Operation machine state monitoring method and device, operation machine and readable storage medium

Abstract

The invention discloses an operation machine state monitoring method and device, an operation machine and a readable storage medium, and relates to the technical field of engineering machines. The method comprises the steps of obtaining operation parameters of an operation machine and operation data corresponding to all the operation parameters, determining key features of all the operation parameters based on the operation data, and determining a prediction coefficient based on all the key features and a regression analysis algorithm. And determining whether the operation machine is abnormal or not based on the prediction coefficient and a preset coefficient threshold value. And under the condition that the working machine is abnormal, the critical value of preset parameters is reduced based on the prediction coefficient, and the preset parameters comprise the power and the loading weight of the working machine. The operation condition of the operation machine is monitored and evaluated, problems can be found in time, the use mode of the operation machine is optimized, and the operation efficiency of the operation machine is improved.

Description

Technical Field

[0001] The present application relates to the technical field of engineering machinery, and specifically to a method and device for monitoring the state of an operating machinery, an operating machinery, and a readable storage medium. Background Art

[0002] Electric wheel loaders are widely used in various engineering and construction sites. For some rental electric wheel loaders, due to their diverse use environments and variable loads, they need to be monitored more carefully to avoid overuse and extend their service life.

[0003] Traditional monitoring methods are relatively crude and only conduct single parameter monitoring and analysis. This method cannot comprehensively and carefully monitor the operating status of the electric wheel loader, resulting in managers being unable to accurately and timely grasp the actual operating conditions of the electric wheel loader. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the embodiments of the present application is to provide a method and device for monitoring the state of an operating machine, an operating machine and a readable storage medium.

[0005] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a method for monitoring the state of an operating machine, comprising:

[0006] Obtaining the operating parameters of the operating machinery and the operating data corresponding to each operating parameter;

[0007] Determine the key characteristics of each operating parameter based on the operating data;

[0008] Determine prediction coefficients based on all key features and regression analysis algorithms;

[0009] Based on the prediction coefficient and the preset coefficient threshold, determining whether the operating machine has an abnormality;

[0010] In the event of an abnormality in the operating machine, a critical value of a preset parameter is reduced based on a prediction coefficient, wherein the preset parameters include power and load weight of the operating machine.

[0011] In the embodiment of the present application, the key features of each operating parameter are determined based on the operating data, including:

[0012] respectively determining the standard scores of the operating data corresponding to each operating parameter;

[0013] Determine the abnormal operation data corresponding to each operation parameter based on the preset score threshold and the standard score of each operation parameter;

[0014] The key features of each operating parameter are determined based on the quantity of abnormal operating data corresponding to each operating parameter.

[0015] In the embodiment of the present application, the standard scores of the operating data corresponding to each operating parameter are determined respectively, including:

[0016] Determine the data mean and standard deviation of the operating data corresponding to each operating parameter respectively;

[0017] For each operating parameter, a ratio of a data difference to a standard deviation is determined as a standard score of each operating data, wherein the data difference is a difference between the operating data and the data mean.

[0018] In the embodiment of the present application, the preset coefficient threshold includes a first coefficient threshold, and determining whether the operating machine has an abnormality based on the prediction coefficient and the preset coefficient threshold includes:

[0019] determining whether the prediction coefficient is greater than a first coefficient threshold;

[0020] When the prediction coefficient is greater than the first coefficient threshold, determining that the operating machine has an abnormality;

[0021] When the prediction coefficient is less than or equal to the first coefficient threshold, it is determined that there is no abnormality in the working machine.

[0022] In the embodiment of the present application, the preset coefficient threshold value also includes a second coefficient threshold value. When the operating machine is abnormal, the critical value of the preset parameter is reduced based on the prediction coefficient, including:

[0023] In the case where the operating machine has an abnormality, determining whether the prediction coefficient is less than or equal to a second coefficient threshold;

[0024] When the prediction coefficient is less than or equal to the second coefficient threshold, the critical value of the preset parameter is reduced based on the prediction coefficient.

[0025] In the embodiment of the present application, the operating machine status monitoring method further includes:

[0026] When the prediction coefficient is greater than the second coefficient threshold, an alarm message is output and an abnormal record is generated.

[0027] In the embodiment of the present application, the operating parameters include voltage data of the battery in the operating machine, current data of the motor, speed data of the motor, temperature data of the motor, pressure data of the hydraulic system, and oil quantity data of the hydraulic oil.

[0028] A second aspect of the present application provides a working machine state monitoring device, comprising:

[0029] a memory configured to store instructions;

[0030] The processor is configured to call instructions from the memory and implement the working machine state monitoring method as described in the above embodiment when executing the instructions.

[0031] A third aspect of the present application provides a working machine, comprising:

[0032] A working machine state monitoring device as described in the above embodiment.

[0033] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the working machinery status monitoring method as described in the above-mentioned embodiment.

[0034] Through the above technical solution, the operating parameters of the operating machinery and the operating data corresponding to each operating parameter are obtained, the key features of each operating parameter are determined based on the operating data, and the prediction coefficient is determined based on all the key features and the regression analysis algorithm. Based on the prediction coefficient and the preset coefficient threshold, it is determined whether the operating machinery has an abnormality. The prediction coefficient is determined through comprehensive operating parameters to provide an effective data reference for subsequent abnormal judgment of the operating machinery. In the event of an abnormality in the operating machinery, the critical value of the preset parameter is reduced based on the prediction coefficient, where the preset parameters include the power and load weight of the operating machinery. Monitoring and evaluating the operation of the operating machinery helps to promptly discover problems, optimize the use of the operating machinery, and improve the operating efficiency of the operating machinery.

[0035] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0037] Figure 1 The flowchart of the working machine state monitoring method according to an embodiment of the present application is schematically shown;

[0038] Figure 2 The figure schematically shows a flow chart of a method for monitoring the state of a working machine according to another embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme 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. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0040] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0041] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0042] Figure 1 The following schematically shows a flow chart of a method for monitoring the state of a working machine according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a method for monitoring the state of an operating machine, which may include the following steps.

[0043] Step 100, obtaining operating parameters of the operating machine and operating data corresponding to each operating parameter;

[0044] It should be noted that, in the present embodiment, the operating machine is described as an electric wheel loader, and the control of the electric wheel loader can be to generate power through the electric energy drive motor, and transmit the power to various actuators of the electric wheel loader through the hydraulic transmission system to work. At the same time, the electric energy drive and hydraulic transmission parts are coordinated and intelligently controlled as a whole through the integrated control system, so as to achieve efficient and stable operation of the electric wheel loader. The operating parameters of the operating machine refer to the operating status of each component of the internal system of the operating machine when it is running. It can include motor current, motor voltage, motor temperature, motor speed, pressure of the hydraulic transmission system, and hydraulic oil temperature and oil volume of the hydraulic transmission system. The operating data corresponding to each operating parameter refers to the corresponding values ​​of the operating parameters at different time points. By considering the diversity of operating parameters, the misreporting or omission of the operating machine status due to the single reference data is reduced, and the situation that the real state of the operating machine cannot be accurately reflected occurs.

[0045] In one embodiment, the operating parameters include voltage data of a battery in the working machine, current data of a motor, speed data of a motor, temperature data of a motor, pressure data of a hydraulic system, and amount data of hydraulic oil.

[0046] It should be noted that in order to improve the diversity and comprehensiveness of data references while reducing data collection and analysis work, in this embodiment, the operating parameters at least include the voltage data of the battery in the operating machine, the current data of the motor, the motor speed data, the temperature data of the motor, the pressure data of the hydraulic system, and the amount of hydraulic oil, and the above operating parameters can basically ensure the general data accuracy. In the case of strict requirements on data accuracy, the operating parameters may include motor current, motor voltage, motor temperature, motor speed, pressure of the hydraulic transmission system, and hydraulic oil temperature and amount of the hydraulic transmission system.

[0047] It is understandable that in one embodiment, the collection of various operating parameters can be realized through different sensors, for example, the voltage data of the battery and the motor can be collected through the voltage sensor; the current data of the motor can be collected through the current sensor; the temperature data of the motor and the hydraulic system can be collected through the temperature sensor; the speed data of the motor can be collected through the speed sensor; the pressure data of the hydraulic system can be collected through the pressure sensor; the oil volume data of the hydraulic oil can be collected through the level sensor; the data from different sensors can be integrated into a unified data packet, stored in the database, and the relevant data can be backed up regularly, and the collected relevant data can be cleaned, denoised and other pre-processing operations can be performed, and the real-time data processing framework can be used to perform real-time analysis on the data to extract key features. The data pre-processing operation can ensure the integrity of the data by deleting or interpolating missing values, and identify and process abnormal data points. Specifically, if the key parameters in some records are missing, these records can be deleted, but this may lose some valid data. Missing values ​​can also be interpolated by using the mean, median, and front and back values ​​to fill in the missing values.

[0048] It is understood that, in one embodiment, the operating parameters may also include motor torque, battery current, transmission temperature, hydraulic flow, tire pressure, tire temperature, and ambient temperature, etc. The motor torque is monitored to evaluate the working state and efficiency of the motor; the battery discharge and charging current is monitored to prevent over-discharge or over-charge of the battery; the temperature of the transmission is monitored to prevent failures caused by overheating; the flow of the hydraulic system is monitored to evaluate the working efficiency and state of the hydraulic system; the tire pressure is monitored to ensure the service life and safety of the tire; the tire temperature is monitored to prevent tire burst caused by overheating; and the temperature of the working environment is monitored to evaluate its impact on the operating state of the equipment.

[0049] Step 200, determining key features of each operating parameter based on the operating data;

[0050] It should be noted that by analyzing and calculating the operating data corresponding to each operating parameter within a certain time range, the key features of each operating parameter are obtained, and the prediction coefficient is calculated using the key features of different operating parameters and the regression analysis algorithm. The state of the operating machinery is further predicted and analyzed by the prediction coefficient. The key features can characterize whether there is an abnormality in the operating parameter within the certain time range and the degree of abnormality when there is an abnormality. The key features of each operating parameter can be determined by taking the average value of the operating data within a certain time range for each operating parameter, or calculating the median, etc.

[0051] In this embodiment, the key features of each operating parameter are determined based on the operating data, including:

[0052] respectively determining the standard scores of the operating data corresponding to each operating parameter;

[0053] Determine the abnormal operation data corresponding to each operation parameter based on the preset score threshold and the standard score of each operation parameter;

[0054] The key features of each operating parameter are determined based on the quantity of abnormal operating data corresponding to each operating parameter.

[0055] It should be noted that, in this embodiment, each operating parameter corresponds to a preset score threshold, and the preset score threshold is used to limit whether the standard score is normal. When the absolute value of the standard score of the operating data is greater than the preset score threshold, the operating data is determined to be abnormal operating data. The standard score of each operating data is obtained by calculating the operating data corresponding to each operating parameter. The absolute value of the standard score of each operating data is compared with the preset score threshold, and the standard score whose absolute value is greater than the preset score threshold is screened out, and the operating data corresponding to the standard score is determined as abnormal operating data. The number of abnormal operating data in all operating data corresponding to each operating parameter is determined, and the number of abnormal data is used as the key feature of the operating parameter.

[0056] Specifically, in one embodiment, respectively determining the standard score of the operating data corresponding to each operating parameter includes:

[0057] Determine the data mean and standard deviation of the operating data corresponding to each operating parameter respectively;

[0058] For each operating parameter, a ratio of a data difference to a standard deviation is determined as a standard score of each operating data, wherein the data difference is a difference between the operating data and the data mean.

[0059] It should be noted that each operating parameter corresponds to multiple operating data, and the data mean and standard deviation are calculated for all operating data corresponding to each operating parameter. Then, the standard score of each operating data is calculated, specifically, it can be calculated by the following formula:

[0060]

[0061] Among them, Z represents the standard score; X represents the running data; μ represents the mean; σ represents the standard deviation.

[0062] In one embodiment, taking the voltage parameter of the battery in the working machine as the operating parameter as an example, the multiple operating data corresponding to the voltage parameter are voltage values ​​within a certain time range. Referring to Table 1, Table 1 shows the voltage values ​​at time points T1-T10.

[0063] Table 1

[0064] Time Voltage value T1 220 T2 225 T3 230 T4 240 T5 250 T6 260 T7 270 T8 280 T9 290 T10 300

[0065] Through calculation, we know that the mean value of the operating data in Table 1 is 256, and the standard deviation is about 24.58. The standard score of the voltage value corresponding to each time point is calculated, and the calculation results are shown in Table 2.

[0066] Table 2

[0067]

[0068]

[0069] Set the preset score threshold to 1, traverse all standard scores, and determine that the absolute values ​​of the standard scores at time points T1, T2, T3, T9, and T10 are greater than 1. Then mark T1, T2, T3, T9, and T10 as abnormal operation data. The number of abnormal operation data of the voltage parameter during the monitoring time period is 5, and the number of abnormal operation data is the key feature of the voltage parameter during the monitoring time period.

[0070] In this embodiment, the accuracy and effectiveness of judging the operating status of the operating machinery are improved by calculating and filtering abnormal operating data and taking the number of abnormal operating data as the key feature of the operating parameters.

[0071] Step 300, determining prediction coefficients based on all key features and regression analysis algorithms;

[0072] It should be noted that, in this embodiment, the acquired key features are used to construct a comprehensive feedback mechanism based on the regression analysis algorithm, and the operation state changes of the electric wheel loader are predicted based on the comprehensive feedback mechanism, and the operation of the electric wheel loader is divided into normal, warning, and abnormal states according to the prediction coefficient. The logical factors of the prediction coefficient are as follows: first, the operating parameter index, that is, the factors that may cause the electric wheel loader to be abnormal, specifically the impact of the key features on the electric wheel loader; second, the weight of these parameter indicators, that is, the proportion of each key feature when it is generated; third, the operation equation, that is, what kind of mathematical operation process is used to obtain the result, and the parameter indicators with their respective weights are calculated through the operation equation to obtain the prediction coefficient.

[0073] Taking the Logistic regression analysis method as an example, the key features of all operating parameters are set to the Y set, which are represented as {y1, y2, ..., y n}, n represents the number of parameters in the set Y. Based on the influence of different operating parameters on the operation of the operating machinery, different regression coefficients are set for the key characteristics of different operating parameters. The exponential expression of the prediction coefficient is:

[0074]

[0075] Among them, YCz represents the prediction coefficient; w n Represents the regression coefficient, with a value range of (0, 1]; y n represents the key feature; Q is a constant term, which represents the impact of aging of the electric wheel loader and can be adaptively adjusted.

[0076] In one embodiment, by monitoring the battery voltage, current, voltage, temperature, speed, hydraulic pressure, oil temperature and oil quantity of the electric wheel loader, the key features of the battery voltage, current, voltage, temperature, speed, hydraulic pressure, oil temperature and oil quantity during the monitoring period are obtained, and a comprehensive feedback mechanism is constructed based on the Logistic regression analysis method, and the key features of all battery voltage, current, voltage, temperature, speed, hydraulic pressure, oil temperature and oil quantity are set to a y set, expressed as {y1, y2, y3, y4, y5, y6, y7, y8}, and the prediction coefficient is calculated by the Logistic regression analysis method, and the exponential expression is:

[0077]

[0078] Among them, YC z represents the prediction coefficient; Q is a constant term, and its value in this embodiment is 0.118; y1, y2, y3, y4, y5, y6, y7, y8 represent the battery voltage key feature, current key feature, voltage key feature, temperature key feature, speed key feature, hydraulic pressure key feature, oil temperature key feature and oil quantity key feature respectively; w1, w2, w3, w4, w5, w6, w7, w8 represent the regression coefficient of the battery voltage key feature, the regression coefficient of the current key feature, the regression coefficient of the voltage key feature, the regression coefficient of the temperature key feature, the regression coefficient of the speed key feature, the regression coefficient of the hydraulic pressure key feature, the regression coefficient of the oil temperature key feature and the regression coefficient of the oil quantity key feature respectively.

[0079] Step 400, determining whether the operating machine has an abnormality based on the prediction coefficient and the preset coefficient threshold;

[0080] It should be noted that the prediction coefficient can reflect the operating status of the electric wheel loader. In this embodiment, the preset coefficient threshold is used as a standard for judging the prediction coefficient. By comparing the prediction coefficient with the preset coefficient threshold, it is determined whether the operating machinery has an abnormality based on the comparison result. In one embodiment, when the prediction coefficient is greater than the preset coefficient threshold, it is considered that the operating machinery has an abnormality at this time. The preset coefficient threshold can also be used to judge the degree of abnormality. One or more preset coefficient thresholds can be set, corresponding to one or more abnormality levels, and the preset coefficient is compared with the one or more preset coefficient thresholds to determine the degree of abnormality corresponding to the operating machinery.

[0081] Step 500, when an abnormality occurs in the operating machine, reducing the critical value of the preset parameter based on the prediction coefficient, wherein the preset parameter includes the power and load weight of the operating machine.

[0082] It should be noted that, in this embodiment, when it is determined that the operating machinery has an abnormality, the operating control of the operating machinery is adjusted to improve the operating stability of the operating machinery. The preset parameters include the power and load weight of the operating machinery. When the operating machinery leaves the factory or for different operating environments, critical values ​​are set for the power and load weight, for example, the maximum power and the maximum load weight. In this embodiment, when it is found that the operating machinery has an abnormality, the critical value of the preset parameter is dynamically reduced to achieve dynamic adjustment of the operation of the operating machinery, thereby alleviating or eliminating the abnormality. Specifically, the critical value of the preset parameter is reduced based on the prediction coefficient, and the degree of reduction can refer to the following formula:

[0083]

[0084] Among them, C_value new Indicates the critical value after reduction; C_value old Indicates the original critical value; YC z represents the prediction coefficient.

[0085] For example, if the preset parameter is the loading weight of the operating machinery, when it is monitored that there is a slight abnormality in the current overall operation of the operating machinery, the critical value of the loading weight is lowered, the maximum loading weight is reduced, and the operating load of the operating machinery is reduced, thereby improving the stability of the operation of the operating machinery.

[0086] In this embodiment, the operating parameters of the operating machinery and the operating data corresponding to each operating parameter are obtained, the key features of each operating parameter are determined based on the operating data, and the prediction coefficient is determined based on all the key features and the regression analysis algorithm. Based on the prediction coefficient and the preset coefficient threshold, it is determined whether the operating machinery is abnormal. The prediction coefficient is determined by comprehensive operating parameters to provide an effective data reference for subsequent abnormal judgment of the operating machinery. In the case of abnormality of the operating machinery, the critical value of the preset parameter is reduced based on the prediction coefficient, wherein the preset parameter includes the power and load weight of the operating machinery. Monitoring and evaluating the operation of the operating machinery helps to find problems in a timely manner, optimize the use of the operating machinery, and improve the operating efficiency of the operating machinery.

[0087] In one embodiment, the preset coefficient threshold includes a first coefficient threshold, and determining whether the working machine has an abnormality based on the prediction coefficient and the preset coefficient threshold includes:

[0088] determining whether the prediction coefficient is greater than a first coefficient threshold;

[0089] When the prediction coefficient is greater than the first coefficient threshold, determining that the operating machine has an abnormality;

[0090] When the prediction coefficient is less than or equal to the first coefficient threshold, it is determined that there is no abnormality in the working machine.

[0091] In this embodiment, the first coefficient threshold is used as a judgment standard for the degree of slight abnormality based on the prediction coefficient. When the prediction coefficient is greater than the first coefficient threshold, it can be determined that the operating machine has an abnormality; conversely, when the prediction coefficient is less than or equal to the first coefficient threshold, it is determined that the operating machine does not have an abnormality.

[0092] refer to Figure 2 In one embodiment, the preset coefficient threshold value further includes a second coefficient threshold value. When the operating machine is abnormal, the critical value of the preset parameter is reduced based on the prediction coefficient, including:

[0093] In the case where the operating machine has an abnormality, determining whether the prediction coefficient is less than or equal to a second coefficient threshold;

[0094] When the prediction coefficient is less than or equal to the second coefficient threshold, the critical value of the preset parameter is reduced based on the prediction coefficient.

[0095] In this embodiment, it should be noted that the preset coefficient thresholds include two, one is the first coefficient threshold, and the other is the second coefficient threshold, wherein the second coefficient threshold is greater than the first coefficient threshold. When the prediction coefficient is greater than the first coefficient threshold and less than or equal to the second coefficient threshold, it is determined that the operating machinery has an abnormality and the degree of abnormality is relatively minor. In the case of a minor abnormality in the operating machinery, it will not have a significant impact on the operation of the operating machinery. At this time, if timely intervention is made and the operating control of the operating machinery is appropriately adjusted, the abnormality is likely to be alleviated or even eliminated. It is understandable that, in one embodiment, the preset coefficient thresholds may also include more, and the degree of abnormality of the operating machinery may be divided more finely through multiple step-by-step increasing coefficient thresholds, so as to further improve the accuracy of the control of the operating machinery.

[0096] In this embodiment, the critical value of the preset parameter is dynamically reduced based on the degree of abnormality, thereby improving the stability of the operation of the working machine.

[0097] refer to Figure 2 In one embodiment, the working machine state monitoring method further includes:

[0098] When the prediction coefficient is greater than the second coefficient threshold, an alarm message is output and an abnormal record is generated.

[0099] In this embodiment, it should be noted that the second coefficient threshold is used as a judgment standard for the degree of serious abnormality based on the prediction coefficient. If the prediction coefficient is greater than the second coefficient threshold, it means that the operating machine is currently in a serious abnormality. At this time, it is impossible to eliminate the abnormality by dynamically adjusting the operating control of the operating machine, and it is necessary to inform the administrator so that the administrator can handle the relevant abnormality. Therefore, when there is an abnormality in the operating machine and the prediction coefficient is greater than the second coefficient threshold, an alarm message is output to prompt the administrator to handle the abnormality, and by generating an abnormal record, a data basis for abnormal analysis is provided to the administrator, so that the administrator can quickly and accurately locate the abnormal point and improve the efficiency of abnormality handling.

[0100] In this embodiment, the judgment of abnormal conditions of the operating machinery is improved by pre-setting coefficient thresholds, and by accurately locating the degree of abnormality, real-time monitoring and effective evaluation of the operating conditions of the operating machinery are achieved, which helps to promptly discover problems, optimize the use of the operating machinery, and improve the operating efficiency of the operating machinery.

[0101] The present application also provides a working machine state monitoring device, including:

[0102] a memory configured to store instructions;

[0103] The processor is configured to call instructions from the memory and implement the working machine state monitoring method as described in the above embodiment when executing the instructions.

[0104] The present application also provides a working machine, including:

[0105] A working machine state monitoring device as described in the above embodiment.

[0106] An embodiment of the present application also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the working machine status monitoring method as described in the above embodiment.

[0107] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0108] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0111] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0112] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0113] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0114] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0115] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for monitoring the state of an operating machine, It is characterized in that include: Obtaining the operating parameters of the operating machinery and the operating data corresponding to each operating parameter; Determining key features of each of the operating parameters based on the operating data; Determining prediction coefficients based on all of the key features and the regression analysis algorithm; Based on the prediction coefficient and a preset coefficient threshold, determining whether the working machine has an abnormality; In the event that an abnormality occurs in the working machine, a critical value of a preset parameter is reduced based on the prediction coefficient, wherein the preset parameter includes power and load weight of the working machine.

2. The method for monitoring the working machine status according to claim 1, It is characterized in that The determining of the key features of each of the operating parameters based on the operating data includes: respectively determining the standard scores of the operating data corresponding to each of the operating parameters; Determining abnormal operation data corresponding to each of the operation parameters based on the preset score threshold and the standard score of each of the operation parameters; The key feature of each of the operating parameters is determined based on the quantity of abnormal operating data corresponding to each of the operating parameters.

3. The method for monitoring the working machine status according to claim 2, It is characterized in that The step of respectively determining the standard scores of the operating data corresponding to the operating parameters comprises: Respectively determine the data mean and standard deviation of the operating data corresponding to each of the operating parameters; For each of the operating parameters, a ratio of a data difference value to the standard deviation is determined as a standard score of each of the operating data, wherein the data difference value is a difference between the operating data and the data mean value.

4. The method for monitoring the working machine status according to claim 1, It is characterized in that The preset coefficient threshold includes a first coefficient threshold, and determining whether the working machine has an abnormality based on the prediction coefficient and the preset coefficient threshold includes: determining whether the prediction coefficient is greater than the first coefficient threshold; When the prediction coefficient is greater than the first coefficient threshold, determining that an abnormality exists in the working machine; When the prediction coefficient is less than or equal to the first coefficient threshold, it is determined that there is no abnormality in the working machine.

5. The method for monitoring the working machine state according to claim 4, It is characterized in that The preset coefficient threshold value also includes a second coefficient threshold value, and when the working machine is abnormal, the critical value of the preset parameter is reduced based on the prediction coefficient, including: In the case where the working machine has an abnormality, determining whether the prediction coefficient is less than or equal to the second coefficient threshold; When the prediction coefficient is less than or equal to the second coefficient threshold, the critical value of the preset parameter is reduced based on the prediction coefficient.

6. The method for monitoring the working machine state according to claim 5, It is characterized in that Also includes: When the prediction coefficient is greater than the second coefficient threshold, an alarm message is output and an abnormal record is generated.

7. The method for monitoring the working machine state according to claim 1, It is characterized in that The operating parameters include voltage data of the battery in the working machine, current data of the motor, speed data of the motor, temperature data of the motor, pressure data of the hydraulic system, and oil quantity data of the hydraulic oil.

8. A device for monitoring the state of an operating machine, It is characterized in that include: a memory configured to store instructions; A processor is configured to call the instructions from the memory and implement the working machine condition monitoring method according to any one of claims 1 to 7 when executing the instructions.

9. A working machine, It is characterized in that include: The working machine condition monitoring device according to claim 8.

10. A machine-readable storage medium, It is characterized in that The machine-readable storage medium stores instructions for causing the machine to execute the working machine condition monitoring method according to any one of claims 1 to 7.

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