Intelligent abnormal water quality identifying and monitoring system and method based on Internet of Things

Abstract

The invention discloses an intelligent abnormal water quality identifying and monitoring system and method based on Internet of Things, and relates to the technical field of intelligent abnormal water quality identifying and monitoring. The system comprises a data acquisition module, an edge calculation module and a cloud analysis module, the output end of the data acquisition module is connected with the input end of the edge calculation module; and the output end of the edge calculation module is connected with the input end of the cloud analysis module. According to the invention, through twice analysis and calculation of water quality data, the identification capability of conventional water quality change is improved, and the sensitivity of potential water quality abnormity is also obviously enhanced, so that the system can find out the water quality problem earlier and take countermeasures before the water quality problem develops into a serious event, thereby improving the water quality detection efficiency. Therefore, potential threats of water body pollution to the environment and human health are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent identification and monitoring of abnormal water quality, and in particular to an abnormal water quality intelligent identification and monitoring system and method based on the Internet of Things. Background Art

[0002] Water quality identification and monitoring is an important part of water pollution prevention and control, which not only ensures the sustainable use of water resources, but also protects the aquatic ecosystem.

[0003] With the advancement of science and technology, water quality identification and monitoring technology is also constantly innovating and improving, which can realize intelligent analysis and diagnosis of water quality monitoring data. However, the existing water quality identification and monitoring technology still faces a key challenge: most systems only analyze water quality data once. This single-time analysis mode is difficult to fully and accurately reflect the true condition of the water body. Summary of the invention

[0004] The purpose of the present invention is to provide an abnormal water quality intelligent identification and monitoring system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: an abnormal water quality intelligent identification and monitoring method based on the Internet of Things, the method comprising the following steps: S1, the data acquisition module collects water quality data of the water body in real time, pre-processes the data, and encapsulates the water quality data into a water quality data packet; S2. The water quality data packet is transmitted to the edge computing platform. The edge computing platform performs a water quality identification based on the water quality data to determine whether the water quality data is abnormal; S3. If the output result of a water quality identification shows that the water quality is normal, the system will continue to monitor the water quality; if the output result of a water quality identification shows that the water quality is abnormal, the system will immediately trigger the early warning mechanism; S4. The edge computing platform sends the water quality data that confirms the abnormal water quality to the cloud platform. The cloud platform performs secondary water quality identification based on the water quality data that confirms the abnormal water quality and generates a water quality identification monitoring report.

[0006] According to the above technical solution, in step S1, it specifically includes: The data acquisition module collects water quality data of the water body in real time according to a preset acquisition frequency. The water quality data includes the acquisition time, acquisition location and water quality index data of the water body. The water quality index data includes the temperature value, pH value, dissolved oxygen content, turbidity value and heavy metal content of the water body. The water quality data is preprocessed and the preprocessed water quality data is packaged according to a certain format to form a water quality data packet. The water quality data packet includes the acquisition time, acquisition location and water quality index data of the water body.

[0007] According to the above technical solution, in step S2, it also includes: The encapsulated water quality data packet is transmitted to the edge computing platform via a wireless network. The edge computing platform parses the water quality data packet, extracts the water quality index data therein, and performs a water quality identification based on the water quality index data; The process of primary water quality identification is as follows: Acquire historical water quality index data, arrange the acquired historical water quality index data in ascending order according to the same index, extract the 10th percentile historical water quality index data and the 90th percentile historical water quality index data, mark the 10th percentile historical water quality index data as the minimum value of the water quality index standard value, and mark the 90th percentile historical water quality index data as the maximum value of the water quality index standard value; The water quality index data is compared with the water quality index standard value; if the water quality index data is greater than the minimum value of the water quality index standard value and the water quality index is less than the maximum value of the water quality index standard value, the output result of a water quality identification shows that the water quality is normal; if the water quality index data is less than the minimum value of the water quality index standard value or the water quality index data is greater than the maximum value of the water quality index standard value, the output result of a water quality identification shows that the water quality is abnormal.

[0008] In the above technical solution, a water quality identification is performed on the water quality index data, which can quickly determine whether the water quality is within a normal range, thereby reducing the workload for subsequent secondary water quality identification.

[0009] According to the above technical solution, in step S3, the early warning mechanism includes starting an audible and visual alarm and sending an early warning text message.

[0010] According to the above technical solution, in step S4, it also includes: Step S401: The edge computing platform encapsulates the water quality index data for confirming abnormal water quality and the data of the previous set of water quality indexes for confirming abnormal water quality into an abnormal water quality index data packet, and sends it to the cloud platform; Step S402, the cloud platform receives the abnormal water quality index data packet, extracts the water quality index data at the t-th time point, the water quality temperature is X1, the water quality pH value is X2, the water quality dissolved oxygen is X3, the water quality turbidity is X4, and the water quality heavy metal content is X5; extracts the water quality index data at the t-1-th time point, the water quality temperature is X1', the water quality pH value is X2', the water quality dissolved oxygen is X3', the water quality turbidity is X4', and the water quality heavy metal content is X5'; Calculate the change rate R of the water quality index data i , the calculation formula for the change rate of the water quality index data is: ; Among them, R j is the change rate of water quality index data, X j is the water quality index data at the tth time point, X j ' is the water quality index data at the t-1th time point, △t is the time interval between the tth time point and the t-1th time point; Step S403: The process of secondary water quality identification is as follows: Acquire historical water quality data for a period of time to form a sample set of water quality data, arrange the sample set of water quality data into a matrix in chronological order, and extract the time interval between two adjacent collection time points as △t * , each row represents a sample of water quality data at a specific time point, and each column corresponds to a water quality index; extract the water quality indexes in the same column to form a set A j ={Y 1j , Y 2j , Y 3j , ...Y nj}, where A j represents the water quality data of the jth column, Y 1j , Y 2j , Y 3j , ...Y nj represents the water quality index data of the first row and the jth column, the water quality index data of the second row and the jth column, the water quality index data of the third row and the jth column, ... the water quality index data of the nth row and the jth column; Calculate the rate of change of water quality index data in each time interval as follows: ; The average value of the change rate of water quality index data is calculated as: ; Among them, α j is the average value of water quality index data, Y ij is the water quality index data in the i-th row and j-th column, n is the number of samples of water quality data, and △t* is the time interval between two adjacent collection time points; The standard deviation of the water quality index data is calculated as: ; Among them, β j is the standard deviation of water quality index data, Y ij is the water quality index data in the i-th row and j-th column, α j is the average value of water quality index data, and n is the number of samples of water quality data; The threshold for calculating the rate of change of water quality indicators is: ; ; Among them, R jmax is the maximum value of the standard change rate of water quality indicators, R jmin is the minimum value of the water quality index change rate, α j is the average value of water quality index data, β j is the standard deviation of water quality index data; The change rate R of the water quality index i The rate of change R of the water quality index standard jmax and R jmin For comparison; if R jmin <R i <R jmax , then the result of the water quality identification is wrong and the water quality is normal; if R i <R jmin or R i >R jmax , then the result of the water quality identification is correct and the water quality is abnormal; Step S404: The cloud platform generates a water quality identification monitoring report according to the verification result of the secondary water quality identification. The water quality identification monitoring report includes the verification result of the secondary water quality identification, water quality data, and the change rate of water quality index data.

[0011] In the above technical solution, the secondary water quality identification verifies the result of the primary water quality identification and more accurately evaluates whether the water quality is within the normal range.

[0012] An abnormal water quality intelligent identification and monitoring system based on the Internet of Things, the system comprises: a data acquisition module, an edge computing module and a cloud analysis module; The data acquisition module is equipped with sensor equipment for collecting water temperature, pH value, dissolved oxygen, turbidity and heavy metal content data; the edge computing module is used to pre-process the data and perform a primary water quality identification on the water pollution data; the cloud analysis module is used to perform a secondary water quality identification on the water pollution data of abnormal water bodies identified as a primary water quality in the edge computing module, generate a water quality identification monitoring report, and send it to the display terminal; The output end of the data acquisition module is connected to the input end of the edge computing module; the output end of the edge computing module is connected to the input end of the cloud analysis module.

[0013] According to the above technical solution, the data acquisition module includes a water quality data acquisition unit and a water quality data encapsulation unit; The water quality data acquisition unit, i.e., a sensor device, is installed at the confluence of water flows. It detects specific components or properties in the water body and converts the detection results into digital signals through a signal processing module. The water quality data encapsulation unit obtains the digital signal collected by the water quality data acquisition unit, encapsulates it according to adding timestamp, geographic location, data category identifier and data value, and forms a water quality data packet.

[0014] According to the above technical solution, the edge computing module includes a primary water quality identification unit and an early warning prompt unit; The primary water quality identification unit is used to receive the water quality data packet from the data acquisition module, and use the built-in algorithm to analyze and identify the received water quality data to determine whether the water quality data is abnormal; the early warning prompt unit automatically determines whether to trigger the early warning mechanism based on the output result of the primary water quality identification unit. If the output result of the primary water quality identification shows that the water quality is normal, the system does not trigger the early warning mechanism. If the output result of the primary water quality identification shows that the water quality is abnormal, the system triggers the early warning mechanism and generates early warning information.

[0015] According to the above technical solution, the cloud analysis module includes a secondary water quality identification unit and a report generation unit; The secondary water quality identification unit is used to receive water quality data that confirms abnormal water quality from the edge computing module, and use the built-in trend change model to perform secondary analysis on the water quality data that confirms abnormal water quality to verify whether the output result of the edge computing module is correct; The report generating unit is used to receive the verification result, water quality data and water quality index data of the secondary water quality identification unit, and to organize the analysis result into a water quality identification monitoring report.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: through two analyses and calculations of water quality data, the present invention not only improves the ability to identify and monitor water bodies, but also significantly enhances the sensitivity to potential water quality anomalies, enabling the system to detect water quality problems earlier and take countermeasures before they develop into serious incidents, thereby effectively reducing the potential threats posed by water pollution to the environment and human health. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of an abnormal water quality intelligent identification and monitoring method based on the Internet of Things of the present invention; Figure 2 The present invention is a structural schematic diagram of an abnormal water quality intelligent identification and monitoring system based on the Internet of Things. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 In the first embodiment, a method for intelligently identifying and monitoring abnormal water quality based on the Internet of Things is provided, the method comprising: The data acquisition module collects water quality data of the water body in real time according to a preset acquisition frequency. The water quality data includes the acquisition time, acquisition location and water quality index data of the water body. The water quality index data includes the temperature value, pH value, dissolved oxygen content, turbidity value and heavy metal content of the water body. The water quality data is preprocessed and the preprocessed water quality data is packaged according to a certain format to form a water quality data packet. The water quality data packet includes the acquisition time, acquisition location and water quality index data of the water body.

[0020] The water quality data packet is transmitted to the edge computing platform, and the edge computing platform performs a water quality identification based on the water quality data to determine whether the water quality data is abnormal; The encapsulated water quality data packet is transmitted to the edge computing platform via a wireless network. The edge computing platform parses the water quality data packet, extracts the water quality index data therein, and performs a water quality identification based on the water quality index data; The process of primary water quality identification is as follows: Acquire historical water quality index data for a certain period of time, arrange the acquired historical water quality index data in ascending order according to the same index, extract the 10th percentile historical water quality index data and the 90th percentile historical water quality index data, use the 10th percentile historical water quality index data as the minimum value of the water quality index standard value, and use the 90th percentile historical water quality index data as the maximum value of the water quality index standard value; For example, historical water quality index data with a monitoring duration of 5 hours and a collection frequency of 0.5 hours per time are obtained, and the water quality temperature data extracted from the historical water quality index data are 22.5, 23.1, 21.8, 22.2, 22.9, 23.5, 22.0, 21.5, 24.0, and 23.8; And sorted in ascending order: 21.5, 21.8, 22.0, 22.2, 22.5, 22.9, 23.1, 23.5, 23.8, 24.0; The total number of data points is 10, so the 10th percentile is 0.1*10=1, and the first data is taken as the minimum value of the water quality temperature standard value; the 90th percentile is 0.9*10=9, and the ninth data is taken as the maximum value of the water quality temperature standard value; That is, 21.5 is the minimum value of the water quality temperature standard value, and 24.0 is the maximum value of the water quality temperature standard value; The water quality index data is compared with the water quality index standard value; if the water quality index data is greater than the minimum value of the water quality index standard value and the water quality index is less than the maximum value of the water quality index standard value, the output result of a water quality identification shows that the water quality is normal; if the water quality index data is less than the minimum value of the water quality index standard value or the water quality index data is greater than the maximum value of the water quality index standard value, the output result of a water quality identification shows that the water quality is abnormal.

[0021] If the output result of a water quality identification shows that the water quality is normal, the system will continue to monitor the water quality; if the output result of a water quality identification shows that the water quality is abnormal, the system will immediately trigger an early warning mechanism; the early warning mechanism includes starting an audible and visual alarm and sending an early warning text message.

[0022] The edge computing platform sends the water quality data that confirms the abnormal water quality to the cloud platform. The cloud platform performs secondary water quality identification based on the water quality data that confirms the abnormal water quality and generates a water quality identification monitoring report.

[0023] The edge computing platform encapsulates the water quality index data for confirming abnormal water quality and the data of the previous set of water quality indexes for confirming abnormal water quality into an abnormal water quality index data packet, and sends it to the cloud platform; The cloud platform receives abnormal water quality index data packets, extracts the water quality index data at the t-th time point, where the water quality temperature is X1, the water quality pH value is X2, the water quality dissolved oxygen is X3, the water quality turbidity is X4, and the water quality heavy metal content is X5; extracts the water quality index data at the t-1-th time point, where the water quality temperature is X1', the water quality pH value is X2', the water quality dissolved oxygen is X3', the water quality turbidity is X4', and the water quality heavy metal content is X5'; Calculate the change rate R of the water quality index data j , the calculation formula for the change rate of the water quality index data is: ; Among them, R j is the change rate of water quality index data, X j is the water quality index data at the tth time point, X j ' is the water quality index data at the t-1th time point, △t is the time interval between the tth time point and the t-1th time point; For example, the cloud platform obtains abnormal water quality index data packets, extracts the water quality temperature data at the t-th time point as 22.7, extracts the water quality temperature data at the t-1-th time point as 22.8, and the time interval between the t-th time point and the t-1-th time point is 0.5h; Calculate the change rate of water quality temperature data as: ; The process of secondary water quality identification is as follows: Acquire historical water quality data for a period of time to form a sample set of water quality data, arrange the sample set of water quality data into a matrix in chronological order, and extract the time interval between two adjacent collection time points as △t * , each row represents a sample of water quality data at a specific time point, and each column corresponds to a water quality index; extract the water quality indexes in the same column to form a set A j ={Y 1j , Y 2j , Y 3j , ...Y nj}, where A j represents the water quality data of the jth column, Y 1j , Y 2j , Y 3j , ...Y nj represents the water quality index data of the first row and the jth column, the water quality index data of the second row and the jth column, the water quality index data of the third row and the jth column, ... the water quality index data of the nth row and the jth column; Calculate the rate of change of water quality index data in each time interval as follows: ; The average value of the change rate of water quality index data is calculated as: ; Among them, α j is the average value of water quality index data, Y ij is the water quality index data in the i-th row and j-th column, n is the number of samples of water quality data, and △t* is the time interval between two adjacent collection time points; The standard deviation of the water quality index data is calculated as: ; Among them, β j is the standard deviation of water quality index data, Y ij is the water quality index data in the i-th row and j-th column, α j is the average value of water quality index data, and n is the number of samples of water quality data; The threshold for calculating the rate of change of water quality indicators is: ; ; Among them, R jmax is the maximum value of the standard change rate of water quality indicators, R jmin is the minimum value of the water quality index change rate, α j is the average value of water quality index data, β j is the standard deviation of water quality index data; For example, historical water quality index data with a monitoring duration of 5 hours and a collection frequency of 0.5 hours / time are obtained to form a sample set of water quality data, and the sample set of water quality data is arranged into a matrix in chronological order, each row represents a sample of water quality data at a specific time point, and each column corresponds to a water quality index. The water quality index of the first column is extracted to form a set A1={22.5, 23.1, 21.8, 22.2, 22.9, 23.5, 22.0, 21.5, 24.0, 23.8}; Calculate the rate of change of the water quality temperature data in column 1 at each time interval as follows: ; ; ; ; ; ; .0; .0; ; The average value of the change rate of the water quality index data in column 1 is calculated as follows: ; Calculate the standard deviation of the rate of change of the water quality index data in column 1 as:

[0024]

[0025] 4; The threshold for calculating the rate of change of water quality indicators in column 1 is: ; ; The maximum value of the water quality index standard change rate R jmax is 6.787, the minimum value of the standard change rate of water quality indicators R jmin for ; The change rate R of the water quality index i The rate of change R of the water quality index standard jmax and R jmin For comparison; if R jmin <R j <R jmax , then the result of the water quality identification is wrong and the water quality is normal; if R i <R jmin or R j >R jmax , then the result of the water quality identification is correct and the water quality is abnormal; The cloud platform generates a water quality identification monitoring report according to the verification result of the secondary water quality identification, and the water quality identification monitoring report includes the verification result of the secondary water quality identification, water quality data and the change rate of water quality index data.

[0026] In the second embodiment, an abnormal water quality intelligent identification and monitoring system based on the Internet of Things is also provided, the system comprising: a data acquisition module, an edge computing module and a cloud analysis module; The data acquisition module is installed with sensor equipment for collecting and packaging water temperature, pH value, dissolved oxygen, turbidity and heavy metal content data; the edge computing module is used to pre-process the data and perform a primary water quality identification on the water pollution data; the cloud analysis module is used to perform a secondary water quality identification on the water pollution data of the abnormal water body identified as a primary water quality in the edge computing module, generate a water quality identification monitoring report, and send it to the display terminal; The output end of the data acquisition module is connected to the input end of the edge computing module; the output end of the edge computing module is connected to the input end of the cloud analysis module.

[0027] The data acquisition module includes a water quality data acquisition unit and a water quality data encapsulation unit; The water quality data acquisition unit, i.e., a sensor device, is installed at the confluence of water flows. It detects specific components or properties in the water body and converts the detection results into digital signals through a signal processing module. The water quality data encapsulation unit obtains the digital signal collected by the water quality data acquisition unit, encapsulates it according to adding timestamp, geographic location, data category identifier and data value, and forms a water quality data packet.

[0028] The edge computing module includes a primary water quality identification unit and an early warning prompt unit; The primary water quality identification unit is used to receive the water quality data packet from the data acquisition module, and use the built-in algorithm to analyze and identify the received water quality data to determine whether the water quality data is abnormal; the early warning prompt unit automatically determines whether to trigger the early warning mechanism based on the output result of the primary water quality identification unit. If the output result of the primary water quality identification shows that the water quality is normal, the system does not trigger the early warning mechanism. If the output result of the primary water quality identification shows that the water quality is abnormal, the system triggers the early warning mechanism and generates early warning information. The cloud analysis module includes a secondary water quality identification unit and a report generation unit; The secondary water quality identification unit is used to receive water quality data that confirms abnormal water quality from the edge computing module, and use the built-in trend change model to perform secondary analysis on the water quality data that confirms abnormal water quality to verify whether the output result of the edge computing module is correct; The report generating unit is used to receive the verification result, water quality data and water quality index data of the secondary water quality identification unit, and to organize the analysis result into a water quality identification monitoring report.

[0029] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for intelligent identification and monitoring of abnormal water quality based on the Internet of Things, characterized by: The method comprises the following specific steps: S1, the data acquisition module collects water quality data of the water body in real time, pre-processes the data, and encapsulates the water quality data into a water quality data packet; S2. The water quality data packet is transmitted to the edge computing platform. The edge computing platform performs a water quality identification based on the water quality data to determine whether the water quality data is abnormal; S3. If the output result of a water quality identification shows that the water quality is normal, the system will continue to monitor the water quality; if the output result of a water quality identification shows that the water quality is abnormal, the system will immediately trigger the early warning mechanism; S4. The edge computing platform sends the water quality data that confirms the abnormal water quality to the cloud platform. The cloud platform performs secondary water quality identification based on the water quality data that confirms the abnormal water quality and generates a water quality identification monitoring report.

2. According to claim 1, the method for intelligent identification and monitoring of abnormal water quality based on the Internet of Things is characterized by: In step S1, it specifically includes: The data acquisition module collects water quality data of the water body in real time according to a preset acquisition frequency. The water quality data includes the acquisition time, acquisition location and water quality index data of the water body. The water quality index data includes the temperature value, pH value, dissolved oxygen content, turbidity value and heavy metal content of the water body. The water quality data is preprocessed and the preprocessed water quality data is packaged according to a certain format to form a water quality data packet. The water quality data packet includes the acquisition time, acquisition location and water quality index data of the water body.

3. According to claim 1, a method for intelligent identification and monitoring of abnormal water quality based on the Internet of Things is characterized by: In step S2, it also includes: The encapsulated water quality data packet is transmitted to the edge computing platform via a wireless network. The edge computing platform parses the water quality data packet, extracts the water quality index data therein, and performs a water quality identification based on the water quality index data; The process of primary water quality identification is as follows: Acquire historical water quality index data, arrange the acquired historical water quality index data in ascending order according to the same index, extract the 10th percentile historical water quality index data and the 90th percentile historical water quality index data, mark the 10th percentile historical water quality index data as the minimum value of the water quality index standard value, and mark the 90th percentile historical water quality index data as the maximum value of the water quality index standard value; The water quality index data is compared with the water quality index standard value; if the water quality index data is greater than the minimum value of the water quality index standard value and the water quality index is less than the maximum value of the water quality index standard value, the output result of a water quality identification shows that the water quality is normal; if the water quality index data is less than the minimum value of the water quality index standard value or the water quality index data is greater than the maximum value of the water quality index standard value, the output result of a water quality identification shows that the water quality is abnormal.

4. According to claim 1, the method for intelligent identification and monitoring of abnormal water quality based on the Internet of Things is characterized by: In step S3, the early warning mechanism includes starting an audible and visual alarm and sending an early warning text message.

5. According to claim 1, the method for intelligent identification and monitoring of abnormal water quality based on the Internet of Things is characterized by: In step S4, it also includes: Step S401: The edge computing platform encapsulates the water quality index data for confirming abnormal water quality and the data of the previous set of water quality indexes for confirming abnormal water quality into an abnormal water quality index data packet, and sends it to the cloud platform; Step S402, the cloud platform receives the abnormal water quality index data packet, extracts the water quality index data at the t-th time point, the water quality temperature is X1, the water quality pH value is X2, the water quality dissolved oxygen is X3, the water quality turbidity is X4, and the water quality heavy metal content is X5; extracts the water quality index data at the t-1-th time point, the water quality temperature is X1', the water quality pH value is X2', the water quality dissolved oxygen is X3', the water quality turbidity is X4', and the water quality heavy metal content is X5'; Calculate the change rate R of the water quality index data i , the calculation formula for the change rate of the water quality index data is: ； Among them, R j is the change rate of water quality index data, X j is the water quality index data at the tth time point, X j ' is the water quality index data at the t-1th time point, △t is the time interval between the tth time point and the t-1th time point; Step S403: The process of secondary water quality identification is as follows: Acquire historical water quality data for a period of time to form a sample set of water quality data, arrange the sample set of water quality data into a matrix in chronological order, and extract the time interval between two adjacent collection time points as △t * , each row represents a sample of water quality data at a specific time point, and each column corresponds to a water quality index; extract the water quality indexes in the same column to form a set A j ={Y 1j , Y 2j , Y 3j , ...Y nj }, where A j represents the water quality data of the jth column, Y 1j , Y 2j , Y 3j , ...Y nj represents the water quality index data of the first row and the jth column, the water quality index data of the second row and the jth column, the water quality index data of the third row and the jth column, ... the water quality index data of the nth row and the jth column; Calculate the rate of change of water quality index data in each time interval as follows: ； The average value of the change rate of water quality index data is calculated as: ； Among them, α j is the average value of water quality index data, Y ij is the water quality index data in the i-th row and j-th column, n is the number of samples of water quality data, and △t* is the time interval between two adjacent collection time points; The standard deviation of the water quality index data is calculated as: ； Among them, β j is the standard deviation of water quality index data, Y ij is the water quality index data in the i-th row and j-th column, α j is the average value of water quality index data, and n is the number of samples of water quality data; The threshold for calculating the rate of change of water quality indicators is: ； ； Among them, R jmax is the maximum value of the standard change rate of water quality indicators, R jmin is the minimum value of the water quality index change rate, α j is the average value of water quality index data, β j is the standard deviation of water quality index data; The change rate R of the water quality index i The rate of change R of the water quality index standard jmax and R jmin For comparison; if R jmin <R i <R jmax , then the result of the water quality identification is wrong and the water quality is normal; if R i <R jmin or R i >R jmax , then the result of the water quality identification is correct and the water quality is abnormal; Step S404: The cloud platform generates a water quality identification monitoring report according to the verification result of the secondary water quality identification. The water quality identification monitoring report includes the verification result of the secondary water quality identification, water quality data, and the change rate of water quality index data.

6. An abnormal water quality intelligent identification and monitoring system based on the Internet of Things, which is applied to the abnormal water quality intelligent identification and monitoring method based on the Internet of Things according to any one of claims 1 to 5, characterized in that: The system includes a data acquisition module, an edge computing module, and a cloud analysis module; The data acquisition module is equipped with sensor equipment for collecting and packaging water temperature, pH value, dissolved oxygen, turbidity and heavy metal content data; The edge computing module is used to pre-process the data and perform a primary water quality identification on the water pollution data; the cloud analysis module is used to perform a secondary water quality identification on the water pollution data of the abnormal water body identified in the primary water quality identification result in the edge computing module, generate a water quality identification monitoring report, and send it to the display end; The output end of the data acquisition module is connected to the input end of the edge computing module; The output end of the edge computing module is connected to the input end of the cloud analysis module.

7. According to claim 5, the abnormal water quality intelligent identification and monitoring system based on the Internet of Things is characterized by: The data acquisition module includes a water quality data acquisition unit and a water quality data encapsulation unit; The water quality data acquisition unit, i.e., a sensor device, is installed at the confluence of water flows. It detects specific components or properties in the water body and converts the detection results into digital signals through a signal processing module. The water quality data encapsulation unit obtains the digital signal collected by the water quality data acquisition unit, encapsulates it according to adding timestamp, geographic location information, data category identifier and data value, and forms a water quality data packet.

8. According to claim 5, the abnormal water quality intelligent identification and monitoring system based on the Internet of Things is characterized by: The edge computing module includes a primary water quality identification unit and an early warning prompt unit; The primary water quality identification unit is used to receive the water quality data packet from the data acquisition module, and use the built-in algorithm to analyze and identify the received water quality data to determine whether the water quality data is abnormal; The early warning prompt unit automatically determines whether to trigger the early warning mechanism based on the output result of the primary water quality identification unit. If the output result of the primary water quality identification shows that the water quality is normal, the system does not trigger the early warning mechanism. If the output result of the primary water quality identification shows that the water quality is abnormal, the system triggers the early warning mechanism and generates early warning information.

9. According to claim 5, the abnormal water quality intelligent identification and monitoring system based on the Internet of Things is characterized by: The cloud analysis module includes a secondary water quality identification unit and a report generation unit; The secondary water quality identification unit is used to receive water quality data that confirms abnormal water quality from the edge computing module, and use the built-in trend change model to perform secondary analysis on the water quality data that confirms abnormal water quality to verify whether the output result of the edge computing module is correct; The report generating unit is used to receive the verification result, water quality data and water quality index data of the secondary water quality identification unit, and to organize the analysis result into a water quality identification monitoring report.

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