Surface water pollution real-time monitoring system

Abstract

The invention discloses a surface water pollution real-time monitoring system, relates to the technical field of surface water pollution monitoring, and solves the technical problems of real-time monitoring and timely prediction of surface water pollution. Collecting monitoring data through a data collection module; the monitoring data are stored in the cloud platform; the data processing module compares the monitoring data with a standard interval thereof to obtain a monitoring data abnormal signal; acquiring a monitoring data change rate based on the monitoring data, acquiring a state label according to the monitoring data change rate and the rate detection model, identifying the state label, and acquiring a monitoring data change rate abnormal signal when the monitoring data is in an abnormal state; the early warning module gives a safety alarm according to the monitoring data abnormal signal and the monitoring data change rate abnormal signal; the system realizes real-time monitoring of the pollution condition of the surface water, predicts the change rate of monitoring data, performs prevention in advance, avoids the increase of the pollution range and degree of the surface water, and reduces the maintenance cost of the surface water.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of sewage treatment, and relates to a surface water pollution monitoring technology, in particular to a surface water pollution real-time monitoring system. BACKGROUND

[0002] Ground water pollution is also called surface water pollution. It is mainly caused by human activities to discharge pollutants, resulting in water pollution of ground water. In recent years, the trend of sudden water pollution incidents has increased year by year. The sudden water pollution accident has uncertainty and emergency harm, which can rapidly affect the water supply system in a short time, cause water stop events, and seriously affect the urban ecological system through the mechanism of spread, transformation and coupling, and further cause complex social problems, becoming the primary threat factor affecting the safety of drinking water sources. In order to minimize the adverse effects of water pollution accidents, in addition to strengthening real-time monitoring of water quality, it is necessary to establish a system technology capable of online risk assessment and prediction.

[0003] Therefore, a surface water pollution real-time monitoring system is provided. SUMMARY

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application provides a surface water pollution real-time monitoring system, which solves the problems of real-time monitoring and timely prediction of surface water pollution.

[0005] To achieve the above-mentioned purpose, according to the first aspect of the embodiment of the present application, a surface water pollution real-time monitoring system is provided, which comprises a cloud platform, and a data acquisition module, a data processing module and a warning module connected thereto; the information interaction between each module is based on digital signal; The data acquisition module is used for acquiring monitoring data; and storing the monitoring data to the cloud platform; The data processing module is used for comparing the monitoring data with its standard interval, obtaining a monitoring data abnormal signal; and obtaining a monitoring data change rate based on the monitoring data, obtaining a state label according to the monitoring data change rate and the rate detection model, identifying the state label, and obtaining a monitoring data change rate abnormal signal when the monitoring data is in an abnormal state; wherein the rate detection model is established based on an artificial intelligence model; Obtaining a monitoring data change rate based on the monitoring data comprises the following steps: The data processing module establishes a monitoring data curve graph changing with time according to the monitoring data; The data processing module obtains a monitoring data change rate curve graph by differentiating the monitoring data curve graph; The cloud platform sets a collection period of the monitoring data change rate, marks the collection period of the monitoring data change rate as T, and the unit is s; The data processing module collects the monitoring data change rate every interval Ts according to the monitoring data change rate curve; The early warning module is used for safety alarm according to the monitoring data abnormal signal and the monitoring data change rate abnormal signal.

[0006] Preferably, the monitoring data includes pH, dissolved oxygen concentration, permanganate index, total phosphorus concentration, and ammonia nitrogen concentration. Preferably, the data collection module adopts a periodic collection method when collecting monitoring data, and the collection period is set by the cloud platform; and when sending device information to the cloud platform, the collection period is numbered, and the number of the period is marked as i.

[0007] Preferably, the data processing module compares the monitoring data with its standard interval to obtain a monitoring data abnormal signal, including the following steps: The data processing module obtains the monitoring data; The data processing module sets the standard interval of the monitoring data; The monitoring data is compared with the standard interval of the monitoring data, and when the monitoring data is not in the corresponding standard interval, the data processing module generates a monitoring data abnormal signal and sends the monitoring data abnormal signal to the early warning module; When the monitoring data is in its corresponding standard interval, the data processing module does not make other processing.

[0008] Preferably, the data processing module obtains a state label according to the monitoring data change rate and the rate detection model, including the following steps: Obtain the rate detection model from the data processing module; Take the time when the data processing module collects the monitoring data change rate as the reference time, extract N monitoring data change rates before the reference time from the monitoring data change rate, and integrate to generate original data; wherein N is an integer greater than or equal to 10; Input the original data into the rate detection model to obtain the corresponding state label.

[0009] Preferably, the state label takes value 0 or 1, when the state label is 0, it means that the corresponding monitoring data change rate is in normal state, when the state label is 1, it means that the corresponding monitoring data change rate is in abnormal state.

[0010] Preferably, the rate detection model is established based on an artificial intelligence model, including the following steps: acquire standard training data from the data processing module; train the artificial intelligence model through the standard training data, and mark the trained artificial intelligence model as a rate detection model.

[0011] Preferably, the cloud platform is in communication and / or electrical connection with the data acquisition module; the cloud platform is in communication and / or electrical connection with the data processing module; the cloud platform is in communication and / or electrical connection with the early warning module.

[0012] Compared with the prior art, the beneficial effects of the present application are: The present application collects monitoring data through the data acquisition module; and stores the monitoring data to the cloud platform; the data processing module compares the monitoring data with its standard interval to obtain an abnormal signal of the monitoring data; and obtains a change rate of the monitoring data based on the monitoring data, obtains a state label according to the change rate of the monitoring data and the rate detection model, identifies the state label, obtains an abnormal signal of the change rate of the monitoring data when the monitoring data is in an abnormal state; the early warning module performs safety alarm according to the abnormal signal of the monitoring data and the abnormal signal of the change rate of the monitoring data; realizes real-time monitoring of the pollution condition of the surface water, predicts the change rate of the monitoring data, prevents in advance, avoids the increase of the pollution range and degree of the surface water, and reduces the maintenance cost of the surface water. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 The flowchart of the present application. DETAILED DESCRIPTION

[0014] The technical solutions of the present application will be described below in conjunction with the embodiments, obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0015] As Figure 1 shown, a real-time monitoring system for surface water pollution, comprising a cloud platform, and a data acquisition module, a data processing module and an early warning module connected thereto; the information interaction between each module is based on digital signal; The data acquisition module is used for collecting monitoring data; wherein the monitoring data includes pH, dissolved oxygen concentration, permanganate index, total phosphorus concentration and ammonia nitrogen concentration; and stores the monitoring data to the cloud platform; Specifically, the data acquisition module adopts a periodic acquisition mode when acquiring monitoring data, and the acquisition period is set by the cloud platform; and when sending device information to the cloud platform, the acquisition period is numbered, and the number of the period is marked as i.

[0016] The data processing module is used for processing the monitoring data, including the following steps: The data processing module acquires the monitoring data, and marks the monitoring data as , and marks the pH, the dissolved oxygen concentration, the permanganate index, the total phosphorus concentration, and the ammonia nitrogen concentration as , respectively; wherein i is the number of the acquisition period; The data processing module sets a standard interval of the monitoring data, and it needs to be further explained that the standard interval of the monitoring data is the standard interval of the pH, the dissolved oxygen concentration, the permanganate index, the total phosphorus concentration, and the ammonia nitrogen concentration; Specifically, the standard interval of the monitoring data is set by a professional; The monitoring data is compared with the standard interval of the monitoring data, and when the monitoring data is not within the corresponding standard interval, the data processing module generates a monitoring data abnormal signal and sends the monitoring data abnormal signal to the early warning module; When the monitoring data is within its corresponding standard interval, the data processing module does not make other processing; Specifically, the pH, the dissolved oxygen concentration, the permanganate index, the total phosphorus concentration, and the ammonia nitrogen concentration are compared with their standard intervals, respectively; For example: The data processing module sets the standard interval of the pH as ; That is, the pH is not within the standard interval, and the data processing module sends a pH abnormal signal to the cloud platform; That is, the pH is within the standard interval, and the data processing module does not make other processing; The data processing module establishes a monitoring data curve graph varying with time according to the monitoring data; The data processing module obtains a monitoring data change rate curve graph by taking the derivative of the monitoring data curve graph; The data processing module obtains a monitoring data change rate according to the monitoring data change rate curve graph, including the following steps: The cloud platform sets a collection period of the monitoring data change rate, and marks the collection period of the monitoring data change rate as T, in seconds; The data processing module collects the monitoring data change rate every interval Ts, and marks the monitoring data change rate as ; wherein n is the number of the collection period of the monitoring data change rate, n takes values of 1, 2, 3, …, N, and N is the total number of collection of the monitoring data change rate; The data processing module obtains a state label based on the monitoring data change rate, including the following steps: Obtain a rate detection model from the data processing module; Take the time when the data processing module collects the monitoring data change rate as a reference time, extract N monitoring data change rates before the reference time from the monitoring data change rate, and integrate to generate original data; Input the original data into the rate detection model to obtain a corresponding state label; The data processing module identifies the state label, and when the monitoring data change rate is in an abnormal state, the data processing module sends a monitoring data change rate abnormal signal to the cloud platform; When the monitoring data change rate is in a normal state, the data processing module does not perform other processing.

[0017] In this embodiment, N is an integer greater than or equal to 10, and it has been verified that at least 10 monitoring data change rates are required to ensure that an accurate state label is obtained; for example: Suppose the monitoring data change rate is collected once per second, and the reference time is 10:00:00 am, 15 monitoring data change rates before the reference time are extracted, that is, the data collected at 9:59:46 is the first monitoring data change rate, and there are a total of 15 monitoring data change rates until the reference time. The 15 monitoring data change rates are sorted according to the collection time to form the original data.

[0018] In an optional embodiment, the rate detection model is established based on an artificial intelligence model, including: Obtain standard training data from the data processing module; Train the artificial intelligence model through the standard training data, and mark the trained artificial intelligence model as the rate detection model.

[0019] In this embodiment, the standard training data includes a plurality of input data and corresponding state labels, and the input data and the original data have consistent content attributes; it can be understood that the input data and the original data both include a selected N monitoring data change rates, only the numerical values of the monitoring data change rates are different.

[0020] In the embodiment, the artificial intelligence model includes a deep convolutional neural network model or an RBF neural network model, or other models with strong nonlinear fitting capability.

[0021] In the embodiment, the state label has a value of 0 or 1, when the state label is 0, it indicates that the corresponding monitoring data change rate is in a normal state, and when the state label is 1, it indicates that the corresponding monitoring data change rate is in an abnormal state; in some other preferred schemes, the state label can also be distinguished by other marks, such as the state label having a value of A or B, when the state label is A, it indicates that the corresponding monitoring data change rate is in a normal state, and when the state label is B, it indicates that the corresponding monitoring data change rate is in an abnormal state.

[0022] The pre-warning module is used for safety alarm according to the monitoring data abnormal signal and the monitoring data change rate abnormal signal, including the following steps: After the cloud platform receives the monitoring data abnormal signal, the pre-warning module is controlled to perform monitoring data abnormal alarm, and the corresponding supervisor is notified; After the cloud platform receives the monitoring data change rate abnormal signal, the pre-warning module is controlled to perform monitoring data change rate abnormal alarm, and the corresponding supervisor is notified.

[0023] In the embodiment, the cloud platform is in communication and / or electrical connection with the data acquisition module; The cloud platform is in communication and / or electrical connection with the data processing module; The cloud platform is in communication and / or electrical connection with the pre-warning module.

[0024] The above formulas are all calculated by removing the dimension and taking the numerical value, the formula is obtained by software simulation of a large amount of data to obtain a formula closest to the real situation, and the preset parameters and the preset threshold in the formula are set by the person skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0025] The above embodiments are only used to illustrate the technical method of the present application and not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A real-time monitoring system for surface water pollution, characterized in that, The cloud platform is connected with a data acquisition module, a data processing module and an early warning module; information interaction is carried out among the modules based on digital signals; The data acquisition module is used for collecting monitoring data and storing the monitoring data into the cloud platform; The data processing module is used for comparing the monitoring data with standard intervals thereof to obtain monitoring data abnormal signals; The data processing module is used for obtaining a state label based on the monitoring data change rate and a rate detection model, identifying the state label, and obtaining a monitoring data change rate abnormal signal when the monitoring data is in an abnormal state; the rate detection model is established based on an artificial intelligence model; The data processing module is used for obtaining a monitoring data change rate based on the monitoring data, including the following steps: The data processing module establishes a monitoring data curve graph changing with time according to the monitoring data; The data processing module obtains a monitoring data change rate curve graph by differentiating the monitoring data curve graph; The cloud platform sets a monitoring data change rate collection period, and marks the monitoring data change rate collection period as T, with the unit being s; The data processing module collects the monitoring data change rate every interval Ts according to the monitoring data change rate curve graph; The early warning module is used for safety alarm according to the monitoring data abnormal signal and the monitoring data change rate abnormal signal.

2. A real-time monitoring system for surface water pollution according to claim 1, characterized in that, The monitoring data includes pH, dissolved oxygen concentration, permanganate index, total phosphorus concentration and ammonia nitrogen concentration.

3. The real-time monitoring system for surface water pollution according to claim 1, wherein, The data acquisition module adopts a periodic collection mode when collecting the monitoring data, and the collection period is set by the cloud platform; and when sending the device information to the cloud platform, the collection period is numbered, and the period number is marked as i.

4. The real-time monitoring system for surface water pollution according to claim 1, wherein, The data processing module compares the monitoring data with standard intervals thereof to obtain monitoring data abnormal signals, including the following steps: The data processing module obtains the monitoring data; The data processing module sets the standard intervals of the monitoring data; The monitoring data is compared with the standard intervals of the monitoring data; when the monitoring data is not in the corresponding standard interval, the data processing module generates a monitoring data abnormal signal and sends the monitoring data abnormal signal to the early warning module; When the monitoring data is in the corresponding standard interval, the data processing module does not make other processing.

5. The real-time monitoring system for surface water pollution according to claim 1, wherein, The data processing module obtains a state label based on the monitoring data change rate and a rate detection model, including the following steps: The rate detection model is obtained from the data processing module; N monitoring data change rates before the reference time are extracted from the monitoring data change rate with the time when the data processing module collects the monitoring data change rate as the reference time, and original data are integrated and generated; N is an integer greater than or equal to 10; The original data are input into the rate detection model to obtain a corresponding state label.

6. The real-time monitoring system for surface water pollution according to claim 5, wherein, The state label is 0 or 1, when the state label is 0, it indicates that the corresponding monitoring data change rate is in a normal state, and when the state label is 1, it indicates that the corresponding monitoring data change rate is in an abnormal state.

7. The real-time monitoring system for surface water pollution according to claim 1, wherein, The rate detection model is established based on an artificial intelligence model, and includes the following steps: Obtaining standard training data from the data processing module; Training the artificial intelligence model through the standard training data, and marking the trained artificial intelligence model as the rate detection model.

8. The real-time monitoring system for surface water pollution according to claim 1, wherein, The cloud platform is in communication and / or electrical connection with the data acquisition module; The cloud platform is in communication and / or electrical connection with the data processing module; The cloud platform is in communication and / or electrical connection with the early warning module.

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