A wireless temperature and pressure measurement and transmission system for geothermal drilling

Abstract

The present invention relates to the field of geothermal drilling technology, specifically a wireless temperature and pressure measurement and transmission system for geothermal drilling, the system comprising: a temperature and pressure data analysis module, a fault information diagnosis module, a power consumption data analysis module, a battery power management module, a transmission condition analysis module, and a signal adjustment and optimization module. In the present invention, by real-time monitoring of temperature and pressure change data, adjusting sensor parameters to send out warning signals, reducing safety accidents and economic losses caused by fault delays, real-time collection and analysis of power consumption data, improving the efficiency of energy management, adjusting the operating frequency and wake-up cycle, matching performance requirements and actual energy consumption, extending the service life of equipment and reducing maintenance costs, combining geological characteristic data to evaluate the impact of various geological characteristics on wireless signal transmission, adjusting the channel and power settings of wireless transmission equipment, optimizing the accuracy and stability of data transmission, and improving the overall efficiency and safety of geothermal energy development.

Description

Technical Field

[0001] The present invention relates to the technical field of geothermal drilling, and in particular to a wireless temperature and pressure measurement and transmission system for geothermal drilling. Background Art

[0002] The field of geothermal drilling technology focuses on utilizing the thermal energy resources inside the earth. It detects, mines and utilizes geothermal energy through a variety of methods and technologies, including geological exploration, drilling technology, thermal energy extraction and conversion technology, aiming to improve the depth and accuracy of drilling and ensure the safety and economic benefits of operations. Through the development of drilling tools under high temperature and high pressure, drilling fluid technology, well wall stability analysis, real-time monitoring of drilling data, and real-time geological condition analysis, geothermal resources are detected and developed to provide energy for geothermal power stations and heating systems.

[0003] Among them, the wireless temperature and pressure measurement and transmission system for geothermal drilling is designed to monitor the geothermal drilling process. It collects temperature and pressure data in real time during the drilling process and transmits the data to the ground monitoring station wirelessly to monitor environmental changes during the drilling process. Engineers can monitor the drilling status in real time and adjust operations in time to optimize drilling efficiency and safety, avoid equipment failures, reduce the risk of safety accidents, optimize resource extraction efficiency, and improve the efficiency and safety of geothermal energy development.

[0004] Traditional temperature and pressure measurement technology relies on wired connections to transmit data. It is susceptible to physical damage in extreme drilling environments, resulting in data loss and signal interruption. It does not respond promptly enough to the rapidly changing drilling environment and cannot effectively warn of potential dangers or failures. It lacks energy efficiency management and cannot adjust power consumption to match multiple operating states, increasing sensor energy consumption and maintenance requirements. It cannot consider the impact of geological characteristics on signal stability, resulting in unstable data transmission, affecting the accuracy of decision-making, and increasing the complexity and cost of operations. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a wireless temperature and pressure measurement and transmission system for geothermal drilling.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a wireless temperature and pressure measurement and transmission system for geothermal drilling comprises:

[0007] The temperature and pressure data analysis module is based on temperature and pressure sensors, collects and records temperature and pressure data in real time, identifies abnormal changes in temperature and pressure, and generates abnormal data identification results;

[0008] The fault information diagnosis module analyzes the cause of the abnormal data based on the abnormal data identification result, adjusts and resets the working parameters of the sensor, issues a warning signal, and generates a fault self-check processing record;

[0009] The power consumption data analysis module collects and analyzes the sensor power consumption data in real time based on the fault self-check processing record, evaluates the energy consumption level of the sensor under differentiated working conditions, and generates energy consumption mode analysis results;

[0010] The battery power management module adjusts the sensor operating frequency, sleep and wake-up cycles according to the energy consumption mode analysis results to match the actual energy consumption and performance requirements and generate power supply strategy adjustment parameters;

[0011] The transmission condition analysis module adjusts the parameters based on the power supply strategy and combines the geological characteristic data to evaluate the impact of various geological characteristics, temperature environments, and pressure levels on wireless signal transmission and generate environmental impact analysis results;

[0012] The signal adjustment and optimization module adjusts the channel and power settings of the wireless transmission device according to the environmental impact analysis results and combines the temperature and pressure data of the sensor to generate wireless data transmission parameters.

[0013] As a further solution of the present invention, the abnormal data identification results include abnormal temperature point data, abnormal pressure point information, and data fluctuation frequency analysis results; the fault self-detection processing record includes fault cause identification information, sensor adjustment parameters, and fault warning level information; the energy consumption mode analysis results include energy consumption trend analysis results, energy-saving potential point identification results, and energy consumption factor analysis results; the power supply strategy adjustment parameters include energy-saving frequency setting parameters, sleep cycle duration adjustment data, and wake-up trigger condition parameters; the environmental impact analysis results include signal attenuation interval information, geological obstacle feature information, and temperature and pressure impact rating data; the wireless data transmission parameters include channel selection results, power adjustment range, and signal enhancement parameters.

[0014] As a further solution of the present invention, the temperature and pressure data analysis module includes:

[0015] The sensor data acquisition submodule is based on the temperature and pressure sensor, which collects and records the temperature and pressure data of the current drilling depth in real time and generates temperature and pressure data records;

[0016] The historical data comparison submodule calculates the deviation between data points based on the temperature and pressure data records, combines the historical temperature and pressure data, compares with the real-time data, identifies the data change trend, and generates data deviation analysis results;

[0017] The temperature and pressure anomaly calibration submodule identifies and records abnormal data points that deviate from the normal range based on the data deviation analysis results, including sudden changes in temperature and pressure data, and generates abnormal data identification results.

[0018] As a further solution of the present invention, the fault information diagnosis module includes:

[0019] The sensor abnormality identification submodule analyzes the causes of abnormal data based on the abnormal data identification results, including performance fluctuations of temperature and pressure sensors and external environmental influences, and generates abnormal cause analysis results;

[0020] The sensor parameter calibration submodule adjusts the working parameters of the sensor with abnormal data based on the abnormal cause analysis result, including calibrating the offset, resetting the fault state, and generating fault reset calibration information;

[0021] The warning information matching submodule calculates warning levels for multiple faults based on the fault reset calibration information, combined with fault causes and processing results, and matches warning information, including fault causes, fault time, and processing records, to generate fault self-check processing records.

[0022] As a further solution of the present invention, the power consumption data analysis module includes:

[0023] The operation data acquisition submodule collects sensor power consumption and operation status data in real time based on the fault self-check processing record, analyzes and records energy consumption information of multiple sensors, and generates real-time energy consumption analysis data;

[0024] The working energy consumption evaluation submodule analyzes the changes in sensor power consumption under various working states based on the real-time energy consumption analysis data, analyzes the relationship between working efficiency and energy consumption, and generates multi-mode power consumption data;

[0025] The energy consumption level calculation submodule uses a support vector machine algorithm based on the multi-mode power consumption data and combines the working status to analyze and calculate the energy consumption levels of various sensors and generate energy consumption mode analysis results.

[0026] As a further solution of the present invention, the support vector machine algorithm is according to the formula:

[0027]

[0028] Calculate the sensor energy consumption level, where f is the calculation function, x is the current power consumption data point, T is the current temperature, P is the current pressure, D is the current drilling depth, H is the historical energy consumption average, α i is the Lagrange multiplier, y i is the category label of the data point, K is the kernel function, x i is the support vector, b is the bias term, w T is the temperature weight coefficient, w P is the weight coefficient of pressure, w D is the weight coefficient of drilling depth, w H is the weight coefficient of historical energy consumption, n is the number of support vectors, and i is the index used to identify the serial number of the support vector.

[0029] As a further solution of the present invention, the battery power management module includes:

[0030] The operation status evaluation submodule evaluates the power requirements and operation efficiency of various sensors based on the energy consumption mode analysis results, adjusts the operation frequency and sleep cycle parameters, optimizes power consumption, and generates power demand analysis results;

[0031] The operation mode adjustment submodule adjusts the operation frequency and power output of multiple sensors based on the power demand analysis result, adjusts the sleep and wake-up cycles to match the energy consumption and performance requirements, and generates power supply configuration parameters;

[0032] The power supply mode matching submodule uses a greedy algorithm based on the power supply configuration parameters, combines the sensor energy consumption and performance data, adjusts the sensor power supply strategy, optimizes the stability of the sensor operation, and generates power supply strategy adjustment parameters.

[0033] As a further solution of the present invention, the greedy algorithm is according to the formula:

[0034]

[0035] Calculate the total energy consumption of the sensor, where E is the total energy consumption and P i is the power consumption of the ith sensor, t i is the operating time of the i-th sensor, U is the environmental factor, V is the energy efficiency level, W is the temperature adjustment coefficient, i is the index variable used to represent the target sensor, and n is the total number of sensors.

[0036] As a further solution of the present invention, the transmission condition analysis module includes:

[0037] The drilling environment analysis submodule adjusts parameters based on the power supply strategy, collects soil and rock samples at the current drilling location, combines temperature and pressure sensors, records temperature and pressure information, and generates geological characteristic data records;

[0038] The signal impact assessment submodule analyzes the impact of temperature, pressure and geological environment on wireless signal transmission based on the geological characteristic data records, evaluates the factors that interfere with signal transmission, and generates signal interference analysis results;

[0039] The impact level calculation submodule analyzes the impact levels of various geological characteristics and environmental factors on wireless transmission based on the signal interference analysis results, and generates environmental impact analysis results.

[0040] As a further solution of the present invention, the signal adjustment optimization module includes:

[0041] The wireless signal analysis submodule analyzes the signal strength and interference area of ​​the wireless transmission equipment in real time based on the environmental impact analysis result, identifies the signal weak area and the channel and power setting that need to be optimized, and generates a signal strength analysis result;

[0042] The signal transmission adjustment submodule adjusts the channel selection of the wireless transmission equipment based on the signal strength analysis result, adjusts the channel and power parameters in combination with the geological environment and signal interference, optimizes the signal stability and transmission efficiency, and generates the transmission power adjustment parameters;

[0043] The power parameter setting submodule updates the channel and power parameter settings of the wireless transmission device based on the transmission power adjustment parameters and combines the energy efficiency and performance requirements of the sensor to generate wireless data transmission parameters.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by real-time monitoring of temperature and pressure changes, timely identification of abnormal data, combined with adjustment of sensor parameters and issuance of early warning signals, safety accidents and economic losses caused by fault delays are reduced, power consumption data is collected and analyzed in real time, and the energy consumption level of the sensor is evaluated to make energy management more efficient. By adjusting the operating frequency and wake-up cycle, performance requirements and actual energy consumption are matched, the service life of the equipment is extended and the maintenance cost is reduced. The impact of various geological characteristics on wireless signal transmission is evaluated in combination with geological characteristic data, and the channel and power settings of the wireless transmission equipment are adjusted according to environmental influences, thereby improving the accuracy and stability of data transmission, enhancing the adaptability and flexibility in the drilling process, and optimizing the overall efficiency and safety of geothermal energy development. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0048] Figure 3 It is a flow chart of the temperature and pressure data analysis module of the present invention;

[0049] Figure 4 This is a flow chart of the fault information diagnosis module of the present invention;

[0050] Figure 5 It is a flow chart of the power consumption data analysis module of the present invention;

[0051] Figure 6 This is a flow chart of the battery power management module of the present invention;

[0052] Figure 7 It is a flow chart of the transmission condition analysis module of the present invention;

[0053] Figure 8 This is a flow chart of the signal adjustment and optimization module of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] Embodiment 1

[0057] See also Figure 1 to Figure 2 , a wireless temperature and pressure measurement and transmission system for geothermal drilling comprises:

[0058] The temperature and pressure data analysis module is based on temperature and pressure sensors, collects and records temperature and pressure data in real time, identifies abnormal changes in temperature and pressure, and generates abnormal data identification results;

[0059] The fault information diagnosis module analyzes the causes of abnormal data based on the abnormal data identification results, adjusts and resets the working parameters of the sensor, issues a warning signal, and generates a fault self-check processing record;

[0060] The power consumption data analysis module collects and analyzes the sensor power consumption data in real time based on the fault self-diagnosis processing records, evaluates the energy consumption level of the sensor under differentiated working conditions, and generates energy consumption mode analysis results;

[0061] The battery power management module adjusts the sensor operating frequency, sleep and wake-up cycles according to the energy consumption mode analysis results to match the actual energy consumption and performance requirements and generate power supply strategy adjustment parameters;

[0062] The transmission condition analysis module adjusts parameters based on the power supply strategy and combines geological characteristic data to evaluate the impact of various geological characteristics, temperature environments, and pressure levels on wireless signal transmission and generate environmental impact analysis results;

[0063] The signal adjustment and optimization module adjusts the channel and power settings of the wireless transmission equipment according to the environmental impact analysis results and combines the sensor temperature and pressure data to generate wireless data transmission parameters.

[0064] The abnormal data identification results include abnormal temperature point data, abnormal pressure point information, and data fluctuation frequency analysis results. The fault self-detection processing records include fault cause identification information, sensor adjustment parameters, and fault warning level information. The energy consumption mode analysis results include energy consumption trend analysis results, energy-saving potential point identification results, and energy consumption factor analysis results. The power supply strategy adjustment parameters include energy-saving frequency setting parameters, sleep cycle duration adjustment data, and wake-up trigger condition parameters. The environmental impact analysis results include signal attenuation range information, geological obstacle feature information, and temperature and pressure impact rating data. The wireless data transmission parameters include channel selection results, power adjustment range, and signal enhancement parameters.

[0065] See also Figure 2 and Figure 3 , the temperature and pressure data analysis module includes:

[0066] The sensor data acquisition submodule is based on the temperature and pressure sensor, which collects and records the temperature and pressure data of the current drilling depth in real time. The specific process of generating temperature and pressure data records is as follows;

[0067] In the sensor data acquisition submodule, based on the temperature and pressure sensors, a continuous data acquisition process is configured to monitor and record the temperature and pressure at the drilling depth in real time at intervals of 1 second. Through the set data interface, the sensor output data, including timestamp, depth, temperature and pressure values, is received in real time to monitor the data integrity and accuracy in real time to ensure the reliability of the data. The data recording model D(t)=s(t)·α+p(t)·β is used, where D(t) represents the comprehensive data record at time t, s(t) is the temperature reading, p(t) is the pressure reading, α and β are the correction coefficients for temperature and pressure, respectively, which are used to adjust the sensor deviation and environmental interference to generate temperature and pressure data records.

[0068] The historical data comparison submodule is based on temperature and pressure data records, combines historical temperature and pressure data, compares with real-time data, calculates the deviation between data points, and identifies data change trends. The specific process of generating data deviation analysis results is as follows;

[0069] In the historical data comparison submodule, based on the temperature and pressure data records, the real-time collected data is compared with the historical data. The real-time data and historical data are matched and compared through the time series analysis method, and the data deviation analysis model is used to analyze the real-time data and historical data. Among them, B(t) represents the deviation ratio of the data point at time t, D(t) is the real-time data point, and H(t) is the historical data point for the same period. It identifies data stability and abnormal fluctuations, calculates the deviation between data points, and generates data deviation analysis results.

[0070] The temperature and pressure anomaly calibration submodule identifies and records abnormal data points that deviate from the normal range based on the data deviation analysis results, including temperature and pressure data mutations. The specific process of generating abnormal data identification results is as follows;

[0071] In the temperature and pressure anomaly calibration submodule, based on the data deviation analysis results, abnormal data points that deviate from the normal range, including temperature and pressure data, are identified and recorded. The abnormal threshold is automatically set according to the information obtained from the data deviation analysis results, and the multiple data points currently collected are continuously monitored. Each newly collected temperature and pressure data point is detected in real time. By comparing the relationship between the data point and the threshold, the temperature and pressure data that exceeds the threshold is identified and marked as anomaly points. The data is recorded in the abnormal event log, including the timestamp, temperature value, pressure value and deviation from the normal range of the data point, to identify and handle equipment failure risks and environmental anomalies, and generate abnormal data identification results.

[0072] See also Figure 2 and Figure 4 , the fault information diagnosis module includes:

[0073] The sensor abnormality identification submodule analyzes the causes of abnormal data based on the abnormal data identification results, including the performance fluctuations of temperature and pressure sensors and the influence of the external environment. The specific process of generating the abnormal cause analysis results is as follows;

[0074] In the sensor anomaly identification submodule, based on the abnormal data identification results, the output fluctuations of multiple sensors and the degree of deviation from historical data are analyzed to identify potential performance degradation and failures, including calculating the coefficient of variation and abnormal proportion of each sensor output, evaluating the performance stability of the sensor, and using the abnormal cause analysis model, the formula is: Among them, R represents the coefficient of variation, which is used to evaluate the fluctuation range, σ is the standard deviation of the measured data, and μ is the mean value. The causes of abnormal data are analyzed, including performance fluctuations of temperature and pressure sensors and external environmental influences, and the abnormal cause analysis results are generated.

[0075] The sensor parameter calibration submodule adjusts the working parameters of the sensor with abnormal data based on the abnormal cause analysis results, including calibration offset and resetting fault status. The specific process of generating fault reset calibration information is as follows:

[0076] In the sensor parameter calibration submodule, based on the abnormal cause analysis results, the parameters that affect the sensor performance are adjusted, including calibrating the sensor offset to match the historical calibration data and resetting the sensor's fault state to restore normal operation. The parameter adjustment formula is P new =P old +ΔP, where P new is the adjusted parameter, P old is the original parameter, ΔP is the offset calculated according to the cause of the abnormality, and the working parameters of the sensor with abnormal data are adjusted to generate fault reset calibration information.

[0077] The warning information matching submodule calculates the warning level for multiple faults and matches the warning information based on the fault reset calibration information, combined with the fault cause and processing result, including the fault cause, fault time, and processing record. The specific process of generating the fault self-check processing record is as follows;

[0078] In the early warning information matching submodule, based on the fault reset calibration information, combined with the fault cause and processing results, the early warning level allocation model is applied to define an early warning level for each fault according to the fault severity and impact range. The formula is Among them, L is the warning level, ranging from 1 to 5, and severity is the fault severity score, which is calculated based on the system scope and potential risks affected by the fault. The warning level is calculated for multiple faults and the warning information is matched, including the cause of the fault, fault time, and processing record, to generate a fault self-check processing record.

[0079] See also Figure 2 and Figure 5 , the power consumption data analysis module includes:

[0080] The operation data acquisition submodule collects sensor power consumption and operation status data in real time based on fault self-check processing records, analyzes and records energy consumption information of multiple sensors, and generates real-time energy consumption analysis data. The specific process is as follows;

[0081] In the operation data acquisition submodule, based on the fault self-detection processing records, the real-time monitoring system continuously tracks the power consumption and operating status of multiple sensors, including collecting the voltage, current, and working cycle data of each sensor, and calculating the total energy consumption. The real-time energy consumption calculation formula is E=V×I×t, where E represents energy consumption, V is voltage, I is current, and t is operating time. The energy consumption information of multiple sensors is analyzed and recorded to generate real-time energy consumption analysis data.

[0082] The working energy consumption evaluation submodule analyzes the changes in sensor power consumption under various working states based on real-time energy consumption analysis data, analyzes the relationship between working efficiency and energy consumption, and generates multi-mode power consumption data in the following specific process:

[0083] In the working energy consumption evaluation submodule, based on real-time energy consumption analysis data, the data trend analysis technology is used to analyze the changes in sensor power consumption under differentiated working conditions, including the calculation of standard deviation and average power consumption changes, and the impact on work efficiency. The relationship between energy consumption and efficiency is: Where η represents the work efficiency percentage, P output is the output power, E input For input energy, the relationship between work efficiency and energy consumption is analyzed, and multi-mode power consumption data is generated.

[0084] The energy consumption level calculation submodule uses the support vector machine algorithm based on the multi-mode power consumption data and combines the working status to analyze and calculate the energy consumption levels of various sensors. The specific process of generating the energy consumption mode analysis results is as follows;

[0085] In the energy consumption level calculation submodule, based on multi-mode power consumption data, power consumption information from various sensors, including temperature and pressure, is collected and analyzed. The data is preprocessed, including cleaning and format standardization, to ensure data consistency and quality. The support vector machine algorithm is used to analyze the energy consumption of each sensor in various working conditions, and the average energy consumption and peak energy consumption are calculated. The energy consumption level of each sensor is calculated according to the set evaluation criteria, including the absolute value of energy consumption, stability and predictability of changes, the energy efficiency performance of the sensor is identified, energy management is optimized and equipment energy efficiency is improved, and energy consumption pattern analysis results are generated.

[0086] Support vector machine algorithm, according to the formula:

[0087]

[0088] Calculate the sensor energy consumption level, where f is the calculation function used to predict the energy consumption level, x is the current power consumption data point, T is the current temperature, P is the current pressure, D is the current drilling depth, H is the historical energy consumption average, α i is the Lagrange multiplier, corresponding to each support vector, y i is the category label of the data point, K is the kernel function used to calculate the similarity between input data points, x i is the support vector, which is the key data point for model training, b is the bias term, which is used to adjust the prediction threshold, and w T is the temperature weight coefficient, w P is the weight coefficient of pressure, w D is the weight coefficient of drilling depth, w H is the weight coefficient of historical energy consumption, n is the number of support vectors, and i is the index used to identify the serial number of the support vector.

[0089] The specific execution process of the formula is as follows:

[0090] Real-time data collection of temperature T, pressure P, drilling depth D, and historical energy consumption average H are used to determine the introduced weight coefficient w through the gradient descent method. T , w P , w D , and w H , to ensure that the influence of environmental variables is reflected, real-time parameters and historical data are substituted into the model, the interaction between data points and the weighted contribution of weight coefficients are processed through kernel functions, and the level prediction value is generated. The energy consumption level of multiple sensors is determined in combination with the bias term b, ensuring that the model can provide accurate energy consumption analysis results in a variety of drilling environments.

[0091] See also Figure 2 and Figure 6 , the battery power management module includes:

[0092] The operation status evaluation submodule evaluates the power requirements and operation efficiency of various sensors based on the energy consumption mode analysis results, adjusts the operation frequency and sleep cycle parameters, optimizes power consumption, and generates the specific process of power demand analysis results as follows;

[0093] In the operation status evaluation submodule, based on the energy consumption mode analysis results, the power demand and operation efficiency of various sensors are evaluated, including analyzing the current energy consumption level and performance output of each sensor, identifying the optimal operation frequency and sleep cycle, and the power demand and operation efficiency adjustment formula is: Among them, F new Represents the adjusted operating frequency, F current is the current operating frequency, L is the energy consumption level, the operating frequency and sleep cycle parameters are adjusted to optimize power consumption and generate power demand analysis results.

[0094] The operation mode adjustment submodule adjusts the operation frequency and power output of multiple sensors based on the power demand analysis results, adjusts the sleep and wake-up cycles to match the energy consumption and performance requirements, and generates the specific process of power supply configuration parameters as follows;

[0095] In the operation mode adjustment submodule, based on the power demand analysis results, the ideal operating frequency and power output of each sensor are identified, performance requirements and energy consumption are balanced, and the operating frequency and power output adjustment formula is used. Among them, P adjust is the adjusted power output, P current is the current power output, ΔF is the frequency adjustment, F max Generate power supply configuration parameters for maximum operating frequency, adjusting sleep and wake cycles to match energy consumption and performance requirements.

[0096] The power supply mode matching submodule uses a greedy algorithm based on the power supply configuration parameters, combined with the sensor energy consumption and performance data, to adjust the sensor's power supply strategy and optimize the stability of the sensor's operation. The specific process of generating the power supply strategy adjustment parameters is as follows;

[0097] In the power supply mode matching submodule, based on the power supply configuration parameters, a greedy algorithm is used to collect and analyze the energy consumption and performance data of multiple sensors, including the power consumption and performance of the sensors under differentiated working conditions, evaluate the power requirements of each sensor, and optimize the power supply strategy by a step-by-step adjustment method based on the performance requirements. By simulating the impact of differentiated power supply modes on sensor performance, the strategy is adjusted to identify the optimal energy consumption and performance balance parameters, ensure the stability of the operation of multiple sensors, and generate power supply strategy adjustment parameters.

[0098] Greedy algorithm, according to the formula:

[0099]

[0100] Calculate the total energy consumption of the sensor, where E is the total energy consumption, which represents the cumulative energy consumption of all sensors, and P i is the power consumption of the ith sensor, which means the power consumed by the ith sensor per unit time, t i is the running time of the i-th sensor, indicating the length of time the i-th sensor is active. U is the environmental factor, which is used to adjust the sensor energy consumption calculation and reflects the impact of the surrounding environment on energy consumption. V is the energy efficiency level, which indicates the efficiency of the sensor in energy use. W is the temperature adjustment coefficient, which adjusts the energy consumption according to the temperature change of the sensor's environment. It is used to calculate the energy consumption changes at various temperatures. i is the index variable used to represent the target sensor. n is the total number of sensors, indicating the total number of sensors involved in the energy consumption calculation.

[0101] The specific execution process of the formula is as follows:

[0102] Collect basic operating data of multiple sensors under differentiated working conditions, including power consumption and operating time. According to the actual environment in which the sensor is located, adjust the environmental factor U, energy efficiency level V, and temperature adjustment coefficient W. Combine the original power and time data to calculate the total energy consumption of each sensor. Adjust the power supply strategy of the sensor based on the calculation results to optimize energy efficiency.

[0103] See also Figure 2 and Figure 7 , the transmission condition analysis module includes:

[0104] The drilling environment analysis submodule adjusts parameters based on the power supply strategy, collects soil and rock samples at the current drilling location, combines temperature and pressure sensors, records temperature and pressure information, and generates geological characteristic data records. The specific process is as follows;

[0105] In the drilling environment analysis submodule, parameters are adjusted based on the power supply strategy, soil and rock sample collection at the drilling location is implemented, and the geological characteristics of the environment are analyzed in combination with the temperature and pressure sensor data, including the use of enhanced geological analysis tools to perform composition analysis and structural analysis on the collected samples, and record the mineral content and distribution of multiple samples. The geological characteristic data recording formula is G=f(C,T,P), where G represents geological characteristic data, C is the chemical composition of rock and soil, T and P are the measured values ​​of temperature and pressure, respectively, to identify the geological stability and risk factors of the current drilling location, and generate geological characteristic data records.

[0106] The signal impact assessment submodule analyzes the impact of temperature, pressure and geological environment on wireless signal transmission based on geological characteristic data records, evaluates the factors that interfere with signal transmission, and generates the specific process of signal interference analysis results as follows;

[0107] In the signal impact assessment submodule, based on the geological characteristic data records, the impact of temperature, pressure, and geological environment changes on wireless signal transmission is evaluated, the signal attenuation, reflection, and refraction indicators are analyzed and calculated, and various interference factors are identified and quantified. The calculation formula for signal interference analysis is: Where I represents the signal interference index, k is the environmental sensitivity coefficient, ΔT and ΔP represent the temperature and pressure changes respectively, T 0 and P 0 is the reference temperature and pressure, R is the geological reflection coefficient, and the factors interfering with signal transmission are evaluated to generate signal interference analysis results.

[0108] The impact level calculation submodule analyzes the impact level of various geological characteristics and environmental factors on wireless transmission based on the signal interference analysis results. The specific process of generating environmental impact analysis results is as follows:

[0109] In the impact level calculation submodule, based on the signal interference analysis results, the impact of differentiated geological characteristics and environmental factors, including terrain undulations and mineral content on wireless transmission, is analyzed using an environmental impact assessment model with the formula: Where L represents the total impact level, w i is the weight of the target factor, I i Evaluate and quantify the impact of environmental factors on wireless transmission performance for individual impact levels of target factors, and generate environmental impact analysis results.

[0110] See also Figure 2 and Figure 8 , the signal adjustment and optimization module includes:

[0111] The wireless signal analysis submodule analyzes the signal strength and interference area of ​​the wireless transmission equipment in real time based on the environmental impact analysis results, identifies signal weak areas and channels and power settings that need to be optimized, and generates the signal strength analysis results in the following specific process:

[0112] In the wireless signal analysis submodule, based on the environmental impact analysis results, the signal strength and interference area of ​​the wireless transmission equipment are monitored in real time, including using the signal strength and interference data acquisition algorithm to measure and record the signal quality indicators of each area, including signal attenuation and noise ratio. The signal strength analysis formula is S strength =S received -(L path +L interference ), where S strength Represents the signal strength,

[0113] S received is the received signal strength, L path is the path loss, L interference Generates signal strength analysis results for interference loss, identifying areas of signal weakness and channels and power settings that require optimization.

[0114] The signal transmission adjustment submodule adjusts the channel selection of the wireless transmission equipment based on the signal strength analysis results, adjusts the channel and power parameters in combination with the geological environment and signal interference, optimizes the signal stability and transmission efficiency, and generates the specific process of the transmission power adjustment parameters as follows;

[0115] In the signal transmission adjustment submodule, based on the signal strength analysis results, the channel and power of the wireless transmission equipment are adjusted, the combined impact of the geological environment and signal interference is considered, and the signal loss and interference are optimized. The channel and power adjustment formula is: Among them, P new is the adjusted power, P old is the original power, ΔS is the signal strength difference, S optimal It is the target signal strength. Through this adjustment, the signal stability and transmission efficiency are optimized and the transmission power adjustment parameters are generated.

[0116] The power parameter setting submodule updates the channel and power parameter settings of the wireless transmission device based on the transmission power adjustment parameters and combines the sensor energy efficiency and performance requirements to generate the specific process of wireless data transmission parameters as follows;

[0117] In the power parameter setting submodule, based on the transmission power adjustment parameters, the channel and power settings of the wireless transmission equipment are updated. Combined with the energy efficiency and performance requirements of the sensor, the optimal channel allocation and power output are calculated. The formula for updating the parameters is P final =adjust(P new ,E efficiency), where P final is the final set power, P new is the recommended power adjustment value, E efficiency Considering the energy efficiency of sensors, the operating energy consumption and data transmission efficiency of wireless devices are optimized, and wireless data transmission parameters are generated.

[0118] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A wireless temperature and pressure measurement and transmission system for geothermal drilling, characterized in that: The system comprises: The temperature and pressure data analysis module is based on temperature and pressure sensors, collects and records temperature and pressure data in real time, identifies abnormal changes in temperature and pressure, and generates abnormal data identification results; The fault information diagnosis module analyzes the cause of the abnormal data based on the abnormal data identification result, adjusts and resets the working parameters of the sensor, issues a warning signal, and generates a fault self-check processing record; The power consumption data analysis module collects and analyzes the sensor power consumption data in real time based on the fault self-check processing record, evaluates the energy consumption level of the sensor under differentiated working conditions, and generates energy consumption mode analysis results; The battery power management module adjusts the sensor operating frequency, sleep and wake-up cycles according to the energy consumption mode analysis results to match the actual energy consumption and performance requirements and generate power supply strategy adjustment parameters; The transmission condition analysis module adjusts the parameters based on the power supply strategy and combines the geological characteristic data to evaluate the impact of various geological characteristics, temperature environments, and pressure levels on wireless signal transmission and generate environmental impact analysis results; The signal adjustment and optimization module adjusts the channel and power settings of the wireless transmission device according to the environmental impact analysis results and combines the temperature and pressure data of the sensor to generate wireless data transmission parameters; The power consumption data analysis module includes: The operation data acquisition submodule collects sensor power consumption and operation status data in real time based on the fault self-check processing record, analyzes and records energy consumption information of multiple sensors, and generates real-time energy consumption analysis data; The working energy consumption evaluation submodule analyzes the changes in sensor power consumption under various working states based on the real-time energy consumption analysis data, analyzes the relationship between working efficiency and energy consumption, and generates multi-mode power consumption data; The energy consumption level calculation submodule uses a support vector machine algorithm based on the multi-mode power consumption data and combines the working status to analyze and calculate the energy consumption levels of various sensors and generate energy consumption mode analysis results; The battery power management module comprises: The operation status evaluation submodule evaluates the power requirements and operation efficiency of various sensors based on the energy consumption mode analysis results, adjusts the operation frequency and sleep cycle parameters, optimizes power consumption, and generates power demand analysis results; The operation mode adjustment submodule adjusts the operation frequency and power output of multiple sensors based on the power demand analysis result, adjusts the sleep and wake-up cycles to match the energy consumption and performance requirements, and generates power supply configuration parameters; The power supply mode matching submodule uses a greedy algorithm based on the power supply configuration parameters, combines the sensor energy consumption and performance data, adjusts the sensor power supply strategy, optimizes the stability of the sensor operation, and generates power supply strategy adjustment parameters; The transmission condition analysis module comprises: The drilling environment analysis submodule adjusts parameters based on the power supply strategy, collects soil and rock samples at the current drilling location, combines temperature and pressure sensors, records temperature and pressure information, and generates geological characteristic data records; The signal impact assessment submodule analyzes the impact of temperature, pressure and geological environment on wireless signal transmission based on the geological characteristic data records, evaluates the factors that interfere with signal transmission, and generates signal interference analysis results; The impact level calculation submodule analyzes the impact levels of various geological characteristics and environmental factors on wireless transmission based on the signal interference analysis results, and generates environmental impact analysis results; The signal adjustment and optimization module comprises: The wireless signal analysis submodule analyzes the signal strength and interference area of ​​the wireless transmission equipment in real time based on the environmental impact analysis result, identifies the signal weak area and the channel and power setting that need to be optimized, and generates a signal strength analysis result; The signal transmission adjustment submodule adjusts the channel selection of the wireless transmission equipment based on the signal strength analysis result, adjusts the channel and power parameters in combination with the geological environment and signal interference, optimizes the signal stability and transmission efficiency, and generates the transmission power adjustment parameters; The power parameter setting submodule updates the channel and power parameter settings of the wireless transmission device based on the transmission power adjustment parameters and combines the energy efficiency and performance requirements of the sensor to generate wireless data transmission parameters.

2. The wireless temperature and pressure measurement and transmission system for geothermal drilling according to claim 1, characterized in that: The abnormal data identification results include abnormal temperature point data, abnormal pressure point information, and data fluctuation frequency analysis results; the fault self-detection processing record includes fault cause identification information, sensor adjustment parameters, and fault warning level information; the energy consumption mode analysis results include energy consumption trend analysis results, energy-saving potential point identification results, and energy consumption factor analysis results; the power supply strategy adjustment parameters include energy-saving frequency setting parameters, sleep cycle duration adjustment data, and wake-up trigger condition parameters; the environmental impact analysis results include signal attenuation interval information, geological obstacle feature information, and temperature and pressure impact rating data; the wireless data transmission parameters include channel selection results, power adjustment range, and signal enhancement parameters.

3. The wireless temperature and pressure measurement and transmission system for geothermal drilling according to claim 1, characterized in that: The temperature and pressure data analysis module includes: The sensor data acquisition submodule is based on the temperature and pressure sensor, which collects and records the temperature and pressure data of the current drilling depth in real time and generates temperature and pressure data records; The historical data comparison submodule calculates the deviation between data points based on the temperature and pressure data records, combines the historical temperature and pressure data, compares with the real-time data, identifies the data change trend, and generates data deviation analysis results; The temperature and pressure anomaly calibration submodule identifies and records abnormal data points that deviate from the normal range based on the data deviation analysis results, including sudden changes in temperature and pressure data, and generates abnormal data identification results.

4. The wireless temperature and pressure measurement and transmission system for geothermal drilling according to claim 1, characterized in that: The fault information diagnosis module includes: The sensor abnormality identification submodule analyzes the causes of abnormal data based on the abnormal data identification results, including performance fluctuations of temperature and pressure sensors and external environmental influences, and generates abnormal cause analysis results; The sensor parameter calibration submodule adjusts the working parameters of the sensor with abnormal data based on the abnormal cause analysis result, including calibrating the offset, resetting the fault state, and generating fault reset calibration information; The warning information matching submodule calculates warning levels for multiple faults based on the fault reset calibration information, combined with fault causes and processing results, and matches warning information, including fault causes, fault time, and processing records, to generate fault self-check processing records.

5. The wireless temperature and pressure measurement and transmission system for geothermal drilling according to claim 1, characterized in that: The support vector machine algorithm is based on the formula: Calculate the sensor energy consumption level, where f is the calculation function, x is the current power consumption data point, T is the current temperature, P is the current pressure, D is the current drilling depth, H is the historical energy consumption average, α i is the Lagrange multiplier, y i is the category label of the data point, K is the kernel function, x i is the support vector, b is the bias term, w T is the temperature weight coefficient, w P is the weight coefficient of pressure, w D is the weight coefficient of drilling depth, w H is the weight coefficient of historical energy consumption, n is the number of support vectors, and i is the index used to identify the serial number of the support vector.

6. The wireless temperature and pressure measurement and transmission system for geothermal drilling according to claim 1, characterized in that: The greedy algorithm, according to the formula: Calculate the total energy consumption of the sensor, where E is the total energy consumption and P i is the power consumption of the ith sensor, t i is the operating time of the i-th sensor, U is the environmental factor, V is the energy efficiency level, W is the temperature adjustment coefficient, i is the index variable used to represent the target sensor, and n is the total number of sensors.

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