Data acquisition method and system for variable frequency driver system of petroleum drilling machine

Abstract

The invention discloses a data acquisition method and system for a variable frequency driver system of a petroleum drilling machine. The method comprises the following steps: acquiring key parameter data of a variable frequency driver system of the petroleum drilling machine according to an initial data acquisition frequency; training the key parameter prediction model, and obtaining key parameter prediction data by using the trained key parameter prediction model; dynamically adjusting the data acquisition frequency by using an adaptive control method, and acquiring key parameter data of the variable frequency driver system of the petroleum drilling machine by using the dynamically adjusted data acquisition frequency; and packaging and storing the key parameter data of the variable frequency driver system of the petroleum drilling machine. According to the method, the collection frequency is dynamically adjusted, the frequency is reduced under the stable working condition, resources are saved, the frequency is improved under the abnormal working condition, accurate capture of key data is ensured, resource optimization is achieved, working condition changes are flexibly coped, the resource utilization efficiency is improved, and the response capacity of the system to emergency situations is enhanced.

Description

Technical Field

[0001] The invention relates to the field of data acquisition of a variable frequency drive system of an oil drilling rig, and in particular to a data acquisition method and system of a variable frequency drive system of an oil drilling rig. Background Art

[0002] In oil drilling operations, variable frequency drive systems are widely used to control the motors of drilling rigs to ensure efficient and stable operation of the drilling rigs under various complex working conditions. To ensure the performance and safety of the variable frequency drive system, real-time monitoring and data collection of key parameters in its operation are required. These parameters include but are not limited to voltage, current, speed, torque, vibration, temperature, etc. High-frequency data collection can provide important basis for system optimization, fault diagnosis and preventive maintenance.

[0003] The existing data acquisition methods of oil rig variable frequency drive systems have the following main disadvantages:

[0004] (1) Fixed acquisition frequency. Existing methods usually use a fixed acquisition frequency, which cannot be adjusted dynamically according to the working conditions of the variable frequency drive system. This fixed frequency will lead to unnecessary waste of resources, such as storage space and processor resources, when the drilling rig load is stable. When the working conditions of the variable frequency drive system change dramatically, the fixed low-frequency acquisition cannot capture key high-frequency data and miss important abnormal information. Ultimately, this method limits the variable frequency drive system's ability to respond to emergencies and affects the effectiveness of monitoring and fault detection;

[0005] (2) Insufficient flexibility in data collection. Existing methods lack flexibility and it is difficult to adjust the acquisition frequency according to changes in operating conditions or sensor types. The operating conditions in different operation stages vary greatly, but existing methods cannot intelligently adjust the data acquisition strategy for each channel. Some key parameters require a higher acquisition frequency, but existing methods cannot effectively respond to these requirements, resulting in low resource utilization and affecting the effectiveness and accuracy of data collection. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a data acquisition method and system for a variable frequency drive system of an oil drilling rig.

[0007] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0008] A data acquisition method for a variable frequency drive system of an oil drilling rig comprises the following steps:

[0009] S1, initializing the data acquisition frequency, and collecting key parameter data of the oil rig variable frequency drive system according to the initial data acquisition frequency;

[0010] S2, constructing a key parameter prediction model, using the key parameter data of the oil drilling rig variable frequency drive system in step S1 to train the key parameter prediction model, and using the trained key parameter prediction model to obtain key parameter prediction data;

[0011] S3, based on the key parameter prediction data in step S2, dynamically adjust the data acquisition frequency using an adaptive control method, and use the dynamically adjusted data acquisition frequency to collect key parameter data of the oil drilling rig variable frequency drive system;

[0012] S4, encapsulating the key parameter data of the oil drilling rig variable frequency drive system in step S3 and storing them in a database.

[0013] Further, in step S1, the key parameter data of the oil drilling rig variable frequency drive system is constructed into input sample data, which is expressed as:

[0014]

[0015] Where: X is the input sample, x11 is the first key parameter at the first observation time, x12 is the second key parameter at the first observation time, x1 n is the nth key parameter at the first observation time, x21 is the first key parameter at the second observation time, x22 is the second key parameter at the second observation time, and x2 n is the nth key parameter at the second observation time, xn1 is the first key parameter at the nth observation time, xn2 is the second key parameter at the nth observation time, and xn n is the nth key parameter at the nth observation moment.

[0016] Furthermore, in step S2, a key parameter prediction model is constructed, including the following steps:

[0017] A1. Build ARIMA forecasting models for all key parameters;

[0018] A2. The ARIMA forecasting model in step A1 is used as an element to construct a matrix to obtain the key parameter forecasting model, which is expressed as:

[0019]

[0020] Where: ξ is the key parameter prediction model, ξ1 is the ARIMA prediction model of the first key parameter, ξ2 is the ARIMA prediction model of the second key parameter, ξ n is the ARIMA forecasting model for the nth key parameter.

[0021] Further, in step A1, the ARIMA forecasting model includes a time series model and a non-stationary time series model, and the time series model is expressed as:

[0022]

[0023] Where: y t is the predicted value of the key parameter at time t, c is a constant term, is the first autoregressive coefficient, y t-1 is the predicted value of the key parameter at time t-1, is the second autoregressive coefficient, y t-2 is the predicted value of the key parameter at time t-2, is the pth autoregressive coefficient, p is the first model parameter, y t-p is the predicted value of the key parameter at time tp, θ1 is the first moving average coefficient, z t-1 is the white noise at time t-1, θ2 is the second moving average coefficient, z t-2 is the white noise at time t-2, θ q is the qth moving average coefficient, q is the second model parameter, z t-q is the white noise at time tq, z t is the white noise at time t;

[0024] The non-stationary time series model is expressed as:

[0025]

[0026] Where: Δ d is the d-time difference operation symbol, where d is the number of differences.

[0027] Further, the key parameter prediction model is trained using the key parameter data of the oil drilling rig variable frequency drive system in step S1, and the key parameter prediction data is obtained using the trained key parameter prediction model, including the following steps:

[0028] B1. Select the number of differences and use the autocorrelation function and the partial correlation function to determine the first model parameter and the second model parameter;

[0029] B2, according to the number of differences, the first model parameter and the second model parameter in step B1, the key parameter prediction model is trained using the key parameter data of the oil rig variable frequency drive system in step S1, the autoregressive coefficient and the moving average coefficient in the model are determined, and the optimal autoregressive coefficient and the moving average coefficient are determined by the maximum likelihood estimation method to obtain the trained key parameter prediction model;

[0030] B3. Determine the set prediction future point and use the trained key parameter prediction model to obtain key parameter prediction data.

[0031] Furthermore, in step S3, based on the key parameter prediction data in step S2, the data collection frequency is dynamically adjusted using the adaptive control method. The specific process is: based on the key parameter prediction data in step S2, the predicted value change rate is calculated; it is determined whether the predicted value change rate continuously exceeds the change rate threshold within the set time step length; if so, the data collection frequency is increased using the adaptive control method, otherwise the data collection frequency is reduced using the adaptive control method.

[0032] Furthermore, the adaptive control method is used to increase the data acquisition frequency, which is expressed as:

[0033] f new =min(f max ,f now +Δf),

[0034]

[0035] Where: f new is the adjusted data collection frequency, min is the minimum value symbol, f max is the maximum data acquisition frequency, f now is the current data acquisition frequency, Δf is the dynamic increment of the data acquisition frequency, λ is the adjustment coefficient, and y t is the predicted value of the key parameter at time t, y t-1 is the predicted value of the key parameter at time t-1;

[0036] Adaptive control method is used to reduce the frequency of data acquisition, which can be expressed as:

[0037] f new =max(f min ,f now -Δf)

[0038] Among them: max is the maximum value symbol, f min is the minimum data collection frequency.

[0039] A data acquisition system for a variable frequency drive system of an oil drilling rig using the above method, comprising a data reading module, a sliding window analysis module, an adaptive control module and a data storage module;

[0040] The data reading module is used to initialize the data acquisition frequency and collect key parameter data of the oil drilling rig variable frequency drive system according to the initial data acquisition frequency;

[0041] The sliding window analysis module is used to construct a key parameter prediction model, train the key parameter prediction model using the key parameter data of the oil rig variable frequency drive system, and obtain key parameter prediction data using the trained key parameter prediction model;

[0042] The adaptive control module is used to predict data according to key parameters, dynamically adjust the data acquisition frequency by using the adaptive control method, and collect key parameter data of the oil rig variable frequency drive system by using the dynamically adjusted data acquisition frequency;

[0043] The data storage module is used to encapsulate the key parameter data of the oil rig variable frequency drive system and store it in the database.

[0044] The present invention has the following beneficial effects:

[0045] (1) The present invention dynamically adjusts the data acquisition frequency by using key parameter prediction data and an adaptive control method, and uses the dynamically adjusted data acquisition frequency to collect key parameter data of the oil drilling rig variable frequency drive system. Specifically, the frequency is reduced under stable working conditions to save resources, and the frequency is increased under abnormal working conditions to ensure accurate capture of key data, thereby achieving resource optimization and flexible response to working condition changes. This not only improves resource utilization efficiency, but also enhances the system's ability to respond to emergencies;

[0046] (2) The present invention can ensure that the high-frequency changes of the oil drilling rig variable frequency drive system can be quickly captured under critical working conditions by dynamically adjusting the acquisition frequency, effectively improving the accuracy and integrity of data acquisition, and ensuring accurate monitoring of the system operation status and timely warning of potential faults;

[0047] (3) The present invention reduces the amount of unnecessary data and the system's demand for storage and transmission resources by dynamically adjusting the acquisition frequency, which not only optimizes the efficiency of subsequent data processing but also provides a guarantee for long-term stable operation and subsequent data analysis;

[0048] (4) The present invention provides a data acquisition system for an oil rig variable frequency drive system, comprising a data reading module, a sliding window analysis module, an adaptive control module and a data storage module; the data reading module can be used to initialize the data acquisition frequency, and collect key parameter data of the oil rig variable frequency drive system according to the initial data acquisition frequency; the sliding window analysis module can be used to construct a key parameter prediction model, and train the key parameter prediction model using the key parameter data of the oil rig variable frequency drive system, and obtain the key parameter prediction data using the trained key parameter prediction model; the adaptive control module can be used to dynamically adjust the data acquisition frequency according to the key parameter prediction data using an adaptive control method, and collect the key parameter data of the oil rig variable frequency drive system using the dynamically adjusted data acquisition frequency; the data storage module can be used to encapsulate the key parameter data of the oil rig variable frequency drive system and store them in a database. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1A flow chart of a data acquisition method for a variable frequency drive system of an oil drilling rig;

[0050] Figure 2 The diagram is a structural diagram of a data acquisition system for a variable frequency drive system of an oil drilling rig. DETAILED DESCRIPTION

[0051] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0052] like Figure 1 As shown, a data acquisition method for a variable frequency drive system of an oil drilling rig includes steps S1-S4, which are specifically as follows:

[0053] S1. Initialize the data acquisition frequency, and collect key parameter data of the oil rig variable frequency drive system according to the initial data acquisition frequency.

[0054] In an optional embodiment of the present invention, after collecting the key parameter data of the oil drilling rig variable frequency drive system, the present invention constructs the key parameter data of the oil drilling rig variable frequency drive system into input sample data, which is expressed as:

[0055]

[0056] Where: X is the input sample, x11 is the first key parameter at the first observation time, x12 is the second key parameter at the first observation time, x1 n is the nth key parameter at the first observation time, x21 is the first key parameter at the second observation time, x22 is the second key parameter at the second observation time, and x2 n is the nth key parameter at the second observation time, xn1 is the first key parameter at the nth observation time, xn2 is the second key parameter at the nth observation time, and xn n is the nth key parameter at the nth observation moment.

[0057] The key parameters of the oil drilling rig variable frequency drive system of the present invention include but are not limited to voltage, current, torque, speed, vibration and temperature.

[0058] S2, constructing a key parameter prediction model, using the key parameter data of the oil drilling rig variable frequency drive system in step S1 to train the key parameter prediction model, and using the trained key parameter prediction model to obtain key parameter prediction data.

[0059] In an optional embodiment of the present invention, the present invention constructs a key parameter prediction model, comprising the following steps:

[0060] A1. Construct ARIMA forecasting models for all key parameters.

[0061] The ARIMA forecasting model includes time series models and non-stationary time series models. The time series model is expressed as:

[0062]

[0063] Where: y t is the predicted value of the key parameter at time t, c is a constant term, is the first autoregressive coefficient, y t-1 is the predicted value of the key parameter at time t-1, is the second autoregressive coefficient, y t-2 is the predicted value of the key parameter at time t-2, is the pth autoregressive coefficient, p is the first model parameter, y t-p is the predicted value of the key parameter at time tp, θ1 is the first moving average coefficient, z t-1 is the white noise at time t-1, θ2 is the second moving average coefficient, z t-2 is the white noise at time t-2, θ q is the qth moving average coefficient, q is the second model parameter, z t-q is the white noise at time tq, z t is the white noise at time t.

[0064] The non-stationary time series model is expressed as:

[0065]

[0066] Where: Δ d is the d-time difference operation symbol, where d is the number of differences.

[0067] A2. The ARIMA forecasting model in step A1 is used as an element to construct a matrix to obtain the key parameter forecasting model, which is expressed as:

[0068]

[0069] Where: ξ is the key parameter prediction model, ξ1 is the ARIMA prediction model of the first key parameter, ξ2 is the ARIMA prediction model of the second key parameter, ξ n is the ARIMA forecasting model for the nth key parameter.

[0070] The present invention uses the key parameter data of the oil drilling rig variable frequency drive system in step S1 to train the key parameter prediction model, and uses the trained key parameter prediction model to obtain key parameter prediction data, including the following steps:

[0071] B1. Select the number of differences and use the autocorrelation function and partial correlation function to determine the first model parameters and the second model parameters.

[0072] The present invention uses the autocorrelation function and the partial correlation function to determine the first model parameter and the second model parameter. The specific process is as follows:

[0073] The present invention calculates the autocorrelation function of the key parameter sequence, which is expressed as:

[0074]

[0075] Where: p(k) is the autocorrelation coefficient with lag order k, t is time, T is the number of samples, y t is the predicted value of the key parameter at time t, is the mean of the predicted values ​​of the key parameters in the sample, y t-k is the predicted value of the key parameter at time tk;

[0076] The present invention calculates the partial correlation function of the key parameter sequence, which is expressed as:

[0077]

[0078] in: is the partial autocorrelation coefficient with lag order k, p(ki) is the autocorrelation coefficient with lag order ki, is the partial autocorrelation coefficient with lag order i, and is used to calculate the partial autocorrelation coefficient with lag order k.

[0079] The present invention dynamically adjusts the first model parameter and the second model parameter through the analysis results of the autocorrelation function of the key parameter sequence and the partial correlation function of the key parameter sequence. When the autocorrelation and the partial autocorrelation are significant, that is, when the calculation result is greater than the set threshold, the first model parameter and the second model parameter are adjusted to obtain the adjusted optimal result to determine the first model parameter and the second model parameter.

[0080] B2. According to the number of differences, the first model parameter and the second model parameter in step B1, the key parameter prediction model is trained using the key parameter data of the oil rig variable frequency drive system in step S1 to determine the autoregressive coefficient and the moving average coefficient in the model, and the optimal autoregressive coefficient and the moving average coefficient are determined by the maximum likelihood estimation method to obtain the trained key parameter prediction model.

[0081] B3. Determine the set prediction future point and use the trained key parameter prediction model to obtain key parameter prediction data.

[0082] Specifically, after the trained key parameter prediction model is used to obtain key parameter prediction data, the prediction error of the trained key parameter prediction model is calculated by the root mean square method, which is expressed as:

[0083]

[0084] Where: MSE is the prediction error of the key parameter prediction model, N is the total number of samples of the predicted data, i is the sequence number of the predicted data, y i is the predicted value of the i-th key parameter, It is the mean of the predicted values ​​of the key parameters in the total sample of the predicted data.

[0085] The present invention monitors whether the prediction error is within an acceptable range; if the prediction error continues to be high, it indicates that the current first model parameters and the second model parameters are not suitable for the current working conditions and need to be dynamically adjusted, otherwise no adjustment is performed.

[0086] S3. Based on the key parameter prediction data in step S2, the data acquisition frequency is dynamically adjusted using an adaptive control method, and the key parameter data of the oil rig variable frequency drive system is collected using the dynamically adjusted data acquisition frequency.

[0087] In an optional embodiment of the present invention, the present invention dynamically adjusts the data acquisition frequency based on the key parameter prediction data in step S2 using an adaptive control method. The specific process is: based on the key parameter prediction data in step S2, the predicted value change rate is calculated; it is determined whether the predicted value change rate continuously exceeds the change rate threshold within a set time step length; if so, the data acquisition frequency is increased using the adaptive control method, otherwise the data acquisition frequency is reduced using the adaptive control method.

[0088] The present invention calculates the predicted value change rate, which is expressed as:

[0089]

[0090] Where: σ is the rate of change of the predicted value from time t-1 to time t, y t is the predicted value of the key parameter at time t, y t-1 is the predicted value of the key parameter at time t-1.

[0091] The present invention uses an adaptive control method to increase the data acquisition frequency, which is expressed as:

[0092] f new =min(f max ,f now +Δf),

[0093]

[0094] Where: f new is the adjusted data collection frequency, min is the minimum value symbol, f max is the maximum data acquisition frequency, f now is the current data acquisition frequency, Δf is the dynamic increment of the data acquisition frequency, λ is the adjustment coefficient, and y t is the predicted value of the key parameter at time t, y t-1 is the predicted value of the key parameter at time t-1;

[0095] The present invention uses an adaptive control method to reduce the data acquisition frequency, which is expressed as:

[0096] f new =max(f min ,f now -Δf)

[0097] Among them: max is the maximum value symbol, f min is the minimum data collection frequency.

[0098] S4, encapsulating the key parameter data of the oil drilling rig variable frequency drive system in step S3 and storing them in a database.

[0099] In an optional embodiment of the present invention, the present invention constructs a data storage module, and uses the data storage module to encapsulate key parameter data of the oil drilling rig variable frequency drive system and store it in a database.

[0100] like Figure 2 As shown, a data acquisition system of a variable frequency drive system of an oil drilling rig using the above method comprises a data reading module, a sliding window analysis module, an adaptive control module and a data storage module.

[0101] In an optional embodiment of the present invention, the data reading module is connected to each device sensor, and the data reading module is used to initialize the data acquisition frequency, and collect the key parameter data of the oil rig variable frequency drive system by collecting the data of each device sensor according to the initial data acquisition frequency.

[0102] In an optional embodiment of the present invention, the sliding window analysis module is used to construct a key parameter prediction model, train the key parameter prediction model using key parameter data of the oil rig variable frequency drive system, and obtain key parameter prediction data using the trained key parameter prediction model.

[0103] In an optional embodiment of the present invention, the adaptive control module is used to predict data based on key parameters, dynamically adjust the data acquisition frequency using an adaptive control method, and collect key parameter data of the oil rig variable frequency drive system using the dynamically adjusted data acquisition frequency.

[0104] In an optional embodiment of the present invention, the data storage module is used to encapsulate key parameter data of the oil drilling rig variable frequency drive system and store it in a database.

[0105] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0108] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0109] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A data acquisition method for a variable frequency drive system of an oil drilling rig, characterized in that: The following steps are involved: S1, initializing the data acquisition frequency, and collecting key parameter data of the oil rig variable frequency drive system according to the initial data acquisition frequency; S2, constructing a key parameter prediction model, using the key parameter data of the oil drilling rig variable frequency drive system in step S1 to train the key parameter prediction model, and using the trained key parameter prediction model to obtain key parameter prediction data; S3, based on the key parameter prediction data in step S2, dynamically adjust the data acquisition frequency using an adaptive control method, and use the dynamically adjusted data acquisition frequency to collect key parameter data of the oil drilling rig variable frequency drive system; S4, encapsulating the key parameter data of the oil drilling rig variable frequency drive system in step S3 and storing them in a database.

2. The data acquisition method of the oil drilling rig variable frequency drive system according to claim 1 is characterized in that: In step S1, the key parameter data of the oil drilling rig variable frequency drive system is constructed into input sample data, which is expressed as: Where: X is the input sample, x11 is the first key parameter at the first observation time, x12 is the second key parameter at the first observation time, x1 n is the nth key parameter at the first observation time, x21 is the first key parameter at the second observation time, x22 is the second key parameter at the second observation time, and x2 n is the nth key parameter at the second observation time, xn1 is the first key parameter at the nth observation time, xn2 is the second key parameter at the nth observation time, and xn n is the nth key parameter at the nth observation moment.

3. The data acquisition method of the oil drilling rig variable frequency drive system according to claim 1 is characterized in that: In step S2, a key parameter prediction model is constructed, including the following steps: A1. Build ARIMA forecasting models for all key parameters; A2. The ARIMA forecasting model in step A1 is used as an element to construct a matrix to obtain the key parameter forecasting model, which is expressed as: Where: ξ is the key parameter prediction model, ξ1 is the ARIMA prediction model of the first key parameter, ξ2 is the ARIMA prediction model of the second key parameter, ξ n is the ARIMA forecasting model for the nth key parameter.

4. The data acquisition method of the oil drilling rig variable frequency drive system according to claim 3 is characterized in that: In step A1, the ARIMA forecasting model includes a time series model and a non-stationary time series model. The time series model is expressed as: Where: y t is the predicted value of the key parameter at time t, c is a constant term, is the first autoregressive coefficient, y t-1 is the predicted value of the key parameter at time t-1, is the second autoregressive coefficient, y t-2 is the predicted value of the key parameter at time t-2, is the pth autoregressive coefficient, p is the first model parameter, y t-p is the predicted value of the key parameter at time tp, θ1 is the first moving average coefficient, z t-1 is the white noise at time t-1, θ2 is the second moving average coefficient, z t-2 is the white noise at time t-2, θ q is the qth moving average coefficient, q is the second model parameter, z t-q is the white noise at time tq, z t is the white noise at time t; The non-stationary time series model is expressed as: Where: Δ d is the d-time difference operation symbol, where d is the number of differences.

5. The data acquisition method of the oil drilling rig variable frequency drive system according to claim 4 is characterized in that: The key parameter prediction model is trained using the key parameter data of the oil drilling rig variable frequency drive system in step S1, and the key parameter prediction data is obtained using the trained key parameter prediction model, including the following steps: B1. Select the number of differences and use the autocorrelation function and the partial correlation function to determine the first model parameter and the second model parameter; B2, according to the number of differences, the first model parameter and the second model parameter in step B1, the key parameter prediction model is trained using the key parameter data of the oil rig variable frequency drive system in step S1, the autoregressive coefficient and the moving average coefficient in the model are determined, and the optimal autoregressive coefficient and the moving average coefficient are determined by the maximum likelihood estimation method to obtain the trained key parameter prediction model; B3. Determine the set prediction future point and use the trained key parameter prediction model to obtain key parameter prediction data.

6. The data acquisition method of the oil drilling rig variable frequency drive system according to claim 1, characterized in that: In step S3, based on the key parameter prediction data in step S2, the data collection frequency is dynamically adjusted using the adaptive control method. The specific process is: based on the key parameter prediction data in step S2, the predicted value change rate is calculated; Determine whether the predicted value change rate exceeds the change rate threshold continuously within the set time step length; if so, use the adaptive control method to increase the data collection frequency, otherwise use the adaptive control method to reduce the data collection frequency.

7. The data acquisition method of the oil drilling rig variable frequency drive system according to claim 6 is characterized in that: The adaptive control method is used to increase the data acquisition frequency, which is expressed as: f new =min(f max ,f now +Δf), Where: f new is the adjusted data collection frequency, min is the minimum value symbol, f max is the maximum data acquisition frequency, f now is the current data acquisition frequency, Δf is the dynamic increment of the data acquisition frequency, λ is the adjustment coefficient, and y t is the predicted value of the key parameter at time t, y t-1 is the predicted value of the key parameter at time t-1; Adaptive control method is used to reduce the frequency of data acquisition, which can be expressed as: f new =max(f min ,f now -Δf) Among them: max is the maximum value symbol, f min is the minimum data collection frequency.

8. A data acquisition system for a petroleum drilling rig variable frequency drive system using the method described in any one of claims 1 to 7, characterized in that: It includes a data reading module, a sliding window analysis module, an adaptive control module and a data storage module; The data reading module is used to initialize the data acquisition frequency and collect key parameter data of the oil drilling rig variable frequency drive system according to the initial data acquisition frequency; The sliding window analysis module is used to construct a key parameter prediction model, train the key parameter prediction model using the key parameter data of the oil rig variable frequency drive system, and obtain key parameter prediction data using the trained key parameter prediction model; The adaptive control module is used to predict data according to key parameters, dynamically adjust the data acquisition frequency by using the adaptive control method, and collect key parameter data of the oil rig variable frequency drive system by using the dynamically adjusted data acquisition frequency; The data storage module is used to encapsulate the key parameter data of the oil rig variable frequency drive system and store it in the database.

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