An intelligent hydraulic power generation device and system for deep-sea oil drilling

Abstract

The present invention relates to the technical field of hydraulic generators, and more particularly to an intelligent hydraulic power generation device and system for deep-sea oil drilling. The device comprises a data monitoring module, a drilling monitoring module, a stability monitoring module, and a generator management module. The module is configured to calculate a management gap value based on the flow matching of the hydraulic generator, determine a hydraulic generator management mode based on the management gap value, determine the necessity of auxiliary operation based on the stability of the hydraulic generator and the simulation prediction analysis results, perform auxiliary optimization of the hydraulic generator management mode based on the auxiliary necessity and auxiliary power generation data, determine an auxiliary margin based on energy storage data, and adjust the auxiliary optimization process based on the auxiliary margin. The present invention improves the energy conversion efficiency of hydraulic generators in deep-sea oil drilling environments and maintains power stability.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic generators, and in particular to an intelligent hydraulic power generation device and system for deep-sea oil drilling. Background Art

[0002] The application prospects of hydraulic generators in deep-sea oil drilling are relatively broad. In deep-sea environments, traditional power supply methods, such as relying on sea-surface platforms to transmit electricity or using independent power generation equipment, may be affected by various factors, resulting in unstable power supply. Hydraulic generators, as backup or auxiliary power sources, can provide timely power support when problems occur in the main power supply, ensuring the operation of key equipment. From a long-term operation perspective, hydraulic generators can significantly reduce mining costs by recycling energy and reducing external energy consumption. The power of hydraulic generators comes from the flow of drilling mud. During the actual drilling process, the mud flow will fluctuate frequently due to factors such as formation characteristics and drill bit working conditions, affecting the normal operation of electrical equipment on board and may even damage precision instruments. In addition, in the complex deep-sea environment, the energy conversion efficiency of hydraulic generators is difficult to maintain constant. Changes in environmental factors such as temperature and pressure, as well as wear and aging of the equipment itself, will cause changes in turbine efficiency, hydraulic transmission efficiency, and generator power generation efficiency, further exacerbating the instability of power output and increasing the difficulty of maintaining a stable power supply.

[0003] Chinese Patent Publication No. CN105736216B discloses a device for generating electricity using formation pressure energy and residual pressure energy during oil drilling. The device comprises: a formation pressure energy generating device comprising a single-inlet, dual-outlet speed tuning valve and a speed-control linkage actuator, a filter and separation buffer, a multi-head spiral plate centrifugal impeller rotor-type gas-liquid phase turbine, a high-backpressure natural gas turbine, and two generators; and a residual pressure energy generating device comprising a piston-type single-stroke natural gas engine unit and a piston-type two-stroke natural gas engine unit connected in parallel to the high-pressure outlet of a higher-level station. This solution only addresses how to generate electricity using the power source generated during oil drilling and does not consider unstable power output. This makes it unsuitable for applications such as deep-sea oil drilling, which require high power stability and operate in harsh environments. Summary of the Invention

[0004] To this end, the present invention provides an intelligent hydraulic power generation device and system for deep-sea oil drilling, so as to overcome the problem of low power stability caused by the difficulty in maintaining constant energy conversion efficiency of hydraulic generators in deep-sea oil drilling environments in the prior art.

[0005] To achieve the above objectives, the present invention provides, on the one hand, an intelligent hydraulic power generation device for deep-sea oil drilling, comprising:

[0006] Drilling ships, used to carry hydraulic generators, auxiliary generators, smart sensors and smart hydraulic power generation systems for deep-sea oil drilling;

[0007] Hydraulic generator, used for generating hydraulic power to provide power for oil drilling, which is installed on the drilling ship;

[0008] Auxiliary generator, used to generate electricity when there are fluctuations in hydraulic power generation, to provide power for oil drilling, which is installed on the drilling ship;

[0009] Intelligent sensors, used to collect oil drilling information, hydraulic power generation information, and auxiliary power generation information, are installed on the drilling ship and connected to the hydraulic generator and auxiliary generator;

[0010] The intelligent hydraulic power generation system is used to intelligently control the hydraulic generator and the auxiliary generator based on the oil drilling data, hydraulic power generation data and auxiliary power generation data collected by the intelligent sensors. The system is connected to the hydraulic generator, the auxiliary generator and the intelligent sensors.

[0011] On the other hand, the present invention also provides an intelligent hydraulic power generation system for deep-sea oil drilling, comprising:

[0012] a data monitoring module for performing calibration monitoring on data acquisition results of the smart sensor to obtain calibration monitoring results, wherein the data acquisition results include oil drilling data, hydraulic power generation data, auxiliary power generation data, and energy storage data, and for calibrating the smart sensor based on the calibration monitoring results;

[0013] a drilling monitoring module for monitoring a current drilling flow interval based on the oil drilling data and for adjusting a calibration monitoring process of an intelligent sensor based on the current drilling flow interval;

[0014] a stability monitoring module for analyzing the flow matching of the hydraulic generator based on the current drilling flow interval and hydraulic power generation data, and monitoring the stability of the hydraulic generator based on the flow matching of the hydraulic generator; analyzing the mutation properties of the flow matching of the hydraulic generator, and optimizing the adjustment scheme of the intelligent sensor calibration monitoring process based on the mutation properties of the flow matching of the hydraulic generator; and performing simulation prediction analysis based on the flow matching of the hydraulic generator, and adjusting the mutation properties of the flow matching of the hydraulic generator based on the simulation prediction analysis results;

[0015] The generator management module is used to calculate the management gap value based on the flow matching of the hydraulic generator, and to determine the management mode of the hydraulic generator based on the management gap value. It is also used to determine the necessity of assistance based on the stability of the hydraulic generator and the simulation prediction analysis results, and to perform auxiliary optimization of the hydraulic generator management mode based on the auxiliary necessity and auxiliary power generation data. It is also used to determine the auxiliary margin based on the energy storage data, and to adjust the auxiliary optimization process based on the auxiliary margin.

[0016] Furthermore, the data monitoring module calculates the data difference ΔU of the homologous sensor according to the data acquisition result, compares the data difference ΔU of the homologous sensor with the preset data difference ΔU0 of the homologous sensor, and calibrates and monitors the data acquisition result of the smart sensor according to the comparison result, wherein:

[0017] When △U≤△U0, the data monitoring module determines that the data acquisition result of the smart sensor corresponding to the data difference of the homologous sensor is accurate;

[0018] When ΔU>ΔU0, the data monitoring module determines that the data collection result of the smart sensor corresponding to the data difference of the homologous sensor is inaccurate.

[0019] Furthermore, the data monitoring module calibrates the smart sensor according to the calibration monitoring result, wherein:

[0020] If the data acquisition result of the smart sensor corresponding to the data difference of the homologous sensor is accurate, the data monitoring module does not calibrate the smart sensor;

[0021] If the data collection result of the smart sensor corresponding to the homologous sensor data difference is inaccurate, the data monitoring module identifies the homologous sensor and performs smart calibration on the homologous sensor through a sensor smart calibration method, and uses the sensor smart calibration method as an adjustment scheme for the smart sensor calibration monitoring process.

[0022] Furthermore, the drilling monitoring module compares the real-time mud flow Q in the oil drilling data with each preset mud flow, and performs initial monitoring of the current drilling flow interval based on the comparison result, wherein:

[0023] When Q≤Q L When , the drilling monitoring module determines that the current drilling flow interval is a low flow interval;

[0024] When Q L <Q≤Q N When , the drilling monitoring module determines that the current drilling flow interval is a medium flow interval;

[0025] When Q N When <Q, the drilling monitoring module determines that the current drilling flow interval is a high flow interval.

[0026] Furthermore, when the current drilling flow interval is a low flow interval, the drilling monitoring module compares the real-time drilling pressure P in the oil drilling data with each preset drilling pressure, judges the drilling status based on the comparison result, and performs secondary monitoring of the current drilling flow interval based on the drilling status, wherein:

[0027] When P≤P L When , the drilling monitoring module determines that the drilling state is low-power drilling and determines that the current drilling flow interval is a normal flow interval;

[0028] When P L <P≤P N When , the drilling monitoring module determines that the drilling state is medium-power drilling and determines that the current drilling flow interval is a normal flow interval;

[0029] When P N When <P, the drilling monitoring module determines that the drilling state is high-power drilling and determines that the current drilling flow rate interval is an abnormal flow rate interval.

[0030] Furthermore, the drilling monitoring module adjusts the calibration monitoring process of the smart sensor according to the current drilling flow interval, wherein:

[0031] If the current drilling flow interval is a high flow interval, the drilling monitoring module does not adjust the calibration monitoring process of the smart sensor;

[0032] If the current drilling flow interval is a medium flow interval, the drilling monitoring module does not adjust the calibration monitoring process of the smart sensor;

[0033] If the current drilling flow interval is a low flow interval and is a normal flow interval, the drilling monitoring module does not adjust the calibration monitoring process of the smart sensor;

[0034] If the current drilling flow interval is a low flow interval and an abnormal flow interval, the drilling monitoring module adjusts the calibration monitoring process of the smart sensor and adjusts the smart sensor to a fine sensor.

[0035] Furthermore, the stability monitoring module calculates the current hydraulic power generation coefficient K based on the hydraulic generator blade length L, the hydraulic generator blade angle θ, the hydraulic generator channel cross-sectional area S, and the hydraulic generator channel surface roughness R in the hydraulic power generation data, sets K=α1×(L / Lmax)×sinθ+α2×(S / Smax)×(1-R / Rmax), compares the current hydraulic power generation coefficient K with the current drilling flow interval adaptive power generation coefficient threshold km, analyzes the hydraulic generator flow matching according to the comparison result, and monitors the hydraulic generator stability according to the hydraulic generator flow matching, wherein:

[0036] When K∈km, the stability monitoring module determines that the flow matching of the hydraulic generator is high and the stability of the hydraulic generator is high;

[0037] when When , the stability monitoring module determines that the flow matching of the hydraulic generator is low and the stability of the hydraulic generator is low.

[0038] Furthermore, the drilling monitoring module compares the matching change time t with the preset matching change time t0, analyzes the sudden change properties of the flow matching of the hydraulic generator according to the comparison result, and optimizes the adjustment scheme of the smart sensor calibration monitoring process according to the sudden change properties of the flow matching of the hydraulic generator, wherein:

[0039] When t≤t0, the drilling monitoring module determines that the mutation attribute of the flow matching of the hydraulic generator is a mutation, and optimizes the adjustment scheme of the smart sensor calibration monitoring process to no adjustment;

[0040] When t>t0, the drilling monitoring module determines that the mutation attribute of the flow matching of the hydraulic generator does not exist, and does not optimize the adjustment scheme of the smart sensor calibration monitoring process.

[0041] Furthermore, the drilling monitoring module inputs the oil drilling data and the hydraulic power generation data into a flow matching simulation prediction model, performs simulation prediction analysis on the flow matching of the hydraulic generator through the flow matching simulation prediction model, obtains a simulation analysis continuous change time ta, uses the simulation analysis continuous change time ta as a simulation prediction analysis result, compares the simulation analysis continuous change time ta with a preset simulation analysis continuous change time ta0, judges the sudden change continuous attribute according to the comparison result, and adjusts the sudden change attribute of the flow matching of the hydraulic generator according to the judgment result, wherein:

[0042] When ta≤ta0, the drilling monitoring module determines that the mutation continuity attribute does not have a continuous mutation, and does not adjust the mutation attribute of the flow matching of the hydraulic generator;

[0043] When ta>ta0, the drilling monitoring module determines that the mutation continuity attribute is the presence of continuous mutations, and adjusts the mutation attribute of the flow matching of the hydraulic generator from the presence of mutations to the absence of mutations.

[0044] Compared with the prior art, the beneficial effect of the present invention is that the system calibrates and monitors various data such as oil drilling, hydraulic power generation, auxiliary power generation and energy storage collected by smart sensors through the data monitoring module, and judges the accuracy of the data by comparing data with the same content but different collection sources, so as to ensure that subsequent modules perform analysis and decision-making based on accurate data, avoid misjudgment and improper operation caused by data errors, and lay the foundation for the stable operation and efficient management of the entire system. In addition, the system calibrates the smart sensors according to the calibration monitoring results through the data monitoring module. When the data is found to be inaccurate, the sensor intelligent calibration method is used to calibrate the same-source sensor to maintain the high-precision data of the smart sensor. The system can improve the data collection capability, reduce the cost and time of manual calibration, improve the automation and intelligence level of data collection, and ensure the long-term stability of data quality. The system uses the drilling monitoring module to accurately judge the current drilling flow range based on the real-time mud flow in the oil drilling data, and provide a basis for subsequent targeted equipment management and optimization, so as to grasp the intensity and status of the drilling operation, discover abnormal situations in time, and provide support for ensuring the safety and efficiency of the drilling operation. In addition, the system uses the drilling monitoring module to adjust the calibration and monitoring process of the smart sensor according to different drilling flow ranges. When in the low flow range and the abnormal flow range, the smart sensor is adjusted to a fine sensor to adapt to the data under different working conditions. The system meets the requirements of accuracy, thereby improving the adaptability and reliability of data acquisition and ensuring that accurate data can be obtained under various drilling conditions. The system analyzes the flow matching of the hydraulic generator in combination with the current drilling flow range and hydraulic power generation data through the stability monitoring module, and then monitors the stability of the hydraulic generator. By setting the current hydraulic power generation coefficient and comparing it with the adaptive power generation coefficient threshold, the stability status of the generator can be accurately judged, helping operators to promptly discover potential stability problems, take measures to prevent failures in advance, and ensure the stable operation of the hydraulic generator. The system analyzes the mutation properties of the flow matching of the hydraulic generator through the stability monitoring module, and optimizes the calibration and monitoring process of the intelligent sensor accordingly. The adjustment plan makes the calibration more scientific and reasonable. At the same time, the subsequent continuous change time of the flow matching of the hydraulic generator is analyzed through simulation prediction, and the mutation attributes are adjusted according to the results, so as to predict the change trend of the generator operation status in advance, provide forward-looking guidance for equipment maintenance and management, and improve the reliability and adaptability of the entire system. The system also calculates the management gap value according to the flow matching of the hydraulic generator through the generator management module, and then determines the management mode of the hydraulic generator, providing a quantitative basis for the operation management of the hydraulic generator, and comprehensively considers the stability of the hydraulic generator and the simulation prediction analysis results to determine the necessity of auxiliary power generation, and combines the auxiliary power generation data to assist in optimizing the management mode of the hydraulic generator.At the same time, the auxiliary margin is judged based on the energy storage data, and the auxiliary optimization process is adjusted to achieve reasonable distribution and efficient utilization of energy, thereby improving the economy and stability of the entire power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic structural diagram of an intelligent hydraulic power generation device for deep-sea oil drilling according to this embodiment;

[0046] Figure 2 Schematic diagram of the structure of the intelligent hydraulic power generation system for deep-sea oil drilling according to this embodiment. DETAILED DESCRIPTION

[0047] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0048] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0050] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0051] See also Figure 1 , which is a schematic structural diagram of an intelligent hydraulic power generation device for deep-sea oil drilling according to this embodiment, the device comprises:

[0052] A drilling vessel 1 is used to carry a hydraulic generator 2, an auxiliary generator 3, an intelligent sensor 4, and an intelligent hydraulic power generation system 5 for oil drilling in the deep sea;

[0053] A hydraulic generator 2, used for generating hydraulic power to provide power for oil drilling, is provided on the drilling ship 1;

[0054] An auxiliary generator 3 is used to generate electricity when there are fluctuations in hydraulic power generation to provide power for oil drilling, and is provided on the drilling ship 1;

[0055] The intelligent sensor 4 is used to collect oil drilling information, hydraulic power generation information and auxiliary power generation information. It is set on the drilling ship 1 and is connected to the hydraulic generator 2 and the auxiliary generator 3;

[0056] The intelligent hydraulic power generation system 5 is used to intelligently control the hydraulic generator 2 and the auxiliary generator 3 based on the oil drilling data, hydraulic power generation data and auxiliary power generation data collected by the intelligent sensor 4. It is connected to the hydraulic generator 2, the auxiliary generator 3 and the intelligent sensor 4.

[0057] Specifically, the device is set up in a deep-sea environment to carry out oil drilling operations efficiently and stably. The drilling ship serves as the carrier of the entire device, enabling the hydraulic generator, auxiliary generator, intelligent sensor and intelligent hydraulic power generation system to carry out oil drilling operations in the deep sea. The drilling ship has good stability and maneuverability, and can locate and perform drilling operations in different deep-sea areas, thereby providing a feasible platform for the exploitation of deep-sea oil resources. The hydraulic generator uses the energy of mud flow in the oil drilling process to generate hydraulic power, converting kinetic energy that may have been wasted into electrical energy to provide power for oil drilling, thereby improving energy utilization efficiency, reducing dependence on traditional energy, and reducing operating costs. The hydraulic generator is specially designed for the working conditions of deep-sea oil drilling, and can realize energy conversion more efficiently according to changes in parameters such as mud flow and pressure. When the hydraulic power generation of the auxiliary generator fluctuates due to various reasons and cannot meet the stable demand for electricity for oil drilling, the auxiliary generator quickly starts to generate electricity, thereby ensuring that the drilling equipment can obtain a continuous and stable power supply under any circumstances, avoiding power outages caused by electricity. Power outages cause drilling operations to stall, ensuring the continuity and safety of drilling operations. The system flexibly adjusts power generation based on actual power demand and works in conjunction with the hydraulic generator. When the hydraulic generator's power generation is insufficient but not completely interrupted, the auxiliary generator can supplement power on demand, enabling the entire power generation system to better adapt to the dynamic changes in power demand during drilling. The intelligent sensors simultaneously collect oil drilling information, hydraulic power generation information, and auxiliary power generation information, providing a comprehensive and accurate basis for subsequent intelligent control and equipment status monitoring. Real-time data collection reflects the operating status of each device in real time. The intelligent hydraulic power generation system intelligently controls the hydraulic generator and auxiliary generator based on the data collected by the intelligent sensors, precisely adjusting the power generation of the two generators according to different drilling conditions and power demand, achieving coordinated operation. Furthermore, by analyzing large amounts of data, it predicts power demand trends and adjusts the generator's operating mode in advance, achieving optimized energy management, avoiding energy waste, and improving the overall energy utilization efficiency. This also helps extend the service life of equipment and reduce maintenance costs.

[0058] See also Figure 2 FIG. 1 is a schematic diagram of the structure of an intelligent hydraulic power generation system for deep-sea oil drilling according to an embodiment of the present invention. The system includes:

[0059] a data monitoring module for performing calibration monitoring on data acquisition results of the smart sensor to obtain calibration monitoring results, wherein the data acquisition results include oil drilling data, hydraulic power generation data, auxiliary power generation data, and energy storage data, and for calibrating the smart sensor based on the calibration monitoring results;

[0060] a drilling monitoring module, configured to monitor a current drilling flow interval based on the oil drilling data and to adjust a calibration monitoring process of the smart sensor based on the current drilling flow interval, the drilling monitoring module being connected to the data monitoring module;

[0061] a stability monitoring module for analyzing the flow matching of the hydraulic generator based on the current drilling flow interval and hydraulic power generation data, and monitoring the stability of the hydraulic generator based on the flow matching of the hydraulic generator; analyzing the mutation properties of the flow matching of the hydraulic generator, and optimizing the adjustment scheme of the intelligent sensor calibration monitoring process based on the mutation properties of the flow matching of the hydraulic generator; performing simulation prediction analysis based on the flow matching of the hydraulic generator, and adjusting the mutation properties of the flow matching of the hydraulic generator based on the simulation prediction analysis results; and connecting the stability monitoring module to the drilling monitoring module;

[0062] The generator management module is used to calculate the management gap value based on the flow matching of the hydraulic generator, and to determine the management mode of the hydraulic generator based on the management gap value. It is also used to determine the necessity of assistance based on the stability of the hydraulic generator and the simulation prediction analysis results, and to perform auxiliary optimization of the hydraulic generator management mode based on the auxiliary necessity and auxiliary power generation data. It is also used to determine the auxiliary margin based on the energy storage data, and to adjust the auxiliary optimization process based on the auxiliary margin. The generator management module is connected to the stability monitoring module.

[0063] Specifically, the system is set in an intelligent hydraulic power generation device for deep-sea oil drilling. By intelligently controlling the intelligent hydraulic power generation device, the energy conversion efficiency of the hydraulic generator in the deep-sea oil drilling environment is improved, and the power stability is improved. The system calibrates and monitors the oil drilling, hydraulic power generation, auxiliary power generation and energy storage data collected by the intelligent sensor through the data monitoring module. By comparing data with the same content but different collection sources, the accuracy of the data is judged to ensure that subsequent modules perform analysis and decisions based on accurate data, avoid erroneous judgments and improper operations caused by data errors, and lay the foundation for the stable operation and efficient management of the entire system. The system also calibrates and monitors the intelligent sensor based on the calibration monitoring results through the data monitoring module. The sensor is calibrated. When inaccurate data is found, the sensor intelligent calibration method is used to calibrate the same source sensor to maintain the high-precision data acquisition capability of the intelligent sensor, reduce the cost and time of manual calibration, improve the automation and intelligence level of data acquisition, and ensure the long-term stability of data quality. The system uses the drilling monitoring module to accurately judge the current drilling flow range based on the real-time mud flow in the oil drilling data, providing a basis for subsequent targeted equipment management and optimization, so as to grasp the intensity and status of the drilling operation, discover abnormal situations in time, and provide support for ensuring the safety and efficiency of the drilling operation. In addition, the system uses the drilling monitoring module to calibrate the intelligent sensor according to different drilling flow ranges. Adjustment: In the low flow range and abnormal flow range, the smart sensor is adjusted to a fine sensor to adapt to the requirements for data accuracy under different working conditions, thereby improving the adaptability and reliability of data acquisition, and ensuring that accurate data can be obtained under various drilling conditions. The system combines the current drilling flow range and hydraulic power generation data through the stability monitoring module to analyze the flow matching of the hydraulic generator, and then monitors the stability of the hydraulic generator. By setting the current hydraulic power generation coefficient and comparing it with the adaptive power generation coefficient threshold, the stability of the generator can be accurately judged, helping operators to promptly discover potential stability problems, take measures to prevent failures in advance, and ensure the stable operation of the hydraulic generator. The system uses the stability monitoring module to The sudden change properties of the flow matching of the hydraulic generator are analyzed, and the adjustment plan of the intelligent sensor calibration monitoring process is optimized accordingly to make the calibration more scientific and reasonable. At the same time, the subsequent continuous change time of the flow matching of the hydraulic generator is analyzed through simulation prediction, and the sudden change properties are adjusted according to the results, so as to predict the changing trend of the generator operation status in advance, provide forward-looking guidance for equipment maintenance and management, and improve the reliability and adaptability of the entire system. The system also calculates the management gap value according to the flow matching of the hydraulic generator through the generator management module, and then determines the management mode of the hydraulic generator, providing a quantitative basis for the operation management of the hydraulic generator, and comprehensively considers the stability of the hydraulic generator and the simulation prediction analysis results to determine the necessity of auxiliary power generation.The auxiliary power generation data is combined to assist in optimizing the management of the hydraulic generator. At the same time, the auxiliary margin is determined based on the energy storage data, and the auxiliary optimization process is adjusted to achieve reasonable distribution and efficient utilization of energy, thereby improving the economy and stability of the entire power generation system.

[0064] Specifically, the data monitoring module calculates the data difference ΔU of the homologous sensor according to the data acquisition result, compares the data difference ΔU of the homologous sensor with the preset data difference ΔU0 of the homologous sensor, and calibrates and monitors the data acquisition result of the smart sensor according to the comparison result, wherein:

[0065] When △U≤△U0, the data monitoring module determines that the data acquisition result of the smart sensor corresponding to the data difference of the homologous sensor is accurate;

[0066] When ΔU>ΔU0, the data monitoring module determines that the data collection result of the smart sensor corresponding to the data difference of the homologous sensor is inaccurate.

[0067] Specifically, the homologous sensor data difference refers to the data difference obtained by collecting the same data at the same position using different types of sensors. For example, for mud flow measurement, an electromagnetic flowmeter and an ultrasonic flowmeter can be installed at the same time, and the electromagnetic flow data and ultrasonic flow data collected by the electromagnetic flowmeter and the ultrasonic flowmeter at the same position are respectively used as the first homologous sensor data and the second homologous sensor data, and the absolute value of the difference between the first homologous sensor data and the second homologous sensor data is used as the homologous sensor data difference. One homologous sensor data difference corresponds to the data of two smart sensors. The preset homologous sensor data difference refers to a preset value of the homologous sensor data difference that indicates whether the data collection result of the smart sensor corresponding to the homologous sensor data difference is accurate. This embodiment does not limit the preset means of the preset homologous sensor data difference. Those skilled in the art can freely set it according to the homologous sensor data type, and only need to meet the calibration and monitoring requirements of the data collection results of the smart sensor, such as setting the preset homologous sensor data difference of mud flow to 1m 3 / h, the data collection results include oil drilling data, hydraulic power generation data, auxiliary power generation data and energy storage data. The oil drilling data refers to various parameter information directly related to deep-sea oil drilling operations, including real-time mud flow and real-time drilling pressure. The real-time mud flow refers to the volume of the cross section of the mud passing through the intelligent sensor setting point per unit time during the deep-sea oil drilling process. In this embodiment, the real-time mud flow is collected by the electromagnetic flowmeter in the intelligent sensor. The hydraulic power generation data refers to various operating parameters related to the hydraulic generator during the power generation process, including the hydraulic generator blade length, hydraulic generator blade angle, hydraulic generator channel cross-sectional area and hydraulic generator channel surface roughness. The hydraulic The length of the generator blade refers to the distance from the root of the blade to the tip of the blade. In this embodiment, the length of the hydraulic generator blade is collected through the design drawings. The hydraulic generator blade angle refers to the angle between the blade and the plane perpendicular to the generator rotation axis. In this embodiment, multiple displacement sensors are installed on the blade to measure the displacement changes at different positions of the blade, and then the hydraulic generator blade angle is calculated using a mathematical model. The surface roughness of the hydraulic generator channel refers to the irregularity of the microscopic geometric shape of the inner surface of the channel. In this embodiment, the surface roughness of the hydraulic generator channel is collected through the surface roughness meter in the intelligent sensor. The auxiliary power generation data refers to various parameter information related to the operation of the auxiliary generator, such as the auxiliary generator. output power, rated output power, start-up time, response speed and operating time; the energy storage data refers to a set of various parameters in terms of the working status, performance and remaining available energy of the energy storage system in the intelligent hydraulic power generation system, including remaining power, capacity decay rate, charge and discharge efficiency, load power, current passing through the energy storage battery, internal resistance of the energy storage battery and open circuit voltage of the energy storage battery; the remaining power refers to the available electrical energy stored in the energy storage system at the current moment; this embodiment collects the remaining power based on a battery management system (BMS) chip; the capacity decay rate refers to the degree of decay of the actual available capacity of the energy storage system relative to the initial rated capacity during use; the charge and discharge efficiency refers to the output of the energy storage system during charging and discharging The ratio of energy to input energy. In this embodiment, the charge and discharge efficiency is collected through regular full-charge and full-discharge tests. The load power refers to the power consumed by the load of the electrical equipment connected to the energy storage system. In this embodiment, the load power is collected through the power analyzer in the intelligent sensor. The current passing through the energy storage battery refers to the current flowing through the energy storage battery during the charging and discharging process of the energy storage system. In this embodiment, the current sensor in the intelligent sensor is used. The internal resistance of the energy storage battery refers to the resistance encountered when the current passes through the energy storage battery of the energy storage system during operation. In this embodiment, the internal resistance of the energy storage battery is collected through the AC impedance method. The open circuit voltage of the energy storage battery refers to the voltage when the battery is in an open circuit state, that is, without an external load.The potential difference between the positive and negative electrodes when no current flows through the battery. In this embodiment, the open circuit voltage of the energy storage battery is collected by a high-precision voltmeter.

[0068] Specifically, the data monitoring module calibrates the smart sensor according to the calibration monitoring result, wherein:

[0069] If the data acquisition result of the smart sensor corresponding to the data difference of the homologous sensor is accurate, the data monitoring module does not calibrate the smart sensor;

[0070] If the data collection result of the smart sensor corresponding to the homologous sensor data difference is inaccurate, the data monitoring module identifies the homologous sensor and performs smart calibration on the homologous sensor through a sensor smart calibration method, and uses the sensor smart calibration method as an adjustment scheme for the smart sensor calibration monitoring process.

[0071] It can be understood that this embodiment does not limit the specific content of the sensor intelligent calibration method. Those skilled in the art can freely set it according to actual conditions, and only need to meet the calibration requirements of the homologous sensors. For example, it can be set to identify faulty sensors in the homologous sensors by building a deep learning model and replace the faulty sensors. It can also be set to calculate the calibration coefficient by building a deep learning model and calibrate the homologous sensors according to the calibration coefficient.

[0072] Specifically, the drilling monitoring module compares the real-time mud flow Q in the oil drilling data with each preset mud flow, and performs initial monitoring of the current drilling flow interval based on the comparison result, wherein:

[0073] When Q≤Q L When , the drilling monitoring module determines that the current drilling flow interval is a low flow interval;

[0074] When Q L <Q≤Q N When , the drilling monitoring module determines that the current drilling flow interval is a medium flow interval;

[0075] When Q N <Q, the drilling monitoring module determines that the current drilling flow interval is a high flow interval;

[0076] 0<Q L <Q N , Q L is the first preset mud flow, Q N The second preset mud flow rate.

[0077] Specifically, the first preset mud flow rate refers to the boundary value that divides the low flow interval and the medium flow interval. L =30m 3 / h, the second preset mud flow rate refers to the boundary value dividing the medium flow interval and the high flow interval, setting Q N =80m 3 / h.

[0078] Specifically, when the current drilling flow interval is a low flow interval, the drilling monitoring module compares the real-time drilling pressure P in the oil drilling data with each preset drilling pressure, judges the drilling status based on the comparison result, and performs secondary monitoring of the current drilling flow interval based on the drilling status, wherein:

[0079] When P≤P L When , the drilling monitoring module determines that the drilling state is low-power drilling and determines that the current drilling flow interval is a normal flow interval;

[0080] When P L <P≤P N When , the drilling monitoring module determines that the drilling state is medium-power drilling and determines that the current drilling flow interval is a normal flow interval;

[0081] When P N <P, the drilling monitoring module determines that the drilling state is high-power drilling and determines that the current drilling flow interval is an abnormal flow interval;

[0082] 0<P L <P N , P L is the first preset drilling pressure, P N The second preset drilling pressure.

[0083] Specifically, the first preset drilling pressure refers to the pressure preset value for further judging the drilling state in the low flow interval. L =5MPa, the second preset drilling pressure refers to the preset pressure value used to refine the drilling status judgment in the low flow range, and the setting P N =10MPa.

[0084] Specifically, the drilling monitoring module adjusts the calibration monitoring process of the smart sensor according to the current drilling flow interval, wherein:

[0085] If the current drilling flow interval is a high flow interval, the drilling monitoring module does not adjust the calibration monitoring process of the smart sensor;

[0086] If the current drilling flow interval is a medium flow interval, the drilling monitoring module does not adjust the calibration monitoring process of the smart sensor;

[0087] If the current drilling flow interval is a low flow interval and is a normal flow interval, the drilling monitoring module does not adjust the calibration monitoring process of the smart sensor;

[0088] If the current drilling flow interval is a low flow interval and an abnormal flow interval, the drilling monitoring module adjusts the calibration monitoring process of the smart sensor and adjusts the smart sensor to a fine sensor.

[0089] Specifically, adjusting the smart sensor to a fine sensor refers to optimizing the working mode, parameter setting or data processing method of the smart sensor to improve its data acquisition accuracy and reliability under specific abnormal working conditions (low flow and abnormal flow range). This embodiment does not limit the method of adjusting the smart sensor to a fine sensor. Those skilled in the art can freely set it according to actual conditions, and only need to meet the optimization requirements of the smart sensor. For example, the smart sensor can be set to reduce the deviation between the measured value and the true value through internal algorithm or hardware adjustment. For example, for a sensor measuring mud flow, the measurement accuracy is ±2% under normal circumstances. After adjusting to a fine sensor, the measurement accuracy can be improved to ±1% through more accurate calibration and compensation algorithms, so that the collected mud flow data can more accurately reflect the actual flow, providing more reliable data support for subsequent drilling operation analysis and decision-making.

[0090] Specifically, the stability monitoring module calculates the current hydraulic power generation coefficient K based on the hydraulic generator blade length L, the hydraulic generator blade angle θ, the hydraulic generator channel cross-sectional area S, and the hydraulic generator channel surface roughness R in the hydraulic power generation data, sets K=α1×(L / Lmax)×sinθ+α2×(S / Smax)×(1-R / Rmax), compares the current hydraulic power generation coefficient K with the current drilling flow interval adaptive power generation coefficient threshold km, analyzes the hydraulic generator flow matching according to the comparison result, and monitors the hydraulic generator stability according to the hydraulic generator flow matching, wherein:

[0091] When K∈km, the stability monitoring module determines that the flow matching of the hydraulic generator is high and the stability of the hydraulic generator is high;

[0092] when When , the stability monitoring module determines that the flow matching of the hydraulic generator is low and the stability of the hydraulic generator is low.

[0093] Specifically, the current drilling flow interval adaptation power generation coefficient threshold refers to the preset hydraulic power generation coefficient range according to different drilling flow intervals, such as low flow interval, medium flow interval, and high flow interval. This embodiment presets the current drilling flow interval adaptation power generation coefficient threshold through experimental data, theoretical analysis, and actual drilling operation experience. The flow matching of the hydraulic generator refers to the degree of adaptation of the hydraulic generator blade length L, hydraulic generator blade angle θ, hydraulic generator channel cross-sectional area S, and hydraulic generator channel surface roughness R of the hydraulic generator to the mud flow in the current drilling process. The stability of the hydraulic generator refers to the ability of the hydraulic generator to maintain stability during operation.

[0094] Specifically, the drilling monitoring module compares the matching change time t with the preset matching change time t0, analyzes the sudden change properties of the flow matching of the hydraulic generator according to the comparison result, and optimizes the adjustment scheme of the intelligent sensor calibration monitoring process according to the sudden change properties of the flow matching of the hydraulic generator, wherein:

[0095] When t≤t0, the drilling monitoring module determines that the mutation attribute of the flow matching of the hydraulic generator is a mutation, and optimizes the adjustment scheme of the smart sensor calibration monitoring process to no adjustment;

[0096] When t>t0, the drilling monitoring module determines that the mutation attribute of the flow matching of the hydraulic generator does not exist, and does not optimize the adjustment scheme of the smart sensor calibration monitoring process.

[0097] Specifically, the matching change time refers to the length of time from the time point of the last change in the flow matching of the hydraulic generator to the time point of the next change. For example, if the flow matching of the hydraulic generator was high last time, the flow matching of the current hydraulic generator is low, and the flow matching of the hydraulic generator will be high next time, the matching change time is the length of time from the time point when the flow matching of the hydraulic generator last changed to the time point when the flow matching of the current hydraulic generator changed to the time point when the flow matching of the current hydraulic generator changed to the time point when the flow matching of the next hydraulic generator changed. This embodiment obtains the matching change time through a built-in clock. The mutation attribute of the flow matching of the hydraulic generator refers to whether the flow matching of the hydraulic generator has an attribute of short-term change.

[0098] Specifically, the drilling monitoring module inputs the oil drilling data and the hydraulic power generation data into a flow matching simulation prediction model, performs simulation prediction analysis on the flow matching of the hydraulic generator through the flow matching simulation prediction model, obtains a simulation analysis continuous change time ta, uses the simulation analysis continuous change time ta as a simulation prediction analysis result, compares the simulation analysis continuous change time ta with a preset simulation analysis continuous change time ta0, judges the sudden change continuous attribute based on the comparison result, and adjusts the sudden change attribute of the flow matching of the hydraulic generator based on the judgment result, wherein:

[0099] When ta≤ta0, the drilling monitoring module determines that the mutation continuity attribute does not have a continuous mutation, and does not adjust the mutation attribute of the flow matching of the hydraulic generator;

[0100] When ta>ta0, the drilling monitoring module determines that the mutation continuity attribute is the presence of continuous mutations, and adjusts the mutation attribute of the flow matching of the hydraulic generator from the presence of mutations to the absence of mutations.

[0101] Specifically, the flow matching simulation prediction model refers to a neural network model that uses the oil drilling data and the hydraulic power generation data as input and simulates and analyzes the continuous change time as output. This embodiment does not limit the setting method of the flow matching simulation prediction model. Those skilled in the art can freely set it according to actual conditions, as long as it meets the need for accurate prediction of the simulation analysis continuous change time. For example, it can be set to obtain historical data of the oil drilling data and the hydraulic power generation data and convert it into image format, use it as a model training set to train the convolutional neural network model, and use the convolutional neural network model that meets the training standards as the flow matching simulation prediction model. The simulation analysis continuous change time refers to the length of time that the flow matching of the hydraulic generator continuously changes after the flow matching of the hydraulic generator is simulated and analyzed by the flow matching simulation prediction model. The preset simulation analysis continuous change time refers to a preset value of the duration used to distinguish whether the change in the flow matching of the hydraulic generator is a short-term, acceptable fluctuation, or a continuous mutation that requires intervention and adjustment. The mutation continuity attribute refers to the continuity of the mutation of the flow matching of the hydraulic generator.

[0102] Specifically, the generator management module calculates the management gap value according to the flow matching of the hydraulic generator, wherein:

[0103] When the flow matching of the hydraulic generator is high, the management gap value is not calculated;

[0104] When the flow matching of the hydraulic generator is low, the management gap value is calculated. The generator management module obtains the real-time mud flow Q and the power generation demand matching flow Qs, and calculates the management gap value △Q according to the real-time mud flow Q and the power generation demand matching flow Qs, setting △Q=Q-Qs. The generator management module inputs the management gap value △Q, real-time drilling pressure P, current drilling flow range, hydraulic generator blade length L, hydraulic generator blade angle θ, hydraulic generator channel cross-sectional area S and hydraulic generator channel surface roughness R into the hydraulic generator management model, obtains the hydraulic generator management method output by the hydraulic generator management model, and manages the hydraulic generator according to the hydraulic generator management method.

[0105] Specifically, the power generation demand matching flow refers to the mud flow corresponding to the hydraulic generator being able to achieve optimal power generation efficiency and stable operating state under the current drilling operation conditions and power generation demand. This embodiment collects data through a normally operating hydraulic generator and establishes a preset database to obtain the power generation demand matching flow. The hydraulic generator management model refers to a deep learning model with management gap value, real-time drilling pressure P, current drilling flow range, hydraulic generator blade length L, hydraulic generator blade angle θ, hydraulic generator channel cross-sectional area S and hydraulic generator channel surface roughness R as inputs and hydraulic generator management method as output. This embodiment does not affect the hydraulic generator. The construction process of the motor management model is limited, and technical personnel in this field can freely set it according to actual conditions. For example, the historical management gap value, real-time drilling pressure P, current drilling flow range, hydraulic generator blade length L, hydraulic generator blade angle θ, hydraulic generator channel cross-sectional area S and hydraulic generator channel surface roughness R can be obtained as the hydraulic generator management model training set, and the convolutional neural network model is trained according to the hydraulic generator management model training set, and the trained convolutional neural network model is used as the hydraulic generator management model. The hydraulic generator management method refers to the operation adjustment strategy formulated for the hydraulic generator under different working conditions, which is output by the hydraulic generator management model.

[0106] Specifically, the generator management module determines the necessity of assistance based on the stability of the hydraulic generator and the simulation prediction analysis results, wherein:

[0107] When the stability of the hydraulic generator is high and ta>ta0, the generator management module determines that the auxiliary necessity is unnecessary auxiliary power generation;

[0108] When the stability of the hydraulic generator is high and ta≤ta0, the generator management module determines that the auxiliary necessity is unnecessary auxiliary power generation;

[0109] When the stability of the hydraulic generator is low and ta≤ta0, the generator management module determines that the auxiliary necessity is unnecessary auxiliary power generation;

[0110] When the stability of the hydraulic generator is low and ta>ta0, the generator management module determines that the auxiliary necessity is necessary auxiliary power generation and there is fluctuation in hydraulic power generation.

[0111] Specifically, the auxiliary necessity refers to an evaluation indicator in the intelligent hydraulic power generation system of deep-sea oil drilling operations, which determines whether it is necessary to start the auxiliary generator to generate electricity to ensure a stable power supply for drilling operations based on the operating status (stability) of the hydraulic generator and the simulation prediction analysis results of its flow matching.

[0112] Specifically, when the auxiliary necessity is necessary auxiliary power generation and there is fluctuation in hydraulic power generation, the generator management module inputs the auxiliary power generation data into the auxiliary optimization power generation model, obtains the auxiliary optimization power generation mode output by the auxiliary optimization power generation model, supplements the auxiliary optimization power generation mode to the hydraulic generator management mode, obtains the auxiliary optimized hydraulic generator management mode, and replaces the hydraulic generator management mode output by the hydraulic generator management model with the auxiliary optimized hydraulic generator management mode.

[0113] Specifically, the auxiliary optimization power generation model refers to the ability to calculate and output the auxiliary power generation strategy that best suits the current operating conditions based on real-time auxiliary power generation data. This embodiment does not limit the construction method of the auxiliary optimization power generation model. For example, the decision tree model can be trained through historical data sets, and the trained decision tree model can be used as the auxiliary optimization power generation model. The auxiliary optimization power generation method refers to the optimized auxiliary power generation strategy output by the auxiliary optimization power generation model based on the input auxiliary power generation data.

[0114] Specifically, the generator management module calculates the energy storage power supply time t according to the remaining power Er, capacity attenuation rate α, charge and discharge efficiency η, load power Pi, energy storage battery current I, energy storage battery internal resistance Ri and energy storage battery open circuit voltage Uo in the energy storage data, and sets The generator management module compares the energy storage power supply duration t with the preset energy storage power supply duration t0, and determines the auxiliary margin based on the comparison result, where:

[0115] When t≥t0, the generator management module determines that the auxiliary margin is sufficient and does not adjust the auxiliary optimization process;

[0116] When t<t0, the generator management module determines that the auxiliary margin is insufficient, and adjusts the auxiliary optimization process to remove the auxiliary optimization power generation mode from the auxiliary optimized hydraulic generator management mode.

[0117] Specifically, the preset energy storage power supply duration refers to a duration pre-set according to the actual needs and safety standards of deep-sea oil drilling operations. The preset energy storage power supply duration is set according to the full power supply duration of the energy storage battery of the energy storage system. For example, the preset energy storage power supply duration can be set to half the full power supply duration of the energy storage battery. The auxiliary margin refers to a measurement indicator of the additional power generation capacity and continuous power supply capacity of the auxiliary power generation system in addition to the power that the current hydraulic power generation and energy storage system can provide.

[0118] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. An intelligent hydraulic power generation device for deep-sea oil drilling, characterized in that: The device comprises: Drilling ships, used to carry hydraulic generators, auxiliary generators, smart sensors and smart hydraulic power generation systems for deep-sea oil drilling; Hydraulic generators, used to generate hydraulic power to power oil drilling; Auxiliary generators, used to generate electricity when there are fluctuations in hydraulic power generation, providing power for oil drilling; Intelligent sensors, used to collect oil drilling information, hydraulic power generation information, and auxiliary power generation information, are connected to the hydraulic generator and the auxiliary generator; Intelligent hydraulic power generation system, including: a data monitoring module for performing calibration monitoring on data acquisition results of the smart sensor to obtain calibration monitoring results, wherein the data acquisition results include oil drilling data, hydraulic power generation data, auxiliary power generation data, and energy storage data, and for calibrating the smart sensor based on the calibration monitoring results; a drilling monitoring module for monitoring a current drilling flow interval based on the oil drilling data and for adjusting a calibration monitoring process of an intelligent sensor based on the current drilling flow interval; a stability monitoring module for analyzing the flow matching of the hydraulic generator based on the current drilling flow interval and hydraulic power generation data, and monitoring the stability of the hydraulic generator based on the flow matching of the hydraulic generator; analyzing the mutation properties of the flow matching of the hydraulic generator, and optimizing the adjustment scheme of the intelligent sensor calibration monitoring process based on the mutation properties of the flow matching of the hydraulic generator; and performing simulation prediction analysis based on the flow matching of the hydraulic generator, and adjusting the mutation properties of the flow matching of the hydraulic generator based on the simulation prediction analysis results; a generator management module, configured to calculate a management gap value based on the flow matching of the hydraulic generator, determine a management mode of the hydraulic generator based on the management gap value, determine the necessity of assistance based on the stability of the hydraulic generator and the simulation prediction analysis results, perform auxiliary optimization of the hydraulic generator management mode based on the necessity of assistance and auxiliary power generation data, determine the auxiliary margin based on the energy storage data, and adjust the auxiliary optimization process based on the auxiliary margin; The stability monitoring module calculates the current hydraulic power generation coefficient K based on the hydraulic generator blade length L, the hydraulic generator blade angle θ, the hydraulic generator channel cross-sectional area S, and the hydraulic generator channel surface roughness R in the hydraulic power generation data, and sets K=α1×(L / Lmax)×sinθ+α2×(S / Smax)×(1-R / Rmax). The current hydraulic power generation coefficient K is compared with the current drilling flow range adaptive power generation coefficient threshold km, and the flow matching of the hydraulic generator is analyzed based on the comparison result. The stability of the hydraulic generator is monitored based on the flow matching of the hydraulic generator.

2. The intelligent hydraulic power generation device for deep-sea oil drilling according to claim 1, characterized in that: The data monitoring module calculates the data difference ΔU of the homologous sensor according to the data acquisition result, compares the data difference ΔU of the homologous sensor with the preset data difference ΔU0 of the homologous sensor, and calibrates and monitors the data acquisition result of the smart sensor according to the comparison result, wherein: When △U≤△U0, the data monitoring module determines that the data acquisition result of the smart sensor corresponding to the data difference of the homologous sensor is accurate; When ΔU>ΔU0, the data monitoring module determines that the data collection result of the smart sensor corresponding to the data difference of the homologous sensor is inaccurate.

3. The intelligent hydraulic power generation device for deep-sea oil drilling according to claim 2, characterized in that: The data monitoring module calibrates the smart sensor according to the calibration monitoring result, wherein: If the data acquisition result of the smart sensor corresponding to the data difference of the homologous sensor is accurate, the data monitoring module does not calibrate the smart sensor; If the data collection result of the smart sensor corresponding to the homologous sensor data difference is inaccurate, the data monitoring module identifies the homologous sensor and performs smart calibration on the homologous sensor through a sensor smart calibration method, and uses the sensor smart calibration method as an adjustment scheme for the smart sensor calibration monitoring process.

4. The intelligent hydraulic power generation device for deep-sea oil drilling according to claim 1, characterized in that: The drilling monitoring module compares the real-time mud flow Q in the oil drilling data with each preset mud flow, and performs initial monitoring of the current drilling flow interval based on the comparison result, wherein: When Q≤Q L When , the drilling monitoring module determines that the current drilling flow interval is a low flow interval; When Q L <Q≤Q N When , the drilling monitoring module determines that the current drilling flow interval is a medium flow interval; When Q N <Q, the drilling monitoring module determines that the current drilling flow interval is a high flow interval; 0<Q L <Q N , Q L is the first preset mud flow, Q N The second preset mud flow rate.

5. The intelligent hydraulic power generation device for deep-sea oil drilling according to claim 4, characterized in that: When the current drilling flow interval is a low flow interval, the drilling monitoring module compares the real-time drilling pressure P in the oil drilling data with each preset drilling pressure, judges the drilling status based on the comparison result, and performs secondary monitoring of the current drilling flow interval based on the drilling status, wherein: When P≤P L When , the drilling monitoring module determines that the drilling state is low-power drilling and determines that the current drilling flow interval is a normal flow interval; When P L <P≤P N When , the drilling monitoring module determines that the drilling state is medium-power drilling and determines that the current drilling flow interval is a normal flow interval; When P N <P, the drilling monitoring module determines that the drilling state is high-power drilling and determines that the current drilling flow interval is an abnormal flow interval; 0<P L <P N , P L is the first preset drilling pressure, P N The second preset drilling pressure.

6. The intelligent hydraulic power generation device for deep-sea oil drilling according to claim 5, characterized in that: The drilling monitoring module adjusts the calibration monitoring process of the smart sensor according to the current drilling flow interval, wherein: If the current drilling flow interval is a high flow interval, the drilling monitoring module does not adjust the calibration monitoring process of the smart sensor; If the current drilling flow interval is a medium flow interval, the drilling monitoring module does not adjust the calibration monitoring process of the smart sensor; If the current drilling flow interval is a low flow interval and is a normal flow interval, the drilling monitoring module does not adjust the calibration monitoring process of the smart sensor; If the current drilling flow interval is a low flow interval and an abnormal flow interval, the drilling monitoring module adjusts the calibration monitoring process of the smart sensor and adjusts the smart sensor to a fine sensor.

7. The intelligent hydraulic power generation device for deep-sea oil drilling according to claim 1, characterized in that: The stability monitoring module monitors the stability of the hydraulic generator according to the flow matching of the hydraulic generator, wherein: When K∈km, the stability monitoring module determines that the flow matching of the hydraulic generator is high and the stability of the hydraulic generator is high; When K∉km, the stability monitoring module determines that the flow matching of the hydraulic generator is low and the stability of the hydraulic generator is low.

8. The intelligent hydraulic power generation device for deep-sea oil drilling according to claim 1, characterized in that: The drilling monitoring module compares the matching change time t with the preset matching change time t0, analyzes the sudden change properties of the flow matching of the hydraulic generator according to the comparison result, and optimizes the adjustment scheme of the intelligent sensor calibration monitoring process according to the sudden change properties of the flow matching of the hydraulic generator, wherein: When t≤t0, the drilling monitoring module determines that the mutation attribute of the flow matching of the hydraulic generator is a mutation, and optimizes the adjustment scheme of the smart sensor calibration monitoring process to no adjustment; When t>t0, the drilling monitoring module determines that the mutation attribute of the flow matching of the hydraulic generator does not exist, and does not optimize the adjustment scheme of the smart sensor calibration monitoring process.

9. The intelligent hydraulic power generation device for deep-sea oil drilling according to claim 8, characterized in that: The drilling monitoring module inputs the oil drilling data and the hydraulic power generation data into a flow matching simulation prediction model, performs simulation prediction analysis on the flow matching of the hydraulic generator through the flow matching simulation prediction model, obtains a simulation analysis continuous change time ta, uses the simulation analysis continuous change time ta as a simulation prediction analysis result, compares the simulation analysis continuous change time ta with a preset simulation analysis continuous change time ta0, judges the sudden change continuous attribute based on the comparison result, and adjusts the sudden change attribute of the flow matching of the hydraulic generator based on the judgment result, wherein: When ta≤ta0, the drilling monitoring module determines that the mutation continuity attribute does not have a continuous mutation, and does not adjust the mutation attribute of the flow matching of the hydraulic generator; When ta>ta0, the drilling monitoring module determines that the mutation continuity attribute is the presence of continuous mutations, and adjusts the mutation attribute of the flow matching of the hydraulic generator from the presence of mutations to the absence of mutations.

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