A water station system with intelligent control function

Abstract

The invention discloses a water station system with intelligent management and control functions, which belongs to the technical field of water quality detection. The system comprises a float, a monitoring unit, a five-parameter unit, a permanganate index analysis station, an ammonia nitrogen analysis station, a total nitrogen analysis station, a total phosphorus analysis station, and an operation and maintenance control unit. The float is provided with a water level gauge, and the float is used to be placed in a water area. The monitoring unit comprises a camera and a retractable wire machine, and the camera is used to capture image information in the water area. The retractable wire machine is connected to the float through a rope and is used to adjust the retraction and release of the float. The five-parameter unit is used to detect the pH value, water temperature, turbidity, conductivity, and dissolved oxygen of a water sample. The permanganate index analysis station is used to monitor the permanganate index of a water sample, and the ammonia nitrogen analysis station is used to monitor the ammonia nitrogen index of a water sample. The invention realizes the recognition of the float in the water area by utilizing a convolutional neural network model, so as to achieve the purpose of water area early warning analysis, thereby ensuring that the water station system monitors the water sample more reliably and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality detection, and in particular to a water station system with intelligent management and control functions. Background Art

[0002] The water station system is a comprehensive online automatic detection system with water quality online analysis stations as the main body, using modern technologies of multiple disciplines such as automatic measurement technology, automatic analysis technology, automatic control technology, computer application technology, and communication network technology. It is an important technical means for environmental protection departments to effectively monitor water quality changes in rivers and drinking water sources and control water pollution. Its main function is to monitor water quality changes in real time, qualitatively and quantitatively grasp the pollutant flux flowing through the monitoring section, monitor the time variation of pollutant concentration and flux in the section, and provide a scientific basis for basin pollution prevention and control and supervision and management;

[0003] The current water station system does not have the water area AI algorithm recognition function, and cannot achieve the purpose of water area early warning. It also does not have real-time monitoring function for the liquid medicine in each analysis station. When there is not much liquid medicine left, it cannot be replenished in time. Summary of the invention

[0004] The present invention provides a water station system with intelligent management and control functions in order to solve the above-mentioned problems.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A water station system with intelligent management and control function, comprising:

[0007] A float, on which a water level gauge is arranged, and the float is used to be placed in a water area;

[0008] The monitoring unit includes a camera and a retractable wire machine, wherein the camera is used to capture image information in the water area, and the retractable wire machine is connected to the buoy through a rope and is used to adjust the retraction and extension of the buoy;

[0009] Five parameter units, used to detect pH value, water temperature, turbidity, conductivity, and dissolved oxygen of water samples;

[0010] Permanganate index analysis station, used to monitor the permanganate index of water samples;

[0011] Ammonia nitrogen analysis station, used to monitor the ammonia nitrogen index of water samples;

[0012] Total nitrogen analysis station, used to monitor the sum of nitrogen from all nitrogen-containing compounds in water samples;

[0013] Total phosphorus analysis station, used for determination of total phosphorus in water samples;

[0014] The operation and maintenance control unit is used to control the forward and reverse operation of the pay-off and take-up machine and collect the data information fed back by the water level meter and the monitoring unit, and make water area early warning analysis based on the data information. The specific analysis method is as follows:

[0015] First, the value fed back by the water level meter is obtained in real time, and the real-time value is compared with the preset value. When the real-time value exceeds the preset value, it is determined that the float is in a damaged state and sinks to the bottom, and a float damage signal is generated;

[0016] When the real-time value does not exceed the preset value, the image information is further retrieved and input into a trained convolutional neural network model to obtain the image recognition result. When the recognition is successful, a normal float status signal is generated. When the recognition is unsuccessful, an abnormal float status signal is generated.

[0017] Furthermore, the five-parameter unit includes a sampling pump, a water sample tube, a parameter detection chamber, a drain pipe, and a filter. The water sample is drawn in by the sampling pump, and the water sample enters the parameter detection chamber and the filter respectively through the water sample tube. The water sample in the parameter detection chamber is discharged through the drain pipe, and the filter is transported to the permanganate index analysis station, the ammonia nitrogen analysis station, the total nitrogen analysis station, and the total phosphorus analysis station through corresponding pipelines, and the water sample is then discharged through the drain pipes corresponding to each analysis station.

[0018] Furthermore, the parameter detection chamber is provided with a pH sensor, a water temperature sensor, a turbidity sensor, a conductivity sensor, and a dissolved oxygen sensor.

[0019] Furthermore, the operation and maintenance control unit is composed of a data operation and maintenance module and a display terminal. The data operation and maintenance module is used to collect monitoring information fed back by the five-parameter unit, the permanganate index analysis station, the ammonia nitrogen analysis station, the total nitrogen analysis station, and the total phosphorus analysis station, and visualize the monitoring information.

[0020] Furthermore, the operation and maintenance control unit, five-parameter unit, permanganate index analysis station, ammonia nitrogen analysis station, total nitrogen analysis station, and total phosphorus analysis station also include a face recognition module, an electromagnetic lock, a safety door, and several weight sensors. The face recognition module is used to capture the facial information of the on-site staff and send the facial information to the data operation and maintenance module to compare it with the database personnel pre-entered by the data operation and maintenance module. When the comparison fails, the safety door in front of the illegal person standing remains closed. When the comparison is successful, the corresponding electromagnetic lock is opened, and the safety door in front of the legal person standing is opened.

[0021] Furthermore, the five parameter units, the permanganate index analysis station, the ammonia nitrogen analysis station, the total nitrogen analysis station, and the total phosphorus analysis station all transmit the monitored information to the operation and maintenance control unit through the COM port.

[0022] Furthermore, each of the weight sensors is provided with a corresponding electronic tag, and different medicine liquids can be placed on each weight sensor.

[0023] Furthermore, the weight sensor sends the weight of the medicine liquid to the operation and maintenance control unit, and the operation and maintenance control unit can query the weight of different medicine liquids according to the electronic tags.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention uses a convolutional neural network model to identify the floating ball in the water area, so as to achieve the purpose of water area early warning analysis, thereby ensuring that the water station system monitors water samples more reliably and accurately;

[0026] The water station system can also realize real-time monitoring of each liquid medicine through the operation and maintenance control unit and weight sensor to facilitate timely replenishment of the liquid medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a system composition diagram of the present invention. DETAILED DESCRIPTION

[0028] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further described below in conjunction with specific implementation methods.

[0029] Refer to Figure 1 As shown, an embodiment of the present invention provides a water station system with intelligent management and control functions, including

[0030] A float ball, on which a water level gauge is arranged, and the float ball is placed in the water area;

[0031] The monitoring unit includes a camera and a retractable wire machine, wherein the camera is used to capture image information in the water area, and the retractable wire machine is connected to the buoy through a rope and is used to adjust the retraction and extension of the buoy;

[0032] The operation and maintenance control unit is used to control the forward and reverse operation of the pay-off and take-up machine and collect the data information fed back by the water level meter and the monitoring unit, and make water area early warning analysis based on the data information. The specific analysis method is as follows:

[0033] First, the value fed back by the water level meter is obtained in real time, and the real-time value is compared with the preset value. When the real-time value exceeds the preset value, it is determined that the float is in a damaged and sunk state, and a float damage signal is generated, which means that the float is currently in a damaged and sunk state. There is no need to identify and analyze the image information. The operation and maintenance control unit can be used to start the reel-and-reel machine to rotate, and the damaged float can be retracted with a rope to facilitate the replacement of a new float.

[0034] When the real-time value does not exceed the preset value, the image information is further retrieved and input into a trained convolutional neural network model to obtain the recognition result of the float in the image. When the float is successfully recognized, a normal float state signal is generated, indicating that the float is currently within the shooting range of the camera, and there is no debris entangled on the surface of the float, and it can be recognized normally. The water area is in a normal state, and there is no large amount of debris that makes the float unrecognizable, and there is no large amount of water that causes the float to float away. At this time, it is more accurate to monitor the water samples in the area;

[0035] When the recognition is unsuccessful, a signal indicating that the float is abnormal is generated, indicating that the float is not in the camera's shooting range or that there are debris around the float, which makes it impossible to identify the float normally. In other words, the current water area is affected by debris or other water bodies and is in an abnormal state. The system does not monitor water samples in this state until the water area returns to normal. The steps for forming the above convolutional neural network model are as follows:

[0036] The original image is grayed, processed by Sobel operator gradient, mean filter, and binarized to find the largest contour in the image;

[0037] Select the key information in the image, crop the image after selection and fill it into a square image, and use the data enhancement algorithm to flip all the original images horizontally, vertically, and horizontally and vertically, so that the data volume becomes four times the original amount. Then rotate the flipped images 90°, 180°, and 270°, so that the data volume becomes twelve times the initial amount. Normalize the processed images and label each image accordingly. Use 80% of the images as training data sets and 20% of the images as test data sets.

[0038] After the model is built, training is performed. First, all weights and biases are initialized, and all training set data are forward propagated along the convolutional neural network in batches to obtain the predicted value. The predicted value is compared with the corresponding input label value to obtain the error value, that is, the value of the loss function.

[0039] According to the loss function, the stochastic gradient descent method is used to update the values ​​of the layer weights and biases to optimize the parameters to complete the subsequent propagation of the convolutional neural network, and the accuracy of the training set results is calculated. If the accuracy meets the requirements, the training is stopped. If not, the training is continued until the number of training times reaches the required number.

[0040] Furthermore, the operation and maintenance control unit is composed of a data operation and maintenance module and a display terminal. The data operation and maintenance module is used to collect monitoring information fed back by the five-parameter unit, the permanganate index analysis station, the ammonia nitrogen analysis station, the total nitrogen analysis station, and the total phosphorus analysis station, and visualize the monitoring information. Among them, the five-parameter unit, the permanganate index analysis station, the ammonia nitrogen analysis station, the total nitrogen analysis station, and the total phosphorus analysis station all transmit the monitoring information to the operation and maintenance control unit through the COM port;

[0041] Among them, the five-parameter unit is used to detect the pH value, water temperature, turbidity, conductivity, and dissolved oxygen of water samples; the permanganate index analysis station is used to monitor the permanganate index of water samples; the ammonia nitrogen analysis station is used to monitor the ammonia nitrogen index of water samples; the total nitrogen analysis station is used to monitor the sum of nitrogen of all nitrogen-containing compounds in water samples; and the total phosphorus analysis station is used to determine the total phosphorus in water samples;

[0042] The five-parameter unit includes a sampling pump, a water sample tube, a parameter detection chamber, a drain pipe, and a filter. The water sample is drawn in by the sampling pump, and the water sample enters the parameter detection chamber and the filter respectively through the water sample tube. The water sample in the parameter detection chamber is discharged through the drain pipe, and the filter is transported to the permanganate index analysis station, the ammonia nitrogen analysis station, the total nitrogen analysis station, and the total phosphorus analysis station through corresponding pipelines. The water sample is then discharged through the drain pipes corresponding to each analysis station. Among them, the parameter detection chamber is provided with a pH sensor, a water temperature sensor, a turbidity sensor, a conductivity sensor, and a dissolved oxygen sensor, which can be used to detect the pH value, water temperature, turbidity, conductivity, and dissolved oxygen of the water sample respectively;

[0043] Based on the above, the operation and maintenance control unit, five-parameter unit, permanganate index analysis station, ammonia nitrogen analysis station, total nitrogen analysis station, and total phosphorus analysis station also include a face recognition module, an electromagnetic lock, a security door, and several weight sensors;

[0044] The face recognition module is used to capture the facial information of the on-site staff and send the facial information to the data operation and maintenance module for comparison with the database personnel pre-entered by the data operation and maintenance module. If the comparison fails, the safety door in front of the illegal person will remain closed. If the comparison succeeds, the corresponding electromagnetic lock will open, and the safety door in front of the legal person will open. Compared with the traditional mechanical lock, the present invention uses face recognition unlocking, which is more convenient.

[0045] Each weight sensor is provided with a corresponding electronic tag, which can be customized and edited. The electronic tag can be the name of the medicine liquid. Different medicine liquids can be placed on each weight sensor. The weight sensor sends the weight of the medicine liquid to the operation and maintenance control unit, and the operation and maintenance control unit can query the weight of different medicine liquids according to the electronic tag.

[0046] When the present invention is in use, the float is placed in the water area, the monitoring unit takes pictures and feeds back the image information to the operation and maintenance control unit. When the real-time value fed back by the water level gauge on the float does not exceed the preset value, the operation and maintenance control unit identifies the float in the image to achieve the purpose of early warning analysis. When the float is recognized normally, the system can perform normal detection operations. When the float is recognized abnormally, the current water area is not suitable for detection operations. The detection operation of the water area is performed by the five-parameter unit, the permanganate index analysis station, the ammonia nitrogen analysis station, the total nitrogen analysis station, and the total phosphorus analysis station. All detected information is uniformly fed back to the operation and maintenance control unit for display. The operation and maintenance control unit can also cooperate with the weight sensor to achieve real-time monitoring of each liquid medicine to facilitate timely replenishment of the liquid medicine.

[0047] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A water station system with intelligent management and control function, characterized in that: include A float, on which a water level gauge is arranged, and the float is used to be placed in a water area; The monitoring unit includes a camera and a retractable wire machine, wherein the camera is used to capture image information in the water area, and the retractable wire machine is connected to the buoy through a rope and is used to adjust the retraction and extension of the buoy; Five parameter units, used to detect pH value, water temperature, turbidity, conductivity, and dissolved oxygen of water samples; Permanganate index analysis station, used to monitor the permanganate index of water samples; Ammonia nitrogen analysis station, used to monitor the ammonia nitrogen index of water samples; Total nitrogen analysis station, used to monitor the sum of nitrogen from all nitrogen-containing compounds in water samples; Total phosphorus analysis station, used for determination of total phosphorus in water samples; The operation and maintenance control unit is used to control the forward and reverse operation of the pay-off and take-up machine and collect the data information fed back by the water level meter and the monitoring unit, and make water area early warning analysis based on the data information. The specific analysis method is as follows: First, the value fed back by the water level meter is obtained in real time, and the real-time value is compared with the preset value. When the real-time value exceeds the preset value, it is determined that the float is in a damaged and sunk state, and a float damage signal is generated, which means that the float is currently in a damaged and sunk state. There is no need to identify and analyze the image information. The operation and maintenance control unit can be used to start the reel-and-reel machine to rotate, and the damaged float can be retracted with a rope to facilitate the replacement of a new float. When the real-time value does not exceed the preset value, the image information is further retrieved and input into a trained convolutional neural network model to obtain the recognition result of the float in the image. When the float is successfully recognized, a normal float signal is generated, indicating that the float is currently within the camera's shooting range, and there is no debris entangled on the surface of the float, and it can be recognized normally. The water area is in a normal state, and there is no large amount of debris that makes the float unrecognizable, and there is no large amount of water that causes the float to float away. At this time, it is more accurate to monitor the water samples in the area; When the recognition is unsuccessful, a float status abnormal signal is generated, indicating that the float is not currently within the camera's shooting range or there are debris entangled around the float, resulting in the inability to identify the float normally. That is, the current water area is affected by debris or other water bodies and is in an abnormal state. The system does not monitor water samples in this state until the water area returns to normal.

2. A water station system with intelligent management and control function according to claim 1, characterized in that: The five-parameter unit includes a sampling pump, a water sample tube, a parameter detection chamber, a drain pipe, and a filter. The water sample is drawn in by the sampling pump, and the water sample enters the parameter detection chamber and the filter respectively through the water sample tube. The water sample in the parameter detection chamber is discharged through the drain pipe, and the filter is transported to the permanganate index analysis station, the ammonia nitrogen analysis station, the total nitrogen analysis station, and the total phosphorus analysis station through corresponding pipelines. The water sample is then discharged through the drain pipes corresponding to each analysis station.

3. A water station system with intelligent management and control function according to claim 2, characterized in that: The parameter detection chamber is provided with a pH sensor, a water temperature sensor, a turbidity sensor, a conductivity sensor, and a dissolved oxygen sensor.

4. According to claim 1, a water station system with intelligent management and control function is characterized in that: The operation and maintenance control unit consists of a data operation and maintenance module and a display terminal. The data operation and maintenance module is used to collect monitoring information fed back by the five parameter units, the permanganate index analysis station, the ammonia nitrogen analysis station, the total nitrogen analysis station, and the total phosphorus analysis station, and to visualize the monitoring information.

5. The water station system with intelligent management and control function according to claim 1, characterized in that: The operation and maintenance control unit, five-parameter unit, permanganate index analysis station, ammonia nitrogen analysis station, total nitrogen analysis station, and total phosphorus analysis station also include a face recognition module, an electromagnetic lock, a safety door, and several weight sensors. The face recognition module is used to capture the facial information of on-site staff and send the facial information to the data operation and maintenance module to compare with the database personnel pre-entered by the data operation and maintenance module. When the comparison fails, the safety door in front of the illegal person standing remains closed. When the comparison is successful, the corresponding electromagnetic lock is opened and the safety door in front of the legal person standing is opened.

6. A water station system with intelligent management and control function according to claim 1, characterized in that: The five parameter units, permanganate index analysis station, ammonia nitrogen analysis station, total nitrogen analysis station, and total phosphorus analysis station all transmit the monitored information to the operation and maintenance control unit through the COM port.

7. A water station system with intelligent management and control function according to claim 5, characterized in that: Each of the weight sensors is provided with a corresponding electronic tag, and different medicine liquids can be placed on each weight sensor.

8. A water station system with intelligent management and control function according to claim 7, characterized in that: The weight sensor sends the weight of the medicine liquid to the operation and maintenance control unit, and the operation and maintenance control unit can query the weight of different medicine liquids according to the electronic tags.

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