Intelligent detection method, device and system for oil leakage of hydraulic support oil cylinder

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent detection method, device and system for oil leakage of a hydraulic support oil cylinder, and the method specifically comprises the steps: carrying out the data division of the change of each parameter, taking each parameter as a target parameter, and determining a corresponding time interval according to the data division result of the target parameter; through the determined time interval, the asynchronous response characteristics of the target parameter compared with other parameters in the face of oil cylinder leakage of different degrees can be accurately extracted; on the basis of the asynchronous response features and in combination with various parameter values at the current moment, hydraulic support oil cylinder oil leakage detection is carried out; the accuracy of asynchronous response feature extraction of each parameter for oil leakage of the oil cylinder is improved, the accuracy of oil leakage detection results of the oil cylinder of the hydraulic support is improved, and interference caused by leakage conditions of different degrees to intelligent detection of oil leakage of the oil cylinder is effectively avoided.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an intelligent detection method, device and system for oil leakage of a hydraulic support cylinder. Background Art

[0002] As a very common key equipment in coal mining operations, hydraulic supports are widely used in roof support work in coal mines. It mainly relies on hydraulic cylinders to convert hydraulic energy into mechanical energy, thereby achieving effective support for the roof. However, in actual application scenarios, the coal mining environment is extremely harsh and complex, and factors such as temperature, humidity, and pressure are variable, which makes the hydraulic support cylinder frequently leak oil during long-term use.

[0003] Once the hydraulic support cylinder leaks oil, it will have a serious impact on the normal operation of the equipment, not only significantly shortening the service life of the equipment, but may even cause damage to the equipment in extreme cases. Therefore, in the use of hydraulic supports, realizing intelligent detection of cylinder oil leakage is of great significance for extending the service life of equipment, ensuring safe production in coal mines, and improving the working environment of mines.

[0004] At present, sensors are generally used for data collection and detection in order to monitor the sealing of the oil cylinder during the use of hydraulic supports. Among them, the more commonly used detection method is the threshold judgment detection method. For example, in the "A remote wireless control method for mining equipment and its system and electronic equipment" with the publication number CN117145551A, it is mentioned that the pressure and temperature thresholds are collected to determine whether the oil cylinder of the hydraulic support is leaking. Although this method avoids the complex algorithm modeling process and is relatively simple to operate, it is very susceptible to many interference factors during the data collection process due to the complex working environment of the hydraulic support. At the same time, there are many reasons for the leakage of the oil cylinder of the hydraulic support, and when leakage occurs, the characteristic changes of the monitoring data in the oil cylinder are extremely complex. While improving the detection efficiency, relying only on the threshold judgment method may produce large errors, resulting in the current intelligent detection accuracy of oil leakage in the hydraulic support cylinder is generally not high, which is difficult to meet the actual production needs. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of this application is to provide an intelligent detection method, device and system for oil leakage of hydraulic support cylinder. The technical solutions adopted are as follows:

[0006] In a first aspect, an embodiment of the present application provides an intelligent detection method for oil leakage of a hydraulic support cylinder, the method comprising the following steps:

[0007] Collect various parameter data of the oil cylinder in real time, including pressure, temperature, flow and piston rod displacement data, and construct various parameter sequences;

[0008] Take any parameter as the target parameter, divide the sequence according to the data mutation in the target parameter sequence, and obtain each subsequence of the target parameter sequence; obtain the time interval corresponding to each subsequence, divide the other parameter sequences according to each time interval, and obtain each subsequence of the other parameter sequences;

[0009] The characteristic value of each parameter change in each time interval is calculated based on the data dispersion degree and data change trend in each subsequence; the characteristic weight of each parameter change in each time interval is calculated based on the stability of data change in each subsequence;

[0010] Calculating a feature extraction result of an asynchronous response of a target parameter in each time interval based on the feature value and the feature weight; calculating a first monitoring coefficient of the target parameter based on the feature extraction results of all time intervals;

[0011] Based on the first monitoring coefficient and in combination with various parameters at the current moment, the hydraulic support cylinder oil leakage detection is performed.

[0012] In one embodiment, the process of acquiring each subsequence of the target parameter sequence is: taking the target parameter sequence as the input of a segmentation algorithm, and outputting the subsequences of the target parameter sequence.

[0013] In one embodiment, each subsequence of the other parameter sequences is a sequence of the other parameter sequences in each time interval.

[0014] In one embodiment, the process of acquiring the characteristic value of each parameter change in each time interval is as follows:

[0015] In each time interval, the coefficient of variation of all data in each parameter subsequence is calculated, and the trend statistics of each parameter subsequence are obtained through a trend verification algorithm. The product of the coefficient of variation and the trend statistics is used as the characteristic value of each parameter change in the time interval.

[0016] In one embodiment, the process of obtaining the characteristic weight of each parameter change in each time interval is as follows:

[0017] A first-order difference sequence of each parameter subsequence in each time interval is obtained, and the normalized value of the entropy of all elements in the first-order difference sequence is used as the characteristic weight of each parameter change in each time interval.

[0018] In one embodiment, the expression of the feature extraction result is:

[0019] , where The target parameter The feature extraction results of asynchronous responses within a time interval; and Respectively represent In the time interval The characteristic value and the characteristic weight of the parameter; Indicates the number of parameter types.

[0020] In one embodiment, the process of obtaining the first monitoring coefficient of the target parameter is as follows:

[0021] For the time intervals divided by the target parameter sequence, the normalized mean value of the feature extraction results in all time intervals is used as the first monitoring coefficient of the target parameter.

[0022] In one embodiment, the oil leakage detection of the hydraulic support cylinder is specifically performed as follows:

[0023] Set response thresholds and abnormal thresholds for various parameters; calculate the first monitoring coefficient mean of all parameters. If the first monitoring coefficient mean is greater than or equal to the preset response threshold, or if the value of any parameter at the current moment is greater than or equal to the corresponding abnormal threshold, it is determined that a leak has occurred in the current hydraulic support cylinder.

[0024] In a second aspect, the embodiment of the present application further provides an intelligent detection device for oil leakage of a hydraulic support cylinder, comprising:

[0025] Data acquisition module: real-time collection of various parameter data of the oil cylinder, including pressure, temperature, flow and piston rod displacement data, to build various parameter sequences;

[0026] Data analysis module: taking any parameter as the target parameter, dividing the sequence according to the data mutation in the target parameter sequence to obtain each subsequence of the target parameter sequence; obtaining the time interval corresponding to each subsequence, dividing the other parameter sequences according to each time interval, and obtaining each subsequence of the other parameter sequences;

[0027] Feature extraction module: Calculate the feature value of each parameter change in each time interval based on the data dispersion degree and data change trend in each subsequence; Calculate the feature weight of each parameter change in each time interval based on the stability of data change in each subsequence;

[0028] Data detection module: calculates the feature extraction result of the asynchronous response of the target parameter in each time interval based on the feature value and the feature weight; calculates the first monitoring coefficient of the target parameter based on the feature extraction results of all time intervals;

[0029] Oil leakage monitoring module: Based on the first monitoring coefficient and in combination with various parameters at the current moment, the oil leakage detection of the hydraulic support cylinder is performed.

[0030] In the third aspect, an embodiment of the present application also provides an intelligent detection system for oil leakage of a hydraulic support cylinder, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0031] The embodiments of the present application have at least the following beneficial effects:

[0032] When a hydraulic support cylinder leaks, different degrees of leakage will cause different changes in various parameters; based on this, the present application divides the data of the changes in each parameter, regards each parameter as a target parameter, and determines the corresponding time interval based on the data division result of the target parameter; through the determined time interval, the asynchronous response characteristics of the target parameter compared to other parameters when facing different degrees of cylinder leakage can be accurately extracted; different from the traditional overall and local data feature change analysis method, the present application fully considers the complexity of the correlation changes between various parameters under different degrees of cylinder leakage; in this way, the asynchronous response characteristics of each parameter facing cylinder leakage can be more accurately extracted, thereby significantly improving the accuracy of the hydraulic support cylinder leakage detection results, and effectively avoiding the interference of different degrees of leakage to the intelligent detection of cylinder leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 A flowchart of the steps of an intelligent detection method for oil leakage of a hydraulic support cylinder provided in one embodiment of the present application;

[0035] Figure 2 Schematic diagram of the process of obtaining characteristic values ​​of parameter changes;

[0036] Figure 3 The present invention is a structural schematic diagram of an intelligent detection device for oil leakage of a hydraulic support cylinder. DETAILED DESCRIPTION

[0037] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the intelligent detection method, device and system for oil leakage of a hydraulic support cylinder proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0038] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0039] The specific scheme of an intelligent detection method, device and system for oil leakage of a hydraulic support cylinder provided by the present application is described in detail below with reference to the accompanying drawings.

[0040] See also Figure 1 , which shows a flowchart of the steps of an intelligent detection method for oil leakage of a hydraulic support cylinder provided by an embodiment of the present application, the method comprising the following steps:

[0041] Step S1, real-time collection of various parameter data of the oil cylinder, including pressure, temperature, flow rate and piston rod displacement data, to construct various parameter sequences.

[0042] Automatic data collection is performed by installing pressure sensors, temperature sensors, flow sensors and displacement sensors at key locations of the hydraulic support cylinder, including the cylinder inlet and outlet, piston seal and cylinder barrel. Specifically, the cylinder pressure, temperature, flow and piston rod displacement data are collected in real time at a preset time interval (this application is set to collect data once per second). Considering that the complex electromagnetic environment underground in coal mines may interfere with data collection, sensors with strong anti-interference capabilities are selected, and shielded cables are used for data transmission to ensure the accuracy and stability of the collected data and provide a reliable data basis for subsequent analysis.

[0043] Each parameter after preprocessing is sorted into a sequence according to the order of acquisition time, and each parameter sequence is obtained, namely, pressure sequence, temperature sequence, flow sequence and piston rod displacement sequence.

[0044] Step S2, taking any parameter as the target parameter, dividing the sequence according to the data mutation in the target parameter sequence to obtain each subsequence of the target parameter sequence; obtaining the time interval corresponding to each subsequence, dividing the other parameter sequences according to each time interval, and obtaining each subsequence of the other parameter sequences.

[0045] Since the working environment of the hydraulic support is extremely complex, it is easily affected by various environmental interference factors during the actual operation, resulting in large noise in the collected data. Therefore, it is necessary to preprocess the collected data. Specifically, the collected data is used as input, and the Kalman filter algorithm is used in the data acquisition and preprocessing module to perform noise reduction on the pressure, temperature, flow and piston rod displacement data, so as to obtain more accurate and reliable noise-reduced data.

[0046] When the hydraulic support cylinder leaks, different degrees of leakage will cause different parameter change characteristics. If it is a slight oil leak, the pressure will usually show a slow downward trend, which is difficult to detect within a certain period of time. Once the pressure changes, the support effect of the hydraulic support on the top plate will also be affected. When the support is under the pressure of the top plate, the piston may slowly retract, which will cause the piston displacement to change. In addition, oil leakage will increase the leakage inside the hydraulic system, and the friction of the oil circulating inside the system will increase, causing the oil temperature to rise. Moreover, due to factors such as unstable pressure, the components in the system may generate additional friction and impact, further causing the oil temperature to rise. As the leakage continues to develop, the inlet or outlet flow of the hydraulic support cylinder will also fluctuate abnormally. Due to the change in the flow in the cylinder, the piston rod will extend more slowly or even fail to extend normally.

[0047] If the hydraulic support cylinder leaks seriously, the pressure, flow, temperature and displacement of the piston will change dramatically during the actual monitoring process. In this case, the intelligent monitoring of the hydraulic support cylinder oil leakage by threshold judgment can obtain the detection results more efficiently and accurately. However, in actual use, when there is a slight leak in the hydraulic support, the detection by threshold judgment will face two problems. On the one hand, the data changes caused by slight leakage are relatively slow, and it is impossible to detect the leakage in a timely and effective manner by relying solely on the set monitoring data threshold. On the other hand, if only a smaller monitoring data threshold is set for optimization and adjustment, due to the influence of interference factors, slight interference changes in the data may trigger a threshold alarm, thereby reducing the accuracy of the hydraulic support cylinder oil leakage detection.

[0048] In order to timely and effectively determine whether the hydraulic support cylinder is leaking oil, this application conducts an in-depth analysis of the change characteristics between various detection parameters when the hydraulic support cylinder leaks oil to different degrees during the actual intelligent detection process. The specific process is as follows:

[0049] (1) First, considering that the changes of various parameters for oil cylinder leakage detection during the operation of the hydraulic support are different in time, any parameter is used as the target parameter, and the target parameter sequence is used as the input of the Bernaola Galvan segmentation algorithm to divide the target parameter sequence to obtain various subsequences of the target parameter sequence. Generally, when the oil cylinder leaks, due to the different usage of the hydraulic support, there will be different degrees of lag differences in the time period when the parameters change during the actual data acquisition process. Therefore, in view of the complexity of the oil cylinder leakage situation, for each parameter in the intelligent detection process of oil cylinder leakage, according to the division result of the corresponding parameter sequence, the time interval corresponding to each subsequence is determined, and other parameter sequences are divided based on the time interval. The purpose of this is to consider that under normal circumstances, when facing the change of the state of the hydraulic support, the changes of various parameters are generally synchronous. Therefore, according to the change of the current parameter in different time periods, the asynchronous response analysis of other parameters in the same time period is performed. Among them, the Bernaola Galvan segmentation algorithm is a well-known technology, and the specific process is not repeated.

[0050] It should be noted that, for the division of the target parameter sequence, this application only provides one segmentation method. There are many existing segmentation methods, and implementers can also use other segmentation methods to divide the target parameter sequence. This application does not make specific restrictions.

[0051] (2) Taking any parameter as an example, the parameter is taken as the target parameter. For each subsequence of the target parameter sequence, the interval between the first data collection moment and the last data collection moment in the subsequence is taken as the time interval of the subsequence; further, for each parameter sequence other than the target parameter sequence, the sequence of the other parameter sequence in each time interval is taken as each subsequence of the other parameter sequence;

[0052] Step S3, calculating the characteristic value of each parameter change in each time interval based on the data dispersion degree and data change trend in each subsequence; calculating the characteristic weight of each parameter change in each time interval based on the data change stability in each subsequence.

[0053] Further, in each time interval, the coefficient of variation of all data in each parameter subsequence is calculated, and the trend statistics of each parameter subsequence are obtained by the MK trend verification algorithm, and the product of the coefficient of variation of each subsequence and the trend statistics is calculated, and the product is used as the characteristic value of each parameter change in the time interval. The characteristic value can reflect the change characteristics of each parameter data in each time interval determined by the target parameter. Among them, the coefficient of variation and the MK trend verification algorithm are both well-known technologies, and the specific process will not be repeated.

[0054] Considering that in each time interval, different parameters have different significances for the data changes of oil cylinder leakage; for example, when a slight leakage occurs, the stability of the flow rate is greatly affected. Then, in the current time period, the flow rate data characteristics can more accurately reflect the oil cylinder leakage situation than other parameters. Therefore, the first-order difference sequence of the parameter sequence of various parameters in each time interval is obtained, and the entropy of all elements in the first-order difference sequence is calculated. The larger the entropy value, the worse the data change stability of the current parameter data in the time interval, and the more significant the parameter response change characteristics caused by the leakage situation. Further, the Softmax function is used to normalize the entropy calculated for all parameters in the time interval, and the normalized result is used as the characteristic weight of each parameter data change in each time interval. The larger the characteristic weight, the greater the significance of the asynchronous characteristics of the parameter data change facing the oil cylinder leakage situation. Among them, the Softmax function is a well-known technology, and the specific process will not be repeated.

[0055] Step S4, calculating the feature extraction result of the asynchronous response of the target parameter in each time interval based on the feature value and the feature weight; and calculating the first monitoring coefficient of the target parameter based on the feature extraction results of all time intervals.

[0056] After the above analysis, based on the time interval division results of the target parameters in the intelligent detection process of hydraulic cylinder oil leakage, the feature analysis of the asynchronous differences of the parameters in different time intervals compared with the target parameters is carried out. Specifically, for each time interval divided by the target parameter sequence, according to the eigenvalues ​​and eigenweights corresponding to each parameter data in each time interval, the feature extraction results of the asynchronous response to the influence of cylinder oil leakage in each time interval are calculated, and the expression is:

[0057] , where The target parameter The feature extraction results of the asynchronous response to the oil cylinder leakage in a time interval; and Respectively represent In the time interval The characteristic values ​​and the characteristic weights of the parameter data; Indicates the number of parameter types.

[0058] Based on the asynchronous response feature extraction results for the impact of oil cylinder leakage in each time interval, the asynchronous response features of the target parameters in the intelligent detection of hydraulic cylinder oil leakage are comprehensively evaluated to construct the first monitoring coefficient. Specifically, for the time intervals divided by the target parameter sequence, the feature extraction results in all time intervals are used as the input of the Z-score normalization algorithm, all the feature extraction results are normalized, and then the mean of the normalized results is calculated, and the mean is used as the first monitoring coefficient of the intelligent detection of oil cylinder oil leakage of the target parameter.

[0059] Step S5: Based on the first monitoring coefficient and in combination with various parameters at the current moment, perform oil leakage detection on the hydraulic support cylinder.

[0060] After the above processing, in the case of slight leakage in the hydraulic support cylinder, although each parameter data may show the characteristics of slow change, in the actual operation process, due to the complex operation of the hydraulic support and the significant asynchronous change characteristics of the correlation between the parameters caused by oil leakage. This application analyzes the phased change characteristics of the asynchronous response for intelligent detection of oil cylinder leakage by conducting a comparative analysis on the time interval of each parameter change, based on the change time interval of one of the parameters, namely the target parameter. Different from the existing overall and local data feature change analysis methods, this application fully combines the complexity of the correlation changes between different parameters under different degrees of oil cylinder leakage, accurately extracts the asynchronous response characteristics of each parameter to oil cylinder leakage, and then obtains the first monitoring coefficient, so as to accurately reflect the data change characteristics under different degrees of leakage.

[0061] Calculate the mean of the first monitoring coefficient of all parameters, and set the response threshold for minor leakage; because the significance of the asynchronous response characteristics of different parameters in the case of minor leakage will increase with time, the value range of the response threshold is 0.3-0.6. In order to further improve the detection accuracy of hydraulic support cylinder oil leakage, this application sets the response threshold to 0.3. If the mean is greater than or equal to the response threshold during the intelligent monitoring process of the hydraulic support cylinder, it indicates that the hydraulic support cylinder has leaked.

[0062] Furthermore, the standard lines of various parameters required by the project during the operation of the hydraulic support cylinder are used as the abnormal thresholds of various parameters, and the pressure, flow, temperature and piston rod displacement data collected at the current moment are used as the second monitoring coefficient. If any of the second monitoring coefficients is greater than or equal to the corresponding abnormal threshold, it means that the hydraulic cylinder may have leaked oil and needs to be repaired in time. It should be noted that when the above judgment is made by the first monitoring coefficient or the second monitoring coefficient, if the judgment result of any of the monitoring coefficients is that the hydraulic cylinder has leaked, it is determined that the current hydraulic support cylinder has leaked.

[0063] The schematic diagram of the process of obtaining the characteristic value of parameter changes is as follows Figure 2 shown.

[0064] See also Figure 3 , Figure 3 1 is a schematic diagram of the structure of an intelligent detection device for oil leakage of a hydraulic support cylinder provided in an embodiment of the present application. In this embodiment, each unit included in the terminal is used to execute each step in the embodiment corresponding to an intelligent detection method for oil leakage of a hydraulic support cylinder. Figure 3 , the intelligent detection device includes:

[0065] Data acquisition module: real-time collection of various parameter data of the oil cylinder, including pressure, temperature, flow and piston rod displacement data, to build various parameter sequences;

[0066] Data analysis module: taking any parameter as the target parameter, dividing the sequence according to the data mutation in the target parameter sequence to obtain each subsequence of the target parameter sequence; obtaining the time interval corresponding to each subsequence, dividing the other parameter sequences according to each time interval, and obtaining each subsequence of the other parameter sequences;

[0067] Feature extraction module: Calculate the feature value of each parameter change in each time interval based on the data dispersion degree and data change trend in each subsequence; Calculate the feature weight of each parameter change in each time interval based on the stability of data change in each subsequence;

[0068] Data detection module: calculates the feature extraction result of the asynchronous response of the target parameter in each time interval based on the feature value and the feature weight; calculates the first monitoring coefficient of the target parameter based on the feature extraction results of all time intervals;

[0069] Oil leakage monitoring module: Based on the first monitoring coefficient and in combination with various parameters at the current moment, the oil leakage detection of the hydraulic support cylinder is performed.

[0070] Based on the same inventive concept as the above method, an embodiment of the present application also provides an intelligent detection system for oil leakage of a hydraulic support cylinder, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned intelligent detection methods for oil leakage of a hydraulic support cylinder are implemented.

[0071] In summary, the embodiment of the present application provides an intelligent detection method for oil leakage of a hydraulic support cylinder. When a hydraulic support cylinder leaks, different degrees of leakage will cause different changes in various parameters. Based on this, the present application divides the data of the changes in each parameter, regards each parameter as a target parameter, and determines the corresponding time interval according to the data division result of the target parameter. Through the determined time interval, the asynchronous response characteristics of the target parameter compared to other parameters when facing different degrees of cylinder leakage can be accurately extracted. Unlike the traditional overall and local data feature change analysis method, the present application fully considers the complexity of the correlation changes between various parameters under different degrees of cylinder leakage. In this way, the asynchronous response characteristics of each parameter facing cylinder leakage can be more accurately extracted, thereby significantly improving the accuracy of the hydraulic support cylinder leakage detection results, and effectively avoiding the interference of different degrees of leakage to the intelligent detection of cylinder leakage.

[0072] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0074] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. An intelligent detection method for oil leakage of hydraulic support cylinder, characterized in that: The method comprises the following steps: Collect various parameter data of the oil cylinder in real time, including pressure, temperature, flow and piston rod displacement data, and construct various parameter sequences; Take any parameter as the target parameter, divide the sequence according to the data mutation in the target parameter sequence, and obtain each subsequence of the target parameter sequence; obtain the time interval corresponding to each subsequence, divide the other parameter sequences according to each time interval, and obtain each subsequence of the other parameter sequences; The characteristic value of each parameter change in each time interval is calculated based on the data dispersion degree and data change trend in each subsequence; the characteristic weight of each parameter change in each time interval is calculated based on the stability of data change in each subsequence; Calculating a feature extraction result of an asynchronous response of a target parameter in each time interval based on the feature value and the feature weight; calculating a first monitoring coefficient of the target parameter based on the feature extraction results of all time intervals; Based on the first monitoring coefficient and in combination with various parameters at the current moment, the hydraulic support cylinder oil leakage detection is performed.

2. The intelligent detection method for oil leakage of hydraulic support cylinder according to claim 1, characterized in that: The process of acquiring each subsequence of the target parameter sequence is as follows: taking the target parameter sequence as the input of the segmentation algorithm, and outputting each subsequence of the target parameter sequence.

3. The intelligent detection method for oil leakage of hydraulic support cylinder according to claim 1, characterized in that: Each subsequence of the other parameter sequences is a sequence of the other parameter sequences in each time interval.

4. The intelligent detection method for oil leakage of hydraulic support cylinder according to claim 1, characterized in that: The process of obtaining the characteristic value of each parameter change in each time interval is as follows: In each time interval, the coefficient of variation of all data in each parameter subsequence is calculated, and the trend statistics of each parameter subsequence are obtained through a trend verification algorithm. The product of the coefficient of variation and the trend statistics is used as the characteristic value of each parameter change in the time interval.

5. The intelligent detection method for oil leakage of hydraulic support cylinder according to claim 1, characterized in that: The process of obtaining the characteristic weight of each parameter change in each time interval is as follows: A first-order difference sequence of each parameter subsequence in each time interval is obtained, and the normalized value of the entropy of all elements in the first-order difference sequence is used as the characteristic weight of each parameter change in each time interval.

6. The intelligent detection method for oil leakage of hydraulic support cylinder according to claim 1, characterized in that: The expression of the feature extraction result is: , where The target parameter The feature extraction results of asynchronous responses within a time interval; and Respectively represent In the time interval The characteristic value and the characteristic weight of the parameter; Indicates the number of parameter types.

7. The intelligent detection method for oil leakage of hydraulic support cylinder according to claim 1, characterized in that: The process of obtaining the first monitoring coefficient of the target parameter is as follows: For the time intervals divided by the target parameter sequence, the normalized mean value of the feature extraction results in all time intervals is used as the first monitoring coefficient of the target parameter.

8. The intelligent detection method for oil leakage of hydraulic support cylinder according to claim 1, characterized in that: The oil leakage detection of the hydraulic support cylinder is specifically performed as follows: Set response thresholds and abnormal thresholds for various parameters; calculate the first monitoring coefficient mean of all parameters. If the first monitoring coefficient mean is greater than or equal to the preset response threshold, or if the value of any parameter at the current moment is greater than or equal to the corresponding abnormal threshold, it is determined that a leak has occurred in the current hydraulic support cylinder.

9. An intelligent detection device for oil leakage of hydraulic support cylinder, realizing the method as claimed in claim 1, characterized in that: The device comprises: Data acquisition module: real-time collection of various parameter data of the oil cylinder, including pressure, temperature, flow and piston rod displacement data, to build various parameter sequences; Data analysis module: taking any parameter as the target parameter, dividing the sequence according to the data mutation in the target parameter sequence to obtain each subsequence of the target parameter sequence; obtaining the time interval corresponding to each subsequence, dividing the other parameter sequences according to each time interval, and obtaining each subsequence of the other parameter sequences; Feature extraction module: Calculate the feature value of each parameter change in each time interval based on the data dispersion degree and data change trend in each subsequence; Calculate the feature weight of each parameter change in each time interval based on the stability of data change in each subsequence; Data detection module: calculates the feature extraction result of the asynchronous response of the target parameter in each time interval based on the feature value and the feature weight; calculates the first monitoring coefficient of the target parameter based on the feature extraction results of all time intervals; Oil leakage monitoring module: Based on the first monitoring coefficient and in combination with various parameters at the current moment, the oil leakage detection of the hydraulic support cylinder is performed.

10. An intelligent detection system for oil leakage of a hydraulic support cylinder, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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