Signal acquisition method and system of underground intelligent monitoring driving device

Abstract

The invention discloses a signal acquisition method and system for an underground intelligent monitoring driving device, and the method comprises the following steps: S1, equipment initialization: after the equipment is started for power supply, automatically completing the precision calibration of a sensor and the matching of a communication protocol, guaranteeing the compatibility of acquisition and transmission, enabling the whole equipment to enter a low-power-consumption standby state, and enabling the equipment to be in a low-power-consumption standby state; and the energy consumption loss in a non-data acquisition stage is reduced. The system can be controlled through wireless remote transmission or an on-site manual operator without personnel descending into a well, the operation risk in a closed space is avoided, the emergency response time is greatly shortened, IP68 protection, anti-explosion characteristics and multi-mode power supply of the system are adapted to the underground wet and corrosive complex environment without a stable power supply, multiple sensors can be externally connected to realize multi-dimensional monitoring, and the system can be widely applied to the field of underground monitoring. And by matching with functions of accurate control, flexible installation and safe transmission, the intelligent monitoring and operation and maintenance requirements of industries such as municipal heat supply, tap water and fuel gas are efficiently met.

Description

Technical Field

[0001] This invention relates to the field of downhole intelligent monitoring and drive technology, and in particular to a signal acquisition method and system for a downhole intelligent monitoring and drive device. Background Technology

[0002] Downhole manholes are an important part of urban municipal infrastructure and are widely used in heating, water supply, gas pipelines and other fields. Traditional downhole valve control and status monitoring rely on manual operation in the well, which has problems such as high risk of working in confined spaces, untimely signal acquisition and delayed emergency response. At the same time, the downhole environment is humid and corrosive, there is no stable power supply and the space is small, which leads to insufficient protection performance and poor endurance of conventional signal acquisition equipment, making it difficult to achieve comprehensive monitoring and stable transmission of multi-dimensional parameters.

[0003] In existing technologies, some downhole monitoring solutions have limited functionality, only capable of valve control or single environmental parameter acquisition, lacking the ability to fuse and process multi-source signals, and having limited communication methods, failing to form hierarchical transmission, encryption mechanisms, and complete closed-loop management processes, thus failing to meet the intelligent monitoring needs of complex downhole scenarios. Considering the above, there is an urgent need for a signal acquisition method and system that is adaptable to complex downhole environments, highly secure, fast-responding, and has a closed-loop management system. To this end, this application proposes a signal acquisition method and system for an intelligent downhole monitoring drive device. Summary of the Invention

[0004] Based on the technical problems existing in the background technology, the present invention proposes a signal acquisition method and system for a downhole intelligent monitoring and drive device.

[0005] The present invention proposes a signal acquisition method for a downhole intelligent monitoring and drive device, comprising the following steps:

[0006] S1: Device Initialization: After the device is powered on, it automatically completes sensor accuracy calibration and communication protocol matching to ensure compatibility between data acquisition and transmission. The device then enters a low-power standby state to reduce energy consumption during periods without data acquisition.

[0007] S2: Multi-source signal acquisition: According to the preset sampling period, acquire downhole temperature, pipeline pressure, well liquid level, gas concentration, well cover status, power supply status, valve opening, output torque, operating fault, battery power and related signals;

[0008] S3: Signal Processing and Fusion: Targeted processing of the acquired multi-source raw signals, using Kalman filtering algorithm to remove interference noise generated by the complex downhole environment, followed by signal amplification and digital calibration to ensure data accuracy. Then, the environmental status signals are correlated and fused with equipment operating parameters, such as binding torque overload conditions with pipeline pressure and liquid level data at the corresponding time, to provide complete data support for subsequent fault diagnosis.

[0009] S4: Wireless signal transmission: Prioritize signals according to their urgency. For emergency alarms such as excessive gas concentration or abnormal opening of manhole covers, high-speed wireless communication is used for real-time transmission to ensure timely emergency response. For routine monitoring signals, low-power wireless communication is used for timed transmission to balance real-time performance and energy consumption. Data encryption is enabled during transmission to prevent data leakage or tampering.

[0010] S5: Platform Analysis and Early Warning: After receiving the transmitted signal data, the remote control platform compares it with the preset safety threshold in real time. If the parameter is detected to exceed the threshold range, an early warning message containing the abnormal parameter type, specific value, location of occurrence, and suggested handling measures is automatically generated and pushed to the management personnel terminal via SMS or APP. At the same time, the remote control platform automatically stores all historical signal data, supporting data traceability and trend analysis.

[0011] S6: Closed-loop feedback control: Managers can view signal data and early warning information through the remote control platform. They can issue valve opening and closing commands, sampling cycle adjustments, torque parameter settings, and emergency shutdown control commands according to actual needs. During the execution of commands, the equipment collects operation status signals in real time and synchronously feeds them back to the remote control platform, forming a complete closed-loop control process of "collection-processing-transmission-decision-execution-feedback".

[0012] Preferably, the specific logical steps of S3 are as follows:

[0013] S301: Standardize the format of the multi-source raw signals collected in S2, unify the data sampling frequency and quantization accuracy, and remove obvious outliers;

[0014] S302: To address random noise caused by electromagnetic interference and vibration in the well, a Kalman filter algorithm is used to filter each signal in real time, separating the effective signal from the noise component;

[0015] The S303 amplifies the filtered weak signal using a programmable control to match the digital processing range. It then combines this with the sensor's factory calibration parameters and eliminates system errors using a linear calibration formula. The linear calibration formula used is:

[0016] ;

[0017] in The signal value after calibration. Here, is the filtered signal value, a is the calibration slope, and b is the calibration intercept, which is obtained by fitting the sensor calibration data.

[0018] S304: Based on the timestamp of the device's built-in clock, align environmental status signals with device operating parameters to ensure the correlation of data under the same timestamp;

[0019] S305: Employs a weighted fusion algorithm to bind device status and environmental signals. The formula used is as follows:

[0020] ;

[0021] in For time k, data is fused. For device status signal weights, Let k be the device status signal at time k. , ... All environmental signals are assigned weights: pipeline pressure is weighted at 0.2, well level at 0.3, and other environmental signals are assigned weights according to monitoring priority, with a total weight of 0.5. , For each time k, the environmental signals are standardized.

[0022] S306: Set threshold ranges based on the physical laws of municipal pipeline network operation, eliminate fusion anomalies that exceed reasonable ranges, and output standardized data.

[0023] Preferably, in step S302, the Kalman filter algorithm achieves noise suppression through a "prediction-update" iteration, as follows:

[0024] S3021: A predictive model is constructed based on the historical trend of the signal to predict the state. The formula used is as follows:

[0025] ;

[0026] in Let A be the predicted state value at time k, and let A be the state transition matrix. Here, B represents the state estimate at time k-1, and B is the control input matrix, which takes a value of 0 and is independent of external control. The control input is set at time k-1;

[0027] S3022: Calculate the uncertainty of the predicted value and perform covariance prediction. The formula used is as follows:

[0028] ;

[0029] in Predict the covariance matrix at time k. Estimate the covariance matrix at time k-1. Let A be the transpose of A. This is the process noise covariance matrix, whose values ​​are taken according to the characteristics of the downhole environment. ;

[0030] S3023: Perform Kalman gain calculation and adjust the weights of the predicted and observed values. The formula used is:

[0031] ;

[0032] in Let K be the Kalman gain at time k, and H be the observation matrix. Let H be the transpose of H, and R be the observation noise covariance matrix, which is set based on the sensor accuracy.

[0033] S3024: The predicted values ​​and the original observed signals are fused for state updates, using the following formula:

[0034] ;

[0035] in The effective signal after denoising at time k. This represents the original observation signal at time k;

[0036] S3025: To update the uncertainty of the estimated value, a covariance update is performed, using the following formula:

[0037] ;

[0038] in The optimal covariance matrix at time k is... It is an identity matrix.

[0039] Preferably, the specific logical steps of S4 are as follows:

[0040] S401: Extract key features of the signal, compare them with preset safety thresholds, and construct a priority determination model to determine emergency alarm signals and routine monitoring signals. The key features of the signal include, but are not limited to, gas concentration values, manhole cover switch status indicators, and equipment fault codes.

[0041] When judging emergency alarm signals, if the signal meets any of the following conditions: "gas concentration ≥ safety threshold", "manhole cover status is abnormally open", or "equipment fault code is fatal", it is judged as a first-level priority.

[0042] When performing routine monitoring signal judgment, signals that do not trigger the above-mentioned emergency conditions, including but not limited to routine temperature, pressure, valve opening degree and battery power, are judged as secondary priority;

[0043] The formula used is: ;

[0044] Where P represents priority, 1 represents urgent, 2 represents normal, and S represents the signal characteristic value. The upper limit threshold for signal security. The lower limit threshold for signal safety. A set of abnormal states The set of normal states;

[0045] S402: Select the communication mode. The first priority signal uses 4Gcat1 high-speed wireless communication and encapsulates data frames based on the Modbus protocol. The transmission rate is set to 1Mbps to ensure real-time performance and communication latency ≤100ms. The second priority signal uses NB-IoT / LORA low-power wireless communication. The transmission period is linked to the sampling period and the transmission rate is set to 50kbps to reduce energy consumption.

[0046] S403: The transmitted data is encrypted using the AES-256 symmetric encryption algorithm. The specific logical steps are as follows:

[0047] S4031: Key Generation: Based on the device's unique hardware identifier and timestamp, a 256-bit session key K is generated using the SHA-256 hash algorithm. The formula used is as follows:

[0048] ;

[0049] in For device hardware address, This is the current timestamp. This is an XOR operation;

[0050] S4032: The standardized data to be transmitted is grouped into 128-bit blocks, and those blocks less than 128 bits are padded using PKCS7 padding.

[0051] S4033: Using the generated key K as the encryption key, encrypt each group of data in AES-256-ECB mode. The encryption formula is as follows:

[0052] ;

[0053] in For the i-th encrypted ciphertext, This represents the i-th set of original data;

[0054] S4034: Concatenate the encrypted ciphertext, timestamp, and device identifier into a complete transmission frame. The frame format is "Device Identifier + Timestamp + Ciphertext + Checksum". The checksum is calculated using the CRC32 algorithm, with the following formula:

[0055] ;

[0056] in This is a string concatenation operation.

[0057] S404: Emergency signals use a "send-confirm" mechanism. After the device sends encrypted data, it waits for the remote platform to send a confirmation frame within 100ms. If no confirmation is received, the data is immediately retransmitted, with a maximum of 3 retransmissions. Regular signals use a "timed send-batch confirmation" mechanism. The device sends encrypted data periodically, and the remote platform sends a confirmation frame every 5 sets of data received. Unconfirmed data is included in the next batch of retransmissions.

[0058] Preferably, the specific logical steps of S5 are as follows:

[0059] S501: After receiving the transmission frame, the remote monitoring platform extracts the device identifier and timestamp, and generates a new key using the same key algorithm. Restore the session key, decrypt the ciphertext using AES-256-ECB mode to obtain the original data, and verify the data integrity using the CRC32 check formula. If the CRC check matches the checksum in the transmission frame, the data is valid; otherwise, discard it and request retransmission.

[0060] S502: Based on the device identifier and signal type fields, the decrypted raw data is classified into environmental status data, device operation data, and safety status data, and associated with the device installation location information;

[0061] S503: Three threshold levels are preset for each type of data, including an early warning threshold. Alarm threshold Emergency shutdown threshold ,and < < The data status is determined by a threshold comparison formula, as follows:

[0062] (1) Normal state: S< or S> The platform only updates real-time data dashboards and does not send any warnings or push notifications.

[0063] (2) Warning status: ≤S< It was determined to be a Level 1 warning, and the data status was marked as "attention".

[0064] (3) Alarm status: ≤S< It was determined to be a Level II warning, and the data status was marked as "dangerous";

[0065] (4) Emergency stop state: S≥ The alert was determined to be a Level 3 warning, the data status was marked as "emergency", and an emergency response command was triggered.

[0066] Where S is the parsed actual data value;

[0067] S504: Calculates the degree of deviation between outlier data and the corresponding threshold, using the following formula:

[0068] ;

[0069] in The percentage of deviation. The threshold used to trigger the event is used to quantify the severity of the anomaly.

[0070] S505: Generates corresponding early warning information based on data status, in the format of "[Early Warning Level]-[Equipment Location]-[Parameter Type]-[Current Value]-[Degree of Deviation]-[Handling Suggestion]". Level 1 early warnings are pushed only through platform pop-up windows, Level 2 early warnings are pushed through platform pop-up windows and APP, and Level 3 early warnings are pushed through platform pop-up windows, APP, and SMS to ensure that managers receive them in a timely manner.

[0071] S506: Stores the parsed raw data, threshold comparison results, early warning information and event timestamps in the database. It uses a time-series database to store real-time monitoring data and a relational database to store early warning events and processing records. It supports historical data tracing and trend analysis by device number, time range and parameter type.

[0072] Preferably, the specific logical steps of S6 are as follows:

[0073] S601: The device listens in real time to control commands issued by the remote monitoring platform via the wireless communication module. The command format is "Device Identifier + Command Type + Parameter Value + Checksum". The checksum is generated using the MD5 algorithm, and the formula used is:

[0074] ;

[0075] S602: After receiving the instruction, the device extracts each field and calculates the checksum using the same MD5 algorithm. If the checksum matches the one in the instruction, the instruction is valid; otherwise, it is discarded. Then, the instruction type and reference value are parsed.

[0076] S603: The device executes the corresponding operation according to the instruction type, as follows:

[0077] (1) Valve control command: Based on the current opening value fed back by the multi-turn absolute encoder, the valve is adjusted through the planetary gear transmission mechanism, with a control accuracy of ≤0.1%. The target opening calculation formula is as follows:

[0078] ;

[0079] in For the target opening, For the current opening, For adjustment coefficients, This is the amount of adjustment for the opening.

[0080] (2) Sampling period adjustment command: Update the preset sampling period parameters to ensure that they are linked and adapted to the transmission period;

[0081] (3) Torque parameter setting command: Adjust the torque protection threshold, ranging from 0-500 Nm, and the formula used is:

[0082] ;

[0083] in The actual torque protection threshold. Set a value for the instruction;

[0084] (4) Emergency shutdown command: Immediately cut off the valve drive power supply, trigger the torque protection mechanism, and ensure that the valve remains in its current position or is closed to a safe position;

[0085] S604: During instruction execution, the device operating parameters are collected in real time, and data is recorded every 100ms to form an execution process curve;

[0086] S605: After the instruction is executed, the device generates feedback information, including the instruction execution result, final status parameters and execution time. After being encrypted with AES-256, the information is transmitted to the remote monitoring platform. After receiving the feedback, the remote monitoring platform updates the instruction execution status. If the execution fails, a fault prompt is pushed, forming a closed-loop control of "instruction issuance - execution - status acquisition - feedback". The total feedback time is ≤500ms.

[0087] The present invention also proposes a signal acquisition system for an intelligent downhole monitoring and drive device, comprising an intelligent downhole monitoring and drive device body, a multi-type sensor module, an information processing module, a wireless communication module, a remote monitoring platform, and a power supply module;

[0088] The downhole intelligent monitoring drive device body is made of cast steel with anti-corrosion coating or stainless steel shell, with a protection level of not less than IP68 and an explosion-proof level that meets the ATEXExdIICT5 / T6 standard. It has a built-in planetary gear transmission mechanism and a multi-turn absolute encoder, and the valve opening acquisition accuracy is not less than 0.1%.

[0089] The multi-type sensor module includes sensors for downhole temperature, pipeline pressure, wellbore level, gas detection, well cover opening alarm, and power failure monitoring. It is detachably connected to the downhole intelligent monitoring and drive unit via an M20x1.5 standard interface and operates within a temperature range of -60℃. +80℃;

[0090] The signal processing module is integrated inside the downhole intelligent monitoring and drive device and is used for signal filtering, amplification, digital calibration and multi-source data fusion. It also has a built-in torque detection unit.

[0091] The wireless communication module supports multiple wireless communication protocols such as 4Gcat1, NB-IoT, and LoRa, and is compatible with Modbus / Mbus bus protocols.

[0092] The power supply module includes a recyclable lithium battery compartment, a solar power supply unit, and an emergency mains power interface. It has an intelligent sleep function and is used to supply power to the main body of the downhole intelligent monitoring drive device, multiple types of sensor modules, and information processing module.

[0093] The remote monitoring platform is used for signal reception, storage, and analysis, enabling status visualization, early warning push, and command issuance. Managers can complete remote monitoring and equipment operation without going down into the well, significantly reducing operational risks.

[0094] Preferably, the downhole intelligent monitoring and drive device is equipped with an emergency handwheel to support manual operation and has a built-in torque protection function to prevent mechanical damage;

[0095] The various sensor modules are designed to be waterproof and corrosion resistant, and can withstand long-term immersion in wells, making them suitable for monitoring the underground pipeline network.

[0096] Compared with existing technologies, the beneficial effects of this invention are:

[0097] 1. Supports multiple non-underground operation modes, including wireless remote transmission and on-site handheld devices, completely eliminating safety risks such as falls, poisoning, and oxygen deficiency that may be caused by working in confined underground spaces. Combined with the manhole cover opening alarm and gas concentration monitoring functions, it achieves dual safety protection of "active early warning + passive protection", greatly reducing the safety hazards of underground pipeline network operation and maintenance.

[0098] 2. In emergency situations, there is no need for manual personnel to travel to the site or go down into the well. Valves can be quickly controlled through a remote control platform or on-site handheld device. The response time is effectively shortened compared to traditional manual operation. In addition, with the multi-rotation planetary gear transmission mechanism, the valve adjustment speed can be flexibly adjusted between 17-60 r / min, further improving the efficiency of emergency response.

[0099] 3. The device casing is made of cast steel with anti-corrosion coating or stainless steel, with a protection level of IP68. It can be buried deep underground or submerged in water for a long time and can withstand temperatures as low as -60℃. It supports a wide temperature range of +80℃ and humid and corrosive environments; it supports multiple power supply methods such as battery compartment, solar power, and emergency mains power. With intelligent sleep function, it can run continuously for several months in wells without stable power supply, completely solving the short battery life of conventional equipment.

[0100] 4. It can be connected to various external sensors such as temperature, pressure, liquid level, and gas, and combined with the device's own status information, it can realize multi-dimensional monitoring of well environment, pipeline status, and equipment operation; equipped with a multi-turn absolute encoder, the valve control accuracy is effectively improved. In addition, the built-in torque protection function can effectively avoid mechanical overload damage, ensuring the accuracy of management and control and the safety of equipment.

[0101] 5. With a maximum diameter of only 340mm, it is compatible with all manhole sizes in the municipal industry. Installation does not require modification of the manhole structure. It provides professional customized connection solutions and support mechanical analysis, and is compatible with various urban pipeline valves such as gate valves, ball valves, and butterfly valves. In addition, it has a built-in self-diagnostic function, which can upload equipment fault information in real time, reducing the frequency of manual inspections and maintenance costs.

[0102] 6. Supports multiple communication protocols such as 4Gcat1, NB-IoT, LoRa, Modbus / Mbus, etc., adapting to different underground signal coverage scenarios to ensure stable long-distance data transmission; equipped with AES-256 encryption algorithm and CRC32 verification mechanism to prevent leakage or tampering during data transmission, ensuring the security and reliability of monitoring data;

[0103] 7. Construct a closed-loop system covering the entire process of "multi-source acquisition - signal processing - hierarchical transmission - platform analysis - early warning push - remote control - status feedback". Managers can view the well status and issue control commands in real time through the cloud platform. At the same time, the system automatically stores historical data and event logs, supports trend analysis and traceability, and realizes intelligent and refined management and control of downhole equipment.

[0104] This invention allows for operation via wireless remote transmission or on-site handheld device without requiring personnel to go down into the well. It avoids the risks of working in confined spaces and significantly shortens emergency response time. Its IP68 protection, explosion-proof characteristics, and multi-mode power supply adapt to the complex environment of underground wells, which are humid and corrosive and lack stable power. It can be connected to various external sensors to achieve multi-dimensional monitoring. With precise control, flexible installation, and safe transmission functions, it efficiently meets the intelligent monitoring and operation and maintenance needs of industries such as municipal heating, tap water, and gas. Attached Figure Description

[0105] Figure 1 This is a flowchart of a signal acquisition method for an intelligent monitoring and drive device for downhole applications proposed in this invention.

[0106] Figure 2 This is a block diagram of a signal acquisition system for an intelligent downhole monitoring and drive device proposed in this invention. Detailed Implementation

[0107] The present invention will be further explained below with reference to specific embodiments.

[0108] Example

[0109] Reference Figure 1-2 This embodiment proposes a signal acquisition method for a downhole intelligent monitoring and drive device, including the following steps:

[0110] S1: Device Initialization: After the device is powered on, it automatically completes sensor accuracy calibration and communication protocol matching to ensure compatibility between data acquisition and transmission. The device then enters a low-power standby state to reduce energy consumption during periods without data acquisition.

[0111] S2: Multi-source signal acquisition: According to the preset sampling period, acquire downhole temperature, pipeline pressure, well liquid level, gas concentration, well cover status, power supply status, valve opening, output torque, operating fault, battery power and related signals;

[0112] S3: Signal Processing and Fusion: Targeted processing of the acquired multi-source raw signals, using Kalman filtering algorithm to remove interference noise generated by the complex downhole environment, followed by signal amplification and digital calibration to ensure data accuracy. Then, the environmental status signals are correlated and fused with equipment operating parameters, such as binding torque overload conditions with pipeline pressure and liquid level data at the corresponding time, to provide complete data support for subsequent fault diagnosis.

[0113] The specific logical steps are as follows:

[0114] S301: Standardize the format of the multi-source raw signals collected in S2, unify the data sampling frequency and quantization accuracy, and remove obvious outliers;

[0115] S302: To address random noise caused by electromagnetic interference and vibration in the well, a Kalman filter algorithm is used to filter each signal in real time, separating the effective signal from the noise component;

[0116] The Kalman filter algorithm achieves noise suppression through a "prediction-update" iteration, as detailed below:

[0117] S3021: A predictive model is constructed based on the historical trend of the signal to predict the state. The formula used is as follows:

[0118] ;

[0119] in Let A be the predicted state value at time k, and let A be the state transition matrix. Here, B represents the state estimate at time k-1, and B is the control input matrix, which takes a value of 0 and is independent of external control. The control input is set at time k-1;

[0120] S3022: Calculate the uncertainty of the predicted value and perform covariance prediction. The formula used is as follows:

[0121] ;

[0122] in Predict the covariance matrix at time k. Estimate the covariance matrix at time k-1. Let A be the transpose of A. This is the process noise covariance matrix, whose values ​​are taken according to the characteristics of the downhole environment. ;

[0123] S3023: Perform Kalman gain calculation and adjust the weights of the predicted and observed values. The formula used is:

[0124] ;

[0125] in Let K be the Kalman gain at time k, and H be the observation matrix. Let H be the transpose of H, and R be the observation noise covariance matrix, which is set based on the sensor accuracy.

[0126] S3024: The predicted values ​​and the original observed signals are fused for state updates, using the following formula:

[0127] ;

[0128] in The effective signal after denoising at time k. This represents the original observation signal at time k;

[0129] S3025: To update the uncertainty of the estimated value, a covariance update is performed, using the following formula:

[0130] ;

[0131] in The optimal covariance matrix at time k is... It is the identity matrix;

[0132] The S303 amplifies the filtered weak signal using a programmable control to match the digital processing range. It then combines this with the sensor's factory calibration parameters and eliminates system errors using a linear calibration formula. The linear calibration formula used is:

[0133] ;

[0134] in The signal value after calibration. Here, is the filtered signal value, a is the calibration slope, and b is the calibration intercept, which is obtained by fitting the sensor calibration data.

[0135] S304: Based on the timestamp of the device's built-in clock, align environmental status signals with device operating parameters to ensure the correlation of data under the same timestamp;

[0136] S305: Employs a weighted fusion algorithm to bind device status and environmental signals. The formula used is as follows:

[0137] ;

[0138] in For time k, data is fused. For device status signal weights, Let k be the device status signal at time k. , ... All environmental signals are assigned weights: pipeline pressure is weighted at 0.2, well level at 0.3, and other environmental signals are assigned weights according to monitoring priority, with a total weight of 0.5. , For each time k, the environmental signals are standardized.

[0139] S306: Set threshold ranges based on the physical laws of municipal pipeline network operation, eliminate fusion anomalies that exceed reasonable ranges, and output standardized data;

[0140] S4: Wireless signal transmission: Prioritize signals according to their urgency. For emergency alarms such as excessive gas concentration or abnormal opening of manhole covers, high-speed wireless communication is used for real-time transmission to ensure timely emergency response. For routine monitoring signals, low-power wireless communication is used for timed transmission to balance real-time performance and energy consumption. Data encryption is enabled during transmission to prevent data leakage or tampering.

[0141] The specific logical steps are as follows:

[0142] S401: Extract key features of the signal, compare them with preset safety thresholds, and construct a priority determination model to determine emergency alarm signals and routine monitoring signals. The key features of the signal include, but are not limited to, gas concentration values, manhole cover switch status indicators, and equipment fault codes.

[0143] When judging emergency alarm signals, if the signal meets any of the following conditions: "gas concentration ≥ safety threshold", "manhole cover status is abnormally open", or "equipment fault code is fatal", it is judged as a first-level priority.

[0144] When performing routine monitoring signal judgment, signals that do not trigger the above-mentioned emergency conditions, including but not limited to routine temperature, pressure, valve opening degree and battery power, are judged as secondary priority;

[0145] The formula used is: ;

[0146] Where P represents priority, 1 represents urgent, 2 represents normal, and S represents the signal characteristic value. The upper limit threshold for signal security. The lower limit threshold for signal safety. A set of abnormal states The set of normal states;

[0147] S402: Select the communication mode. The first priority signal uses 4Gcat1 high-speed wireless communication and encapsulates data frames based on the Modbus protocol. The transmission rate is set to 1Mbps to ensure real-time performance and communication latency ≤100ms. The second priority signal uses NB-IoT / LORA low-power wireless communication. The transmission period is linked to the sampling period and the transmission rate is set to 50kbps to reduce energy consumption.

[0148] S403: The transmitted data is encrypted using the AES-256 symmetric encryption algorithm. The specific logical steps are as follows:

[0149] S4031: Key Generation: Based on the device's unique hardware identifier and timestamp, a 256-bit session key K is generated using the SHA-256 hash algorithm. The formula used is as follows:

[0150] ;

[0151] in For device hardware address, This is the current timestamp. This is an XOR operation;

[0152] S4032: The standardized data to be transmitted is grouped into 128-bit blocks, and those blocks less than 128 bits are padded using PKCS7 padding.

[0153] S4033: Using the generated key K as the encryption key, encrypt each group of data in AES-256-ECB mode. The encryption formula is as follows:

[0154] ;

[0155] in For the i-th encrypted ciphertext, This represents the i-th set of original data;

[0156] S4034: Concatenate the encrypted ciphertext, timestamp, and device identifier into a complete transmission frame. The frame format is "Device Identifier + Timestamp + Ciphertext + Checksum". The checksum is calculated using the CRC32 algorithm, with the following formula:

[0157] ;

[0158] in This is a string concatenation operation.

[0159] S404: Emergency signals use a "send-confirm" mechanism. After the device sends encrypted data, it waits for the remote platform to send a confirmation frame within 100ms. If no confirmation is received, the data is immediately retransmitted, with a maximum of 3 retransmissions. Regular signals use a "timed send-batch confirmation" mechanism. The device sends encrypted data periodically, and the remote platform sends a confirmation frame every 5 sets of data received. Unconfirmed data is included in the next batch of retransmissions.

[0160] S5: Platform Analysis and Early Warning: After receiving the transmitted signal data, the remote control platform compares it with the preset safety threshold in real time. If the parameter is detected to exceed the threshold range, an early warning message containing the abnormal parameter type, specific value, location of occurrence, and suggested handling measures is automatically generated and pushed to the management personnel terminal via SMS or APP. At the same time, the remote control platform automatically stores all historical signal data, supporting data traceability and trend analysis.

[0161] The specific logical steps are as follows:

[0162] S501: After receiving the transmission frame, the remote monitoring platform extracts the device identifier and timestamp, and generates a new key using the same key algorithm. Restore the session key, decrypt the ciphertext using AES-256-ECB mode to obtain the original data, and verify the data integrity using the CRC32 check formula. If the CRC check matches the checksum in the transmission frame, the data is valid; otherwise, discard it and request retransmission.

[0163] S502: Based on the device identifier and signal type fields, the decrypted raw data is classified into environmental status data, device operation data, and safety status data, and associated with the device installation location information;

[0164] S503: Three threshold levels are preset for each type of data, including an early warning threshold. Alarm threshold Emergency shutdown threshold ,and < < The data status is determined by a threshold comparison formula, as follows:

[0165] (1) Normal state: S< or S> The platform only updates real-time data dashboards and does not send any warnings or push notifications.

[0166] (2) Warning status: ≤S< It was determined to be a Level 1 warning, and the data status was marked as "attention".

[0167] (3) Alarm status: ≤S< It was determined to be a Level II warning, and the data status was marked as "dangerous";

[0168] (4) Emergency stop state: S≥ The alert was determined to be a Level 3 warning, the data status was marked as "emergency", and an emergency response command was triggered.

[0169] Where S is the parsed actual data value;

[0170] S504: Calculates the degree of deviation between outlier data and the corresponding threshold, using the following formula:

[0171] ;

[0172] in The percentage of deviation. The threshold used to trigger the event is used to quantify the severity of the anomaly.

[0173] S505: Generates corresponding early warning information based on data status, in the format of "[Early Warning Level]-[Equipment Location]-[Parameter Type]-[Current Value]-[Degree of Deviation]-[Handling Suggestion]". Level 1 early warnings are pushed only through platform pop-up windows, Level 2 early warnings are pushed through platform pop-up windows and APP, and Level 3 early warnings are pushed through platform pop-up windows, APP, and SMS to ensure that managers receive them in a timely manner.

[0174] S506: Stores the parsed raw data, threshold comparison results, early warning information and event timestamps in the database. It uses a time-series database to store real-time monitoring data and a relational database to store early warning events and processing records. It supports historical data tracing and trend analysis by device number, time range and parameter type.

[0175] S6: Closed-loop feedback control: Managers can view signal data and early warning information through the remote control platform. They can issue valve opening and closing, sampling cycle adjustment, torque parameter setting, and emergency shutdown control commands according to actual needs. During the execution of commands, the equipment collects operation status signals in real time and feeds them back to the remote control platform, forming a complete closed-loop control process of "collection-processing-transmission-decision-execution-feedback".

[0176] The specific logical steps are as follows:

[0177] S601: The device listens in real time to control commands issued by the remote monitoring platform via the wireless communication module. The command format is "Device Identifier + Command Type + Parameter Value + Checksum". The checksum is generated using the MD5 algorithm, and the formula used is:

[0178] ;

[0179] S602: After receiving the instruction, the device extracts each field and calculates the checksum using the same MD5 algorithm. If the checksum matches the one in the instruction, the instruction is valid; otherwise, it is discarded. Then, the instruction type and reference value are parsed.

[0180] S603: The device executes the corresponding operation according to the instruction type, as follows:

[0181] (1) Valve control command: Based on the current opening value fed back by the multi-turn absolute encoder, the valve is adjusted through the planetary gear transmission mechanism, with a control accuracy of ≤0.1%. The target opening calculation formula is as follows:

[0182] ;

[0183] in For the target opening, For the current opening, For adjustment coefficients, This is the amount of adjustment for the opening.

[0184] (2) Sampling period adjustment command: Update the preset sampling period parameters to ensure that they are linked and adapted to the transmission period;

[0185] (3) Torque parameter setting command: Adjust the torque protection threshold, ranging from 0-500 Nm, and the formula used is:

[0186] ;

[0187] in The actual torque protection threshold. Set a value for the instruction;

[0188] (4) Emergency shutdown command: Immediately cut off the valve drive power supply, trigger the torque protection mechanism, and ensure that the valve remains in its current position or is closed to a safe position;

[0189] S604: During instruction execution, the device operating parameters are collected in real time, and data is recorded every 100ms to form an execution process curve;

[0190] S605: After the instruction is executed, the device generates feedback information, including the instruction execution result, final status parameters and execution time. After being encrypted with AES-256, the information is transmitted to the remote monitoring platform. After receiving the feedback, the remote monitoring platform updates the instruction execution status. If the execution fails, a fault prompt is pushed, forming a closed-loop control of "instruction issuance - execution - status acquisition - feedback". The total feedback time is ≤500ms.

[0191] This embodiment also proposes a signal acquisition system for an intelligent downhole monitoring and drive device, including an intelligent downhole monitoring and drive device body, multiple types of sensor modules, an information processing module, a wireless communication module, a remote monitoring platform, and a power supply module;

[0192] The downhole intelligent monitoring drive unit body is made of cast steel with anti-corrosion coating or stainless steel shell, with a protection level of not less than IP68 and an explosion-proof level that meets ATEXExdIICT5 / T6 standards. It has a built-in planetary gear transmission mechanism and a multi-turn absolute encoder, and the valve opening acquisition accuracy is not less than 0.1%.

[0193] The downhole intelligent monitoring drive unit is equipped with an emergency handwheel, which supports manual operation and has a built-in torque protection function to prevent mechanical damage.

[0194] Multiple sensor modules, including downhole temperature, pipeline pressure, wellbore level, gas detection, well cover opening alarm, and power failure monitoring sensors, are detachably connected to the downhole intelligent monitoring and drive unit via an M20x1.5 standard interface. The operating temperature range is -60℃. +80℃;

[0195] Multiple types of sensor modules adopt a waterproof and corrosion-resistant design, which can withstand long-term immersion in wells and is suitable for the monitoring needs of municipal pipeline networks.

[0196] The signal processing module is integrated inside the downhole intelligent monitoring and drive device for signal filtering, amplification, digital calibration and multi-source data fusion, and has a built-in torque detection unit;

[0197] The wireless communication module supports multiple wireless communication protocols, including 4Gcat1, NB-IoT, and LoRa, and is compatible with Modbus / Mbus bus protocols.

[0198] The power supply module includes a recyclable lithium battery compartment, a solar power unit, and an emergency mains power interface. It has an intelligent sleep function and is used to power the downhole intelligent monitoring drive device, various types of sensor modules, and information processing modules.

[0199] The remote monitoring platform is used for signal reception, storage, and analysis, enabling status visualization, early warning push, and command issuance. Managers can complete remote monitoring and equipment operation without going down into the mine, greatly reducing operational risks.

[0200] This embodiment allows for operation via wireless remote transmission or on-site handheld device without requiring personnel to enter the well. It avoids the risks of working in confined spaces and significantly shortens emergency response time. Its IP68 protection, explosion-proof characteristics, and multi-mode power supply adapt to the complex environment of dampness, corrosion, and lack of stable power supply in wells. It can be connected to various external sensors to achieve multi-dimensional monitoring. With precise control, flexible installation, and safe transmission functions, it efficiently meets the intelligent monitoring and operation and maintenance needs of industries such as municipal heating, tap water, and gas.

[0201] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A signal acquisition method for an intelligent monitoring and drive device for downhole drilling, characterized in that, Includes the following steps: S1: Device initialization: After the device is powered on, it automatically completes sensor accuracy calibration and communication protocol matching, and the device as a whole enters a low-power standby state. S2: Multi-source signal acquisition: According to the preset sampling period, acquire downhole temperature, pipeline pressure, well liquid level, gas concentration, well cover status, power supply status, valve opening, output torque, operating fault, battery power and related signals; S3: Signal Processing and Fusion: The acquired multi-source raw signals are processed in a targeted manner. The Kalman filter algorithm is used to remove interference noise generated by the complex downhole environment. The signals are then amplified and digitally calibrated. Finally, the environmental status signals are correlated and fused with the equipment operating parameters. S4: Wireless signal transmission: Prioritize signals according to their urgency. For emergency alarm signals such as excessive gas concentration or abnormal opening of manhole covers, high-speed wireless communication is used for real-time transmission. For routine monitoring signals, low-power wireless communication is used for timed transmission to balance real-time performance and energy consumption. Data encryption is enabled during transmission. S5: Platform Analysis and Early Warning: After receiving the transmitted signal data, the remote control platform compares it with the preset safety threshold in real time. If the parameter is detected to exceed the threshold range, an early warning message containing the abnormal parameter type, specific value, location of occurrence, and suggested handling measures is automatically generated and pushed to the management personnel terminal via SMS or APP. At the same time, the remote control platform automatically stores all historical signal data, supporting data traceability and trend analysis. S6: Closed-loop feedback control: Managers can view signal data and early warning information through the remote control platform. They can issue valve opening and closing commands, sampling cycle adjustments, torque parameter settings, and emergency shutdown control commands according to actual needs. During the execution of commands, the equipment collects operation status signals in real time and synchronously feeds them back to the remote control platform, forming a complete closed-loop control process of "collection-processing-transmission-decision-execution-feedback".

2. The signal acquisition method for a downhole intelligent monitoring and drive device according to claim 1, characterized in that, The specific logical steps of S3 are as follows: S301: Standardize the format of the multi-source raw signals collected in S2, unify the data sampling frequency and quantization accuracy, and remove obvious outliers; S302: To address random noise caused by electromagnetic interference and vibration in the well, a Kalman filter algorithm is used to filter each signal in real time, separating the effective signal from the noise component; The S303 amplifies the filtered weak signal using a programmable control to match the digital processing range. It then combines this with the sensor's factory calibration parameters and eliminates system errors using a linear calibration formula. The linear calibration formula used is: ； in The signal value after calibration. Here, is the filtered signal value, a is the calibration slope, and b is the calibration intercept, which is obtained by fitting the sensor calibration data. S304: Based on the timestamp of the device's built-in clock, align environmental status signals with device operating parameters to ensure the correlation of data under the same timestamp; S305: Employs a weighted fusion algorithm to bind device status and environmental signals. The formula used is as follows: ； in For time k, data is fused. For device status signal weights, Let k be the device status signal at time k. , ... All environmental signals are assigned weights: pipeline pressure is weighted at 0.2, well level at 0.3, and other environmental signals are assigned weights according to monitoring priority, with a total weight of 0.

5. , For each time k, the environmental signals are standardized. S306: Set threshold ranges based on the physical laws of municipal pipeline network operation, eliminate fusion anomalies that exceed reasonable ranges, and output standardized data.

3. The signal acquisition method for a downhole intelligent monitoring and drive device according to claim 2, characterized in that, In step S302, the Kalman filter algorithm achieves noise suppression through a "prediction-update" iteration, as detailed below: S3021: A predictive model is constructed based on the historical trend of the signal to predict the state. The formula used is as follows: ； in Let A be the predicted state value at time k, and let A be the state transition matrix. Here, B represents the state estimate at time k-1, and B is the control input matrix, which takes a value of 0 and is independent of external control. The control input is set at time k-1; S3022: Calculate the uncertainty of the predicted value and perform covariance prediction. The formula used is as follows: ； in Predict the covariance matrix at time k. Estimate the covariance matrix at time k-1. Let A be the transpose of A. This is the process noise covariance matrix, whose values ​​are taken according to the characteristics of the downhole environment. ; S3023: Perform Kalman gain calculation and adjust the weights of the predicted and observed values. The formula used is: ； in Let K be the Kalman gain at time k, and H be the observation matrix. Let H be the transpose of H, and R be the observation noise covariance matrix, which is set based on the sensor accuracy. S3024: The predicted values ​​and the original observed signals are fused for state updates, using the following formula: ； in The effective signal after denoising at time k. This represents the original observation signal at time k; S3025: To update the uncertainty of the estimated value, a covariance update is performed, using the following formula: ； in The optimal covariance matrix at time k is... It is an identity matrix.

4. The signal acquisition method for a downhole intelligent monitoring and drive device according to claim 1, characterized in that, The specific logical steps of S4 are as follows: S401: Extract key features of the signal, compare them with preset safety thresholds, and construct a priority determination model to determine emergency alarm signals and routine monitoring signals. The key features of the signal include, but are not limited to, gas concentration values, manhole cover switch status indicators, and equipment fault codes. When judging emergency alarm signals, if the signal meets any of the following conditions: "gas concentration ≥ safety threshold", "manhole cover status is abnormally open", or "equipment fault code is fatal", it is judged as first priority. When performing routine monitoring signal judgment, signals that do not trigger the above-mentioned emergency conditions, including but not limited to routine temperature, pressure, valve opening degree and battery power, are judged as secondary priority; The formula used is: ; Where P represents priority, 1 represents urgent, 2 represents normal, and S represents the signal characteristic value. The upper limit threshold for signal security. The lower limit threshold for signal safety. A set of abnormal states The set of normal states; S402: Select the communication mode. The first priority signal uses 4Gcat1 high-speed wireless communication and encapsulates data frames based on the Modbus protocol. The transmission rate is set to 1Mbps to ensure real-time performance and communication latency ≤100ms. The second priority signal uses NB-IoT / LORA low-power wireless communication. The transmission period is linked to the sampling period and the transmission rate is set to 50kbps to reduce energy consumption. S403: The transmitted data is encrypted using the AES-256 symmetric encryption algorithm. The specific logical steps are as follows: S4031: Key Generation: Based on the device's unique hardware identifier and timestamp, a 256-bit session key K is generated using the SHA-256 hash algorithm. The formula used is as follows: ； in For device hardware address, This is the current timestamp. This is an XOR operation; S4032: The standardized data to be transmitted is grouped into 128-bit blocks, and those blocks less than 128 bits are padded using PKCS7 padding. S4033: Using the generated key K as the encryption key, encrypt each group of data in AES-256-ECB mode. The encryption formula is as follows: ； in For the i-th encrypted ciphertext, This represents the i-th set of original data; S4034: Concatenate the encrypted ciphertext, timestamp, and device identifier into a complete transmission frame. The frame format is "Device Identifier + Timestamp + Ciphertext + Checksum". The checksum is calculated using the CRC32 algorithm, with the following formula: ； in This is a string concatenation operation. S404: Emergency signals use a "send-confirm" mechanism. After the device sends encrypted data, it waits for the remote platform to send a confirmation frame within 100ms. If no confirmation is received, the data is immediately retransmitted, with a maximum of 3 retransmissions. Regular signals use a "timed send-batch confirmation" mechanism. The device sends encrypted data periodically, and the remote platform sends a confirmation frame every 5 sets of data received. Unconfirmed data is included in the next batch for retransmission.

5. The signal acquisition method for a downhole intelligent monitoring and drive device according to claim 1, characterized in that, The specific logical steps of S5 are as follows: S501: After receiving the transmission frame, the remote monitoring platform extracts the device identifier and timestamp, and generates a new key using the same key algorithm. Restore the session key, decrypt the ciphertext using AES-256-ECB mode to obtain the original data, and verify the data integrity using the CRC32 check formula. If the CRC check matches the checksum in the transmission frame, the data is valid; otherwise, discard it and request retransmission. S502: Based on the device identifier and signal type fields, the decrypted raw data is classified into environmental status data, device operation data, and safety status data, and associated with the device installation location information; S503: Three threshold levels are preset for each type of data, including an early warning threshold. Alarm threshold Emergency shutdown threshold ,and < < The data status is determined by a threshold comparison formula, as follows: (1) Normal state: S< or S> The platform only updates real-time data dashboards and does not send any warnings or push notifications. (2) Warning status: ≤S< This is classified as a Level 1 warning, and the data status is marked as "attention". (3) Alarm status: ≤S< It was determined to be a Level 2 warning, and the data status was marked as "dangerous"; (4) Emergency stop state: S≥ The alert was determined to be a Level 3 warning, the data status was marked as "emergency", and an emergency response command was triggered. Where S is the parsed actual data value; S504: Calculates the degree of deviation between outlier data and the corresponding threshold, using the following formula: ； in The percentage of deviation. The threshold used to trigger the event is used to quantify the severity of the anomaly. S505: Generates corresponding early warning information based on data status, in the format of "[Early Warning Level]-[Device Location]-[Parameter Type]-[Current Value]-[Degree of Deviation]-[Handling Suggestion]". Level 1 early warnings are pushed only through platform pop-up windows, Level 2 early warnings are pushed through platform pop-up windows and APP, and Level 3 early warnings are pushed through platform pop-up windows, APP, and SMS to ensure that managers receive them in a timely manner. S506: Stores the parsed raw data, threshold comparison results, early warning information and event timestamps in the database. It uses a time-series database to store real-time monitoring data and a relational database to store early warning events and processing records. It supports historical data tracing and trend analysis by device number, time range and parameter type.

6. The signal acquisition method for a downhole intelligent monitoring and drive device according to claim 1, characterized in that, The specific logical steps of S6 are as follows: S601: The device listens in real time to control commands issued by the remote monitoring platform via the wireless communication module. The command format is "Device Identifier + Command Type + Parameter Value + Checksum". The checksum is generated using the MD5 algorithm, and the formula used is: ； S602: After receiving the instruction, the device extracts each field and calculates the checksum using the same MD5 algorithm. If the checksum matches the one in the instruction, the instruction is valid; otherwise, it is discarded. Then, the instruction type and reference value are parsed. S603: The device executes the corresponding operation according to the instruction type, as follows: (1) Valve control command: Based on the current opening value fed back by the multi-turn absolute encoder, the valve is adjusted through the planetary gear transmission mechanism, with a control accuracy of ≤0.1%. The target opening calculation formula is as follows: ； in For the target opening, For the current opening, For adjustment coefficients, This is the amount of adjustment for the opening. (2) Sampling period adjustment command: Update the preset sampling period parameters to ensure that they are linked and adapted to the transmission period; (3) Torque parameter setting command: Adjust the torque protection threshold, ranging from 0-500 Nm, and the formula used is: ； in The actual torque protection threshold. Set a value for the instruction; (4) Emergency shutdown command: Immediately cut off the valve drive power supply, trigger the torque protection mechanism, and ensure that the valve remains in its current position or is closed to a safe position; S604: During instruction execution, the device operating parameters are collected in real time, and data is recorded every 100ms to form an execution process curve; S605: After the instruction is executed, the device generates feedback information, including the instruction execution result, final status parameters and execution time. After being encrypted with AES-256, the information is transmitted to the remote monitoring platform. After receiving the feedback, the remote monitoring platform updates the instruction execution status. If the execution fails, a fault prompt is pushed, forming a closed-loop control of "instruction issuance - execution - status acquisition - feedback". The total feedback time is ≤500ms.

7. A signal acquisition system for an intelligent downhole monitoring and drive device, used to implement the method described in any one of claims 1-6, characterized in that, It includes the main body of the downhole intelligent monitoring and drive device, multiple types of sensor modules, information processing module, wireless communication module, remote monitoring platform, and power supply module; The downhole intelligent monitoring drive device body is made of cast steel with anti-corrosion coating or stainless steel shell, with a protection level of not less than IP68 and an explosion-proof level that meets the ATEXExdIICT5 / T6 standard. It has a built-in planetary gear transmission mechanism and a multi-turn absolute encoder, and the valve opening acquisition accuracy is not less than 0.1%. The multi-type sensor module includes sensors for downhole temperature, pipeline pressure, wellbore level, gas detection, well cover opening alarm, and power failure monitoring. It is detachably connected to the downhole intelligent monitoring and drive unit via an M20x1.5 standard interface and operates within a temperature range of -60℃. +80℃; The signal processing module is integrated inside the downhole intelligent monitoring and drive device and is used for signal filtering, amplification, digital calibration and multi-source data fusion. It also has a built-in torque detection unit. The wireless communication module supports multiple wireless communication protocols such as 4Gcat1, NB-IoT, and LoRa, and is compatible with Modbus / Mbus bus protocols. The power supply module includes a recyclable lithium battery compartment, a solar power supply unit, and an emergency mains power interface. It has an intelligent sleep function and is used to supply power to the main body of the downhole intelligent monitoring drive device, multiple types of sensor modules, and information processing module. The remote monitoring platform is used for signal reception, storage, and analysis, enabling status visualization, early warning push, and command issuance.

8. The signal acquisition system for a downhole intelligent monitoring and drive device according to claim 7, characterized in that, The downhole intelligent monitoring and drive device is equipped with an emergency handwheel, which supports manual operation and has a built-in torque protection function to prevent mechanical damage. The various sensor modules are designed to be waterproof and corrosion resistant, and can withstand long-term immersion in wells, making them suitable for monitoring the underground pipeline network.

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