CN-121997015-A - Sampling data comparison analysis method based on parameter real-time monitoring

CN121997015ACN 121997015 ACN121997015 ACN 121997015ACN-121997015-A

Abstract

The invention relates to the field of analysis of data of mining and transportation, in particular to a data of mining and transportation contrast analysis method based on parameter real-time monitoring; technical problems: the adopted data comparison analysis method in the prior art mostly relies on manual work to perform data analysis, lacks intelligent data processing and abnormality identification capability, lacks advanced algorithm support, has imperfect data model and has limitation of system architecture, so that complex data analysis and processing tasks are difficult to automatically complete; the technical scheme is as follows: the sampling data comparison analysis method based on parameter real-time monitoring comprises the steps of setting parameter threshold values and standard ranges, comparing and analyzing real-time data with historical data, and identifying abnormal points and trend changes in the data; according to the invention, the data are ordered and arranged 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 the algorithm model, and the identified abnormal data and trend are analyzed to realize automatic analysis of the data.

Inventors

XU BIN
GAO LEI
HUANG BIN
CHEN GANG
WANG JIAN
LI ERYANG
LIU XIANGHAO
YANG QIAN

Assignees

中国石油天然气股份有限公司

Dates

Publication Date: 20260508
Application Date: 20241108

Claims (10)

1. The sampling data comparison analysis method based on parameter real-time monitoring is characterized in that: s1, setting data acquisition points at key positions by using an intelligent terminal and parameter sensing equipment, and acquiring data in real time; s2, preprocessing the collected original data at an edge computing node; s3, carrying out preliminary analysis on the preprocessed data, and extracting key information; S4, transmitting the preprocessed data to the cloud processing platform in real time by adopting a data encryption and verification mechanism; s5, establishing a database on the cloud processing platform, and storing the collected historical data and real-time data; s6, performing time stamp processing on the received data, and sequencing and sorting the data according to a time sequence; s7, setting a parameter threshold and a standard range, comparing and analyzing the real-time data with the historical data, identifying abnormal points and trend changes in the data, and performing preliminary judgment; s8, analyzing interference factors which possibly influence the accuracy of the data, and eliminating the influence of the interference factors on the data through an algorithm model; S9, analyzing the identified abnormal data and trend, performing fault investigation by combining equipment state, operation record and other information, and generating a fault report; s10, extracting key information fragments related to a gathering and transportation pipe network from data, and further analyzing and processing the information fragments to extract useful information; and S11, triggering an emergency alarm mechanism when serious abnormal conditions or potential faults are detected, and pushing alarm information to related personnel or systems in real time.
2. The method for comparing and analyzing the acquired and transmitted data based on the real-time parameter monitoring according to claim 1 is characterized by comprising the following specific steps of setting a data acquisition point at a key position by utilizing an intelligent terminal and parameter sensing equipment, acquiring data in real time, preprocessing the acquired original data at an edge computing node, carrying out preliminary analysis on the preprocessed data, and extracting key information: S201, deploying an intelligent terminal and parameter sensing equipment at a key position where data acquisition is required; s202, configuring sampling frequency and data transmission protocol parameters of an intelligent terminal; S203, the intelligent terminal reads data from the sensor equipment according to preset sampling frequency and parameters through a built-in data acquisition module; S204, recording time stamp information of the data, and performing CRC data check on the acquired data; s205, the acquired data is transmitted to an edge computing node in a wireless transmission mode; S206, the edge computing node receives the original data transmitted from the intelligent terminal; s207, performing secondary verification on the received data; S208, removing repeated data from the data, checking and deleting the repeated records; s209, carrying out missing value processing on the missing data by adopting a mean filling and interpolation method; s210, removing random noise in the data by adopting a moving average and exponential smoothing algorithm; S211, removing noise components in the signals by adopting a wavelet transformation and median filtering method; S212, converting the acquired data into a unified data format, including data type conversion and time stamp standardization; S213, calculating the mean value, standard deviation, maximum value and minimum value of the preprocessed data for statistical analysis; and S214, extracting key information features from the preprocessed data.
3. The method for comparing and analyzing the acquired and transmitted data based on the real-time parameter monitoring as set forth in claim 2, wherein the method is characterized in that the preprocessed data is transmitted to the cloud processing platform in real time by adopting a data encryption and verification mechanism, so that the safety and the reliability in the data transmission process are ensured, and the method specifically comprises the following steps: s301, initializing a transmission environment, checking a network connection state to ensure the network to be unobstructed; S302, configuring a data transmission protocol; S303, initializing an AES encryption algorithm and a CRC algorithm of a data encryption key; s304, packaging the preprocessed data according to a protocol specification; s305, encrypting the packaged data by applying a data encryption algorithm; S306, sending the encrypted data packet to a designated port of the cloud processing platform by using the selected network protocol; s307, monitoring the transmission process and recording the transmission state; S308, waiting for receiving confirmation response of the cloud processing platform, and retransmitting data according to a preset retry strategy if the confirmation is not received or the confirmation fails; and S309, recording key information in the data transmission process, including transmission time, data quantity, transmission state and the like.
4. The method for comparing and analyzing the acquired data based on the real-time monitoring of the parameters according to claim 3 is characterized by comprising the following specific steps of establishing a database on a cloud processing platform, storing the acquired historical data and the real-time data, classifying, indexing and backing up the data: s401, creating a database on a cloud processing platform, wherein the database comprises a table structure and an index; s402, configuring database connection parameters; s403, monitoring a designated port, and receiving an encrypted data packet sent from transmission; s404, decrypting the data packet by applying a decryption algorithm to restore the original data; S405, classifying the data according to the data type, the equipment identification, the time stamp and other information, and establishing an index for the data; S406, storing the classified and indexed data into corresponding tables in a database, and monitoring the storage process; s407, periodically executing data backup operation to backup important data in the database to a safe position; And S408, recording key information in the data storage process, including storage time, data quantity, storage state and the like.
5. The method for comparing and analyzing data based on real-time monitoring of parameters according to claim 4, wherein the method comprises the steps of performing time stamping processing on the received data to ensure the consistency of the time sequence of the data, and sorting and arranging the data according to the time sequence, wherein the method comprises the following specific steps: S501, creating an empty timestamp list and an empty data point list; S502, extracting a time stamp for each element in the received data set, and extracting time stamp information from the current element; S503, checking whether the extracted timestamp format is consistent with the target format, if not, converting the timestamp format into a unified format by using a date-time conversion function; S504, adding the converted time stamp into a time stamp list and adding corresponding data into the time stamp list, and if the data set is in the form of key value pairs, directly storing the key value pairs; S505, updating the data set, and rearranging elements in the data point list according to the ordered timestamp list so as to maintain the corresponding relation between the timestamp and the data; S506, traversing the ordered timestamp list, checking whether the time interval is larger than a preset threshold value, if so, estimating missing data points by using a linear interpolation method, and marking the missing points; S507, traversing the time stamp list by the sample to check whether the time stamp is repeated, if the repeated time stamp is found, selecting to keep the first or last data point, or merging the values of the repeated data points and taking an average value; and S508, updating the data set to reflect any changes made according to the time deficiency and the result of the repeated check.
6. The method for comparing analysis of sampled and transmitted data based on real-time parameter monitoring as set forth in claim 5, wherein the method comprises the steps of setting a parameter threshold and a standard range, comparing real-time data with historical data, identifying abnormal points and trend changes in the data, and performing preliminary judgment, and the specific steps are as follows: S601, analyzing historical data, and knowing the conventional fluctuation range, seasonal change and abnormal value frequency characteristics of each parameter; S602, setting reasonable upper and lower thresholds for each monitoring parameter according to statistical analysis of historical data; s603, setting a normal fluctuation interval of each parameter, namely a standard range, based on a stable interval of the historical data; S604, respectively sorting the real-time data set and the historical data set according to time sequence, and ensuring that analysis is carried out according to time sequence; s605, traversing the sequenced data set by using an iterator, and acquiring real-time data and corresponding historical data at each time point; S606, comparing the real-time data with the historical data, checking whether the real-time data exceeds a set threshold value, and if the real-time data exceeds the threshold value, recording abnormal points, including information such as time stamps, abnormal values, types exceeding the threshold value and the like; S607, checking whether the real-time data deviate from the standard range, and further evaluating the degree of abnormality; s608, trend analysis is carried out on the real-time data and the historical data by applying a time sequence analysis technology ARIMA; s609, calculating trend lines or predicted values for comparison with real-time data; S610, comparing the real-time data with trend lines or predicted values, and identifying data points deviating significantly from the trend; S611, preliminarily judging whether the deviations are potential abnormal or normal fluctuations according to the degree and duration of the deviations; and S612, marking the identified trend change, and recording related information including a time stamp, a change type and an influence degree.
7. The method for comparing and analyzing the acquired data based on the real-time monitoring of the parameters according to claim 6, wherein the method is characterized by analyzing external factors which possibly affect the accuracy of the data, such as environmental factors, equipment faults and the like, and eliminating the influence of the interference factors on the data through an algorithm model, and comprises the following specific steps: s701, collecting external environment data possibly related to the monitored parameters; S702, acquiring current state information of equipment from channels such as an equipment management system, a fault log, maintenance records and the like; s703, analyzing which factors in the external environment data and the equipment state data possibly affect the accuracy of the monitoring parameters; S704, screening out key factors which have obvious influence on monitoring parameters from potential interference factors through correlation analysis; S705, analyzing the property of each interference factor, including continuous variable or classified variable, and the potential relation between the interference factor and the monitored parameter, namely linearity, nonlinearity and periodicity; S706, selecting a linear regression model based on the linear relation between the interference factors and the monitoring parameters; s707, performing feature selection by using a feature importance assessment and correlation analysis method, and selecting features which have important influence on the model prediction performance, namely interference factors, from the data; s708, constructing a quantitative evaluation model by using the training set data, and setting parameters of the model; S709, training the model by using training set data to enable the model to learn the mapping relation between the interference factors and the monitoring parameters; S710, dividing the training set data into a plurality of subsets by using a cross-validation method, and evaluating the performance of the model by using each subset as a validation set in turn; s711, optimizing the model according to the cross verification result, and improving the prediction performance of the model by adjusting the model parameters and changing the model structure; S712, inputting real-time or new external environment data and equipment state data into the trained model; S713, the model calculates the influence degree of interference factors on the monitoring parameters according to the input real-time data, and outputs quantitative evaluation results, wherein the results can be specific numerical values, namely interference intensity, probability values, namely the possibility of interference occurrence, or classification labels, such as normal, slight interference and serious interference; s714, according to the interference evaluation result, formulating a corresponding adjustment strategy comprising direct correction of monitoring parameter values, weighting processing and filtering processing; s715, according to the formulated adjustment strategy, the monitoring parameter value is correspondingly adjusted, and the adjusted parameter value is ensured to accord with the specification.
8. The method for comparing and analyzing the acquired data based on the real-time monitoring of the parameters according to claim 7, wherein the method is characterized by analyzing the identified abnormal data and trend, performing fault detection by combining information such as equipment state, operation record and the like, and generating a fault report, and comprises the following specific steps: S801, screening out different types of abnormal data from an abnormal data set, and classifying according to the abnormal types of the monitoring parameters, such as exceeding an upper limit, a lower limit, fluctuation outside a standard range and the like; s802, distributing unique identifiers for each type of abnormal data, and counting key information such as time, frequency, duration and the like of occurrence of the unique identifiers; S803, carrying out visual display on the abnormal data by using a chart, and analyzing the mode and possible relevance of the abnormal data by combining the visual result; S804, carrying out association analysis on the abnormal data, the external environment data, the equipment state data, the operation records and other information; S805, starting from the top system level fault, decomposing to the bottom basic event step by using a fault tree analysis method; s806, adding possible fault reasons into a fault tree according to the analysis result of the abnormal data, and determining the logic relationship between the fault reasons; S807 analyzing root causes of faults using causal graphs for complex fault conditions, listing and grouping all possible causes with exception data as a result, analyzing interactions and effects between factors S808, determining the specific position, property and influence range of the fault according to the analysis results of the fault tree and the causal graph; S809, writing fault description, describing fault phenomenon in detail, and describing the influence of the fault on the production process; S810, accurately recording the specific position, property and influence range of the fault, and attaching a fault tree and a causal graph; S811, specific processing suggestions are provided for possible reasons of faults; And S812, outputting a fault report.
9. The method for comparing and analyzing the acquired data based on the real-time monitoring of the parameters according to claim 8, wherein key information fragments related to the gathering and transportation pipe network are extracted from the data, the information fragments are further analyzed and processed, and useful information is extracted, and the method comprises the following specific steps: s901, determining a time range in which data needs to be extracted according to analysis requirements; s902, filtering data by using a preset keyword list to quickly locate data fragments related to a gathering and transportation pipe network; S903, defining 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 the data; S904, for time series data, identifying key information fragments by using the technologies of sliding window, time series pattern matching and the like; s905, extracting specific parameter values from the key information fragments; S906, collecting context information related to the key information pieces; S907, aggregating key information fragments with similar characteristics or belonging to the same time period; s908, comparing the extracted information with historical data, and checking the extracted information with real-time data, and verifying the extracted information; S909, formatting the extracted useful information into a form of a table, a chart or a report; and S910, storing the processed data and transmitting the processed data to a subsequent alarm system.
10. The method for comparing and analyzing data based on real-time parameter monitoring as set forth in claim 9, wherein when serious abnormal conditions or potential faults are detected, an emergency alarm mechanism is triggered to push alarm information to related personnel or systems in real time, and the method comprises the following specific steps: S1001, evaluating the severity of abnormality according to the nature of abnormal data in a fault report, the degree of deviation from a normal range, the duration and other factors; S1002, when the trigger alarm is determined, generating an alarm signal containing abnormal information, severity, potential fault analysis and the like; s1004, setting priority for the alarm signal according to the severity and the emergency degree of the abnormality; s1005, the alarm information is arranged into a clear format, and the alarm information formats suitable for different channels are prepared according to different receivers; S1006, selecting a proper pushing channel according to a preset alarm notification strategy, and pushing alarm information to related personnel or systems in real time through the selected channel; S1007, alarm information, response conditions, processing results, and the like are recorded.

Description

Sampling data comparison analysis method based on parameter real-time monitoring Technical Field The invention relates to the field of analysis of data of mining and transportation, in particular to a data of mining and transportation comparison analysis method based on parameter real-time monitoring. Background The method is a widely applied technical means in the production management of oil and gas fields, and the method is used for revealing abnormal conditions, performance changes and potential problems in the production process by carrying out comparative analysis on the real-time data of key links such as collected oil and gas wells, gathering and transportation pipe networks, production stations and the like; the existing data comparison analysis method mainly depends on the traditional data acquisition, transmission and processing technology, and usually adopts a statistical method and a simple data processing algorithm to compare the acquired data in the data analysis stage so as to identify abnormal changes or potential problems in the production process, but the data comparison analysis method in the prior art mostly depends on manual data analysis, lacks intelligent data processing and abnormal identification capability, lacks advanced algorithm support, imperfect data model and limitation of system architecture, and is difficult to automatically complete complex data analysis and processing tasks; Disclosure of Invention In order to overcome the problems that the adopted data comparison analysis method in the prior art mostly relies on manpower to perform data analysis, lacks intelligent data processing and abnormality identification capability, lacks advanced algorithm support, has imperfect data model and has limitation of system architecture, and is difficult to automatically complete complex data analysis and processing tasks. The technical scheme of the invention is that the sampling data contrast analysis method based on parameter real-time monitoring comprises the following steps: s1, setting data acquisition points at key positions by using an intelligent terminal and parameter sensing equipment, and acquiring data in real time; s2, preprocessing the collected original data at an edge computing node; s3, carrying out preliminary analysis on the preprocessed data, and extracting key information; S4, transmitting the preprocessed data to the cloud processing platform in real time by adopting a data encryption and verification mechanism; s5, establishing a database on the cloud processing platform, and storing the collected historical data and real-time data; s6, performing time stamp processing on the received data, and sequencing and sorting the data according to a time sequence; s7, setting a parameter threshold and a standard range, comparing and analyzing the real-time data with the historical data, identifying abnormal points and trend changes in the data, and performing preliminary judgment; s8, analyzing interference factors which possibly influence the accuracy of the data, and eliminating the influence of the interference factors on the data through an algorithm model; S9, analyzing the identified abnormal data and trend, performing fault investigation by combining equipment state, operation record and other information, and generating a fault report; s10, extracting key information fragments related to a gathering and transportation pipe network from data, and further analyzing and processing the information fragments to extract useful information; and S11, triggering an emergency alarm mechanism when serious abnormal conditions or potential faults are detected, and pushing alarm information to related personnel or systems in real time. The method comprises the following specific steps of setting a data acquisition point at a key position by using an intelligent terminal and parameter sensing equipment, acquiring data in real time, preprocessing acquired original data at an edge computing node, carrying out preliminary analysis on the preprocessed data, and extracting key information: S201, deploying an intelligent terminal and parameter sensing equipment at a key position where data acquisition is required; s202, configuring sampling frequency and data transmission protocol parameters of an intelligent terminal; S203, the intelligent terminal reads data from the sensor equipment according to preset sampling frequency and parameters through a built-in data acquisition module; S204, recording time stamp information of the data, and performing CRC data check on the acquired data; s205, the acquired data is transmitted to an edge computing node in a wireless transmission mode; S206, the edge computing node receives the original data transmitted from the intelligent terminal; s207, performing secondary verification on the received data; S208, removing repeated data from the data, checking and deleting the repeated records; s209, carrying out missing value processing on the missing