Self-adaptive multi-parameter frequency conversion control system and method for intelligent fracturing of underground coal mine

Abstract

The invention belongs to the field of coal mine safety and hydraulic mining, and particularly relates to a self-adaptive multi-parameter frequency conversion control system and method for underground intelligent fracturing of a coal mine. The system comprises an intelligent control system, a variable frequency fracturing pump assembly, an intelligent monitoring and control assembly, a self-adaptive pressure regulation execution assembly, a communication and data fusion assembly and a safety explosion-proof and pressure relief protection assembly. The intelligent monitoring and control assembly fuses multi-source signals through pressure, flow, acoustic emission, vibration and temperature sensors arranged at fracturing key points via an edge computing unit and inputs the fused signals into the intelligent control system; and the self-adaptive pressure regulating execution assembly drives the electric control high-pressure valve group and the pulsating pressure regulating unit according to an AI instruction sent by the intelligent control system to generate variable-frequency pulsating pressure with a specific waveform. A highly-integrated modular intelligent system is formed, variable frequency driving, intelligent sensing, AI calculation, self-adaptive execution, reliable communication and the like are organically integrated, and wide application prospects and popularization values are achieved.

Description

Technical Field

[0001] This invention belongs to the field of coal mine safety and hydraulic mining technology, specifically relating to an adaptive frequency conversion control system and method for intelligent fracturing, and more specifically to an adaptive multi-parameter frequency conversion control system and method for intelligent fracturing in underground coal mines. Background Technology

[0002] With the continuous increase in the depth and intensity of coal mining in my country, the geological conditions faced are becoming increasingly complex. In particular, complex coal seams with low permeability, high stress, and hard roof coal seams have poor original permeability, making gas extraction difficult and severely restricting the safe and efficient production of coal mines. Hydraulic permeability enhancement and fracturing technology, as an effective means of coal seam modification, injects high-pressure fluid into the coal seam to fracture the coal and rock mass, creating a fracture network, thereby improving the efficiency of gas desorption and migration. It has become one of the key technologies for coal mine gas control and permeability enhancement. However, traditional hydraulic fracturing control technologies mostly adopt constant pressure or simple step-pressurization modes, which have low levels of intelligence and weak adaptive capabilities, making it difficult to meet the requirements of precise, efficient, and safe fracturing under complex geological conditions.

[0003] The current underground fracturing control system in coal mines has the following main shortcomings: The existing systems suffer from a lack of dynamic adaptability due to their simplistic control modes. They rely heavily on pre-set, fixed pressure-flow programs for open-loop control, failing to provide real-time feedback and adjustments based on dynamic changes in the coal and rock mass fracturing state (such as initiation point identification and fracture propagation morphology). This "one-size-fits-all" control approach easily leads to low energy utilization, uneven fracturing effects, and the generation of single main fractures in hard or heterogeneous coal seams. Insufficient fracture network complexity further impacts the overall permeability enhancement effect.

[0004] Isolated monitoring parameters and incomplete decision-making basis: Most systems only monitor basic parameters such as pressure and flow rate, lacking simultaneous acquisition and fusion analysis of multi-dimensional information such as acoustic emission, microseismic activity, vibration, and temperature. Because they fail to comprehensively utilize multi-source information to depict the complete process of fracture initiation, propagation, and convergence, the control system cannot accurately determine the fracturing stage (such as completion of setting, fracture initiation, and stable fracture propagation), and it is even more difficult to achieve optimal pressure waveform matching for different stages, resulting in a certain degree of blindness in the fracturing process.

[0005] The system suffers from low intelligence and reliance on human experience: the start-up, shutdown, and parameter adjustment of the fracturing process heavily depend on the operator's experience and judgment, lacking the self-learning, self-judgment, and self-regulation capabilities based on artificial intelligence (AI) algorithms. This not only demands high skill levels from operators but also makes it difficult to ensure the consistency and optimization of the fracturing process, resulting in a delayed response to sudden geological anomalies and posing safety hazards.

[0006] Insufficient communication reliability and weak collaborative control capabilities: The harsh downhole environment poses challenges to the reliability and real-time performance of data transmission. Existing communication methods may lack stability in complex roadways, preventing the surface control center from obtaining timely and accurate downhole parameter status and hindering the rapid issuance of precise control commands to actuators. This impacts the collaborative response and closed-loop control performance of the entire fracturing system.

[0007] Therefore, developing an underground fracturing control system that can integrate multi-source information, dynamically identify fracturing stages, adaptively generate and apply optimal variable frequency pulsating pressure waveforms, and possess highly intelligent decision-making and intrinsic safety characteristics is of urgent practical significance and important application value for breaking through the technical bottleneck of efficient permeability enhancement in complex coal seams and improving the level of coal mine gas control and mining safety. Summary of the Invention

[0008] The technical problem to be solved by this invention is to provide an adaptive variable frequency control system and method for intelligent fracturing. This system can integrate and control multiple parameters, specifically an adaptive multi-parameter variable frequency control system and method for intelligent fracturing in coal mines. The variable frequency control system integrates a variable frequency fracturing pump group, intelligent monitoring and control, adaptive pressure regulation execution, underground communication and data fusion, and safety explosion-proof and pressure relief protection components, forming a complete intelligent closed loop. This achieves adaptive intelligent control of the entire fracturing process, significantly improving process accuracy and effect controllability.

[0009] The specific technical solution adopted is as follows: An intelligent fracturing adaptive variable frequency control system includes an intelligent control system, as well as a variable frequency fracturing pump assembly, an intelligent monitoring and control assembly, an adaptive pressure regulation execution assembly, a communication and data fusion assembly, and a safety explosion-proof and pressure relief protection assembly. The variable frequency fracturing pump assembly includes an explosion-proof variable frequency motor, a high-pressure plunger pump, and a flow regulating valve, and its output is adjusted in real time by the variable frequency control cabinet. The intelligent monitoring and control component includes a pressure sensor, a flow meter, an acoustic emission sensor, a vibration sensor, a temperature sensor, and an edge computing unit for data preprocessing and feature extraction. The intelligent monitoring and control component receives data signals sensed by the pressure sensor, flow meter, acoustic emission sensor, vibration sensor, temperature sensor, and edge computing unit, and transmits the data signals to the intelligent control system in the control panel. The adaptive pressure regulating actuator includes an electronically controlled high-pressure valve group, a pulse pressure regulating unit, and a fixed-stroke automatic liquid return device. It receives instructions from the intelligent control system, adjusts the frequency and amplitude of the output pressure, and generates a target pulse waveform. The downhole communication and data fusion component adopts a heterogeneous communication structure combining LoRa and RFID to establish a two-way data link between the downhole equipment and the ground control terminal. The safety explosion-proof and pressure relief protection components include an explosion-proof electrical enclosure, a temperature sensing module, an automatic pressure relief valve, and an overcurrent protection device to realize an automatic protection system for overvoltage, overtemperature, and overcurrent faults.

[0010] Preferably, the intelligent control system incorporates a hybrid intelligent algorithm (AI algorithm) that integrates RBF neural network and fuzzy adaptive control. This hybrid intelligent algorithm is capable of analyzing different parameter characteristic curves of coal seam stress response, acoustic emission energy release rate, and pressure gradient change rate.

[0011] Preferably, the adaptive pressure regulation execution component can receive instructions (AI instructions) from the intelligent control system, automatically determine the coal seam fracturing point based on the acoustic emission energy sequence and pressure gradient change rate identified in real time by the hybrid intelligent algorithm, and adjust the pressure boosting strategy and frequency conversion pulsation waveform accordingly. Sine, triangular, or rectangular waveforms can be applied.

[0012] Preferably, at least one of the pressure sensor, flow meter, acoustic emission sensor, vibration sensor, and temperature sensor is provided in the system as needed. Multiple pressure sensors, temperature sensors, acoustic emission sensors, vibration sensors, etc., can be arranged at key fracturing points to meet the needs of intelligent monitoring.

[0013] Preferably, the fixed-stroke automatic liquid return device can automatically open the pressure relief channel after the fracturing process ends or after receiving a safety command, so as to realize the flexible unloading and safe reset of the system.

[0014] An adaptive frequency conversion control method for intelligent fracturing, employing an adaptive frequency conversion control system for intelligent fracturing according to the present invention, specifically includes the following steps: (1) Parameter initialization and model calibration stage: Initialization settings: Input the geomechanical parameters of the target coal seam into the intelligent control system, set the thresholds for acoustic emission energy release rate and pressure gradient change rate to M and N respectively, and calibrate the initial pressure-flow-acoustic emission characteristic data based on historical data in the database; (2) Adaptive control during the setting stage: The control system outputs the initial setting pressure and adjusts the setting position in real time until it stabilizes based on the dynamic signal fed back by the vibration sensor. (3) Dynamic identification and triggering of the initiation stage: pressurization, based on the hybrid intelligent algorithm (AI algorithm), the acoustic emission energy sequence and the pressure gradient change rate are analyzed simultaneously. When both exceed the preset threshold, the initiation point is automatically determined and the fracturing stage is triggered. (4) Intelligent pulsating pressure stabilization during the fracturing stage: Based on the pressure boosting strategy and frequency-modulated pulsating waveform made by the adaptive pressure regulating execution component, it acts on the coal seam fracture surface, and dynamically adjusts the pulsating parameters based on the real-time feedback of acoustic emission and vibration energy release rate to maintain the optimal expansion morphology of the fracture. (5) Intelligent pressure relief and liquid return stage: When the acoustic emission energy release rate is detected to enter a stable plateau period, it is determined that the crack has expanded sufficiently, and the system automatically commands the liquid return device to start and perform fixed-stroke flexible pressure relief; (6) Data closed loop and model iteration: After the execution is completed, all data is uploaded to the database of the intelligent control system to enrich the database content, update the AI ​​model parameters, and optimize the control strategy for subsequent fracturing operations.

[0015] Preferably, during the fracturing stage, the edge computing unit uses the sliding time window method to calculate the pressure gradient change rate, and is supplemented by short-time Fourier transform analysis of acoustic emission signals to improve the accuracy of fracturing point identification in complex downhole noise environments; the pressure increase rate is 0.3-0.8 MPa / s.

[0016] Preferably, during the fracturing stage, the hybrid intelligent algorithm integrates and analyzes the emission energy release rate and vibration spectrum information to assess the expansion state of the fracture network in real time, and adaptively adjusts the frequency and amplitude of the pulsating pressure accordingly.

[0017] Preferably, the frequency control range is 0.1Hz to 3Hz, and the pressure amplitude control range is 1MPa to 50MPa.

[0018] Preferably, during the entire fracturing process, the output power of the explosion-proof variable frequency motor is dynamically matched with the optimal energy demand of the coal seam through the variable frequency control cabinet, so as to minimize the overall energy consumption of the system.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) Adaptive intelligent control of the entire fracturing process has been achieved, significantly improving the precision of the process and the controllability of the effect. By integrating a multi-source sensor array and an AI intelligent control system, this invention can integrate and analyze multi-dimensional information such as pressure, flow rate, acoustic emission, and vibration in real time, dynamically identify and accurately judge key stages such as setting, fracturing initiation, propagation, pressure stabilization, and pressure relief. Based on AI algorithms, the system can automatically generate and issue optimal control commands according to the real-time response characteristics of the coal and rock mass, realizing a fundamental shift from "fixed program control" to "adaptive intelligent feedback control". This overcomes the drawbacks of traditional technology that relies on manual experience and has rigid parameters, ensuring the precise execution of the fracturing process and the effective guidance of fracture morphology under complex and variable geological conditions.

[0020] (2) This invention, through its adaptive pressure regulation execution component, can identify pressure gradient changes and acoustic emission characteristic data in real time based on AI algorithms, and adjust the pressure boosting strategy and the frequency-varying pulsating pressure with varying waveforms, frequencies, and amplitudes accordingly. This controllable dynamic loading method can effectively overcome stress concentration, activate primary microfractures in coal and rock, and promote multi-branched fracture propagation, thereby forming a more uniform and complex three-dimensional fracture network. Compared with traditional constant pressure or simple step pressurization, it can achieve a better permeability enhancement range and effect under the same or lower injection energy, and is particularly suitable for the modification of low-permeability, high-stress, and hard coal seams.

[0021] (3) This invention constructs a reliable underground communication and data fusion network, ensuring the real-time performance and stability of closed-loop control. A dual-channel heterogeneous network using LoRa and RFID is employed to achieve reliable, low-latency, bidirectional transmission of multi-source monitoring data and control commands underground. This design enhances the system's communication anti-interference and fault-tolerant capabilities in complex roadway environments, ensuring that the ground control terminal can monitor the overall operating conditions in real time and quickly and reliably send precise control commands to the execution terminal.

[0022] (4) This invention strengthens the safety protection system of active early warning and linkage protection, and improves the inherent safety level of the system. Through the safety explosion-proof and pressure relief protection components, the temperature sensing module, automatic pressure relief valve, explosion-proof shell and intelligent control system are deeply linked to monitor the risk precursors such as abnormal temperature rise and pressure change in real time, and automatically execute protection actions such as early warning, power cut-off and pressure relief, realizing the upgrade from "passive pressure relief" to "active protection".

[0023] (5) This invention forms a highly integrated modular intelligent system, improving the convenience and universality of engineering applications. This invention organically integrates five major functional modules: frequency conversion drive, intelligent sensing, AI decision-making, adaptive execution, reliable communication, and safety protection, forming a standardized and modular complete system. This design not only facilitates underground installation, debugging, and maintenance, but its core AI algorithm and control system also have the ability to self-optimize and upgrade through data accumulation, adapting to the differentiated needs of different mining areas and different coal seam geological conditions, and has broad engineering application prospects and promotional value. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the frequency conversion control system described in this invention.

[0025] Figure 2 This is a schematic diagram of the intelligent control system of the present invention.

[0026] Figure 3 This is a schematic diagram of the intelligent monitoring and control components.

[0027] Figure 4This is a schematic diagram of the adaptive voltage regulation execution component.

[0028] Figure 5 This is a schematic diagram of a safety explosion-proof and pressure relief protection component.

[0029] In the figure, 1-Variable frequency fracturing pump assembly, 2-Intelligent monitoring and control assembly, 3-Adaptive pressure regulation execution assembly, 4-Communication and data fusion assembly, 5-Safety explosion-proof and pressure relief protection assembly, 6-Intelligent control system, 7-Flow regulating valve, 8-Variable frequency control cabinet, 9-Acoustic emission sensor (omitted in the figure), 10-Pressure sensor, 11-Flow meter, 12-Temperature sensor, 13-Vibration sensor. Detailed Implementation

[0030] The accompanying drawings are for illustrative purposes only. In the description of this invention, it should be noted that the terms "inner", "outer", "upper", "lower", "front", "rear", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0031] To make the objectives, technical solutions, and beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0032] Example 1 like Figure 1 As shown, an intelligent fracturing adaptive frequency conversion control system can be applied in underground coal mines to perform frequency conversion control of multiple parameters. The system is composed of a frequency conversion fracturing pump assembly 1, an intelligent monitoring and control assembly 2, an adaptive pressure regulation execution assembly 3, a communication and data fusion assembly 4, and a safety explosion-proof and pressure relief protection assembly 5.

[0033] like Figure 2 As shown, the variable frequency fracturing pump assembly 1 includes an explosion-proof variable frequency motor, a high-pressure plunger pump, and a flow regulating valve 7, the output of which is adjusted in real time by the variable frequency control cabinet 8.

[0034] like Figure 1 , 3 As shown, the system arranges pressure sensors 10, flow meters 11, acoustic emission sensors, vibration sensors 13, and temperature sensors 12 at multiple points as needed. The intelligent monitoring and control component 2 includes each pressure sensor 10, flow meter 11, acoustic emission sensor, vibration sensor 13, and temperature sensor 12, as well as an edge computing unit for data preprocessing and feature extraction. All monitoring, sensing, and computing signals and data are ultimately fed into the intelligent control system 6 in the operating console.

[0035] like Figure 4 As shown, the adaptive pressure regulating actuator 3 includes an electrically controlled high-pressure valve group, a pulsating pressure regulating unit, and a fixed-stroke automatic liquid return device. It receives AI commands from the intelligent control system 6 and can adjust the frequency and amplitude of the output pressure to generate a target pulsating waveform. The fixed-stroke automatic liquid return device can automatically open the pressure relief channel after the fracturing process ends or after receiving a safety command, thereby achieving flexible unloading and safe reset of the system.

[0036] The communication and data fusion component 4 adopts a heterogeneous communication structure that combines LoRa and RFID to establish a two-way data link between the downhole equipment and the ground control terminal.

[0037] like Figure 5 As shown, the safety explosion-proof and pressure relief protection component 5 includes an explosion-proof electrical housing, a temperature sensing module, an automatic pressure relief valve, and an overcurrent protection device to realize an automatic protection system for overvoltage, overtemperature, and overcurrent faults.

[0038] The intelligent control system incorporates a hybrid intelligent algorithm (AI algorithm) that integrates RBF neural network and fuzzy adaptive control. This hybrid intelligent algorithm can analyze the characteristic curves of different parameters, such as coal seam stress response, acoustic emission energy release rate, and pressure gradient change rate.

[0039] The adaptive pressure regulation execution component can receive instructions (AI instructions) from the intelligent control system, automatically determine the coal seam fracturing point based on the acoustic emission energy sequence and pressure gradient change rate identified in real time by the hybrid intelligent algorithm, and adjust the pressure boosting strategy and frequency conversion pulsation waveform accordingly.

[0040] An adaptive frequency conversion control method for intelligent fracturing, employing an adaptive frequency conversion control system for intelligent fracturing according to the present invention, the method specifically includes: (1) Parameter initialization and model calibration stage: Initialization settings: Input the geomechanical parameters of the target coal seam into the intelligent control system, set the thresholds for acoustic emission energy release rate and pressure gradient change rate to M and N respectively, and calibrate the initial pressure-flow-acoustic emission characteristic data based on historical data in the database; (2) Adaptive control during the setting stage: The control system outputs the initial setting pressure and adjusts the setting position in real time until it stabilizes based on the dynamic signal fed back by the vibration sensor. (3) Dynamic identification and triggering of the fracturing stage: The pressure is increased at a rate of 0.1 MPa / s. The acoustic emission energy sequence and the pressure gradient change rate are analyzed simultaneously according to the hybrid intelligent algorithm (AI algorithm). When both exceed the preset threshold, the fracturing point is automatically determined and the fracturing stage is triggered. (4) Intelligent pulsating pressure stabilization during the fracturing stage: Based on the pressure boosting strategy and frequency-modulated pulsating waveform made by the adaptive pressure regulating execution component, it acts on the coal seam fracture surface, and dynamically adjusts the pulsating parameters based on the real-time feedback of acoustic emission and vibration energy release rate to maintain the optimal expansion morphology of the fracture; wherein, the frequency is 0.5Hz and the pressure is 1MPa.

[0041] (5) Intelligent pressure relief and liquid return stage: When the acoustic emission energy release rate is detected to enter a stable plateau period, it is determined that the crack has expanded sufficiently, and the system automatically commands the liquid return device to start and perform fixed-stroke flexible pressure relief; (6) Data closed loop and model iteration: After the execution is completed, all data is uploaded to the database of the intelligent control system to enrich the database content, update the AI ​​model parameters, and optimize the control strategy for subsequent fracturing operations.

[0042] During the fracturing stage, the hybrid intelligent algorithm integrates the analysis of the emission energy release rate and vibration spectrum information to assess the expansion state of the fracture network in real time, and adaptively adjusts the frequency and amplitude of the pulsating pressure accordingly.

[0043] Throughout the fracturing process, the output power of the explosion-proof variable frequency motor is dynamically matched with the optimal energy demand of the coal seam through the variable frequency control cabinet, so as to minimize the overall energy consumption of the system.

[0044] Example 2 An adaptive variable frequency control method for intelligent fracturing, used in underground coal mines, is disclosed, which enables variable frequency control of multiple parameters. The method specifically includes: (1) Parameter initialization and model calibration stage: Initialization settings: Input the geomechanical parameters of the target coal seam into the intelligent control system, set the thresholds for acoustic emission energy release rate and pressure gradient change rate to M and N respectively, and calibrate the initial pressure-flow-acoustic emission characteristic data based on historical data in the database; (2) Adaptive control during the setting stage: The control system outputs the initial setting pressure and adjusts the setting position in real time until it stabilizes based on the dynamic signal fed back by the vibration sensor. (3) Dynamic identification and triggering of the fracturing stage: The pressure is increased at a rate of 0.8 MPa / s. The acoustic emission energy sequence and the pressure gradient change rate are analyzed simultaneously according to the hybrid intelligent algorithm (AI algorithm). When both exceed the preset threshold, the fracturing point is automatically determined and the fracturing stage is triggered. (4) Intelligent pulsating pressure stabilization during the fracturing stage: Based on the pressure boosting strategy and frequency-modulated pulsating waveform made by the adaptive pressure regulating execution component, it acts on the coal seam fracture surface, and dynamically adjusts the pulsating parameters based on the real-time feedback of acoustic emission and vibration energy release rate to maintain the optimal expansion morphology of the fracture; wherein, the frequency is 3Hz and the pressure is 5MPa.

[0045] (5) Intelligent pressure relief and liquid return stage: When the acoustic emission energy release rate is detected to enter a stable plateau period, it is determined that the crack has expanded sufficiently, and the system automatically commands the liquid return device to start and perform fixed-stroke flexible pressure relief; (6) Data closed loop and model iteration: After the execution is completed, all data is uploaded to the database of the intelligent control system to enrich the database content, update the AI ​​model parameters, and optimize the control strategy for subsequent fracturing operations.

[0046] Other areas not mentioned are the same as in Example 1.

[0047] Application Example 1 In this application example, for the working face conditions of a low-permeability, hard top coal seam in a coal mine, the system integration and debugging were first completed on the ground. The geomechanical parameters of the target coal seam and the historical fracturing data of the adjacent area were entered into the ground main control computer to initialize the AI ​​control model and complete the knowledge accumulation.

[0048] After the system is installed and positioned in the well, the fracturing operation starts automatically. During the setting stage, the intelligent control system instructs the variable frequency pump unit to output an initial pressure of approximately 2.5 MPa and continuously monitors the vibration sensor signals. When the vibration frequency of the setter approaches a stable spectrum, the system determines that setting is successful and prepares to proceed to the next stage.

[0049] During the fracturing initiation stage, the system steadily increased pressure at a rate of 0.5 MPa / s. When the pressure reached approximately 6.8 MPa, the edge computing unit detected in real-time that within a 200-millisecond time window, the pressure gradient value jumped sharply from 0.02 to 0.15. Simultaneously, the acoustic emission system captured a dense occurrence of acoustic emission events with energy exceeding 50 dB. The built-in AI algorithm, based on the RBF-fuzzy model, fused these two key features to instantly confirm that the fracturing initiation point had been reached and immediately issued instructions to the execution components to switch to the fracturing stage.

[0050] The fracturing stage is the critical period for the formation of the fracture network. Based on AI decision-making, the system generates sinusoidal pulsating waves with a frequency of 1.2 Hz and a pressure that varies periodically between 8.5 and 10.5 MPa. During the subsequent approximately 300 seconds of fracturing, the AI ​​system continuously tracks the spatiotemporal distribution of acoustic emission events and the spectral characteristics of the vibration signals. When analysis reveals that the distribution of acoustic emission event locations at the fracture propagation front becomes sparse and the total energy release rate curve enters a stable plateau, the system intelligently determines that the fracture has expanded to the predetermined range, and the fracturing objective has been achieved.

[0051] Subsequently, the system automatically entered the depressurization and liquid return phase. The automatic liquid return device was activated under control, depressurizing at a gradual rate of approximately 0.8 MPa per minute, and completing the safe unloading of system pressure in about 10 minutes, effectively avoiding the impact of sudden pressure drops on the already formed crack network.

[0052] All data from the entire fracturing process, including pressure, flow rate, acoustic emission, vibration, and equipment status, are transmitted in real time and reliably to the ground control center through a heterogeneous communication network consisting of a LoRa backbone and RFID-assisted positioning, thus fully achieving the engineering goal of efficient permeability enhancement in low-permeability, hard coal seams.

[0053] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. An intelligent fracturing adaptive frequency conversion control system, comprising an intelligent control system, characterized in that, It also includes variable frequency fracturing pump components, intelligent monitoring and control components, adaptive pressure regulation and execution components, communication and data fusion components, and safety explosion-proof and pressure relief protection components; The variable frequency fracturing pump assembly includes an explosion-proof variable frequency motor, a high-pressure plunger pump, and a flow regulating valve, and its output is adjusted in real time by the variable frequency control cabinet. The intelligent monitoring and control component includes a pressure sensor, a flow meter, an acoustic emission sensor, a vibration sensor, a temperature sensor, and an edge computing unit for data preprocessing and feature extraction. The intelligent monitoring and control component receives data signals sensed by the pressure sensor, flow meter, acoustic emission sensor, vibration sensor, temperature sensor, and edge computing unit, and transmits the data signals to the intelligent control system in the control panel. The adaptive pressure regulating actuator includes an electronically controlled high-pressure valve group, a pulse pressure regulating unit, and a fixed-stroke automatic liquid return device. It receives instructions from the intelligent control system, adjusts the frequency and amplitude of the output pressure, and generates a target pulse waveform. The downhole communication and data fusion component adopts a heterogeneous communication structure combining LoRa and RFID to establish a two-way data link between the downhole equipment and the ground control terminal. The safety explosion-proof and pressure relief protection components include an explosion-proof electrical enclosure, a temperature sensing module, an automatic pressure relief valve, and an overcurrent protection device.

2. The intelligent fracturing adaptive frequency conversion control system according to claim 1, characterized in that, The intelligent control system incorporates a hybrid intelligent algorithm that integrates RBF neural networks and fuzzy adaptive control. This hybrid intelligent algorithm can analyze the characteristic curves of different parameters, such as coal seam stress response, acoustic emission energy release rate, and pressure gradient change rate.

3. The intelligent fracturing adaptive frequency conversion control system according to claim 2, characterized in that, The adaptive pressure regulation execution component can receive instructions from the intelligent control system, automatically determine the coal seam fracturing point based on the acoustic emission energy sequence and pressure gradient change rate identified in real time by the hybrid intelligent algorithm, and adjust the pressure boosting strategy and frequency conversion pulsation waveform accordingly.

4. The intelligent fracturing adaptive frequency conversion control system according to claim 1, characterized in that, Pressure sensor, flow meter, acoustic emission sensor, vibration sensor, and temperature sensor shall be provided in the system as needed.

5. The intelligent fracturing adaptive frequency conversion control system according to claim 1, characterized in that, The automatic return fluid device can automatically open the pressure relief channel after the fracturing process ends or after receiving a safety command, so as to realize the flexible unloading and safe reset of the system.

6. An adaptive frequency conversion control method for intelligent fracturing, employing the adaptive frequency conversion control system for intelligent fracturing as described in any one of claims 1-5, characterized in that, Specifically, the steps include the following: (1) Parameter initialization and model calibration stage: Initialization settings: Input the geomechanical parameters of the target coal seam into the intelligent control system, set the thresholds for acoustic emission energy release rate and pressure gradient change rate to M and N respectively, and calibrate the initial pressure-flow-acoustic emission characteristic data based on historical data in the database; (2) Adaptive control during the setting stage: The control system outputs the initial setting pressure and adjusts the setting position in real time until it stabilizes based on the dynamic signal fed back by the vibration sensor. (3) Dynamic identification and triggering of the initiation stage: pressurization, according to the hybrid intelligent algorithm, the acoustic emission energy sequence and the pressure gradient change rate are analyzed simultaneously. When both exceed the preset threshold, the initiation point is automatically determined and the fracturing stage is triggered. (4) Intelligent pulsating pressure stabilization during the fracturing stage: Based on the pressure boosting strategy and frequency-modulated pulsating waveform made by the adaptive pressure regulating execution component, it acts on the coal seam fracture surface, and dynamically adjusts the pulsating parameters based on the real-time feedback of acoustic emission and vibration energy release rate to maintain the optimal expansion morphology of the fracture. (5) Intelligent pressure relief and liquid return stage: When the acoustic emission energy release rate is detected to enter a stable plateau period, it is determined that the crack has expanded sufficiently, and the system automatically commands the liquid return device to start and perform fixed-stroke flexible pressure relief; (6) Data closed loop and model iteration: After the execution is completed, all data is uploaded to the database of the intelligent control system to update the AI ​​model parameters and optimize the control strategy for subsequent fracturing operations.

7. The adaptive frequency conversion control method for intelligent fracturing according to claim 6, characterized in that, During the fracturing stage, the edge computing unit uses the sliding time window method to calculate the pressure gradient change rate, and is supplemented by short-time Fourier transform analysis of acoustic emission signals to improve the accuracy of fracturing point identification in complex downhole noise environments; the pressure increase rate is 0.3-0.8 MPa / s.

8. The adaptive frequency conversion control method for intelligent fracturing according to claim 6, characterized in that, During the fracturing stage, the hybrid intelligent algorithm integrates the analysis of the emission energy release rate and vibration spectrum information to assess the expansion state of the fracture network in real time, and adaptively adjusts the frequency and amplitude of the pulsating pressure accordingly.

9. The adaptive frequency conversion control method for intelligent fracturing according to claim 7, characterized in that, The frequency control range is 0.1Hz to 3Hz, and the pressure amplitude control range is 1 MPa to 50 MPa.

10. The adaptive frequency conversion control method for intelligent fracturing according to claim 6, characterized in that, Throughout the fracturing process, the output power of the explosion-proof variable frequency motor is dynamically matched with the optimal energy demand of the coal seam through the variable frequency control cabinet, so as to minimize the overall energy consumption of the system.

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