CN113373961A - Automatic water level stabilizing system for dewatering well - Google Patents
Automatic water level stabilizing system for dewatering well Download PDFInfo
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- CN113373961A CN113373961A CN202110630130.3A CN202110630130A CN113373961A CN 113373961 A CN113373961 A CN 113373961A CN 202110630130 A CN202110630130 A CN 202110630130A CN 113373961 A CN113373961 A CN 113373961A
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
- E02D19/00—Keeping dry foundation sites or other areas in the ground
- E02D19/06—Restraining of underground water
- E02D19/10—Restraining of underground water by lowering level of ground water
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
- E02D19/00—Keeping dry foundation sites or other areas in the ground
- E02D19/06—Restraining of underground water
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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Abstract
The invention relates to an automatic water level stabilizing system of a dewatering well, which comprises an input type liquid level meter, a water pump, an ultrasonic flowmeter, a control panel and a neural network algorithm operated in a big data center, wherein the input type liquid level meter and the water pump are respectively arranged in the dewatering well, the ultrasonic flowmeter is arranged outside a pipeline of the water pump and is used for monitoring working parameters of the water pump, the control panel is in signal connection with the big data center to form data interaction, the control panel inputs the working parameters of the water pump measured by the ultrasonic flowmeter and the measurement result of the input type liquid level meter into the neural network algorithm, the neural network algorithm outputs calculation working parameters of the water pump according to historical water level data, and the control panel is connected and controls the working state of the water pump according to the output of the neural network algorithm. The invention has the advantages that: guarantee the water level stability in the precipitation well, avoid precipitation not enough with the excessive problem of precipitation, and then avoid because the foundation ditch excavation difficulty that the precipitation is not enough to lead to and because the excessive foundation ditch that leads to of precipitation warp.
Description
Technical Field
The invention relates to the technical field of water level control equipment in dewatering wells, in particular to an automatic water level stabilizing system for a dewatering well.
Background
With the development of cities, both super high buildings and municipal works are actively developed, so that more and more deep foundation pit works are developed in big cities in recent years. A large risk source facing a deep foundation pit is underground water, and how to safely, reasonably and efficiently reduce water is a problem to be solved urgently for foundation pit water reduction.
The dewatering well is the main means for reducing the groundwater in the foundation pit engineering. Scientifically and reasonably reducing the underground water is beneficial to the construction safety and the construction efficiency of the foundation pit engineering and also beneficial to controlling the influence of the surrounding environment on the foundation pit construction. Insufficient precipitation can cause difficulty in excavation of the foundation pit and reduction of excavation efficiency, and excessive precipitation can cause accidents of settlement of the surrounding earth surface and overlarge deformation of the enclosure.
Disclosure of Invention
The invention aims to provide an automatic water level stabilizing system of a precipitation well according to the defects of the prior art, which intelligently adjusts the water yield of a water pump through various sensors and an artificial neural network algorithm, realizes precipitation according to requirements, accurately reduces the water, avoids the problems of insufficient precipitation and excessive precipitation, and further avoids difficult excavation of a foundation pit caused by insufficient precipitation and deformation of the foundation pit caused by excessive precipitation.
The purpose of the invention is realized by the following technical scheme:
the utility model provides a precipitation well water level automatic stabilization system which characterized in that: the automatic water level stabilizing system of the dewatering well comprises a drop-in type liquid level meter, a water pump, an ultrasonic flowmeter, a control panel and a neural network algorithm running in a big data center, wherein the drop-in type liquid level meter and the water pump are respectively arranged in the dewatering well, the ultrasonic flowmeter is installed on the outer side of a pipeline of the water pump and used for monitoring working parameters of the water pump, the control panel is in signal connection with the big data center to form data interaction, the control panel inputs the working parameters of the water pump actually measured by the ultrasonic flowmeter and the measurement result of the drop-in type liquid level meter into the neural network algorithm, the neural network algorithm outputs calculation working parameters of the water pump according to historical water level data, and the control panel controls the working state of the water pump according to the output connection of the neural network algorithm.
The control panel is connected with an alarm device, two sets of the drop-in type liquid level meters are arranged in the dewatering well, and the two sets of the drop-in type liquid level meters are mutually checked; and when the difference value of the measuring results of the two sets of the input type liquid level meters exceeds a certain value, the control panel controls the alarm device to give an alarm.
The control panel is connected with an alarm device; and when the difference value between the working parameters of the water pump actually measured by the ultrasonic flowmeter and the calculated working parameters of the water pump output by the neural network algorithm exceeds a certain value, the control panel controls the alarm device to alarm.
The water pump is connected with an electronic transformer, and the control panel controls the working state of the water pump through the electronic transformer.
The neural network algorithm is a BP neural network algorithm and comprises an input layer, a hidden layer and an output layer, wherein the input layer comprises the top thickness of an impermeable layer, the thickness of a confined water layer, the permeability coefficient of the confined water layer, the water supply rate of the confined water layer, the actual measurement flow of the water pump by the ultrasonic flowmeter and the measurement water head of the dewatering well measured by the drop-in type liquid level meter, and the output layer comprises the variation quantity of the water head of the dewatering well and the calculated flow of the water pump.
The neural network algorithm adopts a reverse error propagation algorithm, and the neural network is reversely corrected by using the difference value between the calculated value and the measured value of the output layer.
The automatic precipitation well water level stabilizing system is integrated into a BIM family and forms signal connection with the BIM system through the control panel so as to carry out data interaction.
The control panel is connected with a waterproof display.
The invention has the advantages that: the water level in the precipitation well is ensured to be stable, the problems of insufficient precipitation and excessive precipitation are avoided, and further the difficult excavation of the foundation pit caused by insufficient precipitation and the deformation of the foundation pit caused by excessive precipitation are avoided; the degree of automation is high, the precipitation control precision is high, and meanwhile, the safety is improved through the alarm device; the integration is BIM clan, and the designer of being convenient for calls this system fast to look over the operating condition of precipitation well in this system, reduce repetitive work.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a diagram of the neural network algorithm architecture of the present invention;
FIG. 3 is a training relationship diagram of the neural network algorithm of the present invention.
Detailed Description
The features of the present invention and other related features are described in further detail below by way of example in conjunction with the following drawings to facilitate understanding by those skilled in the art:
as shown in fig. 1-3, the labels 1-22 are respectively shown as: the system comprises an input type liquid level meter 1, a water pump 2, an electronic transformer 3, an ultrasonic flowmeter 4, a control panel 5, a 5g signal 6, an intelligent mobile terminal 7, a waterproof display 8, an alarm lamp 9, a BIM group 10, a big data center 11, a waterproof layer top thickness 12, a pressure-bearing water layer thickness 13, a pressure-bearing water layer permeability coefficient 14, a pressure-bearing water layer water feeding rate 15, a water pump flow 16, a dewatering well water head 17, a dewatering well water head variable quantity 18, a water pump flow 19, a dewatering system 20, a trained neural network algorithm 21 and a trained neural network algorithm 22.
Example (b): as shown in fig. 1, the automatic stabilizing system for the water stability of the precipitation well in the embodiment mainly comprises a drop-in type liquid level meter 1, a water pump 2, an electronic transformer 3, an ultrasonic flowmeter 4, a control panel 5 and a big data center 11. The drop-in type liquid level meter 1 is arranged in a precipitation well, and the water level height in the precipitation well is determined by calculating the water pressure and the atmospheric pressure difference. The water pump 2 is arranged in the precipitation well and used for carrying out precipitation in the well. The electronic transformer 3 is connected with the water pump 2 and can control the working state of the water pump 2, such as the power and the running time of the water pump 2; the electronic transformer 3 adjusts the power of the water pump 2 by adjusting the power supply voltage of the water pump 2, so that the water pump without the frequency conversion function can also adjust the speed, and the applicability is wide. The ultrasonic flowmeter 4 is arranged outside a pipeline of the water reducing pipeline of the water pump 2 and is used for monitoring the real-time flow and the accumulated flow of the water pump 2; since the ultrasonic flow meter 4 is installed outside the pipe so that it does not directly contact the groundwater in the pipe, there is no risk of sand clogging the flow meter. The ultrasonic flow meter 4 also functions as follows: checking whether the precipitation system has faults or not; and judging the possibility of leakage of the building envelope.
The control board 5, the input liquid level meter 1 and the ultrasonic flow meter 4 are in signal connection to receive monitoring data of the input liquid level meter 1 and the ultrasonic flow meter 4; the control board 5 is also in signal connection with the big data center 11 through a 5g signal 6 to realize data interaction.
When the device is applied, the input type liquid level meter 1 monitors the height of the water level in the dewatering well, the ultrasonic flow meter 4 monitors the real-time flow and the accumulated flow of the water pump 2, the control panel 5 receives the monitoring data and transmits the monitoring data to the big data center 11 through the 5g signal 6, the big data center 11 calculates the monitored actual measurement data through the BP neural network algorithm operated by the big data center, the current required water pump flow and the theoretically reached water outlet flow of the dewatering well are calculated according to historical data, the output result is transmitted back to the control panel 5 through the 5g signal 6, the control panel 5 controls the working parameters of the water pump 2 through the electronic transformer 3 according to the calculated working parameters of the water pump 2, and therefore the automatic accurate control of the water level in the dewatering well is achieved.
As shown in fig. 2, the BP neural network algorithm running in the large data center 11 in this embodiment includes an input layer, three hidden layers, and an output layer, where the input layer includes an impermeable layer top thickness 12, a confined water layer thickness 13, a confined water layer permeability coefficient 14, a confined water layer water supply rate 15, a water pump flow 16, and a dewatering well head 17; the output layer comprises a dewatering well water head variable quantity 18 and a water pump flow 19; the BP neural network algorithm is input from an input layer and output from an output layer in cooperation with the calculation of the hidden layer. The control panel 5 controls the working state of the water pump 2 through the electronic transformer 3 according to the precipitation well water head variable quantity 18 and the water pump flow 19, when the precipitation well water head variable quantity 18 is large, the power of the water pump 2 can be correspondingly reduced, otherwise, when the precipitation well water head variable quantity 18 is small, the power of the water pump 2 is correspondingly increased; the same applies to the pump flow 19 of the pump 2.
As shown in fig. 1, the control board 5 may send monitoring data of the drop-in level gauge 1 and the ultrasonic flow meter 4 to the intelligent mobile terminal 7 through the 5g signal 6, so as to implement remote monitoring. Simultaneously, control panel 5 still is connected with waterproof display 8, and this waterproof display 8 can set up in arranging the place of precipitation well, and it can show water level and flow in the precipitation well in real time, makes things convenient for the on-the-spot staff to observe.
As shown in fig. 1, the control panel 5 is connected with an alarm lamp 9 as an alarm device to alarm when abnormal conditions occur, so as to remind field personnel to repair the precipitation well or the precipitation system at once.
Specifically, two sets of drop-in type liquid level meters 1 are arranged in each precipitation well, and the two sets of drop-in type liquid level meters 1 monitor the water level height in the precipitation well so as to realize mutual check of the two. When the difference of the reading between the two input type liquid level meters 1 is larger than 0.5m, the abnormality of one input type liquid level meter 1 is judged, and at the moment, the control panel 5 controls the alarm lamp 9 to light up to remind long and narrow personnel to overhaul.
Meanwhile, when the control panel 5 finds that the flow measured by the ultrasonic flowmeter 4 and the flow calculated by the BP neural network algorithm have large deviation during data interaction, the control panel 5 judges that the precipitation of the precipitation well is abnormal, and the alarm lamp 9 is turned on to remind workers of overhauling. The deviation value between the measured flow and the calculated flow can be set by combining historical data and specific geological conditions in a construction site.
As shown in figure 1, the system is integrated into a BIM family 10, so that designers can conveniently and quickly call the BIM modeling process, and constructors can conveniently check the working state of the dewatering well in the BIM system.
The big data center 11 records the working data of the precipitation well and the soil layer condition of the precipitation well position, and continuously optimizes the neural network algorithm through distributed calculation and data mining, so that the algorithm is automatically updated along with the use. And training the neural network by adopting a reverse error propagation algorithm, and reversely correcting the neural network by using the difference value between the calculated value and the measured value of the output layer. As shown in fig. 3, there is a connection between the deployed precipitation system 20, the trained neural network 21 and the trained neural network 22. The trained neural network 21 controls the precipitation system 20 to perform precipitation. Precipitation system 20 transmits precipitation data to big data center 11, retraining neural network 22 in training within big data center 11 with newly collected data at 0 a day. After the training of the neural network 22 in the training is completed, the trained neural network 21 is updated, and the deployed precipitation system 20 performs precipitation, so that the BP neural network algorithm used in the embodiment is in a continuously updated state, the precision of automatic control is improved, and the water level in the precipitation well is ensured to be automatically stable.
In the embodiment, in specific implementation: the drop-in type liquid level meter 1 adopts a 4-20mA signal which is stable and reliable and is not influenced by the length of a signal transmission line.
The electronic transformer 3 is controlled by 485 signals, and the signals have the characteristics of stability, reliability and long transmission distance, so that the control precision of the water pump 2 is ensured.
The control panel 5 adopts a low-power-consumption mainboard, and adopts an automatic dormancy-awakening cycle to further reduce power consumption and realize energy conservation. The control panel 5 can also be provided with a watchdog function to automatically restart and correct errors which may occur in the running process.
Although the conception and the embodiments of the present invention have been described in detail with reference to the drawings, those skilled in the art will recognize that various changes and modifications can be made therein without departing from the scope of the appended claims, and therefore, they are not to be considered repeated herein.
Claims (8)
1. The utility model provides a precipitation well water level automatic stabilization system which characterized in that: the automatic water level stabilizing system of the dewatering well comprises a drop-in type liquid level meter, a water pump, an ultrasonic flowmeter, a control panel and a neural network algorithm running in a big data center, wherein the drop-in type liquid level meter and the water pump are respectively arranged in the dewatering well, the ultrasonic flowmeter is installed on the outer side of a pipeline of the water pump and used for monitoring working parameters of the water pump, the control panel is in signal connection with the big data center to form data interaction, the control panel inputs the working parameters of the water pump actually measured by the ultrasonic flowmeter and the measurement result of the drop-in type liquid level meter into the neural network algorithm, the neural network algorithm outputs calculation working parameters of the water pump according to historical water level data, and the control panel controls the working state of the water pump according to the output connection of the neural network algorithm.
2. The automatic water level stabilizing system for dewatering wells as claimed in claim 1, wherein the automatic water level stabilizing system comprises: the control panel is connected with an alarm device, two sets of the drop-in type liquid level meters are arranged in the dewatering well, and the two sets of the drop-in type liquid level meters are mutually checked; and when the difference value of the measuring results of the two sets of the input type liquid level meters exceeds a certain value, the control panel controls the alarm device to give an alarm.
3. The automatic water level stabilizing system for dewatering wells as claimed in claim 1, wherein the automatic water level stabilizing system comprises: the control panel is connected with an alarm device; and when the difference value between the working parameters of the water pump actually measured by the ultrasonic flowmeter and the calculated working parameters of the water pump output by the neural network algorithm exceeds a certain value, the control panel controls the alarm device to alarm.
4. The automatic water level stabilizing system for dewatering wells as claimed in claim 1, wherein the automatic water level stabilizing system comprises: the water pump is connected with an electronic transformer, and the control panel controls the working state of the water pump through the electronic transformer.
5. The automatic water level stabilizing system for dewatering wells as claimed in claim 1, wherein the automatic water level stabilizing system comprises: the neural network algorithm is a BP neural network algorithm and comprises an input layer, a hidden layer and an output layer, wherein the input layer comprises the top thickness of an impermeable layer, the thickness of a confined water layer, the permeability coefficient of the confined water layer, the water supply rate of the confined water layer, the actual measurement flow of the water pump by the ultrasonic flowmeter and the measurement water head of the dewatering well measured by the drop-in type liquid level meter, and the output layer comprises the variation quantity of the water head of the dewatering well and the calculated flow of the water pump.
6. The automatic water level stabilizing system for dewatering wells as claimed in claim 5, wherein the automatic water level stabilizing system comprises: the neural network algorithm adopts a reverse error propagation algorithm, and the neural network is reversely corrected by using the difference value between the calculated value and the measured value of the output layer.
7. The automatic water level stabilizing system for dewatering wells as claimed in claim 1, wherein the automatic water level stabilizing system comprises: the automatic precipitation well water level stabilizing system is integrated into a BIM family and forms signal connection with the BIM system through the control panel so as to carry out data interaction.
8. The automatic water level stabilizing system for dewatering wells as claimed in claim 1, wherein the automatic water level stabilizing system comprises: the control panel is connected with a waterproof display.
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Cited By (1)
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