Generator management method and device, storage medium and electronic equipment

Abstract

The invention discloses a generator management method and device, a storage medium and electronic equipment. The method comprises the steps that a first parameter set and a second parameter set of a target engine are acquired; determining a first correlation coefficient between the ith parameter in the first parameter set and the jth parameter in the second parameter set by using a Pearson correlation coefficient function; determining a second correlation coefficient between the ith parameter in the first parameter set and the jth parameter in the second parameter set by using a Spearman correlation coefficient function; determining a third correlation coefficient between the ith parameter in the first parameter set and the jth parameter in the second parameter set by using a Kendall correlation coefficient function; obtaining a target coefficient based on the first correlation coefficient, the second correlation coefficient and the third correlation coefficient; and under the condition that the target coefficient is greater than the reference threshold value, adjusting the ith parameter. The technical problem that the management efficiency of the generator is low is solved.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular, to a method and device for managing a generator, a storage medium, and an electronic device. Background Art

[0002] As a key component of a wind turbine, the generator inputs high speed from the gearbox and outputs high-quality, high-stability power generation required for grid connection. Generators generate vibration and noise when working, which not only affects the service life of the generator itself, but also has a negative impact on the surrounding environment.

[0003] In order to avoid the problem of vibration and noise generated by the generator during operation, the method commonly used in the related art is to manually check the various components of the generator to determine the components that cause vibration and noise, and then replace or adjust the components.

[0004] However, the manual inspection of the generator provided in the above-mentioned related art takes a lot of time, resulting in a technical problem of low management efficiency of the generator.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present application provide a method and device for managing a generator, a storage medium, and an electronic device to at least solve the technical problem of low management efficiency of the generator.

[0007] According to one aspect of an embodiment of the present application, a method for managing an engine is provided, comprising: obtaining a first parameter set and a second parameter set of a target engine, wherein the first parameter set includes equipment state parameters of the target engine in a first time period and environmental parameters of the environment in which the target engine is located in the first time period, and the second parameter set includes vibration parameters of the target engine in the first time period and sound print parameters of the target engine in the first time period; determining a first correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set using a Pearson correlation coefficient function, wherein i is greater than or equal to 1 and i is an integer, and j is greater than or equal to 1 and j is an integer; determining a second correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set using a Spearman correlation coefficient function; determining a third correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set using a Kendall correlation coefficient function; obtaining a target coefficient based on the first correlation coefficient, the second correlation coefficient and the third correlation coefficient; and determining that the i-th parameter is correlated with the j-th parameter when the target coefficient is greater than a reference threshold, and adjusting the i-th parameter.

[0008] According to another aspect of an embodiment of the present application, an engine management device is also provided, including: a first acquisition unit, used to acquire a first parameter set and a second parameter set of a target engine, wherein the first parameter set includes equipment state parameters of the target engine in a first time period and environmental parameters of the environment in which the target engine is located in the first time period, and the second parameter set includes vibration parameters of the target engine in the first time period and sound print parameters of the target engine in the first time period; a first determination unit, used to determine a first correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set using a Pearson correlation coefficient function, wherein i is greater than or equal to 1 and i is an integer, j is greater than or equal to 1 and j is an integer; a second determining unit is used to determine the second correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set by using the Spearman correlation coefficient function; a third determining unit is used to determine the third correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set by using the Kendall correlation coefficient function; a second acquiring unit is used to acquire a target coefficient based on the first correlation coefficient, the second correlation coefficient and the third correlation coefficient; a fourth determining unit is used to determine that the i-th parameter is correlated with the j-th parameter when the target coefficient is greater than a reference threshold, and adjust the i-th parameter.

[0009] According to another aspect of the embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned engine management method when running.

[0010] According to another aspect of the embodiment of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above engine management method.

[0011] According to another aspect of an embodiment of the present application, there is also provided an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the engine management method through the computer program.

[0012] In an embodiment of the present application, a first parameter set and a second parameter set of a target engine are obtained, wherein the first parameter set includes equipment state parameters of the target engine in a first time period and environmental parameters of the environment in which the target engine is located in the first time period, and the second parameter set includes vibration parameters of the target engine in the first time period and sound print parameters of the target engine in the first time period; a first correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set is determined using a Pearson correlation coefficient function, wherein i is greater than or equal to 1 and i is an integer, and j is greater than or equal to 1 and j is an integer; a second correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set is determined using a Spearman correlation coefficient function; a third correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set is determined using a Kendall correlation coefficient function; a target coefficient is obtained based on the first correlation coefficient, the second correlation coefficient and the third correlation coefficient; when the target coefficient is greater than a reference threshold, it is determined that there is a correlation between the i-th parameter and the j-th parameter, and the i-th parameter is adjusted. In other words, by using the embodiments of the present application, by analyzing the correlation between parameters, it is possible to identify which equipment status parameters or environmental parameters have a direct or indirect impact on vibration and soundprint. For example, if there is a high correlation between the cooling water temperature and the engine vibration, the operator can adjust the temperature or flow of the cooling water to reduce the vibration, thereby extending the engine life and improving the operating efficiency. This enables the engine management to shift from passive response to active optimization, achieving more intelligent equipment operation management. Furthermore, based on the correlation analysis, the operating status of the engine can be monitored. Once the correlation between the equipment status parameters, environmental parameters and the vibration parameters and soundprint parameters is abnormal (that is, the target coefficient exceeds the preset reference threshold), the relevant parameters are immediately adjusted to avoid equipment failures caused by abnormal parameters. This real-time monitoring and adjustment mechanism improves the response speed of management, thereby solving the technical problem of low management efficiency in the engine management method provided in the relevant technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0014] Figure 1 is a flow chart of an optional engine management method according to an embodiment of the present application;

[0015] Figure 2 is a flow chart of an optional engine management method according to an embodiment of the present application;

[0016] Figure 3is a schematic structural diagram of an optional engine management device according to an embodiment of the present application;

[0017] Figure 4 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] Alternatively, as an alternative, Figure 1 As shown, the engine management method includes:

[0021] S102, obtaining a first parameter set and a second parameter set of the target engine, wherein the first parameter set includes equipment state parameters of the target engine in a first time period and environmental parameters of the environment in which the target engine is located in the first time period, and the second parameter set includes vibration parameters of the target engine in the first time period and voiceprint parameters of the target engine in the first time period.

[0022] Optionally, the engine management method can be applied to, but not limited to, the engine maintenance and overhaul scenario, and the target transmitter is the engine to be maintained. Further, the equipment status parameters can include, but are not limited to, inverter temperature, hub temperature, cabin temperature, generator temperature, hub speed, hub angle, blade angle, yaw state value, etc., which are not limited in this embodiment. The environmental parameters can include, but are not limited to, wind speed, wind direction absolute value, atmospheric pressure, and wind tower ambient temperature of the cabin weather station. Wind speed and wind direction information can be collected by anemometers and wind vanes, and temperature and weather condition information can be counted by thermometers or meteorological systems. The vibration parameters can include, but are not limited to, vibration data including vibration sensors installed on gearboxes, main bearings, motors, etc., which can be judged by changes in vibration amplitude, main frequency, vibration trend, and fault frequency. The voiceprint parameters can include, but are not limited to, parameters such as main frequency, main frequency amplitude, and sound wave amplitude extracted by detecting voiceprint parameters of equipment such as blades, gearboxes, and motors.

[0023] It should be noted that, for each parameter, the first parameter set includes N values ​​thereof in the first time period, and correspondingly, for each parameter, the second parameter set also includes N values ​​thereof in the first time period. For example, for the inverter temperature, the first parameter set includes N values ​​of the inverter temperature in the first time period, such as: a first temperature collected at a first sub-time in the first time period, a second temperature collected at a second sub-time in the second time period, and so on, which will not be repeated in this example.

[0024] S104, using a Pearson correlation coefficient function to determine a first correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set, wherein i is greater than or equal to 1 and is an integer, and j is greater than or equal to 1 and is an integer.

[0025] It should be noted that the above Pearson correlation coefficient function can be but is not limited to the calculation formula for indicating the Pearson correlation coefficient, wherein the Pearson correlation coefficient, also known as the Pearson product-moment correlation coefficient, is a statistic used to measure the degree of linear correlation between two variables. It is a value between -1 and 1, indicating the strength and direction of the linear relationship between the two variables.

[0026] Optionally, the above-mentioned determination of the first correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using the Pearson correlation coefficient function may include, but is not limited to: obtaining N i-th parameters within the first time period from the first parameter set, and obtaining N j-th parameters within the first time period from the second parameter set, where N is greater than 1 and N is an integer; obtaining a first average value of the N i-th parameters, and obtaining a second average value of the N j-th parameters; inputting the first average value, the second average value, the N i-th parameters and the N j-th parameters into the Pearson correlation coefficient function to obtain the first correlation coefficient.

[0027] S106: Determine a second correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using a Spearman correlation coefficient function.

[0028] It should be noted that the above-mentioned Spearman correlation coefficient function can be but is not limited to the calculation formula for indicating the Spearman correlation coefficient, wherein the Spearman correlation coefficient is also called the Spearman rank correlation coefficient. It measures the correlation between two variables by comparing their rank order. The first step in calculating the Spearman correlation coefficient is to convert the original data into rank data, that is, to sort the values ​​in each variable, and then calculate the Pearson correlation coefficient between the rank data. The range of the Spearman correlation coefficient is also between -1 and 1, similar to the Pearson correlation coefficient: a value close to 1 indicates a strong positive correlation, a value close to -1 indicates a strong negative correlation, and a value close to 0 indicates no correlation.

[0029] Optionally, using the Spearman correlation coefficient function to determine the second correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set may include, but is not limited to: obtaining N i-th parameters within the first time period from the first parameter set, and obtaining N j-th parameters within the first time period from the second parameter set, where N is greater than 1 and N is an integer; sorting the N i-th parameters to obtain N first sorting ranks, and sorting the N j-th parameters to obtain N second sorting ranks, wherein the n-th first sorting rank among the N first sorting ranks is used to represent the sorting rank of the n-th parameter among the N i-th parameters, and the n-th second sorting rank among the N second sorting ranks is used to represent the sorting rank of the n-th parameter among the N i-th parameters; inputting the N first sorting ranks and the N second sorting ranks into the Spearman correlation coefficient function to obtain the second correlation coefficient.

[0030] S108: Determine a third correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using a Kendall correlation coefficient function.

[0031] It should be noted that the above Kendall correlation coefficient function can be, but is not limited to, used to indicate the calculation formula of the Kendall correlation coefficient, wherein the Kendall correlation coefficient is a non-parametric statistic that measures the correlation between two measurement columns, and is used to determine whether the two variables are statistically correlated and the direction of the correlation (positive or negative correlation). Unlike the Pearson correlation coefficient and the Spearman correlation coefficient, the Kendall correlation coefficient does not assume that the data follows a specific distribution or has a linear relationship, so it is more applicable when processing discontinuous, nonlinear or small sample data.

[0032] Optionally, using the Kendall correlation coefficient function to determine the third correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set may include, but is not limited to: obtaining N i-th parameters within the first time period from the first parameter set, and obtaining N j-th parameters within the first time period from the second parameter set; determining each i-th parameter in the N i-th parameters as the current i-th parameter in turn, and determining each i-th parameter in the N j-th parameters as the current j-th parameter, and performing the following steps: randomly selecting a target i-th parameter from the N i-th parameters, and randomly selecting a target j-th parameter from the N j-th parameters; obtaining a first difference between the current i-th parameter and the target i-th parameter, and obtaining a second difference between the current j-th parameter and the target j-th parameter; obtaining the product of the first difference and the second difference; when the product is greater than the first threshold, accumulating the first value; when the product is less than or equal to the first threshold, accumulating the second value; outputting the first value and the second value into the Kendall correlation coefficient function to obtain the third correlation coefficient.

[0033] S110, obtaining a target coefficient based on the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient.

[0034] Optionally, the above-mentioned obtaining of the target coefficient based on the first correlation coefficient, the second correlation coefficient and the third correlation coefficient may include but is not limited to: obtaining the first target coefficient based on the first correlation coefficient and the first weight; obtaining the second target coefficient based on the second correlation coefficient and the second weight; obtaining the third target coefficient based on the third correlation coefficient and the third weight; and summing the first target coefficient, the second target coefficient and the third target coefficient to obtain the target coefficient.

[0035] S112: When the target coefficient is greater than the reference threshold, determine that the i-th parameter is correlated with the j-th parameter, and adjust the i-th parameter.

[0036] It should be noted that after determining that there is a correlation between the i-th parameter and the j-th parameter, it can include but is not limited to: using the obtained correlation relationship (i.e., the conclusion that there is a correlation between the i-th parameter and the j-th parameter) as a training label to train the fault diagnosis model, wherein the above fault diagnosis model can be used to predict which parameters of the transmitter are unreasonable.

[0037] Optionally, the above fault diagnosis model may be trained by, but is not limited to, the following steps:

[0038] S1, data preprocessing: ensure that all collected parameter data (including device status parameters, environmental parameters, vibration parameters and voiceprint parameters) have undergone necessary preprocessing, such as missing value processing, data cleaning and standardization, to reduce noise and bias and improve the accuracy of model training.

[0039] S2, feature selection: Use the target coefficients obtained from CCI analysis to screen out device status parameters and environmental parameters that are highly correlated with vibration parameters and voiceprint parameters. These parameters will be used as input features for training the model, while vibration parameters or voiceprint parameters will be used as output targets (or labels). Ensure that the selected features have predictive value and avoid redundancy or model overfitting caused by excessive features.

[0040] S3, training data set preparation: The "feature-label" pairs obtained from the correlation analysis are combined into a training data set, where each sample consists of a set of equipment state parameters, environmental parameters (input features), and corresponding vibration parameters or voiceprint parameters (output labels). If there is historical fault data, it can be labeled as "fault" or a specific fault type for model training.

[0041] S4, Model selection and training: Select the appropriate model for training based on the nature of the data and the prediction requirements. Possible models include: Supervised learning models: such as decision trees, random forests, support vector machines (SVM), gradient boosted trees (GBM) or deep learning models such as convolutional neural networks (CNN) and long short-term memory networks (LSTM). These models can predict vibration or voiceprint parameters based on features, or directly predict fault conditions. Anomaly detection models: such as statistical-based anomaly detection, isolation forests or autoencoders. These models are suitable for unsupervised learning scenarios and can learn patterns from normal operating data to identify abnormal vibration or voiceprint parameters. Time series analysis models: such as ARIMA, state space models or recurrent neural networks (RNN), are particularly useful for capturing patterns of vibration and voiceprint parameters changing over time.

[0042] When training a model, you need to use a training data set to optimize the model parameters to minimize the gap between the predicted results and the actual results. Techniques such as cross-validation can be used to evaluate the generalization ability of the model and prevent overfitting.

[0043] S5, Model Validation and Testing: Use independent validation and test sets to evaluate the performance of the model. The validation set is used to adjust the model parameters, while the test set is used to finally evaluate the predictive ability of the model. Performance indicators can include accuracy, recall, F1 score, AUC-ROC, etc., depending on the application scenario and goals of the model.

[0044] S6, Model Adjustment: Based on the performance of the model on the validation set and test set, it may be necessary to adjust the model parameters, feature selection, or model type to improve the prediction performance.

[0045] S7, Model deployment and monitoring: Deploy the trained model to a real-time monitoring system to monitor the operating status of the engine. The model should be retrained with new data regularly to adapt to equipment aging, environmental changes, or new failure modes.

[0046] In this way, a fault diagnosis model can be trained to predict vibration or sound pattern parameters based on equipment status parameters and environmental parameters, or directly predict the fault status, thereby achieving early fault warning and status maintenance, improving the operating efficiency of the engine and the accuracy of the maintenance strategy. When selecting a model, the characteristics of the data (such as whether it is time series data), the limitations of computing resources, and the accuracy requirements of the diagnosis should be considered.

[0047] It should be noted that, in this embodiment, the selection of the above-mentioned reference threshold can refer to the following: Industry standards and equipment manuals: Consult relevant standards in the industry or technical manuals provided by equipment manufacturers to understand what the correlation threshold between equipment status parameters and vibration parameters is usually for a specific type of engine or wind turbine. Manufacturers may give recommended values ​​based on years of experience and testing. Historical data: Analyze the relationship between equipment status parameters and vibration parameters in historical data to find out the change pattern of correlation indicators when equipment failure or abnormal state occurs. Statistical methods can be used to determine the mean and standard deviation of the correlation indicators in these cases, thereby defining a threshold range that usually covers the correlation indicator values ​​of most abnormal situations. Expert knowledge: Combine the experience and knowledge of domain experts, and judge which correlation indicator values ​​are worthy of attention based on actual operating experience and failure cases. Sensitivity and specificity: When setting the reference threshold, it is necessary to balance sensitivity and specificity. Sensitivity refers to the ability of the model to correctly identify situations that are truly problematic, while specificity refers to the ability of the model to correctly exclude situations without problems. If the threshold is set too low, there may be a high false positive rate (i.e., a non-problematic situation is mistakenly reported as a problem), resulting in unnecessary equipment inspection and maintenance; if the threshold is set too high, there may be a high false negative rate (i.e., a problematic situation is mistakenly judged as a non-problematic situation), thus missing the opportunity for early warning. Usually, a suitable balance can be found by adjusting the threshold and evaluating the sensitivity and specificity of the model on the validation data set. Business needs and cost considerations: The selection of the reference threshold should take into account business needs and cost factors. If the cost of early fault warning is lower than the loss after the failure occurs, then consider setting a lower reference threshold to increase the sensitivity of the warning. Conversely, if the cost of frequent false alarms (such as unnecessary maintenance and downtime) is high, it may be necessary to set a higher reference threshold to increase the specificity of the warning.

[0048] In an embodiment of the present application, a first parameter set and a second parameter set of a target engine are obtained, wherein the first parameter set includes equipment state parameters of the target engine in a first time period and environmental parameters of the environment in which the target engine is located in the first time period, and the second parameter set includes vibration parameters of the target engine in the first time period and sound print parameters of the target engine in the first time period; a first correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set is determined using a Pearson correlation coefficient function, wherein i is greater than or equal to 1 and i is an integer, and j is greater than or equal to 1 and j is an integer; a second correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set is determined using a Spearman correlation coefficient function; a third correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set is determined using a Kendall correlation coefficient function; a target coefficient is obtained based on the first correlation coefficient, the second correlation coefficient and the third correlation coefficient; when the target coefficient is greater than a reference threshold, it is determined that there is a correlation between the i-th parameter and the j-th parameter, and the i-th parameter is adjusted. In other words, by using the embodiments of the present application, by analyzing the correlation between parameters, it is possible to identify which equipment status parameters or environmental parameters have a direct or indirect impact on vibration and soundprint. For example, if there is a high correlation between the cooling water temperature and the engine vibration, the operator can adjust the temperature or flow of the cooling water to reduce the vibration, thereby extending the engine life and improving the operating efficiency. This enables the engine management to shift from passive response to active optimization, achieving more intelligent equipment operation management. Furthermore, based on the correlation analysis, the operating status of the engine can be monitored. Once the correlation between the equipment status parameters, environmental parameters and the vibration parameters and soundprint parameters is abnormal (that is, the target coefficient exceeds the preset reference threshold), the relevant parameters are immediately adjusted to avoid equipment failures caused by abnormal parameters. This real-time monitoring and adjustment mechanism improves the response speed of management, thereby solving the technical problem of low management efficiency in the engine management method provided in the relevant technology.

[0049] Optionally, as an optional solution, determining the first correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using the Pearson correlation coefficient function includes:

[0050] S1, obtaining N i-th parameters within a first time period from a first parameter set, and obtaining N j-th parameters within the first time period from a second parameter set, wherein N is greater than 1 and is an integer.

[0051] It should be noted that the N i-th parameters within the above-mentioned first time period can be, but are not limited to, used to indicate the N numerical values ​​corresponding to the i-th parameter within the first time period. For example, assuming that the above-mentioned i-th parameter is the inverter temperature, then the above-mentioned N i-th parameters can be, but are not limited to, including: the first temperature collected at the first sub-time within the first time period, the second temperature collected at the second sub-time within the second time period, and so on, which will not be repeated in this example.

[0052] S2, obtaining a first average value of N i-th parameters, and obtaining a second average value of N j-th parameters.

[0053] S3, inputting the first average value, the second average value, the N i-th parameters and the N j-th parameters into a Pearson correlation coefficient function to obtain a first correlation coefficient.

[0054] For example, the above steps may be described based on, but not limited to, the following examples:

[0055]

[0056] The Pearson correlation coefficient function can refer to formula (1), r (x，y) is used to represent the first correlation coefficient, x is used to represent the i-th parameter, and y is used to represent the j-th parameter. It is used to represent the first average value of N i-th parameters, Used to represent the second mean of the N j-th parameters, x n Used to represent the nth i-th parameter among N i-th parameters, y n Used to represent the nth jth parameter among N jth parameters.

[0057] It should be noted that the above example is an optional example provided for the convenience of explaining the above steps. In this embodiment, the above steps can also be implemented through other implementation methods, and this is not limited in this embodiment.

[0058] In an embodiment of the present application, N i-th parameters within a first time period are obtained from a first parameter set, and N j-th parameters within a first time period are obtained from a second parameter set, wherein N is greater than 1 and N is an integer; a first average value of the N i-th parameters is obtained, and a second average value of the N j-th parameters is obtained; the first average value, the second average value, the N i-th parameters, and the N j-th parameters are input into the Pearson correlation coefficient function to obtain a first correlation coefficient. In other words, using an embodiment of the present application, based on correlation analysis, the operating state of the engine can be monitored. Once the correlation between the equipment state parameters, environmental parameters, vibration parameters, and soundprint parameters is abnormal (i.e., the target coefficient exceeds the preset reference threshold), the relevant parameters are immediately adjusted to avoid equipment failures caused by abnormal parameters. This real-time monitoring and adjustment mechanism improves the response speed of management, thereby solving the technical problem of low management efficiency in the engine management method provided in the relevant technology.

[0059] Optionally, as an optional solution, determining the second correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using the Spearman correlation coefficient function includes:

[0060] S1, obtaining N i-th parameters within a first time period from a first parameter set, and obtaining N j-th parameters within the first time period from a second parameter set, where N is greater than 1 and is an integer.

[0061] S2, sorting the N i-th parameters to obtain N first sorting ranks, and sorting the N j-th parameters to obtain N second sorting ranks, wherein the n-th first sorting rank among the N first sorting ranks is used to represent the sorting rank of the n-th i-th parameter among the N i-th parameters, and the n-th second sorting rank among the N second sorting ranks is used to represent the sorting rank of the n-th j-th parameter among the N i-th parameters.

[0062] For example, assuming that the N i-th parameters include: 0.1, 0.2, 0.4, 0.3 in sequence, assuming that the N j-th parameters include: 0.5, 0.6, 0.8, 0.7 in sequence, then the N first sorting orders include: 1, 2, 4, 3, and the N second sorting orders include: 1, 2, 4, 3.

[0063] S3, inputting the N first sorting orders and the N second sorting orders into a Spearman correlation coefficient function to obtain a second correlation coefficient.

[0064] For example, the above steps may be described based on, but not limited to, the following examples:

[0065]

[0066] The above Spearman correlation coefficient function can refer to formula (2), ρ x,y is used to represent the second correlation coefficient, x is used to represent the i-th parameter, y is used to represent the j-th parameter, R n It is used to indicate the first sorting order of the nth i-th parameter among N i-th parameters, Q n Used to indicate the second sorting order of the nth jth parameter among N jth parameters.

[0067] It should be noted that the above example is an optional example provided for the convenience of explaining the above steps. In this embodiment, the above steps can also be implemented through other implementation methods, and this is not limited in this embodiment.

[0068] In the embodiment of the present application, N i-th parameters in the first time period are obtained from the first parameter set, and N j-th parameters in the first time period are obtained from the second parameter set, where N is greater than 1 and N is an integer; the N i-th parameters are sorted to obtain N first sorting positions, and the N j-th parameters are sorted to obtain N second sorting positions, wherein the n-th first sorting position in the N first sorting positions is used to represent the sorting position of the n-th i-th parameter in the N i-th parameters, and the n-th second sorting position in the N second sorting positions is used to represent the sorting position of the n-th j-th parameter in the N i-th parameters; the N first sorting positions and the N second sorting positions are input into the Spearman correlation coefficient function to obtain the second correlation coefficient. That is to say, by using the embodiment of the present application, based on correlation analysis, the running state of the engine can be monitored. Once the correlation between the equipment state parameters, environmental parameters, vibration parameters, and voiceprint parameters is detected to be abnormal (that is, the target coefficient exceeds the preset reference threshold), the relevant parameters are adjusted immediately to avoid equipment failure caused by abnormal parameters. This real-time monitoring and adjustment mechanism improves the response speed of management, thereby solving the technical problem of low management efficiency in the engine management method provided in the relevant technology.

[0069] Optionally, as an optional solution, determining the third correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using the Kendall correlation coefficient function includes:

[0070] S1, obtaining N i-th parameters within a first time period from a first parameter set, and obtaining N j-th parameters within the first time period from a second parameter set.

[0071] S2, sequentially determine each i-th parameter among the N i-th parameters as the current i-th parameter, and determine each i-th parameter among the N j-th parameters as the current j-th parameter, and perform the following steps: randomly select a target i-th parameter from the N i-th parameters, and randomly select a target j-th parameter from the N j-th parameters; obtain a first difference between the current i-th parameter and the target i-th parameter, and obtain a second difference between the current j-th parameter and the target j-th parameter; obtain the product of the first difference and the second difference; when the product is greater than a first threshold, accumulate the first value; when the product is less than or equal to the first threshold, accumulate the second value.

[0072] It should be noted that the first value and the second value may be set to the same initial value, for example, both may be set to 0. Further, the first threshold may be set to 0, but is not limited to 0. The accumulation of the first value may include, but is not limited to: adding 1 to the first value, and the accumulation of the second value may include, but is not limited to: adding 1 to the second value.

[0073] S3, outputting the first value and the second value into a Kendall correlation coefficient function to obtain a third correlation coefficient.

[0074] For example, the above steps may be described based on, but not limited to, the following examples:

[0075]

[0076] The above Kendall correlation coefficient function can refer to formula (3), τ (x，y) is used to represent the third correlation coefficient, x is used to represent the i-th parameter, y is used to represent the j-th parameter, N c Used to represent the first value mentioned above, N d Used to represent the second numerical value mentioned above.

[0077] It should be noted that the above example is an optional example provided for the convenience of explaining the above steps. In this embodiment, the above steps can also be implemented through other implementation methods, and this is not limited in this embodiment.

[0078] In an embodiment of the present application, N i-th parameters within a first time period are obtained from a first parameter set, and N j-th parameters within the first time period are obtained from a second parameter set; each i-th parameter in the N i-th parameters is determined as a current i-th parameter in turn, and each i-th parameter in the N j-th parameters is determined as a current j-th parameter, and the following steps are performed: a target i-th parameter is randomly selected from the N i-th parameters, and a target j-th parameter is randomly selected from the N j-th parameters; a first difference between the current i-th parameter and the target i-th parameter is obtained, and a second difference between the current j-th parameter and the target j-th parameter is obtained; the product of the first difference and the second difference is obtained; when the product is greater than a first threshold, the first value is accumulated; when the product is less than or equal to the first threshold, the second value is accumulated; the first value and the second value are output to a Kendall correlation coefficient function to obtain a third correlation coefficient. That is to say, by using the embodiment of the present application, based on correlation analysis, the operating status of the engine can be monitored. Once the correlation between the equipment status parameters, environmental parameters, vibration parameters, and soundprint parameters is abnormal (that is, the target coefficient exceeds the preset reference threshold), the relevant parameters are adjusted immediately to avoid equipment failure caused by abnormal parameters. This real-time monitoring and adjustment mechanism improves the response speed of management, thereby solving the technical problem of low management efficiency in the engine management method provided in the relevant technology.

[0079] Optionally, as an optional solution, obtaining the target coefficient based on the first correlation coefficient, the second correlation coefficient and the third correlation coefficient includes:

[0080] S1, obtaining a first target coefficient based on a first correlation coefficient and a first weight.

[0081] S2, obtaining a second target coefficient based on the second correlation coefficient and the second weight.

[0082] S3, obtaining a third target coefficient based on the third correlation coefficient and the third weight.

[0083] S4, summing the first target coefficient, the second target coefficient and the third target coefficient to obtain the target coefficient.

[0084] For example, assuming that weights are set for the Pearson, Spearman and Kendall correlation coefficients, respectively, W1=0.4 (i.e., the first weight), W2=0.3 (i.e., the second weight) and W3=0.3 (i.e., the third weight), assuming that the first correlation coefficient is 0.65, the second correlation coefficient is 0.75, and the third correlation coefficient is 0.60; then the first target coefficient=0.65×0.4=0.26, the second target coefficient=0.75*0.3=0.225, the third target coefficient=0.60*0.3=0.18; target coefficient=0.26+0.225+0.18=0.665.

[0085] It should be noted that the above example is an optional example provided for the convenience of explaining the above steps, and there is no limitation on the management method of the above engine.

[0086] In an embodiment of the present application, a first target coefficient is obtained based on a first correlation coefficient and a first weight; a second target coefficient is obtained based on a second correlation coefficient and a second weight; a third target coefficient is obtained based on a third correlation coefficient and a third weight; and the first target coefficient, the second target coefficient, and the third target coefficient are summed to obtain a target coefficient. In other words, using an embodiment of the present application, by using three different statistical correlation coefficients, the scheme can comprehensively evaluate the relationship between parameters from three dimensions: linear correlation (Pearson), rank correlation (Spearman), and trend correlation (Kendall). This multi-dimensional evaluation can more comprehensively understand the complex correlations between parameters and avoid information that may be missed by a single method.

[0087] Optionally, as an optional solution, the management method of the transmitter further includes:

[0088] S1, determine the elastic support assembly configured for the target engine.

[0089] S2, obtaining a first vibration parameter of a first subcomponent in the elastic support assembly, and obtaining a second vibration parameter of a second subcomponent in the elastic support assembly, wherein the first subcomponent is located at the upper end of the elastic support assembly, and the second subcomponent is located at the lower end of the elastic support assembly, the first vibration parameter is used to characterize the vibration degree of the first subcomponent, and the second vibration parameter is used to characterize the vibration degree of the second subcomponent.

[0090] S3, when the difference between the first vibration parameter and the second vibration parameter is greater than a second threshold, prompting to adjust the elastic support assembly.

[0091] It should be noted that the elastic support assembly mentioned above can be but is not limited to the elastic support for indicating the generator, also known as vibration isolation support or vibration reduction support, which is a component installed between the generator base and the fixed structure, and its main function is to reduce the transmission of vibration generated during the operation of the generator to the surrounding structure, thereby reducing noise, protecting equipment and extending the service life of the generator. The elastic support is usually made of elastic materials (such as rubber, springs or hydraulic dampers), which can absorb and attenuate the vibration energy caused by the rotation of the generator and load changes.

[0092] Optionally, in some embodiments, the first subcomponent may include but is not limited to the components configured at the upper end of the elastic support component, and the first vibration parameters may include but are not limited to: axial, radial, horizontal vibrations of the generator at the upper end of the elastic support, voiceprint data, etc.; the second subcomponent may include but is not limited to the components configured at the lower end of the elastic support component, and the second vibration parameters may include but are not limited to: axial, radial, horizontal vibrations of the generator at the lower end of the elastic support, voiceprint data, etc.

[0093] Furthermore, after obtaining the first vibration parameter of the first subassembly in the elastic support assembly and obtaining the second vibration parameter of the second subassembly in the elastic support assembly, it may include, but is not limited to: comparing each calculated difference with a preset second threshold. The second threshold is based on the equipment manufacturer's recommendations, industry standards, historical operating data, or obtained through experiments, and it defines the difference level between normal operation and the need for adjustment. If any difference exceeds the second threshold, it indicates that the performance of the elastic support assembly has deteriorated and there is a need for adjustment or maintenance.

[0094] It should be noted that the elastic support assembly can be adjusted by, but is not limited to, sending a prompt message to the maintenance system for maintaining the generator, wherein the specific way of adjusting the elastic support assembly depends on the design of the assembly and the nature of the fault. For example, replacing aged or damaged elastic materials such as rubber pads or springs; adjusting the hardness or damping coefficient of the support assembly, which may involve adjusting the hydraulic damper; tightening or adjusting the connection parts of the support assembly to ensure the stability of the generator; in some cases, it may be necessary to rebalance the generator rotor to reduce the original vibration source.

[0095] In an embodiment of the present application, an elastic support assembly configured for a target engine is determined; a first vibration parameter of a first subassembly in the elastic support assembly is obtained, and a second vibration parameter of a second subassembly in the elastic support assembly is obtained, wherein the first subassembly is located at the upper end of the elastic support assembly, and the second subassembly is located at the lower end of the elastic support assembly, the first vibration parameter is used to characterize the vibration degree of the first subassembly, and the second vibration parameter is used to characterize the vibration degree of the second subassembly; when the difference between the first vibration parameter and the second vibration parameter is greater than the second threshold value, a prompt is given to adjust the elastic support assembly. In other words, by using an embodiment of the present application, by separately monitoring the vibration conditions of the upper end and the lower terminal assembly of the elastic support assembly, it is possible to quickly identify whether there is a problem of asymmetric vibration or local excessive vibration. This helps to quickly locate the source of the fault and avoid additional vibration caused by uneven elastic support during engine operation, thereby reducing damage to the engine structure.

[0096] Optionally, as an optional example, it is possible but not limited to Figure 2 The following steps are shown to illustrate the management method of the above engine by way of example:

[0097] S202, download indicators. Specifically, the collected state parameters can be mainly divided into four categories: natural environment data, equipment status data, vibration data and voiceprint data. Natural environment data mainly include wind speed, wind direction absolute value, atmospheric pressure and wind tower ambient temperature of the cabin weather station. Wind speed and direction information can be collected through anemometers and wind vanes, and temperature and weather conditions can be counted through thermometers or meteorological systems; equipment status data mainly include inverter temperature, hub temperature, cabin temperature, generator temperature, hub speed, hub angle, blade angle, yaw state value, etc.; vibration data includes vibration amplitude changes, main frequency changes, vibration trend changes and fault frequency determined by installing vibration sensors on equipment such as gearboxes, main bearings and motors; voiceprint data includes detection of voiceprint parameters of equipment such as blades, gearboxes and motors, and extraction of parameters such as main frequency, main frequency amplitude and sound wave amplitude.

[0098] S204, comprehensive correlation analysis, specifically, a comprehensive correlation index (CCI) is established by combining a linear correlation index and two nonlinear indicators to analyze the correlation between the state parameters of the wind turbines. The linear correlation index is selected as the Pearson correlation coefficient, and the nonlinear correlation index is selected as the Spearman correlation coefficient and the Kendall correlation coefficient, and the comprehensive correlation index is established using different weights of the three.

[0099] S206, training the early warning model, specifically, using the establishment of a comprehensive correlation indicator training mechanism and a data-driven early warning model.

[0100] S208, generator elastic support performance evaluation, specifically, detect the generator axial, radial, horizontal vibration, and soundprint data at the upper end of the elastic support, compare the generator axial, radial, horizontal vibration, and soundprint data at the lower end of the elastic support, and detect the health status of the elastic support based on the difference between the upper and lower end indicators.

[0101] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0102] According to another aspect of the embodiment of the present application, there is also provided an engine management device for implementing the above engine management method. Figure 3 As shown, the device comprises:

[0103] A first acquisition unit 302 is used to acquire a first parameter set and a second parameter set of a target engine, wherein the first parameter set includes equipment state parameters of the target engine in a first time period and environmental parameters of an environment where the target engine is located in the first time period, and the second parameter set includes vibration parameters of the target engine in the first time period and voiceprint parameters of the target engine in the first time period;

[0104] A first determining unit 304 is used to determine a first correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set using a Pearson correlation coefficient function, wherein i is greater than or equal to 1 and is an integer, and j is greater than or equal to 1 and is an integer;

[0105] A second determining unit 306 is used to determine a second correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set by using a Spearman correlation coefficient function;

[0106] A third determining unit 308 is used to determine a third correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set by using a Kendall correlation coefficient function;

[0107] A second acquisition unit 310, configured to acquire a target coefficient based on the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient;

[0108] The fourth determining unit 312 is used to determine whether the i-th parameter is correlated with the j-th parameter and adjust the i-th parameter when the target coefficient is greater than the reference threshold.

[0109] Optionally, in this embodiment, the above-mentioned first determination unit includes: a first acquisition module, used to obtain N i-th parameters within the first time period from the first parameter set, and to obtain N j-th parameters within the first time period from the second parameter set, wherein N is greater than 1 and N is an integer; a second acquisition module, used to obtain a first average value of the N i-th parameters, and to obtain a second average value of the N j-th parameters; a first input acquisition module, used to input the first average value, the second average value, the N i-th parameters and the N j-th parameters into the Pearson correlation coefficient function to obtain a first correlation coefficient.

[0110] Optionally, in this embodiment, the above-mentioned second determination unit includes: a third acquisition module, used to obtain N i-th parameters within the first time period from the first parameter set, and to obtain N j-th parameters within the first time period from the second parameter set, where N is greater than 1 and N is an integer; a sorting module, used to sort the N i-th parameters to obtain N first sorting ranks, and to sort the N j-th parameters to obtain N second sorting ranks, wherein the n-th first sorting rank among the N first sorting ranks is used to represent the sorting rank of the n-th parameter among the N i-th parameters, and the n-th second sorting rank among the N second sorting ranks is used to represent the sorting rank of the n-th parameter among the N i-th parameters; a second input acquisition module, used to input the N first sorting ranks and the N second sorting ranks into the Spearman correlation coefficient function to obtain a second correlation coefficient.

[0111] Optionally, in this embodiment, the third determination unit includes: a fourth acquisition module, used to acquire N i-th parameters within the first time period from the first parameter set, and to acquire N j-th parameters within the first time period from the second parameter set; an accumulation processing module, used to sequentially determine each i-th parameter in the N i-th parameters as the current i-th parameter, and to determine each i-th parameter in the N j-th parameters as the current j-th parameter, and to perform the following steps: randomly select a target i-th parameter from the N i-th parameters, and randomly select a target j-th parameter from the N j-th parameters; acquire a first difference between the current i-th parameter and the target i-th parameter, and acquire a second difference between the current j-th parameter and the target j-th parameter; acquire the product of the first difference and the second difference; when the product is greater than the first threshold, accumulate the first value; when the product is less than or equal to the first threshold, accumulate the second value; a third input acquisition module, used to output the first value and the second value into the Kendall correlation coefficient function to obtain a third correlation coefficient.

[0112] Optionally, in this embodiment, the second acquisition unit includes: a fifth acquisition module, used to acquire a first target coefficient based on the first correlation coefficient and the first weight; a sixth acquisition module, used to acquire a second target coefficient based on the second correlation coefficient and the second weight; a seventh acquisition module, used to acquire a third target coefficient based on the third correlation coefficient and the third weight; and a summation processing module, used to sum the first target coefficient, the second target coefficient and the third target coefficient to obtain the target coefficient.

[0113] Optionally, in this embodiment, the above-mentioned device also includes: a fifth determination unit, used to obtain a first vibration parameter of a first subcomponent in the elastic support assembly, and to obtain a second vibration parameter of a second subcomponent in the elastic support assembly, wherein the first subcomponent is located at the upper end of the elastic support assembly, and the second subcomponent is located at the lower end of the elastic support assembly, and the first vibration parameter is used to characterize the vibration degree of the first subcomponent, and the second vibration parameter is used to characterize the vibration degree of the second subcomponent; a prompt unit, used to prompt to adjust the elastic support assembly when the difference between the first vibration parameter and the second vibration parameter is greater than a second threshold value.

[0114] The specific embodiment can refer to the example shown in the above-mentioned engine management method, and this embodiment will not be described in detail here.

[0115] According to another aspect of the embodiment of the present application, an electronic device for implementing the above engine management method is also provided. This embodiment is described by taking the electronic device as a server. Figure 4 As shown, the electronic device includes a memory 402 and a processor 404. The memory 402 stores a computer program, and the processor 404 is configured to execute the steps in any of the above method embodiments through the computer program.

[0116] Optionally, in this embodiment, the electronic device may be located in at least one network device among a plurality of network devices of a computer network.

[0117] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0118] S1, obtaining a first parameter set and a second parameter set of a target engine, wherein the first parameter set includes equipment state parameters of the target engine in a first time period and environmental parameters of an environment where the target engine is located in the first time period, and the second parameter set includes vibration parameters of the target engine in the first time period and voiceprint parameters of the target engine in the first time period;

[0119] S2, determining a first correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set using a Pearson correlation coefficient function, wherein i is greater than or equal to 1 and is an integer, and j is greater than or equal to 1 and is an integer;

[0120] S3, determining a second correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using a Spearman correlation coefficient function;

[0121] S4, determining a third correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using a Kendall correlation coefficient function;

[0122] S5, obtaining a target coefficient based on the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient;

[0123] S6, when the target coefficient is greater than the reference threshold, determining that the i-th parameter is correlated with the j-th parameter, and adjusting the i-th parameter.

[0124] Alternatively, a person skilled in the art may understand that: Figure 4 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 4 The structure of the electronic device is not limited. Figure 4 More or fewer components (such as network interfaces, etc.) as shown in, or with Figure 4 Different configurations are shown.

[0125] Among them, the memory 402 can be used to store software programs and modules, such as the program instructions / modules corresponding to the engine management method and device in the embodiment of the present application. The processor 404 executes various functional applications and data processing by running the software programs and modules stored in the memory 402, that is, realizing the above-mentioned engine management method. The memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 402 may further include a memory remotely located relative to the processor 404, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. As an example, Figure 4As shown, the memory 402 may include, but is not limited to, the acquisition unit 302, the first determination unit 304, the second determination unit 306, the third determination unit 308, the second acquisition unit 310, and the fourth determination unit 312 in the management device of the engine. In addition, other module units in the management device of the engine may also be included but are not limited to, which will not be repeated in this example.

[0126] Optionally, the transmission device 406 is used to receive or send data via a network. Specific examples of the network may include a wired network and a wireless network. In one example, the transmission device 406 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 406 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0127] In addition, the electronic device further includes: a display 408, and a connection bus 410 for connecting various module components in the electronic device.

[0128] In other embodiments, the terminal device or server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the multiple nodes through network communication. The nodes may form a point-to-point network, and any form of computing device, such as a server, terminal or other electronic device, may become a node in the blockchain system by joining the point-to-point network.

[0129] According to one aspect of the present application, a computer program product is provided, the computer program product comprising a computer program / instruction, the computer program / instruction comprising a program code for executing the above method. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions provided by the embodiments of the present application are executed.

[0130] According to one aspect of the present application, another computer program product is also provided, including a non-volatile computer-readable storage medium, the non-volatile computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method in each embodiment of the present application are implemented.

[0131] According to one aspect of the present application, a computer-readable storage medium is provided, and a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above method.

[0132] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0133] S1, obtaining a first parameter set and a second parameter set of a target engine, wherein the first parameter set includes equipment state parameters of the target engine in a first time period and environmental parameters of an environment where the target engine is located in the first time period, and the second parameter set includes vibration parameters of the target engine in the first time period and voiceprint parameters of the target engine in the first time period;

[0134] S2, determining a first correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set using a Pearson correlation coefficient function, wherein i is greater than or equal to 1 and is an integer, and j is greater than or equal to 1 and is an integer;

[0135] S3, determining a second correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using a Spearman correlation coefficient function;

[0136] S4, determining a third correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using a Kendall correlation coefficient function;

[0137] S5, obtaining a target coefficient based on the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient;

[0138] S6, when the target coefficient is greater than the reference threshold, determining that the i-th parameter is correlated with the j-th parameter, and adjusting the i-th parameter.

[0139] Optionally, in the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0140] Optionally, in this embodiment, a person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing hardware related to the terminal device through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0141] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.

[0142] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0143] In the several embodiments provided in the present application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0144] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0146] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for managing an engine, characterized in that: include: Acquire a first parameter set and a second parameter set of a target engine, wherein the first parameter set includes device state parameters of the target engine in a first time period and environmental parameters of an environment where the target engine is located in the first time period, and the second parameter set includes vibration parameters of the target engine in the first time period and voiceprint parameters of the target engine in the first time period; Determine a first correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set using a Pearson correlation coefficient function, wherein i is greater than or equal to 1 and is an integer, and j is greater than or equal to 1 and is an integer; Determine a second correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using a Spearman correlation coefficient function; Determine a third correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set using a Kendall correlation coefficient function; Acquire a target coefficient based on the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient; When the target coefficient is greater than a reference threshold, it is determined that there is a correlation between the i-th parameter and the j-th parameter, and the i-th parameter is adjusted.

2. The method according to claim 1, characterized in that Determining the first correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set by using the Pearson correlation coefficient function includes: Obtain N of the i-th parameters in the first time period from the first parameter set, and obtain N of the j-th parameters in the first time period from the second parameter set, where N is greater than 1 and is an integer; Obtaining a first average value of the N i-th parameters, and obtaining a second average value of the N j-th parameters; The first average value, the second average value, N of the i-th parameters, and N of the j-th parameters are input into the Pearson correlation coefficient function to obtain the first correlation coefficient.

3. The method according to claim 1, characterized in that Determining the second correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set by using the Spearman correlation coefficient function includes: Obtain N of the i-th parameters in the first time period from the first parameter set, and obtain N of the j-th parameters in the first time period from the second parameter set, where N is greater than 1 and is an integer; Sorting the N i-th parameters to obtain N first sorting orders, and sorting the N j-th parameters to obtain N second sorting orders, wherein the n-th first sorting order among the N first sorting orders is used to indicate the sorting order of the n-th i-th parameter among the N i-th parameters, and the n-th second sorting order among the N second sorting orders is used to indicate the sorting order of the n-th j-th parameter among the N i-th parameters; The N first sorting orders and the N second sorting orders are input into the Spearman correlation coefficient function to obtain the second correlation coefficient.

4. The method according to claim 1, characterized in that Determining the third correlation coefficient between the i-th parameter in the first parameter set and the j-th parameter in the second parameter set by using the Kendall correlation coefficient function includes: Acquire N of the i-th parameters within the first time period from the first parameter set, and acquire N of the j-th parameters within the first time period from the second parameter set; Sequentially determine each i-th parameter among the N i-th parameters as the current i-th parameter, and determine each i-th parameter among the N j-th parameters as the current j-th parameter, and perform the following steps: randomly select a target i-th parameter from the N i-th parameters, and randomly select a target j-th parameter from the N j-th parameters; obtain a first difference between the current i-th parameter and the target i-th parameter, and obtain a second difference between the current j-th parameter and the target j-th parameter; obtain the product of the first difference and the second difference; if the product is greater than a first threshold, accumulate the first value; if the product is less than or equal to the first threshold, accumulate the second value; The first value and the second value are output to the Kendall correlation coefficient function to obtain the third correlation coefficient.

5. The method according to any one of claims 1 to 4, characterized in that The acquiring a target coefficient based on the first correlation coefficient, the second correlation coefficient and the third correlation coefficient comprises: Acquire a first target coefficient based on the first correlation coefficient and a first weight; Acquire a second target coefficient based on the second correlation coefficient and the second weight; Acquire a third target coefficient based on the third correlation coefficient and a third weight; The first target coefficient, the second target coefficient and the third target coefficient are summed to obtain the target coefficient.

6. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Determining an elastic support assembly configured for the target engine; Acquire a first vibration parameter of a first subassembly in the elastic support assembly, and acquire a second vibration parameter of a second subassembly in the elastic support assembly, wherein the first subassembly is located at the upper end of the elastic support assembly, and the second subassembly is located at the lower end of the elastic support assembly, the first vibration parameter is used to characterize the vibration degree of the first subassembly, and the second vibration parameter is used to characterize the vibration degree of the second subassembly; When the difference between the first vibration parameter and the second vibration parameter is greater than a second threshold, it is prompted to adjust the elastic support assembly.

7. An engine management device, characterized in that: include: A first acquisition unit is used to acquire a first parameter set and a second parameter set of a target engine, wherein the first parameter set includes equipment state parameters of the target engine in a first time period and environmental parameters of an environment where the target engine is located in the first time period, and the second parameter set includes vibration parameters of the target engine in the first time period and voiceprint parameters of the target engine in the first time period; A first determining unit, configured to determine a first correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set using a Pearson correlation coefficient function, wherein i is greater than or equal to 1 and is an integer, and j is greater than or equal to 1 and is an integer; A second determining unit, configured to determine a second correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set by using a Spearman correlation coefficient function; A third determining unit, configured to determine a third correlation coefficient between an i-th parameter in the first parameter set and a j-th parameter in the second parameter set by using a Kendall correlation coefficient function; A second acquisition unit, configured to acquire a target coefficient based on the first correlation coefficient, the second correlation coefficient and the third correlation coefficient; The fourth determining unit is used to determine that there is a correlation between the i-th parameter and the j-th parameter when the target coefficient is greater than a reference threshold, and adjust the i-th parameter.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program is executed by a processor to perform the method described in any one of claims 1 to 6.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

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