Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a schematic diagram of a control system of a production facility, as shown in fig. 1, according to an embodiment of the present application, the control system 100 of the production facility may include a multi-sensor integrated monitoring module 102, a data analysis platform 104, and a remote control and maintenance module 106.
The multi-sensor integrated monitoring module 102 is configured to monitor current operation data of the production equipment, where the current operation data is used to represent an operation state of the production equipment at a current moment.
Alternatively, the control system of the production facility may be referred to as a remote monitoring system. The production facility may be a coal mine production facility. The current operation data can be used for representing the operation state of the production equipment at the current moment, and can be multidimensional parameters such as vibration, pressure and temperature of the coal mine production equipment, gas concentration in the environment where the coal mine production equipment is located and the like.
Alternatively, the multi-sensor integrated monitoring module 102 may employ an integrated multi-sensor node to monitor current operation data (multi-dimensional parameters) of the coal mine production equipment, collect the current operation data in real time through a wireless sensor network, and transmit the current operation data to the central control platform.
The data analysis platform 104 is in communication with the multi-sensor integrated monitoring module through a wireless sensor network and is used for predicting target operation data of the production equipment based on current operation data and historical operation data of the production equipment, wherein the historical operation data is used for representing the operation state of the production equipment at the moment above the current moment, and the target operation data is used for representing the operation state of the production equipment at the moment below the current moment.
Alternatively, the data analysis platform may be a cloud computing and big data analysis platform. The data analysis platform 104 uploads current operation data (i.e. real-time data) to the cloud through a cloud computing technology, and utilizes big data analysis and a machine learning algorithm to automatically analyze the operation state of production equipment and conduct fault prediction and early warning on the production equipment.
Optionally, the cloud computing and big data analysis platform is constructed based on the cloud platform and has big data processing capability. Through the data uploading interface, the data of all the sensor nodes are transmitted to the cloud in real time, and unified storage and management are carried out.
The remote control and maintenance module 106 is in communication with the data analysis platform for displaying the target operation data and controlling the production equipment based on the target operation data.
Alternatively, the remote control and maintenance module 106 may view the status of the production facility in real time and remotely control and debug the production facility, reducing the need for field operations.
Optionally, the user may access the remote monitoring system through a computer (PC) terminal, a mobile phone application, or a browser, to view the real-time running state of the production device. The remote monitoring system provides a friendly visual interface, and equipment state information (such as temperature, pressure, vibration and the like) is displayed in a chart or instrument panel form, so that a user can intuitively know the health condition of the equipment. The remote monitoring system provides a remote control function that allows a user to debug or control the production facility. By this function, the user can remotely restart or adjust parameters of the failed production equipment without requiring on-site operations. The remote monitoring system automatically generates maintenance reminding according to the state and the historical operation data of the production equipment. For example, when vibration data of the production equipment exceeds a set safety threshold, the remote monitoring system may recommend scheduling maintenance to avoid damage to the production equipment.
The embodiment of the application discloses a multi-sensor integrated monitoring module, a data analysis platform and a remote control and maintenance module, wherein the multi-sensor integrated monitoring module is used for monitoring current operation data of production equipment, the current operation data are used for representing the operation state of the production equipment at the current moment, the data analysis platform is communicated with the multi-sensor integrated monitoring module through a wireless sensor network and used for predicting target operation data of the production equipment based on the current operation data and historical operation data of the production equipment, the target operation data are used for representing the operation state of the production equipment at the next moment, the remote control and maintenance module is communicated with the data analysis platform and used for displaying the target operation data and controlling the production equipment based on the target operation data, so that the technical effect of improving the control efficiency of the production equipment is achieved, and the technical problem of low control efficiency of the production equipment is solved.
The above-described embodiments of the present application are further described below.
In some embodiments of the application, the multi-sensor integrated monitoring module comprises a vibration sensor module, a temperature sensor module, a pressure sensor module and a gas concentration sensor module, wherein the vibration sensor module is used for monitoring the vibration state of production equipment, the temperature sensor module is used for monitoring the temperature state of the production equipment, the pressure sensor module is used for monitoring the pressure state of the production equipment, and the gas concentration sensor module is used for monitoring the gas concentration state in the environment where the production equipment is located.
Alternatively, the plurality of sensors integrated by the sensor nodes in the multi-sensor integrated monitoring module can at least comprise a vibration sensor, a temperature sensor, a pressure sensor, a gas concentration sensor and the like. Wherein the vibration sensor is used for monitoring the vibration state of the production equipment. The temperature sensor is used for monitoring the temperature change of the production equipment. The pressure sensor is used for monitoring pressure fluctuation of the production equipment. The gas concentration sensor is used for monitoring the concentration of harmful gas in the working environment of the production equipment.
Optionally, each sensor node is provided with a data acquisition module, so that the operation parameters of the equipment can be acquired in real time, and preliminary preprocessing, such as noise and abnormal data filtering, can be performed to ensure the data quality. Each sensor node sends the collected data to the central control platform in real time through a wireless sensor network (such as Zigbee, loRa, wi-Fi). The wireless sensor network supports low power consumption and wide area coverage, and ensures stability in a complex underground coal mine environment.
In some embodiments of the present application, the multi-sensor integrated monitoring module further includes a data fusion processing unit, configured to fuse data monitored by the vibration sensor module, the temperature sensor module, the pressure sensor module, and the gas concentration sensor module to obtain current operation data.
In this embodiment, the multi-sensor integrated monitoring module may further include a data fusion processing unit. After the vibration sensor monitors the vibration state of the production equipment, the temperature sensor monitors the temperature change of the production equipment, the pressure sensor monitors the pressure fluctuation of the production equipment and the gas concentration sensor monitors the harmful gas concentration in the working environment of the production equipment, the data monitored by the vibration sensor, the temperature sensor, the pressure sensor and the gas concentration sensor can be fused to obtain the current operation data, so that the accuracy of the monitoring data is improved.
In some embodiments of the application, the remote control and maintenance module comprises a fault early warning module for determining early warning information matched with the fault information and sending the early warning information to the target object.
In this embodiment, the remote control and maintenance module includes a fault pre-warning module that may be used to determine pre-warning information that matches the fault information and send the pre-warning information to the target object. The target object may be a worker associated with the coal mine production facility. The early warning information can be sent in the form of voice, telephone, short message, mail, etc., and the corresponding sending mode of the early warning information can be selected according to the grade of the fault information, which is only illustrative and not particularly limited.
In some embodiments of the application, the system further comprises a central control platform in communication with the multi-sensor integrated monitoring module for storing current operation data and historical operation data, and a high-efficiency data transmission module for connecting the multi-sensor integrated monitoring module, the central control platform and the remote control and maintenance module.
Optionally, the high-efficiency data transmission module utilizes the internet of things (Internet of Things, abbreviated as IoT) technology and the fifth-generation mobile network (5 th Generation mobile networks, abbreviated as 5G) communication technology to ensure high-speed transmission of the monitoring data, and can maintain stable communication connection even in complex environments such as coal mines.
Optionally, the efficient data transmission module seamlessly connects the sensor node, the production equipment, and the monitoring platform using IoT technology. And through the edge computing equipment, data transmission delay and network load are reduced, and the efficiency of real-time data uploading is ensured. The high bandwidth and low delay characteristics of 5G are utilized, so that a large amount of monitoring data on a coal mine site can be ensured to be transmitted to a cloud platform at high speed and stably, and the method is particularly suitable for processing real-time video monitoring data and a large amount of equipment parameters.
The embodiment of the application discloses a multi-sensor integrated monitoring module, a data analysis platform and a remote control and maintenance module, wherein the multi-sensor integrated monitoring module is used for monitoring current operation data of production equipment, the current operation data are used for representing the operation state of the production equipment at the current moment, the data analysis platform is communicated with the multi-sensor integrated monitoring module through a wireless sensor network and used for predicting target operation data of the production equipment based on the current operation data and historical operation data of the production equipment, the target operation data are used for representing the operation state of the production equipment at the next moment, the remote control and maintenance module is communicated with the data analysis platform and used for displaying the target operation data and controlling the production equipment based on the target operation data, so that the technical effect of improving the control efficiency of the production equipment is achieved, and the technical problem of low control efficiency of the production equipment is solved.
According to an embodiment of the present application, there is provided a method embodiment of a control method of a production apparatus, it being noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that herein.
Fig. 2 is a flowchart of a control method of a production apparatus according to an embodiment of the present application, as shown in fig. 2, the method may include the steps of:
Step S202, current operation data and historical operation data of the production equipment are obtained, wherein the current operation data are used for representing the operation state of the production equipment at the current moment, and the historical operation data are used for representing the operation state of the production equipment at the moment which is the last time at the current moment.
In the technical solution provided in step S202, current operation data and historical operation data of the production equipment may be obtained. Wherein the production equipment can be coal mine production equipment. The current operation data can be used for representing the operation state of the production equipment at the current moment, and can be multidimensional parameters such as vibration, pressure and temperature of the coal mine production equipment, gas concentration in the environment where the coal mine production equipment is located and the like. The historical operating data may be used to represent an operating state of the production device at a time immediately prior to the current time.
Optionally, the wireless sensor network is utilized to collect current operation data of the production equipment in real time, and the current operation data is transmitted to the central control platform. In addition, the central control platform also stores historical operating data of the production equipment. Optionally, the current operation data may be preliminary preprocessed, such as filtering noise and abnormal data, before being transmitted to the central control platform.
Step S204, predicting target operation data of the production equipment based on the current operation data and the historical operation data, wherein the target operation data is used for representing the operation state of the production equipment at the next time of the current time.
In the technical solution provided in step S204, after the current operation data and the historical operation data of the production equipment are acquired, the target operation data of the production equipment may be predicted based on the acquired current operation data and the historical operation data. Wherein the target operation data may be used to represent an operation state of the production device at a time next to the current time.
Optionally, using a machine learning algorithm, the cloud computing and big data analysis platform analyzes the historical operation data and the current operation data (real-time data), predicts possible faults of the production equipment, obtains target operation data of the production equipment, and generates an early warning report. The cloud computing and big data analysis platform is combined with maintenance records and operation conditions of coal mine production equipment to provide intelligent maintenance suggestions.
Optionally, machine learning algorithms such as anomaly detection, time series analysis, etc. are used to analyze historical operational data and current operational data (real-time data), predict likely failures of the device, and generate an early warning report. For example, based on the historical vibration data model, whether the production equipment has abnormal vibration trend in operation or not can be automatically detected, and fault early warning is sent out in advance. The cloud computing and big data analysis platform is further combined with maintenance records and operation conditions of coal mine equipment, intelligent maintenance suggestions are provided, maintenance period is optimized, and unplanned shutdown is reduced.
Step S206, controlling the production equipment based on the target operation data.
In the technical solution provided in step S206, after predicting the target operation data of the production apparatus, the production apparatus may be controlled based on the obtained target operation data.
Optionally, the visual interface displays status information of the production equipment in a form of a chart or a dashboard, and provides a remote control function, so that a user can debug or control the production equipment.
In the embodiment of the application, the current operation data and the historical operation data of the production equipment are acquired, wherein the current operation data is used for representing the operation state of the production equipment at the current moment, the historical operation data is used for representing the operation state of the production equipment at the last moment of the current moment, the target operation data of the production equipment is predicted based on the current operation data and the historical operation data, the target operation data is used for representing the operation state of the production equipment at the next moment of the current moment, and the production equipment is controlled based on the target operation data, so that the technical effect of improving the control efficiency of the production equipment is realized, and the technical problem of low control efficiency of the production equipment is solved.
The above-described embodiments of the present application are further described below.
In some embodiments of the application, the method comprises the steps of obtaining current operation data of production equipment, and obtaining vibration information, temperature information, pressure information and gas concentration information of the production equipment, wherein the vibration information is used for representing the vibration state of the production equipment, the temperature information is used for representing the temperature state of the production equipment, the pressure information is used for representing the pressure state of the production equipment, and the gas concentration information is used for representing the gas concentration state in the environment where the production equipment is located.
In this embodiment, vibration information, temperature information, pressure information, and gas concentration information of the production apparatus can be acquired. And further carrying out fusion processing on the obtained vibration information, temperature information, pressure information and gas concentration information to obtain current operation data. Wherein the vibration information may be used to represent the vibration state of the production equipment. The temperature information may be used to indicate the temperature status of the production facility. The pressure information may be used to represent the pressure status of the production facility. The gas concentration information may be used to indicate a gas concentration state in the environment in which the production facility is located.
Optionally, each sensor node is provided with a data acquisition module, so that the operation parameters of the equipment can be acquired in real time, and preliminary preprocessing, such as noise and abnormal data filtering, can be performed to ensure the data quality. Each sensor node sends the collected data to the central control platform in real time through a wireless sensor network (such as Zigbee, loRa, wi-Fi). The wireless sensor network supports low power consumption and wide area coverage, and ensures stability in a complex underground coal mine environment.
In some embodiments of the application, the method further comprises determining pre-warning information matched with the target operation data and sending the pre-warning information to the target object.
In this embodiment, after the target operation data is obtained, the early warning information that matches the target operation data may be determined, and the early warning information is sent to the target object. The target object may be a worker associated with the coal mine production facility. The early warning information can be sent in the form of voice, telephone, short message, mail, etc., and the corresponding sending mode of the early warning information can be selected according to the grade of the fault information, which is only illustrative and not particularly limited.
In some embodiments of the application, the method further comprises storing the current operational data and the historical operational data to a central control platform.
In this embodiment, the current operational data and the historical operational data may be stored to the central control platform to predict target operational data of the production facility based on the current operational data and the historical operational data.
In the embodiment of the application, the current operation data and the historical operation data of the production equipment are acquired, wherein the current operation data is used for representing the operation state of the production equipment at the current moment, the historical operation data is used for representing the operation state of the production equipment at the last moment of the current moment, the target operation data of the production equipment is predicted based on the current operation data and the historical operation data, the target operation data is used for representing the operation state of the production equipment at the next moment of the current moment, and the production equipment is controlled based on the target operation data, so that the technical effect of improving the control efficiency of the production equipment is realized, and the technical problem of low control efficiency of the production equipment is solved.
In order to facilitate a better understanding of the technical solution of the present application by a person skilled in the art, a description will now be given with reference to a specific embodiment.
At present, in the coal mine production process, the traditional monitoring system usually depends on site staff for real-time inspection and maintenance, the system usually performs data acquisition through a simple monitoring camera or a sensor, the data needs manual analysis and processing, the monitoring range and the monitoring precision are limited, and the running state of coal mine production equipment cannot be comprehensively reflected in real time. The existing monitoring system is lack of efficient data transmission and analysis means, so that faults or anomalies in production equipment cannot be found in time, the equipment maintenance cost is increased, the production efficiency is reduced, and potential safety hazards exist.
Therefore, the monitoring system in the related art has limited remote monitoring precision and range, is difficult to acquire detailed state information of equipment in real time, lacks automatic data analysis and fault early warning functions, causes fault discovery delay, affects production safety, has insufficient adaptability to complex coal mine environments, is difficult to stably operate in severe environments for a long time, has low data transmission rate, cannot efficiently transmit a large amount of real-time monitoring data, and has the technical problem of low efficiency of controlling production equipment.
In order to solve the problems, the application provides a remote monitoring system for coal mine production equipment, which comprises a multi-sensor integrated monitoring module, a wireless sensor network and a central control platform, wherein the multi-sensor integrated monitoring module is used for monitoring multi-dimensional parameters of the coal mine production equipment by adopting integrated multi-sensor nodes, collecting data in real time and transmitting the data to the central control platform. And the cloud computing and big data analysis platform is used for uploading real-time data to the cloud through a cloud computing technology, and automatically analyzing the running state of equipment by utilizing big data analysis and a machine learning algorithm to perform fault prediction and early warning. And the remote control and maintenance module is used for checking the equipment state in real time, performing remote control and debugging, and reducing the requirement of on-site operation. The high-efficiency data transmission module is used for connecting the sensor node, the equipment and the monitoring platform by utilizing an IoT technology and a 5G communication technology.
Optionally, the sensor nodes in the multi-sensor integrated monitoring module integrate a plurality of sensors such as a vibration sensor, a temperature sensor, a pressure sensor, a gas concentration sensor and the like, and respectively monitor the vibration state, the temperature change, the pressure fluctuation and the harmful gas concentration in the working environment of the equipment. Each sensor node is provided with a data acquisition module, and each sensor node transmits acquired data to the central control platform in real time through a wireless sensor network (such as Zigbee, loRa, wi-Fi). And the cloud computing and big data analysis platform is used for transmitting the data of all the sensor nodes to the cloud in real time through the data uploading interface so as to perform unified storage and management. The remote control and maintenance module provides a friendly visual interface, the equipment state information (such as temperature, pressure, vibration and the like) is displayed in a chart or instrument panel form, and a user can access the remote monitoring system through a PC end, a mobile phone application or a Web browser to debug or control the equipment. All hardware devices in the coal mine production equipment remote monitoring system adopt an explosion-proof design, equipment shells adopt a high-level protection design (such as IP65 or IP 67), the hardware devices adopt a low-power-consumption design, and the sensor nodes can be powered by batteries or solar energy.
The application provides a remote monitoring method of coal mine production equipment, which comprises the steps of collecting data in real time by a wireless sensor network, transmitting the data to a central control platform after preliminary preprocessing by a data acquisition module, analyzing historical data and real-time data by the platform by utilizing a machine learning algorithm, predicting possible faults of the equipment and generating an early warning report, providing an intelligent maintenance suggestion by combining maintenance records and operation conditions of the coal mine equipment by the platform, displaying equipment state information in a chart or instrument panel form by a visual interface, providing a remote control function, and allowing a user to debug or control the equipment.
Optionally, the data preliminary preprocessing refers to filtering noise, abnormal data and the like. The historical data and the real-time data are analyzed by machine learning algorithms such as anomaly detection, time sequence analysis and the like, and the platform also provides intelligent maintenance suggestions by combining maintenance records and operation conditions of coal mine equipment.
According to the embodiment, through multi-sensor integration and high-efficiency data transmission, the running state of the coal mine production equipment can be accurately monitored in real time, the monitoring precision and range are remarkably improved, and the response time of equipment faults is reduced. By adopting the cloud computing and big data analysis platform, equipment faults are automatically identified, early warning is timely carried out, the pressure of manual monitoring is reduced, and the safety and the efficiency of coal mine production are improved. The severe production environment of the coal mine is considered, and the system can stably run for a long time in the complex coal mine environment through the explosion-proof, dust-proof, waterproof and low-power-consumption design, so that the service life and safety of the equipment are prolonged, the durability and adaptability of the equipment are improved, and the faults caused by environmental problems of the system are reduced. Through remote monitoring and automatic maintenance reminding, the requirements of field personnel are reduced, the maintenance and production shutdown cost is reduced, and the overall economic benefit is improved. The application of the Internet of things and the 5G communication technology ensures that the system can maintain high-speed and reliable data transmission under severe environments, and further improves the overall efficiency of the system.
The existing monitoring system for the coal mine production equipment generally depends on a single sensor or local monitoring, and is difficult to monitor the omnibearing running state of the coal mine equipment in real time and accurately. Meanwhile, due to the fact that the equipment operation environment is complex, data acquisition is unstable, and the monitoring system lacks intelligent analysis and early warning functions, equipment faults are difficult to discover in time. In addition, the remote control and maintenance functions of the traditional system are limited, manual inspection is relied on, and the problems of high potential safety hazard and low efficiency exist. In the aspect of data transmission, the transmission efficiency of the prior art in the underground complex environment of the coal mine is low, the prior art is easy to be interfered, the monitoring data cannot be uploaded in time, and the production scheduling and management are affected.
However, the application breaks through the limitations of the existing monitoring system of the coal mine production equipment by technical means such as multi-sensor integration, cloud computing and big data analysis, remote control and maintenance, high-efficiency data transmission and the like. And the multi-dimensional parameters such as the temperature, vibration, pressure, harmful gas concentration and the like of the equipment are monitored in real time by integrating various sensor nodes, so that the comprehensiveness and accuracy of data acquisition are ensured. Through the wireless sensor network, the data can be uploaded to the cloud end in time for storage and analysis. By combining big data with a machine learning algorithm, the system can conduct fault prediction and early warning, and accidental shutdown of equipment is effectively reduced. Meanwhile, the remote control module enables operators to remotely debug equipment through a PC end or a mobile phone, so that the requirements of on-site operation are reduced, and the maintenance efficiency and safety of coal mine equipment are improved.
According to the embodiment, by integrating various sensors, the monitoring precision of equipment is greatly improved, the fault detection early period is obviously shortened, and the equipment downtime is reduced. Secondly, by utilizing big data analysis, the system can automatically generate a device operation report, provide maintenance advice, and avoid the defect of relying on manual experience to check the device. The remote control and maintenance module enables the equipment to operate without the presence of personnel, so that safety and efficiency are greatly improved. Finally, by adopting an internet of things (IoT) technology and a 5G communication technology, the data transmission speed and reliability of the system in a complex environment are guaranteed, and the instantaneity and the effectiveness of production management are ensured.
The conventional coal mine monitoring system is compared with the coal mine remote monitoring system of the embodiment in aspects of monitoring precision, fault detection advance period, data transmission efficiency, maintenance cost, equipment downtime, safety, maintenance advice and the like. Aiming at the monitoring precision, the traditional coal mine monitoring system only monitors partial equipment parameters, the precision is low, the coal mine remote monitoring system of the embodiment is integrated by multiple sensors, the multidimensional parameters are monitored in real time, the monitoring precision is improved by 30%, and the coverage is more comprehensive. Aiming at the early period of fault detection, the traditional coal mine monitoring system relies on manual inspection, so that faults are difficult to find in time, and the coal mine remote monitoring system of the embodiment combines big data analysis and prediction algorithms to early warn in advance, so that the early period of fault detection is shortened by more than 50%. Aiming at the data transmission efficiency, the traditional coal mine monitoring system is easy to be interfered by the environment, the transmission rate is low, and the coal mine remote monitoring system of the embodiment is fast and stable in transmission speed, the data transmission speed is improved by 40% and the reliability is improved based on the IoT and 5G technology.
Aiming at maintenance cost, the traditional coal mine monitoring system is highly dependent on manual inspection, and has high cost, and the remote control and automatic debugging of the coal mine remote monitoring system of the embodiment reduce field operation and maintenance cost by 20% -30%. Aiming at equipment downtime, equipment fault detection of the traditional coal mine monitoring system is delayed, the downtime is long, and the coal mine remote monitoring system of the embodiment reduces the downtime by 40% through fault prediction and early maintenance, so that the equipment is more stable in operation. Aiming at safety, the traditional coal mine monitoring system has a large potential safety hazard in manual operation, and the coal mine remote monitoring system of the embodiment is automatically controlled remotely, so that manual intervention is reduced, the operation safety is improved, the safety is improved obviously, and the operation risk is reduced greatly. Aiming at maintenance advice, the traditional coal mine monitoring system relies on manual experience and does not have automatic analysis capability, and the coal mine remote monitoring system of the embodiment automatically generates equipment operation reports, provides the maintenance advice, and improves the scientificity of maintenance and the rationality of operation.
Compared with the traditional coal mine monitoring system, the method and the system not only improve the reliability and safety of equipment operation, but also reduce maintenance cost and manual operation requirements, and enhance the automation level of the coal mine production process. Through intelligent monitoring and maintenance means, the service life of the coal mine production equipment is prolonged, the production safety is obviously improved, meanwhile, the influence of equipment faults and shutdown on production is reduced, and obvious economic and social benefits are brought.
Fig. 3 is a schematic diagram of a coal mine production facility remote monitoring system according to an embodiment of the present application, and as shown in fig. 3, the coal mine production facility remote monitoring system 300 may include a multi-sensor integrated monitoring module 301, a central control platform 302, a remote control and maintenance module 303, and an efficient data transmission module 304. The multi-sensor integrated monitor module 301 internally integrates a vibration sensor 3011, a temperature sensor 3012, a pressure sensor 3013, a gas concentration sensor 3104, and the like. The central control platform 302 includes a cloud computing and big data analysis platform 3021.
The multi-sensor integrated monitoring module adopts integrated multi-sensor nodes to monitor multi-dimensional parameters of coal mine production equipment, collects data in real time through a wireless sensor network and transmits the data to the central control platform. And the cloud computing and big data analysis platform is used for uploading real-time data to the cloud through a cloud computing technology, automatically analyzing the running state of equipment by utilizing big data analysis and a machine learning algorithm, and carrying out fault prediction and early warning. And the remote control and maintenance module is used for checking the equipment state in real time, performing remote control and debugging, and reducing the requirement of on-site operation. And the high-efficiency data transmission module ensures high-speed transmission of monitoring data by utilizing an IoT technology and a 5G communication technology, and can maintain stable communication connection even in complex environments such as coal mines.
Optionally, the multi-sensor integrated monitoring module, the sensor node integrates a plurality of sensors, such as a vibration sensor, a temperature sensor, a pressure sensor, a gas concentration sensor, and the like, for respectively monitoring the vibration state, the temperature change, the pressure fluctuation, and the concentration of harmful gas in the working environment of the device. Each sensor node is provided with a data acquisition module, can acquire the operation parameters of the equipment in real time, and performs preliminary preprocessing, such as noise and abnormal data filtering, so as to ensure the data quality. Each sensor node transmits the acquired data to the central control platform in real time through a wireless sensor network (such as Zigbee, loRa, wi-Fi). The wireless sensor network supports low power consumption and wide area coverage, and ensures stability in a complex underground coal mine environment. The cloud computing and big data analysis platform is constructed based on the cloud platform and has big data processing capability. Through the data uploading interface, the data of all the sensor nodes are transmitted to the cloud in real time, and unified storage and management are carried out.
As shown in fig. 3, a user may access the remote monitoring system through a PC end, a mobile phone application or a browser to view the real-time running state of the device. The system provides a friendly visual interface, and equipment state information (such as temperature, pressure, vibration and the like) is displayed in a chart or instrument panel form, so that a user can intuitively know the health condition of equipment. The system provides remote control functionality allowing a user to debug or control the device. By this function, the user can remotely restart or adjust parameters of the faulty device without requiring on-site operations. The system automatically generates maintenance reminders according to the state and historical operation data of the equipment. For example, when vibration data of the device exceeds a set safety threshold, the system may recommend maintenance to be scheduled to avoid damage to the device.
The high-efficiency data transmission module adopts the internet of things (IoT) technology to seamlessly connect the sensor nodes, the devices and the monitoring platform. And through the edge computing equipment, data transmission delay and network load are reduced, and the efficiency of real-time data uploading is ensured. The high bandwidth and low delay characteristics of 5G are utilized, so that a large amount of monitoring data on a coal mine site can be ensured to be transmitted to a cloud platform at high speed and stably, and the method is particularly suitable for processing real-time video monitoring data and a large amount of equipment parameters.
Fig. 4 is a schematic diagram of another coal mine production equipment remote monitoring system according to an embodiment of the present application, and as shown in fig. 4, the coal mine production equipment remote monitoring system 400 may include a multi-sensor integrated monitoring module 401, a cloud computing and big data analysis platform 402, a remote control and maintenance module 403, and a high efficiency data transmission module 404. The working principle of the remote monitoring system of the coal mine production equipment in the embodiment is realized by cooperation of a multi-sensor integrated monitoring module, a cloud computing and big data analysis platform, a remote control and maintenance module and a high-efficiency data transmission module.
First, the multi-sensor integrated monitoring module 401 monitors the operating parameters of the coal mine production facility, including the vibration, temperature, pressure and gas concentration in the surrounding environment of the facility, in real time, through integrated various sensors, such as vibration sensors, temperature sensors, pressure sensors, gas concentration sensors, etc. Each sensor node is provided with a data acquisition module, so that noise and abnormal data can be filtered, and the reliability of the acquired data is ensured. All the sensor nodes transmit real-time data to a central control platform through a wireless sensor network, so that stable transmission of the data in a complex underground coal mine environment is ensured.
Next, the cloud computing and big data analysis platform 402 performs data storage and analysis through cloud processing technology. And the data acquired by the sensor nodes are transmitted to the cloud end in real time through the data uploading interface for unified management and processing. The platform analyzes the running state of the equipment based on big data analysis and a machine learning algorithm, identifies a potential failure mode and performs early warning. For example, when a certain parameter of the equipment abnormally fluctuates, the system can automatically trigger an early warning prompt to help coal mine operators to find problems in advance, maintain and adjust the problems, and avoid larger equipment faults or stop.
Further, the remote control and maintenance module 403 enables an operator to access the remote monitoring system through a PC, a mobile phone application, or a browser. The user can view the running state of the equipment in real time, and the data are displayed in the form of an intuitive chart and an instrument panel, so that the health condition of the equipment can be conveniently known. Remote control functions allow a user to debug or maintain a device without entering the field, such as remotely restarting the device, adjusting parameters, etc. The system also has an intelligent maintenance reminding function, and when the operation data of the equipment reaches a preset safety threshold, the system can automatically generate maintenance advice to remind operators to carry out maintenance operation, so that potential equipment faults are prevented.
Finally, the efficient data transfer module 404 ensures seamless connection between each sensor node, device, and monitoring platform through internet of things IoT technology. By adopting the edge computing equipment, partial processing can be carried out on the data in the field, and the delay and network load of data transmission are reduced. Through the high bandwidth and low delay characteristics of the 5G communication technology, the system can realize high-speed stable transmission of a large amount of data, such as real-time video monitoring data, equipment parameters and the like, on a coal mine site, ensures that the data can be timely uploaded to a cloud platform, and is particularly suitable for scenes requiring large data volume and real-time processing.
The implementation scheme of the remote monitoring system of the coal mine production equipment is based on the cooperative work of a plurality of modules, and comprises the technologies of multi-sensor monitoring, cloud computing processing, remote control and maintenance, high-efficiency data transmission and the like. Firstly, a plurality of sensor nodes including vibration sensors, temperature sensors, pressure sensors, gas concentration sensors and the like are installed at key positions of coal mine production equipment and used for monitoring multidimensional parameters in equipment operation in real time. The sensor nodes not only can collect data, but also have a preliminary data preprocessing function, noise and abnormal values are filtered, and the reliability of the data is ensured. In order to adapt to the underground complex environment of the coal mine, the sensors transmit data to a central control platform through a wireless sensing network, so that low power consumption and wide area coverage are ensured.
And secondly, uploading the data acquired by all the sensor nodes to the cloud end in real time through a special interface by the central control platform. Based on cloud computing and big data technology, the platform can uniformly store, manage and analyze data. The platform is provided with big data analysis and machine learning algorithms, and can automatically analyze the running state of equipment, extract potential fault characteristics and predict and early warn. When the system detects that the operation parameters of certain equipment exceed the normal range, the platform can send out early warning, so that operators can perform preventive maintenance before the equipment fails, and the continuity of coal mine production and the safety of the equipment are ensured.
In the aspect of a remote control and maintenance module, the system provides a user-friendly visual interface through a PC end, a mobile phone application or a browser, and a user can view the running state of the equipment in real time. The parameters of the equipment are displayed in the form of charts and instrument panels, so that operators can conveniently and intuitively know the running condition of the equipment. When the system detects an abnormality, a user can remotely control the equipment through the module, such as restarting the equipment, adjusting the operating parameters of the equipment and the like, without on-site intervention. Meanwhile, the system automatically generates maintenance reminding according to historical operation data and the current state of the equipment, prompts operators to arrange maintenance, and avoids the influence of sudden faults on production.
To ensure the efficiency of data transmission, the system employs internet of things IoT technology and edge computing. Data acquired by the sensor nodes are initially processed by the edge computing equipment and then transmitted to the cloud, so that delay and network load in the data transmission process are reduced. The edge calculation can carry out partial filtration and analysis on the data acquired in real time, and only key data is uploaded to the cloud, so that the response speed of the system is improved. In addition, the adopted 5G communication technology can keep high-speed and low-delay data transmission in a complex coal mine environment, is particularly suitable for processing real-time video monitoring data and a large number of equipment parameters, and ensures that the monitoring data can be timely transmitted to a cloud platform.
Furthermore, one of the key applications of the system is the monitoring of exhaust gases and harmful gases. The gas concentration sensor is used for monitoring the gas composition change in the underground coal mine in real time, especially the concentration of harmful gases such as methane, carbon monoxide and the like. When the gas concentration is detected to be out of the safety range, the system can immediately give an alarm to an operator and starts an automatic exhaust device to ventilate, so that the safety of underground personnel is ensured. The system also has the function of recording the gas concentration historical data, and is convenient for accident tracing and safety evaluation in the future.
In summary, the coal mine production equipment remote monitoring system of the embodiment realizes real-time monitoring and intelligent maintenance of the coal mine production equipment through effective combination of multi-sensor integration, cloud big data processing, remote control and 5G data transmission technologies. The system not only improves the operation safety and reliability of coal mine equipment, but also greatly reduces the manual operation requirement by an intelligent means, and improves the overall production efficiency of the coal mine.
The remote monitoring system for the coal mine production equipment adopts an environment adaptability enhancement design, comprising a hardware protection design, aiming at the particularity of the coal mine environment, all hardware equipment in the system, such as sensor nodes, wireless communication equipment and the like, adopt an explosion-proof design, so that the safety of the equipment under the condition of the existence of combustible gas is ensured. The dustproof and waterproof design ensures that the equipment stably operates for a long time in a wet and dusty mine environment by adopting a high-grade protection design (such as IP65 or IP 67) for the equipment shell, and avoids the influence of the external environment on the performance of the equipment. The low-power-consumption design is adopted by the hardware equipment, the sensor nodes can be powered by batteries or solar energy, long-time independent operation is supported, and dependence on a coal mine power system is reduced.
FIG. 5 is a flowchart of a method for remotely monitoring a coal mine production facility according to an embodiment of the present application, and as shown in FIG. 5, the method for remotely monitoring a coal mine production facility may at least include the following steps:
Step S501, the wireless sensor network collects real-time data and transmits the real-time data to the central control platform.
Optionally, the wireless sensor network collects real-time data, and the data acquisition module performs preliminary preprocessing on the real-time data and then transmits the real-time data to the central control platform.
Step S502, the platform analyzes the historical data and the real-time data by using a machine learning algorithm, predicts possible faults of the equipment, and generates an early warning report.
Optionally, machine learning algorithms such as anomaly detection, time series analysis, and the like are used to analyze the historical data and the real-time data, predict possible faults of the device, and generate an early warning report. For example, based on a historical vibration data model, whether the equipment has abnormal vibration trend in operation can be automatically detected, and fault early warning is sent out in advance. The platform also combines maintenance records and operation conditions of coal mine equipment, provides intelligent maintenance suggestions, optimizes maintenance period and reduces unplanned shutdown.
In step S503, the platform combines the maintenance record and the operation condition of the coal mine equipment to provide an intelligent maintenance suggestion.
In step S504, the visual interface displays the device status information in the form of a chart or dashboard, and provides a remote control function to allow the user to debug or control the device.
The embodiment of the application discloses a multi-sensor integrated monitoring module, a data analysis platform and a remote control and maintenance module, wherein the multi-sensor integrated monitoring module is used for monitoring current operation data of production equipment, the current operation data are used for representing the operation state of the production equipment at the current moment, the data analysis platform is communicated with the multi-sensor integrated monitoring module through a wireless sensor network and used for predicting target operation data of the production equipment based on the current operation data and historical operation data of the production equipment, the target operation data are used for representing the operation state of the production equipment at the next moment, the remote control and maintenance module is communicated with the data analysis platform and used for displaying the target operation data and controlling the production equipment based on the target operation data, so that the technical effect of improving the control efficiency of the production equipment is achieved, and the technical problem of low control efficiency of the production equipment is solved.
The embodiment of the application also provides a control device of the production equipment. The control device of the production apparatus of this embodiment may be configured to execute the control method of the production apparatus of the embodiment of the present application.
Fig. 6 is a schematic structural view of a control device of a production apparatus according to an embodiment of the present application, and as shown in fig. 6, the control device 600 of the production apparatus may include an acquisition unit 602, a prediction unit 604, and a control unit 606.
An obtaining unit 602, configured to obtain current operation data and historical operation data of the production device, where the current operation data is used to represent an operation state of the production device at a current time, and the historical operation data is used to represent an operation state of the production device at a time previous to the current time.
And a prediction unit 604 for predicting target operation data of the production equipment based on the current operation data and the historical operation data, wherein the target operation data is used for representing the operation state of the production equipment at the next time of the current time.
And a control unit 606 for controlling the production equipment based on the target operation data.
Optionally, the acquiring unit 602 is configured to acquire current operation data of the production equipment, and includes an acquiring module configured to acquire vibration information, temperature information, pressure information and gas concentration information of the production equipment, where the vibration information is used to represent a vibration state of the production equipment, the temperature information is used to represent a temperature state of the production equipment, the pressure information is used to represent a pressure state of the production equipment, and the gas concentration information is used to represent a gas concentration state in an environment where the production equipment is located, and perform fusion processing on the vibration information, the temperature information, the pressure information and the gas concentration information to obtain the current operation data.
Optionally, the device further comprises a sending unit, which is used for determining the early warning information matched with the target operation data and sending the early warning information to the target object.
Optionally, the device further comprises a storage unit for storing the current operation data and the historical operation data to the central control platform.
In the device, current operation data and historical operation data of the production equipment are acquired through the acquisition unit 602, wherein the current operation data is used for representing the operation state of the production equipment at the current moment, and the historical operation data is used for representing the operation state of the production equipment at the moment which is the last moment of the current moment. The target operation data of the production apparatus is predicted by the prediction unit 604 based on the current operation data and the history operation data, wherein the target operation data is used to represent the operation state of the production apparatus at the next time of the current time. The control unit 606 controls the production equipment based on the target operation data, thereby realizing the technical effect of improving the efficiency of controlling the production equipment and further solving the technical problem of low efficiency of controlling the production equipment.
According to another aspect of the embodiments of the present application, there is also provided a nonvolatile storage medium including a stored program, wherein the device in which the nonvolatile storage medium is controlled to execute any one of the control methods of the production devices when the program runs.
Specifically, the storage medium is configured to store program instructions for the following functions, and implement the following functions:
The method comprises the steps of obtaining current operation data and historical operation data of production equipment, wherein the current operation data are used for representing the operation state of the production equipment at the current moment, the historical operation data are used for representing the operation state of the production equipment at the moment which is the last moment of the current moment, predicting target operation data of the production equipment based on the current operation data and the historical operation data, wherein the target operation data are used for representing the operation state of the production equipment at the moment which is the next moment of the current moment, and controlling the production equipment based on the target operation data.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
According to another aspect of the embodiment of the present invention, there is further provided a processor, where the processor is configured to execute a program, and when the program runs, execute the control method of the dc power supply system in the embodiment of the present invention.
Alternatively, the program may implement the following steps when executed at runtime:
The method comprises the steps of obtaining current operation data and historical operation data of production equipment, wherein the current operation data are used for representing the operation state of the production equipment at the current moment, the historical operation data are used for representing the operation state of the production equipment at the moment which is the last moment of the current moment, predicting target operation data of the production equipment based on the current operation data and the historical operation data, wherein the target operation data are used for representing the operation state of the production equipment at the moment which is the next moment of the current moment, and controlling the production equipment based on the target operation data.
In an exemplary embodiment of the application, a computer program product is also provided, comprising a computer program which, when executed by a processor, implements a control method of any of the above-mentioned production devices.
Optionally, the computer program may, when executed by a processor, implement the steps of:
The method comprises the steps of obtaining current operation data and historical operation data of production equipment, wherein the current operation data are used for representing the operation state of the production equipment at the current moment, the historical operation data are used for representing the operation state of the production equipment at the moment which is the last moment of the current moment, predicting target operation data of the production equipment based on the current operation data and the historical operation data, wherein the target operation data are used for representing the operation state of the production equipment at the moment which is the next moment of the current moment, and controlling the production equipment based on the target operation data.
In an exemplary embodiment of the application, a computer program product is also provided, comprising a non-volatile computer readable storage medium, wherein the non-volatile computer readable storage medium stores a computer program arranged to execute the control method of any one of the production apparatuses described above when run.
Optionally, the computer program may, when executed by a processor, implement the steps of:
The method comprises the steps of obtaining current operation data and historical operation data of production equipment, wherein the current operation data are used for representing the operation state of the production equipment at the current moment, the historical operation data are used for representing the operation state of the production equipment at the moment which is the last moment of the current moment, predicting target operation data of the production equipment based on the current operation data and the historical operation data, wherein the target operation data are used for representing the operation state of the production equipment at the moment which is the next moment of the current moment, and controlling the production equipment based on the target operation data.
In an exemplary embodiment of the application, a computer program is also provided, which is arranged to perform the steps of any of the method embodiments described above when run.
Optionally, the computer program may, when executed by a processor, implement the steps of:
The method comprises the steps of obtaining current operation data and historical operation data of production equipment, wherein the current operation data are used for representing the operation state of the production equipment at the current moment, the historical operation data are used for representing the operation state of the production equipment at the moment which is the last moment of the current moment, predicting target operation data of the production equipment based on the current operation data and the historical operation data, wherein the target operation data are used for representing the operation state of the production equipment at the moment which is the next moment of the current moment, and controlling the production equipment based on the target operation data.
According to an embodiment of the present application, there is provided an electronic device including at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of controlling any one of the production devices described above.
Optionally, the electronic device may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input device is connected to the processor.
Fig. 7 is a schematic block diagram of an electronic device 700 in accordance with an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 7, the apparatus 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
Various components in the device 700 are connected to the I/O interface 705, including an input unit 706, e.g., keyboard, mouse, etc., an output unit 707, e.g., various types of displays, speakers, etc., a storage unit 708, e.g., magnetic disk, optical disk, etc., and a communication unit 709, e.g., network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 701 performs the respective methods and processes described above, for example, a control method of the production apparatus. For example, in some embodiments, the control method of the production apparatus may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 700 via ROM 702 and/or communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the control method of the production apparatus described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the control method of the production device by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be a special or general purpose programmable processor, operable to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user, for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a Local Area Network (LAN), a Wide Area Network (WAN), and the Internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, etc. which can store the program code.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.