CN-121996940-A - Monitoring method, monitoring equipment, monitoring computer equipment and monitoring medium for mud state

CN121996940ACN 121996940 ACN121996940 ACN 121996940ACN-121996940-A

Abstract

The application discloses a monitoring method, equipment, computer equipment and medium for mud state, which are used for solving the problems that manual inspection cannot be monitored in real time, has large error and is difficult to process in time. The method comprises the steps of obtaining state parameters of the slurry through monitoring equipment, analyzing the state parameters based on a neural network model, and controlling adjusting equipment to adjust the slurry state according to analysis results. The method can monitor the state of the slurry in real time, predicts the state of the slurry through the neural network, thereby realizing intelligent regulation and control of the state of the slurry, being capable of adjusting the slurry at the early stage of abnormal slurry performance, being capable of adjusting the state of the slurry in advance without depending on manual experience compared with the traditional manual inspection mode, avoiding delay caused by manual intervention, ensuring the slurry to be always in the optimal state, ensuring the slag carrying capacity and the wall protection stability of the slurry, further ensuring the construction quality of pile foundations and improving the safety and efficiency of construction.

Inventors

ZHOU PU
LUO GUIJUN
ZHANG CAN
Xiao Xiangnan
HUANG WEI
HU TIANYU
XU JINGYI
CHEN YAJUN
SU SICHENG

Assignees

中建五局土木工程有限公司

Dates

Publication Date: 20260508
Application Date: 20251218

Claims (10)

1. A method for monitoring a mud condition, the method comprising: Acquiring state parameters of the slurry through monitoring equipment; Analyzing the state parameters based on a neural network model; And controlling the adjusting equipment to adjust the mud state according to the analysis result.
2. The method of claim 1, wherein analyzing the status parameters based on a neural network model comprises: inputting the state parameters into the neural network model; And predicting the mud state of a future preset period based on the output data of the neural network model.
3. A method of monitoring mud conditions according to claim 1, wherein prior to the step of inputting the condition parameters into the neural network model, the method further comprises: Acquiring historical state data of slurry, and carrying out normalization processing on the historical state data; Extracting key feature vectors of the mud state changing along with time based on the processed historical state data; and training the neural network model by taking the key feature vector as input data and taking the predicted mud state of a preset time period in the future as output data.
4. The method of claim 1, wherein the monitoring device comprises at least one of a density sensor, a viscosity sensor, and a sand content sensor, wherein the status parameter comprises at least one of specific gravity, fluidity, and sand content, and wherein the step of obtaining the status parameter of the slurry by the sensor comprises: Acquiring density data of the slurry through the density sensor to determine the specific gravity of the slurry; And/or acquiring viscosity data of the slurry through the viscosity sensor to determine the fluidity of the slurry; and/or acquiring sand content data of the slurry through the sand content sensor so as to determine the sand content of the slurry.
5. The method according to claim 4, wherein the adjusting device comprises at least one of a raw material feeding device, a sand removing device, a flow rate and pressure adjusting device, and the controlling the adjusting device to adjust the mud state based on the analysis result comprises: Controlling the throwing amount of the raw material throwing device under the condition that the specific gravity of the predicted slurry does not meet the preset condition; And/or controlling the sand removing device to remove redundant sand grains in the slurry under the condition that the sand content of the slurry is predicted to be too high; And/or adjusting the flow rate and/or pumping pressure of the slurry through the flow rate and pressure adjusting device under the condition that the fluidity of the slurry is not predicted to meet the preset condition.
6. The method for monitoring the status of slurry according to claim 1, wherein the step of obtaining the status parameter of the slurry by the monitoring device further comprises: acquiring environmental parameters of the monitoring equipment; And adjusting the monitoring equipment based on the environmental parameters.
7. The method of claim 6, wherein adjusting the monitoring device based on the environmental parameter comprises: adjusting the sensitivity of the monitoring device according to the environmental parameter; and/or adjusting the acquisition frequency of the monitoring device according to the environmental parameter; and/or, carrying out preset processing on the acquisition signals of the monitoring equipment according to the environmental parameters.
8. A device for monitoring the condition of a slurry, the device comprising: the acquisition module is used for acquiring state parameters of the slurry through the monitoring equipment; The analysis module is used for analyzing the state parameters based on a neural network model; And the adjusting module is used for controlling the adjusting equipment to adjust the mud state according to the analysis result.
9. A computer device, the device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of monitoring mud conditions as set forth in any one of claims 1-7.
10. A non-transitory computer storage medium storing computer executable instructions which, when executed, implement the method of monitoring mud conditions of any one of claims 1-7.

Description

Monitoring method, monitoring equipment, monitoring computer equipment and monitoring medium for mud state Technical Field The application relates to the technical field of building construction, in particular to a method, equipment, computer equipment and medium for monitoring mud state. Background In the construction process of buildings such as bridges, pile foundations are used as the foundation of bearing structures, the weight of the whole building is borne, the quality of pile foundation construction directly influences stability and safety, mud is an important wall protecting medium in the pile foundation pore-forming process, the pile pore wall can be supported, hole collapse is prevented, rock fragments in the drilling process can be taken away, the pore wall is kept clean, and the smooth construction of the pile foundations is ensured. At present, the performance state of the slurry is usually detected by a constructor in a manual periodicity, and when the manual detection has poor timeliness, real-time monitoring cannot be performed, and an empty stage without long-time monitoring may exist. When the mud state changes, risks cannot be effectively predicted and prevented, effective measures are difficult to take at the first time, the optimal treatment time is missed, the construction quality of the pile foundation is reduced, and even construction accidents such as hole collapse and the like are likely to occur. And the manual detection and adjustment are easily affected by the external environment and experience of operators, so that the error is larger and the efficiency is lower. Disclosure of Invention The embodiment of the application provides a monitoring method, equipment, computer equipment and medium for mud state, which are used for solving the technical problems that manual inspection in the prior art cannot be monitored in real time, has large error and is difficult to process in time. In one aspect, an embodiment of the present application provides a method for monitoring a mud state, including: Acquiring state parameters of the slurry through monitoring equipment; Analyzing the state parameters based on a neural network model; And controlling the adjusting equipment to adjust the mud state according to the analysis result. According to the embodiment of the application, the state of the slurry can be monitored in real time, and predicted through the neural network, so that the intelligent regulation and control of the state of the slurry can be realized, the slurry can be regulated at the early stage of abnormal slurry performance, compared with the traditional manual inspection mode, the method can be used for regulating the state of the slurry in advance without depending on manual experience, delay caused by manual intervention is avoided, the slurry is always in the optimal state, the slag carrying capacity and the wall protection stability of the slurry are ensured, the construction quality of pile foundations is ensured, and the safety and efficiency of construction are improved. In one implementation of the present application, the analyzing the state parameter based on the neural network model includes: inputting the state parameters into the neural network model; And predicting the mud state of a future preset period based on the output data of the neural network model. According to the embodiment of the application, the neural network prediction model is adopted, and the change condition of the slurry in a preset time period in the future is predicted through real-time data analysis, so that the potential risk in the slurry is detected, an early warning signal is given in advance, personnel is notified or an automatic adjustment mechanism is started, the slurry is processed in time, and compared with the traditional manual judgment, the response time is reduced, and the construction safety is improved. In one implementation of the present application, before the step of inputting the state parameter into the neural network model, the method further comprises: Acquiring historical state data of slurry, and carrying out normalization processing on the historical state data; Extracting key feature vectors of the mud state changing along with time based on the processed historical state data; and training the neural network model by taking the key feature vector as input data and taking the predicted mud state of a preset time period in the future as output data. According to the embodiment of the application, training of the neural network model for predicting the mud state is carried out, training data are firstly required to be acquired, the training data are acquired and preprocessed, after the data preprocessing is completed, feature construction and dimension reduction can be carried out on the processed data, key features of the mud state changing along with time are extracted, the extracted key feature vectors are used as the training data of the neural network model