Collected and transmitted data contrastive analysis method based on parameter real-time monitoring

Abstract

The invention relates to the field of acquisition and transmission data analysis, in particular to an acquisition and transmission data comparative analysis method based on parameter real-time monitoring. The technical problems that in the prior art, most of collected and transmitted data comparative analysis methods depend on manual data analysis, lack intelligent data processing and exception recognition capabilities, lack advanced algorithm support, incomplete data models and limitation of system architecture, and consequently complex data analysis and processing tasks are difficult to complete automatically are solved; according to the technical scheme, the acquisition and transmission data comparative analysis method based on parameter real-time monitoring comprises the steps that a parameter threshold value and a standard range are set, comparative analysis is conducted on real-time data and historical data, and abnormal points and trend changes in the data are recognized; according to the method, the data is sorted and sorted according to the time sequence, the real-time data and the historical data are compared and analyzed, the influence of interference factors on the data is eliminated through an algorithm model, and the identified abnormal data and trend are analyzed to realize automatic analysis of the data.

Description

Technical Field

[0001] This invention relates to the field of mining and transportation data analysis, and in particular to a method for comparative analysis of mining and transportation data based on real-time parameter monitoring. Background Technology

[0002] The comparative analysis of production and transportation data is a widely used technique in oil and gas field production management. It involves comparing and analyzing real-time data from key components such as oil, gas, and water wells, gathering and transportation networks, and production stations to reveal anomalies, performance changes, and potential problems during production. Existing comparative analysis methods primarily rely on traditional data acquisition, transmission, and processing technologies. This approach typically involves periodically or irregularly collecting data from key nodes such as oil, gas, and water wells, key equipment, gathering and transportation networks, and production stations, and then using computer systems for data storage, organization, and analysis. During the data analysis phase, statistical methods and simple data processing algorithms are mainly used to compare the collected data to identify abnormal changes or potential problems in the production process. However, current comparative analysis methods largely rely on manual data analysis, lacking intelligent data processing and anomaly identification capabilities. They also suffer from a lack of advanced algorithm support, incomplete data models, and limitations in system architecture, making it difficult to automatically complete complex data analysis and processing tasks. Summary of the Invention To overcome the problem that existing data comparison and analysis methods mostly rely on manual data analysis, lack intelligent data processing and anomaly identification capabilities, lack advanced algorithm support, have imperfect data models, and have limitations in system architecture, making it difficult to automatically complete complex data analysis and processing tasks.

[0003] The technical solution of this invention is: a method for comparative analysis of data acquisition and transmission based on real-time parameter monitoring. S1: Utilize smart terminals and parameter sensing devices to set up data collection points at key locations for real-time data collection; S2: Preprocess the collected raw data at the edge computing node; S3: Perform preliminary analysis on the preprocessed data and extract key information; S4: Employs data encryption and verification mechanisms to transmit preprocessed data to the cloud processing platform in real time; S5: Establish a database on the cloud processing platform to store collected historical and real-time data; classify, index, and back up the data; S6: Perform timestamp processing on the received data, and sort and organize the data according to the time series; S7: Set parameter thresholds and standard ranges, compare and analyze real-time data with historical data, identify outliers and trend changes in the data, and make preliminary judgments; S8: Analyze the interfering factors that may affect the accuracy of the data, and eliminate the impact of the interfering factors on the data through algorithmic models; S9: Analyze the identified abnormal data and trends, combine information such as equipment status and operation records to troubleshoot faults, and generate fault reports; S10: Extract key information fragments related to the gathering and transmission pipeline network from the data, further analyze and process these information fragments, and extract useful information; S11: When a serious anomaly or potential fault is detected, an emergency alarm mechanism is triggered, and the alarm information is pushed to relevant personnel or systems in real time.

[0004] Using smart terminals and parameter sensing devices, data collection points are set up at key locations to collect data in real time; the collected raw data is preprocessed at edge computing nodes; the preprocessed data is then preliminarily analyzed to extract key information; the specific steps are as follows: S201: Deploy smart terminals and parameter sensing devices at key locations where data collection is required; S202: Configure the sampling frequency and data transmission protocol parameters of the smart terminal; S203: The smart terminal reads data from the sensor device through its built-in data acquisition module, based on preset sampling frequency and parameters; S204: Record the timestamp information of the data and perform CRC data verification on the collected data; S205: The collected data is transmitted to the edge computing node wirelessly; S206: Edge computing nodes receive raw data transmitted from smart terminals; S207: Perform secondary verification on the received data; S208: Remove duplicate data from the data; check and delete duplicate records. S209: For missing data, use mean imputation and interpolation methods to handle missing values; S210: Use moving average and exponential smoothing algorithms to remove random noise from the data; S211: Wavelet transform and median filtering are used to remove noise components from the signal; S212: Convert the collected data into a unified data format, including data type conversion and timestamp standardization; S213: Perform statistical analysis on the preprocessed data, calculating the mean, standard deviation, maximum value, and minimum value. S214: Extract key information features from the preprocessed data.

[0005] Data encryption and verification mechanisms are employed to transmit preprocessed data to the cloud processing platform in real time, ensuring the security and reliability of data transmission. Specifically, this includes the following steps: S301: Initialize the transmission environment and check the network connection status to ensure smooth network operation; S302: Configure data transmission protocol; S303: Initialize the data encryption key using AES encryption algorithm and CRC check algorithm; S304: Encapsulate the preprocessed data according to the protocol specifications; S305: Encrypt the encapsulated data using a data encryption algorithm; S306: Use the selected network protocol to send the encrypted data packet to the specified port of the cloud processing platform; S307: Monitor the sending process and record the sending status; S308: Wait for the cloud processing platform to receive confirmation; if no confirmation is received or the confirmation fails, resend the data according to the preset retry policy; S309: Records key information during data transmission, including transmission time, data volume, and transmission status.

[0006] Establish a database on the cloud processing platform to store the collected historical and real-time data; classify, index, and back up the data; the specific steps are as follows: S401: Create a database on a cloud processing platform, including table structure and indexes; S402: Configure database connection parameters; S403: Listens on the specified port and receives encrypted data packets sent from the transmission; S404: Use a decryption algorithm to decrypt the data packet and recover the original data; S405: Classify data according to data type, device identifier, timestamp, and other information, and create an index for the data; S406: Store the categorized and indexed data into the corresponding tables in the database and monitor the stored procedures; S407: Perform data backup operations regularly to back up important data in the database to a safe location; S408: Records key information during the data storage process, including storage time, data volume, and storage status.

[0007] The received data is timestamped to ensure consistency over time. The data is then sorted and organized according to the time series. The specific steps are as follows: S501: Create an empty list of timestamps and an empty list of data points; S502: Extract the timestamp for each element in the received dataset, and extract the timestamp information from the current element; S503: Check if the extracted timestamp format is consistent with the target format; if not, use a date and time conversion function to convert it to a unified format. S504: Add the converted timestamp to the timestamp list and add the corresponding data to the timestamp list; if the dataset is already in key-value pair format, store the key-value pairs directly. S505: Update the dataset by rearranging the elements in the data point list according to the sorted timestamp list to maintain the correspondence between timestamps and data. S506: Traverse the sorted timestamp list and check if there are any time intervals greater than a preset threshold; if time is missing, use linear interpolation to estimate the missing data points and mark the missing points; S507: Iterate through the timestamp list and check for duplicate timestamps; if duplicate timestamps are found, choose to keep the first or last data point, or merge the values ​​of duplicate data points and take the average. S508: Update the dataset to reflect any changes made based on the results of time missing and duplicate checks.

[0008] Set parameter thresholds and standard ranges, compare and analyze real-time data with historical data, identify outliers and trend changes in the data, and make preliminary judgments; the specific steps are as follows: S601: Analyze historical data to understand the normal fluctuation range, seasonal changes, and outlier frequency characteristics of each parameter; S602: Based on the statistical analysis of historical data, set reasonable upper and lower threshold values ​​for each monitoring parameter; S603: Based on the stable range of historical data, set the normal fluctuation range, i.e., the standard range, for each parameter; S604: Sort the real-time dataset and the historical dataset in chronological order to ensure that the analysis is performed in chronological order; S605: Use an iterator to traverse the sorted dataset and obtain real-time data and corresponding historical data at each time point; S606: Compare real-time data with historical data; check whether the real-time data exceeds the set threshold. If the real-time data exceeds the threshold, record the anomaly, including timestamp, outlier, type of exceeding threshold, etc. S607: Check whether the real-time data deviates from the standard range and further assess the degree of anomaly; S608: Apply ARIMA time series analysis technology to perform trend analysis on real-time and historical data; S609: Calculates trend lines or forecasts for comparison with real-time data; S610: Compare real-time data with trend lines or forecasts to identify data points that deviate significantly from the trend; S611: Based on the degree and duration of the deviation, make a preliminary judgment as to whether these deviations are potential anomalies or normal fluctuations; S612: Mark the identified trend changes and record relevant information, including timestamps, type of change, and degree of impact.

[0009] Analyze external factors that may affect data accuracy, such as environmental factors and equipment malfunctions, and use algorithmic models to eliminate the influence of these interfering factors on the data; the specific steps are as follows: S701: Collect external environmental data that may be related to the monitoring parameters; S702: Obtain current status information of equipment from channels such as equipment management system, fault logs, and maintenance records; S703: Analyze which factors in external environmental data and equipment status data may affect the accuracy of monitoring parameters; S704: Screen out key factors that significantly affect monitoring parameters from potential interference factors through correlation analysis; S705: Analyze the nature of each interfering factor, including whether it belongs to a continuous or categorical variable, and the potential relationship between the interfering factor and the monitoring parameter, i.e., linear, nonlinear, or periodic. S706: Based on the linear relationship between interference factors and monitoring parameters, a linear regression model is selected; S707: Feature selection is performed using feature importance assessment and correlation analysis methods to select features from the data that have a significant impact on the model's predictive performance, i.e., confounding factors. S708: Use the training set data to build a quantitative evaluation model and set the model parameters; S709: Train the model using training set data to learn the mapping relationship between interfering factors and monitoring parameters; monitor the model's loss function and accuracy metrics; S710: Use cross-validation to divide the training set data into multiple subsets and use each subset as a validation set in turn to evaluate the model's performance. S711: Based on the results of cross-validation, optimize the model by adjusting model parameters and changing model structure to improve the model's predictive performance. S712: Input real-time or new external environment data and device status data into the trained model; S713: The model calculates the degree of influence of interfering factors on monitoring parameters based on the input real-time data and outputs quantitative evaluation results. These results can be specific numerical values, i.e., interference intensity, probability values, i.e., the likelihood of interference occurring, or classification labels such as normal, slight interference, and severe interference; S714: Based on the interference assessment results, formulate corresponding adjustment strategies, including directly correcting the monitoring parameter values, weighted processing, and filtering processing; S715: Adjust the monitoring parameter values ​​accordingly based on the established adjustment strategy to ensure that the adjusted parameter values ​​meet the specifications.

[0010] The identified abnormal data and trends are analyzed, and fault diagnosis is performed by combining information such as equipment status and operation records, and a fault report is generated; the specific steps are as follows: S801: Filter out different types of abnormal data from the abnormal dataset and classify them according to the abnormality type of the monitored parameters, such as exceeding the upper limit, lower limit, or fluctuation outside the standard range. S802: Assign a unique identifier to each type of abnormal data and collect key information such as the time, frequency, and duration of its occurrence; S803: Use charts to visualize anomalous data, and combine the visualization results to analyze the patterns and possible correlations of the anomalous data; S804: Perform correlation analysis between abnormal data and information such as external environment data, equipment status data, and operation records; S805: Uses fault tree analysis to decompose faults from the top-level system-level faults down to the bottom-level basic events. S806: Based on the results of abnormal data analysis, add possible causes of failure to the fault tree and determine the logical relationships between them; S807: For complex fault situations, use cause-effect graphs to analyze the root causes of the fault; take abnormal data as the result, list all possible causes and group them, and analyze the interactions and influences between the factors. S808: Based on the analysis results of the fault tree and cause-effect diagram, determine the specific location, nature, and scope of impact of the fault; S809: Write a fault description, describing the fault phenomenon in detail and explaining the impact of the fault on the production process; S810: Accurately record the specific location, nature, and scope of impact of the fault, and attach a fault tree and cause-effect diagram; S811: Provide specific handling suggestions based on the possible causes of the fault; S812: Output fault report.

[0011] The key information fragments related to the gathering and transportation pipeline network are extracted from the data, and these fragments are further analyzed and processed to extract useful information; the specific steps are as follows: S901: Determine the time range for data extraction based on the analysis requirements; S902: Use a preset keyword list to filter the data to quickly locate data segments related to the gathering and transmission network; S903: Define rules to match data segments of a specific format or content based on the structure of the data, the range of data values, or the trend of data change. S904: For time series data, techniques such as sliding window and time series pattern matching are used to identify key information segments; S905: Extract specific parameter values ​​from key information segments; S906: Collect contextual information related to key information fragments; S907: Aggregate key information fragments that have similar characteristics or belong to the same time period; S908: Verify the extracted information by comparing it with historical data and checking it against real-time data; S909: Format the extracted useful information into tables, charts, or reports; S910: Stores the processed data and transmits it to the subsequent alarm system.

[0012] When a serious anomaly or potential malfunction is detected, an emergency alarm mechanism is triggered, pushing alarm information to relevant personnel or systems in real time; the specific steps are as follows: S1001: Assess the severity of the anomaly based on factors such as the nature of the abnormal data in the fault report, the degree of deviation from the normal range, and the duration; and decide whether to trigger the emergency alarm mechanism based on the assessment and analysis results. S1002: When an alarm is triggered, an alarm signal containing abnormal information, severity, and potential fault analysis is generated. S1004: Set priority for alarm signals based on the severity and urgency of the anomaly; S1005: Organize alarm information into a clear format, and prepare alarm information formats suitable for different channels according to different recipients; S1006: Select an appropriate push channel according to the preset alarm notification strategy, and push the alarm information to relevant personnel or systems in real time through the selected channel; S1007: Record alarm information, response status, and processing results.

[0013] The beneficial effects of this invention are: 1. Compared to existing data comparison and analysis methods, which mostly rely on manual data analysis and lack intelligent data processing and anomaly identification capabilities, as well as advanced algorithm support, incomplete data models, and limitations in system architecture, making it difficult to automatically complete complex data analysis and processing tasks, this method sorts and organizes data according to time series, compares and analyzes real-time data with historical data, eliminates the influence of interference factors on the data through algorithm models, and analyzes the identified abnormal data and trends to achieve automated data analysis and processing. 2. Data collection points are set up at key locations using smart terminals and parameter sensing devices for real-time data acquisition; the raw data is preprocessed at edge computing nodes; preliminary analysis is performed on the preprocessed data to extract key information; data encryption and verification mechanisms are used to transmit the preprocessed data to the cloud processing platform in real time; a database is established on the cloud processing platform to store historical and real-time data; the data is classified, indexed, and backed up; the received data is timestamped and sorted and organized according to time series; parameter thresholds and standard ranges are set to compare and analyze real-time and historical data, identify anomalies and trend changes, and make preliminary judgments; interference factors that may affect data accuracy are analyzed, and the impact of interference factors on the data is eliminated through algorithm models; the identified abnormal data and trends are analyzed; thus, the analysis of identified abnormal data and trends achieves automated data analysis and processing. 3. Through single-point automatic data acquisition and real-time parameter monitoring technology, high-frequency, low-latency data acquisition and transmission are achieved; combined with field edge computing technology, preliminary data processing and analysis are performed at edge nodes to reduce data transmission latency and bandwidth usage. Attached Figure Description

[0014] Figure 1 The diagram shown is a schematic flowchart of the data acquisition and transmission comparison and analysis method based on real-time parameter monitoring of the present invention. Figure 2 The diagram illustrates the abnormal data analysis and fault report generation process of the data comparison and analysis method based on real-time parameter monitoring of the present invention. Detailed Implementation

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] Please see Figure 1-2 This invention provides an embodiment: a method for comparative analysis of data acquisition and transmission based on real-time parameter monitoring. S1: Utilize smart terminals and parameter sensing devices to set up data collection points at key locations for real-time data collection; S2: Preprocess the collected raw data at the edge computing node; S3: Perform preliminary analysis on the preprocessed data and extract key information; S4: Employs data encryption and verification mechanisms to transmit preprocessed data to the cloud processing platform in real time; S5: Establish a database on the cloud processing platform to store collected historical and real-time data; classify, index, and back up the data; S6: Perform timestamp processing on the received data, and sort and organize the data according to the time series; S7: Set parameter thresholds and standard ranges, compare and analyze real-time data with historical data, identify outliers and trend changes in the data, and make preliminary judgments; S8: Analyze the interfering factors that may affect the accuracy of the data, and eliminate the impact of the interfering factors on the data through algorithmic models; S9: Analyze the identified abnormal data and trends, combine information such as equipment status and operation records to troubleshoot faults, and generate fault reports; S10: Extract key information fragments related to the gathering and transmission pipeline network from the data, further analyze and process these information fragments, and extract useful information; S11: When a serious anomaly or potential fault is detected, an emergency alarm mechanism is triggered, and the alarm information is pushed to relevant personnel or systems in real time.

[0017] Using smart terminals and parameter sensing devices, data collection points are set up at key locations to collect data in real time; the collected raw data is preprocessed at edge computing nodes; the preprocessed data is then preliminarily analyzed to extract key information; the specific steps are as follows: S201: Deploy smart terminals and parameter sensing devices at key locations where data collection is required; S202: Configure the sampling frequency and data transmission protocol parameters of the smart terminal; S203: The smart terminal reads data from the sensor device through its built-in data acquisition module, based on preset sampling frequency and parameters; S204: Record the timestamp information of the data and perform CRC data verification on the collected data; S205: The collected data is transmitted to the edge computing node wirelessly; S206: Edge computing nodes receive raw data transmitted from smart terminals; S207: Perform secondary verification on the received data; S208: Remove duplicate data from the data; check and delete duplicate records. S209: For missing data, use mean imputation and interpolation methods to handle missing values; S210: Use moving average and exponential smoothing algorithms to remove random noise from the data; S211: Wavelet transform and median filtering are used to remove noise components from the signal; S212: Convert the collected data into a unified data format, including data type conversion and timestamp standardization; S213: Perform statistical analysis on the preprocessed data, calculating the mean, standard deviation, maximum value, and minimum value. S214: Extract key information features from the preprocessed data.

[0018] Data encryption and verification mechanisms are employed to transmit preprocessed data to the cloud processing platform in real time, ensuring the security and reliability of data transmission. Specifically, this includes the following steps: S301: Initialize the transmission environment and check the network connection status to ensure smooth network operation; S302: Configure data transmission protocol; S303: Initialize the data encryption key using AES encryption algorithm and CRC check algorithm; S304: Encapsulate the preprocessed data according to the protocol specifications; S305: Encrypt the encapsulated data using a data encryption algorithm; S306: Use the selected network protocol to send the encrypted data packet to the specified port of the cloud processing platform; S307: Monitor the sending process and record the sending status; S308: Wait for the cloud processing platform to receive confirmation; if no confirmation is received or the confirmation fails, resend the data according to the preset retry policy; S309: Records key information during data transmission, including transmission time, data volume, and transmission status.

[0019] Establish a database on the cloud processing platform to store the collected historical and real-time data; classify, index, and back up the data; the specific steps are as follows: S401: Create a database on a cloud processing platform, including table structure and indexes; S402: Configure database connection parameters; S403: Listens on the specified port and receives encrypted data packets sent from the transmission; S404: Use a decryption algorithm to decrypt the data packet and recover the original data; S405: Classify data according to data type, device identifier, timestamp, and other information, and create an index for the data; S406: Store the categorized and indexed data into the corresponding tables in the database and monitor the stored procedures; S407: Perform data backup operations regularly to back up important data in the database to a safe location; S408: Records key information during the data storage process, including storage time, data volume, and storage status.

[0020] The received data is timestamped to ensure consistency over time. The data is then sorted and organized according to the time series. The specific steps are as follows: S501: Create an empty list of timestamps and an empty list of data points; S502: Extract the timestamp for each element in the received dataset, and extract the timestamp information from the current element; S503: Check if the extracted timestamp format is consistent with the target format; if not, use a date and time conversion function to convert it to a unified format. S504: Add the converted timestamp to the timestamp list and add the corresponding data to the timestamp list; if the dataset is already in key-value pair format, store the key-value pairs directly. S505: Update the dataset by rearranging the elements in the data point list according to the sorted timestamp list to maintain the correspondence between timestamps and data. S506: Traverse the sorted timestamp list and check if there are any time intervals greater than a preset threshold; if time is missing, use linear interpolation to estimate the missing data points and mark the missing points; S507: Iterate through the timestamp list and check for duplicate timestamps; if duplicate timestamps are found, choose to keep the first or last data point, or merge the values ​​of duplicate data points and take the average. S508: Update the dataset to reflect any changes made based on the results of time missing and duplicate checks.

[0021] Set parameter thresholds and standard ranges, compare and analyze real-time data with historical data, identify outliers and trend changes in the data, and make preliminary judgments; the specific steps are as follows: S601: Analyze historical data to understand the normal fluctuation range, seasonal changes, and outlier frequency characteristics of each parameter; S602: Based on the statistical analysis of historical data, set reasonable upper and lower threshold values ​​for each monitoring parameter; S603: Based on the stable range of historical data, set the normal fluctuation range, i.e., the standard range, for each parameter; S604: Sort the real-time dataset and the historical dataset in chronological order to ensure that the analysis is performed in chronological order; S605: Use an iterator to traverse the sorted dataset and obtain real-time data and corresponding historical data at each time point; S606: Compare real-time data with historical data; check whether the real-time data exceeds the set threshold. If the real-time data exceeds the threshold, record the anomaly, including timestamp, outlier, type of exceeding threshold, etc. S607: Check whether the real-time data deviates from the standard range and further assess the degree of anomaly; S608: Apply ARIMA time series analysis technology to perform trend analysis on real-time and historical data; S609: Calculates trend lines or forecasts for comparison with real-time data; S610: Compare real-time data with trend lines or forecasts to identify data points that deviate significantly from the trend; S611: Based on the degree and duration of the deviation, make a preliminary judgment as to whether these deviations are potential anomalies or normal fluctuations; S612: Mark the identified trend changes and record relevant information, including timestamps, type of change, and degree of impact.

[0022] Analyze external factors that may affect data accuracy, such as environmental factors and equipment malfunctions, and use algorithmic models to eliminate the influence of these interfering factors on the data; the specific steps are as follows: S701: Collect external environmental data that may be related to the monitoring parameters; S702: Obtain current status information of equipment from channels such as equipment management system, fault logs, and maintenance records; S703: Analyze which factors in external environmental data and equipment status data may affect the accuracy of monitoring parameters; S704: Screen out key factors that significantly affect monitoring parameters from potential interference factors through correlation analysis; S705: Analyze the nature of each interfering factor, including whether it belongs to a continuous or categorical variable, and the potential relationship between the interfering factor and the monitoring parameter, i.e., linear, nonlinear, or periodic. S706: Based on the linear relationship between interference factors and monitoring parameters, a linear regression model is selected; S707: Feature selection is performed using feature importance assessment and correlation analysis methods to select features from the data that have a significant impact on the model's predictive performance, i.e., confounding factors. S708: Use the training set data to build a quantitative evaluation model and set the model parameters; S709: Train the model using training set data to learn the mapping relationship between interfering factors and monitoring parameters; monitor the model's loss function and accuracy metrics; S710: Use cross-validation to divide the training set data into multiple subsets and use each subset as a validation set in turn to evaluate the model's performance. S711: Based on the results of cross-validation, optimize the model by adjusting model parameters and changing model structure to improve the model's predictive performance. S712: Input real-time or new external environment data and device status data into the trained model; S713: The model calculates the degree of influence of interfering factors on monitoring parameters based on the input real-time data and outputs quantitative evaluation results. These results can be specific numerical values, i.e., interference intensity, probability values, i.e., the likelihood of interference occurring, or classification labels such as normal, slight interference, and severe interference; S714: Based on the interference assessment results, formulate corresponding adjustment strategies, including directly correcting the monitoring parameter values, weighted processing, and filtering processing; S715: Adjust the monitoring parameter values ​​accordingly based on the established adjustment strategy to ensure that the adjusted parameter values ​​meet the specifications.

[0023] The identified abnormal data and trends are analyzed, and fault diagnosis is performed by combining information such as equipment status and operation records, and a fault report is generated; the specific steps are as follows: S801: Filter out different types of abnormal data from the abnormal dataset and classify them according to the abnormality type of the monitored parameters, such as exceeding the upper limit, lower limit, or fluctuation outside the standard range. S802: Assign a unique identifier to each type of abnormal data and collect key information such as the time, frequency, and duration of its occurrence; S803: Use charts to visualize anomalous data, and combine the visualization results to analyze the patterns and possible correlations of the anomalous data; S804: Perform correlation analysis between abnormal data and information such as external environment data, equipment status data, and operation records; S805: Uses fault tree analysis to decompose faults from the top-level system-level faults down to the bottom-level basic events. S806: Based on the results of abnormal data analysis, add possible causes of failure to the fault tree and determine the logical relationships between them; S807: For complex fault situations, use cause-effect graphs to analyze the root causes of the fault; take abnormal data as the result, list all possible causes and group them, and analyze the interactions and influences between the factors. S808: Based on the analysis results of the fault tree and cause-effect diagram, determine the specific location, nature, and scope of impact of the fault; S809: Write a fault description, describing the fault phenomenon in detail and explaining the impact of the fault on the production process; S810: Accurately record the specific location, nature, and scope of impact of the fault, and attach a fault tree and cause-effect diagram; S811: Provide specific handling suggestions based on the possible causes of the fault; S812: Output fault report.

[0024] The key information fragments related to the gathering and transportation pipeline network are extracted from the data, and these fragments are further analyzed and processed to extract useful information; the specific steps are as follows: S901: Determine the time range for data extraction based on the analysis requirements; S902: Use a preset keyword list to filter the data to quickly locate data segments related to the gathering and transmission network; S903: Define rules to match data segments of a specific format or content based on the structure of the data, the range of data values, or the trend of data change. S904: For time series data, techniques such as sliding window and time series pattern matching are used to identify key information segments; S905: Extract specific parameter values ​​from key information segments; S906: Collect contextual information related to key information fragments; S907: Aggregate key information fragments that have similar characteristics or belong to the same time period; S908: Verify the extracted information by comparing it with historical data and checking it against real-time data; S909: Format the extracted useful information into tables, charts, or reports; S910: Stores the processed data and transmits it to the subsequent alarm system.

[0025] When a serious anomaly or potential malfunction is detected, an emergency alarm mechanism is triggered, pushing alarm information to relevant personnel or systems in real time; the specific steps are as follows: S1001: Assess the severity of the anomaly based on factors such as the nature of the abnormal data in the fault report, the degree of deviation from the normal range, and the duration; and decide whether to trigger the emergency alarm mechanism based on the assessment and analysis results. S1002: When an alarm is triggered, an alarm signal containing abnormal information, severity, and potential fault analysis is generated. S1004: Set priority for alarm signals based on the severity and urgency of the anomaly; S1005: Organize alarm information into a clear format, and prepare alarm information formats suitable for different channels according to different recipients; S1006: Select an appropriate push channel according to the preset alarm notification strategy, and push the alarm information to relevant personnel or systems in real time through the selected channel; S1007: Record alarm information, response status, and processing results.

[0026] Example 1 Optionally, during the data acquisition and preprocessing of oil and gas field production and transportation data, intelligent terminals and various parameter sensing devices, such as temperature sensors, pressure sensors, and flow sensors, are deployed at key locations in the oil and gas field, such as oil and gas wells, gathering and transportation stations, and pipeline nodes. These devices can monitor various parameters in the production process in real time. Based on production needs, parameters such as the sampling frequency and data transmission protocol of the intelligent terminals are configured to ensure the accuracy and real-time performance of data acquisition. For high-risk or sensitive areas, a higher sampling frequency is set to capture more details. The data acquisition module built into the intelligent terminal reads data from the sensors according to preset parameters and records timestamp information. The acquired data is transmitted to the edge computing node in real time via LoRa wireless communication technology. After receiving raw data from the smart terminal, the edge computing node first performs secondary verification to ensure the integrity and accuracy of the data. Then, it performs preprocessing steps such as deduplication, missing value handling, and noise removal. Missing data is handled using mean imputation and interpolation methods, and data noise is removed using methods such as moving average, exponential smoothing, wavelet transform, and median filtering to improve data quality. Simultaneously, the data is formatted for easier subsequent analysis. At the edge computing node, preliminary statistical analysis is performed on the preprocessed data, calculating statistics such as mean, standard deviation, maximum, and minimum values ​​to understand the basic distribution of the data. Key information features, such as pressure fluctuation trends and abnormal flow changes, are extracted from the data; these features will be used for subsequent comparative analysis.

[0027] Example 2 Optionally, when conducting anomaly detection and trend analysis of oil, gas and water well monitoring data, monitoring data from the past period can be obtained from the oil, gas and water well monitoring system. Obvious erroneous data points and missing values ​​can be removed to ensure the accuracy and completeness of the data. Statistical quantities such as mean, standard deviation, maximum value, and minimum value of each parameter can be calculated to understand the normal fluctuation range, seasonal changes, and outlier frequency characteristics of the parameters. Based on the statistical analysis of historical data, reasonable upper and lower bound thresholds are set for each monitoring parameter. The mean plus / minus a certain multiple of the standard deviation can be used as the threshold. Based on the stable interval of historical data, a normal fluctuation range (standard range) is set for each parameter. This range should encompass most normal data points while excluding obvious outliers. Real-time and historical datasets are sorted chronologically to ensure the analysis is performed sequentially. An iterator is used to traverse the sorted datasets, acquiring real-time data and corresponding historical data at each time point. The real-time data is compared to see if it exceeds the set threshold. If it does, outlier information is recorded, including timestamp, outlier, and type of threshold exceedance. Further checks are conducted to determine if the real-time data deviates from the standard range, assessing the degree of anomaly. Significant deviations may require further analysis or appropriate measures. The ARIMA time series analysis model is applied to perform trend analysis on the real-time and historical data. Trend lines or predicted values ​​are calculated and compared with the real-time data. Data points that significantly deviate from the trend are identified, and based on the degree and duration of deviation, a preliminary judgment is made as to whether these deviations are potential anomalies or normal fluctuations. Large deviations with long durations are likely anomalies. In oil, gas, and water well monitoring, temperature is a crucial parameter. Historical data analysis reveals that temperatures typically fluctuate between 20°C and 120°C, exhibiting seasonal variations. Therefore, an upper threshold of 130°C and a lower threshold of 10°C can be set, with a standard range of 20°C to 120°C. During real-time monitoring, if a temperature of 135°C is detected at a given time point, it exceeds the upper threshold and is recorded as an anomaly. Furthermore, if the temperature data consistently deviates from the standard range, such as exceeding 120°C at multiple consecutive time points, further analysis may be necessary to determine if equipment malfunctions or production anomalies are present. Trend analysis indicates that a sustained upward trend in temperature data, deviating from the normal fluctuation range of historical data, may indicate potential problems, such as reduced wellbore heat loss or rising formation temperature, requiring attention from production management personnel.

[0028] Example 3 Optionally, when analyzing and eliminating interference factors in oil, gas and water well monitoring data, data such as temperature, humidity, wind speed, wind direction, and rainfall are collected. These data may affect the monitoring parameters of oil, gas and water wells, such as temperature and pressure. Data such as formation pressure and seismic activity may also be collected, which may affect wellbore stability and monitoring data. The operating status, maintenance history, and fault records of oil, gas and water well monitoring equipment are queried. The calibration status, aging degree, and fault alarm information of sensors such as temperature, pressure, and flow are checked. External environmental factors such as extreme weather and geological activity, as well as equipment status such as sensor failure and equipment aging degree, may affect the accuracy of monitored temperature, pressure, and flow parameters. Correlation analysis using Pearson correlation coefficients is used to determine which factors have the most significant impact on monitoring parameters. Determine whether each interfering factor is a continuous variable (e.g., temperature, humidity) or a categorical variable (e.g., equipment status: normal / faulty), and preliminarily assess its potential relationship with monitoring parameters (linear, nonlinear, periodic). Build and train a quantitative evaluation model using training data, monitor the model's loss function and accuracy metrics, apply K-fold cross-validation to evaluate model performance, and adjust the model's learning rate and iteration count, add hidden layers, and adjust the number of neurons to improve predictive performance based on the results. Input real-time or new external environmental data and equipment status data into the trained model. The model calculates the degree of influence of interfering factors on monitoring parameters based on the input data and outputs quantitative evaluation results, such as interference intensity, probability value, or classification label. Based on the interference evaluation results, formulate corresponding adjustment strategies. For data deviations caused by sensor faults, methods such as directly correcting monitoring parameter values, weighted processing, or filtering can be used to adjust the monitoring parameter values ​​accordingly, ensuring that the adjusted parameter values ​​meet specifications and providing a more accurate basis for production management decisions. The system was initially monitoring three key parameters: temperature, pressure, and flow rate. Recently, abnormal fluctuations in the temperature data were detected, which may be related to external environmental conditions and equipment status. To accurately assess and eliminate the influence of these interfering factors, a detailed analysis and model building were performed following the steps outlined above: Step 1: Data Collection 1. External environment data: Temperature: Daily average temperatures over the past month were collected from weather stations, ranging from 10°C to 30°C, showing seasonal variation; Humidity: Humidity data were also collected, but correlation analysis showed that humidity had no significant effect on temperature monitoring, so it was not considered as a confounding factor in this case. 2. Equipment status data: Sensor status: The calibration records, fault alarm information and maintenance history of the temperature sensor were obtained from the equipment management system; it was found that the sensor was calibrated two weeks ago, but there are no recent fault records. Equipment aging level: The aging level of the sensor was assessed by its years of use and maintenance records. It was estimated that the sensor had been used for 5 years and was in the medium aging stage. Step 2: Analysis of Interference Factors 1. Preliminary Analysis Ambient temperature: It is preliminarily determined that ambient temperature is an important factor affecting temperature monitoring parameters, especially under extreme weather conditions; Sensor aging: Sensor aging may cause reading deviations. Although the sensor has been recently calibrated, the degree of aging may still affect the accuracy of the data. 2. Correlation analysis: Correlation analysis was performed on ambient temperature and temperature monitoring data using Pearson correlation coefficient, and the results showed a significant positive correlation between the two (r>0.7). Step 3: Model Building and Training 1. Model selection: Given the clear linear relationship between ambient temperature and temperature monitoring data, a linear regression model was chosen as the basic model. To handle the nonlinear factor of sensor aging, a quadratic term was added to the model or a simple layer of a neural network model was used to capture its nonlinear effects. 2. Feature selection and training: Select ambient temperature and sensor aging (quantified by years of use) as input features for the model; use historical data from the past year as the training set, including temperature monitoring data, ambient temperature data, and corresponding sensor usage years for days with no fault records; train the model and monitor its mean squared error of loss function (MSE) and adjust the model parameters until satisfactory performance is achieved; Step 3: Real-time data evaluation and adjustment: 1. Real-time data input: The system acquires real-time ambient temperature and sensor status information and inputs this data into the trained model. 2. Interference assessment results: The model outputs the interference assessment results of the current temperature monitoring data, including the deviation caused by the ambient temperature and the additional deviation caused by sensor aging; the model outputs: "The current temperature monitoring value is 2°C higher due to the influence of the ambient temperature, and an additional 0.5°C higher due to sensor aging." 3. Based on the assessment results, formulate adjustment strategies. In this example, since the influence of ambient temperature is predictable and usually significant, we choose to directly correct the temperature monitoring value to eliminate its influence. For deviations caused by sensor aging, considering that the sensor has been calibrated and its aging level is moderate, we adopt weighted processing or periodic sensor replacement methods. We make corresponding adjustments to the temperature monitoring value to ensure the accuracy and reliability of the data, providing support for subsequent production management decisions.

[0029] Example 4 Optionally, when conducting anomaly analysis and troubleshooting for decreased efficiency of oil, gas, and water well pumps, the following steps may be taken: collecting historical and real-time data on key parameters such as pump efficiency, flow rate, inlet and outlet pressure, motor current, and vibration; obtaining information such as pump operating time, most recent maintenance record, and sensor status; reviewing recent operation logs, including pump start-up and shutdown records and parameter adjustment operations; and cleaning and standardizing the collected data to ensure its accuracy and consistency. By comparing and analyzing historical and real-time data, it was found that the pump efficiency value had been below the normal range for several consecutive days, with a significant downward trend. Efficiency decline was identified as the primary anomaly, and key information such as the time, magnitude, and duration of its occurrence was recorded. A line graph was used to display the pump efficiency trend over time, clearly showing the decrease in efficiency value. Simultaneously, comparing changes in parameters such as flow rate, pressure, and motor current revealed a slight decrease in flow rate, while pressure remained stable and motor current increased. Combining equipment status data and operation records, it was found that the pump efficiency decline was related to the pump's operating time (approaching its design life), the increase in motor current (potentially indicating increased motor load or reduced efficiency), and recent frequent start-stop operations. Starting from the top-level fault of "pump efficiency decline," possible causes were identified, such as "accelerated pump wear," "reduced motor efficiency," and "control system malfunction." Further analysis attributed "accelerated pump wear" to "prolonged operation" and "medium corrosion," and "reduced motor efficiency" to "motor aging" and "increased load." A cause-and-effect diagram was drawn, with the decrease in pump efficiency as the result, listing all possible causes, including "pump design life expired," "corrosive substances in the medium," "motor aging," and "control system malfunction." By analyzing the interactions and effects between the factors, "pump design life expired" and "motor aging" were identified as the main causes. Fault confirmation was conducted, confirming severe wear of internal pump parts, especially the impeller and bearings, and a decrease in the insulation performance of the motor. Write a fault report, describing in detail the phenomenon of pump efficiency decline, including the time of occurrence, the scope of impact, and the specific impact on the production process. Summarize the results of correlation analysis and cause-effect graph analysis, and clearly point out that pump wear and motor aging are the main causes of efficiency decline. Propose handling suggestions such as replacing the pump and motor, strengthening equipment maintenance, and optimizing operating procedures. Compile the fault description, analysis, and handling suggestions into a complete fault report and output it.

[0030] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for comparative analysis of data acquisition and transportation based on real-time parameter monitoring; characterized in that: S1: Utilize smart terminals and parameter sensing devices to set up data collection points at key locations for real-time data collection; S2: Preprocess the collected raw data at the edge computing node; S3: Perform preliminary analysis on the preprocessed data and extract key information; S4: Employs data encryption and verification mechanisms to transmit preprocessed data to the cloud processing platform in real time; S5: Establish a database on the cloud processing platform to store collected historical and real-time data; classify, index, and back up the data; S6: Perform timestamp processing on the received data, and sort and organize the data according to the time series; S7: Set parameter thresholds and standard ranges, compare and analyze real-time data with historical data, identify outliers and trend changes in the data, and make preliminary judgments; S8: Analyze the interfering factors that may affect the accuracy of the data, and eliminate the impact of the interfering factors on the data through algorithmic models; S9: Analyze the identified abnormal data and trends, combine information such as equipment status and operation records to troubleshoot faults, and generate fault reports; S10: Extract key information fragments related to the gathering and transmission pipeline network from the data, further analyze and process these information fragments, and extract useful information; S11: When a serious anomaly or potential fault is detected, an emergency alarm mechanism is triggered, and the alarm information is pushed to relevant personnel or systems in real time.

2. The method for comparative analysis of data acquisition and transmission based on real-time parameter monitoring according to claim 1, characterized in that: Using smart terminals and parameter sensing devices, data collection points are set up at key locations to collect data in real time; the collected raw data is preprocessed at edge computing nodes; the preprocessed data is then preliminarily analyzed to extract key information; the specific steps are as follows: S201: Deploy smart terminals and parameter sensing devices at key locations where data collection is required; S202: Configure the sampling frequency and data transmission protocol parameters of the smart terminal; S203: The smart terminal reads data from the sensor device through its built-in data acquisition module, based on preset sampling frequency and parameters; S204: Record the timestamp information of the data and perform CRC data verification on the collected data; S205: The collected data is transmitted to the edge computing node wirelessly; S206: Edge computing nodes receive raw data transmitted from smart terminals; S207: Perform secondary verification on the received data; S208: Remove duplicate data from the data; check and delete duplicate records. S209: For missing data, use mean imputation and interpolation methods to handle missing values; S210: Use moving average and exponential smoothing algorithms to remove random noise from the data; S211: Wavelet transform and median filtering are used to remove noise components from the signal; S212: Convert the collected data into a unified data format, including data type conversion and timestamp standardization; S213: Perform statistical analysis on the preprocessed data, calculating the mean, standard deviation, maximum value, and minimum value. S214: Extract key information features from the preprocessed data.

3. The method for comparative analysis of data acquisition and transmission based on real-time parameter monitoring according to claim 2, characterized in that: Data encryption and verification mechanisms are used to transmit preprocessed data to the cloud processing platform in real time, ensuring the security and reliability of data transmission. Specifically, the following steps are included: S301: Initialize the transmission environment and check the network connection status to ensure smooth network operation; S302: Configure data transmission protocol; S303: Initialize the data encryption key using AES encryption algorithm and CRC check algorithm; S304: Encapsulate the preprocessed data according to the protocol specifications; S305: Encrypt the encapsulated data using a data encryption algorithm; S306: Use the selected network protocol to send the encrypted data packet to the specified port of the cloud processing platform; S307: Monitor the sending process and record the sending status; S308: Wait for the cloud processing platform to receive confirmation; if no confirmation is received or the confirmation fails, resend the data according to the preset retry policy; S309: Records key information during data transmission, including transmission time, data volume, and transmission status.

4. The data comparison and analysis method based on real-time parameter monitoring according to claim 3, characterized in that: Establish a database on the cloud processing platform to store the collected historical and real-time data; classify, index, and back up the data; the specific steps are as follows: S401: Create a database on a cloud processing platform, including table structure and indexes; S402: Configure database connection parameters; S403: Listens on the specified port and receives encrypted data packets sent from the transmission; S404: Use a decryption algorithm to decrypt the data packet and recover the original data; S405: Classify data according to data type, device identifier, timestamp, and other information, and create an index for the data; S406: Store the categorized and indexed data into the corresponding tables in the database and monitor the stored procedures; S407: Perform data backup operations regularly to back up important data in the database to a safe location; S408: Records key information during the data storage process, including storage time, data volume, and storage status.

5. The data comparison and analysis method based on real-time parameter monitoring according to claim 4, characterized in that: The received data is timestamped to ensure consistency over time. The data is then sorted and organized according to the time series. The specific steps are as follows: S501: Create an empty list of timestamps and an empty list of data points; S502: Extract the timestamp for each element in the received dataset, and extract the timestamp information from the current element; S503: Check if the extracted timestamp format is consistent with the target format; If they are inconsistent, use date and time conversion functions to convert them to a uniform format; S504: Add the converted timestamp to the timestamp list and add the corresponding data to the timestamp list; If the dataset is already in key-value pair format, then store the key-value pairs directly. S505: Update the dataset by rearranging the elements in the data point list according to the sorted timestamp list to maintain the correspondence between timestamps and data. S506: Traverse the sorted timestamp list and check if there are any time intervals greater than a preset threshold. If time gaps are found, use linear interpolation to estimate the missing data points and mark the missing points; S507: Iterate through the timestamp list and check for duplicate timestamps; If duplicate timestamps are found, choose to keep the first or last data point, or merge the values ​​of duplicate data points and take the average. S508: Update the dataset to reflect any changes made based on the results of time missing and duplicate checks.

6. The data comparison and analysis method based on real-time parameter monitoring according to claim 5, characterized in that: Set parameter thresholds and standard ranges, compare and analyze real-time data with historical data, identify outliers and trend changes in the data, and make preliminary judgments; the specific steps are as follows: S601: Analyze historical data to understand the normal fluctuation range, seasonal changes, and outlier frequency characteristics of each parameter; S602: Based on the statistical analysis of historical data, set reasonable upper and lower threshold values ​​for each monitoring parameter; S603: Based on the stable range of historical data, set the normal fluctuation range, i.e., the standard range, for each parameter; S604: Sort the real-time dataset and the historical dataset in chronological order to ensure that the analysis is performed in chronological order; S605: Use an iterator to traverse the sorted dataset and obtain real-time data and corresponding historical data at each time point; S606: Compare real-time data with historical data; check whether the real-time data exceeds the set threshold. If the real-time data exceeds the threshold, record the anomaly, including timestamp, outlier, type of exceeding threshold, etc. S607: Check whether the real-time data deviates from the standard range and further assess the degree of anomaly; S608: Apply ARIMA time series analysis technology to perform trend analysis on real-time and historical data; S609: Calculates trend lines or forecasts for comparison with real-time data; S610: Compare real-time data with trend lines or forecasts to identify data points that deviate significantly from the trend; S611: Based on the degree and duration of the deviation, make a preliminary judgment as to whether these deviations are potential anomalies or normal fluctuations; S612: Mark the identified trend changes and record relevant information, including timestamps, type of change, and degree of impact.

7. The method for comparative analysis of data acquisition and transmission based on real-time parameter monitoring according to claim 6, characterized in that: Analyze external factors that may affect data accuracy, such as environmental factors and equipment malfunctions, and use algorithmic models to eliminate the influence of these interfering factors on the data; the specific steps are as follows: S701: Collect external environmental data that may be related to the monitoring parameters; S702: Obtain current status information of equipment from channels such as equipment management system, fault logs, and maintenance records; S703: Analyze which factors in external environmental data and equipment status data may affect the accuracy of monitoring parameters; S704: Screen out key factors that significantly affect monitoring parameters from potential interference factors through correlation analysis; S705: Analyze the nature of each interfering factor, including whether it belongs to a continuous or categorical variable, and the potential relationship between the interfering factor and the monitoring parameter, i.e., linear, nonlinear, or periodic. S706: Based on the linear relationship between interference factors and monitoring parameters, a linear regression model is selected; S707: Feature selection is performed using feature importance assessment and correlation analysis methods to select features from the data that have a significant impact on the model's predictive performance, i.e., confounding factors. S708: Use the training set data to build a quantitative evaluation model and set the model parameters; S709: Train the model using training set data to learn the mapping relationship between interfering factors and monitoring parameters; monitor the model's loss function and accuracy metrics; S710: Use cross-validation to divide the training set data into multiple subsets and use each subset as a validation set in turn to evaluate the model's performance. S711: Based on the results of cross-validation, optimize the model by adjusting model parameters and changing model structure to improve the model's predictive performance. S712: Input real-time or new external environment data and device status data into the trained model; S713: The model calculates the degree of influence of interference factors on monitoring parameters based on the input real-time data and outputs quantitative evaluation results. These results can be specific numerical values, i.e., interference intensity, probability values, i.e., the possibility of interference occurring, or classification labels such as normal, slight interference, and severe interference. S714: Based on the interference assessment results, formulate corresponding adjustment strategies, including directly correcting the monitoring parameter values, weighted processing, and filtering processing; S715: Adjust the monitoring parameter values ​​accordingly based on the established adjustment strategy to ensure that the adjusted parameter values ​​meet the specifications.

8. The method for comparative analysis of data acquisition and transmission based on real-time parameter monitoring according to claim 7, characterized in that: The identified abnormal data and trends are analyzed, and fault diagnosis is performed by combining information such as equipment status and operation records, and a fault report is generated; the specific steps are as follows: S801: Filter out different types of abnormal data from the abnormal dataset and classify them according to the abnormality type of the monitored parameters, such as exceeding the upper limit, lower limit, or fluctuation outside the standard range. S802: Assign a unique identifier to each type of abnormal data and collect key information such as the time, frequency, and duration of its occurrence; S803: Use charts to visualize anomalous data, and combine the visualization results to analyze the patterns and possible correlations of the anomalous data; S804: Perform correlation analysis between abnormal data and information such as external environment data, equipment status data, and operation records; S805: Uses fault tree analysis to decompose faults from the top-level system-level faults down to the bottom-level basic events. S806: Based on the results of abnormal data analysis, add possible causes of failure to the fault tree and determine the logical relationships between them; S807: For complex fault situations, use cause-effect graphs to analyze the root causes of the fault; take abnormal data as the result, list all possible causes and group them, and analyze the interactions and influences between the factors. S808: Based on the analysis results of the fault tree and cause-effect diagram, determine the specific location, nature, and scope of impact of the fault; S809: Write a fault description, describing the fault phenomenon in detail and explaining the impact of the fault on the production process; S810: Accurately record the specific location, nature, and scope of impact of the fault, and attach a fault tree and cause-effect diagram; S811: Provide specific handling suggestions based on the possible causes of the fault; S812: Output fault report.

9. The method for comparative analysis of data acquisition and transmission based on real-time parameter monitoring according to claim 8, characterized in that: The key information fragments related to the gathering and transportation pipeline network are extracted from the data, and these fragments are further analyzed and processed to extract useful information; the specific steps are as follows: S901: Determine the time range for data extraction based on the analysis requirements; S902: Use a preset keyword list to filter the data to quickly locate data segments related to the gathering and transmission network; S903: Define rules to match data segments of a specific format or content based on the structure of the data, the range of data values, or the trend of data change. S904: For time series data, techniques such as sliding window and time series pattern matching are used to identify key information segments; S905: Extract specific parameter values ​​from key information segments; S906: Collect contextual information related to key information fragments; S907: Aggregate key information fragments that have similar characteristics or belong to the same time period; S908: Verify the extracted information by comparing it with historical data and checking it against real-time data; S909: Format the extracted useful information into tables, charts, or reports; S910: Stores the processed data and transmits it to the subsequent alarm system.

10. The method for comparative analysis of data acquisition and transmission based on real-time parameter monitoring according to claim 9, characterized in that: When a serious anomaly or potential malfunction is detected, an emergency alarm mechanism is triggered, pushing alarm information to relevant personnel or systems in real time; the specific steps are as follows: S1001: Assess the severity of the anomaly based on factors such as the nature of the abnormal data in the fault report, the degree of deviation from the normal range, and the duration; and decide whether to trigger the emergency alarm mechanism based on the assessment and analysis results. S1002: When an alarm is triggered, an alarm signal containing abnormal information, severity, and potential fault analysis is generated. S1004: Set priority for alarm signals based on the severity and urgency of the anomaly; S1005: Organize alarm information into a clear format, and prepare alarm information formats suitable for different channels according to different recipients; S1006: Select an appropriate push channel according to the preset alarm notification strategy, and push the alarm information to relevant personnel or systems in real time through the selected channel; S1007: Record alarm information, response status, and processing results.

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