Pointer data buffer modulation and demodulation method and system for petroleum drilling

Abstract

The invention provides a pointer data buffer modulation and demodulation method and system for petroleum drilling, and relates to the technical field of wireless communication, and the method comprises the steps: constructing a pointer data buffer pool which is of a nonlinear topological buffer structure and is used for receiving data stream input from a plurality of sensors; performing quality scoring through a data quality analyzer, and establishing a data priority label; obtaining the current state of the pointer data buffer pool, inputting the current state to a modulation control channel, and generating a dequeuing sequence; state prediction is executed, and if a preset proportion threshold value is met, an active compression instruction is generated; clustering compression is carried out; and performing modulation and demodulation management according to a clustering compression result and a dequeuing sequence. The technical problems that in the prior art, the processing capacity of a single-chip microcomputer used for petroleum drilling is limited, a complex data modulation and demodulation algorithm cannot be efficiently executed, and then the real-time performance and accuracy of data processing in drilling operation are limited are solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a pointer data buffer modulation and demodulation method and system for oil drilling. Background Art

[0002] In the field of oil drilling, a large amount of real-time data needs to be collected and transmitted during the drilling operation, especially the sensor data near the drill bit. These data need to be transmitted to the MWD (Measurement While Drilling) receiving circuit module through wireless electromagnetic waves, and the data modulation and demodulation technology plays an important role in this process.

[0003] Currently, in conventional wireless communication applications, data modulation and demodulation are mostly completed by FPGA (Field Programmable Gate Array). FPGA has powerful parallel computing capabilities, can handle complex algorithms, and has multiple dedicated IP cores suitable for data modulation and demodulation built-in. Although FPGA has obvious advantages in data processing, it still has some deficiencies in high-demand real-time and accuracy, especially in the tasks of real-time data transmission and precise modulation and demodulation in drilling operations; in addition, the amount of data in drilling operations is usually huge, and a large amount of sensor data needs to be effectively managed and processed. Traditional single-chip microcomputers have limitations in data processing capabilities and cannot efficiently handle complex modulation and demodulation algorithms, especially in situations where strong real-time performance and large data volume are required, which poses high requirements for the computing power of single-chip microcomputers. Summary of the Invention

[0004] This application provides a pointer data buffer modulation and demodulation method and system for oil drilling, aiming to solve the technical problem that the processing power of the single-chip microcomputer for oil drilling in the prior art is limited and cannot efficiently execute complex data modulation and demodulation algorithms, thereby restricting the real-time performance and accuracy of data processing in drilling operations.

[0005] In the first aspect disclosed in this application, a pointer data buffer modulation and demodulation method for oil drilling is provided. The method includes: constructing a pointer data buffer pool, which is a non-linear topological cache structure for receiving data stream inputs from multiple sensors; when any data stream inputs the pointer data buffer pool, performing quality scoring through a data quality analyzer to establish a data priority label; obtaining the current state of the pointer data buffer pool, inputting the current state and the data priority label into a modulation control channel to generate a dequeue order; performing state prediction on the pointer data buffer pool to establish a prediction result, and if the prediction result meets a preset ratio threshold, generating an active compression instruction; performing clustering compression based on the data priority label and data correlation degree according to the active compression instruction; and performing modulation and demodulation management according to the clustering compression result and the dequeue order.

[0006] The second aspect disclosed in this application provides a pointer data buffer modulation and demodulation system for oil drilling. The system is used for the pointer data buffer modulation and demodulation method for oil drilling described above. The system includes: a buffer pool construction module for constructing a pointer data buffer pool, which is a non-linear topological cache structure for receiving data stream inputs from multiple sensors; a quality scoring module for, when any data stream is input into the pointer data buffer pool, performing quality scoring through a data quality analyzer to establish data priority tags; a sequence generation module for obtaining the current state of the pointer data buffer pool, inputting the current state and the data priority tags into a modulation control channel to generate a dequeue sequence; a state prediction module for performing state prediction of the pointer data buffer pool to establish a prediction result, and if the prediction result meets a preset proportion threshold, generating an active compression instruction; a clustering compression module for performing clustering compression based on the data priority tags and data correlation degree according to the active compression instruction; and a demodulation management module for performing modulation and demodulation management according to the clustering compression result and the dequeue sequence.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects: Construct a pointer data buffer pool with a non - linear topological cache structure to receive data stream inputs from multiple sensors. This design can more efficiently handle complex data input scenarios, especially suitable for drilling operations in ultra - high - temperature environments, improving the flexibility of data stream management and making the scheduling of data input and output more efficient. The data quality analyzer scores the quality of the input data stream and assigns a priority label to each data stream. It ensures that in the case of frequent or complex sensor data, the processing efficiency of high - priority data can be guaranteed, enhancing the real - time performance and safety of the operation. The modulation control channel can dynamically manage the scheduling of data streams, making the data processing process more flexible and efficient. By combining the real - time cache pool status and data priority labels, it can adjust the dequeue order according to the actual situation, ensuring that important data can always be processed first under high - load conditions, improving the stability of the system in ultra - high - temperature environments. By predicting the state of the pointer data buffer pool, compression instructions can be actively generated when the data flow rate exceeds the system's processing capacity, enabling the system to adaptively handle sudden increases in data traffic, avoiding data loss, and enhancing the stability and reliability of the system. Cluster compression is performed on the data according to the data priority labels and data correlation, optimizing the use of storage and transmission resources. Through cluster compression, low - priority data streams can be more efficiently managed, ensuring that high - priority data streams receive more resources, thereby improving the system processing efficiency and reducing energy consumption. According to the cluster compression results and dequeue order, a suitable modulation method is selected for modulation and demodulation management. By flexibly selecting the modulation method, data can be adaptively transmitted under various operating conditions, ensuring the efficient and stable transmission of data. Especially in extreme environments such as ultra - high - temperature, the integrity of operation data is ensured.

[0008] The above description is only an overview of the technical solution of this application. In order to more clearly understand the technical means of this application, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically presents the specific embodiments of this application. Brief Description of the Drawings

[0009] Figure 1 It is a schematic flow chart of the pointer data buffer modulation and demodulation method for oil drilling provided by an embodiment of this application.

[0010] Figure 2 It is a schematic structural diagram of the pointer data buffer modulation and demodulation system for oil drilling provided by an embodiment of this application.

[0011] Description of the reference numerals: Buffer pool construction module 10, Quality scoring module 20, Order generation module 30, State prediction module 40, Cluster compression module 50, Demodulation management module 60. Detailed Description of the Embodiments

[0012] By providing a pointer data buffer modulation and demodulation method and system for oil drilling, the embodiment of the present application solves the technical problem in the prior art that the processing capacity of the single-chip microcomputer for oil drilling is limited, resulting in the inability to efficiently execute complex data modulation and demodulation algorithms, thereby restricting the real-time performance and accuracy of data processing in drilling operations.

[0013] After introducing the basic principle of the present application, the various non-limiting implementation manners of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Embodiment 1, as Figure 1 shown, the embodiment of the present application provides a pointer data buffer modulation and demodulation method for oil drilling, and the method includes: Construct a pointer data buffer pool, which is a non-linear topological cache structure for receiving data stream inputs from multiple sensors.

[0015] Construct a pointer data buffer pool, which is a non-linear topological cache structure for storing data streams from multiple sensors. Its design purpose is to cope with high-frequency data inputs in ultra-high temperature environments, ensure data is not lost, and at the same time be able to flexibly handle inputs of different data streams. Among them, the pointer data buffer pool is not a simple linear queue, but uses a more complex data structure to store data according to factors such as data type, priority, and arrival time. This design helps to achieve more efficient scheduling and access operations, reduce data conflicts. Non-linear topology means using non-linear data structures such as circular queues, tree structures, and graph structures for storage. Each node represents a data stream or a data block of multiple sensors, and priority scheduling is performed through different paths.

[0016] Multiple sensors will simultaneously input data into the pointer data buffer pool. These sensors are used to measure physical quantities such as temperature, pressure, and vibration during the drilling process. These data streams have different frequencies and priorities. Therefore, the buffer pool needs to be designed flexibly enough to adapt to various input modes. Due to the complex and unstable on-site operating conditions, the buffer pool needs to be able to dynamically adjust the capacity of the buffer area, and expand or contract the buffer area in real time according to the flow rate and burst flow of data input to avoid data loss.

[0017] After any data stream is input into the pointer data buffer pool, it is subjected to quality scoring by a data quality analyzer to establish a data priority label.

[0018] After the data enters the pointer data buffer pool, the quality of these data is scored, and a priority label is established for each data stream according to the scoring results. Specifically, the data quality analyzer evaluates the quality of each data stream according to a series of evaluation indicators, including the change rate indicator, the volatility indicator, the abnormal deviation indicator, the sampling time indicator, and the sensor weight indicator. Among them, different abnormal situations may occur during the operation process, such as equipment failures or sudden environmental changes. At this time, the weight factor of the data will be adjusted according to the abnormal situation to ensure that important data can be processed in a timely manner. Through the quality analysis of the data stream and the calculation of the weight factor, a corresponding priority label is finally generated for each data stream, and this label indicates the priority of the data stream during processing. The establishment of the priority label enables the pointer data buffer pool to efficiently manage the data stream and ensure that important data will not be delayed in the case of limited resources.

[0019] Obtain the current state of the pointer data buffer pool, and input the current state and the data priority label into the modulation control channel to generate the dequeue order.

[0020] Obtain the current state of the pointer data buffer pool, which reflects the distribution of data inside the buffer pool, the stored quantity, the real-time status of the data stream, etc. For example, check the current storage state of the pointer data buffer pool, including information such as the amount of data in the buffer area, the amount of processed data, and the amount of data to be processed; check the free space and occupied space of the buffer pool. If the capacity of the buffer pool is close to full load, it indicates a risk of data overflow.

[0021] After obtaining the current state and the data priority label, input them into the modulation control channel. The modulation control channel determines the dequeue order according to the priority and state of the data. The dequeue order determines the processing order of the data in the buffer pool. High-priority data will be processed first, while low-priority data will be processed later. During the processing process, the modulation control channel will dynamically adjust the dequeue order to ensure that critical data is processed first and avoid data loss or processing delays.

[0022] Execute the state prediction of the pointer data buffer pool, establish the prediction result, and if the prediction result meets the preset proportion threshold, generate an active compression instruction.

[0023] Perform state prediction using historical data, real-time data streams, and the current state of the buffer pool. This prediction can be based on statistical methods or machine learning algorithms. By analyzing factors such as the input frequency of the data stream, data priority, and the real-time occupancy status of the buffer pool, predict the occupancy rate of the buffer pool and the data stream input speed within a certain period in the future, and evaluate whether the buffer pool will quickly approach saturation based on these factors. Preset a percentage threshold, such as 80% or 90%, as a warning sign. When the occupancy rate of the buffer pool approaches this threshold, it indicates that the buffer pool is about to be nearly full. At this time, generate a compression instruction. The purpose of this instruction is to release space by compressing data, ensure that the buffer pool does not overflow, and maintain the stability of the data stream.

[0024] Perform clustering compression based on data priority tags and data correlation according to the active compression instruction.

[0025] Data correlation refers to the relationship between data. If there is a high correlation between multiple data items, such as the same physical quantity measured by sensors, then their compression can be achieved through clustering, that is, compressing adjacent data items with similar characteristics or values together. When compressing low-priority data, through clustering technology, divide the data items into several groups, where each group of data is relatively consistent in the change pattern, and the data in each group can be stored using differential coding, that is, record the change of the data instead of the data itself. Taking temperature as an example, assume that the temperature data measured by the sensor is 100°C, 102°C, 105°C, 107°C, 110°C in sequence. Through differential compression, only the change of the data needs to be stored: 100°C, +2, +3, +2, +3, where the first value retains the original value, and only the change amount of each step is recorded subsequently. This can significantly reduce the required storage space.

[0026] The compressed data stores only the data change information, rather than the complete data value, so the occupied storage space will be significantly reduced, ensuring that the buffer pool can effectively manage more data streams.

[0027] Perform modulation and demodulation management according to the clustering compression result and the dequeue order.

[0028] Modulation and demodulation management is responsible for selecting an appropriate modulation method for data transmission and reception based on the priority of the data, the compression result, and the dequeue order. In oil drilling operations, data transmission needs to be carried out in an ultra-high temperature environment. Therefore, an appropriate modulation technology needs to be selected to ensure stable data transmission in complex environments. The modulation methods include FSK (Frequency Shift Keying), BPSK (Binary Phase Shift Keying), UART (Universal Asynchronous Receiver-Transmitter), and ASK (Amplitude Shift Keying). Among them, FSK is suitable for low-frequency and high-noise environments and is usually used for low-speed data transmission; BPSK is used for digital signal transmission and is suitable for transmissions with a low error rate; UART is suitable for serial communication and is often used for low-bandwidth data transmission; ASK represents different digital data by changing the amplitude of the signal. Among them, the dequeue order plays a key role in modulation and demodulation management because it determines which data should be processed first. High-priority data will be dequeued first and transmitted using a more reliable modulation method to ensure that the most critical data can be sent in a timely manner and will not be lost during transmission.

[0029] According to the selected modulation method, the input data is encoded into a format suitable for channel transmission. For example, when using FSK modulation, the data is converted into a frequency signal, and an appropriate frequency range is selected according to the transmission environment. After the receiving end receives the signal, it uses the corresponding demodulation algorithm to decode the signal and recover the original data. The above steps ensure the effective management and transmission of data in an ultra-high temperature and complex environment, while minimizing the need for data storage and improving the efficiency and reliability of the system.

[0030] Furthermore, the quality scoring is performed by the data quality analyzer to establish data priority tags, including: Using the index extraction unit of the data quality analyzer to extract the indexes of the data stream and establish an evaluation index set. The evaluation index set includes a change rate index, a fluctuation index, an abnormal deviation index, a sampling time index, and a sensor weight index; calling the context state of the drilling operation status and configuring a dynamic weight factor according to the context state; using the dynamic weight factor to perform weighted calculation on the evaluation index set to establish data priority tags.

[0031] The data quality analyzer is used to evaluate the quality of the sensor data stream in order to assign corresponding priority tags to each data stream. The index extraction unit of the data quality analyzer extracts the key information of the data according to the predefined evaluation indexes, thereby establishing an evaluation index set. These evaluation indexes are used to quantify the quality of the data and help with subsequent priority sorting, including a change rate index, a fluctuation index, an abnormal deviation index, a sampling time index, and a sensor weight index.

[0032] Among them, the change rate index measures the speed at which data changes over time and is used to describe the dynamic characteristics of the data stream, especially the degree of fluctuation of the data in a short period of time. For example, in temperature monitoring, a sharp change in temperature may represent an important physical phenomenon or equipment failure. The change rate index can be obtained by calculating the difference between adjacent data points and dividing it by the time interval. A larger change rate indicates a significant change in the data stream in a short period of time and usually requires higher-priority processing; the fluctuation degree index measures the stability of the data and describes the amplitude of data fluctuation. Data with a larger fluctuation degree often shows more unstable changes, which may be a sign of poor data quality or represent abnormal situations in the system. The fluctuation degree is obtained by calculating the standard deviation or variance of the data stream; the abnormal deviation index measures the deviation between the data and the normal expected value or historical data. When the change of sensor data exceeds the normal expected range, it represents equipment failure or external interference, so it needs to be processed first. This index is calculated by comparing the difference between the current sensor data and the historical mean or reference data; the sampling time index measures the real-time nature of the data. The latest data of the sensor is usually more important than the historical data. Therefore, the timestamp is also a key factor in evaluating the data priority. This index evaluates the freshness of the data based on the timestamp of the data. The latest, real-time collected data usually has a higher priority; the sensor weight index reflects the reliability and importance of different sensors. Some sensors are more critical than others or have higher accuracy. Therefore, the data they generate requires a higher priority. The sensor weight can be calculated based on factors such as the accuracy of the sensor, historical performance, and maintenance status.

[0033] The context state refers to various actual situations and environmental changes in the drilling operation, such as the depth of the drilling operation, the flow rate of the drilling fluid, the current equipment state, the bit pressure, etc. These factors directly affect which data is more critical. For example, in some drilling operation environments, temperature data is more important than other data, or pressure data requires more attention when the equipment is running at a high load. The dynamic weight factor changes in real time according to the operation state. If the drilling operation enters a more dangerous stage, such as an increase in equipment load, depth increase, etc., the weight of the sensor data related to these key factors will be increased, so that this type of data can obtain more processing resources and a higher priority. Exemplarily, when the drilling depth reaches a certain depth, the weight factor of the pressure data will increase because the pressure fluctuation has a greater impact on the drilling safety at this time; when the equipment has an abnormality, such as too high temperature, the weight factor of the temperature sensor will temporarily rise to ensure that the system can give priority to processing the temperature data.

[0034] Apply dynamic weight factors to each indicator in the evaluation index set of the data stream, obtain a comprehensive score through weighted calculation, and assign high-priority tags to the data streams with higher comprehensive scores. These data are usually recent, highly variable, or contain abnormal deviations, representing the critical situations of the job and may require immediate processing. The data streams with lower comprehensive scores are usually relatively stable and less variable, representing normal operating states or less important environmental data, and are assigned low-priority tags and can be processed later. Through these priority tags, the data streams can be effectively scheduled, important and urgent data can be processed first, and fewer computing resources and storage spaces can be allocated to low-priority data streams.

[0035] Furthermore, the obtaining the current state of the pointer data buffer pool and inputting the current state and the data priority tag into the modulation control channel to generate a dequeue order includes: Execute the division of the modulation sub-task pool to establish N modulation sub-task pools; perform matching competition between the modulation sub-task pools and the data under the sequential priority according to the current state and the data priority tag; after binding the modulation sub-task pools according to the matching competition result, establish a dequeue order.

[0036] Divide the modulation sub-task pool. Each modulation sub-task pool can independently process the modulation tasks of a part of the data stream. The purpose of dividing the tasks into pools is to perform parallel processing and improve the overall processing efficiency. The number N of the modulation sub-task pools is set according to the computing resources, data traffic, and complexity of the job of the system. Through reasonable division, the load can be balanced and the data processing efficiency can be improved.

[0037] At the same time, multiple modulation sub-task pools need to process different data streams. To ensure that the data streams can be processed in a timely manner according to the priority, matching competition is carried out according to the priority tags of the data streams, that is, the data streams with high priority will strive to occupy more modulation resources, and the data streams are matched according to the resource usage situation and task priority of each sub-task pool. For example, if a certain task pool is processing a data stream with low priority while the tasks in another task pool are waiting for a data stream with higher priority, then the data stream with high priority is reallocated to another pool. Adjust the data stream allocation in the task pool according to the characteristics and priority of the data stream. The data streams in each modulation sub-task pool will ensure that important data streams can be processed and transmitted in the shortest time through sorting or competition mechanisms.

[0038] Through matching competition, it is determined which tasks should be assigned to which modulation sub-task pools. The matched tasks are bound to the corresponding pools, and each pool starts to process the task flow bound to it. According to the matching competition result and the task priorities in the task pools, a dequeue order is generated, and the dequeue order determines the processing order of the data stream. During the modulation and demodulation process, the tasks are processed step by step according to the dequeue order to ensure that the real-time requirements of high-priority tasks are met.

[0039] Furthermore, the modulation sub-task pool and the data matching competition under the sequential priority according to the current state and data priority label further include: Establish a pool-level priority in each modulation sub-task pool. The pool-level priority is a dynamic priority, and the pool-level priority is dynamically set by the urgency of the data in the pool, the historical packet loss rate, and the modulation resource occupancy rate; based on the pool-level priority, data matching competition is carried out after sorting the priority labels in order.

[0040] In each modulation sub-task pool, the pool-level priority is dynamically set according to the urgency of the data in the pool, the historical packet loss rate, and the modulation resource occupancy rate. The pool-level priority reflects the processing priority of each modulation sub-task pool and determines the processing order and resource allocation of the data tasks in the pool. When multiple tasks are waiting for modulation resources at the same time, the task pool with a higher pool-level priority will be processed first.

[0041] Among them, each modulation sub-task pool may contain multiple task data streams, and the urgency of these data streams will affect the pool-level priority. For example, high-priority data streams, such as device failure data and key sensor data, will increase the pool-level priority of the task pool. Urgent data streams usually require faster processing time to avoid affecting job safety or job efficiency; record the data processing history of each modulation sub-task pool, including the ratio of task data lost during transmission or processing. If the historical packet loss rate of a certain pool is high, it means that there may be risks in the task processing of this pool, then increase the priority of this pool to resolve resource shortages or transmission problems as soon as possible and avoid further packet loss; the modulation resource occupancy rate refers to the proportion of resources consumed by each modulation sub-task pool when performing data modulation tasks. If a certain task pool has occupied a high proportion of resources, its pool-level priority needs to be reduced to ensure that resources can be fairly allocated to other pools. On the contrary, if the resource occupancy of a certain pool is low, its priority can be appropriately increased to process tasks more quickly.

[0042] The pool-level priority is adjusted in real time according to the above factors. For example, when the data stream in a certain task pool suddenly becomes more urgent, the pool-level priority will immediately increase to ensure that the tasks in this pool can be processed first. Similarly, if a high historical packet loss rate appears in the pool, the priority of this pool will be automatically adjusted to ensure that the lost data can be restored or retransmitted as soon as possible.

[0043] Based on the data flow priority tags and pool-level priorities, comprehensively sort the data flows within the pool to ensure that tasks with higher priorities can be scheduled first when resources are limited. The process of dynamic adjustment ensures that resources are optimally configured, improving the response ability and stability of the overall system.

[0044] Furthermore, the clustering compression based on data priority tags and data correlation degree according to the active compression instruction includes: Using the data priority tags for priority screening to identify low-priority data; performing priority similarity clustering on the low-priority data to generate a first clustering constraint; performing data correlation degree clustering on the low-priority data to establish a second clustering constraint, where the data correlation degree clustering includes data type association and data value association; performing intersection recognition on the data under the first clustering constraint and the second clustering constraint, and selecting data according to the intersection recognition result to complete clustering compression.

[0045] Perform priority screening on the data according to a preset standard to identify and mark low-priority data. Low-priority data refers to those data that have less impact on the current job status and lower real-time requirements, and can usually be processed, compressed, or discarded later to save resources or relieve data backlog.

[0046] Low-priority data flows usually have similar characteristics and attributes, such as low real-time requirements and small change ranges. Aggregating these data together can help the system optimize resource allocation and data compression. Determine which data flows should be grouped together based on the similarity of priority tags. For example, define a threshold. When the difference in data priority tags is less than this threshold, cluster them together. The data within the cluster are similar in priority and can be operated on together in subsequent processing and compression.

[0047] Data correlation degree clustering refers to dividing data into different groups according to the correlation between data. Data correlation degree clustering includes data type association and data value association. Among them, data type association refers to the type similarity between different data flows. For example, data from the same type of sensor, such as temperature sensor data and temperature sensor data, may have similar structures and change patterns, and these data can be clustered together to reduce the computational burden during processing; data value association refers to the numerical similarity between different data flows. For example, temperature and pressure data show a certain correlation in some cases. If one data value changes and the other data value also shows a similar change trend, then there is a strong data value association between these data flows, and these data flows are put into the same cluster for subsequent processing.

[0048] Combine the first clustering constraint (clustering based on priority tags) and the second clustering constraint (clustering based on data correlation), find the intersection between the two. Specifically, identify which data meet both the priority requirements and the data correlation requirements to form a more precise clustering. Execute the compression strategy according to the clustering result. The result of compression is an effective storage optimization for low-priority data, reducing the storage requirement and saving resources for the system.

[0049] Furthermore, the selecting data according to the intersection recognition result to complete clustering compression includes: Take any data in the intersection recognition result as the reach center and execute reach evaluation; adaptively select the real reach center point according to the reach evaluation result, and complete differential compression with the real reach center point.

[0050] During the process of data compression and clustering, the reach center is the center point representing the data features. These points can represent the overall features of the cluster or data stream where they are located. The purpose of selecting the reach center is to reduce redundancy when storing data and retain the main information of the data as much as possible. From the data after intersection recognition, select any data as the reach center. The selected data points are usually the data with larger changes or the most representative in the cluster. For example, if within a cluster, some data streams change significantly over time or have a greater impact on the overall system state, these data points are more suitable to be the reach center.

[0051] The goal of reach evaluation is to evaluate the effectiveness of these reach center points in data compression, evaluate the selected reach centers, and check whether they can represent the change trends of most data streams in the cluster. The evaluation metrics include: the representativeness of the center point, that is, whether the selected reach center can better express the data distribution of the entire cluster; the compression benefit, that is, whether the reach center can retain the key information of the cluster after compression and minimize the redundancy of the data.

[0052] The result of reach evaluation will directly affect the subsequent compression strategy. If the reach centers perform reasonably, continue to use these data points for compression; if the evaluation result is not ideal, it is necessary to reselect the reach centers.

[0053] Decide which data points to select as the final real reach centers according to the reach evaluation result. In practical applications, sometimes one reach center is not enough to cover the data features of the entire cluster. In this case, multiple real reach centers can be adaptively selected to ensure that all key features can be represented. The selected multiple real reach centers help the system represent the changes of data more precisely in the subsequent compression process.

[0054] After determining the true reach center points, these center points are used to perform differential compression. Instead of directly storing the complete values of each data stream, differential compression calculates the difference between each data stream and its nearest reach center. Specifically, for each data stream within a cluster, the difference between the data stream and the closest reach center is calculated, and only these differences are stored. This means that the compressed data will occupy less storage space than the original data because only the changes need to be stored instead of the complete data. The core advantage of differential compression is that for data streams with small changes, the differences are small and the storage space required is also small. Only when the data changes significantly will the stored differences be large, but usually this situation is relatively rare. Therefore, it can effectively reduce the storage requirements.

[0055] Furthermore, the modulation and demodulation management based on the clustering compression result and the dequeue order further includes: The modulation control channel selects the modulation method according to the data priority label and data attributes of the dequeue order, including FSK, BPSK, UART, and ASK.

[0056] The modulation control channel selects an appropriate modulation method according to the data priority label and data attributes. The selection of the modulation method affects the quality, speed, and reliability of data transmission. In a communication system, the modulation method determines how digital data is encoded into an analog signal for transmission over a physical medium.

[0057] The data priority label determines the urgency or importance of the data during transmission. High-priority data usually requires a more reliable and faster transmission method to ensure its timely delivery. Low-priority data can choose a modulation method that is more bandwidth- and resource-saving; data attributes include the characteristics, size, real-time requirements, etc. of the data. Different types of data require different modulation methods to optimize their transmission performance. For example, data with high real-time requirements selects a fast and low-latency modulation method, while non-real-time data can adopt a lower-bandwidth and low-complexity modulation method.

[0058] Among them, FSK is suitable for low-frequency and high-noise environments and is usually used for low-speed data transmission. In data streams with high priority and high requirements for reliability, FSK will be selected for modulation; BPSK is used for digital signal transmission and is suitable for transmissions with a low error rate. For data with higher importance and a lower required bit error rate, BPSK is a suitable modulation method; UART is suitable for serial communication and is often used for low-bandwidth data transmission. For applications with low priority and no high-speed requirements, UART is a more suitable choice; ASK represents different digital data by changing the amplitude of the signal. If the priority of the data stream is low and the requirement for transmission speed is low, the ASK modulation method can be selected.

[0059] Furthermore, after performing modulation and demodulation management based on the clustering compression result and the dequeue order, the following steps are also included: Record the clustering compression result, and obtain ground feedback after data upload; establish compression optimization for similar scenarios based on the ground feedback, and perform clustering compression adjustment according to the compression optimization.

[0060] Record the data after clustering compression, including information such as the compression ratio of the data after clustering, the data clustering center, and the differential compressed data. Recording these data helps to track the data processing process, analyze the compression effect, and provide a basis for subsequent optimization.

[0061] Upload the compressed data to the ground system. The ground system performs a recovery operation on the received data, attempting to restore the compressed data to the original format or an approximate original format. The key metrics feedback by the ground system include: whether the recovery is successful, that is, check whether the compressed data can be successfully restored to the original data. A successful recovery means that no key data is lost during the compression process, and the compressed data retains complete information; error detection. Even if the data recovery is successful, the ground system may detect a certain amount of error, especially during the compression of low-priority data. It is necessary to evaluate the size of the error to determine whether the compression strategy needs to be adjusted. The ground feedback is used for subsequent compression strategy adjustment. If the recovery is successful and the error is acceptable, it indicates that the compression strategy is effective; if the recovery fails or the error is too large, the compression method needs to be re-evaluated and adjusted.

[0062] By analyzing the ground feedback, the performance of data compression in different scenarios can be identified. These scenarios may include factors such as device status, environmental changes, and data stream types. For example, certain types of data, such as sensor temperature data, may have better compression effects in some environments, while more key information may be lost in other environments.

[0063] Classify different working scenarios, and then define the optimal compression strategy for each scenario. Specifically, according to the identified similar scenarios, adjust the compression strategy for each scenario. Specifically, the compression effect can be optimized by adjusting the granularity of clustering, selecting different reach centers, or adjusting the threshold of differential compression. The clustering algorithm can also be adjusted according to the feedback, such as changing the clustering criteria, adjusting the threshold of data association degree clustering, or performing more refined compression on the data according to the priority to improve the recovery accuracy. Perform dynamic optimization according to each feedback. Whenever data recovery failure or a large error is detected, automatically adjust the compression strategy to ensure more successful compression next time and reduce errors.

[0064] Furthermore, the step of inputting the current state and the data priority label into the modulation control channel to generate the dequeue order also includes: Configure an event detector to identify scheduling exception events based on the event detector. When the event detector identifies a scheduling exception event, trigger a burst interruption priority modulation strategy and perform dequeue management according to the burst interruption priority modulation strategy.

[0065] The event detector is an algorithm module used to monitor in real time whether there are any abnormalities during data scheduling and processing. By analyzing various status parameters, task flows, resource occupancy, etc. in the system, and according to specific job requirements and preset criteria, it detects whether there are abnormal conditions beyond the normal range. For example, if the resource occupancy of some modulation sub-task pools exceeds the predetermined threshold during scheduling, it is identified as an abnormality; if the delay in data processing or transmission exceeds the expected time, it is identified as an abnormality; if some data streams are lost during transmission, it is identified as an abnormality.

[0066] When the event detector identifies a scheduling exception event, it immediately triggers a burst interruption priority modulation strategy. This strategy is an emergency mechanism automatically started when an abnormal event is identified, aiming to give priority to processing critical data streams in the current task pool, ensuring that abnormal events are processed in a timely manner and minimizing the negative impact on the overall system performance. After triggering the burst interruption priority modulation strategy, adjust the dequeue order of the task pool, suspend or postpone the processing of non-critical tasks, and give priority to processing high-priority tasks affected by the abnormal event to ensure that critical data is not delayed or lost when an abnormal event occurs.

[0067] In summary, the pointer data buffer modulation and demodulation method for oil drilling provided by the embodiments of the present application has the following technical effects: Construct a pointer data buffer pool with a non-linear topological cache structure for receiving data stream inputs from multiple sensors. This design can more efficiently handle complex data input scenarios, especially suitable for drilling operations in ultra-high temperature environments, improving the flexibility of data stream management and making the scheduling of data input and output more efficient; use a data quality analyzer to score the quality of the input data stream and establish a priority label for each data stream, which ensures that in the case of frequent or complex sensor data, the processing efficiency of high-priority data can be guaranteed, enhancing the real-time performance and safety of the operation; the modulation control channel can dynamically manage the scheduling of the data stream, making the data processing process more flexible and efficient. By combining the real-time cache pool status and data priority labels, the dequeue order can be adjusted according to the actual situation, ensuring that important data can always be preferentially processed under high-load conditions and improving the stability of the system in ultra-high temperature environments; by predicting the status of the pointer data buffer pool, compression instructions can be actively generated when the data flow rate exceeds the system processing capacity, enabling the system to adaptively respond to sudden increases in data traffic, avoiding data loss, and enhancing the stability and reliability of the system; cluster and compress the data according to the data priority label and data correlation degree, optimizing the use of storage and transmission resources. Through cluster compression, low-priority data streams can be more efficiently managed to ensure that high-priority data streams receive more resources, thereby improving the system processing efficiency and reducing energy consumption; select an appropriate modulation method for modulation and demodulation management according to the cluster compression result and dequeue order. By flexibly selecting the modulation method, data transmission can be adaptively performed under various operating conditions to ensure the efficient and stable transmission of data. Especially in extreme environments such as ultra-high temperature, the integrity of operation data is ensured.

[0068] Embodiment 2, based on the same inventive concept as the pointer data buffer modulation and demodulation method for oil drilling in the foregoing embodiment, as Figure 2 shown, the embodiment of the present application provides a pointer data buffer modulation and demodulation system for oil drilling, and the system includes: The buffer pool construction module 10 is used to construct a pointer data buffer pool. The pointer data buffer pool is a non-linear topological cache structure and is used to receive data stream inputs from multiple sensors. The quality scoring module 20 is used to, when any data stream is input into the pointer data buffer pool, perform quality scoring through a data quality analyzer and establish data priority tags. The sequence generation module 30 is used to obtain the current state of the pointer data buffer pool, input the current state and the data priority tags into a modulation control channel, and generate a dequeue sequence. The state prediction module 40 is used to perform state prediction on the pointer data buffer pool, establish a prediction result, and generate an active compression instruction if the prediction result meets a preset proportion threshold. The clustering compression module 50 is used to perform clustering compression based on the data priority tags and data correlation degree according to the active compression instruction. The demodulation management module 60 is used to perform modulation and demodulation management according to the clustering compression result and the dequeue sequence.

[0069] Furthermore, the quality scoring module 20 is used to perform the following operation steps: Use the index extraction unit of the data quality analyzer to extract the indexes of the data stream and establish an evaluation index set. The evaluation index set includes a change rate index, a fluctuation degree index, an abnormal deviation index, a sampling time index, and a sensor weight index. Call the context state of the drilling operation status and configure a dynamic weight factor according to the context state. Use the dynamic weight factor to perform weighted calculation on the evaluation index set and establish data priority tags.

[0070] Furthermore, the sequence generation module 30 is used to perform the following operation steps: Execute the division of the modulation subtask pool and establish N modulation subtask pools. Perform data matching competition between the modulation subtask pools and the data under the sequential priority according to the current state and the data priority tags. After binding the modulation subtask pools according to the matching competition result, establish a dequeue sequence.

[0071] Furthermore, the sequence generation module 30 is used to perform the following operation steps: Establish a pool-level priority in each modulation subtask pool. The pool-level priority is a dynamic priority, and the pool-level priority is dynamically set by the urgency of the data in the pool, the historical packet loss rate, and the modulation resource occupancy rate. Perform data matching competition after sorting the priority tags in order based on the pool-level priority.

[0072] Furthermore, the clustering compression module 50 is used to perform the following operation steps: Perform priority screening using the data priority tags to identify low-priority data; perform priority similarity clustering on the low-priority data to generate a first clustering constraint; perform data correlation clustering on the low-priority data to establish a second clustering constraint, where the data correlation clustering includes data type correlation and data value correlation; perform intersection identification on the data under the first clustering constraint and the second clustering constraint, and select data according to the intersection identification result to complete clustering compression.

[0073] Furthermore, the clustering compression module 50 is used to perform the following operation steps: Take any data in the intersection identification result as a reach center, and perform reach evaluation; adaptively select a true reach center point according to the reach evaluation result, and complete differential compression with the true reach center point.

[0074] Furthermore, the demodulation management module 60 is used to perform the following operation steps: The modulation control channel selects a modulation method according to the data priority tag and data attributes of the dequeued order, including FSK, BPSK, UART, and ASK.

[0075] Furthermore, the demodulation management module 60 is used to perform the following operation steps: Record the data of the clustering compression result, and obtain ground feedback after the data is uploaded; establish compression optimization for similar scenarios according to the ground feedback, and perform clustering compression adjustment according to the compression optimization.

[0076] Furthermore, the sequence generation module 30 is used to perform the following operation steps: Configure an event detector, and identify scheduling exception events according to the event detector; when the event detector identifies a scheduling exception event, trigger a burst interruption priority modulation strategy, and perform dequeue management according to the burst interruption priority modulation strategy.

[0077] Through the foregoing detailed description of the pointer data buffer modulation and demodulation method for oil drilling in this specification, those skilled in the art can clearly know the pointer data buffer modulation and demodulation system for oil drilling in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for related parts, refer to the description in the method part.

[0078] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A pointer data buffering modulation and demodulation method for oil drilling, characterized in that: The method comprises: Constructing a pointer data buffer pool, wherein the pointer data buffer pool is a non-linear topological cache structure for receiving data stream input from multiple sensors; When any data stream enters the pointer data buffer pool, it is scored for quality by the data quality analyzer and a data priority label is established; Acquire the current state of the pointer data buffer pool, input the current state and the data priority tag into the modulation control channel, and generate a dequeue order; Execute state prediction of the pointer data buffer pool, establish a prediction result, and if the prediction result meets a preset proportion threshold, generate an active compression instruction; Performing cluster compression based on data priority labels and data relevance according to the active compression instruction; Modem management is performed based on cluster compression results and dequeue order.

2. The pointer data buffering modulation and demodulation method for oil drilling as claimed in claim 1, characterized in that: The data quality analyzer is used to perform quality scoring and establish data priority labels, including: Extracting indicators of the data stream using an indicator extraction unit of the data quality analyzer, and establishing an evaluation indicator set, wherein the evaluation indicator set includes a change rate indicator, a fluctuation indicator, an abnormal deviation indicator, a sampling time indicator, and a sensor weight indicator; Calling the context state of the drilling operation state, and configuring the dynamic weight factor according to the context state; The dynamic weight factor is used to perform weighted calculation on the evaluation index set to establish a data priority label.

3. The pointer data buffering modulation and demodulation method for oil drilling according to claim 1, characterized in that: The obtaining of the current state of the pointer data buffer pool, inputting the current state and the data priority tag into the modulation control channel, and generating a dequeue order includes: Execute the division of the modulation subtask pool and establish N modulation subtask pools; According to the current state and the data priority label, the subtask pool is modulated to compete with the data matching under the sequential priority; After binding the modulation subtask pool according to the matching competition results, the dequeue order is established.

4. The pointer data buffering modulation and demodulation method for oil drilling as claimed in claim 3, characterized in that: The step of performing the modulation subtask pool and the data matching competition under the sequential priority according to the current state and the data priority tag also includes: Establishing a pool-level priority in each modulation subtask pool, wherein the pool-level priority is a dynamic priority, and the pool-level priority is dynamically set according to the urgency of the data in the pool, the historical packet loss rate, and the modulation resource occupancy rate; The data matching competition is performed after the priority tags are sorted in order based on the pool-level priority.

5. The pointer data buffering modulation and demodulation method for oil drilling as claimed in claim 1, characterized in that: The performing cluster compression based on the data priority label and the data association degree according to the active compression instruction includes: Using the data priority label to perform priority screening and identify low priority data; Execute priority similarity clustering of the low priority data to generate a first clustering constraint; Execute data association degree clustering of the low priority data to establish a second clustering constraint, wherein the data association degree clustering includes data type association and data value association; Intersection identification is performed on the data under the first clustering constraint and the second clustering constraint, and data is selected according to the intersection identification result to complete clustering compression.

6. The pointer data buffering modulation and demodulation method for oil drilling as claimed in claim 5, characterized in that: The selecting of data according to the intersection recognition result to complete clustering compression includes: Taking any data in the intersection recognition result as the reach center, performing reach evaluation; The true touch center point is adaptively selected according to the touch evaluation result, and the differential compression is completed with the true touch center point.

7. The pointer data buffering modulation and demodulation method for oil drilling as claimed in claim 1, characterized in that: The modulation and demodulation management according to the cluster compression result and the dequeue order also includes: The modulation control channel selects a modulation mode according to the data priority tag and data attribute of the dequeue order, including FSK, BPSK, UART, and ASK.

8. The pointer data buffering modulation and demodulation method for oil drilling as claimed in claim 1, characterized in that: After the modulation and demodulation management is performed according to the cluster compression result and the dequeue order, the method further includes: Recording the cluster compression results, and obtaining ground feedback after uploading the data; Compression optimization of similar scenarios is established according to the ground feedback, and cluster compression adjustment is performed according to the compression optimization.

9. The pointer data buffering modulation and demodulation method for oil drilling as claimed in claim 1, characterized in that: The step of inputting the current state and the data priority tag into a modulation control channel to generate a dequeue order further includes: configuring an event detector, and identifying scheduling abnormal events according to the event detector; When the event detector identifies a scheduling abnormality event, a burst interrupt priority modulation strategy is triggered, and dequeue management is performed according to the burst interrupt priority modulation strategy.

10. A pointer data buffering modem system for oil drilling, characterized in that: A pointer data buffering modulation and demodulation method for oil drilling according to any one of claims 1 to 9, the system comprising: A buffer pool construction module is used to construct a pointer data buffer pool, wherein the pointer data buffer pool is a non-linear topological cache structure and is used to receive data stream inputs from multiple sensors; The quality scoring module is used to perform quality scoring through a data quality analyzer and establish a data priority label when any data stream enters the pointer data buffer pool; A sequence generation module, used for obtaining the current state of the pointer data buffer pool, inputting the current state and the data priority tag into the modulation control channel, and generating a dequeue sequence; A state prediction module, used for performing state prediction of the pointer data buffer pool, establishing a prediction result, and generating an active compression instruction if the prediction result meets a preset proportion threshold; A cluster compression module, used for performing cluster compression based on data priority labels and data relevance according to the active compression instruction; The demodulation management module is used to manage modulation and demodulation according to the cluster compression results and the dequeue order.

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