Pointer data buffering modulation and demodulation method and system for oil drilling

Abstract

The present invention provides a pointer data buffering modulation and demodulation method and system for oil drilling, relating to the field of wireless communication technology, including: constructing a pointer data buffer pool, which is a nonlinear topological cache structure for receiving data stream inputs from multiple sensors; performing quality scoring through a data quality analyzer to establish data priority tags; obtaining the current state of the pointer data buffer pool, inputting it into a modulation control channel, and generating a dequeue order; performing state prediction, and generating an active compression instruction if a preset percentage threshold is met; performing cluster compression; and performing modulation and demodulation management based on the cluster compression results and the dequeue order. The present invention solves the technical problem in the prior art that single-chip microcomputers used for oil drilling have limited processing power, making it impossible to efficiently execute complex data modulation and demodulation algorithms, thereby limiting the real-time and accuracy of data processing during drilling operations.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a pointer data buffering 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 collection and transmission is required during the drilling operation, especially the sensor data near the drill bit. This data needs to be transmitted to the MWD (measurement real-time data) receiving circuit module via radio electromagnetic waves. Data modulation and demodulation technology plays a key role in this process.

[0003] Currently, in conventional wireless communication applications, data modulation and demodulation are mostly performed by FPGAs (field programmable gate arrays). FPGAs have powerful parallel computing capabilities, can handle complex algorithms, and have multiple built-in dedicated IP cores suitable for data modulation and demodulation. Although FPGAs have obvious advantages in data processing, they still have some shortcomings in terms of high real-time and accuracy requirements, especially in the real-time data transmission and precise modulation and demodulation tasks in drilling operations. In addition, the amount of data in drilling operations is usually huge, requiring effective management and processing of large amounts of sensor data. Traditional microcontrollers have limitations in data processing capabilities and cannot efficiently handle complex modulation and demodulation algorithms, especially in situations where strong real-time processing and large amounts of data are required. This places high demands on the computing power of the microcontroller. 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 in the existing technology that the processing power of the single-chip microcomputer used for oil drilling is limited, and it is unable to efficiently execute complex data modulation and demodulation algorithms, thereby limiting the real-time and accuracy of data processing in drilling operations.

[0005] The first aspect disclosed in the present application provides a pointer data buffer modulation and demodulation method for oil drilling, the method comprising: constructing a pointer data buffer pool, the pointer data buffer pool being a nonlinear topological cache structure for receiving data stream inputs from multiple sensors; when any data stream is input into the pointer data buffer pool, performing a quality score 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 and the data priority label into a modulation control channel and generating a dequeueing order; executing a state prediction of the pointer data buffer pool and establishing a prediction result, and generating an active compression instruction if the prediction result meets a preset proportion threshold; performing cluster compression based on the data priority label and data correlation according to the active compression instruction; and performing modulation and demodulation management according to the cluster compression result and the dequeueing order.

[0006] The second aspect disclosed in the present application provides a pointer data buffer modulation and demodulation system for oil drilling, which is used for the above-mentioned pointer data buffer modulation and demodulation method for oil drilling. The system includes: a buffer pool construction module, which is used to construct a pointer data buffer pool, and the pointer data buffer pool is a nonlinear topological cache structure, which is used to receive data stream inputs from multiple sensors; a quality scoring module, which is used to perform quality scoring through a data quality analyzer when any data stream is input into the pointer data buffer pool, and establish a data priority label; a sequence generation module, which is used to obtain the current state of the pointer data buffer pool, input the current state and the data priority label into the modulation control channel, and generate a dequeue order; a state prediction module, which is used to perform state prediction of the pointer data buffer pool and establish a prediction result. If the prediction result meets a preset proportion threshold, an active compression instruction is generated; a cluster compression module, which is used to perform cluster compression based on the data priority label and data correlation according to the active compression instruction; and a demodulation management module, which is used to perform modulation and demodulation management according to the cluster compression result and the dequeue order.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects:

[0008] A pointer data buffer pool with a nonlinear topological cache structure is constructed to receive data stream inputs from multiple sensors. This design can more efficiently cope with complex data input scenarios, and is especially suitable for drilling operations in ultra-high temperature environments. It improves the flexibility of data stream management and makes the scheduling of data input and output more efficient. The input data stream is scored by the data quality analyzer, and a priority label is established for each data stream. This ensures the processing efficiency of high-priority data when the sensor data is frequent or complex, and enhances the real-time and security 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, the queue order can be adjusted according to the actual situation, ensuring that important data can always be processed first under high load conditions, improving the system's Stability in ultra-high temperature environments; by predicting the status of the pointer data buffer pool, it can actively generate compression instructions when the data flow rate exceeds the system processing capacity, so that the system can adaptively respond to the increase in sudden data traffic, avoid data loss, and improve the stability and reliability of the system; cluster and compress data according to data priority tags and data correlation, optimize the use of storage and transmission resources, and through cluster compression, it can more efficiently manage low-priority data streams, ensuring that high-priority data streams get more resources, thereby improving system processing efficiency and reducing energy consumption; according to the cluster compression results and the dequeue order, select the appropriate modulation method for modulation and demodulation management, and through flexible selection of modulation methods, it can adaptively transmit data under various operating conditions to ensure efficient and stable data transmission, especially in extreme environments such as ultra-high temperatures, to ensure the integrity of operating data.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flowchart of a pointer data buffering modulation and demodulation method for oil drilling provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of the structure of a pointer data buffering modulation and demodulation system for oil drilling provided in an embodiment of the present application.

[0012] Description of reference numerals: buffer pool construction module 10 , quality scoring module 20 , sequence generation module 30 , state prediction module 40 , cluster compression module 50 , demodulation management module 60 . DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a pointer data buffer modulation and demodulation method and system for oil drilling, thereby solving the technical problem in the prior art that the processing power of the single-chip microcomputer used for oil drilling is limited, resulting in the inability to efficiently execute complex data modulation and demodulation algorithms, thereby limiting the real-time and accuracy of data processing in drilling operations.

[0014] After introducing the basic principles of this application, various non-limiting embodiments of this application will be specifically described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0015] Example 1, as Figure 1 As shown, an embodiment of the present application provides a pointer data buffering modulation and demodulation method for oil drilling, the method comprising:

[0016] A pointer data buffer pool is constructed, wherein the pointer data buffer pool is a non-linear topological cache structure for receiving data stream inputs from multiple sensors.

[0017] A pointer data buffer pool is constructed as a nonlinear topological cache structure for storing data streams from multiple sensors. It is designed to cope with high-frequency data input in ultra-high temperature environments, ensuring data loss, while also being able to flexibly handle the input of different data streams. The pointer data buffer pool is not a simple linear queue, but rather uses a more complex data structure to store data based on factors such as data type, priority, and arrival time. This design facilitates more efficient scheduling and access operations and reduces data conflicts. Nonlinear topology refers to the use of nonlinear data structures such as circular queues, tree structures, and graph structures for storage. Each node represents a data stream or a data block from multiple sensors, and is prioritized and scheduled through different paths.

[0018] Multiple sensors simultaneously input data into the pointer data buffer. These sensors measure physical quantities such as temperature, pressure, and vibration during drilling. These data streams have varying frequencies and priorities, so the buffer needs to be designed to be flexible enough to accommodate various input modes. Due to the complex and unstable field operating conditions, the buffer needs to be able to dynamically adjust the buffer capacity, expanding or contracting the buffer in real time based on the data input flow rate and burst traffic to avoid data loss.

[0019] When any data stream enters the pointer data buffer pool, it is scored by the data quality analyzer and a data priority label is established.

[0020] After the data enters the pointer data buffer pool, the data is scored for quality, and a priority label is established for each data stream based on the scoring results. Specifically, the data quality analyzer evaluates the quality of each data stream based on a series of evaluation indicators. The evaluation indicators include rate of change index, volatility index, abnormal deviation index, sampling time index, and sensor weight index. Different abnormal situations may occur during the operation, such as equipment failure 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. This label indicates the priority of the data stream in processing. The establishment of priority labels enables the pointer data buffer pool to efficiently manage data streams and ensure that important data will not be delayed when resources are limited.

[0021] The current state of the pointer data buffer pool is obtained, and the current state and the data priority tag are input into the modulation control channel to generate a dequeue order.

[0022] Get the current status of the pointer data buffer pool. This status reflects the distribution of data within the buffer pool, the amount of storage, the real-time status of the data flow, etc. For example, check the current storage status of the pointer data buffer pool, including information such as the amount of data in the cache, the amount of processed data, and the amount of data to be processed; check the free space and occupied space in the buffer pool. If the buffer pool capacity is close to full, it indicates that there is a risk of data overflow.

[0023] After obtaining the current status and data priority label, it is input into the modulation control channel. The modulation control channel determines the dequeue order based on the priority and status 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.

[0024] A state prediction of the pointer data buffer pool is performed to establish a prediction result, and if the prediction result meets a preset proportion threshold, an active compression instruction is generated.

[0025] Utilizing historical data, real-time data streams, and the current state of the buffer pool, state predictions are performed. This prediction, based on statistical methods or machine learning algorithms, analyzes factors such as data stream input frequency, data priority, and the real-time occupancy status of the buffer pool. This prediction predicts the buffer pool's occupancy rate and data stream input speed over a certain period of time. Based on these factors, the system assesses whether the buffer pool will rapidly approach saturation. A pre-set occupancy threshold, such as 80% or 90%, serves as a warning sign. When the buffer pool occupancy rate approaches this threshold, indicating that the buffer pool is nearing full capacity, a compression instruction is generated. This instruction aims to free up space by compressing data, ensuring that the buffer pool does not overflow and maintaining a stable data flow.

[0026] Cluster compression based on data priority labels and data association is performed according to the active compression instruction.

[0027] Data association refers to the degree of correlation between data. If multiple data items have a high degree of correlation, such as the same physical quantity measured by a sensor, their compression can be achieved through clustering, compressing adjacent data items with similar characteristics or values together. When compressing low-priority data, clustering techniques are used to divide the data items into several groups, each with a relatively consistent change pattern. The data in each group can be stored using differential encoding, which records the changes in the data rather than the data itself. For example, if the temperature data measured by the sensor is 100°C, 102°C, 105°C, 107°C, and 110°C, differential compression only stores the changes in the data: 100°C, +2, +3, +2, +3. The first bit retains the original value, and subsequent steps only record the change. This significantly reduces the storage space required.

[0028] The compressed data only stores data change information rather than complete data values, so the storage space occupied is significantly reduced, ensuring that the buffer pool can effectively manage more data streams.

[0029] Modem management is performed based on cluster compression results and dequeue order.

[0030] Modem management is responsible for selecting the appropriate modulation method for data transmission and reception based on data priority, compression results, and dequeue order. In oil drilling operations, data transmission must occur in extremely high-temperature environments, necessitating the selection of appropriate modulation technologies to ensure stable data transmission in such complex environments. Modulation methods include FSK (Frequency Shift Keying), BPSK (Binary Phase Shift Keying), UART (Universal Asynchronous Receiver / Transmitter), and ASK (Amplitude Shift Keying). FSK is suitable for low-frequency and high-noise environments and is typically used for low-speed data transmission; BPSK is used for digital signal transmission and is suitable for low-error rates; UART is suitable for serial communication and is often used for low-bandwidth data transmission; and ASK represents different digital data by varying the signal amplitude. Dequeue order plays a key role in modem management, as it determines which data is prioritized. High-priority data is dequeued first and transmitted using a more reliable modulation method, ensuring that the most critical data is delivered promptly and without loss during transmission.

[0031] Based on 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 the appropriate frequency range is selected based on the transmission environment. After receiving the signal, the receiver uses the corresponding demodulation algorithm to decode the signal and recover the original data. These steps ensure the effective management and transmission of data in ultra-high temperature and complex environments, while minimizing data storage requirements and improving system efficiency and reliability.

[0032] Furthermore, the quality scoring by the data quality analyzer and the establishment of data priority labels include:

[0033] The indicator extraction unit of the data quality analyzer is used to extract indicators from the data stream and establish an evaluation indicator set, which includes a rate of change indicator, a volatility indicator, an abnormal deviation indicator, a sampling time indicator, and a sensor weight indicator. The context state of the drilling operation state is called, and a dynamic weight factor is configured according to the context state. The dynamic weight factor is used to perform a weighted calculation on the evaluation indicator set to establish a data priority label.

[0034] The data quality analyzer is used to evaluate the quality of sensor data streams in order to assign corresponding priority labels to each data stream. The indicator extraction unit of the data quality analyzer extracts key information of the data based on predefined evaluation indicators, thereby establishing an evaluation indicator set. These evaluation indicators are used to quantify the quality of the data and assist in subsequent priority sorting, including rate of change indicators, volatility indicators, abnormal deviation indicators, sampling time indicators, and sensor weight indicators.

[0035] Among them, the rate of change index measures the speed at which data changes over time and is used to describe the dynamic characteristics of data streams, especially the degree of fluctuation of 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 rate of change index can be obtained by calculating the difference between adjacent data points and dividing it by the time interval. A larger rate of change indicates that the data stream has changed significantly in a short period of time and usually requires higher priority processing; the volatility index measures the stability of the data and describes the amplitude of data fluctuations. Data with larger fluctuations often show more unstable changes, which may be a sign of poor data quality or represent an abnormality in the system. The volatility 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 in sensor data exceeds the normal expected range, it indicates equipment failure or external interference, and therefore requires priority processing. This indicator is calculated by comparing the difference between the current sensor data and the historical average or benchmark data; the sampling time indicator measures the real-time nature of the data. The latest data from the sensor is usually more important than historical data, so the timestamp is also a key factor in evaluating data priority. This indicator 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 indicator reflects the reliability and importance of different sensors. Some sensors are more critical than other sensors, or have higher accuracy, so the data they generate requires a higher priority. The sensor weight can be calculated based on factors such as the sensor's accuracy, historical performance, and maintenance status.

[0036] The contextual state refers to the various actual conditions and environmental changes during drilling operations, such as drilling depth, drilling fluid flow rate, current equipment status, and drill bit pressure. These factors directly influence which data is more critical. For example, in certain drilling environments, temperature data may be more important than other data, or pressure data may require greater attention when equipment is operating at high load. The dynamic weight factor changes in real time based on the operational state. If the drilling operation enters a more dangerous phase, such as increased equipment load or depth, the weight of sensor data related to these critical factors will be increased, allowing this type of data to receive more processing resources and a higher priority. For example, when the drilling depth reaches a certain depth, the weight factor of pressure data will increase because pressure fluctuations at this time have a greater impact on drilling safety. When equipment anomalies occur, such as excessive temperature, the weight factor of the temperature sensor will temporarily increase to ensure that the system can prioritize temperature data.

[0037] Dynamic weighting factors are applied to each indicator in the data flow evaluation index set, and a comprehensive score is obtained through weighted calculation. Data flows with higher comprehensive scores are assigned high-priority tags. This data is usually recent, changes dramatically, or contains abnormal deviations, representing critical operation conditions and may require immediate processing. Data flows with lower comprehensive scores are usually relatively stable and less volatile, representing normal operating conditions or less important environmental data. They are assigned low-priority tags and can be processed later. These priority tags can effectively schedule data flows, prioritize important and urgent data, and allocate less computing resources and storage space to low-priority data flows.

[0038] Furthermore, the acquiring 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:

[0039] Execute the division of the modulation subtask pool to establish N modulation subtask pools; perform data matching competition between the modulation subtask pool and the sequential priority according to the current state and the data priority label; after binding the modulation subtask pool according to the matching competition result, establish the dequeue order.

[0040] Modulation subtask pools are divided into separate groups. Each modulation subtask pool can independently handle modulation tasks for a portion of the data stream. The purpose of task pooling is to enable parallel processing and improve overall processing efficiency. The number of modulation subtask pools N is determined based on the system's computing resources, data flow, and job complexity. Through reasonable division, load balancing can be achieved, improving data processing efficiency.

[0041] At the same time, multiple modulation subtask pools need to process different data streams. To ensure that data streams are processed promptly based on their priority, they compete for them based on their priority tags. This means that high-priority data streams will compete to occupy more modulation resources. Data streams are matched based on each subtask pool's resource usage and task priority. For example, if a task pool is processing a low-priority data stream while tasks in another task pool are waiting for a higher-priority data stream, the higher-priority data stream will be reallocated to the other pool. Data stream allocation within the task pool is adjusted based on the data stream's characteristics and priority. Data streams within each modulation subtask pool are sorted or competed for to ensure that important data streams are processed and transmitted in the shortest possible time.

[0042] Through matching competition, it is determined which tasks should be assigned to which modulation subtask pools, and the matched tasks are bound to the corresponding pools. Each pool starts to process the task flow bound to it. According to the matching competition results and the task priority in the task pool, the dequeue order is generated. The dequeue order determines the processing order of the data stream. During the modulation and demodulation process, tasks are processed step by step according to the dequeue order to ensure that the real-time requirements of high-priority tasks are met.

[0043] Furthermore, the step of performing the matching competition between the subtask pool and the data under the sequential priority according to the current state and the data priority tag further includes:

[0044] A pool-level priority is established in each modulation subtask pool. 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; data matching competition is performed after the priority label order is sorted based on the pool-level priority.

[0045] In each modulation subtask pool, the pool-level priority is dynamically set based on 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 subtask pool and determines the processing order and resource allocation of 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.

[0046] Among them, each modulation subtask 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 equipment failure data and critical 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; the data processing history of each modulation subtask pool is recorded, including the rate of task data loss during transmission or processing. If the historical packet loss rate of a pool is high, it means that there may be risks in the task processing of this pool. In this case, the priority of this pool is increased to resolve resource shortages or transmission problems as soon as possible to avoid further packet loss; the modulation resource occupancy rate refers to the proportion of resources consumed by each modulation subtask pool when executing data modulation tasks. If a task pool has already occupied a high proportion of resources, its pool-level priority needs to be lowered to ensure that resources can be fairly allocated to other pools. Conversely, if the resource occupancy of a pool is low, its priority can be appropriately increased to process tasks more quickly.

[0047] Pool-level priorities are adjusted in real time based on the above factors. For example, if the data flow within a task pool suddenly becomes more urgent, the pool-level priority will be immediately increased to ensure that the tasks in that pool are processed first. Similarly, if a pool has a high historical packet loss rate, the priority of the pool will be automatically adjusted to ensure that the lost data can be recovered or retransmitted as soon as possible.

[0048] Based on the data flow priority label and pool-level priority, the data flows in the pool are comprehensively sorted to ensure that higher-priority tasks are scheduled first when resources are limited. The dynamic adjustment process ensures that resources are optimally configured, improving the responsiveness and stability of the overall system.

[0049] Furthermore, performing cluster compression based on data priority labels and data relevance according to the active compression instruction includes:

[0050] The data priority label is used to perform priority screening to identify low-priority data; priority similarity clustering of the low-priority data is performed to generate a first clustering constraint; data association clustering of the low-priority data is performed to establish a second clustering constraint, wherein the data association 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.

[0051] Prioritize data based on preset standards and identify and mark low-priority data. Low-priority data refers to data that has little impact on the current job status and has low real-time requirements. It can usually be processed later, compressed, or discarded to save resources or alleviate data backlogs.

[0052] Low-priority data streams often have similar characteristics and attributes, such as low real-time requirements and small fluctuations. Grouping these data streams together can help the system optimize resource allocation and data compression. The similarity of their priority labels determines which data streams should be grouped together. For example, a threshold can be defined. When the difference in data priority labels is less than this threshold, they are clustered together. Data within a cluster has similar priorities and can be processed and compressed together in subsequent operations.

[0053] Data association clustering refers to dividing data into different groups based on the correlation between the data. Data association clustering includes data type association and data value association. Among them, data type association refers to the type similarity between different data streams. 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. These data can be clustered together to reduce the computational burden during processing; data value association refers to the numerical similarity between different data streams. 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 these data streams have a strong data value association, and these data streams are placed in the same cluster for subsequent processing.

[0054] The first clustering constraint (clustering based on priority labels) and the second clustering constraint (clustering based on data association) are combined to find the intersection between the two. Specifically, which data meets both priority requirements and data association requirements is identified to form a more accurate clustering. The compression strategy is executed based on the clustering results. The result of compression is effective storage optimization of low-priority data, reducing storage requirements and saving resources for the system.

[0055] Furthermore, selecting data according to the intersection recognition result to complete clustering compression includes:

[0056] Any data in the intersection recognition result is used as the touch center to perform touch evaluation; the real touch center point is adaptively selected according to the touch evaluation result, and differential compression is completed with the real touch center point.

[0057] During data compression and clustering, a touch center is a central point representing data characteristics. These points can represent the overall characteristics of the cluster or data stream. The purpose of selecting a touch center is to reduce redundancy when storing data while preserving the data's key information as much as possible. From the data identified after intersection, any data point is selected as a touch center. The selected data point is usually the data with the largest variation or the most representative data in the cluster. For example, if certain data streams within a cluster vary significantly over time or have a significant impact on the overall system state, these data points are more suitable as touch centers.

[0058] The goal of reach evaluation is to evaluate the effectiveness of these reach centers in data compression, evaluate the selected reach centers, and check whether they can represent the changing trends of most data flows in the cluster. The evaluation indicators include: the representativeness of the center point, that is, whether the selected reach center can better express the data distribution of the entire cluster; compression efficiency, that is, whether the reach center can retain the key information of the cluster after compression and minimize data redundancy.

[0059] The results of the reach evaluation will directly affect the subsequent compression strategy. If the reach center performs reasonably, continue to use these data points for compression; if the evaluation result is not ideal, you need to reselect the reach center.

[0060] The data points selected as the final true reach centers are determined based on the reach evaluation results. In practical applications, sometimes one reach center is not enough to cover the data features of the entire cluster. In this case, multiple true reach centers can be adaptively selected to ensure that all key features are represented. The selected multiple true reach centers help the system represent data changes more accurately during the subsequent compression process.

[0061] After determining the true reach center points, these center points are used to perform differential compression. Differential compression does not directly store the complete value of each data stream, but calculates the difference between each data stream and its nearest reach center. Specifically, for each data stream in the 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 take up 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 smaller changes, the differences are smaller and the required storage space is also small. Only when the data changes significantly will the stored differences be larger, but this situation is usually relatively rare, so it can effectively reduce storage requirements.

[0062] Furthermore, the modulation and demodulation management according to the cluster compression result and the dequeue order further includes:

[0063] The modulation control channel selects a modulation mode according to the data priority tag and data attributes of the dequeue order, including FSK, BPSK, UART, and ASK.

[0064] The modulation control channel selects the appropriate modulation method based on the data priority tag and data attributes. The choice of modulation method affects the quality, speed and reliability of data transmission. In the communication system, the modulation method determines how to encode digital data into analog signals for transmission on the physical medium.

[0065] The data priority label determines the urgency or importance of the data during transmission. High-priority data usually requires more reliable and faster transmission methods to ensure its timely delivery. Low-priority data can choose modulation methods that save more bandwidth and resources. Data attributes include data characteristics, size, real-time requirements, etc. Different types of data require different modulation methods to optimize their transmission performance. For example, data with high real-time requirements choose fast, low-latency modulation methods, while non-real-time data can use lower bandwidth, low-complexity modulation methods.

[0066] 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 reliability requirements, FSK will be selected for modulation; BPSK is used for digital signal transmission and is suitable for transmission with low error rates. For data that is of high importance and requires a lower 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 data stream has a lower priority and has lower requirements for transmission speed, ASK modulation can be selected.

[0067] Furthermore, after performing modulation and demodulation management according to the cluster compression result and the dequeue order, the method further includes:

[0068] Data is recorded for the cluster compression results, and ground feedback is obtained after the data is uploaded; compression optimization for similar scenarios is established based on the ground feedback, and cluster compression adjustment is performed based on the compression optimization.

[0069] The data that has been clustered and compressed is recorded, including the compression ratio of the data after clustering, the center of the data cluster, the differential compression data and other information. Recording this data helps to track the data processing process, analyze the compression effect, and provide a basis for subsequent optimization.

[0070] The compressed data is uploaded to the ground system, which performs a recovery operation on the received data, attempting to restore the compressed data to its original format or a near-original format. Key indicators of the ground system feedback include: whether the recovery is successful, that is, checking whether the compressed data can be successfully restored to the original data. 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 is successfully recovered, the ground system may detect certain errors, especially during the compression of low-priority data. The size of the error needs to be evaluated to determine whether the compression strategy needs to be adjusted. Ground feedback is used for subsequent compression strategy adjustments. If the recovery is successful and the error is acceptable, 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.

[0071] By analyzing ground feedback, we can identify the performance of data compression in different scenarios. These scenarios may include factors such as device status, environmental changes, and data stream type. For example, certain types of data, such as sensor temperature data, may compress better in some environments, but may lose more critical information in other environments.

[0072] Different work scenarios are classified and the optimal compression strategy is defined for each scenario. Specifically, the compression strategy is adjusted for each scenario based on the identified similar scenarios. Specifically, the compression effect can be optimized by adjusting the clustering granularity, selecting different reach centers, or adjusting the differential compression threshold. The clustering algorithm can also be adjusted based on feedback, such as changing the clustering criteria, adjusting the threshold for data association clustering, or performing more refined data compression based on priority to improve recovery accuracy. Dynamic optimization is performed based on each feedback. Whenever a data recovery failure or large error is detected, the compression strategy is automatically adjusted to ensure that the next compression is more successful and reduces errors.

[0073] Furthermore, inputting the current state and the data priority tag into a modulation control channel to generate a dequeue order further includes:

[0074] An event detector is configured, and scheduling abnormal events are identified according to the event detector; when the event detector identifies the scheduling abnormal event, a burst interrupt priority modulation strategy is triggered, and dequeue management is performed according to the burst interrupt priority modulation strategy.

[0075] The event detector is an algorithmic module that monitors data scheduling and processing for anomalies in real time. By analyzing various system status parameters, task flows, resource usage, and other information, it detects abnormalities beyond normal limits based on specific job requirements and pre-set standards. For example, if the resource usage of certain modulation subtask pools during scheduling exceeds a predetermined threshold, this is identified as an anomaly; if data processing or transmission delays exceed expected times, this is also identified as an anomaly; and if certain data streams are lost during transmission, this is also identified as an anomaly.

[0076] When the event detector identifies a scheduling anomaly, it immediately triggers the burst interrupt priority modulation strategy. This strategy is an emergency mechanism that automatically activates upon identification of an anomaly. Its purpose is to prioritize critical data streams in the current task pool, ensuring that the anomaly is handled promptly and minimizing the negative impact on overall system performance. Once the burst interrupt priority modulation strategy is triggered, the dequeue order of the task pool is adjusted, pausing or postponing the processing of non-critical tasks. Prioritizing high-priority tasks affected by the anomaly ensures that critical data is not delayed or lost in the event of an anomaly.

[0077] In summary, the pointer data buffering modulation and demodulation method for oil drilling provided by the embodiments of the present application has the following technical effects:

[0078] A pointer data buffer pool with a nonlinear topological cache structure is constructed to receive data stream inputs from multiple sensors. This design can more efficiently cope with complex data input scenarios, and is especially suitable for drilling operations in ultra-high temperature environments. It improves the flexibility of data stream management and makes the scheduling of data input and output more efficient. The input data stream is scored by the data quality analyzer, and a priority label is established for each data stream. This ensures the processing efficiency of high-priority data when the sensor data is frequent or complex, and enhances the real-time and security 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, the queue order can be adjusted according to the actual situation, ensuring that important data can always be processed first under high load conditions, improving the system's Stability in ultra-high temperature environments; by predicting the status of the pointer data buffer pool, it can actively generate compression instructions when the data flow rate exceeds the system processing capacity, so that the system can adaptively respond to the increase in sudden data traffic, avoid data loss, and improve the stability and reliability of the system; cluster and compress data according to data priority tags and data correlation, optimize the use of storage and transmission resources, and through cluster compression, it can more efficiently manage low-priority data streams, ensuring that high-priority data streams get more resources, thereby improving system processing efficiency and reducing energy consumption; according to the cluster compression results and the dequeue order, select the appropriate modulation method for modulation and demodulation management, and through flexible selection of modulation methods, it can adaptively transmit data under various operating conditions to ensure efficient and stable data transmission, especially in extreme environments such as ultra-high temperatures, to ensure the integrity of operating data.

[0079] Embodiment 2 is based on the same inventive concept as the pointer data buffer modulation and demodulation method for oil drilling in the above embodiment. Figure 2 As shown, an embodiment of the present application provides a pointer data buffering modulation and demodulation system for oil drilling, the system comprising:

[0080] The buffer pool construction module 10 is used to construct a pointer data buffer pool, which is a nonlinear topological cache structure and is used to receive data stream inputs from multiple sensors; the quality scoring module 20 is used to perform quality scoring through a data quality analyzer when any data stream is input into the pointer data buffer pool, and to establish a data priority label; 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 label into the modulation control channel, and generate a dequeue order; the state prediction module 40 is used to perform state prediction of the pointer data buffer pool and establish a prediction result. If the prediction result meets a preset proportion threshold, an active compression instruction is generated; the cluster compression module 50 is used to perform cluster compression based on the data priority label and data correlation according to the active compression instruction; the demodulation management module 60 is used to perform modulation and demodulation management according to the cluster compression result and the dequeue order.

[0081] Furthermore, the quality scoring module 20 is configured to perform the following steps:

[0082] The indicator extraction unit of the data quality analyzer is used to extract indicators from the data stream and establish an evaluation indicator set, which includes a rate of change indicator, a volatility indicator, an abnormal deviation indicator, a sampling time indicator, and a sensor weight indicator. The context state of the drilling operation state is called, and a dynamic weight factor is configured according to the context state. The dynamic weight factor is used to perform a weighted calculation on the evaluation indicator set to establish a data priority label.

[0083] Furthermore, the sequence generation module 30 is configured to perform the following steps:

[0084] Execute the division of the modulation subtask pool to establish N modulation subtask pools; perform data matching competition between the modulation subtask pool and the sequential priority according to the current state and the data priority label; after binding the modulation subtask pool according to the matching competition result, establish the dequeue order.

[0085] Furthermore, the sequence generation module 30 is configured to perform the following steps:

[0086] A pool-level priority is established in each modulation subtask pool. 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; data matching competition is performed after the priority label order is sorted based on the pool-level priority.

[0087] Furthermore, the cluster compression module 50 is configured to perform the following steps:

[0088] The data priority label is used to perform priority screening to identify low-priority data; priority similarity clustering of the low-priority data is performed to generate a first clustering constraint; data association clustering of the low-priority data is performed to establish a second clustering constraint, wherein the data association 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.

[0089] Furthermore, the cluster compression module 50 is configured to perform the following steps:

[0090] Any data in the intersection recognition result is used as the touch center to perform touch evaluation; the real touch center point is adaptively selected according to the touch evaluation result, and differential compression is completed with the real touch center point.

[0091] Furthermore, the demodulation management module 60 is configured to perform the following steps:

[0092] The modulation control channel selects a modulation mode according to the data priority tag and data attributes of the dequeue order, including FSK, BPSK, UART, and ASK.

[0093] Furthermore, the demodulation management module 60 is configured to perform the following steps:

[0094] Data is recorded for the cluster compression results, and ground feedback is obtained after the data is uploaded; compression optimization for similar scenarios is established based on the ground feedback, and cluster compression adjustment is performed based on the compression optimization.

[0095] Furthermore, the sequence generation module 30 is configured to perform the following steps:

[0096] An event detector is configured, and scheduling abnormal events are identified according to the event detector; when the event detector identifies the scheduling abnormal event, a burst interrupt priority modulation strategy is triggered, and dequeue management is performed according to the burst interrupt priority modulation strategy.

[0097] Through the above detailed description of the pointer data buffering modulation and demodulation method for oil drilling in this specification, those skilled in the art can clearly understand the pointer data buffering 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 the relevant parts can be referred to the method part description.

[0098] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to 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 nonlinear topological cache structure for receiving data stream input from multiple sensors; When any data stream enters the pointer data buffer pool, it is scored by the data quality analyzer and a data priority label is established; Obtaining a current state of the pointer data buffer pool, inputting the current state and the data priority tag into a modulation control channel, and generating a dequeue order; Execute state prediction of the pointer data buffer pool, establish a prediction result, and generate an active compression instruction if the prediction result meets a preset proportion threshold; Performing cluster compression based on data priority labels and data relevance according to the active compression instruction; Perform modulation and demodulation management based on cluster compression results and dequeue order; The step of 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 order includes: Perform the division of the modulation subtask pool and establish N modulation subtask pools; Performing a modulation subtask pool and data matching competition under sequential priority according to the current state and the data priority label; After binding the modulation subtask pool according to the matching competition results, the queue order is established; The step of performing the matching competition between the modulation subtask pool and the data under the sequential priority according to the current state and the data priority tag further includes: Establishing 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 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.

2. The pointer data buffering modulation and demodulation method for oil drilling according to claim 1, characterized in that: The data quality analyzer is used to perform quality scoring and establish data priority labels, including: Extracting indicators from the data stream using an indicator extraction unit of a data quality analyzer to establish an evaluation indicator set, wherein the evaluation indicator set includes a rate of change indicator, a volatility indicator, an abnormal deviation indicator, a sampling time indicator, and a sensor weight indicator; calling a context state of a drilling operation state, and configuring a 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, wherein: The performing cluster compression based on the data priority label and the data association according to the active compression instruction includes: Using the data priority tag to perform priority screening and identify low-priority data; performing priority similarity clustering of the low-priority data to generate a first clustering constraint; Performing data association clustering of the low-priority data to establish a second clustering constraint, wherein the data association 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 cluster compression.

4. The pointer data buffering modulation and demodulation method for oil drilling according to claim 3, characterized in that: The selecting of data according to the intersection recognition result to complete clustering compression includes: Using 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 differential compression is performed using the true touch center point.

5. The pointer data buffering modulation and demodulation method for oil drilling according to claim 1, wherein: 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 attributes of the dequeue order, including FSK, BPSK, UART, and ASK.

6. The pointer data buffering modulation and demodulation method for oil drilling according to claim 1, wherein: After performing modulation and demodulation management 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.

7. The pointer data buffering modulation and demodulation method for oil drilling according to 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 based on 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.

8. A pointer data buffering modulation and demodulation system for oil drilling, characterized in that: A system for implementing the pointer data buffering modulation and demodulation method for oil drilling according to any one of claims 1 to 7, comprising: A buffer pool construction module is used to construct a pointer data buffer pool, wherein the pointer data buffer pool is a nonlinear 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 the data quality analyzer when any data stream enters the pointer data buffer pool, and to establish a data priority label; An order generation module is used to obtain 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; A state prediction module, configured to perform state prediction of the pointer data buffer pool, establish a prediction result, and generate an active compression instruction if the prediction result meets a preset proportion threshold; A cluster compression module, configured to perform 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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