Method and device for monitoring state of mining equipment

Abstract

The invention discloses a mining equipment state monitoring method and device, and the method comprises the steps: collecting the operation data of mining equipment through a plurality of sensors, packaging the operation data into a message based on a preset protocol, and transmitting the message to a processing end; analyzing the message through the processing end to obtain to-be-monitored data; and inputting the to-be-monitored data into a preset model to obtain a state classification result, so that the state data of the mining equipment can be accurately transmitted outwards in real time in a complex mine operation environment, and the state of the mining equipment can be comprehensively monitored in real time.

Description

Technical Field

[0001] The invention belongs to the technical field of equipment monitoring, and in particular relates to a method and a device for monitoring the status of mining equipment. Background Art

[0002] In the field of mining production, with the continuous expansion of mining scale and the leap in automation level, the safe and stable operation of mining equipment has become an indispensable key link for safe and efficient mining operations. However, due to the extreme complexity and high dynamics of the mining operating environment, traditional monitoring methods have a narrow monitoring range, delayed response and human errors when monitoring mining equipment, making it difficult to adapt to the real-time, comprehensive and accurate requirements of mining equipment status monitoring.

[0003] Therefore, how to conduct real-time, comprehensive and accurate status monitoring of mining equipment in a complex mining operation environment is a technical problem to be solved by those skilled in the art. Summary of the invention

[0004] The purpose of the present invention is to solve the technical problem in the prior art that it is impossible to perform real-time, comprehensive and accurate status monitoring of mining equipment in a complex mining operation environment.

[0005] To achieve the above technical objectives, on the one hand, the present invention provides a method for monitoring the state of mining equipment, the method comprising: The operating data of mining equipment is collected through multiple sensors, and encapsulated into messages based on the preset protocol and transmitted to the processing end; The processing end parses the message to obtain the data to be monitored; The data to be monitored is input into a preset model to obtain a state classification result.

[0006] Furthermore, the preset protocol is specifically a message queue telemetry transmission protocol, and encapsulating it into a message based on the preset protocol specifically includes: Preprocessing the operation data collected by each sensor to obtain corresponding first data; Extracting feature data after fusing all first data; The characteristic data is encapsulated based on the preset protocol to obtain a message.

[0007] Furthermore, the fusion is specifically to align the first data according to timestamps or data tags for fusion.

[0008] Furthermore, the preset model also includes the following training process: Obtain historical operating data of mining equipment; Establish a model and initialize parameters to obtain the model to be trained; The final preset model is obtained after training the model to be trained with the historical operation data, wherein the historical operation data includes a training set, a validation set and a test set. Specifically, the model to be trained is iteratively trained with the training set, and the error between the output value and the true value is determined based on the output value, validation set and test set of the model to be trained in each iteration, and then the parameters of the model to be trained are updated according to the error.

[0009] Furthermore, the model to be trained is specifically a recurrent neural network model, and the hidden layer state update formula of the model to be trained is specifically: , In the formula, is the hidden layer state at time t, is the activation function, is the weight matrix input to the hidden state, is the input at time t, is the weight matrix from the previous hidden state to the current hidden state, is the hidden layer state at time t-1, is the bias term.

[0010] Furthermore, the method further includes optimizing parameters of the model to be trained, and the parameter optimization is specifically performed by the following formula: , In the formula, is the parameter of the t+1th iteration, is the parameter of the tth iteration, is the learning rate, is the exponentially decaying average of the historical gradient after the deviation is corrected at the tth iteration, is the exponentially decaying average of the historical gradient after the deviation is corrected at the tth iteration, A preset positive number.

[0011] Furthermore, the method also includes randomly discarding part of the output of neurons of the model to be trained in each training when training the model to be trained.

[0012] On the other hand, the present invention also provides a mining equipment status monitoring device, the device comprising: The acquisition module is used to collect the operating data of mining equipment through multiple sensors, and encapsulate it into messages based on a preset protocol and transmit it to the processing end; A parsing module, used for parsing the message through the processing end to obtain the data to be monitored; The classification module is used to input the data to be monitored into a preset model to obtain a status classification result.

[0013] The present invention provides a mining equipment status monitoring method and device. Compared with the prior art, the method first collects the operating data of the mining equipment through multiple sensors, and encapsulates the data into messages based on a preset protocol and transmits them to a processing end; the processing end parses the messages to obtain the data to be monitored; the data to be monitored is input into a preset model to obtain a status classification result, which can accurately transmit the status data of the mining equipment in real time in a complex mining operation environment, and can comprehensively monitor the status of the mining equipment in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 The figure is a flow chart of a method for monitoring the state of mining equipment provided in an embodiment of this specification; Figure 2 Shown is a schematic diagram of the structure of a mining equipment status monitoring device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0017] like Figure 1The flowchart of the mining equipment status monitoring method provided by the embodiment of this specification is shown. Although this specification provides the method operation steps or device structures shown in the following embodiments or drawings, the method or device may include more or fewer operation steps or module units after partial merger based on routine or no creative labor. In the steps or structures where there is no necessary causal relationship logically, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure shown in the embodiments or drawings of this specification. When the method or module structure is applied in actual devices, servers or terminal products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or drawings (for example, in a parallel processor or multi-threaded processing environment, or even in a distributed processing, server cluster implementation environment).

[0018] The mining equipment status monitoring method provided in the embodiments of the present invention can be applied to terminal devices such as clients and servers. Figure 1 As shown, the method specifically comprises the following steps: Step S101: collect the operation data of mining equipment through multiple sensors, encapsulate it into a message based on a preset protocol, and transmit it to the processing end.

[0019] In the embodiment of the present application, the preset protocol is specifically a message queue telemetry transmission protocol, and the encapsulation of it into a message based on the preset protocol specifically includes: Preprocessing the operation data collected by each sensor to obtain corresponding first data; Extracting feature data after fusing all first data; The characteristic data is encapsulated based on the preset protocol to obtain a message.

[0020] Specifically, the collected operating data is preprocessed immediately while the operating data is being collected. The collected data, such as temperature, humidity, pressure, and vibration, etc., preprocessing includes: 1. Data cleaning: Clean the original operating data collected by each sensor to remove obvious erroneous data, duplicate data, and invalid data to ensure the accuracy and reliability of the data.

[0021] 2. Data normalization: Since the data collected by different sensors have different dimensions and units, in order to facilitate subsequent data processing and analysis, these data need to be normalized and converted to the same level.

[0022] 3. Data smoothing: In view of the noise and fluctuations that may appear in the sensor data, data smoothing technology: exponential smoothing method is used to eliminate these interference factors and improve the stability and reliability of the data.

[0023] After the above preprocessing, the first data corresponding to each sensor is obtained, and then all the first data are fused. Fusion specifically refers to aligning the first data according to the timestamp or data tag to form a complete data set. This process can ensure the synchronization and consistency of the data, and ensure that the fused data can accurately reflect the operating status of the device. Then, feature data is extracted from the fused data. Feature extraction is to extract feature data related to the device status from the fused data through statistical analysis, machine learning algorithms, etc. These feature data include key indicators such as the device's operating speed, temperature, vibration frequency, and their changing trends and abnormal fluctuations.

[0024] The characteristic data is extracted through the above operation, and the characteristic data is encapsulated to obtain a message. The preset protocol is specifically the message queue telemetry transmission protocol, which has the characteristics of low power consumption and low latency, and can ensure the timeliness and integrity of message transmission. The encapsulation process is specifically to first conduct an in-depth analysis of the message format and transmission specifications of the message queue telemetry transmission protocol to obtain the protocol requirements, and then encapsulate the extracted characteristic data according to the protocol requirements, including adding necessary information such as message headers, data fields, and checksums. In this process, it is necessary to ensure the integrity and accuracy of the data so that the receiving end can correctly parse and process the message. The message includes a fixed header, a variable header, and a payload. The encapsulated message is sent through the message queue telemetry transmission protocol and transmitted to the specified receiving end. During the sending process, it is necessary to ensure the stability and reliability of the network to avoid data loss or transmission errors.

[0025] Step S102: The processing end parses the message to obtain the data to be monitored.

[0026] Specifically, the message is parsed by the processing end, and the processing end can be placed according to actual application needs. Parsing the message means parsing out the effective load, and composing it into the data to be monitored.

[0027] Step S103: input the data to be monitored into a preset model to obtain a state classification result.

[0028] In the embodiment of the present application, the preset model also includes the following training process: Obtain historical operating data of mining equipment; Establish a model and initialize parameters to obtain the model to be trained; The final preset model is obtained after training the model to be trained with the historical operation data, wherein the historical operation data includes a training set, a validation set and a test set. Specifically, the model to be trained is iteratively trained with the training set, and the error between the output value and the true value is determined based on the output value, validation set and test set of the model to be trained in each iteration, and then the parameters of the model to be trained are updated according to the error.

[0029] In the specific application process, the final preset model is obtained after training the model to be trained by the historical operation data, wherein the historical operation data includes a training set, a validation set and a test set. Specifically, the training model to be trained is iteratively trained by the training set, and in each iteration, the validation set is used to monitor the performance of the model to prevent overfitting; finally, the generalization ability of the model is evaluated by the test set. Based on the output value of the model to be trained in each iteration, the validation set (used to adjust the model hyperparameters and monitor the training process to prevent overfitting) and the test set (used to evaluate the performance of the model on unseen data), the error between the output value and the true value is determined, and this error is usually measured by a loss function. Then, according to the error, the optimization algorithm is used to update the parameters of the model to be trained to minimize the loss function and improve the accuracy of the model prediction.

[0030] In view of the complexity and diversity of mining equipment, the model to be trained is specifically a recurrent neural network model. In order to customize the structure of the recurrent neural network model to better adapt to the characteristics of mining equipment, hyperparameters such as the number of network layers, the number of hidden units, and the activation function can be adjusted. In addition, gated recurrent units (such as LSTM or GRU) are used to better capture the time series characteristics of the equipment status. These gating mechanisms can help the model effectively handle long-distance dependency problems.

[0031] In order to better bring out the performance of the preset model, it needs to be better trained, starting with the optimization of parameters. Here, an optimization algorithm based on gradient descent is used, and momentum is introduced (implemented through first-order moment estimation and second-order moment estimation) to accelerate convergence and reduce oscillation. The specific steps include the following: First-order moment estimation: Calculate the exponentially decaying average of historical gradients ; Second-order moment estimation: Calculate the exponentially decaying average of the squared historical gradients ; Correcting Bias: Solving the Cold Start Problem in Weighted Average Calculation and ; Parameter update: ; In the formula, is the exponentially decaying average of the historical gradient at the tth iteration, is the first hyperparameter, is the exponentially decaying average of the historical gradient at the t-1th iteration, is the temporal gradient at the t-th iteration, is the exponentially decaying average of the square of the historical gradient at the tth iteration, is the exponentially decaying average of the square of the historical gradient at the t-1th iteration, is the second hyperparameter, is the parameter of the t+1th iteration, is the parameter of the tth iteration, is the learning rate, which is used to control the step size of parameter update. is the exponentially decaying average of the historical gradient after the deviation is corrected at the tth iteration, is the exponentially decaying average of the historical gradient after the deviation is corrected at the tth iteration, It is a preset positive number, which is a very small positive number used to prevent numerical instability caused by the denominator being 0.

[0032] Moreover, during the training process of the model to be trained, part of the neuron output of the model to be trained is randomly discarded in each training.

[0033] To prevent the neural network from overfitting, we randomly discard some of the outputs of neurons. During the training phase, for each neuron’s output y, we randomly discard some of the outputs using the following formula: , In the formula, is the output of the neuron, m is a binary mask vector of the same dimension as y, with a value of 0 or 1, It is an element-wise multiplication operation.

[0034] In the testing phase of the model to be trained, in order to maintain the consistency of the expected output value, the output of each neuron needs to be multiplied by the retention probability , To test the output, by discarding the output of some neurons as mentioned above, the dependency between neurons can be reduced, the generalization ability and robustness of the model can be improved, and overfitting can be prevented.

[0035] The hidden layer state update formula of the model to be trained is specifically: , In the formula, is the hidden layer state at time t, is the activation function, is the weight matrix input to the hidden state, is the input at time t, is the weight matrix from the previous hidden state to the current hidden state, is the hidden layer state at time t-1, is the bias term.

[0036] After the preset model is trained, the data to be monitored can be input to obtain the operating status of the mining equipment. The sequence modeling capability of the preset model is combined with the attention mechanism to conduct in-depth analysis of the data sequence before and after the fault occurs, and automatically identify the fault type, location and cause. At the same time, combined with the expert knowledge base and the fault case library, detailed explanations and repair suggestions are provided for the diagnosis results to assist maintenance personnel in quickly locating problems and taking corresponding treatment measures. The data to be monitored adopts real-time data stream processing technology to ensure that the operating data collected by the sensor can be processed by the model in real time and accurately. At the same time, an intelligent early warning algorithm is designed, combined with multi-parameter comprehensive evaluation and historical data comparison, to achieve accurate prediction and timely early warning of equipment status. Once the equipment status is predicted to be abnormal, the early warning mechanism is triggered immediately, and relevant personnel are notified in time to handle it.

[0037] In addition, a model performance monitoring system is established to regularly evaluate key indicators such as the accuracy and recall rate of the model, and promptly detect the trend of declining model performance. According to the evaluation results, the structure and parameters of the model are adjusted, such as increasing the number of network layers and adjusting the learning rate, to improve the performance of the model. At the same time, as the mine production environment and equipment status change, data is collected again and a new model version is trained to ensure that the model can always accurately reflect the actual status of the equipment. In addition, an online learning mechanism is introduced to allow the model to continuously receive new data and make fine-tuning in the production environment to adapt to the dynamic changes in the equipment status.

[0038] Based on the above-mentioned mining equipment status monitoring method, one or more embodiments of this specification also provide a platform and terminal for mining equipment status monitoring. The platform or terminal may include devices, software, modules, plug-ins, servers, clients, etc. that use the methods described in the embodiments of this specification and are combined with necessary implementation hardware devices. Based on the same innovative concept, the system in one or more embodiments provided in the embodiments of this specification is as described in the following embodiments. Since the implementation scheme and method for solving the problem of the system are similar, the implementation of the specific system in the embodiments of this specification can refer to the implementation of the aforementioned method, and the repetitions will not be repeated. The terms "unit" or "module" used below can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, hardware, and the combination of software and hardware are also possible and conceived.

[0039] Specifically, Figure 2 This is a schematic diagram of the module structure of an embodiment of a mining equipment status monitoring device provided in this specification. Figure 2As shown, the mining equipment status monitoring device provided in this specification includes: The acquisition module 201 is used to collect the operation data of the mining equipment through multiple sensors, and encapsulate it into a message based on a preset protocol and transmit it to the processing end; The parsing module 202 is used to parse the message through the processing end to obtain the data to be monitored; The classification module 203 is used to input the data to be monitored into a preset model to obtain a state classification result.

[0040] It should be noted that the above-mentioned system may also include other implementation modes according to the description of the corresponding method embodiment. The specific implementation modes may refer to the description of the above-mentioned corresponding method embodiment, which will not be described one by one here.

[0041] The present application also provides an electronic device, including: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the method provided in the above embodiment.

[0042] The electronic device provided in the embodiment of the present application stores the executable instructions of the processor in a memory. When the processor executes the executable instructions, it can first collect the operating data of the mining equipment through multiple sensors, and encapsulate it into a message based on a preset protocol and transmit it to the processing end; the processing end parses the message to obtain the data to be monitored; the data to be monitored is input into a preset model to obtain a status classification result, and the status data of the mining equipment can be transmitted to the outside in real time and accurately in a complex mining operation environment, and the status of the mining equipment can be monitored in real time and comprehensively.

[0043] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0044] The methods or devices described in the above embodiments provided in this specification can implement business logic through computer programs and record them on storage media, and the storage media can be read and executed by computers to achieve the effects of the solutions described in the embodiments of this specification, such as: The operating data of mining equipment is collected through multiple sensors, and encapsulated into messages based on the preset protocol and transmitted to the processing end; The processing end parses the message to obtain the data to be monitored; The data to be monitored is input into a preset model to obtain a state classification result.

[0045] The storage medium may include a physical device for storing information, which is usually a medium that digitizes information and then stores it in an electrical, magnetic or optical manner. The storage medium may include: a device that stores information in an electrical energy manner, such as various memories, such as RAM, ROM, etc.; a device that stores information in a magnetic energy manner, such as a hard disk, a floppy disk, a magnetic tape, a magnetic core memory, a magnetic bubble memory, a USB flash drive; a device that stores information in an optical manner, such as a CD or a DVD. Of course, there are other readable storage media, such as quantum memory, graphene memory, etc.

[0046] The embodiments of this specification are not limited to complying with industry communication standards, standard computer resource data update and data storage rules, or the situations described in one or more embodiments of this specification. Certain industry standards or slightly modified implementation plans based on the implementation described in the custom method or embodiment can also achieve the same, equivalent or similar, or predictable implementation effects after deformation of the above-mentioned embodiments. The embodiments obtained by using these modified or deformed data acquisition, storage, judgment, processing methods, etc. can still fall within the scope of the optional implementation plans of the embodiments of this specification.

[0047] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a computer-readable medium such as a microprocessor or processor and a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, ATMEL AT91SAM, MICROCHIP PIC18F26K20, and SILICONE LABS C8051F320. The memory controller can also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0048] The device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. There may be other divisions in actual implementation, such as multiple units or plug-ins may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interface, indirect coupling or communication connection of the device or unit, which may be electrical, mechanical or other forms.

[0049] These computer program instructions may also be loaded onto a computer or other programmable resource data updating 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 executed on the computer or other programmable device 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.

[0050] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. In the description of this specification, the description of the reference term "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0051] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for monitoring the state of mining equipment, characterized in that: The method comprises: The operating data of mining equipment is collected through multiple sensors, and encapsulated into messages based on the preset protocol and transmitted to the processing end; The processing end parses the message to obtain the data to be monitored; The data to be monitored is input into a preset model to obtain a state classification result.

2. The mining equipment status monitoring method according to claim 1, characterized in that: The preset protocol is specifically a message queue telemetry transmission protocol, and the encapsulation into a message based on the preset protocol specifically includes: Preprocessing the operation data collected by each sensor to obtain corresponding first data; Extracting feature data after fusing all first data; The characteristic data is encapsulated based on the preset protocol to obtain a message.

3. The mining equipment status monitoring method according to claim 2, characterized in that: The fusion specifically includes aligning the first data according to timestamps or data tags for fusion.

4. The mining equipment status monitoring method according to claim 1, characterized in that: The preset model also includes the following training process: Obtain historical operating data of mining equipment; Establish a model and initialize parameters to obtain the model to be trained; The final preset model is obtained after training the model to be trained with the historical operation data, wherein the historical operation data includes a training set, a validation set and a test set. Specifically, the model to be trained is iteratively trained with the training set, and the error between the output value and the true value is determined based on the output value, validation set and test set of the model to be trained in each iteration, and then the parameters of the model to be trained are updated according to the error.

5. The mining equipment status monitoring method according to claim 4, characterized in that: The model to be trained is specifically a recurrent neural network model, and the hidden layer state update formula of the model to be trained is specifically: ， In the formula, is the hidden layer state at time t, is the activation function, is the weight matrix input to the hidden state, is the input at time t, is the weight matrix from the previous hidden state to the current hidden state, is the hidden layer state at time t-1, is the bias term.

6. The mining equipment status monitoring method according to claim 4, characterized in that: The method further includes optimizing parameters of the model to be trained, and the parameter optimization is specifically performed by the following formula: ， In the formula, is the parameter of the t+1th iteration, is the parameter of the tth iteration, is the learning rate, is the exponentially decaying average of the historical gradient after the deviation is corrected at the tth iteration, is the exponentially decaying average of the historical gradient after the deviation is corrected at the tth iteration, A preset positive number.

7. The mining equipment status monitoring method according to claim 4, characterized in that: The method also includes randomly discarding part of the output of neurons of the model to be trained in each training when training the model to be trained.

8. A mining equipment status monitoring device, characterized in that: The device comprises: The acquisition module is used to collect the operating data of mining equipment through multiple sensors, and encapsulate it into messages based on a preset protocol and transmit it to the processing end; A parsing module, used for parsing the message through the processing end to obtain the data to be monitored; The classification module is used to input the data to be monitored into a preset model to obtain a status classification result.

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