical air monitoring data set by using the air quality grade to obtain a first marking data set;
performing deviation degree analysis on the first marked data set and the historical monitoring scheme set to obtain a first analysis result;
based on the first analysis result, constructing a mapping relation between the air quality grade and a monitoring scheme set;
and obtaining the second monitoring scheme based on the mapping relation and the air quality grade.
5. A remote intelligent diagnostic and operation system for an air quality monitoring system, the system comprising:
a first execution unit for obtaining a first target area, based on which a plurality of sensors are matched;
A first obtaining unit for performing air quality monitoring using the plurality of sensors based on a first monitoring scheme, obtaining a first data set;
the second execution unit is used for transmitting the first data set to an air quality monitoring platform in a wireless communication mode, converting the data signals received by the air quality monitoring platform into data through the existing signal conversion technology, and obtaining the first data set, wherein the air quality monitoring platform comprises a visualization component;
the second obtaining unit is used for carrying out data preprocessing on the first data set received by the air quality monitoring platform to obtain a second data set;
the third obtaining unit is used for checking the second data set based on a monitoring data abnormality detection model, uploading an abnormality detection result to the visualization component for content rendering, and obtaining a first display result;
the third execution unit is used for generating a first regulation and control instruction based on the first display result, and regulating and controlling the air quality monitoring system;
The fourth execution unit is used for carrying out data cleaning and data integration on the first data set received by the air quality monitoring platform;
a fifth execution unit, configured to render the integrated data to a first radar map, where the first radar map includes response values of the plurality of sensors;
the eighth obtaining unit is used for uploading the first radar map to a visualization component for content rendering and obtaining a second display result;
a sixth execution unit, configured to take a result of the data cleansing and the data integration as the second data set;
the system comprises:
a fourth obtaining unit configured to obtain a variance and an average value of each data in the second data set;
the fifth obtaining unit is used for training the anomaly detection model according to the variance and the average value of each data through a Gaussian distribution algorithm to obtain a monitored data anomaly detection model;
a sixth obtaining unit configured to obtain an abnormal data set in the second data set based on the monitoring data abnormality detection model;
A seventh obtaining unit, configured to upload the abnormal data set to the visualization component for content rendering, to obtain the first display result;
a nineteenth obtaining unit, configured to obtain an association monitoring link based on the first display result, where the association monitoring link includes an association monitoring device and an association transmission path;
the twentieth acquisition unit is used for carrying out fault finding on the associated monitoring equipment and the associated transmission path to obtain a fault finding result;
the eighth execution unit is used for generating a first regulation and control instruction based on the fault finding result;
and the ninth execution unit is used for regulating and controlling the air quality monitoring system based on the first regulation and control instruction.
6. A remote intelligent diagnostic and operation system for an air quality monitoring system, comprising: a processor coupled to a memory for storing a program which, when executed by the processor, causes the system to perform a remote intelligent diagnostic and operational method of an air quality monitoring system as claimed in any one of claims 1 to 4.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104133038A (en) * | 2013-06-21 | 2014-11-05 | 赵时旻 | Vehicle internal environment monitoring and control system |
| CN107085070A (en) * | 2017-03-14 | 2017-08-22 | 深圳市金立通信设备有限公司 | A kind of based reminding method and terminal based on air quality |
| CN108489877A (en) * | 2018-05-31 | 2018-09-04 | 安徽中疆环境科技有限公司 | A kind of atmospheric monitoring system |
| CN109146434A (en) * | 2018-10-24 | 2019-01-04 | 云南电网有限责任公司信息中心 | A kind of visualizing monitor platform and method towards digital electric network business |
| CN109523510A (en) * | 2018-10-11 | 2019-03-26 | 浙江大学 | River water quality free air anomaly method for detecting area based on multi-spectrum remote sensing image |
| CN110909896A (en) * | 2019-11-27 | 2020-03-24 | 北京搜狐新媒体信息技术有限公司 | Visual operation and maintenance monitoring method and device |
| CN111460059A (en) * | 2020-03-02 | 2020-07-28 | 平安国际智慧城市科技股份有限公司 | Ambient air quality data visualization method, device, equipment and storage medium |
| CN111679050A (en) * | 2020-07-03 | 2020-09-18 | 广州傲马科技有限公司 | Water quality monitoring and early warning system based on Internet of things |
| CN111983961A (en) * | 2020-08-19 | 2020-11-24 | 詹晨 | Resident drinking water safety intelligent monitoring management system based on big data |
| CN112100843A (en) * | 2020-09-10 | 2020-12-18 | 中国电力科学研究院有限公司 | Visual analysis method and system for power system safety event simulation verification |
| CN113030666A (en) * | 2021-03-22 | 2021-06-25 | 三门核电有限公司 | Large-scale transformer discharge fault diagnosis method and device |
| CN113552817A (en) * | 2021-07-27 | 2021-10-26 | 珠海格力电器股份有限公司 | Movable air detection device, detection method and air detector |
| CN113655175A (en) * | 2021-08-05 | 2021-11-16 | 重庆亿森动力环境科技有限公司 | Gridding air monitoring method, system, computer equipment and storage medium |
| CN113687609A (en) * | 2021-07-21 | 2021-11-23 | 浙江微科机电有限公司 | Intelligent monitoring system and monitoring method for Internet of things applied to abnormal environment |
| CN215263344U (en) * | 2021-04-29 | 2021-12-21 | 福建农林大学 | Air pollution on-line monitoring device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BR112019017301A2 (en) * | 2017-10-09 | 2020-04-22 | Bl Technologies, Inc. | intelligent methods and systems for health diagnosis of a water treatment plant, anomaly detection and control |
-
2021
- 2021-12-31 CN CN202111677810.7A patent/CN114487283B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104133038A (en) * | 2013-06-21 | 2014-11-05 | 赵时旻 | Vehicle internal environment monitoring and control system |
| CN107085070A (en) * | 2017-03-14 | 2017-08-22 | 深圳市金立通信设备有限公司 | A kind of based reminding method and terminal based on air quality |
| CN108489877A (en) * | 2018-05-31 | 2018-09-04 | 安徽中疆环境科技有限公司 | A kind of atmospheric monitoring system |
| CN109523510A (en) * | 2018-10-11 | 2019-03-26 | 浙江大学 | River water quality free air anomaly method for detecting area based on multi-spectrum remote sensing image |
| CN109146434A (en) * | 2018-10-24 | 2019-01-04 | 云南电网有限责任公司信息中心 | A kind of visualizing monitor platform and method towards digital electric network business |
| CN110909896A (en) * | 2019-11-27 | 2020-03-24 | 北京搜狐新媒体信息技术有限公司 | Visual operation and maintenance monitoring method and device |
| CN111460059A (en) * | 2020-03-02 | 2020-07-28 | 平安国际智慧城市科技股份有限公司 | Ambient air quality data visualization method, device, equipment and storage medium |
| CN111679050A (en) * | 2020-07-03 | 2020-09-18 | 广州傲马科技有限公司 | Water quality monitoring and early warning system based on Internet of things |
| CN111983961A (en) * | 2020-08-19 | 2020-11-24 | 詹晨 | Resident drinking water safety intelligent monitoring management system based on big data |
| CN112100843A (en) * | 2020-09-10 | 2020-12-18 | 中国电力科学研究院有限公司 | Visual analysis method and system for power system safety event simulation verification |
| CN113030666A (en) * | 2021-03-22 | 2021-06-25 | 三门核电有限公司 | Large-scale transformer discharge fault diagnosis method and device |
| CN215263344U (en) * | 2021-04-29 | 2021-12-21 | 福建农林大学 | Air pollution on-line monitoring device |
| CN113687609A (en) * | 2021-07-21 | 2021-11-23 | 浙江微科机电有限公司 | Intelligent monitoring system and monitoring method for Internet of things applied to abnormal environment |
| CN113552817A (en) * | 2021-07-27 | 2021-10-26 | 珠海格力电器股份有限公司 | Movable air detection device, detection method and air detector |
| CN113655175A (en) * | 2021-08-05 | 2021-11-16 | 重庆亿森动力环境科技有限公司 | Gridding air monitoring method, system, computer equipment and storage medium |
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| Publication number | Publication date |
|---|---|
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