Geotechnical engineering exploration intelligent management system and method based on Internet of Things

Abstract

The invention discloses a geotechnical engineering exploration intelligent management system and method based on the Internet of Things. Data are transmitted to a cloud platform for storage and analysis through an Internet of Things gateway. The cloud platform combines big data analysis and a city information model, carries out descriptive statistics, trend analysis and spatial analysis on the data, identifies potential geological risks in an exploration process, predicts the risks by using an ARMA model, identifies and evaluates the potential risks in advance, provides intelligent decision support, and then carries out decision making on the potential geological risks; on-site data and equipment states are remotely monitored through an informatization management system. When a risk is found, the safety management system automatically executes a corresponding safety operation instruction, generates a safety report and gives an alarm. According to the invention, the whole process of geotechnical engineering exploration can be monitored in real time, risk identification, emergency response capability and operation safety are significantly improved, and efficient and safe exploration operation is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of geotechnical engineering exploration, and in particular, relates to an intelligent management system and method for geotechnical engineering exploration based on the Internet of Things. Background Art

[0002] Geotechnical engineering exploration is a process of systematic investigation and analysis of geological bodies, aiming to provide reliable geological data and basic parameters for engineering projects. Through geotechnical engineering exploration, we can understand the geological conditions below the surface in detail, including soil layer structure, rock layer distribution, groundwater level, and physical and mechanical properties of soil and rock. This information is crucial for the safety and stability of engineering design and construction. The exploration results help engineers evaluate the adaptability of the geological environment, identify potential geological risks, and provide a scientific basis for formulating appropriate basic design plans. In addition, geotechnical engineering investigation can also predict and solve problems that may be encountered during engineering construction and use, such as foundation settlement, slope instability, groundwater impact, etc., to ensure the smooth implementation of engineering projects and the reliability of long-term operation. Through comprehensive geotechnical engineering exploration, the design plan can be optimized, the construction risk can be reduced, and the economy and sustainability of the project can be improved.

[0003] However, the existing geotechnical engineering survey process usually relies on local calculations and manual work to perform data analysis and risk assessment, mainly through the limited computing power of on-site equipment and manual judgment to analyze the collected environmental and equipment data. This method is not only inefficient in processing large amounts of complex data, but also difficult to monitor and analyze dynamically changing environments in real time, resulting in limited timeliness and accuracy in risk identification and assessment. In addition, manual analysis relies on the experience and subjective judgment of operators, which can easily lead to inconsistent and erroneous risk assessment results. At the same time, due to the lack of automated data processing and decision support capabilities, the existing system cannot respond and adjust quickly when risks occur, limiting the degree of automation and overall efficiency of risk management.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide an intelligent management system and method for geotechnical engineering exploration based on the Internet of Things to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0007] An intelligent management system and method for geotechnical engineering exploration based on the Internet of Things, comprising: an Internet of Things device, which is used to be installed in the drilling equipment and environment at the exploration site, and collects environmental data and equipment data in real time through multiple sensors;

[0008] A data acquisition module, connected to the IoT device, for collecting data collected by sensors and transmitting the data to the cloud computing platform through the IoT gateway;

[0009] A cloud computing platform for receiving and storing data transmitted by the data acquisition module and performing data analysis in combination with big data analysis and urban information models;

[0010] The information management system communicates with the cloud computing platform, provides a monitoring interface and management tools, and supports remote viewing of exploration data and on-site status;

[0011] Intelligent decision-making module, integrated into the cloud computing platform, is used to identify and evaluate potential risks in the exploration process based on the analysis and processing results, and provide targeted safety precautions and emergency response suggestions;

[0012] The safety management system is connected with IoT devices, data acquisition modules, cloud computing platforms and intelligent decision-making modules to execute safety operating procedures based on real-time monitoring data, generate safety management reports, and issue alarms when risks are detected.

[0013] Optionally, the steps of collecting environmental data and equipment data in real time through multiple sensors are: installing multiple sensors at the geotechnical engineering exploration site, the sensors including but not limited to temperature sensors, humidity sensors, pressure sensors, accelerometers, vibration sensors, etc., then, the sensors monitor the environmental parameters and equipment operating status of the geotechnical engineering exploration site in real time, and convert these physical quantities into electrical signals, and finally the electrical signals converted by the sensors are transmitted to the data acquisition module via wireless communication.

[0014] Optionally, when transmitting data to the cloud computing platform through the Internet of Things gateway, the data acquisition module processes the received data, then packs the processed data into data packets, and encapsulates the data packets according to a preset protocol, and then sends the packaged data packets to the Internet of Things gateway through the wireless network. Finally, the Internet of Things gateway sends the acquired data to the cloud computing platform.

[0015] Optionally, the steps for establishing a city information model are:

[0016] Collect geological data of the exploration area, then use geographic information system technology to obtain the geospatial data of the area, then collect environmental and meteorological data of the area, and finally format and integrate the collected geological data, geospatial data and meteorological data;

[0017] Based on the integrated geological data and drilling records, an underground geological model is constructed using 3D modeling software. Then, on the geographic information system platform, multiple spatial data layers are created based on the integrated geospatial data and environmental data. Finally, the created geological model and each spatial data layer are superimposed and integrated to form a city information model.

[0018] Optionally, the steps for data analysis using big data and the established city information model are:

[0019] Aggregate the required data from the city information model and other data sources, then perform descriptive statistical analysis on the collected data to calculate the mean, variance and skewness, and then identify the changing trends and cyclical patterns of the data through moving average and difference methods;

[0020] Next, we conduct spatial analysis. When conducting spatial analysis, we use the formula The weighted average values ​​of the characteristics of different regions are calculated to identify regional risks. Then, the formula Measures the degree of discreteness of data in space, where σ spatial is the standard deviation of spatial data, μ spatial is the average value of spatial data, where is the weighted average, w j is the weight of the jth region, w j is the characteristic value of the region, M represents the number of regions;

[0021] Collect historical geological data from geological databases and other relevant data sources, and pre-process the acquired data. Then, trend analysis is performed on time series data through moving average and difference transformation to eliminate short-term fluctuations and identify long-term trends and periodic patterns. Moving average is used to smooth data to observe the overall trend, and difference transformation is used to eliminate trends and make data stable.

[0022] The results of descriptive statistics, trend analysis, and spatial analysis are integrated to form a comprehensive analysis report. Then, the risk level is quantified through the risk index RI = α·T + β·S + γ·P to provide an overall overview of potential problems in each region. Finally, the correlation coefficient and regression analysis are used to identify potential causal relationships. The causal relationship is identified, and its expression is: Among them, r represents the correlation between two variables x and y, μ x and μ y are the average values ​​of two variables respectively, where T is the trend value, S is the spatial correlation factor, P is the frequency of event occurrence, and α, β, and γ are weight coefficients.

[0023] Optionally, when using the management system, it is also necessary to collect time series data, which includes historical geological observations or economic indicators. Then, the orders p and q of the ARMA model are determined, where p represents the order of the autoregressive part and q represents the order of the moving average part. Then, based on the identified orders p and q, the ARMA model is established. Its expression is: Among them, x t is the predicted value at time t, c is a constant term, φ i is the regression coefficient, is the weight of the i-th lag term, θ j is the moving average coefficient, which is the weight of the jth lagged error term, ∈ t is the white noise error term;

[0024] Then, the historical time series data is used to train the model and set the parameters c, φ i and θ j , whose expression is Where N is the total number of sample points, is the model prediction value;

[0025] Finally, use the trained ARMA model to predict future time points. The formula is: in, is the predicted value h steps after time t.

[0026] Optionally, when using an information management system to remotely view exploration data and field status, first, develop a RESTful API interface on the cloud computing platform to allow the monitoring interface and management tools to access the latest data and status information through these interfaces. Next, use CSS to develop a monitoring interface and integrate data charts and maps. Then, users can remotely view the real-time data and status information of the site through the monitoring interface and send control instructions through the monitoring interface. The control instructions are transmitted to the cloud computing platform through the API interface and sent from the cloud computing platform to the field equipment for execution.

[0027] Optionally, when identifying and evaluating potential risks in the exploration process based on the analysis and processing results, and providing targeted safety precautions and emergency response suggestions, it is necessary to first establish a rule base. When establishing the rule base, first, collect data from past exploration projects, equipment operation records, safety incident reports and monitoring data, and identify patterns and trends in historical data. Then, based on the data analysis results, identify common risk categories in the exploration process. Subsequently, for each risk category, determine the corresponding risk parameters and indicators. According to the identified risk parameters, set rule conditions and write logical rules for each risk condition. Finally, for each rule condition, define the corresponding safety precautions and write the countermeasures into a template, listing the execution steps, required resources, and condition restrictions in detail. Finally, the established rule base is integrated into the cloud technology platform;

[0028] The intelligent decision module obtains the processed analysis results from the cloud computing platform and applies the rule base to identify risks based on the analysis results. Then, the intelligent decision module evaluates the identified potential risks, considering the severity, impact scope and probability of occurrence of the risks;

[0029] Based on the identified risks, the intelligent decision-making module automatically searches for matching rules and measure templates in the rule base, automatically generates targeted security precautions and emergency response suggestions, and finally sends the security precautions and emergency response suggestions to on-site operators. When executing the security precautions and emergency response suggestions, the on-site equipment and personnel will feedback the execution status and results to the cloud computing platform.

[0030] Optionally, the safety management system automatically executes corresponding safety operating procedures based on the risk identification results, such as adjusting equipment parameters, stopping equipment or taking emergency measures. Then, based on the risk identification results, it automatically executes corresponding safety operating procedures. After performing the safety operation, the system automatically generates a safety management report, records the monitoring data, operating steps, risk assessment and processing results, and compares them according to the preset safety threshold. If the identified risk exceeds the preset safety threshold, the system immediately issues an alarm and sends the alarm to relevant personnel. Before comparing the safety threshold, it is necessary to generate the preset safety threshold by analyzing previous data.

[0031] An intelligent management method for geotechnical engineering exploration based on the Internet of Things comprises the following steps:

[0032] Install IoT devices at geotechnical engineering exploration sites to collect environmental data and equipment data in real time and convert the collected sensor data into electrical signals;

[0033] Collect the data collected by the sensor, pre-process the data, and finally package the processed data into data packets and transmit them to the cloud computing platform through the Internet of Things gateway using a secure communication protocol;

[0034] The cloud computing platform receives and stores data from the data collection module and uses big data analysis technology and urban information models to conduct in-depth analysis of the data;

[0035] Send the analysis results to the information management system, which enables users to view various data and status information of the exploration site in real time;

[0036] Automatically identify and evaluate potential risks in the exploration process based on data analysis results. Finally, based on these risk assessments, the intelligent decision-making module generates targeted safety precautions and emergency response recommendations;

[0037] Based on the acquired safety precautions and emergency response recommendations and their monitoring data, safety operating procedures are executed and safety management reports are automatically generated. Finally, when it is detected that the risk exceeds the preset safety threshold, the safety management system will immediately issue an alarm and send the alarm to relevant personnel.

[0038] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all of the following advantages at the same time:

[0039] The present invention integrates the cloud computing platform with the intelligent decision-making module, fully utilizes big data analysis and urban information model technology, and can process and analyze a large amount of sensor data and equipment operation data from the exploration site in real time. This integration can not only quickly identify and evaluate potential risks in the exploration process, but also automatically generate targeted safety precautions and emergency response suggestions based on real-time data changes. This method can greatly improve the accuracy of on-site monitoring and ensure that potential risks can be discovered and responded to in a timely manner. At the same time, through automated data analysis and decision support, the efficiency of risk management can be significantly improved, the errors and response time of human operations can be reduced, and the safety and reliability of the exploration process can be ensured.

[0040] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0042] In the figure:

[0043] Figure 1 This is the block diagram of the intelligent management system;

[0044] Figure 2 The figure is a flow chart of the intelligent management method.

[0045] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0046] The present invention will now be described in further detail with reference to the accompanying drawings.

[0047] See also Figure 1-2As shown, in this embodiment, an intelligent management system and method for geotechnical engineering exploration based on the Internet of Things is provided, including an Internet of Things device, which is used to be installed in the drilling equipment and environment of the exploration site, and collects environmental data and equipment data in real time through multiple sensors. The sensors are used to monitor the environmental parameters and equipment operating status of the geotechnical engineering exploration site;

[0048] A data acquisition module, connected to the IoT device, for collecting data collected by sensors and transmitting the data to the cloud computing platform through the IoT gateway;

[0049] A cloud computing platform for receiving and storing data transmitted by the data acquisition module and performing data analysis in combination with big data analysis and urban information models;

[0050] The information management system communicates with the cloud computing platform, provides a monitoring interface and management tools, and supports remote viewing of exploration data and on-site status;

[0051] Intelligent decision-making module, integrated into the cloud computing platform, is used to identify and evaluate potential risks in the exploration process based on the analysis and processing results, and provide targeted safety precautions and emergency response suggestions;

[0052] The safety management system is connected with IoT devices, data acquisition modules, cloud computing platforms and intelligent decision-making modules to execute safety operating procedures based on real-time monitoring data, generate safety management reports, and issue alarms when risks are detected.

[0053] In this embodiment, the steps of collecting environmental data and equipment data in real time through multiple sensors are as follows: multiple sensors are placed at the geotechnical engineering exploration site, and the sensors include temperature sensors, humidity sensors, pressure sensors, accelerometers, vibration sensors, etc. Then, the sensors monitor the environmental parameters (such as temperature, humidity, wind speed, air pressure, etc.) and equipment operation status (such as equipment vibration, drill speed, torque, displacement, etc.) of the geotechnical engineering exploration site in real time, and convert these physical quantities into electrical signals. Finally, the electrical signals converted by the sensors are transmitted to the data acquisition module via wireless communication. By arranging multiple sensors (such as temperature, humidity, pressure, acceleration, vibration sensors, etc.) at the geotechnical engineering exploration site and on the exploration equipment, environmental parameters and equipment operation status can be monitored in real time. This multi-sensor arrangement ensures comprehensive coverage of site conditions and helps to fully understand the geological and equipment status of the site. In addition, the sensors can monitor and collect data in real time and convert these data into electrical signals. These electrical signals are transmitted to the data acquisition module via wireless communication, ensuring the real-time and accuracy of the data. Real-time data acquisition and transmission can reflect the site situation in a timely manner and provide data support for quick decision-making.

[0054] In this embodiment, when the data is transmitted to the cloud computing platform through the Internet of Things gateway, the data acquisition module processes the received data, and the processing method includes but is not limited to compression, encoding or data cleaning. Then, the processed data is packaged into a data packet, and the data packet is encapsulated according to a preset protocol. Subsequently, the packaged data packet is sent to the Internet of Things gateway through a wireless network. Finally, the Internet of Things gateway sends the acquired data to the cloud computing platform. The Internet of Things gateway sends the acquired data to the cloud computing platform, and uses the powerful processing power of the cloud computing platform to perform more advanced data analysis and storage. This integration method ensures the scalability of the system, and more data sources and analysis functions can be added according to demand to adapt to the ever-changing engineering needs. Through the combination of the Internet of Things gateway and the cloud computing platform, centralized monitoring and management of the on-site environment and equipment status can be achieved. The cloud platform can analyze and store a large amount of data in real time, and use the analysis results for decision support and risk warning, thereby improving the overall efficiency and effectiveness of monitoring and management.

[0055] In this embodiment, the steps of establishing the city information model are:

[0056] Collect geological data of the exploration area, including geological profiles, borehole coordinates, borehole data, lithology distribution, groundwater level, soil characteristics, etc. Then, use geographic information system technology to obtain the regional topography, elevation, rivers, roads, buildings and other geospatial data. Then, collect the environmental and meteorological data of the area, such as rainfall, temperature, humidity, wind speed, etc. Finally, format and integrate the collected geological data, geospatial data, and meteorological data; form a comprehensive urban information model by superimposing and integrating urban information models and multiple spatial data layers. This model integrates multi-dimensional geographic and geological information, provides a panoramic view, and helps to comprehensively evaluate and manage geological engineering projects.

[0057] Based on the integrated geological data and drilling records, the underground geological model is constructed using 3D modeling software. Then, on the GIS platform, multiple spatial data layers such as terrain layer, soil layer, and groundwater layer are created based on the integrated geospatial data and environmental data. Finally, the created geological model and each spatial data layer are superimposed and integrated to form an urban information model. The underground geological model constructed based on the integrated geological data and drilling records using 3D modeling software can accurately reflect the underground geological structure and lithology distribution. This modeling method improves the visualization and understanding of underground conditions, which helps to better plan and manage exploration activities.

[0058] Specifically, by comprehensively collecting and integrating geological, geographical and environmental data, an accurate three-dimensional geological model and multi-level spatial data layers were constructed. The urban information model formed by superimposing this information not only provides a comprehensive view for geological exploration and urban planning, but also supports scientific decision-making and risk management, improving overall work efficiency and prediction capabilities.

[0059] In this embodiment, the steps of using big data and the established city information model to perform data analysis are as follows:

[0060] Aggregate the required data from the city information model and other data sources (such as sensor data, historical exploration data, meteorological data, etc.). The data includes spatial data (such as topography and geological structure), time series data (such as groundwater level changes), and environmental data (such as temperature and rainfall). Then, perform descriptive statistical analysis on the collected data to calculate the mean, variance, and skewness. Then, identify the change trend and periodic pattern of the data through moving average and difference method;

[0061] Next, we conduct spatial analysis. When conducting spatial analysis, we use the formula The weighted average values ​​of the characteristics of different regions are calculated to identify regional risks. Then, the formula Measures the degree of discreteness of data in space, where σ spatial is the standard deviation of spatial data, μ spatial is the average value of spatial data, where is the weighted average, w j is the weight of the jth region, x j is the characteristic value of the region, M represents the number of regions;

[0062] Collect historical geological data from geological databases and other relevant data sources, and pre-process the acquired data. Then, trend analysis is performed on time series data through moving average and difference transformation to eliminate short-term fluctuations and identify long-term trends and periodic patterns. Moving average is used to smooth data to observe the overall trend, and difference transformation is used to eliminate trends and make data stable.

[0063] The results of descriptive statistics, trend analysis, and spatial analysis are integrated to form a comprehensive analysis report. Then, the risk level is quantified through the risk index RI = α·T + β·S + γ·P to provide an overall overview of potential problems in each region. Finally, the correlation coefficient and regression analysis are used to identify potential causal relationships. The causal relationship is identified, and its expression is: Among them, r represents the correlation between two variables x and y, μ x and μ yare the average values ​​of two variables respectively, where T is the trend value, S is the spatial correlation factor, P is the frequency of event occurrence, and α, β, and γ are weight coefficients.

[0064] For example: In water resource management, data including geological structure, monthly changes in groundwater level, rainfall and temperature are collected from urban information models, groundwater monitoring sensors, historical exploration reports and meteorological databases, and the data are standardized and outliers are removed to ensure consistency. Through descriptive statistical analysis, the average groundwater level, variance and skewness are calculated to show the overall characteristics and fluctuations of the data. Then, moving average and smoothing techniques are used to identify long-term trends, and differential transformation is used to make the data stable for modeling. Spatial analysis calculates the weighted average groundwater level and spatial coefficient of variation of each region to measure the difference in water level in different regions. The results of descriptive statistics, trend analysis and spatial analysis are integrated into a water resource management report, the regional risk level is quantified through risk index, and the causal relationship between rainfall and groundwater level change is verified using correlation coefficient and regression analysis.

[0065] In this embodiment, when using the management system, time series data also needs to be collected. The time series data includes historical geological observations or economic indicators. Then, the orders p and q of the ARMA model are determined, where p represents the order of the autoregressive part and q represents the order of the moving average part. Then, based on the identified orders p and q, the ARMA model is established, and its expression is: Among them, x t is the predicted value at time t, c is a constant term, φ i is the regression coefficient, is the weight of the i-th lag term, θ j is the moving average coefficient, which is the weight of the jth lagged error term, ∈ t is the white noise error term;

[0066] Then, the historical time series data is used to train the model and set the parameters c, φ i and θ j , whose expression is Where N is the total number of sample points, is the model prediction value;

[0067] Finally, use the trained ARMA model to predict future time points. The formula is: in, is the predicted value h steps after time t

[0068] In geotechnical engineering investigation management, time series data including soil settlement, surface displacement and groundwater level change are collected from urban information models, sensor data and historical exploration reports, and standardized and outlier processing is performed to ensure data consistency and accuracy. The autoregressive order p and moving average order q of the ARMA model are determined using the autocorrelation function and partial autocorrelation function graphs. Then the ARMA model is established and trained through maximum likelihood estimation, and the residual sum of squares is calculated to optimize the model. After training, the model is used to predict future geological changes, and the risk index is calculated in combination with big data analysis methods to evaluate and predict geological risks (such as landslides and ground subsidence). Finally, the correlation coefficient and regression analysis are used to identify the causal relationship between geological factors, provide decision support and early warning capabilities for engineering projects, and ensure that preventive measures are taken in time to protect the safety of projects and personnel.

[0069] In this embodiment, when using the information management system to remotely view the exploration data and field status, first, a RESTful API interface is developed on the cloud computing platform to allow the monitoring interface and management tools to access the latest data and status information through these interfaces. Then, the monitoring interface is developed using CSS and integrated with data charts and maps. Then, the user can remotely view the real-time data and status information of the field through the monitoring interface and send control instructions through the monitoring interface. The control instructions are transmitted to the cloud computing platform through the API interface and sent to the field equipment by the cloud computing platform for execution. By developing a RESTful API interface on the cloud computing platform, the information management system can realize remote viewing of exploration data and field status. Users can obtain various data in the exploration process in real time without being on site, which greatly improves the convenience and coverage of monitoring. Users can directly view the real-time data and status information of the field through the monitoring interface and send control instructions. This operation method simplifies the process, improves management efficiency, and reduces the need for intermediate links and manual intervention.

[0070] Control instructions are transmitted to the cloud computing platform through the API interface, and then sent to the on-site equipment for execution by the cloud computing platform. This real-time control capability ensures that when problems are identified or equipment needs to be adjusted, quick action can be taken to reduce delays and risks.

[0071] In this embodiment, when identifying and evaluating potential risks in the exploration process based on the analysis and processing results, and providing targeted safety precautions and emergency response suggestions, it is necessary to first establish a rule base. When establishing the rule base, first, collect data from past exploration projects, equipment operation records, safety incident reports and monitoring data, and identify patterns and trends in historical data. Then, based on the data analysis results, identify common risk categories in the exploration process, such as: equipment overload, excessive temperature, excessive pressure, excessive torque, abnormal vibration, etc. Subsequently, for each risk category, determine the corresponding risk parameters and indicators, such as temperature, pressure, vibration frequency, etc. These parameters will be used for the definition and monitoring of subsequent rules. According to the identified risk parameters, set the rule conditions. For example, when the equipment temperature exceeds a certain threshold, it should be considered as an overheating risk; when the vibration amplitude exceeds the normal range, it should be considered as a mechanical failure risk. And write logical rules for each risk condition, which are expressed in "if-then" statements (IF-THEN). For example, "If the equipment temperature>80℃, then reduce the equipment load", and then set a priority for each rule to ensure that when multiple risks occur at the same time, the system can give priority to the most urgent situation. Finally, for each rule condition, corresponding safety precautions are defined. These measures include adjusting equipment parameters, suspending operations, issuing alarms, etc., and the response measures are written into templates, detailing the execution steps, required resources, and conditional restrictions. For example, a response template for overtemperature may include the steps: "1. Reduce load; 2. Check the cooling system; 3. Notify the operator", and the rule base established is integrated into the cloud technology platform. By establishing a rule base, the system can systematically identify and evaluate potential risks in the exploration process, and provide corresponding safety precautions and emergency response recommendations. This approach ensures the standardization and consistency of risk management and avoids the subjectivity and uncertainty of human judgment.

[0072] The intelligent decision-making module obtains the processed analysis results from the cloud computing platform and applies the rule base to identify risks based on the analysis results. Then, the intelligent decision-making module evaluates the identified potential risks, taking into account the severity, scope of impact and probability of occurrence of the risks.

[0073] Based on the identified risks, the intelligent decision-making module automatically searches for matching rules and measures templates in the rule base, automatically generates targeted safety precautions and emergency response suggestions, and finally sends the safety precautions and emergency response suggestions to the on-site operators. When the safety precautions and emergency response suggestions are executed, the on-site equipment and personnel will feedback the execution status and results to the cloud computing platform. The intelligent decision-making module can automatically search for matching rules and measures templates in the rule base, generate safety precautions and send them to the on-site operators. This automated operation reduces the time delay of manual intervention, ensures a rapid response in an emergency, and reduces the risk of accidents.

[0074] In this embodiment, the safety management system automatically executes the corresponding safety operation procedures according to the risk identification results, such as adjusting equipment parameters, stopping equipment or taking emergency measures. After performing the safety operation, a safety management report is automatically generated to record monitoring data, operation steps, risk assessment and processing results, and compared according to the preset safety threshold. If the identified risk exceeds the preset safety threshold, an alarm is immediately issued, and the alarm is sent to relevant personnel through notification. Before comparing the safety threshold, it is necessary to generate a preset safety threshold by analyzing previous data. The system can identify the conditions of safety events or failures that have occurred. These historical data help determine the typical values ​​of various parameters in a safe state and an unsafe state, so as to set the corresponding thresholds. By automatically executing the corresponding safety operation procedures according to the risk identification results, the system can respond to different risk conditions in real time. In this way, measures can be taken quickly, such as adjusting equipment parameters or stopping equipment, to reduce the harm of potential risks to equipment and personnel. By analyzing previous safety events and failure data, the system can understand the risk factors and safety hazards under different conditions. This helps to integrate historical experience into the existing risk management framework and improve the accuracy and effectiveness of risk identification. In addition, based on historical data analysis, the system can identify the typical values ​​of various parameters in a safe state and an unsafe state. These typical values ​​are used to set safety thresholds to ensure that the threshold settings are consistent with actual conditions and to reduce false positives and negatives.

[0075] An intelligent management method for geotechnical engineering exploration based on the Internet of Things comprises the following steps:

[0076] Install IoT devices at geotechnical engineering exploration sites to collect environmental data and equipment data in real time and convert the collected sensor data into electrical signals;

[0077] Collect the data collected by the sensor, pre-process the data, and finally package the processed data into data packets and transmit them to the cloud computing platform through the Internet of Things gateway using a secure communication protocol;

[0078] The cloud computing platform receives and stores data from the data collection module and uses big data analysis technology and urban information models to conduct in-depth analysis of the data;

[0079] Send the analysis results to the information management system, which enables users to view various data and status information of the exploration site in real time;

[0080] Automatically identify and evaluate potential risks in the exploration process based on data analysis results. Finally, based on these risk assessments, the intelligent decision-making module generates targeted safety precautions and emergency response recommendations;

[0081] Based on the acquired safety precautions and emergency response recommendations and their monitoring data, safety operating procedures are executed and safety management reports are automatically generated. Finally, when it is detected that the risk exceeds the preset safety threshold, the safety management system will immediately issue an alarm and send the alarm to relevant personnel.

[0082] The monitoring systems of existing solutions are usually unevenly distributed and cannot fully monitor various equipment and environmental parameters at the exploration site in real time. They often rely on regular manual inspections and checks and cannot respond quickly based on real-time data. To this end, the following experiments were conducted:

[0083] At the same geotechnical engineering exploration site, the same sensor configuration (temperature, humidity, pressure, vibration, acceleration, etc.) is selected for data collection. This experiment compares the IoT-based intelligent management system of the present invention with the existing traditional exploration management solution. The traditional solution uses manual recording and analysis of data. The experimental period is 3 months. All devices are installed in the same location and synchronized monitoring and data recording are performed.

[0084]

[0085] It can be seen from the above table that the real-time performance of data collection and processing of the present invention is greatly improved, and potential risks can be identified in time and corresponding emergency response measures can be taken. Secondly, based on the integration of cloud computing platform and intelligent decision-making module, this solution can provide more accurate risk prediction and more efficient decision support, significantly improving the efficiency of risk management. Finally, through automated data analysis, human intervention is reduced, operational errors and response time are reduced, thereby ensuring the safety and efficiency of exploration operations.

[0086] The present invention is not limited to the above-mentioned embodiments. Anyone should be aware that any structural changes made under the enlightenment of the present invention, and any technical solutions that are the same or similar to the present invention, fall within the protection scope of the present invention. The technology, shape, and structural parts not described in detail in the present invention are all well-known technologies.

Claims

1. An intelligent management system for geotechnical engineering exploration based on the Internet of Things, characterized in that: include: IoT devices are used to install on drilling equipment and environments at exploration sites, collecting environmental and equipment data in real time through multiple sensors; A data acquisition module, connected to the IoT device, for collecting data collected by sensors and transmitting the data to the cloud computing platform through the IoT gateway; A cloud computing platform for receiving and storing data transmitted by the data acquisition module and performing data analysis in combination with big data analysis and urban information models; The information management system communicates with the cloud computing platform, provides a monitoring interface and management tools, and supports remote viewing of exploration data and on-site status; Intelligent decision-making module, integrated into the cloud computing platform, is used to identify and evaluate potential risks in the exploration process based on the analysis and processing results, and provide targeted safety precautions and emergency response suggestions; The safety management system is connected with IoT devices, data acquisition modules, cloud computing platforms and intelligent decision-making modules to execute safety operating procedures based on real-time monitoring data, generate safety management reports, and issue alarms when risks are detected.

2. According to claim 1, an intelligent management system for geotechnical engineering exploration based on the Internet of Things is characterized in that: The steps of collecting environmental data and equipment data in real time through multiple sensors are as follows: installing multiple sensors at the geotechnical engineering exploration site, the sensors including but not limited to temperature sensors, humidity sensors, pressure sensors, accelerometers, vibration sensors, etc.; then, the sensors monitor the environmental parameters and equipment operating status of the geotechnical engineering exploration site in real time, and convert these physical quantities into electrical signals, and finally the electrical signals converted by the sensors are transmitted to the data acquisition module via wireless communication.

3. According to claim 1, an intelligent management system for geotechnical engineering exploration based on the Internet of Things is characterized in that: When data is transmitted to the cloud computing platform through the IoT gateway, the data acquisition module processes the received data, then packs the processed data into data packets, and encapsulates the data packets according to the preset protocol. Subsequently, the packaged data packets are sent to the IoT gateway through the wireless network. Finally, the IoT gateway sends the acquired data to the cloud computing platform.

4. According to claim 1, an intelligent management system for geotechnical engineering exploration based on the Internet of Things is characterized in that: The steps to build a city information model are: Collect geological data of the exploration area, then use geographic information system technology to obtain the geospatial data of the area, then collect environmental and meteorological data of the area, and finally format and integrate the collected geological data, geospatial data and meteorological data; Based on the integrated geological data and drilling records, an underground geological model is constructed using 3D modeling software. Then, on the geographic information system platform, multiple spatial data layers are created based on the integrated geospatial data and environmental data. Finally, the created geological model and each spatial data layer are superimposed and integrated to form a city information model.

5. According to claim 1, an intelligent management system for geotechnical engineering exploration based on the Internet of Things is characterized in that: The steps for data analysis using big data and the established urban information model are: Aggregate the required data from the city information model and other data sources, then perform descriptive statistical analysis on the collected data to calculate the mean, variance and skewness, and then identify the changing trends and cyclical patterns of the data through moving average and difference methods; Next, we conduct spatial analysis. When conducting spatial analysis, we use the formula The weighted average values ​​of the characteristics of different regions are calculated to identify regional risks. Then, the formula Measures the degree of discreteness of data in space, where σ spatial is the standard deviation of spatial data, μ spatial is the average value of spatial data, where is the weighted average, w j is the weight of the jth region, x j is the characteristic value of the region, M represents the number of regions; Collect historical geological data from geological databases and other relevant data sources, and pre-process the acquired data. Then, trend analysis is performed on time series data through moving average and difference transformation to eliminate short-term fluctuations and identify long-term trends and periodic patterns. Moving average is used to smooth data to observe the overall trend, and difference transformation is used to eliminate trends and make data stable. The results of descriptive statistics, trend analysis, and spatial analysis are integrated to form a comprehensive analysis report. Then, the risk level is quantified through the risk index RI = α·T + β·S + γ·P to provide an overall overview of potential problems in each region. Finally, the correlation coefficient and regression analysis are used to identify potential causal relationships. The causal relationship is identified, and its expression is: Among them, r represents the correlation between two variables x and y, μ x and μ y are the average values ​​of two variables respectively, where T is the trend value, S is the spatial correlation factor, P is the frequency of event occurrence, and α, β, and γ are weight coefficients.

6. According to claim 1, an intelligent management system for geotechnical engineering exploration based on the Internet of Things is characterized in that: When using the management system, it is also necessary to collect time series data, which includes historical geological observations or economic indicators. Then, determine the orders p and q of the ARMA model, where p represents the order of the autoregressive part and q represents the order of the moving average part. Then, based on the identified orders p and q, establish the ARMA model. Its expression is: Among them, x t is the predicted value at time t, c is a constant term, φ i is the regression coefficient, is the weight of the i-th lag term, θ j is the moving average coefficient, which is the weight of the jth lagged error term, ∈ t is the white noise error term; Then, the historical time series data is used to train the model and set the parameters c, φ i and θ j , whose expression is Where N is the total number of sample points, is the model prediction value; Finally, use the trained ARMA model to predict future time points. The formula is: in, is the predicted value h steps after time t.

7. According to claim 1, an intelligent management system for geotechnical engineering exploration based on the Internet of Things is characterized in that: When using the information management system to remotely view exploration data and field status, first, develop a RESTful API interface on the cloud computing platform to allow the monitoring interface and management tools to access the latest data and status information through these interfaces. Then, use CSS to develop the monitoring interface and integrate data charts and maps. Then, users can remotely view the real-time data and status information of the site through the monitoring interface and send control instructions through the monitoring interface. The control instructions are transmitted to the cloud computing platform through the API interface and sent from the cloud computing platform to the field equipment for execution.

8. According to claim 1, an intelligent management system for geotechnical engineering exploration based on the Internet of Things is characterized by: In order to identify and evaluate potential risks in the exploration process based on the analysis and processing results, and provide targeted safety precautions and emergency response suggestions, it is necessary to first establish a rule base. When establishing the rule base, first, collect data from past exploration projects, equipment operation records, safety incident reports and monitoring data, and identify patterns and trends in historical data; then, based on the data analysis results, identify common risk categories in the exploration process; Then, for each risk category, the corresponding risk parameters and indicators are determined. Based on the identified risk parameters, rule conditions are set and logical rules are written for each risk condition. Finally, for each rule condition, corresponding security precautions are defined and the countermeasures are written into a template, listing the execution steps, required resources, and condition restrictions in detail. Finally, the established rule base is integrated into the cloud technology platform. The intelligent decision module obtains the processed analysis results from the cloud computing platform and applies the rule base to identify risks based on the analysis results; then, the intelligent decision module evaluates the identified potential risks; Based on the identified risks, the intelligent decision-making module automatically searches for matching rules and measure templates in the rule base, automatically generates targeted security precautions and emergency response suggestions, and finally sends the security precautions and emergency response suggestions to on-site operators. When executing the security precautions and emergency response suggestions, the on-site equipment and personnel will feedback the execution status and results to the cloud computing platform.

9. According to claim 1, an intelligent management system for geotechnical engineering exploration based on the Internet of Things is characterized by: The safety management system automatically executes the corresponding safety operating procedures based on the risk identification results; then, based on the risk identification results, it automatically executes the corresponding safety operating procedures. After performing the safety operation, the system automatically generates a safety management report, records the monitoring data, operating steps, risk assessment and processing results, and compares them according to the preset safety threshold. If the identified risk exceeds the preset safety threshold, an alarm is immediately issued and sent to relevant personnel. Before comparing the safety threshold, it is necessary to generate the preset safety threshold through analysis of previous data.

10. An intelligent management method for geotechnical engineering exploration based on the Internet of Things, characterized in that: The following steps are involved: Install IoT devices at geotechnical engineering exploration sites to collect environmental data and equipment data in real time, and convert the collected sensor data into electrical signals; Collect the data collected by the sensor, pre-process the data, and finally package the processed data into data packets and transmit them to the cloud computing platform through the Internet of Things gateway using a secure communication protocol; The cloud computing platform receives and stores data from the data collection module and uses big data analysis technology and urban information models to conduct in-depth analysis of the data; Send the analysis results to the information management system, which enables users to view various data and status information of the exploration site in real time; Automatically identify and evaluate potential risks in the exploration process based on data analysis results. Finally, based on these risk assessments, the intelligent decision-making module generates targeted safety precautions and emergency response recommendations; Safety operating procedures are implemented based on the acquired safety precautions and emergency response recommendations and their monitoring data, and a safety management report is automatically generated; finally, when it is detected that the risk exceeds the preset safety threshold, the safety management system will immediately issue an alarm and send the alarm to relevant personnel.

Images