Temperature and pressure acquisition method and circuit and underground ultrahigh-temperature pressure gauge

Abstract

The invention relates to the technical field of oil field measurement, in particular to a temperature and pressure acquisition method and circuit and an underground ultrahigh-temperature pressure gauge. The method comprises the following steps: initializing a configuration system, including configuring a temperature sensor, a pressure sensor, a sampling rate and a communication protocol, and calibrating the temperature sensor and the pressure sensor to ensure precision; acquiring analog signals of the temperature sensor and the pressure sensor; converting the received analog signal into a digital signal through an analog-to-digital converter for subsequent digital processing; according to the invention, the field programmable gate array is innovatively applied to the digital signal processing method, a temperature and pressure acquisition system for multi-mode signal processing is successfully developed, and stable and reliable data acquisition and transmission can be realized in a high-temperature oil field environment.

Description

Technical Field

[0001] The invention relates to the technical field of oil field measurement, in particular to a temperature and pressure acquisition method, a circuit and an underground ultra-high temperature pressure gauge. Background Art

[0002] my country's oil and gas fields are mainly distributed in continental sedimentary basins, and the types are relatively complex. Due to different geological structures, there are heavy oil blocks with much higher viscosity than general crude oil in the oil fields, and the high viscosity makes it difficult to mine. Thermal mining by injecting steam into heavy oil layers has been widely used in the mining of heavy oil, asphalt and oil shale. The pressure drop and mass flow rate of steam injected into oil wells during thermal mining of heavy oil greatly affect the output of heavy oil. In order to further improve the quality of steam injection and optimize the entire mining process, it is necessary to first obtain the parameters of steam injection wells, the main parameters of which are temperature and pressure.

[0003] However, for some wells, the main feature is that their temperature is extremely high, generally above 200°C. At present, the maximum pressure gauge is 175°C, and for the electronic part, the general design temperature is 125°C. When the actual temperature of the device exceeds its design temperature, the device will be in irreversible danger of reduced signal-to-noise ratio or even damage. Foreign high-temperature resistant electronic technology is relatively mature, but due to technological blockade, it is difficult to obtain domestically; high-temperature resistant chips have high technical barriers, and my country is currently unable to manufacture, package and mass produce them; a small number of high-temperature resistant devices can be obtained from abroad through customization for experimental testing, but such devices are expensive and cannot be used in large quantities for commercial purposes.

[0004] And the high temperature pressure gauges known at present are all of storage type, and cannot read the downhole pressure and temperature in real time; and the known high temperature pressure gauges can only test one set of pressure and temperature, and cannot test other parameters; because the known high temperature uses a thermos bottle setting, the working time is short.

[0005] In order to solve the above technical problems of measurement difficulties caused by high temperature, a technical means is urgently needed to effectively measure underground steam parameters (especially temperature and pressure) under high temperature conditions. Not only must the current difficulty of equipment temperature tolerance be overcome, but also the ability to obtain underground data more immediately and in real time on the table or on the ground must be achieved. Summary of the invention

[0006] The present invention provides a temperature and pressure acquisition method, a circuit and a downhole ultra-high temperature pressure gauge to solve the technical problem of downhole data measurement difficulties caused by high temperature in oil fields.

[0007] The technical solution of the present invention to solve the above technical problems is as follows:

[0008] In one aspect, a method for collecting temperature and pressure is provided, the method comprising the following steps:

[0009] Initializing the configuration system, including configuring the temperature sensor, the pressure sensor, the sampling rate and the communication protocol, and calibrating the temperature sensor and the pressure sensor to ensure accuracy;

[0010] Acquiring analog signals from the temperature sensor and the pressure sensor;

[0011] Converting the received analog signal into a digital signal through an analog-to-digital converter for subsequent digital processing;

[0012] De-noising the digital signal using a digital filtering algorithm, including zero-phase moving average filtering, to eliminate the effects of transient noise;

[0013] Applying a decoding algorithm to the denoised digital signal, wherein the decoding algorithm matches the existing feature pattern recognition decoding template in the algorithm library for accurate signal transmission;

[0014] Segmenting the decoded digital signal into time segments for data analysis and processing;

[0015] The segmented time segments are stored in an internal database for subsequent access and analysis.

[0016] Furthermore, the step of performing denoising processing on the digital signal using a digital filtering algorithm includes:

[0017] performing zero-phase moving average filtering, setting a dynamic window size to accommodate different noise levels of the digital signal;

[0018] The calculation formula of the digital filtering algorithm is:

[0019]

[0020] Where Y(t) is the output signal after filtering, x(tn) is the input signal, t is the current time point, x is the input data value measured at different time points, n is the time point, N is the time window size, and α and β are filter coefficients.

[0021] Furthermore, the step of applying a decoding algorithm to the denoised digital signal comprises:

[0022] Initialize the matching template library and select the best matching mode according to the collected ones;

[0023] Real-time matching, using fast Fourier transform to analyze the frequency spectrum characteristics of the digital signal;

[0024] Pattern recognition, pattern matching is performed through the minimum mean square error algorithm, and accurate decoding is performed by determining the signal matching degree.

[0025] Furthermore, the step of dividing the decoded digital signal into time segments includes:

[0026] Adopt adaptive time segmentation algorithm to segment, the length of the time segment T s The calculation formula is:

[0027]

[0028] In the formula, m is the segment constant, A s is the signal amplitude, f c is the signal center frequency.

[0029] Furthermore, before the decoded digital signal is divided into time segments, the signal is supplemented and a piecewise linear supplementation algorithm is used to interpolate missing data to ensure that the time segments are complete and continuous.

[0030] Furthermore, the step of storing the divided time segments into an internal database includes:

[0031] Implement lossless compression algorithms to reduce storage space requirements, including incremental encoding combined with Huffman coding to ensure that the data compression process does not lose precise information;

[0032] After the step of storing the segmented time segments in the internal database, real-time visualization processing is used to display the selected temperature and pressure data in a graphical form through a graphical user interface, and a user interaction function is provided for data query and historical comparison.

[0033] Furthermore, the step of acquiring the analog signals of the temperature sensor and the pressure sensor includes:

[0034] Receive remotely transmitted signals through wireless communication modules and use custom protocols to achieve time synchronization to ensure data integrity;

[0035] The step of converting the received analog signal into a digital signal by an analog-to-digital converter comprises:

[0036] A dual-channel fast analog-to-digital converter is used to ensure that the data of the temperature sensor and the pressure sensor are acquired simultaneously, and an interleaved sampling technique is adopted to increase the sampling frequency.

[0037] On the other hand, a temperature and pressure acquisition circuit is provided, the circuit is used to implement the temperature and pressure acquisition method as described above, and the circuit includes:

[0038] A signal receiving module, connected to the temperature sensor and the pressure sensor, for receiving analog signals;

[0039] an analog-to-digital conversion module, connected to the signal receiving module, for converting the analog signal into a digital signal;

[0040] A digital signal processing module, connected to the analog-to-digital conversion module, including a digital filter and a decoder, for performing denoising, decoding and segmentation operations;

[0041] A storage module, connected to the digital signal processing module, and used to store the processed digital signal;

[0042] Wireless communication module for remote data transmission;

[0043] Furthermore, the digital filter in the digital signal processing module adopts a field programmable gate array for dynamic real-time adjustment of filtering parameters, including:

[0044] Initialize the field programmable gate array configuration, design the filter architecture, including the filter type and basic parameters, and implement the basic logic of the filter through a hardware description language;

[0045] Dynamically obtain the current signal characteristics of the system, and pass the signal characteristics as input parameters to the field programmable gate array to ensure low-latency input of sensor data;

[0046] The filter parameters are generated according to the real-time characteristics of the input signal, and the filter coefficients are updated on the field programmable gate array to dynamically adjust the filter characteristics. The calculation formula for dynamically adjusting the filter characteristics is:

[0047]

[0048] Where H(z) represents the filter transfer function, k represents the frequency index, z is a complex variable used to represent the frequency domain transform of discrete-time signals, M(n) represents the filter order that changes with time point n, and h k (n) represents the filter coefficient at time point n and frequency index k, z -k represents the delay of the input signal by k time points; M(n) and h k (n) is dynamically updated with the real-time signal characteristics, where the filter coefficient h k The update formula of (n) is:

[0049]

[0050] In the formula, w k is the window function coefficient, a k (n) is the reference filter coefficient, b k is the periodic component amplitude factor, f c (n) is the dynamic cutoff frequency, F sis the sampling frequency;

[0051] The filtering operation is performed in real time inside the field programmable gate array, and the parallel processing capability of the field programmable gate array is utilized to achieve fast filtering.

[0052] On the other hand, a temperature and pressure acquisition method, circuit and downhole ultra-high temperature pressure gauge are provided, wherein the pressure gauge is integrated with the temperature and pressure acquisition circuit as described above, and is used for signal acquisition, signal processing, processing command signals and sending data, and the pressure gauge further comprises:

[0053] A rope cap (1) is used to connect a steel wire to lower the pressure gauge;

[0054] An insulating bottle (2) is used to isolate high temperature so as to ensure that the temperature inside the insulating bottle (2) is not higher than 125° C. during measurement;

[0055] The cable part (3) is used for powering the instrument in direct reading mode and for communicating with the ground;

[0056] A quartz pressure sensor (5) for testing the pressure and the temperature inside the insulated bottle (2);

[0057] A temperature sensor (6) is used to test the well temperature;

[0058] A battery part (7), used for supplying power to the pressure gauge in storage mode;

[0059] A ground controller (8) is used for controlling and supplying power in the pressure gauge mode setting and direct reading mode.

[0060] The beneficial effects of the present invention are:

[0061] The present invention successfully developed a temperature and pressure acquisition method by applying field programmable gate array to digital signal processing method. This method not only realizes stable and reliable data acquisition and transmission in high-temperature oil field environment, but also achieves significant technical breakthrough in solving existing high-temperature measurement problems.

[0062] Specifically, the present invention ensures the accuracy and stability of the system in a high temperature environment by configuring temperature sensors, pressure sensors, sampling rates and communication protocols, and performing calibration, thereby obtaining high-precision injection steam parameters in the heavy oil thermal recovery process, with the main parameters including temperature and pressure. Secondly, by converting the analog signals of the temperature sensor and the pressure sensor into digital signals and processing them using an analog-to-digital converter, a solid foundation is laid for subsequent digital signal processing. Not only does it ensure the integrity and processability of the signal, it also provides technical support for eliminating transient noise and improving signal quality.

[0063] In terms of digital signal processing, the present invention adopts digital filtering algorithms such as zero-phase moving average filtering to effectively eliminate the influence of transient noise and improve the quality and reliability of the signal.

[0064] Furthermore, the present invention fundamentally gets rid of the dependence on expensive imported high-temperature resistant electronic devices, and realizes dynamic real-time adjustment of filtering parameters through field programmable gate array hardware architecture design. The present invention can realize real-time online monitoring with relatively low cost investment under existing infrastructure, improve the quality of oil well acquisition and overall production efficiency under difficult geological conditions, and provide technical support for promoting the modernization and intelligence of oil field measurement in my country. Furthermore, the temperature and pressure acquisition method, circuit and downhole ultra-high temperature pressure gauge (which can be used under working conditions of up to 350°C) of the present invention can measure two pressures at the same time and measure the pressure difference in the well; it can realize the switching between direct reading and storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flow chart of the temperature and pressure acquisition method of the present invention;

[0066] Figure 2 is a block diagram of a control circuit in one embodiment of the present invention;

[0067] Figure 3 A power supply circuit of a temperature and pressure acquisition circuit in one embodiment of the present invention;

[0068] Figure 4 A temperature and pressure acquisition circuit in one embodiment of the present invention;

[0069] Figure 5 This is a waveform diagram of an ST coding signal in one embodiment of the present invention;

[0070] Figure 6 It is a logic diagram of a temperature and pressure acquisition method in one embodiment of the present invention;

[0071] Figure 7 A test flow chart of a temperature and pressure acquisition method, circuit, and downhole ultra-high temperature pressure gauge in one embodiment of the present invention;

[0072] Figure 8 It is a structural diagram of a temperature and pressure acquisition method, a circuit and a downhole ultra-high temperature pressure gauge in one embodiment of the present invention;

[0073] Fig. 9 It is a diagram of the pressure-conducting structure of a temperature and pressure acquisition method, circuit, and downhole ultra-high temperature pressure gauge in one embodiment of the present invention;

[0074] Fig.10 This is a principle block diagram of a quartz pressure sensor in one embodiment of the present invention;

[0075] In the attached figure:

[0076] 1. Rope cap; 2. Insulation bottle; 3. Cable part; 4. Circuit part; 5. Pressure sensor; 6. Temperature sensor; 7. Battery part; 8. Ground controller; 9. Pressure conducting structure; 10. Circuit skeleton; 11. Circuit outer casing; 12. Circuit assembly; 13. Pressure conducting channel; 14. Piston part; 15. Inert gas. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] The term "comprise" and any variation thereof in the specification and claims of the present application are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In addition, the use of "and / or" in the specification and claims to indicate at least one of the connected objects, such as A and / or B, means including A alone, B alone, and A and B.

[0079] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0080] The present invention provides the following preferred embodiments:

[0081] Embodiment 1

[0082] In order to solve the problem of how to accurately acquire and process temperature and pressure signals in a complex oil and gas field exploitation environment, this embodiment proposes a temperature and pressure acquisition method to improve the accuracy of signal acquisition and processing and the operational stability of the system. Figure 1 and Figure 6 to Figure 7 As shown, the specific steps of the method are as follows:

[0083] S100, initializing the configuration system, including configuring the temperature sensor, pressure sensor, sampling rate and communication protocol, and calibrating the temperature sensor and pressure sensor to ensure accuracy.

[0084] S200: Acquire analog signals from the temperature sensor and the pressure sensor.

[0085] S300 , converting the received analog signal into a digital signal through an analog-to-digital converter for subsequent digital processing.

[0086] S400, performing denoising processing on the digital signal using a digital filtering algorithm, including zero-phase moving average filtering, to eliminate the influence of transient noise.

[0087] S500, applying a decoding algorithm to the denoised digital signal, wherein the decoding algorithm matches an existing feature pattern recognition decoding template in an algorithm library for accurate signal transmission.

[0088] S600: Segment the decoded digital signal into time segments for data analysis and processing.

[0089] S700: Store the segmented time segments into an internal database for subsequent access and analysis.

[0090] In this embodiment, during the system initialization phase, the configuration steps cover the settings of the temperature sensor and the pressure sensor. The selected sensor model needs to be able to withstand extreme environmental conditions. For example, a sensor model suitable for high temperature and high pressure environments should be selected to ensure the reliability of data sampling. Both the sampling rate and the communication protocol can be initially set by the system control unit to ensure stable and controllable data transmission. This process includes the calibration of the sensor, and a standard reference source is used during the calibration process to minimize the inherent zero drift and gain errors of the sensor. The calibration step is performed through automated calibration software to coordinate the overall adjustment of the entire system to ensure the consistency and accuracy of each sensor.

[0091] Furthermore, the acquisition of analog signals is completed through the direct output of the sensor; the selection of sensors needs to consider the linearity and range of their output signals, which lays the foundation for subsequent data conversion. Then, these signals are converted into digital signals through analog-to-digital converters (ADCs). Analog-to-digital converters (ADCs) play the role of a bridge in the signal processing process. The use of high-resolution analog-to-digital converters (ADCs) can effectively improve the accuracy of the signal. In this process, it is understandable that the specific sampling rate setting needs to be optimized according to the signal characteristics to meet the real-time requirements of the system. The analog-to-digital converter (ADC) process ensures the unbiased conversion of the signal from analog to digital to avoid information loss.

[0092] Furthermore, denoising is required in the processing of digital signals. Zero-phase moving average filtering can effectively eliminate transient noise caused by random interference during signal acquisition and ensure the integrity of signal transmission. During the filtering process, it should be understood that the size of the filter window is dynamically adjusted by the system in real time to adapt to the specific noise characteristics. This adjustment strategy comprehensively considers the power spectral density of the real-time signal input, and the filter needs to minimize phase distortion while maintaining data validity.

[0093] Furthermore, the ST coding and decoding algorithm is applied to the denoised digital signal. The ST coding and decoding algorithm matches the existing feature pattern recognition decoding template in the algorithm library, and uses the redundant information of the ST code to perform error correction for accurate signal transmission. It is understandable that after the digital signal is converted into a digital signal by the analog-to-digital converter, it will undergo digital filtering. At this point, the processing of the ST coded signal can be integrated into the decoding algorithm and the coding transmission process. ST coding (usually refers to Space-Time coding, which is widely used in error correction and multipath transmission optimization in wireless communications and signal processing) can be used to enhance the transmission stability of the signal, especially in high temperature and noisy environments, such as Figure 5 shown.

[0094] Furthermore, in addition to filtering, in the application of the decoding algorithm, the signal is pattern matched through the feature pattern recognition library for accurate signal transmission. The implementation of the decoding algorithm depends on the accuracy of the model matching of historical data and current sampling data to ensure the robustness and high accuracy of the decoding process. It should be understood that the algorithm library in this link needs to be updated regularly to adapt to the changing signal characteristics and environmental conditions.

[0095] Furthermore, the decoded signal is segmented into different time segments for in-depth analysis. The segmentation method is based on an adaptive dynamic partitioning strategy to adapt to various signal characteristics and system conditions. Understanding this set of actions is related to the balance between the segmentation algorithm and computational accuracy and storage efficiency. These time segments are fed to the system storage module through subsequent processing and analysis and stored in the internal database for further data analysis and historical comparison.

[0096] Through this embodiment, the acquisition system not only meets the actual needs of high precision and high reliability in its design, but also fully considers the complexity of operation under extreme conditions. Its additional benefit is that through the complete signal acquisition and processing chain, the system provides a detailed and reliable data foundation for subsequent oil and gas development analysis without the need for additional calibration and data cleaning processes. This solution ensures the effectiveness and stability of the entire signal processing chain and provides technical support for solving the data collection problem in complex application scenarios.

[0097] Embodiment 2

[0098] In order to solve the high noise signal environment faced in the oil and gas field exploitation process, this embodiment further refines the process of digital signal denoising, wherein zero-phase moving average filtering is used to achieve efficient signal denoising. This filtering method is selected because it has the characteristics of eliminating phase distortion and enhancing signal stability.

[0099] In the specific implementation process, it is first necessary to initialize the basic parameters of the zero-phase moving average filter, such as the setting of the time window size N. The window size needs to dynamically adapt to different noise levels to optimize the signal processing effect. The selection of this parameter is not only closely related to the power spectrum characteristics of the current signal, but also needs to take into account the frequency range of the signal to be processed and the real-time performance requirements of the system. Selecting the time window size means that the window boundary is not fixed, but is adaptively adjusted through the algorithm according to the changes in the real-time signal to ensure that the filter can maintain its effectiveness regardless of whether it is in a relatively stable or violently fluctuating signal environment.

[0100] Furthermore, the calculation formula used by the filtering algorithm at each current time point t is:

[0101]

[0102] In the calculation formula (1), Y(t) is the output signal after filtering, x(tn) is the input signal, t is the current time point, x is the input data value measured at different time points, n is the time point, N is the time window size, and α and β are filter coefficients. It should be understood that the dynamic adjustment of α and β is based on specific signal characteristics, usually automatically combined with historical data and current noise levels. This adjustment mechanism allows the filter parameters to respond quickly under different operating conditions to continuously improve the denoising effect.

[0103] It can be understood that through this algorithm, not only the smoothing of the signal and the reproduction of the true value are achieved, but also the phase delay problem in the traditional moving average filter is effectively solved, further enhancing the fidelity of the signal. In addition, the filtering operation is performed using the parallel advantages of the field programmable gate array (FPGA) to ensure low latency and high efficiency of the entire process.

[0104] The benefit of this embodiment is that through this flexible and efficient denoising processing strategy, the accuracy of signal detection of the system in complex environments is greatly improved, and the processing time is shortened, providing high-quality input for subsequent data analysis.

[0105] Embodiment 3

[0106] In order to solve the problem of signal decoding and recognition in a high noise environment, this embodiment further optimizes the application process of the decoding algorithm. When processing the denoised digital signal, the initialization step of the matching template library is first introduced. The library selects the most reliable signal feature template at present when constructing to ensure the basic accuracy of decoding.

[0107] Furthermore, real-time matching uses the Fast Fourier Transform (FFT) algorithm to analyze the spectral characteristics of digital signals. Through the Fast Fourier Transform (FFT) algorithm, the spectral information of the signal is quickly extracted, so that specific frequency patterns can be quickly located and identified. It should be understood that the computing resources called by real-time matching are coordinated by the system's multi-core processors to improve the immediacy and accuracy of operations by minimizing latency.

[0108] Furthermore, in pattern recognition, the minimum mean square error algorithm (MMSE) is applied for accurate pattern matching. The minimum mean square error algorithm (MMSE) determines the correspondence with the input signal by calculating the mean square error between the current characteristics of the signal and the best matching pattern previously recorded in the template library. It can be understood that the matching process not only relies on the simple selection of a high-deviation pattern, but also adapts to instantaneous changes and increases the robustness of decoding by continuously updating the template library with templates that meet the current signal characteristics.

[0109] Through this embodiment, the signal matching and identification in the complete process from denoising to decoding of digital signals are effectively optimized. In different environments and working conditions, signal decoding becomes a reliable technical means, and also lays a solid foundation for the subsequent extensive application of signals. The advantage of this implementation is that even under extremely complex signal conditions, the accuracy of the decoding process can be maintained and the time consumption can be reduced, thereby improving the overall system operation efficiency and data utilization.

[0110] Embodiment 4

[0111] In order to solve the problem of how to elegantly divide the decoded digital signal into time segments under complex signal conditions, this embodiment further refines the application of an adaptive time segmentation algorithm, which allows flexible adjustment of the length of the time segment while ensuring signal processing efficiency and accuracy. In the specific implementation process, a segmentation strategy based on signal characteristics is adopted, and a segment length calculation formula is introduced into the data processing chain.

[0112] In this embodiment, the length of the time segment Ts is calculated as follows:

[0113]

[0114] In the calculation formula (2), m is a segment constant, which is set according to the specific application environment and requirements. It should be understood that this constant maintains the real-time processing capability of the system while ensuring the segmentation effect. s The signal amplitude is adjusted by the system in real time to detect the dynamic changes of the signal to ensure that the time segmentation matches the signal characteristics; this strategy can achieve the combination of the rationality of the segment length and the flexibility of the segmentation process. c It is the center frequency of the signal. In general, this value is obtained through the Fast Fourier Transform (FFT) algorithm to ensure its accuracy in representing the signal characteristics.

[0115] Furthermore, the segmentation process is accompanied by continuous tracking of signal features in the time-frequency domain, and the adaptive adjustment technology is used to enable the system to adjust the segment length in a more variable signal environment to ensure the continuity and accuracy of the data. It can be understood that the adaptive adjustment of the segment length not only plays a key role in accurately reconstructing the signal, but also provides a stable foundation for subsequent signal analysis.

[0116] Through this embodiment, the system overcomes the problem of inconsistent segments caused by signal fluctuations in actual applications, and realizes efficient connection from decoding to segmentation. Its benefits are not only to improve the efficiency of signal analysis, but also to ensure that data is not lost during the analysis process, and to improve the accuracy of data collection and processing.

[0117] Embodiment 5

[0118] In order to ensure the integrity of signal data before segmentation, this embodiment further optimizes the supplementation processing of the decoded signal and introduces a piecewise linear supplementation algorithm to fix the potential data missing problem. This process ensures the data continuity of each time segment before segmentation through an interpolation algorithm.

[0119] Specifically, when performing the supplementation processing of the digital signal, the piecewise linear supplementation algorithm is used to detect possible missing blocks in the signal, which are usually due to distortion or occasional noise in signal transmission. It should be understood that the piecewise linear supplementation algorithm not only analyzes the boundary value of each segment for linear interpolation, but also compares the trend changes of adjacent segments to fill in the blank areas in the signal while maintaining the consistency of the entire segment data and the integrity of the time sequence.

[0120] Furthermore, the algorithm takes into account the statistical characteristics of the signal itself and anomaly detection during its application, and the implementation details of the dynamic segmentation parameters (such as forward difference and backward difference) gradually fill in the missing data through a prediction mechanism. It is understandable that the algorithm needs to update the interpolation strategy in real time, adjust the segment length and symmetric extension according to the current spectrum changes and amplitude differences of the signal, to ensure that all supplemented data are consistent with the actual signal.

[0121] Through this embodiment, the signal has the ability to be expressed in the form of a complete time series before entering the segmentation stage, which not only solves the reliability problem of the entire signal, but also improves the data quality and the effectiveness of signal decoding during the preprocessing of the time segment. The supplementary algorithm makes the processed signal not only smooth but also has no obvious faults, which improves the value of data analysis and storage after segmentation.

[0122] Embodiment 6

[0123] In order to solve the storage efficiency and real-time display problems of massive data, this embodiment optimizes the data compression and visualization processing mechanism in detail in the storage link after the signal segmentation is completed. A lossless compression algorithm is introduced, and the incremental encoding is combined with Huffman encoding to achieve a significant reduction in storage space requirements without losing the integrity of data information.

[0124] In this embodiment, in the data storage stage, the incremental coding technology is first applied, which reduces the inherent redundancy by calculating the difference between the data instead of storing the absolute data value. It should be understood that this process effectively reduces the size of the storage body while ensuring the accuracy of the data during the reconstruction process. After the incremental coding, Huffman coding compresses the remaining low-redundancy data again, and further compression is achieved through the statistically constructed optimal coding table.

[0125] Furthermore, after the compression and storage, the visualization is performed through a real-time graphical user interface (GUI). This system can dynamically display the temperature and pressure data in the form of charts and has interactive functions, allowing users to query and compare the data historically according to actual needs. It can be understood that the graphical user interface (GUI) provides users with not only data viewing functions, but also analysis options such as dynamic zooming, trend prediction and anomaly detection.

[0126] This embodiment not only effectively improves the flexibility of large-scale data storage and processing, but also greatly improves the data availability for downstream application systems by implementing real-time data interfaces and display platforms, meeting the needs of managers at different levels in on-site decision-making and historical data analysis. This integrated solution constitutes a complete system from data collection to display, which saves storage resources while improving the value of data applications.

[0127] Embodiment 7

[0128] In order to solve the complex problems in remote signal transmission and synchronization, this embodiment further optimizes the method of collecting temperature and pressure signals, wherein the remotely transmitted signals are received through a wireless communication module, and time synchronization is achieved using a custom communication protocol, thereby ensuring data integrity and accurate synchronization.

[0129] Specifically, in the design of the wireless communication module, a low-power, high-reliability wireless protocol is selected to meet the transmission requirements of remote signals. This module coordinates the data transmission of temperature and pressure sensors, takes into account potential interference in the environment, and reduces the possibility of signal loss through an adaptive frequency selection mechanism. The development of the custom protocol focuses on the time synchronization function, which enables accurate time alignment of data at the receiving end by embedding timestamp correction information in the data frame. This mechanism not only ensures the synchronization of data from different sensors, but also provides complete time series support for subsequent data processing.

[0130] Furthermore, in the process of converting analog signals into digital signals, a dual-channel fast analog-to-digital converter (ADC) is used. The main purpose of selecting a dual-channel fast analog-to-digital converter (ADC) is to simultaneously collect temperature and pressure signals, eliminating the cross-interference caused by single-channel switching. Interleaved sampling technology is applied in the operation of the dual-channel fast analog-to-digital converter (ADC) to increase the overall sampling frequency and ensure the accuracy of high-frequency signal acquisition. It should be understood that this technology allows the sampling points to be staggered in time, thereby increasing the effective sampling rate without increasing hardware costs.

[0131] It is understandable that the dual-channel fast analog-to-digital converter (ADC) combined with interleaved sampling used in the embodiment not only meets the requirements of efficient signal acquisition, but also provides technical support for processing data of multiple physical parameters at the same time. Through this embodiment, complete transmission and efficient acquisition of remote data become possible, ensuring performance and reliability under remote and real-time conditions.

[0132] Embodiment 8

[0133] In order to meet the demand of realizing integrated multi-mode signal processing circuit, this embodiment proposes a temperature and pressure acquisition circuit, such as Figures 2 to 4 As shown, the circuit of this embodiment is used to implement a multi-mode signal processing method.

[0134] In the circuit design of this embodiment, a signal receiving module is first set up to connect the temperature and pressure sensors and receive analog signals. This module has high compatibility for different types of sensor input signals and adapts to common industrial standard signal inputs. The analog signal is then input into a dual-channel fast analog-to-digital converter (ADC) to ensure that the analog signal is accurately converted into a digital signal and provide a consistent signal for downstream processing.

[0135] Furthermore, the digital signal processing module includes sub-modules such as digital filters and decoders. The digital filter uses advanced denoising algorithms to remove unnecessary high-frequency noise and achieve signal purification; the decoder performs feature decoding and segmentation operations on the filtered signal according to the designed algorithm logic to ensure that the signal is accurately transmitted to the storage stage.

[0136] Furthermore, the storage module takes into account the need for long-term stable recording in large data volume scenarios. The module uses non-volatile storage chips to ensure the security and long-term preservation of digital signals. In addition, the circuit is also equipped with a wireless communication module to achieve remote transmission of data and reprocessing of the central control unit through optimized remote transmission protocols. The central control unit coordinates the overall processing of the signal and effectively manages all aspects of data transmission, processing and storage.

[0137] It is understandable that, through the collaborative work of the above modules, a complete process from acquisition, conversion, processing to storage and transmission is realized. The benefit of this embodiment is that a highly integrated signal processing solution is realized, which meets the different requirements for data acquisition and processing in various application scenarios and enhances the adaptability of the system.

[0138] Embodiment 9

[0139] In order to solve the problem of achieving real-time filter parameter adjustment in a highly dynamic environment, this embodiment further optimizes the design and implementation of the field programmable gate array (FPGA) in the digital signal processing module for the filter. The field programmable gate array (FPGA) is used to achieve dynamic real-time adjustment of the filter parameters, ensuring that the filter can always maintain the best state when the signal characteristics change frequently.

[0140] In the specific implementation, the first step is to initialize the configuration of the field programmable gate array (FPGA). This process involves selecting a suitable hardware platform and designing the filter architecture through a hardware description language (such as VHDL or Ver il og). The filter type and basic parameters need to be effectively selected according to system requirements at the beginning of the design, such as selecting FIR or IIR filters, determining the initial order, etc. These choices directly affect the filter's performance in real-time performance and resource usage.

[0141] Furthermore, the system needs to obtain the current characteristics of the signal in real time, including but not limited to the frequency components and amplitude changes of the signal. This information is passed to the field programmable gate array (FPGA) through appropriate interfaces, such as dual-channel fast analog-to-digital converter (ADC) sampling and post-processing, as a basis for adjusting the filter parameters. It should be understood that the acquisition and transmission of this information must be achieved with the lowest latency to ensure that subsequent filtering operations do not cause bottlenecks in the entire signal processing chain.

[0142] Further, filter parameters are generated according to the real-time characteristics of the input signal, and filter coefficients are updated on the field programmable gate array to dynamically adjust the filter characteristics. The calculation formula for dynamically adjusting the filter characteristics is:

[0143]

[0144] In the calculation formula (3), H(z) represents the transfer function of the filter, k represents the frequency index, z is a complex variable used to represent the frequency domain transformation of the discrete time signal, M(n) represents the filter order that changes with time point n, and h k (n) represents the filter coefficient at time point n and frequency index k, z -k represents the delay of the input signal by k time points; M(n) and h k (n) Dynamically updated with real-time signal characteristics.

[0145] After obtaining the signal characteristics, the field programmable gate array (FPGA) generates new filter parameters based on them, so that the filter characteristics can be dynamically adjusted according to the real-time input signal. The specific process involves calculating the new filter order M(n) and filter coefficient h k (n), filter coefficient h k The updated calculation formula for (n) is:

[0146]

[0147] In formula (4), w k is the window function coefficient, a k (n) is the reference filter coefficient, b k is the periodic component amplitude factor, f c (n) is the dynamic cutoff frequency, F s is the sampling frequency; it is understandable that the selection and adjustment of these parameters need to be completed through effective digital circuits on the platform of field programmable gate array (FPGA), and the parallel processing capability of field programmable gate array (FPGA) is fully utilized to quickly update the filter coefficients and adapt to real-time signals.

[0148] Furthermore, through the real-time processing capability of the field programmable gate array (FPGA), the filtering operation implemented in this embodiment has significant speed and efficiency advantages. The inherent parallel capability of the field programmable gate array (FPGA) ensures that multiple filtering operations can be performed synchronously and in parallel, thereby reducing time consumption.

[0149] Through this embodiment, the system realizes high-efficiency real-time filtering in a dynamic and complex signal environment, and improves the ability of the signal processing module to adapt to rapidly changing conditions. Through dynamic adjustment at the hardware level, the filter can adapt to changes in different signal spectra, thereby improving the system response capability and data processing accuracy in practical applications. This embodiment provides a more flexible and efficient solution for multi-mode signal processing systems, especially in engineering environments with strict requirements for fast response and high data accuracy.

[0150] Embodiment 10

[0151] In order to solve the problem of achieving accurate measurement and data processing of temperature, pressure and vibration parameters in a high-temperature and high-pressure environment underground, this embodiment provides a temperature and pressure acquisition method, circuit and underground ultra-high temperature pressure gauge. The pressure gauge can not only adapt to the complex underground environment through structural design and circuit optimization, but also stably perform data acquisition and processing.

[0152] like Figures 8 to 10 As shown, the pressure gauge structurally comprises a rope cap 1, which is tightly connected to an insulating bottle 2 through a thread, and the outside of the rope cap is connected to a cable part 3 through a steel cable to ensure reliable fixation and communication of the device.

[0153] Furthermore, the insulation bottle 2 is used to accommodate all electronic components except the temperature sensor. Its main function is to effectively reduce heat conduction by filling inert gas such as argon or carbon dioxide, extend the downhole use time of the instrument, and transmit the external pressure to the quartz pressure sensor 5 through the pressure-conducting structure 9. Therefore, the pressure-conducting structure is modified to a piston type: a pressure-conducting channel 13, a piston part 14, and an inert gas 15. In practice, the improved piston-type pressure-conducting structure uses external pressure to push the piston, compresses the inert gas and transmits the pressure to the sensor. This design not only ensures the thermal insulation protection of the sensor, but also ensures the accurate transmission of the signal.

[0154] Furthermore, the cable section 3 uses inorganically insulated ultra-high temperature cables with excellent insulation properties, which can maintain normal operation under conditions up to 800°C. These cables are treated with coating and curing materials to form a sealed insulation layer to meet the requirements of high-temperature applications underground.

[0155] Furthermore, the circuit part 4 is composed of a circuit skeleton 10, a circuit outer casing 11 and a circuit component 12. The circuit component 12 includes a power supply circuit, a temperature acquisition circuit, a signal processing circuit, a main control circuit, a coding and decoding circuit, a storage circuit and a vibration acquisition circuit. The power supply circuit is responsible for providing adaptive voltage support for different components and monitoring the power supply status in real time. The temperature acquisition circuit uses a PT1000 sensor to obtain accurate temperature data through a constant current circuit. The main control circuit uses advanced algorithms for data processing, and then transmits the data to the ground controller via the coding circuit, or receives instructions from the decoding circuit.

[0156] Furthermore, the quartz pressure sensor 5 is composed of a plurality of crystal oscillators, mixers and frequency counters, which realizes the mixed frequency calculation of temperature and pressure signals. This design improves the accuracy and stability of signal processing by adding multiple oscillators, mixers and filters in the sensor. In addition, the reliability of the output signal is further guaranteed by the median filtering process in the program. On this basis, a redundant storage function is also added, and the data is automatically saved in the storage circuit in the case of loss of ground signal.

[0157] Furthermore, the program setting of the pressure gauge supports direct reading and storage modes. In direct reading mode, monitoring is performed and the continuity of instruction transmission is ensured; in storage mode, test data is reliably saved for analysis in offline mode.

[0158] Through this embodiment, the pressure gauge not only realizes high-precision environmental parameter collection and dynamic data processing, but also provides guarantees for data integrity and equipment reliability under high temperature conditions. This design meets the stringent requirements for test equipment in complex underground environments and expands the application range and service life of the pressure gauge.

[0159] The above embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the present invention should be included in the scope of protection of the present invention.

Claims

1. A temperature and pressure acquisition method, characterized in that: The method comprises the following steps: Initialize the configuration system, including configuring the temperature sensor, the pressure sensor, the sampling rate and the communication protocol, and calibrate the temperature sensor and the pressure sensor to ensure accuracy; Acquiring analog signals from the temperature sensor and the pressure sensor; Converting the received analog signal into a digital signal through an analog-to-digital converter for subsequent digital processing; De-noising the digital signal using a digital filtering algorithm, including zero-phase moving average filtering, to eliminate the effects of transient noise; Applying a decoding algorithm to the denoised digital signal, wherein the decoding algorithm matches the existing feature pattern recognition decoding template in the algorithm library for accurate signal transmission; Segmenting the decoded digital signal into time segments for data analysis and processing; The segmented time segments are stored in an internal database for subsequent access and analysis.

2. The temperature and pressure acquisition method according to claim 1, characterized in that: The step of performing denoising processing on the digital signal by using a digital filtering algorithm comprises: performing zero-phase moving average filtering, setting a dynamic window size to accommodate different noise levels of the digital signal; The calculation formula of the digital filtering algorithm is: Where Y(t) is the output signal after filtering, x(tn) is the input signal, t is the current time point, x is the input data value measured at different time points, n is the time point, N is the time window size, and α and β are filter coefficients.

3. The temperature and pressure acquisition method according to claim 1, characterized in that: The step of applying a decoding algorithm to the denoised digital signal comprises: Initialize the matching template library and select the best matching mode according to the collected ones; Real-time matching, using fast Fourier transform to analyze the frequency spectrum characteristics of the digital signal; Pattern recognition, pattern matching is performed through the minimum mean square error algorithm, and accurate decoding is performed by determining the signal matching degree.

4. The temperature and pressure acquisition method according to claim 1, characterized in that: The step of dividing the decoded digital signal into time segments comprises: Adopt adaptive time segmentation algorithm to segment, the length of the time segment T s The calculation formula is: In the formula, m is the segment constant, A s is the signal amplitude, f c is the signal center frequency.

5. The temperature and pressure acquisition method according to claim 4, characterized in that: Before the decoded digital signal is divided into time segments, the signal is supplemented and a piecewise linear supplementation algorithm is used to interpolate the missing data to ensure that the time segments are complete and continuous.

6. The temperature and pressure acquisition method according to claim 5, characterized in that: The step of storing the divided time segments into an internal database comprises: Implement lossless compression algorithms to reduce storage space requirements, including incremental encoding combined with Huffman coding to ensure that the data compression process does not lose precise information; After the step of storing the segmented time segments in the internal database, real-time visualization processing is used to display the selected temperature and pressure data in a graphical form through a graphical user interface, and a user interaction function is provided for data query and historical comparison.

7. The temperature and pressure acquisition method according to claim 1, characterized in that: The step of acquiring the analog signals of the temperature sensor and the pressure sensor comprises: Receive remotely transmitted signals through wireless communication modules and use custom protocols to achieve time synchronization to ensure data integrity; The step of converting the received analog signal into a digital signal by an analog-to-digital converter comprises: A dual-channel fast analog-to-digital converter is used to ensure that the data of the temperature sensor and the pressure sensor are acquired simultaneously, and an interleaved sampling technique is adopted to increase the sampling frequency.

8. A temperature and pressure acquisition circuit, characterized in that: The circuit is used to implement the temperature and pressure acquisition method according to any one of claims 1 to 7, and the circuit includes: A signal receiving module, connected to the temperature sensor and the pressure sensor, for receiving analog signals; an analog-to-digital conversion module, connected to the signal receiving module, for converting the analog signal into a digital signal; A digital signal processing module, connected to the analog-to-digital conversion module, including a digital filter and a decoder, for performing denoising, decoding and segmentation operations; A storage module, connected to the digital signal processing module, and used to store the processed digital signal; Wireless communication module for long-distance data transmission.

9. The temperature and pressure acquisition circuit according to claim 8, characterized in that: The digital filter in the digital signal processing module adopts a field programmable gate array for dynamic real-time adjustment of filtering parameters, including: Initialize the field programmable gate array configuration, design the filter architecture, including the filter type and basic parameters, and implement the basic logic of the filter through a hardware description language; Dynamically obtain the current signal characteristics of the system, and pass the signal characteristics as input parameters to the field programmable gate array to ensure low-latency input of sensor data; The filter parameters are generated according to the real-time characteristics of the input signal, and the filter coefficients are updated on the field programmable gate array to dynamically adjust the filter characteristics. The calculation formula for dynamically adjusting the filter characteristics is: Where H(z) represents the filter transfer function, k represents the frequency index, z is a complex variable used to represent the frequency domain transform of discrete-time signals, M(n) represents the filter order that changes with time point n, and h k (n) represents the filter coefficient at time point n and frequency index k, z -k represents the delay of the input signal by k time points; M(n) and h k (n) is dynamically updated with the real-time signal characteristics, where the filter coefficient h k The updated calculation formula for (n) is: In the formula, w k is the window function coefficient, a k (n) is the reference filter coefficient, b k is the periodic component amplitude factor, f c (n) is the dynamic cutoff frequency, F s is the sampling frequency; The filtering operation is performed in real time inside the field programmable gate array, and the parallel processing capability of the field programmable gate array is utilized to achieve fast filtering.

10. A temperature and pressure acquisition method, circuit and downhole ultra-high temperature pressure gauge, characterized in that: The pressure gauge is integrated with the temperature and pressure acquisition circuit according to any one of claims 8 to 9, which is used for signal acquisition, signal processing, processing command signals and sending data, and the pressure gauge also includes: A rope cap (1) is used to connect a steel wire to lower the pressure gauge; An insulating bottle (2) is used to isolate high temperature so as to ensure that the temperature inside the insulating bottle (2) is not higher than 125° C. during measurement; The cable part (3) is used for powering the instrument in direct reading mode and for communicating with the ground; A quartz pressure sensor (5) for testing the pressure and the temperature inside the insulated bottle (2); A temperature sensor (6) is used to test the well temperature; A battery part (7), used for supplying power to the pressure gauge in storage mode; A ground controller (8) is used for controlling and supplying power in the pressure gauge mode setting and direct reading mode.

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