Alpine region logging data cloud storage system

Abstract

The invention relates to the technical field of cloud storage, in particular to an alpine region logging data cloud storage system which comprises a network quality monitoring module, a data grading module, a dynamic bandwidth management module and an intelligent data migration module. According to the invention, by collecting the historical data of the network indexes and calculating the score of the current network state, the network condition is effectively evaluated, the data transmission strategy is optimized, the reliability and efficiency of data transmission are enhanced, priority rating is carried out according to the importance and emergency degree of the data, key data can be transmitted preferentially, the network congestion is effectively reduced, and the data transmission efficiency is improved. The data transmission efficiency is improved, the accuracy and the response speed of data management are optimized, the data access speed is improved by predicting the access frequency of the data and dynamically adjusting the storage medium, the storage cost is reduced, the overall data processing capacity and the cost effectiveness are improved, and the method is suitable for popularization and application. And the security and the quick accessibility of the key data in a severe environment are ensured.

Description

Technical Field

[0001] The present invention relates to the field of cloud storage technology, and in particular to a cloud storage system for well logging data in alpine areas. Background Art

[0002] The field of cloud storage technology involves the storage, management and backup of data on remote server clusters connected to the Internet. These servers are usually maintained by professional data center operators. Cloud storage allows data to be virtualized on multiple hardware platforms and provides high scalability and accessibility. Users can access data stored in the cloud from any location through the network. This technology supports different storage architectures, such as file storage, block storage, and object storage, each suitable for different types of data needs. Cloud storage services usually include data encryption, disaster recovery plans, and redundant data backups to enhance data security and durability.

[0003] Among them, the cloud storage system for well logging data in high-cold areas is designed to collect and store well logging data generated during geological exploration in extremely cold areas. The main purpose of this system is to ensure that key data can be safely stored and backed up under environmental conditions affected by high-cold plateaus. By uploading data to the cloud, remote access, analysis and management of data can be achieved, supporting the exploration team to obtain key information in a timely manner. The system uses the high reliability and accessibility of cloud storage to ensure that data can be securely stored and accessed in a timely manner even in remote geographical locations or harsh environments.

[0004] Traditional cloud storage systems lack sufficient flexibility and pertinence when dealing with data transmission and storage under extreme climatic conditions, especially in high-cold areas with unstable network conditions. Conventional cloud storage systems fail to effectively deal with the problems of high latency and high packet loss rates, affecting the real-time and accuracy of data transmission. Traditional cloud storage solutions lack the ability to dynamically adjust the allocation of data storage media, resulting in the use of high-cost storage media for both high-frequency access data and low-frequency access data, increasing the company's operating costs, failing to fully adapt to rapidly changing environmental requirements, and resulting in inefficient data access and waste of resources. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a cloud storage system for well logging data in alpine areas.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a cloud storage system for well logging data in high-cold areas, the system comprising:

[0007] The network quality monitoring module collects historical data of network indicators based on the network environment of well logging in high-cold areas. The network indicators include network delay, packet loss rate and bandwidth utilization, calculates the score of each indicator, and evaluates the current network status in combination with the current environment status to obtain network status analysis information;

[0008] The data classification module identifies the data flow and cloud storage requirements based on the real-time data of logging and well logging. It analyzes the importance of logging and well logging data according to the data content, identifies key data, and rates the data according to its importance and urgency to obtain priority assessment information.

[0009] The dynamic bandwidth management module adjusts the network bandwidth in real time based on the network status analysis information and priority evaluation information, gives priority to the transmission of high-priority logging data, limits the bandwidth usage of non-urgent data, optimizes the overall network resource utilization, and obtains bandwidth allocation optimization results;

[0010] The intelligent data migration module predicts the access frequency of the differentiated logging data in the future period based on the bandwidth allocation optimization result and the historical access data of the cloud storage server and the type of data stored, divides the data into high-frequency access data and low-frequency access data, sets a low-cost storage medium for the low-frequency access data, and sets a high-performance storage medium for the high-frequency access data, so as to obtain a data optimization storage strategy.

[0011] The present invention is improved in that the method for calculating the score of each indicator is:

[0012] Based on the network environment of logging and well logging in high-cold areas, historical data of network indicators are collected, including network delay, packet loss rate and bandwidth utilization, and an initial score value S0 is set for each indicator;

[0013] Based on the initial score value S0, by the formula:

[0014]

[0015] Calculate the adjusted score S for each indicator and evaluate the current status of each indicator;

[0016] Among them, S0 represents the initial score, α is the main adjustment coefficient, β is the smoothing parameter, x is the current measured value of the indicator, θ is the expected target value of the indicator, and S represents the adjusted score.

[0017] The present invention is improved in that the steps of obtaining the network status analysis information are:

[0018] Based on the logging work scenario in the alpine region, collect current environmental data, including ambient temperature and wind speed;

[0019] Based on the adjusted score S of each indicator, combined with environmental data, the formula is:

[0020]

[0021] Calculate the comprehensive network status score S n , and score S according to the comprehensive network status n , evaluate the current network status and obtain network status analysis information;

[0022] Among them, S n is the comprehensive network status score, μ is the environmental status adjustment coefficient, and w d 、w l and w b is the weight coefficient, S d , S l and S b They are the scores for network delay, packet loss rate, and bandwidth utilization respectively.

[0023] The present invention is improved in that the method for analyzing the importance of logging data is:

[0024] Based on the real-time data of logging, the key attributes of each data are extracted, including measurement type and data size, and the attributes are marked as x1, x2, …, x n ;

[0025] Based on the key attributes of each data, the formula is:

[0026]

[0027] Calculate the importance score C of each data point, evaluate the importance of the data, and identify key data based on the size of the C value;

[0028] Among them, C represents the importance score, i is the index, n represents the total number of attributes of the data, and w i is the attribute x i The weight of x i Represents the value of the i-th key attribute of the data, p is the attribute weighted power parameter, σ is the smoothing parameter, and μ represents the average value of the attribute.

[0029] The present invention is improved in that the step of obtaining the priority evaluation information is:

[0030] Based on the importance score C and the urgency of each data, the formula is:

[0031]

[0032] Calculate the priority score P;

[0033] Among them, P is the priority score of the data, C is the importance score of the data, U is the urgency of the data, α is the weight coefficient of the importance score, β is the weight coefficient of the urgency, γ is a constant, and ∈ is the adjustment factor;

[0034] Based on the priority score P, it is compared with a preset priority threshold, and the priority category of the data is divided into three levels: high, medium, and low, to obtain priority evaluation information.

[0035] The present invention is improved in that the steps of obtaining the bandwidth allocation optimization result are:

[0036] Based on the comprehensive network status score S n And the priority score P, through the formula:

[0037]

[0038] Calculate the network bandwidth adjustment value B alloc ;

[0039] Among them, B total represents the total bandwidth value of the current network, P is the priority score of the data, S n is the comprehensive network status score, α and β are weight coefficients, γ is a constant, B alloc Adjust the value for network bandwidth;

[0040] Based on the network bandwidth adjustment value B alloc , adjust the network bandwidth allocation, give priority to the transmission of high-priority logging data, limit the bandwidth usage of non-urgent data, and obtain the bandwidth allocation optimization result.

[0041] The present invention is improved in that the method for predicting the access frequency of the differentiated logging data in the future period is:

[0042] Collect historical access data and storage type information of the cloud storage server, count the access frequency and mode of each data type, and obtain historical access statistics;

[0043] Based on the historical access statistics, the formula is:

[0044]

[0045] Calculate the predicted access frequency R i , obtain the access frequency information of the differentiated logging data in the future period;

[0046] Among them, R i is the predicted access frequency of the i-th data type, V i is the historical number of visits to the i-th data type, α iis the influence adjustment parameter of the i-th data type, β is the sensitivity adjustment parameter, T i is the time-related factor of the i-th data type, θ is the threshold parameter, λ is the adjustment coefficient, and e represents the base of the natural logarithm.

[0047] The present invention is improved in that the steps of obtaining the data optimization storage strategy are:

[0048] Based on the predicted access frequency R i , through the formula:

[0049]

[0050] Calculate the weighted threshold comparison value W i ;

[0051] Among them, R i is the predicted access frequency of the i-th data type, α is the weight coefficient of the predicted access frequency, β is the accuracy weight coefficient, γ is the weight coefficient of the time-related factor, T i is the time-dependent factor, W i is the weighted threshold comparison value:

[0052] Based on the weighted threshold comparison value W i , according to historical data and business needs, set the frequency threshold Y, and W i Data above the threshold Y is classified as high-frequency access data, and W i Data below and equal to the threshold value Y is classified as low-frequency access data, a low-cost storage medium is set for the low-frequency access data, and a high-performance storage medium is set for the high-frequency access data, thereby obtaining a data optimization storage strategy.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are:

[0054] In the present invention, by collecting historical data of network indicators and calculating the current network status score, the network status is effectively evaluated, the data transmission strategy is optimized, the reliability and efficiency of data transmission are enhanced, and priority rating is performed according to the importance and urgency of the data to ensure that key data can be transmitted first, effectively alleviating network congestion, improving the efficiency of data transmission, optimizing the accuracy and response speed of data management, and by predicting the access frequency of data and dynamically adjusting the storage medium, the speed of data access is increased, and the storage cost is reduced, thereby improving the overall data processing capability and cost-effectiveness, ensuring the security and rapid accessibility of key data in harsh environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a system flow chart of the present invention;

[0056] Figure 2A flowchart for calculating the score of each indicator for the present invention;

[0057] Figure 3 A flowchart of the present invention for obtaining network status analysis information;

[0058] Figure 4 Flow chart of the importance of analyzing the logging data for the present invention;

[0059] Figure 5 A flow chart for obtaining priority assessment information for the present invention;

[0060] Figure 6 A flow chart of obtaining bandwidth allocation optimization results according to the present invention;

[0061] Figure 7 A flow chart of predicting the access frequency of the future time period of the differentiated logging data according to the present invention;

[0062] Figure 8 A flow chart of obtaining a data optimization storage strategy for the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0065] Example

[0066] See also Figure 1 The present invention provides a technical solution: a cloud storage system for logging and well logging data in high-cold areas, the system comprising:

[0067] The network quality monitoring module collects historical data of network indicators based on the network environment of well logging in high-cold areas. The network indicators include network delay, packet loss rate and bandwidth utilization, calculates the score of each indicator, and uses the score of each indicator combined with the current environment status to calculate the current network status score, evaluate the current network status, and obtain network status analysis information;

[0068] The data classification module identifies the data flow and cloud storage requirements based on the real-time data of logging and well logging. It analyzes the importance of logging and well logging data according to the data content, identifies key data, and rates the data according to its importance and urgency to obtain priority assessment information.

[0069] The dynamic bandwidth management module adjusts the network bandwidth in real time based on the network status analysis information and priority assessment information, giving priority to the transmission of high-priority logging data, limiting the bandwidth usage of non-urgent data, optimizing the overall network resource utilization, and obtaining bandwidth allocation optimization results;

[0070] Based on the bandwidth allocation optimization results, the intelligent data migration module combines the historical access data of the cloud storage server and the type of data stored to predict the access frequency of the differentiated logging data in the future period, divide the data into high-frequency access data and low-frequency access data, set low-cost storage media for low-frequency access data, and set high-performance storage media for high-frequency access data, thus obtaining a data optimization storage strategy;

[0071] Network status analysis information includes network delay index, packet loss rate score, bandwidth utilization index and overall network score. Priority assessment information includes critical data storage priority identification, non-critical data storage efficiency level and data access speed index. Bandwidth allocation optimization results include critical data transmission bandwidth priority information, non-emergency data bandwidth limitation strategy and overall network bandwidth utilization. Data optimization storage strategy includes data access frequency prediction value, cost-effective storage media allocation and data storage timeliness evaluation.

[0072] See also Figure 2 , the method to calculate the score of each indicator is:

[0073] Based on the network environment of logging and well logging in high-cold areas, historical data of network indicators are collected, including network delay, packet loss rate and bandwidth utilization, and an initial score value S0 is set for each indicator;

[0074] Based on the initial score value S0, through the formula:

[0075]

[0076] Calculate the adjusted score S for each indicator and evaluate the current status of each indicator;

[0077] Among them, S0 represents the initial score, α is the main adjustment coefficient, β is the smoothing parameter, x is the current measured value of the indicator, θ is the expected target value of the indicator, and S represents the adjusted score.

[0078] formula:

[0079]

[0080] Parameter details and how to obtain them:

[0081] S0: Initial score, set as the standard starting point for all network indicators, set by experts based on the theoretical network state to facilitate standardization of the scores to a comparable range.

[0082] α: Main adjustment coefficient, which determines the basic range of score adjustment. The coefficient is set according to the sensitivity of network performance to business impact, and is generally derived through historical data analysis.

[0083] β: Smoothing parameter, used to adjust the growth rate of the score adjustment to prevent the adjustment from being too fast or too drastic. The parameter is determined through statistical analysis of historical data and is usually set within a certain range to ensure that the denominator is not zero and the adjustment process is relatively smooth.

[0084] x: represents the actual observed value, which is obtained directly from the network monitoring system, such as real-time network delay or bandwidth utilization.

[0085] θ: is the expected target value of each network indicator, which is usually set according to network design specifications or business requirements. For example, the expected target value of network delay may be set to the minimum delay value.

[0086] Calculation example:

[0087] Set the parameters as follows:

[0088] S0=100, α=0.5, β=1, x=120 (assuming the current network delay is 120 milliseconds), θ=100 (the expected target delay is 100 milliseconds).

[0089] Calculation process:

[0090] Compute the difference between the actual observed value and the expected target value:

[0091] x-θ=120-100=20

[0092] Calculate the smoothing parameter and the square root part:

[0093]

[0094] Then calculate the adjustment factor multiplied by the difference:

[0095] α·x-θ=0.5·20=10

[0096] Calculate the adjusted rating:

[0097]

[0098] The resulting adjusted score is 98.17, reflecting the drop in score due to the 20 milliseconds of increased latency. The calculation flow clearly shows the specific formula steps used to get the adjusted score from the actual observations.

[0099] See also Figure 3 , the steps to obtain network status analysis information are:

[0100] Based on the logging work scenario in the alpine region, collect current environmental data, including ambient temperature and wind speed;

[0101] Based on the adjusted score S of each indicator, combined with environmental data, the formula is:

[0102]

[0103] Calculate the comprehensive network status score S n , and score S according to the comprehensive network status n , evaluate the current network status and obtain network status analysis information;

[0104] Among them, S n is the comprehensive network status score, μ is the environmental status adjustment coefficient, and w d 、w l and w b is the weight coefficient, S d , S l and S b They are the scores for network delay, packet loss rate, and bandwidth utilization respectively.

[0105] formula:

[0106]

[0107] Parameter details and how to obtain them:

[0108] μ: Environmental status adjustment coefficient, which reflects the actual impact of the environment on network performance. It obtains data on temperature, wind speed, etc. through environmental monitoring equipment and is set by experts or regression models.

[0109] w d 、w l and w b : Weight coefficient, which is allocated according to the importance of each network indicator. It can be obtained by analyzing the impact of historical data. The weight allocation depends on expert experience and data analysis.

[0110] S d , S l and S b : The score of each network indicator, calculated by the previous steps.

[0111] Calculation example:

[0112] Assume the following parameter values:

[0113] μ=0.85

[0114] w d =0.5,w l =0.3,w b =0.2

[0115] S d =80,S l =50,S b =70

[0116] Calculation process:

[0117] Calculate the weighted network latency score portion:

[0118]

[0119] Calculate the weighted packet loss score portion:

[0120]

[0121] Calculate the weighted bandwidth utilization score portion:

[0122] w b ·logS b =0.2·log70≈0.2·1.845=0.369

[0123] Calculate the sum of the weighted scores:

[0124] 3200+2.121+0.369=3202.49

[0125] Application environment status adjustment coefficient:

[0126]

[0127] This calculation shows a composite score of 2722.12, a result that combines multiple influencing factors and reflects the current state of the network.

[0128] See also Figure 4 , the method to analyze the importance of logging data is:

[0129] Based on the real-time data of logging, the key attributes of each data are extracted, including measurement type and data size, and the attributes are marked as x1, x2, …, x n ;

[0130] Based on the key attributes of each data, through the formula:

[0131]

[0132] Calculate the importance score C of each data point, evaluate the importance of the data, and identify key data based on the size of the C value;

[0133] Among them, C represents the importance score, i is the index, n represents the total number of attributes of the data, and w i is the attribute x i The weight of x i Represents the value of the i-th key attribute of the data, p is the attribute weighted power parameter, σ is the smoothing parameter, and μ represents the average value of the attribute.

[0134] formula:

[0135]

[0136] Parameter details and how to obtain them:

[0137] w i : Weight coefficient, obtained by statistical analysis of the influence of historical data on each attribute. The weight reflects the importance of each type of data to the overall result.

[0138] x i : The actual observed value of the ith attribute, obtained directly from the data source such as the logging instrument.

[0139] p: attribute weighted power, which is a tuning parameter determined according to the nature of the attribute. For example, for attributes such as temperature that have a greater impact on the result, a higher power may be assigned.

[0140] σ: Smoothing parameter, used to adjust the sensitivity of the importance score to prevent extreme values ​​from excessively affecting the importance results. The parameter is obtained through variance analysis of the data.

[0141] μ: The average value of all attribute values, obtained by calculating the average of the corresponding attribute values ​​of all data points.

[0142] Calculation Example

[0143] Assume the following parameters and data points:

[0144] n=3: three properties, namely temperature, pressure, and flow rate.

[0145] w1=0.5,w2=0.3,w3=0.2:weight distribution, set according to the importance of the attribute.

[0146] x1=100,x2=50,x3=30: property values, corresponding to the actual readings of temperature, pressure and flow rate respectively.

[0147] p=2: Choose a square power to enhance the influence of important attributes.

[0148] σ=10: Smoothing parameter to prevent extreme deviations.

[0149] μ=60: Mean value, calculated from the average of the attribute values.

[0150] The calculation process is as follows:

[0151] Calculate the adjusted value for each property:

[0152] For temperature (x1):

[0153]

[0154] For pressure (x2):

[0155]

[0156] For flow rate (x3):

[0157] Calculate the importance score C:

[0158] C = 100 + 37.5 + 4.5 = 142

[0159] According to the importance score C, we can compare it with the scores of other data to determine the importance of the data point.

[0160] See also Figure 5 , the steps to obtain priority evaluation information are:

[0161] Based on the importance score C and the urgency of each data, the formula is:

[0162]

[0163] Calculate the priority score P;

[0164] Among them, P is the priority score of the data, C is the importance score of the data, U is the urgency of the data, α is the weight coefficient of the importance score, β is the weight coefficient of the urgency, γ is a constant, and ∈ is the adjustment factor;

[0165] Based on the priority score P, it is compared with the preset priority threshold and the priority category of the data is divided into three levels: high, medium and low to obtain the priority evaluation information.

[0166] formula:

[0167]

[0168] Parameter details and how to obtain them:

[0169] α: is the weight coefficient of the importance score, which is set by analyzing the historical importance of the data and its impact on the business process.

[0170] β: The weight coefficient of the urgency score, which is set by analyzing the historical importance of the data and its impact on business processes, and aims to mitigate the impact of extreme values.

[0171] C: The importance score of the data, calculated by the previous step.

[0172] U: The urgency of the data depends on the time sensitivity and potential risks of the data, and can be preset by experts in combination with historical data.

[0173] γ: constant used to stabilize the denominator and prevent division by zero.

[0174] ∈: Adjustment factor used to adjust the impact of the difference between importance and urgency scores.

[0175] Calculation example:

[0176] Assume that the parameter values ​​have been set as follows:

[0177] α=1.5, β=0.5, C=80 (indicating a higher level of importance), U=50 (indicating medium urgency), γ=1, ∈=0.1.

[0178] Calculation process:

[0179] Calculate the weighted square of the importance score:

[0180] α·C 2 =1.5×80 2 =1.5×6400=9600

[0181] Calculate the weighted square root of urgency:

[0182]

[0183] Calculate the adjusted difference in the denominator:

[0184] γ+∈·|CU|=1+0.1×|80-50|=1+0.1×30=4

[0185] Calculate the priority score according to the improved formula:

[0186]

[0187] The calculation clearly demonstrates a balanced approach to prioritizing data by taking into account both its importance and urgency.

[0188] See also Figure 6, the steps to obtain the bandwidth allocation optimization results are:

[0189] Based on the comprehensive network status score S n And the priority score P, through the formula:

[0190]

[0191] Calculate the network bandwidth adjustment value B alloc ;

[0192] Among them, B total represents the total bandwidth value of the current network, P is the priority score of the data, S n is the comprehensive network status score, α and β are weight coefficients, γ is a constant, B alloc Adjust the value for network bandwidth;

[0193] Adjust value B based on network bandwidth alloc , adjust the network bandwidth allocation, give priority to the transmission of high-priority logging data, limit the bandwidth usage of non-urgent data, and obtain the bandwidth allocation optimization result.

[0194] formula:

[0195]

[0196] Parameter details and how to obtain them:

[0197] B total : The total bandwidth value of the current network is the maximum bandwidth resource available in the network and is obtained in real time through the network monitoring system.

[0198] P: The priority score of the data, which has been obtained in the previous step. It reflects the urgency and importance of data transmission and is usually based on data content and real-time evaluation.

[0199] S n : The comprehensive network status score, which has been obtained in the previous step, reflects the overall performance status of the current network and is calculated by comprehensively evaluating network delay, packet loss rate and bandwidth utilization.

[0200] α: Weight coefficient, used to adjust the influence of the priority score in the bandwidth allocation formula, set through historical data analysis or expert experience.

[0201] β: Weight coefficient, used to adjust the influence of the network status score in the bandwidth allocation formula, set through historical data analysis or expert experience.

[0202] γ: a constant used to stabilize the denominator of the formula to prevent instability when the denominator approaches zero when the priority score and network status score are low. It is set through experience or testing.

[0203] Calculation example:

[0204] Assume the following specific values:

[0205] B total =1000Mbps, P = 2400.884, S n =2722.12, α=1.0β=0.5, γ=1000.

[0206] Calculation process:

[0207] The numerator part of the calculation formula is:

[0208] α·P+β·S n =1.0·2400.884+0.5·2722.12

[0209] =2400.884+1361.06

[0210] =3761.944

[0211] Calculate the denominator part of the formula:

[0212] γ+P·S n =1000+2400.884·2722.12

[0213] =1000+6533960.465

[0214] ≈6534960.465

[0215] Calculate the bandwidth allocation value B alloc :

[0216]

[0217] Based on the current network status and data priority, the bandwidth allocated to data is 575.5Mbps, ensuring optimal utilization of network resources.

[0218] See also Figure 7 , the method for predicting the access frequency of differentiated logging data in the future period is:

[0219] Collect historical access data and storage type information of the cloud storage server, count the access frequency and mode of each data type, and obtain historical access statistics;

[0220] Based on historical access statistics, the formula is:

[0221]

[0222] Calculate the predicted access frequency R i , obtain the access frequency information of the differentiated logging data in the future period;

[0223] Among them, R i is the predicted access frequency of the i-th data type, V i is the historical number of visits to the i-th data type, α i is the influence adjustment parameter of the i-th data type, β is the sensitivity adjustment parameter, T i is the time-related factor of the i-th data type, indicating the time from the last visit, θ is the threshold parameter, λ is the adjustment coefficient, and e represents the base of the natural logarithm.

[0224] formula:

[0225]

[0226] Parameter details and how to obtain them:

[0227] V i : The historical number of visits to the i-th data type. The value is directly extracted from the log of the cloud storage server and is obtained by counting the number of visits in the past certain period of time.

[0228] α i : The influence adjustment parameter for the i-th data type. It is an empirical value, which is set by analyzing the access impact and importance of different data types. It is obtained through regression analysis or historical data influence analysis.

[0229] β: The sensitivity adjustment parameter of the model. The parameter is adjusted according to the historical prediction performance of the model and is used to control the model's sensitivity to the time difference T i -θ response sensitivity.

[0230] T i : Time-related factor, indicating the length of time since the last access to the i-th data type. Calculated based on the timestamp of data access.

[0231] θ: Threshold parameter used to set the baseline for activating the model response. It is set based on the magnitude of changes in historical data and the characteristics of the data type.

[0232] λ: A newly introduced adjustment coefficient that enhances the model’s sensitivity to the time factor. The parameter is set by optimizing the model to adapt to different data access trends, aiming to adjust the weighted impact of time distance.

[0233] Calculation Example

[0234] The calculation example assumes the following parameter values:

[0235] V i =300 (indicates the number of times the i-th data type is accessed)

[0236] αi =1.2 (influence coefficient specially designed for data type)

[0237] β = 0.2 (sensitivity adjustment parameter of the model)

[0238] T i =30 (30 days have passed since the last visit)

[0239] θ=15 (threshold parameter)

[0240] λ=0.1 (adjustment coefficient for time sensitivity)

[0241] Calculation process:

[0242] Calculate historical visits with exponential adjustment:

[0243]

[0244] Calculate the time-sensitive part:

[0245]

[0246] The main part of calculating the predicted visit frequency:

[0247]

[0248] The effect of adding a time factor:

[0249] λ·log1+T i =0.1·log1+30≈0.1·3.433≈0.3433

[0250] Total predicted visit frequency:

[0251] R i =893.97+0.3433≈894.31

[0252] This calculation shows that combining historical access data and time factors to predict future access frequencies for a specific data type can reflect the actual access patterns and predicted demand for the data.

[0253] See also Figure 8 , the steps to obtain the data optimization storage strategy are:

[0254] Based on the predicted access frequency R i , through the formula:

[0255]

[0256] Calculate the weighted threshold comparison value W i ;

[0257] Among them, Ri is the predicted access frequency of the i-th data type, α is the weight coefficient of the predicted access frequency, β is the accuracy weight coefficient, γ is the weight coefficient of the time-related factor, T i is the time-dependent factor, W i is the weighted threshold comparison value:

[0258] Based on the weighted threshold comparison value W i , according to historical data and business needs, set the frequency threshold Y, and W i Data above the threshold Y is classified as high-frequency access data, and W i Data below and equal to the threshold value Y is classified as low-frequency access data, a low-cost storage medium is set for the low-frequency access data, and a high-performance storage medium is set for the high-frequency access data, thereby obtaining a data optimization storage strategy.

[0259] formula:

[0260]

[0261] Parameter details and how to obtain them:

[0262] The context provided by the formula:

[0263] R i : The predicted access frequency of the i-th data type. Calculated by the previous step.

[0264] Y: Frequency threshold, used to distinguish high-frequency access data from low-frequency access data. The threshold is set based on historical data analysis and business needs, for example, through statistical methods or business expert evaluation.

[0265] α: The weight coefficient of the predicted access frequency, which is used to adjust the impact of the predicted frequency on the partition. The value is determined by analyzing the importance and historical access frequency of different data types.

[0266] β: Weight coefficient of the square root part, used to adjust the impact of square root transformation on partitioning. The value improves prediction accuracy by optimizing model parameters.

[0267] γ: The weight of the time-related factor, used to adjust the time impact. The value is determined by analyzing the impact of time factors on data access frequency.

[0268] T i : Time-related factor, indicating the length of time since the last access. The value is obtained directly from the data access log.

[0269] Calculation example:

[0270] Assume the following parameter values:

[0271] Ri =894.31

[0272] Y=1000

[0273] α=0.5

[0274] β=0.3

[0275] γ=0.2

[0276] T i =30

[0277] Calculation process:

[0278] Calculate the weight part of the predicted access frequency:

[0279] α·R i =0.5·894.31=447.155

[0280] Calculate the square root part:

[0281]

[0282] Calculate the time-related factor part:

[0283] γ·log1+T i =0.2·log1+30

[0284] =0.2·log31

[0285] =0.2·3.433

[0286] =0.6866

[0287] Comprehensively calculate the weighted threshold comparison value:

[0288] W i =447.155+8.97+0.6866=456.8116

[0289] Compare W i Sum Y: 456.8116 < 1000

[0290] According to the above steps, data type i is classified as low-frequency access data.

[0291] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A cloud storage system for logging data in high-cold areas, characterized in that: The system comprises: The network quality monitoring module collects historical data of network indicators based on the network environment of well logging in high-cold areas. The network indicators include network delay, packet loss rate and bandwidth utilization, calculates the score of each indicator, and evaluates the current network status in combination with the current environment status to obtain network status analysis information; The data classification module identifies the data flow and cloud storage requirements based on the real-time data of logging and well logging. It analyzes the importance of logging and well logging data according to the data content, identifies key data, and rates the data according to its importance and urgency to obtain priority assessment information. The dynamic bandwidth management module adjusts the network bandwidth in real time based on the network status analysis information and priority evaluation information, gives priority to the transmission of high-priority logging data, limits the bandwidth usage of non-urgent data, optimizes the overall network resource utilization, and obtains bandwidth allocation optimization results; The intelligent data migration module predicts the access frequency of the differentiated logging data in the future period based on the bandwidth allocation optimization result and the historical access data of the cloud storage server and the type of stored data, divides the data into high-frequency access data and low-frequency access data, sets a low-cost storage medium for the low-frequency access data, and sets a high-performance storage medium for the high-frequency access data, so as to obtain a data optimization storage strategy.

2. The cloud storage system for logging data in alpine areas according to claim 1 is characterized in that: The method for calculating the score of each indicator is: Based on the network environment of logging and well logging in high-cold areas, historical data of network indicators are collected, including network delay, packet loss rate and bandwidth utilization, and an initial score value S0 is set for each indicator; Based on the initial score value S0, by the formula: Calculate the adjusted score S for each indicator and evaluate the current status of each indicator; Among them, S0 represents the initial score, α is the main adjustment coefficient, β is the smoothing parameter, x is the current measured value of the indicator, θ is the expected target value of the indicator, and S represents the adjusted score.

3. The cloud storage system for logging data in alpine areas according to claim 2 is characterized in that: The steps for obtaining the network status analysis information are as follows: Based on the logging work scenario in the alpine region, collect current environmental data, including ambient temperature and wind speed; Based on the adjusted score S of each indicator, combined with environmental data, the formula is: Calculate the comprehensive network status score S n , and score S according to the comprehensive network status n , evaluate the current network status and obtain network status analysis information; Among them, S n is the comprehensive network status score, μ is the environmental status adjustment coefficient, and w d 、w l and w b is the weight coefficient, S d , S l and S b They are the scores for network delay, packet loss rate, and bandwidth utilization respectively.

4. The cloud storage system for logging data in alpine areas according to claim 3 is characterized in that: The method for analyzing the importance of logging data is: Based on the real-time data of logging, the key attributes of each data are extracted, including measurement type and data size, and the attributes are marked as x1, x2, ..., x n ; Based on the key attributes of each data, the formula is: Calculate the importance score C of each data point, evaluate the importance of the data, and identify key data based on the size of the C value; Among them, C represents the importance score, i is the index, n represents the total number of attributes of the data, and w i is the attribute x i The weight of x i Represents the value of the i-th key attribute of the data, p is the attribute weighted power parameter, σ is the smoothing parameter, and μ represents the average value of the attribute.

5. The cloud storage system for logging data in alpine areas according to claim 4 is characterized in that: The steps for obtaining the priority evaluation information are: Based on the importance score C and the urgency of each data, the formula is: Calculate the priority score P; Among them, P is the priority score of the data, C is the importance score of the data, U is the urgency of the data, α is the weight coefficient of the importance score, β is the weight coefficient of the urgency, γ is a constant, and ∈ is the adjustment factor; Based on the priority score P, it is compared with a preset priority threshold, and the priority category of the data is divided into three levels: high, medium, and low, to obtain priority evaluation information.

6. The cloud storage system for logging data in alpine areas according to claim 5 is characterized in that: The steps for obtaining the bandwidth allocation optimization result are: Based on the comprehensive network status score S n And the priority score P, through the formula: Calculate the network bandwidth adjustment value B alloc ; Among them, B total represents the total bandwidth value of the current network, P is the priority score of the data, S n is the comprehensive network status score, α and β are weight coefficients, γ is a constant, B alloc Adjust the value for network bandwidth; Based on the network bandwidth adjustment value B alloc , adjust the network bandwidth allocation, give priority to the transmission of high-priority logging data, limit the bandwidth usage of non-urgent data, and obtain the bandwidth allocation optimization result.

7. The cloud storage system for logging data in alpine areas according to claim 6 is characterized in that: The method for predicting the access frequency of the differentiated logging data in the future period is: Collect historical access data and storage type information of the cloud storage server, count the access frequency and mode of each data type, and obtain historical access statistics; Based on the historical access statistics, the formula: Calculate the predicted access frequency R i , obtain the access frequency information of the differentiated logging data in the future period; Among them, R i is the predicted access frequency of the i-th data type, V i is the historical number of visits to the i-th data type, α i is the influence adjustment parameter of the i-th data type, β is the sensitivity adjustment parameter, T i is the time-related factor of the i-th data type, θ is the threshold parameter, λ is the adjustment coefficient, and e represents the base of the natural logarithm.

8. The cloud storage system for logging data in alpine areas according to claim 7 is characterized in that: The steps for obtaining the data optimization storage strategy are: Based on the predicted access frequency R i , through the formula: Calculate the weighted threshold comparison value W i ; Among them, R i is the predicted access frequency of the i-th data type, α is the weight coefficient of the predicted access frequency, β is the accuracy weight coefficient, γ is the weight coefficient of the time-related factor, T i is the time-dependent factor, W i is the weighted threshold comparison value: Based on the weighted threshold comparison value W i , according to historical data and business needs, set the frequency threshold Y, and W i Data above the threshold Y is classified as high-frequency access data, and W i Data below and equal to the threshold value Y is classified as low-frequency access data, a low-cost storage medium is set for the low-frequency access data, and a high-performance storage medium is set for the high-frequency access data, thereby obtaining a data optimization storage strategy.

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