Beidou Satellite-Based Oilfield Data Transmission Method and System

Abstract

The present invention relates to the technical field of data transmission, and discloses an oilfield data transmission method and system based on Beidou satellites. The method includes: obtaining oilfield on-site data and data emergency levels, performing feature detection operations based on the oilfield on-site data to obtain information types corresponding to the oilfield on-site data, extracting feature values from the information types to obtain type feature values, performing deviation calculation operations on the type feature values and preset normal values to obtain deviation values, performing probability conversion operations based on the deviation values to obtain information sensitivity levels, encrypting and compressing the oilfield on-site data according to the information sensitivity levels and the data emergency levels to obtain data packets to be sent, and performing encrypted transmission on the data packets to be sent. When using Beidou satellites to transmit oilfield wellhead information, this method improves the reliability and security of data transmission, and at the same time ensures the efficiency of data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular, to an oilfield data transmission method and system based on Beidou satellites. Background Art

[0002] With the continuous deepening of oil resource development, in order to reduce the impact of oil resource development on the environment, the oil extraction process not only needs to reduce the discharge of waste water, but also minimize energy consumption. This not only helps to protect the environment, but also saves oil resources and their related subsidiary resources. During the underground oil extraction process, the extraction volume and underground pressure are important factors affecting the sustainable oil extraction. Therefore, precise monitoring of these parameters can effectively reduce energy consumption during the extraction process and reduce the impact on the environment.

[0003] In an existing technology, the method of monitoring the oil extraction process includes means for detecting data collected at the oilfield wellhead, and these data are transmitted to the ground through a wired network for analysis. The wired network usually uses cables for transmission. In addition, oil extraction involves the coordinated operation of various equipment and machines to achieve the expected target effect.

[0004] In the existing technology, for oilfield exploitation, the equipment and machines used may be subject to external attacks or fraudulent tampering, resulting in problems of unstable data transmission and insufficient security. Summary of the Invention

[0005] The present invention provides an oilfield data transmission method and system based on Beidou satellites, so as to design a real-time, efficient, and secure transmission method by combining the oilfield data type, characteristics, and sensitivity level when using Beidou satellites for oilfield wellhead information transmission, improve the reliability and security of data transmission, and at the same time ensure the efficiency of data transmission.

[0006] In a first aspect, to solve the above technical problems, the present invention provides an oilfield data transmission method based on Beidou satellites, which is executed by a data sending end and includes:

[0007] Obtain oilfield on-site data and data emergency levels, where the oilfield on-site data includes the temperature, pressure, and seepage information of the oilfield collection wellhead;

[0008] Perform a feature detection operation according to the oilfield on-site data to obtain the information type corresponding to the oilfield on-site data;

[0009] Extract the eigenvalue of the information type to obtain the type eigenvalue;

[0010] Perform a deviation calculation operation on the type eigenvalue with a preset normal value to obtain a deviation value;

[0011] Perform a probability conversion operation based on the deviation value to obtain the information sensitivity level.

[0012] Encrypt and compress the oilfield on-site data according to the information sensitivity level and the data urgency level to obtain a data packet to be sent, and perform encrypted transmission on the data packet to be sent.

[0013] As an alternative implementation, after performing encrypted transmission on the data packet to be sent, which is executed by the data receiving end, the method further includes:

[0014] The data receiving end receives the data packet to be sent to obtain an encrypted data packet.

[0015] The data receiving end calculates a statistic using a statistical decision model based on the encrypted data packet.

[0016] Perform a sensitivity evaluation operation on each data segment of the encrypted data packet according to the statistic to obtain a sensitivity evaluation result for each data segment.

[0017] The data receiving end determines whether to re-receive the data packet to be sent according to the sensitivity evaluation result for each data segment.

[0018] The data receiving end sends the statistic to the data sending end, and the data sending end determines whether to re-encrypt, compress, and transmit the oilfield on-site data according to the statistic.

[0019] As an alternative implementation, the performing a feature detection operation on the oilfield on-site data to obtain the information type corresponding to the oilfield on-site data includes:

[0020] Compare and judge the oilfield on-site data with a preset normal value of on-site data to obtain a judgment result.

[0021] Among them, the normal value of on-site data includes a normal temperature value, a normal pressure value, and a normal seepage information value, and the type feature value includes a temperature feature value, a pressure feature value, and a seepage feature value.

[0022] Obtain the normal situation information, overlimit information, overlimit feature value of the oilfield on-site data, and the information type corresponding to the oilfield on-site data according to the judgment result.

[0023] As an alternative implementation, the method for judging the information type of the oilfield on-site data includes:

[0024] Judge whether the type feature value exceeds the preset normal value of on-site data.

[0025] When the type feature value exceeds the limit, further determine the information type of the on-site oilfield data, and store the on-site oilfield data as numerical data or feature data respectively;

[0026] The numerical data includes wellhead temperature, pressure, and seepage parameters, and the feature data includes abnormal data.

[0027] As an alternative implementation, perform a probability conversion operation based on the deviation value to obtain the information sensitivity degree, including:

[0028] When the transmitted data includes the numerical data of the on-site oilfield data, calculate the deviation value of the numerical data, and convert the deviation value into a probability to obtain a probability result;

[0029] According to the probability result, divide the numerical data into three sensitive ranges:

[0030] When the probability result of the numerical data is lower than one standard deviation of the mean value, the numerical data belongs to the first sensitive range;

[0031] When the probability result of the numerical data exceeds one standard deviation of the mean value and is lower than the mean value plus two standard deviations, the numerical data belongs to the second sensitive range;

[0032] When the probability result of the numerical data is higher than the mean value plus two standard deviations, the numerical data belongs to the third sensitive range;

[0033] Among them, the data in the first sensitive range and the second sensitive range are sensitive data, and the data in the third sensitive range are very sensitive data.

[0034] As an alternative implementation, after the transmitted data includes the feature data of the on-site oilfield data, the method for judging the abnormal situation of the transmitted data includes:

[0035] For temperature information, when the temperature feature value exceeds a preset value of the normal range of the oil well temperature, it belongs to a general anomaly; when the temperature feature value exceeds two preset values of the normal range, it belongs to a serious anomaly;

[0036] For pressure information, when the pressure feature value exceeds a preset value of the normal range of the oil well pressure, it belongs to a general anomaly; when the pressure feature value exceeds two preset values of the normal range, it belongs to a serious anomaly;

[0037] For seepage information, when the seepage feature value is lower than a preset value of the normal range of the oil well seepage, it belongs to a general anomaly; when the seepage feature value is lower than two preset values of the normal range, it belongs to a serious anomaly.

[0038] As an alternative implementation, the method for encrypting and compressing the on-site oilfield data includes:

[0039] Set a normal data level and an abnormal data level;

[0040] For the data within the third sensitive range, the general anomalies, and the severe anomalies, use a separate encryption and compression method;

[0041] For the normal data, use a weak encryption and weak compression method;

[0042] For the abnormal data, use a strong encryption and strong compression method;

[0043] Among them, the encrypted and compressed results of the abnormal data are sent in segments with a fixed length, and the encrypted and compressed results of the normal data are transmitted dynamically according to requirements.

[0044] As an alternative implementation, the method for strongly encrypting and strongly compressing the abnormal data includes:

[0045] Store the abnormal data in a specific area;

[0046] Count the data volume and length of the abnormal data, establish a statistical model, analyze the statistical characteristics of the abnormal data, and obtain attributes such as the length of the abnormal data, the distribution of the abnormal data, and the dispersion of the abnormal data;

[0047] Obtain compression parameters based on the length of the abnormal data or the weighted distribution of the length;

[0048] Generate a key based on the statistical model and use the key to encrypt the abnormal data.

[0049] In a second aspect, the present invention provides an oilfield data transmission system based on Beidou satellites, configured at a data sending end, including:

[0050] A data acquisition module for acquiring on-site oilfield data and a data emergency level, where the on-site oilfield data includes the temperature, pressure, and seepage information at the oilfield collection wellhead;

[0051] A feature detection module for performing feature detection operations based on the on-site oilfield data to obtain the information type corresponding to the on-site oilfield data;

[0052] A feature value extraction module for extracting feature values from the information type to obtain type feature values;

[0053] A deviation calculation module for performing a deviation calculation operation on the type feature value and a preset normal value to obtain a deviation value;

[0054] A probability conversion module performs a probability conversion operation based on the deviation value to obtain the information sensitivity level.

[0055] An oilfield on-site data packet transmission module encrypts and compresses the oilfield on-site data according to the information sensitivity level and the data urgency level to obtain a data packet to be sent, and transmits the data packet to be sent.

[0056] In a third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for transmitting oilfield data based on Beidou satellites described in any one of the above is implemented.

[0057] In a fourth aspect, the present invention further provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for transmitting oilfield data based on Beidou satellites described in any one of the above.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] The present invention provides a method for transmitting oilfield data based on Beidou satellites, including: obtaining oilfield on-site data and a data urgency level, performing a feature detection operation on the oilfield on-site data to obtain an information type corresponding to the oilfield on-site data, extracting a type feature value from the information type, performing a deviation calculation operation on the type feature value and a preset normal value to obtain a deviation value, performing a probability conversion operation based on the deviation value to obtain an information sensitivity level, encrypting and compressing the oilfield on-site data according to the information sensitivity level and the data urgency level to obtain a data packet to be sent, and performing encrypted transmission on the data packet to be sent.

[0060] When this method uses Beidou satellites to transmit oilfield wellhead information, the data sending end obtains the temperature, pressure, and seepage information of the wellhead, performs feature detection, judges the information type, calculates its deviation from the normal value, judges its sensitivity level, classifies, encrypts, and compresses according to different sensitivity levels and emergency situations, and the data sending end encrypts and transmits the data, improving the reliability and security of data transmission.

[0061] In addition, during the data encrypted transmission process of the present invention, a statistical model of data transmission is introduced, and statistics are used to evaluate the sensitivity level of the transmitted data, reducing data expansion, improving system throughput. At the same time, according to the retransmission probability and transmission effect, a retransmission decision is adopted, different algorithms and parameters are selected, optimizing the efficiency of data transmission and enhancing the flexibility and practicality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 FIG. 1 is a schematic flowchart of a method for transmitting oilfield data based on Beidou satellites provided by an embodiment of the present invention;

[0063] Figure 2 FIG. 2 is a schematic diagram of a data transmission process provided by an embodiment of the present invention;

[0064] Figure 3 FIG. 3 is a schematic structural diagram of a system for transmitting oilfield data based on Beidou satellites provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0066] With the continuous deepening of oil resource development, in order to reduce the impact of oil resource development on the environment, the oil extraction process not only needs to reduce the discharge of waste water, but also minimize energy consumption. This not only helps to protect the environment, but also saves oil resources and their related subsidiary resources. During the underground oil extraction process, the extraction volume and underground pressure are important factors affecting the sustainable oil extraction. Therefore, accurate monitoring of these parameters can effectively reduce energy consumption during the extraction process and reduce the impact on the environment.

[0067] In an existing technology, the method for monitoring the oil extraction process includes means for detecting data collected at the oilfield wellhead, and these data are transmitted to the ground through a wired network for analysis. The wired network usually uses cables for transmission. In addition, oil extraction involves the coordinated operation of various equipment and machines to achieve the expected target effect.

[0068] In the existing technology, the oilfield extraction equipment and machines may be subject to external attacks or fraudulent tampering, resulting in problems of unstable data transmission and insufficient security.

[0069] To solve the above problems, referring to Figure 1 , a first embodiment of the present invention provides a method for transmitting oilfield data based on Beidou satellites, including the following steps:

[0070] S1. Obtain oilfield field data and data emergency levels, where the oilfield field data includes the temperature, pressure, and seepage information of the oilfield collection wellhead;

[0071] S2. Perform a feature detection operation based on the on-site oilfield data to obtain the information type corresponding to the on-site oilfield data.

[0072] S3. Extract feature values from the information type to obtain type feature values.

[0073] S4. Perform a deviation calculation operation on the type feature values with respect to preset normal values to obtain deviation values.

[0074] S5. Perform a probability conversion operation based on the deviation values to obtain the information sensitivity level.

[0075] S6. Encrypt and compress the on-site oilfield data according to the information sensitivity level and the data emergency level to obtain a data packet to be sent, and perform encrypted transmission on the data packet to be sent.

[0076] In step S1, obtain the on-site oilfield data and the data emergency level.

[0077] In one implementation, the data sender obtains the real-time temperature, pressure, and seepage information of the oilfield wellhead, and obtains the data emergency level based on the real-time temperature, pressure, and seepage information of the wellhead.

[0078] It should be noted that the acquisition of the real-time information of the oilfield wellhead is achieved by installing various sensors, such as temperature sensors, pressure sensors, flow meters, etc., at key positions in the oilfield, such as wellheads and equipment monitoring points, to realize the real-time collection of key parameters in oilfield production. In the process of determining the emergency level, the system will judge the emergency degree of the data based on preset rules, such as setting the emergency level according to whether parameters such as temperature and pressure exceed the preset normal range. This judgment process is automatically completed by the data acquisition terminal. In special cases, the operator manually adjusts the data emergency level according to actual needs to ensure that important information is given priority.

[0079] In step S2, perform a feature detection operation based on the on-site oilfield data to obtain the information type corresponding to the on-site oilfield data.

[0080] In one implementation, the performing a feature detection operation based on the on-site oilfield data to obtain the information type corresponding to the on-site oilfield data includes:

[0081] Compare and judge the on-site oilfield data with the preset normal values of the on-site data to obtain a judgment result.

[0082] Among them, the normal values of the on-site data include normal temperature values, normal pressure values, and normal seepage information values, and the type feature values include temperature feature values, pressure feature values, and seepage feature values.

[0083] Based on the judgment result, obtain the normal situation information, over-limit information, over-limit characteristic values of the on-site oilfield data, and the information types corresponding to the on-site oilfield data.

[0084] Further, the method for judging the information type of the on-site oilfield data includes:

[0085] Judge whether the type characteristic value exceeds the preset normal value of the on-site data;

[0086] When the type characteristic value is over-limit, further judge the information type of the on-site oilfield data, and store the on-site oilfield data as numerical data or characteristic data respectively;

[0087] The numerical data includes wellhead temperature, pressure, and seepage parameters, and the characteristic data includes abnormal data.

[0088] It should be noted that the method for obtaining the preset normal value of the current wellhead temperature, pressure, and seepage information includes: according to the measured values of the wellhead historical parameters, calculate the mean and standard deviation of the wellhead parameters in different time periods, obtain the mapping relationship between the mean and standard deviation and time, substitute the obtained real-time measured value into this mapping relationship, calculate its corresponding standard score, and then calculate the corresponding probability and confidence level according to the Chapman table to obtain the preset normal value.

[0089] It should be noted that the data sender presets the normal value of the on-site data, and judges the wellhead temperature, pressure, and seepage information obtained in real time to determine whether it is within the value range of the normal value of the on-site data, so as to obtain the data types of the current wellhead temperature, pressure, and seepage information. Among them, the data types of the current wellhead temperature, pressure, and seepage information include: the normal situation information, over-limit information, and over-limit characteristic values of the current wellhead temperature, the normal situation information, over-limit information, and over-limit characteristic values of the pressure, and the normal situation information, over-limit information, and over-limit characteristic values of the seepage.

[0090] Among them, the method for judging the over-limit information degree value of the current wellhead temperature, pressure, and seepage includes: judging whether the characteristic values of the temperature, pressure, and seepage data exceed the preset normal value of the on-site data; if it is over-limit, then judge the data types of the current temperature, pressure, and seepage data, store the data into different sensitive information, and classify the data into numerical data and characteristic data. The numerical data includes wellhead temperature, pressure, and seepage parameters, and the characteristic data includes abnormal data.

[0091] Further, after the transmitted data includes the characteristic data of the on-site oilfield data, the method for judging the abnormal situation of the transmitted data includes:

[0092] For temperature information, when the temperature characteristic value exceeds the normal range of oil well temperature by one preset value, it is a general abnormality; when the temperature characteristic value exceeds the normal range by two preset values, it is a serious abnormality;

[0093] For pressure information, when the pressure characteristic value exceeds the normal range of oil well pressure by one preset value, it is a general abnormality; when the pressure characteristic value exceeds the normal range by two preset values, it is a serious abnormality;

[0094] For the seepage information, when the seepage characteristic value is lower than the normal range of oil well seepage by one preset value, it is a general abnormality; when the seepage characteristic value is lower than the normal range by two preset values, it is a serious abnormality.

[0095] It should be noted that for temperature information, when the temperature characteristic value exceeds the normal temperature range of the oil well by a preset value, it is judged as a general abnormality, which usually indicates that the oil well may have slight temperature fluctuations, but will not seriously affect production. When the temperature characteristic value exceeds the normal range by two preset values, it is judged as a serious abnormality. At this time, the obvious change in temperature may indicate a potential production risk, which requires further investigation and processing.

[0096] For pressure information, when the pressure characteristic value exceeds the normal pressure range of the oil well by a preset value, it is classified as a general abnormality, indicating that the pressure fluctuates within a certain range, which may have a certain impact on production, but has not yet reached a critical level. When the pressure characteristic value exceeds the normal range by two preset values, it is classified as a severe abnormality, indicating that the oil well may face a greater risk of pressure changes, indicating that immediate measures need to be taken.

[0097] For seepage information, when the seepage characteristic value is lower than a preset value in the normal seepage range of the oil well, it is a general abnormality, indicating that the seepage has decreased to a certain extent, which may affect the oil well production. When the seepage characteristic value is lower than two preset values in the normal range, it is a serious abnormality, indicating that the seepage has decreased significantly, indicating that the oil well is blocked or has other problems.

[0098] In step S3, feature value extraction is performed on the information type to obtain a type feature value.

[0099] In one implementation, the data sending end presets the normal value of the field data, and judges the wellhead temperature, pressure, and seepage information obtained in real time to determine whether it is within the value range of the normal value of the field data, and obtains the characteristic value of the current wellhead temperature, the characteristic value of the pressure, and the characteristic value of the seepage.

[0100] It should be noted that the determination of whether the wellhead temperature, pressure and seepage information obtained in real time is within the range of normal values of field data includes:

[0101] For the wellhead temperature, a preset normal temperature range of 60°C to 80°C is set. When the real-time collected wellhead temperature is 75°C, this temperature value is within the preset normal range, so this temperature value is recorded as a normal temperature characteristic value. When the real-time temperature data is 85°C, exceeding the normal range by a preset value, then this temperature characteristic value is marked as "general anomaly". When the real-time temperature data reaches or exceeds 90°C, exceeding the normal range by two preset values, then this temperature characteristic value will be marked as "severe anomaly".

[0102] For the pressure information, a preset normal pressure range of 2 MPa to 5 MPa is set. When the real-time collected pressure value is 4 MPa, which is within the normal range, this pressure value will be used as a normal pressure characteristic value. When the real-time pressure is 1 MPa, lower than the normal range by a preset value, then this pressure characteristic value is marked as "general anomaly". When the real-time pressure drops to 0.5 MPa, lower than the normal range by two preset values, then this pressure characteristic value is marked as "severe anomaly".

[0103] For the seepage information, a preset normal seepage range of 10 m³ / h to 15 m³ / h is set. When the real-time seepage data is 12 m³ / h, the seepage characteristic value is within the normal range. When the real-time seepage data drops to 8 m³ / h, lower than the normal range by a preset value, this seepage characteristic value will be marked as "general anomaly". If the real-time seepage data further drops to 5 m³ / h, lower than the normal range by two preset values, then this seepage characteristic value will be marked as "severe anomaly".

[0104] In step S4, the type characteristic value is subjected to a deviation calculation operation with the preset normal value to obtain a deviation value.

[0105] In one implementation, the data receiving end calculates according to the type characteristic value and the preset normal value of the on-site data according to the deviation calculation formula to obtain a deviation value.

[0106] It should be noted that the deviation calculation formula can be expressed as:

[0107]

[0108] Among them, represents the deviation value of the th type of data, represents the real-time obtained temperature characteristic value, pressure characteristic value and seepage characteristic value, represents the preset normal value of the

[0109] In step S5, according to the deviation value, a probability conversion operation is performed to obtain the information sensitivity degree, including:

[0110] When the data to be transmitted includes the numerical data of the oilfield site data, calculate the deviation value of the numerical data, and convert the deviation value into a probability to obtain a probability result;

[0111] According to the probability result, divide the numerical data into three sensitive ranges:

[0112] When the probability result of the numerical data is lower than one standard deviation from the mean, the numerical data belongs to the first sensitive range;

[0113] When the probability result of the numerical data exceeds one standard deviation from the mean and is lower than two standard deviations from the mean plus the mean, the numerical data belongs to the second sensitive range;

[0114] When the probability result of the numerical data is higher than two standard deviations from the mean plus the mean, the numerical data belongs to the third sensitive range;

[0115] Among them, the data in the first sensitive range and the second sensitive range are sensitive data, and the data in the third sensitive range are very sensitive data.

[0116] It should be noted that after obtaining the deviation value , using a statistical model or distribution function, convert the deviation value into a probability value to obtain a probability result , and the probability result represents the degree of abnormality of the data. The greater the probability, the greater the degree of deviation of the data.

[0117] Based on this probability result, the numerical data can be divided into the following three sensitive ranges:

[0118] First sensitive range: When the probability result is lower than the mean minus one standard deviation , the data belongs to the first sensitive range. At this time, the degree of deviation of the data from the normal range is small, and it is usually considered a minor fluctuation.

[0119] Second sensitive range: When the probability result is between one standard deviation from the mean and two standard deviations from the mean plus the mean (i.e., ), the data belongs to the second sensitive range, indicating that the deviation of the data from the normal range is more significant and there is a certain risk.

[0120] Third sensitive range: When the probability result is higher than two standard deviations from the mean plus the mean , the data belongs to the third sensitive range, indicating that the data seriously deviates from the normal range. At this time, the data is in an abnormal state and is considered very sensitive, requiring immediate attention and processing.

[0121] In step S6, according to the information sensitivity and the data urgency level, encrypt and compress the oilfield field data to obtain a data packet to be sent, and perform encrypted transmission on the data packet to be sent.

[0122] In one implementation, the method for encrypting and compressing the oilfield field data includes:

[0123] Set a normal data level and an abnormal data level;

[0124] For the data in the third sensitive range, the general abnormal data, and the severe abnormal data, use a separate encryption and compression method;

[0125] For the normal data, use a weak encryption and weak compression method;

[0126] For the abnormal data, use a strong encryption and strong compression method;

[0127] Among them, the encrypted and compressed results of the abnormal data are sent in segments with a fixed length, and the encrypted and compressed results of the normal data are transmitted dynamically according to requirements.

[0128] Further, the method for strongly encrypting and strongly compressing the abnormal data includes:

[0129] Store the abnormal data in a specific area;

[0130] Count the data volume and length of the abnormal data, establish a statistical model, analyze the statistical characteristics of the abnormal data, and obtain attributes such as the length of the abnormal data, the distribution of the abnormal data, and the dispersion of the abnormal data;

[0131] Obtain compression parameters based on the length of the abnormal data or the weighted distribution of the length;

[0132] Generate a key based on the statistical model, and use the key to encrypt the abnormal data.

[0133] Further, the general abnormal data is compressed at three levels, the severe abnormal data is compressed at four levels, and the normal data at different levels is compressed at one level. Among them, the encrypted and compressed results of the abnormal data are sent in segments with a fixed length, and the encrypted and compressed results of the normal data are transmitted dynamically according to requirements.

[0134] It should be noted that the implementation methods of the first-level compression, the second-level compression, and the third-level compression include:

[0135] First-level compression: Determine the value size of normal data. For large data, divide it into data segments of equal length according to the large data, convert the data segments into binary data, turn the continuous binary data into discrete data, perform compression, convert the data into 8-bit binary data after compression, and then, according to the statistical model, perform statistical judgment on the data segments in the statistical model, delete redundant strings, and make marks.

[0136] Second-level compression: Determine the value size of normal data. For large data, divide it into data segments of equal length according to the large data, convert the data segments into binary data, turn the continuous binary data into discrete data, perform compression, convert the data into 8-bit binary data after compression, and then, according to the statistical model, perform statistical judgment on the data segments in the statistical model, delete redundant strings, and make marks.

[0137] Third-level compression: Determine the value size of normal data. For large data, divide it into data segments of equal length according to the large data, convert the data segments into binary data, turn the continuous binary data into discrete data, perform compression, convert the data into 8-bit binary data after compression, according to the statistical model, perform statistical judgment on the data segments in the statistical model, delete redundant strings, and make marks.

[0138] Furthermore, the implementation methods of weak encryption and weak compression include:

[0139] Establish an additive encryption or multiplicative encryption model based on the data and the statistical model, that is, F(x) = f((k1x1 + k2x2 + k3x3 + … + knxn) mod 255), establish a statistical dictionary, and compress the encryption result, where F(·) represents the encryption function, f(·) represents the compression function, k1 - kn ∈ R, xn is the value at each moment, and 0 < kn ≤ n.

[0140] For the convenience of understanding the present invention, some preferred embodiments of the present invention will be further described below.

[0141] In a preferred implementation manner, after encrypting and transmitting the to-be-sent data packet, which is executed by the data receiving end, the method further includes:

[0142] The data receiving end receives the to-be-sent data packet to obtain an encrypted data packet;

[0143] The data receiving end calculates according to the encrypted data packet by using the statistical decision model to obtain a statistic;

[0144] According to the statistic, perform a sensitivity evaluation operation on each segment of data information of the encrypted data packet to obtain a sensitivity evaluation result for each segment of data;

[0145] The data receiving end determines whether to re - receive the data packet to be sent according to the sensitivity evaluation result of each data segment;

[0146] The data receiving end sends the statistic to the data sending end, and the data sending end determines whether to re - encrypt, compress, and transmit the oilfield field data according to the statistic.

[0147] It should be noted that the statistic can be used as a quantitative index of the current data packet status for subsequent sensitivity evaluation and data integrity judgment. The sensitivity evaluation operation can be achieved by comparing the abnormality degree of each data segment in the data packet, such as the deviation value or the fluctuation situation, so as to determine which data segments are more important or need to be processed preferentially. The evaluation result is used to judge the reliability and transmission quality of the data packet.

[0148] The statistic is expressed as:

[0149]

[0150] Where, is the statistic, is the th statistic of the data segment, is the number of data segments, is the th weight of the data segment, The calculation method is as follows: , is the th transmission time of the data packet, is the th sensitivity degree of the data packet, is the th requirement of the data segment information for the transmission time, is the abnormality degree of the data segment during the transmission process.

[0151] The following takes a relatively common scenario as an example to describe the working process of the present invention. The working process is as follows:

[0152] Step 1: Collect oilfield field data, perform feature detection, and judge the information type;

[0153] Step 2: Judge the deviation size between the feature value of the information type and the preset normal value, calculate the sensitivity degree of the information. The information is collected to the data sending end. By performing feature detection on the collected data, judge the information type, judge the deviation size between it and the preset normal value, and calculate its sensitivity degree;

[0154] Step 3: Classify, encrypt, and compress according to the sensitivity degree and the urgency of the data, and then encrypt and transmit through the data sending end;

[0155] Step 4: The data receiving end receives the data, obtains the statistic, and evaluates the sensitivity of the transmitted data;

[0156] Preferably, in the said Step 2, there are three types of information types, and each information type corresponds to a certain information characteristic value. The information type with a large deviation from the preset normal value is a sensitive information type, and its corresponding information sensitivity level is high. After the data sending end obtains the information requirements of the data receiving end, it conducts a characteristic test on the collected information, judges its information type and the deviation from the preset normal value. If the information type is "0", it will not be sent; if it is "1", it will be sent.

[0157] Preferably, in the said Step 3, the sensitivity level of the information is set as α, and the emergency situation of the data is set as β. Its transmission process is divided into two steps. In the first step, the data sending end encrypts the collected information. It encrypts the (α×β) sensitive information using an advanced encryption algorithm to obtain an encrypted data packet γ. The data sending end compresses the collected information. It compresses the β(1 - α) normal data according to the sensitivity level using run-length encoding or Huffman encoding to obtain a compressed data packet φ. In the second step, the encrypted data packet γ and the compressed data packet φ are merged into a new data packet η for sending. Among them, when the combined length of the encrypted data packet γ and the compressed data packet φ exceeds the preset length, the method with a larger proportion of the compressed data packet φ in each packet of the sent data is selected to ensure that the encrypted data packet γ is sent first.

[0158] It should be noted that the classification encryption and compression are carried out according to the sensitivity level and the emergency situation of the data. The specific methods include:

[0159] Classification encryption of data: Steps of data classification. Define non-important data such as time, GPS positioning, current, etc. as 0, and important data such as pressure, temperature, and seepage, etc. as 1. Calculate the importance level of each field, that is, the sensitivity level of the data. AES encryption is performed on the data with a sensitivity level of 1, and the data with a sensitivity level of 0 is not encrypted. Steps of data encryption. Represent the data as a hexadecimal string, group it, with 64bit as a group, add a 16 / 64bit key, and fill the part less than 64bit with 0. After each data block is concatenated with the key, encoding is performed. An 8bit non-zero integer is used to represent a valid code. If it is greater than 1, two's complement is performed before encoding. The encoding is iterated multiple times, and byte substitution and row shift operations are performed in each round of iteration.

[0160] Compression processing of data: Calculate the importance degree of each field, perform lossy compression on data with a sensitivity level of 1, perform no compression on unimportant data such as time and GPS positioning, and do not compress data with a sensitivity level of 0. The data compression methods use run-length encoding and Huffman encoding. Based on the principle of a large compression ratio, different encoding methods are used for different types of data. Run-length encoding is an algorithm for compressing data sequences. First, count the number of occurrences of the characters in the encoding object, and represent a series of repeatedly occurring characters with this character and the number of repetitions. For example, when the original data is "FFFFFF", the run-length encoding is W12; when the original data is "ABABABAAAB", the run-length encoding is A16B12; when the original data is "ABABAAAB", the run-length encoding is A16B8. Huffman encoding is a data representation method that defines the encoding length of characters according to probabilities. To increase the encoding efficiency, when encoding characters, the character with the smallest probability is always used as the root node, and a choice is made between left or right exits in the branch with a smaller probability, which is defined using binary 0 and 1. During the character encoding process, each character corresponds to a path, and finally a branched tree structure is obtained. During transmission, it is transmitted according to the symbol sequence corresponding to the path.

[0161] Preferably, in step 4, during the encrypted transmission process, the data receiving end determines the importance degree of the data by evaluating the data transmission volume. When it is determined that the length of the encrypted data packet γ exceeds the preset length, it indicates that the importance degree α is greater than 1, that is, it is considered that new important data has occurred. The data receiving end needs to stop receiving the original data and preferentially receive the important data until the important data transmission is completed to ensure the real-time nature of the data transmission.

[0162] Refer to Figure 2 , the data sending end determines whether to transmit, retransmit, compress, and encrypt the data frame information according to the size of the statistic. The data receiving end evaluates the data sensitivity according to the statistic to determine whether to receive again, and then determines the transmission scheme according to these statistics, reducing the number of data transmissions and the error rate to improve the accuracy and security of the data transmission.

[0163] It should be noted that the statistic is expressed as:

[0164]

[0165] Among them, is the statistic, is the statistic of the th segment of data, is the number of data segments, is the weight of the th segment of data, The calculation method is as follows: , is the transmission time of the packet data, is the sensitivity level of the packet data, is the requirement of the segment data information for the transmission time, is the abnormality degree of the data transmission process of this segment.

[0166] In summary, in the present invention, the data sending end obtains the temperature, pressure, and seepage information of the wellhead, performs feature detection, judges the information type, calculates the deviation from the normal value, judges its sensitivity level, and uses statistical models and statistical decisions to classify, encrypt, and compress according to different sensitivity levels and emergency situations, and encrypts and transmits by the data sending end. During the transmission process, the sensitivity level of each segment of transmitted data is evaluated by statistical quantities to avoid data expansion generated during the encryption and compression processes. In the encryption process of the present invention, a statistical model of data transmission is introduced, and statistical quantities are used to evaluate the sensitivity level of transmitted data, reduce data expansion, improve system throughput. At the same time, according to the retransmission probability and transmission effect, retransmission decisions are adopted, different algorithms and parameters are selected, the efficiency of data transmission is optimized, and the flexibility and practicality of the system are improved.

[0167] Referring to Figure 3 , the second embodiment of the present invention provides an oilfield data transmission system based on Beidou satellites, including:

[0168] A 101 data acquisition module, configured to acquire oilfield on-site data and data emergency levels, where the oilfield on-site data includes the temperature, pressure, and seepage information of the oilfield collection wellhead;

[0169] A 102 feature detection module, configured to perform feature detection operations according to the oilfield on-site data to obtain the information type corresponding to the oilfield on-site data;

[0170] A 103 eigenvalue extraction module, which extracts eigenvalues from the information type to obtain type eigenvalues;

[0171] A 104 deviation calculation module, which performs deviation calculation operations on the type eigenvalues and a preset normal value to obtain deviation values;

[0172] A 105 probability conversion module, which performs probability conversion operations according to the deviation values to obtain information sensitivity levels;

[0173] A 106 oilfield on-site data packet transmission module, which encrypts and compresses the oilfield on-site data according to the information sensitivity level and the data emergency level to obtain a packet to be sent, and transmits the packet to be sent.

[0174] It should be noted that the oilfield data transmission system based on Beidou satellite provided by the embodiments of the present invention is used to execute all the process steps of the oilfield data transmission method based on Beidou satellite in the above embodiments. The working principles and beneficial effects of the two correspond one by one, so they will not be elaborated here.

[0175] In summary, the present invention provides an oilfield data transmission method based on Beidou satellite, including: obtaining oilfield on-site data and data emergency levels, performing feature detection operations according to the oilfield on-site data to obtain the information type corresponding to the oilfield on-site data, extracting feature values from the information type to obtain type feature values, performing deviation calculation operations on the type feature values and preset normal values to obtain deviation values, performing probability conversion operations according to the deviation values to obtain information sensitivity levels, encrypting and compressing the oilfield on-site data according to the information sensitivity levels and the data emergency levels to obtain data packets to be sent, and performing encrypted transmission on the data packets to be sent.

[0176] When this method uses Beidou satellite to transmit oilfield wellhead information, the data sending end obtains the temperature, pressure, and seepage information of the wellhead, performs feature detection, judges the information type, calculates its deviation from the normal value, judges its sensitivity level, classifies, encrypts, and compresses according to different sensitivity levels and emergency situations, and the data sending end encrypts and transmits the data, improving the reliability and security of data transmission.

[0177] In addition, in the process of data encrypted transmission of the present invention, a statistical model of data transmission is introduced, and statistics are used to evaluate the sensitivity level of the transmitted data, reduce data expansion, improve system throughput. At the same time, according to the retransmission probability and transmission effect, retransmission decisions are made, different algorithms and parameters are selected, optimizing the efficiency of data transmission and enhancing the flexibility and practicality of the system.

[0178] The embodiments of the present invention also provide an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an oilfield data transmission method program based on Beidou satellite. When the processor executes the computer program, it implements the steps in the above embodiments of the oilfield data transmission method based on Beidou satellite, such as Figure 1 the steps shown in S1. Or, when the processor executes the computer program, it implements the functions of each module / unit in the above device embodiments, such as the data acquisition module.

[0179] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the electronic device.

[0180] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine certain components, or different components. For example, the electronic device may further include input / output devices, network access devices, a bus, etc.

[0181] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and circuits.

[0182] The memory can be used to store the computer program and / or modules. By running or executing the computer program and / or modules stored in the memory, and invoking the data stored in the memory, the processor realizes various functions of the electronic device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0183] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0184] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationships between the modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0185] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. In particular, for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An oilfield data transmission method based on Beidou satellites, characterized in that, Executed by the data sender, including: Obtain the oilfield field data and the data emergency level, where the oilfield field data includes the temperature, pressure, and seepage information of the oilfield collection wellhead; Perform a feature detection operation based on the oilfield field data to obtain the information type corresponding to the oilfield field data; Extract the feature values of the information type to obtain the type feature values; Perform a deviation calculation operation on the type feature value and a preset normal value to obtain a deviation value; Perform a probability conversion operation based on the deviation value to obtain the information sensitivity; Encrypt and compress the oilfield field data according to the information sensitivity and the data emergency level to obtain a data packet to be sent, and perform encrypted transmission on the data packet to be sent; Wherein, after performing encrypted transmission on the data packet to be sent, it is executed by the data receiver, and the method further includes: The data receiver receives the data packet to be sent to obtain an encrypted data packet; The data receiver calculates a statistic using a statistical decision model based on the encrypted data packet; Perform a sensitivity evaluation operation on each segment of data information of the encrypted data packet according to the statistic to obtain a sensitivity evaluation result for each segment of data; The data receiver determines whether to re-receive the data packet to be sent according to the sensitivity evaluation result for each segment of data; The data receiver sends the statistic to the data sender, and the data sender determines whether to re-encrypt, compress, and transmit the oilfield field data according to the statistic.

2. The oilfield data transmission method based on Beidou satellites according to claim 1, characterized in that, The performing a feature detection operation based on the oilfield field data to obtain the information type corresponding to the oilfield field data includes: Compare and judge the oilfield field data with a preset normal value of the field data to obtain a judgment result; Wherein, the normal value of the field data includes the normal value of temperature, the normal value of pressure, and the normal value of seepage information, and the type feature value includes the temperature feature value, the pressure feature value, and the seepage feature value; According to the judgment result, obtain the normal situation information, overlimit information, and overlimit feature value of the oilfield field data, and the information type corresponding to the oilfield field data.

3. The method for transmitting oilfield data based on Beidou satellites according to claim 2, wherein The information type judgment method of the oilfield field data includes: Judge whether the type feature value exceeds the preset normal value of the field data; When the type feature value is overlimit, further judge the information type of the oilfield field data, and store the oilfield field data as numerical data or feature data respectively; The numerical data includes the wellhead temperature, pressure, and seepage parameters, and the feature data includes abnormal data.

4. The oilfield data transmission method based on Beidou satellites according to claim 3, characterized in that, The performing a probability conversion operation based on the deviation value to obtain the information sensitivity includes: When the transmitted data includes the numerical data of the oilfield field data, calculate the deviation value of the numerical data and convert the deviation value into a probability to obtain a probability result; According to the probability result, divide the numerical data into three sensitive ranges: When the probability result of the numerical data is lower than the mean minus one standard deviation, the numerical data belongs to the first sensitive range; When the probability result of the numerical data exceeds the mean minus one standard deviation and is lower than the mean plus two standard deviations, the numerical data belongs to the second sensitive range; When the probability result of the numerical data is higher than the mean plus two standard deviations, the numerical data belongs to the third sensitive range; Among them, the data in the first sensitive range and the second sensitive range are sensitive data, and the data in the third sensitive range are highly sensitive data.

5. The oilfield data transmission method based on Beidou satellites according to claim 4, wherein When the transmitted data includes the characteristic data of the oilfield field data, the method for judging the abnormal situation of the transmitted data includes: For temperature information, when the temperature characteristic value exceeds a preset value of the normal range of the oil well temperature, it belongs to general abnormality; when the temperature characteristic value exceeds two preset values of the normal range, it belongs to serious abnormality; For pressure information, when the pressure characteristic value exceeds a preset value of the normal range of the oil well pressure, it belongs to general abnormality; when the pressure characteristic value exceeds two preset values of the normal range, it belongs to serious abnormality; For seepage information, when the seepage characteristic value is lower than a preset value of the normal range of the oil well seepage, it belongs to general abnormality; when the seepage characteristic value is lower than two preset values of the normal range, it belongs to serious abnormality.

6. The method for transmitting oilfield data based on Beidou satellites according to claim 5, characterized in that The method for encrypting and compressing the oilfield field data includes: Setting normal data levels and abnormal data levels; For the data in the third sensitive range, the general abnormality and the serious abnormality, adopt separate encryption and compression methods; For the normal data, adopt weak encryption and weak compression methods; For the abnormal data, adopt strong encryption and strong compression methods; Among them, the encrypted and compressed results of the abnormal data are sent in segments with a fixed length, and the encrypted and compressed results of the normal data are transmitted dynamically according to requirements.

7. The method for transmitting oilfield data based on Beidou satellites according to claim 6, characterized in that, The method for strongly encrypting and strongly compressing the abnormal data includes: Storing the abnormal data in a specific area; Counting the data volume and length of the abnormal data, establishing a statistical model, analyzing the statistical characteristics of the abnormal data, and obtaining the length of the abnormal data, the distribution of the abnormal data, and the dispersion of the abnormal data; Obtaining compression parameters based on the length of the abnormal data or the weighted distribution of the length; Generating a key based on the statistical model and using the key to encrypt the abnormal data.

8. An oilfield data transmission system based on Beidou satellites, characterized in that, For implementing the Beidou satellite-based oilfield data transmission method according to any one of claims 1 to 7, configured at the data sending end, includes: A data acquisition module, used to acquire oilfield field data and data emergency levels, and the oilfield field data includes temperature, pressure, and seepage information of the oilfield collection wellhead; A feature detection module, used to perform feature detection operations according to the oilfield field data to obtain the information type corresponding to the oilfield field data; A feature value extraction module, which extracts feature values from the information type to obtain type feature values; A deviation calculation module, which performs a deviation calculation operation on the type feature value and a preset normal value to obtain a deviation value; A probability conversion module, which performs a probability conversion operation according to the deviation value to obtain the information sensitivity level; The oilfield field data packet transmission module encrypts and compresses the oilfield field data according to the information sensitivity and the data urgency level to obtain a data packet to be sent, and transmits the data packet to be sent.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the Beidou satellite-based oilfield data transmission method according to any one of claims 1 to 7.

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