Mining equipment remote monitoring system and method based on Internet of Things

Abstract

The invention provides a mining equipment remote monitoring system and method based on the Internet of Things, and belongs to the technical field of mining equipment monitoring. The system comprises a multi-parameter data collection module which is used for collecting original data parameters of operation of mining equipment in real time through a sensor; the wireless communication module is used for sending the original data parameters to the data processing module through a wireless transmission technology; the data processing module is used for carrying out data cleaning processing on the original data parameters, generating equipment operation parameters and storing the equipment operation parameters in a database; the fault prediction module is used for reading operation parameters of the equipment and predicting the state of the equipment by using a linear regression prediction model according to the data characteristics; the performance evaluation and maintenance suggestion module is used for reading the equipment operation parameters, calculating the performance indexes of the equipment, determining the performance evaluation result of the equipment by comparing preset evaluation indexes, and generating an equipment maintenance plan; and the visual user interface is used for displaying the prediction result and the equipment maintenance plan.

Description

Technical Field

[0001] The present invention relates to the technical field of mine equipment monitoring, and more specifically, to a remote monitoring system and method for mine equipment based on the Internet of Things. Background Art

[0002] With the booming development of the mining industry, the operating status and performance of mine equipment have become key factors affecting the production efficiency and safety level of mines. Traditionally, equipment monitoring mainly relies on manual inspections and regular maintenance, but this method has many limitations. It not only consumes a large amount of human and time resources, but also is difficult to comprehensively and real-time grasp the actual operating status of the equipment, thus leaving potential safety hazards.

[0003] In recent years, with the development of Internet of Things technology, remote monitoring systems based on the Internet of Things have been widely used in many fields, bringing unprecedented convenience to equipment monitoring. However, in the field of mine equipment monitoring, existing remote monitoring systems based on the Internet of Things still face many challenges.

[0004] On the one hand, the sensors used in existing monitoring systems have relatively single functions and usually can only detect single physical or chemical quantities such as temperature, pressure, and vibration. This single detection method is difficult to comprehensively and accurately reflect the overall operating status of the equipment, and is prone to misjudgment or missed judgment, thus affecting the maintenance and upkeep of the equipment. For example, it is difficult to accurately judge the lubrication status or wear degree of the equipment only by a temperature sensor, which requires comprehensive analysis by combining more types of sensor data.

[0005] On the other hand, the data processing and analysis capabilities of existing systems still need to be improved. Due to the lack of accurate assessment and prediction capabilities for the operating status of the equipment, the system is prone to failure shutdowns, which not only affects the production efficiency of the mine, but also poses a certain threat to the production safety of the mine. Therefore, improving the data processing and analysis capabilities of the system and achieving more accurate equipment status monitoring and prediction have become urgent problems to be solved. Summary of the Invention

[0006] Aiming at the above problems, the purpose of the present invention is to provide a remote monitoring system and method for mine equipment based on the Internet of Things, which solves the problems that the sensors used in existing monitoring systems can often only detect single physical or chemical quantities, prone to misjudgment or missed judgment, and the system is difficult to accurately assess and predict the operating status of the equipment, resulting in the system being prone to failure shutdowns.

[0007] To achieve the above object, the present invention is realized through the following technical solutions: In a first aspect, the present invention discloses a remote monitoring system for mining equipment based on the Internet of Things, including: a multi-parameter data collection module, a wireless communication module, a data processing module, a database, a fault prediction module, a performance evaluation and maintenance recommendation module, and a visualization user interface; the database is respectively data-connected to the wireless communication module, the data processing module, the fault prediction module, and the performance evaluation and maintenance recommendation module, the wireless communication module is respectively data-connected to the multi-parameter data collection module and the data processing module, and the performance evaluation and maintenance recommendation module is data-connected to the visualization user interface; The multi-parameter data collection module is used to collect the original data parameters of the operation of mining equipment in real time through preset sensors; The wireless communication module is used to send the original data parameters to the data processing module through wireless transmission technology; The data processing module is used to perform data cleaning on the original data parameters to remove invalid data points and error data points therein, generate equipment operation parameters and store them in the database; The fault prediction module is used to read the equipment operation parameters in the database, extract the data features reflecting the equipment operation state therefrom, and use a linear regression prediction model to predict the equipment state according to the data features, generating a prediction result; The performance evaluation and maintenance recommendation module is used to read the equipment operation parameters in the database, calculate the performance indicators of the equipment, determine the performance evaluation result of the equipment by comparing the performance indicators of the equipment with preset evaluation indicators, and generate an equipment maintenance plan according to the performance evaluation result of the equipment; The visualization user interface is used to provide a graphical interaction interface for remote monitoring and management of mining equipment, and display the prediction result and the equipment maintenance plan.

[0008] Furthermore, the preset sensors include a temperature sensor, a pressure sensor, a vibration sensor, a flow sensor, a liquid level sensor, a gas sensor, and a sound sensor connected through a wireless network; The temperature sensor is used to measure the temperature of the mining equipment; The pressure sensor is used to measure the pressure of the mining equipment; The vibration sensor is used to measure the vibration data of the mining equipment; The flow sensor is used to measure the flow rate of the fluid; The liquid level sensor is used to measure the height and position of the liquid; The gas sensor is used to detect the concentration of a specific gas; The sound sensor is used to detect the sound signal generated by the mining equipment.

[0009] Furthermore, the data processing module includes a data receiving unit, a data preprocessing unit, and a data storage unit; A data receiving unit for receiving the original data parameters transmitted from a wireless communication module; A data preprocessing unit for cleaning the original data parameters to remove invalid and incorrect data points, and performing data standardization processing using a data standardization model to generate device operation parameters; A data storage unit for storing the device operation parameters in a database; The data standardization model includes:

[0010] where x is the original data point, μ is the mean of the data, σ is the standard deviation of the data, and x′ is the standardized data.

[0011] Furthermore, the fault prediction module includes: A feature extraction unit for reading the device operation parameters from the database and extracting the data reflecting the operation state of the mining equipment at different time periods according to the types of preset sensors as the target features; A prediction model establishment unit for using a linear regression prediction model, inputting the extracted target features into the linear regression prediction model for training, and validating and optimizing the model using a preset test data set to generate a state prediction model; A future state prediction unit for predicting the operation state of the mining equipment using the linear regression prediction model according to the real-time device operation parameters, determining whether there is a potential fault risk, generating a prediction result; performing quantization processing on the prediction result to generate a quantization value, comparing the quantization value with a preset warning threshold, and triggering an alarm when the quantization value exceeds the preset warning threshold. Furthermore, the linear regression prediction model includes: y = β 0 + β 1 X 1 + β 2 X 2 + …… β n X n + ϵ where y is the device fault probability, x 1 、x 2 、…… x n are the target features collected by n types of preset sensors, β 0 is the intercept, β 1 、β 2 、…… β n are the regression coefficients corresponding to n types of preset sensors, and ϵ is the error term.

[0012] Furthermore, the performance evaluation and maintenance suggestion module includes: An evaluation index determination unit, which is used to determine performance evaluation indexes according to the operating characteristics and business requirements of mining equipment; A data collection and analysis unit, which is used to collect the historical and current operating parameters of mining equipment, and conduct statistical analysis to calculate various actual performance indexes; A comparison and benchmark unit, which is used to compare the actual performance indexes with the corresponding performance evaluation indexes, evaluate the performance of mining equipment, and generate a performance evaluation result; A maintenance plan formulation unit, which is used to determine the status of mining equipment according to the performance evaluation result, and use a preset mathematical model to formulate a targeted maintenance plan according to the status of mining equipment; The preset mathematical model includes: a preventive maintenance model, a regular maintenance model, and a condition-based maintenance model.

[0013] Furthermore, the preventive maintenance model is used to generate a preventive maintenance plan according to the status of the mining equipment and the current time when the status of the mining equipment shows signs of failure; The regular maintenance model is used to generate a regular maintenance plan based on the time interval according to the current usage status information of the mining equipment and the suggestions in the user manual of the equipment manufacturer; The condition-based maintenance model is used to extract real-time status information and performance data according to the current status of the mining equipment, and generate maintenance conditions for mining according to the real-time status information and performance data to determine whether maintenance is required.

[0014] In a second aspect, the present invention also discloses a remote monitoring method for mining equipment based on the Internet of Things, including the following steps: S1: Continuously monitor and collect the original data parameters of mining equipment through a multi-parameter data collection module. The original data parameters include the temperature, pressure, vibration conditions of the mining equipment, as well as the flow rate of the fluid, the height of the liquid, the concentration of oxygen and carbon dioxide gases, and the sound signals generated by the mining equipment; transmit the collected original data parameters to the wireless communication module; S2: Send the original data parameters to the data processing module through the wireless communication module; S3: Use a data standardization model through the data processing module to perform data cleaning on the received original data parameters to remove invalid data points and error data points therein, generate equipment operating parameters and store them in the database; S4: Read the equipment operating parameters in the database, extract the data characteristics reflecting the equipment operating status, use a linear regression prediction model to predict the equipment status according to the data characteristics, judge whether there is a potential failure risk, and generate a prediction result; perform quantization processing on the prediction result to generate a quantization value, compare the quantization value with a preset warning threshold, and trigger an alarm when the quantization value exceeds the preset warning threshold; S5: Determine the performance evaluation indicators according to the operating characteristics and business requirements of the mining equipment; collect the historical and current operating data of the equipment, conduct statistical analysis on the collected data, calculate the values of various performance indicators to determine the actual performance indicators, compare the actual performance indicators with the performance evaluation indicators, and formulate an equipment maintenance plan based on the performance evaluation results; S6: Display the prediction results and equipment maintenance plan through a visual user interface, enabling the staff to understand the status of the mining equipment, make decisions, and arrange preventive maintenance or emergency repairs.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The multi-parameter data collection module of the present invention can simultaneously collect various types of sensor data, realizing all-round monitoring of the equipment status. This comprehensiveness helps to more fully understand the operating conditions of the equipment, can timely reflect the current state of the equipment, provides real-time equipment operation information for the staff, enables the staff to discover abnormal situations in the first time and take corresponding measures to avoid accidents, and can greatly reduce the failure rate and downtime of the equipment.

[0016] 2. By analyzing the historical data, the present invention establishes a linear regression model for temperature and vibration sensor data, which can predict future temperature and vibration trends. According to the results of the linear regression prediction model, a more reasonable maintenance plan can be formulated to avoid equipment failure and damage; moreover, the alarm mechanism can be automatically triggered to remind relevant personnel to handle potential risks in a timely manner; by predicting the operating status and maintenance requirements of the equipment, maintenance resources and personnel can be more reasonably allocated, which helps to improve maintenance efficiency and reduce maintenance costs.

[0017] 3. By comparing the differences between the actual performance indicators and the preset evaluation index data, the present invention evaluates the operating status and performance of the equipment, helps the manager make decisions, optimize the production process, and improve the resource utilization efficiency; by evaluating the real-time performance data of the equipment, the maintenance requirements of the equipment can be predicted, so as to achieve preventive maintenance instead of simply relying on regular maintenance, which helps to reduce unexpected downtime and extend the equipment life.

[0018] It can be seen that compared with the prior art, the present invention has prominent substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided drawings.

[0020] Figure 1 It is the system structure diagram of the specific implementation manner of the present invention.

[0021] Figure 2 It is the structural schematic diagram of the data processing module of the specific implementation manner of the present invention.

[0022] Figure 3 It is the structural schematic diagram of the fault prediction module of the specific implementation manner of the present invention.

[0023] Figure 4 It is the structural schematic diagram of the performance evaluation and maintenance suggestion module of the specific implementation manner of the present invention.

[0024] Figure 5 It is the method flowchart of the specific implementation manner of the present invention. Specific implementation manner

[0025] To enable those skilled in the art of this technology to better understand the solution of the present invention, the following will further elaborate on the present invention in conjunction with the accompanying drawings and specific implementation manners. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0026] See Figure 1 As shown, this embodiment provides a remote monitoring system for mining equipment based on the Internet of Things, including a multi-parameter data collection module 1, a wireless communication module 2, a data processing module 3, a database 4, a fault prediction module 5, a performance evaluation and maintenance suggestion module 6, and a visualization user interface 7; the database 4 is respectively connected to the wireless communication module 2, the data processing module 3, the fault prediction module 5, and the performance evaluation and maintenance suggestion module 6 for data connection, the wireless communication module 2 is respectively connected to the multi-parameter data collection module 1 and the data processing module 3 for data connection, and the performance evaluation and maintenance suggestion module 6 and the visualization user interface 7 are data-connected.

[0027] The multi-parameter data collection module 1 is used to collect the original data parameters of the operation of mining equipment in real time through preset sensors.

[0028] Exemplarily, the multi-parameter data collection module 1 includes various types of sensors for collecting the original data parameters of the device operation in real time to comprehensively grasp the device status. The multi-parameter data collection module 1 is composed of multiple sensors, including a temperature sensor, a pressure sensor, a vibration sensor, a flow sensor, a liquid level sensor, a gas sensor, and a sound sensor. The sensors are connected through a wireless network to form a collaborative sensing network to achieve real-time data transmission and sharing.

[0029] Among them, the temperature sensor is used to measure the temperature of the device and is installed near the heat source of the device or on key components, such as motors and transformers; the pressure sensor is used to measure the pressure of the device and is installed in the hydraulic system, pneumatic system, or pipeline; the vibration sensor is used to measure the vibration condition of the device and is installed on the bearing seat or casing of rotating equipment (such as pumps and fans); the flow sensor is used to measure the flow rate of the fluid and is installed at the inlet and outlet of the pipeline or container; the liquid level sensor is used to measure the height or position of the liquid and is installed on the top and side of the storage tank or container; the gas sensor is used to detect the concentration of specific gases, such as oxygen and carbon dioxide, and is installed in the ventilation system; the sound sensor is used to detect the sound signal generated by the device and is installed on the outer shell and internal structure of the device. The wireless communication module 2 is used to send the original data parameters to the data processing module through wireless transmission technology.

[0030] Exemplarily, the wireless communication module 2 uses wireless transmission technology to transmit the data collected by the sensors to the data processing module of the ground monitoring center for data processing. The communication protocol of the wireless transmission technology is Wi-Fi. The sensors are wirelessly connected to the gateway device and configured to form a distributed network to ensure that the network coverage is large enough so that all devices can stably transmit data. The collected original data is sent to the database 4 for storage, and the database 4 is a MySQL database.

[0031] The data processing module 3 is used to perform data cleaning on the original data parameters to remove the invalid data points and error data points therein, generate the device operation parameters, and store them in the database 4.

[0032] Exemplarily, the data processing module 3 receives the data transmitted from the wireless communication module 2. The data includes the temperature, pressure, and vibration condition of the device, as well as the flow rate of the fluid, the height of the liquid, the concentration of oxygen and carbon dioxide gases, and the sound signal generated by the device. It is used to clean the data, remove the invalid and error data points, ensure data standardization for subsequent processing, and transmit the cleaned and preprocessed data to the database 4.

[0033] The fault prediction module 5 is used to read the device operation parameters in the database, extract the data features reflecting the device operation status, and predict the device status using a linear regression prediction model based on the data features to generate a prediction result.

[0034] The performance evaluation and maintenance recommendation module 6 is used to read the device operation parameters in the database 4, calculate the performance indicators of the device, determine the performance evaluation result of the device by comparing the performance indicators of the device with the preset evaluation indicators, and generate a device maintenance plan based on the performance evaluation result of the device.

[0035] The visualization user interface 7 is used to provide a graphical interaction interface for remote monitoring and management of mining equipment, and display the prediction result and the device maintenance plan. The visualization user interface 7 provides a graphical interface for staff for remote monitoring and management, supports access by mobile devices, enhances flexibility, and visually displays the operation status and performance indicator information of the device through chart and dashboard visualization tools, enabling staff to quickly understand the overall situation of the device and helping managers quickly identify potential problems and abnormal situations of the device.

[0036] In this embodiment, the multi-parameter data collection module 1 can simultaneously collect various types of sensor data to achieve all-round monitoring of the device status. This comprehensiveness helps to more comprehensively understand the operation status of the device, provides a rich information source for subsequent data analysis and fault diagnosis. By collecting and transmitting data in real time, the multi-parameter data collection module 1 can timely reflect the current status of the device, provide real-time device operation information for staff, enabling staff to discover abnormal situations in the first time and take corresponding measures to avoid accidents. This preventive maintenance method is more efficient and economical than traditional regular maintenance, and can greatly reduce the failure rate and downtime of the device.

[0037] In addition, through wireless communication technology, this system can achieve real-time data transmission and sharing, avoiding the problems of cumbersome wiring and difficult maintenance in traditional wired methods, improving the efficiency of data transmission. The wireless communication module 2 can save a large amount of wiring materials and maintenance costs, while reducing the frequency and cost of manual inspections, thereby reducing the overall operation cost. The wireless communication module can monitor the operation status and performance indicators of the device in real time, discover and handle potential problems in time, avoid production interruptions and safety accidents caused by device failures, and improve the reliability of the system.

[0038] In an embodiment of the present invention, based on the data processing module 3, a possible embodiment will be given below to non-restrictively elaborate on its specific implementation scheme.

[0039] Such as Figure 2As shown in the figure, the data processing module 3 includes a data receiving unit 31, a data preprocessing unit 32, and a data storage unit 33.

[0040] The data receiving unit 31 is configured to receive the original data parameters transmitted from the wireless communication module.

[0041] Exemplarily, the data receiving unit 31 is configured to receive the data transmitted from the wireless communication module 2, and the data includes temperature, vibration, and displacement sensor data.

[0042] The data preprocessing unit 32 is configured to perform data cleaning on the original data parameters, remove invalid and incorrect data points, and perform data standardization processing using a data standardization model to generate device operation parameters.

[0043] Exemplarily, the data preprocessing unit 32 is configured to clean the data, remove invalid and incorrect data points, and then perform data standardization for subsequent processing. For the processing of vibration signals, Fourier transform is used to convert the time-domain signal into a frequency-domain signal to extract the vibration characteristic frequency. For the filtering processing of temperature and pressure parameters, a moving average filter is used to remove noise.

[0044] The data storage unit 33 is configured to store the device operation parameters in the database 4.

[0045] In the data processing module 3, the standard deviation is used to describe the basic characteristics of the sensor data, and dimensionless indicators (such as kurtosis and skewness) are used to further extract the characteristic information of the device state. Among them, the data standardization model includes:

[0046] where x is the original data point, μ is the mean of the data, σ is the standard deviation of the data, and x′ is the standardized data.

[0047] In an embodiment of the present invention, based on the fault prediction module 5, a possible embodiment will be given below to non-restrictively elaborate on its specific implementation scheme.

[0048] As Figure 3 shown, the fault prediction module 5 includes: The feature extraction unit 51 is configured to read the device operation parameters from the database 4, and extract the data reflecting the operation state of the mining equipment at different time periods according to the preset sensor type as the target feature. For example, time-domain and frequency-domain features are extracted from the vibration signal.

[0049] A prediction model unit 52 is established to use a linear regression prediction model. The extracted target features are input into the linear regression prediction model for training, and a preset test data set is used to verify and optimize the model to generate a state prediction model. The prediction model unit 52 uses the linear regression prediction model, inputs the extracted features into the model for training, and uses the test data set to verify and optimize the model.

[0050] The linear regression prediction model includes: y = β 0 + β 1 X 1 + β 2 X 2 + …… β n X n + ϵ where y is the equipment failure probability, x 1 , x 2 , …… x n are the target features collected by n types of preset sensors, β 0 is the intercept, β 1 , β 2 , …… β n are the regression coefficients corresponding to n types of preset sensors, and ϵ is the error term.

[0051] A future state prediction unit 53 is used to predict the operating state of mining equipment based on real-time equipment operating parameters using the linear regression prediction model, determine whether there is a potential failure risk, and generate a prediction result; perform quantization processing on the prediction result to generate a quantization value, compare the quantization value with a preset warning threshold, and trigger an alarm when the quantization value exceeds the preset warning threshold.

[0052] In this embodiment, the linear regression prediction model is a statistical method for predicting a dependent variable (target variable) based on one or more independent variables (explanatory variables). By analyzing historical data, a linear regression model of temperature and vibration sensor data is established, and the future temperature and vibration trends can be predicted. Based on the results of the linear regression prediction model, a more reasonable maintenance plan can be formulated. For example, if it is predicted that the temperature will rise in a certain future period, preventive maintenance can be carried out before that period to avoid equipment damage due to high temperature.

[0053] The linear regression prediction model can provide decision-making support for mine safety management. By analyzing the prediction results, managers can understand the operating conditions of equipment and formulate corresponding safety measures and management strategies. When the linear regression prediction model predicts that the equipment may have abnormalities, it can automatically trigger an alarm mechanism to remind relevant personnel to handle potential risks in a timely manner. By predicting the operating status and maintenance requirements of equipment, maintenance resources and personnel can be allocated more reasonably, which helps to improve maintenance efficiency and reduce maintenance costs.

[0054] In an embodiment of the present invention, based on the performance evaluation and maintenance recommendation module 6, a possible embodiment will be given below to non-restrictively elaborate on its specific implementation scheme.

[0055] As Figure 4 shown, the performance evaluation and maintenance recommendation module 6 includes: an evaluation index determination unit 61, a data collection and analysis unit 62, a comparison and benchmark unit 63, and a maintenance plan formulation unit 64.

[0056] The evaluation index determination unit 61 is used to determine performance evaluation indexes according to the operating characteristics and business requirements of mining equipment. The performance evaluation indexes include production efficiency, energy consumption level, and failure rate.

[0057] The data collection and analysis unit 62 is used to collect the historical and current operating parameters of mining equipment and perform statistical analysis to calculate various actual performance indexes. For example, collect the historical and current operating data of the equipment, including production volume, energy consumption, and failure records, and perform statistical analysis on the collected data to calculate the values of various performance indexes.

[0058] The comparison and benchmark unit 63 is used to compare the actual performance indexes with the corresponding performance evaluation indexes, evaluate the performance of mining equipment, and generate a performance evaluation result.

[0059] The maintenance plan formulation unit 64 is used to determine the status of mining equipment according to the performance evaluation result, and formulate a targeted maintenance plan using a preset mathematical model according to the status of mining equipment.

[0060] Among them, the preset mathematical models include: a preventive maintenance model, a regular maintenance model, and a condition-based maintenance model.

[0061] Preventive maintenance model: Before obvious failure signs appear in the equipment, preventive maintenance is carried out in advance according to the results of the prediction model to avoid the occurrence of failures. The mathematical expression is: PM = F(t, s), where PM represents preventive maintenance, t represents time, and s represents the equipment status; Regular maintenance model: According to the usage of the equipment and the suggestions in the equipment manufacturer's user manual, formulate a regular maintenance plan, which can be mathematically expressed as: TM = F(T), where TM represents regular maintenance and T represents the time interval; Condition-based maintenance model: According to the real-time status and performance data of the equipment, decide whether maintenance is required, which can be mathematically expressed as: CM = F(S), where CM represents condition-based maintenance and S represents the equipment status.

[0062] In this embodiment, the performance evaluation and maintenance recommendation module 6 can provide a detailed analysis of the equipment operation status and efficiency. By comparing the differences between the actual performance indicators and the preset evaluation index data, the operation status and performance of the equipment can be evaluated. If the predicted value and the actual value differ greatly, it may indicate potential problems with the equipment, which requires further inspection and maintenance, helping managers make more informed decisions. By deeply understanding the equipment performance, the production process can be optimized and the resource utilization efficiency can be improved; By evaluating the real-time performance data of the equipment, the maintenance requirements of the equipment can be predicted, thus realizing preventive maintenance instead of relying solely on regular maintenance, which helps reduce unexpected downtime and extend the equipment life; accurate maintenance recommendations can help enterprises identify energy waste and unnecessary expenses, and achieve cost savings by optimizing operation parameters and processes; key performance indicators can be monitored to timely detect potential faults and problems, and thus take measures to avoid system failures and ensure continuous and stable operation.

[0063] Such as Figure 5 shown, the following is an embodiment of a method for remotely monitoring mining equipment based on the Internet of Things provided by the embodiments of the present disclosure. This method and the Internet of Things-based remote monitoring system for mining equipment in the above embodiments belong to the same inventive concept. Details not described in detail in the embodiment of the method for remotely monitoring mining equipment based on the Internet of Things can refer to the embodiments of the Internet of Things-based remote monitoring system for mining equipment.

[0064] A method for remotely monitoring mining equipment based on the Internet of Things includes the following steps: S1: Continuously monitor and collect the original data parameters of the mining equipment through the multi-parameter data collection module. The original data parameters include the temperature, pressure, vibration conditions of the mining equipment, as well as the flow rate of the fluid, the height of the liquid, the concentration of oxygen and carbon dioxide gases, and the sound signals generated by the mining equipment; transmit the collected original data parameters to the wireless communication module.

[0065] Exemplarily, various sensors are used to continuously monitor and collect the parameters of the mining equipment. The parameters include the temperature, pressure, vibration conditions of the equipment, as well as the flow rate of the fluid, the height of the liquid, the concentration of oxygen and carbon dioxide gases, and the sound signals generated by the equipment, and transmit the collected original data to the wireless communication module.

[0066] S2: Send the original data parameters to the data processing module via the wireless communication module.

[0067] The wireless communication module is responsible for transmitting the collected data to the data processing module of the ground monitoring center through wireless communication technology for data processing.

[0068] S3: Use the data standardization model in the data processing module to perform data cleaning on the received original data parameters to remove invalid data points and error data points therein, generate device operation parameters and store them in the database.

[0069] The data processing module uses the data standardization mathematical model to clean the received original data, remove invalid and error data points, and then perform data standardization for subsequent processing. The cleaned and preprocessed data is stored in the database.

[0070] S4: Read the device operation parameters in the database, extract the data features reflecting the device operation status therein, use the linear regression prediction model to predict the device status according to the data features, judge whether there is a potential failure risk, and generate a prediction result; perform quantization processing on the prediction result to generate a quantization value, compare the quantization value with the preset warning threshold, and trigger an alarm when the quantization value exceeds the preset warning threshold.

[0071] Extract the data features reflecting the device operation status from the database by the sensor, use the linear regression prediction model to predict the device status, judge whether there is a potential failure risk, set the corresponding warning threshold according to the prediction result, and trigger an alarm when the predicted value exceeds the threshold.

[0072] S5: Determine the performance evaluation indicators according to the operation characteristics and business requirements of the mining equipment; collect the historical operation data and current operation data of the equipment, perform statistical analysis on the collected data, calculate the values of various performance indicators to determine the actual performance indicators, compare the actual performance indicators with the performance evaluation indicators, and formulate a device maintenance plan according to the performance evaluation result.

[0073] First, determine the performance evaluation indicators according to the operation characteristics and business requirements of the equipment, then collect the historical operation data and current operation data of the equipment, perform statistical analysis on the collected data, calculate the values of various performance indicators to determine the evaluation indicators, compare the actual performance indicators with the preset evaluation indicators, and formulate a targeted maintenance plan according to the performance evaluation result.

[0074] S6: Display the prediction result and the device maintenance plan through the visual user interface, so that the staff can understand the status of the mining equipment, make decisions, and arrange preventive maintenance or emergency repairs.

[0075] The visual user interface displays the processed data to the staff, enabling the staff to understand the equipment status, make decisions, and arrange preventive maintenance or emergency repairs.

[0076] In summary, the present invention utilizes a multi-parameter data collection module to simultaneously collect various types of sensor data, achieving an all-round monitoring of the equipment status, providing real-time equipment operation information for the staff, enabling the staff to detect abnormal situations in a timely manner and take corresponding measures to avoid accidents, and significantly reducing the failure rate and downtime of the equipment.

[0077] Through wireless communication technology, the present invention can achieve real-time data transmission and sharing, avoiding the cumbersome wiring and difficult maintenance problems in the traditional wired method, improving the efficiency of data transmission. The wireless communication module can help monitor the equipment status, prevent accidents, and ensure the safety of employees. The wireless communication module has strong anti-interference ability and adaptability, and can work stably in a complex mine environment to ensure the accuracy and reliability of data transmission.

[0078] By analyzing historical data, the present invention establishes a linear regression model to predict future temperature and vibration trends. According to the results of the linear regression prediction model, a reasonable maintenance plan is formulated. When the linear regression prediction model predicts that the equipment may have abnormalities, an alarm mechanism can be automatically triggered to remind relevant personnel to handle potential risks in a timely manner.

[0079] By comparing the differences between the actual performance indicators and the preset evaluation index data, the present invention can evaluate the operation status and performance of the equipment. If the predicted value differs significantly from the actual value, it may indicate potential problems with the equipment, requiring further inspection and maintenance, helping managers make decisions, optimize the production process, and improve resource utilization efficiency; predict the maintenance requirements of the equipment, achieve cost savings by optimizing operation parameters and processes; can monitor key performance indicators, detect potential failures and problems in a timely manner, and thus take measures to avoid system failures and ensure continuous and stable operation.

[0080] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts between each embodiment can be referred to each other. For the methods disclosed in the embodiments, since they correspond to the systems disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method part.

[0081] Those skilled in the art may further realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0082] In several embodiments provided by the present invention, it should be understood that the disclosed systems, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of systems or units can be in electrical, mechanical, or other forms.

[0083] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] In addition, the functional modules in each embodiment of the present invention can be integrated in a processing unit, or each module can exist physically separately, or two or more modules can be integrated in a unit.

[0085] Similarly, the processing units in each embodiment of the present invention can be integrated in a functional module, or each processing unit can exist physically, or two or more processing units can be integrated in a functional module.

[0086] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of both. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the technical field.

[0087] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0088] The above has introduced in detail the remote monitoring system and method for mining equipment based on the Internet of Things provided by the present invention. Specific examples are used in this text to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and modifications can still be made to the present invention, and these improvements and modifications also fall within the protection scope of the present invention.

Claims

1. A remote monitoring system for mining equipment based on the Internet of Things, characterized in that: include: A multi-parameter data collection module, a wireless communication module, a data processing module, a database, a fault prediction module, a performance evaluation and maintenance suggestion module, and a visual user interface; the database is respectively connected with the wireless communication module, the data processing module, the fault prediction module, the performance evaluation and maintenance suggestion module, the wireless communication module is respectively connected with the multi-parameter data collection module and the data processing module, and the performance evaluation and maintenance suggestion module is connected with the visual user interface; Multi-parameter data collection module, used to collect raw data parameters of mining equipment operation in real time through preset sensors; A wireless communication module, used for sending the raw data parameters to a data processing module through wireless transmission technology; The data processing module is used to perform data cleaning on the original data parameters to remove invalid data points and erroneous data points, generate equipment operating parameters and store them in the database; A fault prediction module is used to read the equipment operating parameters in the database, extract the data features reflecting the equipment operating status, predict the equipment status using a linear regression prediction model based on the data features, and generate a prediction result; The performance evaluation and maintenance suggestion module is used to read the equipment operating parameters in the database, calculate the performance indicators of the equipment, determine the performance evaluation results of the equipment by comparing the performance indicators of the equipment with the preset evaluation indicators, and generate an equipment maintenance plan based on the performance evaluation results of the equipment; The visual user interface is used to provide a graphical interactive interface for remote monitoring and management of mining equipment, and to display prediction results and equipment maintenance plans.

2. The remote monitoring system for mining equipment based on the Internet of Things according to claim 1 is characterized in that: The preset sensors include temperature sensors, pressure sensors, vibration sensors, flow sensors, liquid level sensors, gas sensors and sound sensors connected via a wireless network; Temperature sensors, used to measure the temperature of mining equipment; Pressure sensors, used to measure pressure in mining equipment; Vibration sensors are used to measure vibration data of mining equipment; Flow sensor, used to measure the flow rate of fluid; Liquid level sensors, used to measure the height and position of liquids; Gas sensors, used to detect the concentration of specific gases; Acoustic sensors are used to detect acoustic signals generated by mining equipment.

3. The remote monitoring system for mining equipment based on the Internet of Things according to claim 2 is characterized in that: The wireless communication module is specifically used for: The Wi-Fi communication protocol is used to wirelessly connect the temperature sensor, pressure sensor, vibration sensor, flow sensor, liquid level sensor, gas sensor and sound sensor to the gateway device and configure them to form a distributed network, and the collected raw data parameters are sent to the database for storage.

4. The remote monitoring system for mining equipment based on the Internet of Things according to claim 1 is characterized in that: The data processing module includes a data receiving unit, a data preprocessing unit, and a data storage unit; A data receiving unit, used for receiving original data parameters transmitted from the wireless communication module; The data preprocessing unit is used to clean the original data parameters, remove invalid and erroneous data points, and use the data standardization model to perform data standardization processing to generate equipment operation parameters; A data storage unit, used for storing equipment operating parameters in a database; The data standardization model includes: Among them, x is the original data point, μ is the mean of the data, σ is the standard deviation of the data, and x′ is the standardized data.

5. The remote monitoring system for mining equipment based on the Internet of Things according to claim 1 is characterized in that: The fault prediction module comprises: A feature extraction unit is used to read the equipment operation parameters from the database and extract data reflecting the operation status of the mining equipment in different time periods according to the type of preset sensors as target features; Establish a prediction model unit for using a linear regression prediction model, input the extracted target features into the linear regression prediction model for training, and use a preset test data set to verify and optimize the model to generate a state prediction model; The future state prediction unit is used to use a linear regression prediction model to predict the operating state of mining equipment according to real-time equipment operating parameters, determine whether there is a potential failure risk, and generate a prediction result; quantify the prediction result to generate a quantitative value, compare the quantitative value with a preset warning threshold, and trigger an alarm when the quantitative value exceeds the preset warning threshold.

6. The remote monitoring system for mining equipment based on the Internet of Things according to claim 5 is characterized in that: The linear regression prediction model includes: y=β0+β1X1+β2X2+……β n X n + ϵ Where y is the probability of equipment failure, x1, x2, ... x n is the target feature collected by n types of preset sensors, β0 is the intercept, β1, β2, ... β n is the regression coefficient corresponding to the n types of preset sensors, and ϵ is the error term.

7. The remote monitoring system for mining equipment based on the Internet of Things according to claim 1 is characterized in that: The performance evaluation and maintenance suggestion module includes: An evaluation index determination unit, used to determine performance evaluation indexes according to the operating characteristics and business requirements of mining equipment; Data collection and analysis unit, used to collect historical equipment operating parameters and current equipment operating parameters of mining equipment, conduct statistical analysis, and calculate various actual performance indicators; A comparison and benchmark unit, used to compare the actual performance index with the corresponding performance evaluation index, evaluate the performance of the mining equipment, and generate a performance evaluation result; A maintenance plan making unit is used to determine the state of mining equipment according to the performance evaluation results, and to make a targeted maintenance plan according to the state of mining equipment using a preset mathematical model; The preset mathematical models include: a preventive maintenance model, a periodic maintenance model and a conditional maintenance model.

8. The mining equipment remote monitoring system based on the Internet of Things according to claim 7 is characterized in that: The preventive maintenance model is used to generate a preventive maintenance plan according to the state of the mining equipment and the current time when the state of the mining equipment shows signs of failure; The periodic maintenance model is used to generate a periodic maintenance plan based on time intervals according to the current use status information of the mining equipment and the user manual recommendations of the equipment manufacturer; The conditional maintenance model is used to extract real-time status information and performance data according to the current status of mining equipment, and generate mining maintenance conditions according to the real-time status information and performance data to determine whether to perform maintenance.

9. A remote monitoring method for mining equipment based on the Internet of Things, characterized in that: The method adopts the remote monitoring system for mining equipment based on the Internet of Things as described in any one of claims 1 to 7; The method comprises the following steps: S1: Continuously monitor and collect raw data parameters of mining equipment through the multi-parameter data collection module. The raw data parameters include temperature, pressure, vibration, flow rate of fluid, height of liquid, concentration of oxygen and carbon dioxide gas and sound signals generated by mining equipment; transmit the collected raw data parameters to the wireless communication module; S2: Sending the original data parameters to the data processing module through the wireless communication module; S3: The data processing module uses a data standardization model to clean the received raw data parameter rows to remove invalid data points and erroneous data points, generate equipment operation parameters and store them in a database; S4: reading the equipment operation parameters in the database, and extracting the data features reflecting the equipment operation status, using the linear regression prediction model to predict the equipment status according to the data features, judging whether there is a potential failure risk, and generating a prediction result; quantifying the prediction result to generate a quantized value, comparing the quantized value with a preset warning threshold, and triggering an alarm when the quantized value exceeds the preset warning threshold; S5: Determine performance evaluation indicators based on the operating characteristics and business requirements of mining equipment; Collect historical and current operation data of the equipment, conduct statistical analysis on the collected data, calculate the value of each performance indicator to determine the actual performance indicator, compare the actual performance indicator with the performance evaluation indicator, and formulate an equipment maintenance plan based on the performance evaluation results; S6: Prediction results and equipment maintenance plans are displayed through a visual user interface, allowing personnel to understand the status of mining equipment, make decisions, and arrange preventive maintenance or emergency repairs.

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