Method, device and equipment for evaluating service life of coal mine equipment

Abstract

The embodiment of the invention provides a coal mine equipment service life evaluation method, device and equipment. The method comprises the following steps: acquiring multiple pieces of feature information of current coal mine equipment; fusing the multiple pieces of feature information to obtain health state features of the current coal mine equipment; performing health state prediction on the current coal mine equipment according to the health state characteristics of the current coal mine equipment to obtain a predicted health state; and evaluating the residual life of the current coal mine equipment according to the predicted health state. In this way, the residual life of the current coal mine equipment can be accurately evaluated, and therefore comprehensive monitoring and accurate evaluation of the current coal mine equipment can be achieved according to the multi-dimensional features of the current coal mine equipment.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of industrial equipment operation monitoring, and particularly to the field of coal mine equipment life assessment. BACKGROUND

[0002] In coal mine production, the running stability of equipment directly affects the safety and economic benefits of the mine. Coal mine equipment usually operates under high load, high dust and complex working conditions, and its running state has high nonlinearity and randomness. Real-time monitoring and life assessment of the health state of the equipment are important foundations for realizing scientific maintenance of the equipment, which can effectively reduce unplanned downtime and improve the utilization rate of the equipment. Therefore, how to accurately assess the life of healthy equipment has become a problem to be solved. SUMMARY

[0003] The present disclosure provides a coal mine equipment life assessment method, device, equipment and storage medium.

[0004] According to a first aspect of the present disclosure, a coal mine equipment life assessment method is provided. The method comprises:

[0005] obtaining a plurality of feature information of a current coal mine equipment;

[0006] fusing the plurality of feature information to obtain a health state feature of the current coal mine equipment;

[0007] performing health state prediction on the current coal mine equipment according to the health state feature of the current coal mine equipment to obtain a predicted health state;

[0008] performing life assessment on the current coal mine equipment according to the predicted health state.

[0009] According to the aspect and any possible implementation manner described above, a further implementation manner is provided, and the performing health state prediction on the current coal mine equipment according to the health state feature of the current coal mine equipment to obtain a predicted health state comprises:

[0010] calling a target health state prediction network model, wherein the target health state prediction network model is trained by a convolutional neural network and a bidirectional long short-term memory network;

[0011] inputting the health state feature into the target health state prediction network model to obtain the predicted health state of the current coal mine equipment.

[0012] According to the aspect and any possible implementation manner described above, a further implementation manner is provided, and the obtaining of the target health state prediction network model comprises:

[0013] Obtaining health state features of each of a plurality of preset coal mine devices;

[0014] Inputting the health state features of each of the plurality of preset coal mine devices into a preset health state prediction network model to obtain predicted health states of each of the plurality of preset coal mine devices;

[0015] Obtaining real health states of each of the plurality of preset coal mine devices;

[0016] Calculating loss values of the predicted health states of each of the plurality of preset coal mine devices and the real health states of each of the plurality of preset coal mine devices;

[0017] Optimizing the preset health state prediction network model to obtain a target health state prediction network model, with the objective of minimizing the loss values.

[0018] The aspect and any possible implementation manner described above further provide an implementation manner, wherein the evaluating the remaining service life of the current coal mine device according to the predicted health state comprises:

[0019] Obtaining a probability that a predicted health state at a future time is less than a preset health state;

[0020] Time-integrating the probability to evaluate the remaining service life of the current coal mine device.

[0021] The aspect and any possible implementation manner described above further provide an implementation manner, wherein the method further comprises:

[0022] Obtaining a preset service life threshold;

[0023] Judging whether the remaining service life of the current coal mine device is less than the preset service life threshold;

[0024] If the remaining service life of the current coal mine device is less than the preset service life threshold, performing a device maintenance warning.

[0025] The aspect and any possible implementation manner described above further provide an implementation manner, wherein the obtaining the health state features of the current coal mine device by fusing the plurality of feature information comprises:

[0026] Preprocessing the plurality of feature information to obtain preprocessed feature information, wherein the preprocessing comprises denoising, outlier detection, and time alignment processing;

[0027] Performing feature extraction and fusion on the preprocessed feature information by using a spatio-temporal graph convolution network to obtain the health state features of the current coal mine device.

[0028] According to a second aspect of the present disclosure, a coal mine equipment life evaluation device is provided. The device comprises: a first acquisition module configured to acquire a plurality of characteristic information of a current coal mine equipment;

[0029] a second acquisition module configured to fuse the plurality of characteristic information to obtain a health state characteristic of the current coal mine equipment;

[0030] a prediction module configured to perform health state prediction on the current coal mine equipment according to the health state characteristic of the current coal mine equipment to obtain a predicted health state;

[0031] an evaluation module configured to evaluate the remaining life of the current coal mine equipment according to the predicted health state.

[0032] According to any possible implementation of the above aspect, a further implementation is provided, wherein the prediction module comprises:

[0033] a calling sub-module configured to call a target health state prediction network model trained by a convolutional neural network and a bidirectional long short-term memory network;

[0034] a prediction sub-module configured to input the health state characteristic into the target health state prediction network model to obtain the predicted health state of the current coal mine equipment.

[0035] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the method as described above when executing the program.

[0036] According to a fourth aspect of the present disclosure, a computer readable storage medium is provided, which stores a computer program, and the program is executed by a processor to implement the method according to the first aspect of the present disclosure.

[0037] In the present disclosure, by acquiring a plurality of characteristic information of a current coal mine equipment, the plurality of characteristic information can be fused to obtain a health state characteristic of the current coal mine equipment. Then, according to the health state characteristic of the current coal mine equipment, health state prediction is performed on the current coal mine equipment to obtain a predicted health state. Furthermore, according to the predicted health state, the remaining life of the current coal mine equipment can be accurately evaluated. In this way, according to the multi-dimensional characteristics of the current coal mine equipment, comprehensive monitoring and accurate evaluation of the current coal mine equipment can be achieved.

[0038] It should be understood that the content described in the summary section is not intended to limit or define the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0040] Figure 1 A flow chart of a method for evaluating the life of coal mine equipment according to an embodiment of the present disclosure is shown;

[0041] Figure 2 A flow chart showing another method for evaluating the life of coal mine equipment according to an embodiment of the present disclosure is shown;

[0042] Figure 3 A block diagram of a coal mine equipment life assessment device according to an embodiment of the present disclosure is shown;

[0043] Figure 4 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0044] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0045] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0046] Figure 1 A flow chart of a method 100 for assessing the life of coal mine equipment according to an embodiment of the present disclosure is shown. The method 100 may include:

[0047] Step 110, obtaining multiple characteristic information of current coal mine equipment;

[0048] Step 120: fusing the plurality of feature information to obtain health status features of the current coal mine equipment;

[0049] In step 130, a health state of the current coal mine equipment is predicted according to the health state feature of the current coal mine equipment, to obtain a predicted health state.

[0050] In step 140, a remaining life of the current coal mine equipment is evaluated according to the predicted health state.

[0051] By obtaining a plurality of feature information of the current coal mine equipment, the plurality of feature information can be fused to obtain the health state feature of the current coal mine equipment, and then the health state of the current coal mine equipment is predicted according to the health state feature of the current coal mine equipment, that is, the predicted health state is obtained, and then the remaining life of the current coal mine equipment is accurately evaluated according to the predicted health state, so that the current coal mine equipment can be comprehensively monitored and accurately evaluated according to the multi-dimensional feature of the current coal mine equipment.

[0052] In some embodiments, the health state of the current coal mine equipment is predicted according to the health state feature of the current coal mine equipment, to obtain a predicted health state, including:

[0053] The target health state prediction network model is trained by a convolutional neural network and a bidirectional long short-term memory network;

[0054] The health state feature is input into the target health state prediction network model to obtain the predicted health state of the current coal mine equipment.

[0055] By calling the target health state prediction network model, the health state feature can be automatically input into the target health state prediction network model, so as to obtain the predicted health state of the current coal mine equipment.

[0056] In some embodiments, the target health state prediction network model is obtained as follows:

[0057] The health state feature of each of a plurality of preset coal mine equipment is obtained.

[0058] The preset coal mine equipment can be other coal mine equipment different from the current coal mine equipment.

[0059] The health state feature of each of the plurality of preset coal mine equipment is input into a preset health state prediction network model to obtain a predicted health state of each of the plurality of preset coal mine equipment.

[0060] The real health state of each of the plurality of preset coal mine equipment is obtained.

[0061] The loss value of the predicted health state of each of the plurality of preset coal mine equipment and the real health state of each of the plurality of preset coal mine equipment is calculated.

[0062] The preset health state prediction network model is optimized to obtain the target health state prediction network model with the minimum loss value as the target.

[0063] By obtaining the health state characteristics of each preset coal mine equipment in the plurality of preset coal mine equipments, and then automatically inputting the health state characteristics of each preset coal mine equipment into the preset health state prediction network model, the predicted health state of each preset coal mine equipment is obtained, and then the predicted health state of each preset coal mine equipment is compared with the true health state of each preset coal mine equipment, so as to obtain the loss value corresponding to each preset coal mine equipment, and then the preset health state prediction network model is continuously optimized with the minimum loss value as the target, so as to obtain the target health state prediction network model, thereby realizing accurate prediction and scientific maintenance of the health state of the equipment by using the target health state prediction network model.

[0064] In some embodiments, the remaining life of the current coal mine equipment is evaluated according to the predicted health state, including:

[0065] The probability that the predicted health state at the future time is less than the preset health state is obtained.

[0066] The target health state prediction network model can not only predict the health state of the current coal mine equipment, but also predict the probability that the current coal mine equipment belongs to the health state.

[0067] Of course, the health state is related to time as follows:

[0068]

[0069] wherein, represents the predicted health state at the future time Δt; H t is the health state characteristic at the current time; f is a state prediction model function, that is, a target health state prediction network model, which is realized by a hybrid deep learning model composed of CNN and Bi-LSTM. Through the above formula, the model can predict the future running trend of the equipment based on the current state characteristics, providing an advance for equipment maintenance.

[0070] The probability that the predicted health state at the future time is less than the preset health state is obtained.

[0071] After obtaining the probability that the predicted health state at the future time is less than the preset health state, and then performing time integration on the probability, the remaining life of the current coal mine equipment can be automatically and accurately evaluated.

[0072]

[0073] wherein, RUL is the remaining life; to predict the probability that the health state is below a threshold S th The maintenance strategy is optimized by a reinforcement learning algorithm, so as to reduce the maintenance cost and equipment operation risk and generate an optimal maintenance time node.

[0074] In some embodiments, the method further comprises:

[0075] obtaining a preset service life threshold;

[0076] determining whether the remaining service life of the current coal mine equipment is less than the preset service life threshold;

[0077] If the remaining service life of the current coal mine equipment is less than the preset service life threshold, a device maintenance warning is performed.

[0078] After obtaining the preset service life threshold, it can be determined whether the remaining service life of the current coal mine equipment is less than the preset service life threshold. If the remaining service life of the current coal mine equipment is less than the preset service life threshold, it indicates that the current coal mine equipment is worn out too fast and needs to be maintained. Therefore, a device maintenance warning can be performed.

[0079] In some embodiments, the fusion of the plurality of feature information to obtain the health state feature of the current coal mine equipment comprises:

[0080] The plurality of feature information is preprocessed to obtain preprocessed feature information, wherein the preprocessing comprises denoising, outlier detection and time alignment processing;

[0081] By arranging multiple sensors on key components of the coal mine equipment, multi-modal data such as vibration signals, temperature data, pressure signals and electrical operation parameters are collected in real time to form a comprehensive device operation state representation (i.e., multiple feature information). These data are preprocessed by denoising, outlier detection and time alignment to ensure the quality and consistency of the input data. Wavelet transform is used to remove high-frequency noise, isolated forest algorithm is used to remove abnormal data, and time alignment technology is used to synchronize the timestamps of different data sources to ensure that multi-modal data are modeled on the same time axis.

[0082] The preprocessed feature information is extracted and fused by using a spatio-temporal graph convolution network to obtain the health state feature of the current coal mine equipment.

[0083] By preprocessing the plurality of feature information, preprocessed feature information is obtained, and then the preprocessed feature information is extracted and fused by using a spatio-temporal graph convolution network, so that the health state feature of the current coal mine equipment can be accurately obtained.

[0084] The coal mine equipment service life evaluation method provided by the present application can be used for Figure 2As shown, the evaluation method includes data acquisition and preprocessing, feature extraction and fusion, and health state prediction three main stages. By arranging various sensors on the key components of coal mine equipment, real-time acquisition of vibration signals, temperature data, pressure signals and electrical operation parameters and other multi-modal data form a comprehensive representation of the equipment running state. These data are transmitted to the data processing module through the wireless network, and after the preprocessing steps of denoising, outlier detection and time alignment, the quality and consistency of the input data are ensured. Wavelet transform is used to remove high-frequency noise, and the isolated forest algorithm is used to remove abnormal data, while the time alignment technology synchronizes the timestamps of different data sources to ensure that multi-modal data are modeled on the same time axis.

[0085] In the feature extraction and fusion stage, the present application uses a spatio-temporal graph convolution network (ST-GCN) to extract and fuse the features of the preprocessed multi-modal data, and constructs the health state representation of the equipment. The core formula of feature extraction is:

[0086] H t = σ (W·F t +b)

[0087] Where H t represents the health state feature at time t; F t is the input multi-modal data matrix (i.e. a matrix composed of multiple feature information); W is the weight matrix of the network; b is the bias term; and σ is the activation function. Through this formula, the multi-source data of equipment operation are mapped to a unified health state feature space, while considering the correlation between time and space dimensions.

[0088] Health state prediction, as the core innovation point of the present application, models and predicts the future health state of the equipment through a hybrid deep learning model. In this stage, a convolutional neural network (CNN) is used to extract spatial features, and a bidirectional long short-term memory network (Bi-LSTM) is used to capture the time series relationship of the equipment operation data. The core formula of the prediction model is:

[0089]

[0090] Where, represents the predicted health state at future time Δt; H t is the health state feature at the current time; and f is the state prediction model function, realized by a hybrid deep learning model composed of CNN and Bi-LSTM. Through the above formula, the model can predict the future operation trend of the equipment based on the current state feature, providing an advance for equipment maintenance.

[0091] In addition, in order to improve the prediction accuracy of the model, the present application adopts a prediction loss optimization strategy to dynamically adjust the model parameters through the following formula:

[0092]

[0093] wherein, L is a loss value; is the true health state of the i-th sample (wherein, i = 1, 2, …, N); is the predicted value; N is the number of samples. By minimizing the loss function value, the prediction accuracy of the model can be continuously optimized.

[0094] The i-th sample refers to the i-th time segment data in the multiple time segment data divided at a fixed time interval (such as every 10 minutes) from the operation history of the same coal mine equipment, and i takes a value of 1 to N.

[0095] Finally, in combination with the above prediction results, the application proposes a life assessment and maintenance optimization strategy. A Gamma degradation process model is used to assess the remaining life of the equipment, and the formula is:

[0096]

[0097] wherein, RUL is the remaining life; P(S t <S th ) is the probability that the health state is lower than the threshold S th ; T is the current time point. The reinforcement learning algorithm is used to optimize the maintenance strategy to reduce the maintenance cost and equipment operation risk, and generate the best maintenance time node.

[0098] Overall, the application realizes accurate prediction and scientific maintenance of the equipment health state through the spatio-temporal feature modeling of multi-modal data, the prediction method of deep learning, and the optimization strategy of reinforcement learning. The method has high practicability and innovation.

[0099] Application example

[0100] The coal mine equipment life assessment method and system of the application are applied to the main fan equipment maintenance management of a coal mine. The main fan system is the core equipment of the coal mine, and its operation state directly affects the ventilation efficiency and production safety of the mine. The traditional maintenance mode mainly relies on fixed-period maintenance plans, which is difficult to timely find potential abnormal states of the equipment, increasing the risk of unplanned downtime and equipment failure.

[0101] In actual application, the application first installs multiple types of sensors on the key components (such as bearings, impellers, drive motors, etc.) of the main fan, for real-time collection of operation data of the equipment, including vibration signals, temperature changes, pressure fluctuations, and current and voltage parameters of the motor. The collected data is transmitted to a data processing module through a wireless network, and preprocessing operations such as denoising, outlier detection, and time alignment are performed to ensure the accuracy and consistency of the input data.

[0102] Next, the feature extraction module utilizes a spatio-temporal graph convolution network (ST-GCN) to model the pre-processed data in depth, extracting key spatio-temporal features representing the health status of the equipment. The health status prediction module further analyzes these features, predicting the trend of the equipment's operational status in the coming week through a hybrid deep learning model. For example, the vibration signal prediction for the main fan bearing shows that the vibration amplitude may exceed the safety threshold in the next 48 hours, indicating a possible bearing fatigue problem. Based on this prediction, the system alerts maintenance personnel through a visualization interface to check and replace the bearing components in advance.

[0103] Combining the health status prediction results, the Gamma degradation model is used to quantify the remaining life of the equipment. The evaluation shows that the main fan impeller has less than 200 hours of remaining life, and the system suggests a comprehensive inspection and cleaning of the impeller during the next planned maintenance to ensure the safe operation of the equipment.

[0104] Based on the deep reinforcement learning (DRL) algorithm, the maintenance decision is dynamically optimized. The system compares various maintenance strategies such as immediate maintenance, delayed maintenance, and continued operation, considering maintenance costs and operational risks to generate the best maintenance time node recommendation. For example, by adjusting the operating conditions and load distribution of the fan, the system extends the service life of critical components while minimizing the risk of unplanned downtime.

[0105] The practical application effect of the present example shows that the present application significantly improves the operational stability and safety of the main fan equipment, reduces the unplanned downtime of coal mine equipment, and optimizes the allocation of maintenance resources, having important economic and social benefits.

[0106] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the action sequence described, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0107] The above is the introduction of the method embodiment, and the following will further illustrate the scheme of the present disclosure through the device embodiment.

[0108] Figure 3 A block diagram of a coal mine equipment life evaluation device 300 according to an embodiment of the present disclosure is shown. As shown in Figure 3 The device 300 includes:

[0109] A first acquisition module 310 is configured to acquire a plurality of feature information of a current coal mine equipment.

[0110] The second acquisition module 320 is configured to fuse the plurality of feature information to obtain the health state feature of the current coal mine equipment.

[0111] The prediction module 330 is configured to perform health state prediction on the current coal mine equipment according to the health state feature of the current coal mine equipment to obtain a predicted health state.

[0112] The evaluation module 340 is configured to evaluate the remaining life of the current coal mine equipment according to the predicted health state.

[0113] In some embodiments, the prediction module 330 includes:

[0114] The calling sub-module is configured to call a target health state prediction network model, and the target health state prediction network model is trained by a convolutional neural network and a bidirectional long short-term memory network.

[0115] The prediction sub-module is configured to input the health state feature into the target health state prediction network model to obtain the predicted health state of the current coal mine equipment.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described modules can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0117] According to embodiments of the present disclosure, the present disclosure also provides an electronic device and a non-transitory computer-readable storage medium having computer instructions stored therein.

[0118] Figure 4 A schematic block diagram of an electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0119] The device 800 includes a computing unit 801 that can perform various appropriate actions and processes in accordance with a computer program stored in a read only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0120] A plurality of components in the device 800 are connected to the I / O interface 805, including an input unit 806 such as a keyboard, a mouse, and the like, an output unit 807 such as various types of displays, speakers, and the like, a storage unit 808 such as a magnetic disk, an optical disk, and the like, and a communication unit 809 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0121] The computing unit 801 can be various general and / or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, and the like. The computing unit 801 performs various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the method 100 described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the method 100 by any other appropriate means, such as by means of firmware.

[0122] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0123] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, or entirely on a remote machine or server.

[0124] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0126] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0127] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0128] It should be understood that various forms of flow shown above can be used, re-ordered, added to, or deleted from without departing from the spirit of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, in series, or in different orders, as long as the desired results of the technical solutions of the present disclosure are achieved, and the present disclosure is not limited herein.

[0129] The specific embodiments described above do not constitute an exhaustive list of all possible embodiments as can be made within the scope of the present disclosure. It should be understood that various modifications, combinations, sub-combinations, and alternatives can be practiced without departing from the spirit of the present disclosure. Any modification, equivalent replacement, and improvement, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.

Claims

1. A method for evaluating the life of coal mine equipment, characterized in that: include: Obtain multiple characteristic information of current coal mine equipment; fusing the plurality of feature information to obtain health status features of the current coal mine equipment; Predicting the health status of the current coal mine equipment according to the health status characteristics of the current coal mine equipment to obtain a predicted health status; The remaining life of the current coal mining equipment is evaluated based on the predicted health status.

2. The method according to claim 1, wherein The step of predicting the health status of the current coal mine equipment based on the health status characteristics of the current coal mine equipment to obtain the predicted health status includes: Invoking a target health status prediction network model, wherein the target health status prediction network model is trained by a convolutional neural network and a bidirectional long short-term memory network; The health status characteristics are input into the target health status prediction network model to obtain the predicted health status of the current coal mine equipment.

3. The method according to claim 2, wherein The steps for obtaining the target health status prediction network model are as follows: Obtaining health status characteristics of each preset coal mining equipment among a plurality of preset coal mining equipment; Inputting the health status characteristics of each preset coal mine equipment into a preset health status prediction network model to obtain a predicted health status of each preset coal mine equipment; Obtaining the true health status of each of the preset coal mine equipment; Calculating a loss value between a predicted health state of each of the preset coal mine equipment and an actual health state of each of the preset coal mine equipment; With the goal of minimizing the loss value, the preset health status prediction network model is optimized to obtain the target health status prediction network model.

4. The method according to claim 1, wherein The evaluating the remaining life of the current coal mining equipment according to the predicted health status includes: Obtain the probability that the predicted health state at a future moment is less than the preset health state; Time integration is performed according to the probability to evaluate the remaining life of the current coal mining equipment.

5. The method according to claim 1, wherein The method further comprises: Get the preset lifespan threshold; Determining whether the remaining life of the current coal mining equipment is less than the preset life threshold; If the remaining life of the current coal mine equipment is less than the preset life threshold, an equipment maintenance warning is issued.

6. The method according to claim 1, wherein The fusing of the plurality of feature information to obtain the health status feature of the current coal mine equipment includes: Preprocessing the plurality of feature information to obtain preprocessed feature information, wherein the preprocessing includes: denoising, outlier detection, and time alignment processing; The preprocessed feature information is extracted and fused using a spatiotemporal graph convolutional network to obtain the health status characteristics of the current coal mine equipment.

7. A coal mine equipment life assessment device, characterized in that: include: The first acquisition module is used to obtain multiple characteristic information of current coal mine equipment; A second acquisition module is configured to fuse the plurality of feature information to obtain the health status feature of the current coal mine equipment; a prediction module, configured to predict the health status of the current coal mine equipment according to the health status characteristics of the current coal mine equipment to obtain a predicted health status; An evaluation module is used to evaluate the remaining life of the current coal mine equipment based on the predicted health status.

8. The device according to claim 7, wherein The prediction module includes: A calling submodule is used to call a target health status prediction network model, wherein the target health status prediction network model is trained by a convolutional neural network and a bidirectional long short-term memory network; The prediction submodule is used to input the health status characteristics into the target health status prediction network model to obtain the predicted health status of the current coal mine equipment.

9. An electronic device, characterized in that: include: memory and processor, The memory stores a computer program, and when the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that When the instructions in the storage medium are executed by a processor corresponding to the electronic device, the electronic device can implement the coal mine equipment life assessment method as described in any one of claims 1 to 6.

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