Electric submersible pump fault diagnosis optimization method based on complex data feature extraction

Abstract

The invention discloses an electric submersible pump fault diagnosis optimization method based on complex data feature extraction, and relates to the technical field of electric submersible pumps, and the method comprises the steps: forming at least one detection data index of an electric submersible pump and a collection scheme of the value of the detection data index; judging whether the current diagnosis scheme of the sample fault has an optimization space or not; during optimization, a correlation coefficient of the detection data index and the sample fault is formed; forming a detection data index set; forming at least one to-be-determined detection scheme of the sample fault; verifying the to-be-determined detection scheme; forming at least one overall detection scheme; and calculating detection efficiency coefficients of the overall detection schemes, and taking the overall detection scheme with the maximum detection efficiency coefficient as a target detection scheme. By forming the correlation coefficient of the detection data index and the sample fault and calculating the detection efficiency coefficient of the overall detection scheme, the overall detection efficiency can be improved, and then the effect of optimizing the fault diagnosis of the electric submersible pump is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of electric submersible pumps, and in particular to an electric submersible pump fault diagnosis optimization method based on complex data feature extraction. Background Art

[0002] Electric submersible pumps have become one of the most widely used artificial lift methods in oil and gas fields. Therefore, studying the working status of electric submersible pumps to ensure long-term and efficient operation of the units, thereby extending the pump inspection cycle and reducing production costs has become an important topic in the field of electric pump well production technology.

[0003] Fault detection of electric submersible pumps involves many aspects, and the detection efficiency of electric submersible pumps may not be the best. When detecting multiple faults, there may be too many detection indicators, which will increase the detection time. Summary of the invention

[0004] In order to solve the above technical problems, a method for optimizing fault diagnosis of electric submersible pumps based on complex data feature extraction is provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: An electric submersible pump fault diagnosis optimization method based on complex data feature extraction, comprising: Based on the historical detection data, at least one sample fault of the electric submersible pump is obtained, and a current diagnosis scheme of the sample fault is obtained, wherein the current diagnosis scheme is composed of at least one current detection index and a fault identification range of the current detection index; forming a collection scheme for at least one detection data indicator of the electric submersible pump and a value of the detection data indicator, the detection data indicator being extracted from the image data, the electrical data, and the vibration data; Determine whether there is room for optimization in the current diagnostic scheme of the sample fault. If not, do nothing. If yes, optimize the current diagnostic scheme. During optimization, the correlation coefficient between the detection data index and the sample fault is formed; Aggregate the detection data indicators whose correlation coefficients with the sample faults are greater than 0 to form a detection data indicator set, and pair the detection data indicator set with the sample faults; Based on the set of detection data indicators, forming at least one pending detection scheme for the sample fault; Verify the pending detection plan, use the pending detection plan that has passed the verification as the preliminary detection plan, and match the preliminary detection plan to the corresponding sample fault; Forming at least one overall detection plan, the overall detection plan consisting of sample faults and preliminary detection plans; The detection efficiency coefficient of the overall detection scheme is calculated, and the overall detection scheme with the largest detection efficiency coefficient is used as the target detection scheme. The target detection scheme is used to replace the current diagnosis scheme of all sample faults.

[0006] Preferably, the scheme for collecting at least one detection data indicator and the value of the detection data indicator of the electric submersible pump comprises the following steps: At least one sampling point is set at the cable, impeller, bearing, and mouth ring of the electric submersible pump to obtain image data at the sampling point, wherein the image data is composed of a sample image and an actual image, and the sample image is an image when the sampling point is normal; The difference between the actual image and the sample image at the sampling point is used as a detection data index, and the value of the detection data index is the ratio of the difference between the actual image and the sample image at the sampling point; Collect electrical data when the electric submersible pump is running. The electrical data includes operating load, operating current and operating voltage. The operating load is collected using a load meter, the operating current is collected using an ammeter, and the operating voltage is collected using a voltmeter; The operating load, operating current and operating voltage in the electrical data are respectively used as detection data indicators; Collect the overall vibration intensity of the electric submersible pump, and obtain in advance at least one basic component of the electric submersible pump, the basic component consisting of a cable, an impeller, a bearing, a mouth ring, a stator winding, a filter, a valve, a shaft seal and a casing; Obtaining the vibration frequency of the basic component, obtaining the vibration intensity equal to the vibration frequency of the basic component in the overall vibration intensity, and obtaining the vibration intensity of the basic component; The vibration intensity of basic components is used as the detection data indicator.

[0007] Preferably, the step of determining whether there is room for optimization in the current diagnostic solution for the sample fault comprises the following steps: Under the same conditions, obtaining at least one first detection value of the detection data index as the electric submersible pump runs in a preset time interval, and obtaining at least one second detection value of the current detection index as the electric submersible pump runs for a certain period of time; Fitting the first detection value with respect to time to obtain a first fitting function, and taking the derivative of the first fitting function to obtain a first derivative function; Fitting the second detection value with respect to time to obtain a second fitting function, and taking the derivative of the second fitting function to obtain a second derivative function; Integrate the absolute value of the difference between the first derivative function and the second derivative function over a preset time interval to obtain a difference coefficient between the detection data index and the current detection index; When the difference coefficient between the detection data indicator and the current detection indicator is less than the preset value, and the detection data indicator is different from the current detection indicator, there is room for optimization of the current diagnostic scheme for the sample fault; otherwise, there is no room for optimization of the current diagnostic scheme for the sample fault.

[0008] Preferably, forming the correlation coefficient between the detection data index and the sample fault comprises the following steps: Obtaining at least one fault degree of the sample fault, and obtaining a value of the detection data indicator under the condition of the fault degree of the sample fault; Selecting at least one of the fault levels of the sample fault as a reference fault level, and using the value of the detection data indicator corresponding to the reference fault level as a reference value; Use the correlation formula to calculate the correlation coefficient between the detection data index and the sample fault; The associated formula is as follows: , Where A is the correlation coefficient between the detection data index and the sample fault, i is the subscript, and n is the number of at least one fault degree of the sample fault. is the benchmark value, is the baseline fault level, is the value of the detection data indicator corresponding to the i-th fault degree of the sample fault, is the i-th fault degree of the sample fault.

[0009] Preferably, the forming of at least one pending detection scheme for sample faults based on the detection data indicator set comprises the following steps: Summarize the detection data indicators contained in the subset of the detection data indicator set corresponding to the sample fault as a pending detection plan; All subsets of the detection data indicator set corresponding to the sample fault form at least one pending detection scheme.

[0010] Preferably, the verification of the pending detection scheme comprises the following steps: Use the pending detection scheme to detect the corresponding sample faults and calculate the judgment accuracy of the pending detection scheme; When the difference between the judgment accuracy and 1 is less than the preset difference, the pending detection scheme passes the verification; otherwise, the pending detection scheme fails the verification.

[0011] Preferably, forming at least one overall detection scheme comprises the following steps: taking one of at least one preliminary detection scheme for the sample fault as a candidate detection scheme; Summarize the candidate detection schemes for all sample faults to form an overall detection scheme; When the candidate detection scheme traverses at least one preliminary detection scheme of the sample fault, at least one overall detection scheme is formed.

[0012] Preferably, the calculation of the detection efficiency coefficient of the overall detection scheme comprises the following steps: Acquire at least one sample fault set, where the at least one sample fault set constitutes all combinations of sample faults; Get the detection time of the detection data indicator; Aggregate the preliminary detection schemes corresponding to the sample faults in the sample fault set into a preliminary detection scheme set; Aggregating candidate detection schemes of the overall detection scheme into a candidate detection scheme set; Take the intersection of the candidate detection scheme set and the preliminary detection scheme set to obtain a feature set; Summarize and remove duplicate detection data indicators of the preliminary detection scheme in the feature set to obtain a feature detection scheme, which is a scheme for detecting all sample faults in the sample fault set; The detection data indicators in the feature detection scheme are classified into image indicators, electrical indicators and vibration indicators according to the attributes of the indicators. The attributes of the indicators are image, electrical and vibration. The detection time of the image index is superimposed to obtain the first time, the detection time of the electrical index is superimposed to obtain the second time, and the detection time of the vibration index is superimposed to obtain the third time; Taking the maximum value among the first time, the second time and the third time as the total detection time of the sample fault set; The total detection time of at least one sample fault set is superimposed and the reciprocal is taken to obtain the detection efficiency coefficient of the overall detection scheme.

[0013] Preferably, the step of obtaining the detection time of the detection data indicator comprises the following steps: When the attribute of the detection data indicator is an image, the time for obtaining the value of the detection data indicator through image analysis is counted as the detection time of the detection data indicator; When the attribute of the detection data indicator is electrical, the time when the statistical instrument measures the value of the detection data indicator is used as the detection time of the detection data indicator; When the attribute of the detection data indicator is vibration, the time for obtaining the value of the detection data indicator through vibration frequency analysis is counted as the detection time of the detection data indicator.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By judging whether there is room for optimization in the current diagnostic scheme of sample faults, forming a correlation coefficient between detection data indicators and sample faults, and calculating the detection efficiency coefficient of the overall detection scheme, the current diagnostic scheme of the existing sample faults of the submersible pump can be evaluated. Thus, it can be determined whether the current diagnostic scheme needs to be optimized based on the evaluation results. When optimizing, the overall detection scheme with the largest detection efficiency coefficient is selected as the target detection scheme. Then, when detecting multiple sample faults, it can be ensured that the repetition of the detection data indicators used for the sample faults is large and the time consumed for the overall detection is small. Thus, the overall detection efficiency can be improved, thereby achieving the effect of optimizing the diagnosis of submersible pump faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the electric submersible pump fault diagnosis optimization method based on complex data feature extraction of the present invention; Figure 2 A schematic diagram of a flow chart of a scheme for collecting at least one detection data indicator and a value of the detection data indicator of an electric submersible pump according to the present invention; Figure 3 A schematic diagram of a flow chart of judging whether there is room for optimization in the current diagnostic scheme of a sample fault according to the present invention; Figure 4 A schematic diagram of a process for forming a correlation coefficient between a detection data indicator and a sample fault according to the present invention; Figure 5 A schematic diagram of a flow chart of forming at least one pending detection scheme for a sample fault based on a set of detection data indicators according to the present invention; Figure 6 A schematic diagram of a process for verifying a pending detection scheme of the present invention; Figure 7 A schematic diagram of a process for forming at least one overall detection scheme of the present invention; Figure 8 A schematic diagram of a flow chart for calculating the detection efficiency coefficient of the overall detection scheme of the present invention; Fig. 9 It is a schematic diagram of the process of obtaining the detection time of the detection data indicator according to the present invention. DETAILED DESCRIPTION

[0016] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0017] Reference Figure 1 As shown, an electric submersible pump fault diagnosis optimization method based on complex data feature extraction includes: Based on the historical detection data, at least one sample fault of the electric submersible pump is obtained, and a current diagnosis scheme of the sample fault is obtained, wherein the current diagnosis scheme is composed of at least one current detection index and a fault identification range of the current detection index; forming a collection scheme for at least one detection data indicator of the electric submersible pump and a value of the detection data indicator, the detection data indicator being extracted from the image data, the electrical data, and the vibration data; Determine whether there is room for optimization in the current diagnostic scheme of the sample fault. If not, do nothing. If yes, optimize the current diagnostic scheme. During optimization, the correlation coefficient between the detection data index and the sample fault is formed; Aggregate the detection data indicators whose correlation coefficients with the sample faults are greater than 0 to form a detection data indicator set, and pair the detection data indicator set with the sample faults; Based on the set of detection data indicators, forming at least one pending detection scheme for the sample fault; Verify the pending detection plan, use the pending detection plan that has passed the verification as the preliminary detection plan, and match the preliminary detection plan to the corresponding sample fault; Forming at least one overall detection plan, the overall detection plan consisting of sample faults and preliminary detection plans; The detection efficiency coefficient of the overall detection scheme is calculated, and the overall detection scheme with the largest detection efficiency coefficient is used as the target detection scheme. The target detection scheme is used to replace the current diagnosis scheme of all sample faults.

[0018] Since the detection does not necessarily involve one sample fault, but multiple sample faults, when the detection data indicators of multiple sample faults have a small overlap, relatively more detection data indicators need to be detected. At the same time, the detection time of the detection data indicators used may not be optimal. Therefore, the overall detection time will be longer. In this solution, the diagnostic test is optimized to target this situation, ensuring that the detection data indicators of multiple sample faults have a large overlap. At the same time, the total detection time of the detection data indicators used is minimized, thereby improving the detection efficiency.

[0019] Reference Figure 2 As shown, the scheme for collecting at least one detection data indicator and the value of the detection data indicator of the electric submersible pump includes the following steps: At least one sampling point is set at the cable, impeller, bearing, and mouth ring of the electric submersible pump to obtain image data at the sampling point, wherein the image data is composed of a sample image and an actual image, and the sample image is an image when the sampling point is normal; The difference between the actual image and the sample image at the sampling point is used as a detection data index, and the value of the detection data index is the ratio of the difference between the actual image and the sample image at the sampling point; Collect electrical data when the electric submersible pump is running. The electrical data includes operating load, operating current and operating voltage. The operating load is collected using a load meter, the operating current is collected using an ammeter, and the operating voltage is collected using a voltmeter; The operating load, operating current and operating voltage in the electrical data are respectively used as detection data indicators; Collect the overall vibration intensity of the electric submersible pump, and obtain in advance at least one basic component of the electric submersible pump, the basic component consisting of a cable, an impeller, a bearing, a mouth ring, a stator winding, a filter, a valve, a shaft seal and a casing; Obtaining the vibration frequency of the basic component, obtaining the vibration intensity equal to the vibration frequency of the basic component in the overall vibration intensity, and obtaining the vibration intensity of the basic component; The vibration intensity of basic components is used as the detection data indicator.

[0020] Reference Figure 3 As shown, judging whether there is room for optimization in the current diagnosis scheme of the sample fault includes the following steps: Under the same conditions, obtaining at least one first detection value of the detection data index as the electric submersible pump runs in a preset time interval, and obtaining at least one second detection value of the current detection index as the electric submersible pump runs for a certain period of time; Fitting the first detection value with respect to time to obtain a first fitting function, and taking the derivative of the first fitting function to obtain a first derivative function; Fitting the second detection value with respect to time to obtain a second fitting function, and taking the derivative of the second fitting function to obtain a second derivative function; Integrate the absolute value of the difference between the first derivative function and the second derivative function over a preset time interval to obtain a difference coefficient between the detection data index and the current detection index; When the difference coefficient between the detection data indicator and the current detection indicator is less than the preset value, and the detection data indicator is different from the current detection indicator, there is room for optimization of the current diagnostic scheme for the sample fault; otherwise, there is no room for optimization of the current diagnostic scheme for the sample fault.

[0021] The basis is that when the difference between the change of the detection data index with the operation of the submersible pump and the change of the current detection index is very small, it means that they are the same kind of indicators, that is, the detection effects of the two are consistent, so they can be interchanged. When the detection data index is different from the current detection index, the effect after interchange may be better, so there is room for optimization.

[0022] Reference Figure 4 As shown, forming the correlation coefficient between the detection data index and the sample fault includes the following steps: Obtaining at least one fault degree of the sample fault, and obtaining a value of the detection data indicator under the condition of the fault degree of the sample fault; Selecting at least one of the fault levels of the sample fault as a reference fault level, and using the value of the detection data indicator corresponding to the reference fault level as a reference value; Use the correlation formula to calculate the correlation coefficient between the detection data index and the sample fault; The associated formula is as follows: , Where A is the correlation coefficient between the detection data index and the sample fault, i is the subscript, and n is the number of at least one fault degree of the sample fault. is the benchmark value, is the baseline fault level, is the value of the detection data indicator corresponding to the i-th fault degree of the sample fault, is the i-th fault degree of the sample fault.

[0023] The calculation of the correlation coefficient depends on the change in the value of the detection data indicator caused by the change in the sample fault. When there is no correlation, the change in the sample fault has almost no effect on the value of the detection data indicator, and the value of the detection data indicator will not change. When the correlation coefficient between the detection data indicator and the sample fault is not 0, it means that it can be used to identify and judge the sample fault, but a series of subsequent steps are required for verification.

[0024] Reference Figure 5 As shown, based on the detection data indicator set, forming at least one pending detection scheme for the sample fault includes the following steps: Summarize the detection data indicators contained in the subset of the detection data indicator set corresponding to the sample fault as a pending detection plan; All subsets of the detection data indicator set corresponding to the sample fault form at least one pending detection scheme.

[0025] The detection of sample faults depends on the detection data indicators in its corresponding detection data indicator set. Any subset of the detection data indicator set can be used as a pending detection scheme, but the pending detection scheme is not a scheme that can accurately identify sample faults, that is, the detection values ​​of the detection data indicators in the pending detection scheme are not sufficient to determine whether the sample fault exists or not. Therefore, the pending detection scheme needs to be verified, and the verification depends on the accuracy of the identification of the pending detection scheme. When using the detection data indicators, the normal range of the detection data indicators can be obtained based on historical experience. When the value of the detection data indicator is not within the normal range, the detection data indicator is abnormal. The method for the pending detection scheme to identify sample faults is: when the detection data indicators in the pending detection scheme are all abnormal, the sample fault is determined to exist according to the pending detection scheme.

[0026] Reference Figure 6 As shown, the verification of the pending detection scheme includes the following steps: Use the pending detection scheme to detect the corresponding sample faults, and calculate the judgment accuracy of the pending detection scheme; When the difference between the judgment accuracy and 1 is less than the preset difference, the pending detection scheme passes the verification; otherwise, the pending detection scheme fails the verification.

[0027] Reference Figure 7 As shown, forming at least one overall detection scheme includes the following steps: taking one of at least one preliminary detection scheme for the sample fault as a candidate detection scheme; Summarize the candidate detection schemes for all sample faults to form an overall detection scheme; When the candidate detection scheme traverses at least one preliminary detection scheme of the sample fault, at least one overall detection scheme is formed.

[0028] Since a sample fault has at least one preliminary detection scheme to detect it, and the overall detection scheme is a scheme for detecting all sample faults, it is necessary to determine the preliminary detection scheme adopted for the sample fault in the overall detection scheme. Therefore, each selection of at least one preliminary detection scheme for the sample fault will form an overall detection scheme.

[0029] Reference Figure 8 As shown, calculating the detection efficiency coefficient of the overall detection scheme includes the following steps: Acquire at least one sample fault set, where the at least one sample fault set constitutes all combinations of sample faults; Get the detection time of the detection data indicator; Aggregate the preliminary detection schemes corresponding to the sample faults in the sample fault set into a preliminary detection scheme set; Aggregating candidate detection schemes of the overall detection scheme into a candidate detection scheme set; Take the intersection of the candidate detection scheme set and the preliminary detection scheme set to obtain a feature set; Summarize and remove duplicate detection data indicators of the preliminary detection scheme in the feature set to obtain a feature detection scheme, which is a scheme for detecting all sample faults in the sample fault set; The detection data indicators in the feature detection scheme are classified into image indicators, electrical indicators and vibration indicators according to the attributes of the indicators. The attributes of the indicators are image, electrical and vibration. The detection time of the image index is superimposed to obtain the first time, the detection time of the electrical index is superimposed to obtain the second time, and the detection time of the vibration index is superimposed to obtain the third time; Taking the maximum value among the first time, the second time and the third time as the total detection time of the sample fault set; The total detection time of at least one sample fault set is superimposed and the reciprocal is taken to obtain the detection efficiency coefficient of the overall detection scheme.

[0030] Because in actual detection, it is necessary to detect all possible combinations of sample faults. Therefore, the calculation of the detection efficiency coefficient depends on the possible combinations of all sample faults, that is, the sample fault set. In the overall detection plan, candidate detection plans are determined for all sample faults, and at least one preliminary detection plan corresponding to the sample fault is also known. Therefore, in order to determine all the preliminary detection plans adopted by the sample fault set, it is necessary to take the intersection of the candidate detection plan set and the preliminary detection plan set to obtain the feature set. The preliminary detection plan in the feature set is the plan adopted by the sample fault of the sample fault set. It should be noted that the candidate detection plan here is also the preliminary detection plan. , so the intersection can be taken, but the detection data indicators of the preliminary detection scheme in the feature set will overlap, so it is necessary to remove the duplicates and then summarize, and calculate the time consumption of these indicator identification, but these indicators are different in attributes, and the identification of indicators with different attributes is parallel, because different modules are used for identification, so the first time, the second time and the third time are classified, and the total detection time of the sample fault set is obtained. Since all sample fault sets need to be detected, the total detection time of at least one sample fault set is superimposed and the reciprocal is taken to obtain the detection efficiency coefficient of the overall detection scheme. The reason for taking the reciprocal is: the longer the time, the lower the detection efficiency.

[0031] Reference Fig. 9 As shown, obtaining the detection time of the detection data indicator includes the following steps: When the attribute of the detection data indicator is an image, the time for obtaining the value of the detection data indicator through image analysis is counted as the detection time of the detection data indicator; When the attribute of the detection data indicator is electrical, the time when the statistical instrument measures the value of the detection data indicator is used as the detection time of the detection data indicator; When the attribute of the detection data indicator is vibration, the time for obtaining the value of the detection data indicator through vibration frequency analysis is counted as the detection time of the detection data indicator.

[0032] Furthermore, the present solution also proposes a storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned electric submersible pump fault diagnosis optimization method based on complex data feature extraction is executed.

[0033] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0034] In summary, the advantages of the present invention are: by judging whether there is room for optimization in the current diagnosis scheme of the sample fault, forming the correlation coefficient between the detection data index and the sample fault, and calculating the detection efficiency coefficient of the overall detection scheme, the current diagnosis scheme of the existing sample fault of the submersible pump can be evaluated, thereby, it can be determined whether the current diagnosis scheme needs to be optimized according to the evaluation result, and when optimizing, the overall detection scheme with the largest detection efficiency coefficient is selected as the target detection scheme, then when detecting multiple sample faults, it can be ensured that the repetition of the detection data index used for the sample fault is large and the time consumed for the overall detection is small, thereby, the overall detection efficiency can be improved, and then the effect of optimizing the diagnosis of the submersible pump fault is achieved.

[0035] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An electric submersible pump fault diagnosis optimization method based on complex data feature extraction, characterized in that: include: Based on the historical detection data, at least one sample fault of the electric submersible pump is obtained, and a current diagnosis scheme of the sample fault is obtained, wherein the current diagnosis scheme is composed of at least one current detection index and a fault identification range of the current detection index; forming a collection scheme for at least one detection data indicator of the electric submersible pump and a value of the detection data indicator, the detection data indicator being extracted from the image data, the electrical data, and the vibration data; Determine whether there is room for optimization in the current diagnostic scheme of the sample fault. If not, do nothing. If yes, optimize the current diagnostic scheme. During optimization, the correlation coefficient between the detection data index and the sample fault is formed; Aggregate the detection data indicators whose correlation coefficients with the sample faults are greater than 0 to form a detection data indicator set, and pair the detection data indicator set with the sample faults; Based on the set of detection data indicators, forming at least one pending detection scheme for the sample fault; Verify the pending detection plan, use the pending detection plan that has passed the verification as the preliminary detection plan, and match the preliminary detection plan to the corresponding sample fault; Forming at least one overall detection plan, the overall detection plan consisting of sample faults and preliminary detection plans; The detection efficiency coefficient of the overall detection scheme is calculated, and the overall detection scheme with the largest detection efficiency coefficient is used as the target detection scheme. The target detection scheme is used to replace the current diagnosis scheme of all sample faults.

2. The electric submersible pump fault diagnosis optimization method based on complex data feature extraction according to claim 1 is characterized in that: The scheme for collecting at least one detection data indicator and the value of the detection data indicator of the electric submersible pump comprises the following steps: At least one sampling point is set at the cable, impeller, bearing, and mouth ring of the electric submersible pump to obtain image data at the sampling point, wherein the image data is composed of a sample image and an actual image, and the sample image is an image when the sampling point is normal; The difference between the actual image and the sample image at the sampling point is used as a detection data index, and the value of the detection data index is the ratio of the difference between the actual image and the sample image at the sampling point; Collect electrical data when the electric submersible pump is running. The electrical data includes operating load, operating current and operating voltage. The operating load is collected using a load meter, the operating current is collected using an ammeter, and the operating voltage is collected using a voltmeter; The operating load, operating current and operating voltage in the electrical data are respectively used as detection data indicators; Collect the overall vibration intensity of the electric submersible pump, and obtain in advance at least one basic component of the electric submersible pump, the basic component consisting of a cable, an impeller, a bearing, a mouth ring, a stator winding, a filter, a valve, a shaft seal and a casing; Obtaining the vibration frequency of the basic component, obtaining the vibration intensity equal to the vibration frequency of the basic component in the overall vibration intensity, and obtaining the vibration intensity of the basic component; The vibration intensity of basic components is used as the detection data indicator.

3. The electric submersible pump fault diagnosis optimization method based on complex data feature extraction according to claim 2 is characterized in that: The method of determining whether there is room for optimization of the current diagnostic scheme for the sample fault comprises the following steps: Under the same conditions, obtaining at least one first detection value of the detection data index as the electric submersible pump runs in a preset time interval, and obtaining at least one second detection value of the current detection index as the electric submersible pump runs for a certain period of time; Fitting the first detection value with respect to time to obtain a first fitting function, and taking the derivative of the first fitting function to obtain a first derivative function; Fitting the second detection value with respect to time to obtain a second fitting function, and taking the derivative of the second fitting function to obtain a second derivative function; Integrate the absolute value of the difference between the first derivative function and the second derivative function over a preset time interval to obtain a difference coefficient between the detection data index and the current detection index; When the difference coefficient between the detection data indicator and the current detection indicator is less than the preset value, and the detection data indicator is different from the current detection indicator, there is room for optimization of the current diagnostic scheme for the sample fault; otherwise, there is no room for optimization of the current diagnostic scheme for the sample fault.

4. The electric submersible pump fault diagnosis optimization method based on complex data feature extraction according to claim 3 is characterized in that: The forming of the correlation coefficient between the detection data index and the sample fault comprises the following steps: Obtaining at least one fault degree of the sample fault, and obtaining a value of the detection data indicator under the condition of the fault degree of the sample fault; Selecting at least one of the fault levels of the sample fault as a reference fault level, and using the value of the detection data indicator corresponding to the reference fault level as a reference value; Use the correlation formula to calculate the correlation coefficient between the detection data index and the sample fault; The associated formula is as follows: , Where A is the correlation coefficient between the detection data index and the sample fault, i is the subscript, and n is the number of at least one fault degree of the sample fault. is the benchmark value, is the baseline fault level, is the value of the detection data indicator corresponding to the i-th fault degree of the sample fault, is the i-th fault degree of the sample fault.

5. The electric submersible pump fault diagnosis optimization method based on complex data feature extraction according to claim 4 is characterized in that: The forming of at least one pending detection scheme for the sample fault based on the detection data indicator set comprises the following steps: Summarize the detection data indicators contained in the subset of the detection data indicator set corresponding to the sample fault as a pending detection plan; All subsets of the detection data indicator set corresponding to the sample fault form at least one pending detection scheme.

6. The electric submersible pump fault diagnosis optimization method based on complex data feature extraction according to claim 5 is characterized in that: The verification of the pending detection scheme comprises the following steps: Use the pending detection scheme to detect the corresponding sample faults, and calculate the judgment accuracy of the pending detection scheme; When the difference between the judgment accuracy and 1 is less than the preset difference, the pending detection scheme passes the verification; otherwise, the pending detection scheme fails the verification.

7. The electric submersible pump fault diagnosis optimization method based on complex data feature extraction according to claim 6 is characterized in that: The forming of at least one overall detection scheme comprises the following steps: taking one of at least one preliminary detection scheme for the sample fault as a candidate detection scheme; Summarize the candidate detection schemes for all sample faults to form an overall detection scheme; When the candidate detection scheme traverses at least one preliminary detection scheme of the sample fault, at least one overall detection scheme is formed.

8. The electric submersible pump fault diagnosis optimization method based on complex data feature extraction according to claim 7 is characterized in that: The calculation of the detection efficiency coefficient of the overall detection scheme comprises the following steps: Acquire at least one sample fault set, where the at least one sample fault set constitutes all combinations of sample faults; Get the detection time of the detection data indicator; Aggregate the preliminary detection schemes corresponding to the sample faults in the sample fault set into a preliminary detection scheme set; Aggregating candidate detection schemes of the overall detection scheme into a candidate detection scheme set; Take the intersection of the candidate detection scheme set and the preliminary detection scheme set to obtain a feature set; Summarize and remove duplicate detection data indicators of the preliminary detection scheme in the feature set to obtain a feature detection scheme, which is a scheme for detecting all sample faults in the sample fault set; The detection data indicators in the feature detection scheme are classified into image indicators, electrical indicators and vibration indicators according to the attributes of the indicators. The attributes of the indicators are image, electrical and vibration. The detection time of the image index is superimposed to obtain the first time, the detection time of the electrical index is superimposed to obtain the second time, and the detection time of the vibration index is superimposed to obtain the third time; Taking the maximum value among the first time, the second time and the third time as the total detection time of the sample fault set; The total detection time of at least one sample fault set is superimposed and the reciprocal is taken to obtain the detection efficiency coefficient of the overall detection scheme.

9. The electric submersible pump fault diagnosis optimization method based on complex data feature extraction according to claim 8 is characterized in that: The method of obtaining the detection time of the detection data indicator includes the following steps: When the attribute of the detection data indicator is an image, the time for obtaining the value of the detection data indicator through image analysis is counted as the detection time of the detection data indicator; When the attribute of the detection data indicator is electrical, the time when the statistical instrument measures the value of the detection data indicator is used as the detection time of the detection data indicator; When the attribute of the detection data indicator is vibration, the time for obtaining the value of the detection data indicator through vibration frequency analysis is counted as the detection time of the detection data indicator.

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