CN119620740A - A fault prediction method and device for intelligent operation and maintenance system - Google Patents
A fault prediction method and device for intelligent operation and maintenance system Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
一种用于智能运维系统的故障预测方法及装置，涉及故障预测技术领域。在该方法中，接收用户端发送的预测请求，根据预测请求确定第一设备；根据第一设备对应的第一型号确定预设第一监测数据；判断第一监测数据与预设第一监测数据是否一致；当第一监测数据与预设第一监测数据不一致时，确定第一设备存在第一故障；获取第一设备与第二设备之间的目标关联度；判断目标关联度是否大于或等于预设关联度；若目标关联度大于或等于预设关联度，则确定第一设备与第二设备均存在故障状态，将故障状态向用户端进行发送，以便用户端对应的用户查看故障状态。实施本申请提供的技术方案，使得运维人员有足够的时间进行修复，避免了因故障扩大而导致的不必要损失。
A fault prediction method and device for an intelligent operation and maintenance system, relating to the technical field of fault prediction. In the method, a prediction request sent by a user end is received, and a first device is determined according to the prediction request; preset first monitoring data is determined according to a first model corresponding to the first device; it is determined whether the first monitoring data is consistent with the preset first monitoring data; when the first monitoring data is inconsistent with the preset first monitoring data, it is determined that a first fault exists in the first device; the target correlation between the first device and the second device is obtained; it is determined whether the target correlation is greater than or equal to the preset correlation; if the target correlation is greater than or equal to the preset correlation, it is determined that both the first device and the second device are in a fault state, and the fault state is sent to the user end so that the user corresponding to the user end can view the fault state. The implementation of the technical solution provided by the present application allows operation and maintenance personnel to have enough time to repair and avoid unnecessary losses caused by the expansion of the fault.
Description
Technical Field
The application relates to the technical field of fault prediction, in particular to a fault prediction method and device for an intelligent operation and maintenance system.
Background
Along with the full deepening of the industrial age, industrial enterprises increasingly depend on various devices to improve the production efficiency. However, the equipment inevitably fails during operation, and these failures not only reduce the production efficiency, but also may cause serious safety accidents. Therefore, the fault monitoring of the equipment is particularly critical, and the abnormal condition of the equipment can be found and processed in an early stage, so that the occurrence of major accidents can be effectively prevented.
Currently, to more effectively implement equipment fault monitoring, industrial enterprises tend to employ intelligent operation and maintenance systems. The intelligent operation and maintenance system is characterized in that the front edge technologies such as big data, machine learning, artificial intelligence and the like are comprehensively utilized, and deep mining and fine analysis are carried out on a large amount of data generated in the daily operation and maintenance process so as to accurately identify hidden problems and faults in the equipment operation process. However, the current intelligent operation and maintenance system can only analyze the faults of a single device, and the analysis is mostly limited to passive response after the faults occur, rather than early warning potential faults, so that the maintenance is not timely and unnecessary loss is generated.
Therefore, there is a need for a fault prediction method and device for an intelligent operation and maintenance system, which can solve the above-mentioned technical problems.
Disclosure of Invention
The application provides a fault prediction method and device for an intelligent operation and maintenance system, which solve the problems that the current intelligent operation and maintenance system can only analyze faults of single equipment and the analysis is mostly limited to passive response after the faults occur, so that operation and maintenance personnel have enough time to intervene and repair, and unnecessary loss caused by fault expansion is avoided.
The method comprises the steps of receiving a prediction request sent by a user side, determining a first device according to the prediction request, determining whether the first device is any one of a plurality of devices to be monitored, acquiring first monitoring data corresponding to the first device, wherein the first monitoring data comprises operation data, electric data, vibration data and environment data, determining preset first monitoring data according to a first model corresponding to the first device, judging whether the first monitoring data is consistent with the preset first monitoring data, determining that the first device has a first fault when the first monitoring data is inconsistent with the preset first monitoring data, acquiring a target association degree between the first device and the second device, judging whether the target association degree is greater than or equal to the preset association degree, if the target association degree is greater than or equal to the preset association degree, determining that a fault state exists between the first device and the second device, and sending the fault state to the user side so that a user corresponding to the user side checks the fault state.
By adopting the technical scheme, the prediction request sent by the user terminal is received, any one (first equipment) of the plurality of equipment to be monitored is actively monitored, the first monitoring data of the equipment to be monitored is obtained in real time, the preset first monitoring data is determined according to the model of the first equipment, the preset first monitoring data is based on the known parameters of the equipment in normal operation, the first monitoring data obtained in real time is compared with the preset first monitoring data, and the abnormality of the data can be timely found, so that whether the first equipment has faults or not is judged. When the first equipment has a first fault, the association degree between the first equipment and the second equipment is also obtained, if the target association degree between the first equipment and the second equipment is larger than or equal to the preset association degree, the second equipment can be deduced that the second equipment also has potential risks, the association analysis is helpful for identifying potential cascading faults, the accuracy and the comprehensiveness of fault early warning are further improved, the problem that the current intelligent operation and maintenance system can only analyze faults of single equipment and the analysis is mostly limited to passive response after the faults occur is successfully solved, and once the faults or the potential faults are detected, early warning can be sent out before or at an initial stage of the faults, so that operation and maintenance personnel have enough time to intervene and repair, and unnecessary loss caused by fault expansion is avoided.
The method comprises the steps of obtaining a first monitoring value corresponding to first equipment, wherein the first monitoring value comprises a first temperature, a first pressure value, a first vibration value, a first current value, a first voltage value and a first rotating speed value, obtaining a second monitoring value corresponding to second equipment, wherein the second monitoring value comprises a second temperature, a second pressure value, a second vibration value, a second current value, a second voltage value and a second rotating speed value, calculating the first monitoring value and the second monitoring value by using a preset formula to obtain a plurality of correlation values, determining a plurality of weights according to the plurality of correlation values, multiplying each correlation value by the weight corresponding to each correlation value to obtain a plurality of target values, and adding the plurality of target values to obtain the target correlation.
By adopting the technical scheme, a plurality of monitoring values (including temperature, pressure, vibration, current, voltage, rotating speed and the like) of the first equipment and the second equipment are obtained, so that the running state of the equipment can be comprehensively estimated. And calculating the first monitoring value and the second monitoring value by using a preset formula to obtain a plurality of correlation values, wherein the correlation values reflect the correlations of the two devices on different parameters. A plurality of weights are determined based on the plurality of correlation values, one for each weight. Multiplying each correlation value by a corresponding weight to obtain a plurality of target values, and then adding the target values to obtain a target correlation degree, the comprehensive evaluation method can comprehensively consider a plurality of parameters and the relevance among the parameters, so as to obtain a comprehensive and objective relevance evaluation result.
Optionally, calculating the first monitoring value and the second monitoring value by using a preset formula to obtain a plurality of correlation values, wherein the method specifically comprises the steps of obtaining a first parameter from the first monitoring value, wherein the first parameter comprises a first temperature, a first pressure value, a first vibration value, a first current value, a first voltage value and a first rotation speed value, obtaining a second parameter from the second monitoring value, wherein the second parameter comprises a second temperature, a second pressure value, a second vibration value, a second current value, a second voltage value and a second rotation speed value, and calculating the first parameter and the second parameter by using the preset formula to obtain a plurality of correlation values, wherein the preset formula is as follows: Wherein r represents a correlation value, n represents a maximum value corresponding to the observation times, T1 ij represents a j-th first parameter in a first monitoring value of a first device at an i-th observation time, T2 ij represents a j-th second parameter in a second monitoring value of a second device at the i-th observation time, T1 j represents an average value of the j-th first parameter of the n-th observation times in the first device, and T2 j represents an average value of the j-th second parameter of the n-th observation times in the second device.
By adopting the technical scheme, the calculated correlation value can find out potential association faults among the devices in time, and comprehensive and dynamic monitoring and analysis of the states of the devices are realized.
Optionally, after judging whether the target association degree is greater than or equal to the preset association degree, the method further comprises the steps of acquiring a target device set with a connection relation with the first device when the target association degree is smaller than the preset association degree, judging whether second devices exist in the target device set, and confirming that the second devices do not have fault states when the target devices and the second devices do not exist.
By adopting the technical scheme, when the target association degree is smaller than the preset association degree, the target device set which has a connection relation with the first device is firstly obtained. It is determined whether a second device is present in the set of target devices. If not, it is indicated that the association between the second device and the first device is not strong, or that the connection relationship between them may not be tight. In this case, it can be confirmed that the second device does not have the same fault state as the first device, thereby avoiding unnecessary inspection and maintenance of the second device. According to the accurate judgment whether the second equipment exists in the target equipment set or not, the false alarm rate caused by inaccurate association degree calculation can be reduced.
Optionally, after judging whether the second device exists in the target device set, the method further comprises determining that a fault state exists in both the first device and the second device if the second device exists in the target device set.
By adopting the technical scheme, when the target equipment contains the second equipment in a concentrated manner, the first equipment and the second equipment are indicated to have stronger relevance. If the first equipment has faults, the second equipment is likely to be affected, and the same fault state exists, so that the second equipment is also judged to be in the fault state, the accuracy of fault judgment can be improved, and the possibility of missed judgment is reduced.
Optionally, after judging whether the first monitoring data is consistent with the preset first monitoring data, the method further comprises the steps of acquiring a target image corresponding to the first device when the first monitoring data is consistent with the preset first monitoring data, processing the target image to obtain target feature data, comparing the target feature data with the preset feature data, determining that the first device has a damage fault state when the target feature data is inconsistent with the preset feature data, acquiring a target temperature corresponding to the first device according to the damage fault state, and determining that the first device has an overheat fault if the target temperature is greater than the preset temperature.
By adopting the technical scheme, the first monitoring data is compared with the preset first monitoring data, so that the running state of the first equipment can be monitored in real time. When the first monitoring data are consistent with the preset first monitoring data, acquiring the target image for further analysis, so that continuous monitoring of the equipment state is realized. Once the target characteristic data is inconsistent with the preset characteristic data, the damage fault state of the equipment can be immediately determined, so that early warning is timely sent out, and further development of the fault is avoided. After determining that the equipment has a damage fault state, further acquiring the target temperature of the equipment, and comparing the target temperature with a preset temperature. If the target temperature is higher than the preset temperature, the overheat fault of the equipment can be determined, so that equipment damage or safety accidents possibly caused by overheat can be found and treated in time.
Optionally, after determining that the first device and the second device have fault states if the target association degree is greater than or equal to a preset association degree, the method further comprises the steps of obtaining second monitoring data corresponding to the second device, determining preset second monitoring data according to the second model, wherein the second model is a device model corresponding to the second device, confirming that the second device has a second fault when the second monitoring data is inconsistent with the preset second monitoring data, inputting the first fault and the second fault into a preset operation and maintenance database for inquiring to obtain operation and maintenance measures, generating an operation and maintenance assessment report according to the operation and maintenance measures, and sending the operation and maintenance assessment report to a user side so that the user can maintain according to the operation and maintenance assessment report.
By adopting the technical scheme, the second monitoring data of the second equipment are acquired and compared with the preset second monitoring data determined according to the equipment model (second model), and whether the second equipment has a fault (second fault) can be accurately detected. The customized monitoring data comparison based on the equipment model improves the accuracy and pertinence of fault detection. Once the second equipment is confirmed to have faults, the first faults and the second faults are immediately input into a preset operation and maintenance database for inquiring, and the rapid fault information integration and inquiry are helpful for operation and maintenance teams to rapidly acquire corresponding operation and maintenance measures, so that timely response and treatment of the faults are realized, time for manual intervention and decision is reduced, and operation and maintenance work efficiency is improved.
The second aspect of the application provides a fault prediction device for an intelligent operation and maintenance system, which comprises a receiving unit, a processing unit and a determining unit, wherein the receiving unit is used for receiving a prediction request sent by a user side, determining that first equipment is any one of a plurality of equipment to be monitored according to the prediction request, acquiring first monitoring data corresponding to the first equipment, wherein the first monitoring data comprises operation data, electric data, vibration data and environment data, the processing unit is used for determining preset first monitoring data according to a first model corresponding to the first equipment, judging whether the first monitoring data is consistent with the preset first monitoring data, determining that the first equipment has a first fault when the first monitoring data is inconsistent with the preset first monitoring data, acquiring target association degree between the first equipment and the second equipment, judging whether the target association degree is larger than or equal to the preset association degree, and determining that fault states exist in the first equipment and the second equipment if the target association degree is larger than or equal to the preset association degree, and sending the fault states to the user side so as to check the fault states corresponding to the user side.
Optionally, the receiving unit is configured to obtain a first monitoring value corresponding to the first device, where the first monitoring value includes a first temperature, a first pressure value, a first vibration value, a first current value, a first voltage value, and a first rotation speed value, obtain a second monitoring value corresponding to the second device, where the second monitoring value includes a second temperature, a second pressure value, a second vibration value, a second current value, a second voltage value, and a second rotation speed value, calculate the first monitoring value and the second monitoring value by using a preset formula to obtain a plurality of correlation values, determine a plurality of weights according to the plurality of correlation values, where one correlation value corresponds to one weight, multiply each correlation value with the weight corresponding to each correlation value to obtain a plurality of target values, and add the plurality of target values to obtain the target correlation.
Optionally, the processing unit is configured to obtain a first parameter from the first monitored value, where the first parameter includes a first temperature, a first pressure value, a first vibration value, a first current value, a first voltage value, and a first rotation speed value, obtain a second parameter from the second monitored value, where the second parameter includes a second temperature, a second pressure value, a second vibration value, a second current value, a second voltage value, and a second rotation speed value, and calculate the first parameter and the second parameter using a preset formula to obtain a plurality of correlation values, where the preset formula is as follows: Wherein r represents a correlation value, n represents a maximum value corresponding to the observation times, T1 ij represents a j-th first parameter in a first monitoring value of a first device at an i-th observation time, T2 ij represents a j-th second parameter in a second monitoring value of a second device at the i-th observation time, T1 j represents an average value of the j-th first parameter of the n-th observation times in the first device, and T2 j represents an average value of the j-th second parameter of the n-th observation times in the second device.
Optionally, the receiving unit is used for acquiring a target device set with a connection relation with the first device when the target association degree is smaller than a preset association degree, the processing unit is used for judging whether a second device exists in the target device set, and the determining unit is used for confirming that the second device does not have a fault state when the target device and the second device do not exist in the target device set.
Optionally, the determining unit is configured to determine that the first device and the second device both have a fault state if the second device exists in the target device set.
Optionally, the receiving unit is used for acquiring a target image corresponding to the first device when the first monitoring data are consistent with preset first monitoring data, the processing unit is used for processing the target image to obtain target feature data, comparing the target feature data with the preset feature data, the determining unit is used for determining that the first device has a damage fault state when the target feature data are inconsistent with the preset feature data, acquiring a target temperature corresponding to the first device according to the damage fault state, and determining that the first device has an overheat fault if the target temperature is greater than the preset temperature.
Optionally, the receiving unit is used for obtaining second monitoring data corresponding to the second equipment, the processing unit is used for determining preset second monitoring data according to a second model, the second model is the equipment model corresponding to the second equipment, the determining unit is used for confirming that the second equipment has a second fault when the second monitoring data is inconsistent with the preset second monitoring data, the first fault and the second fault are input into a preset operation and maintenance database to be inquired to obtain operation and maintenance measures, an operation and maintenance assessment report is generated according to the operation and maintenance measures, and the operation and maintenance assessment report is sent to the user side so that the user can maintain according to the operation and maintenance assessment report.
In a third aspect the application provides an electronic device comprising a processor, a memory for storing instructions, a user interface and a network interface for communicating with other devices, the processor being arranged to execute the instructions stored in the memory, such that an electronic device performs a method according to any of the above-mentioned applications.
In a fourth aspect the application provides a computer readable storage medium storing instructions which, when executed, perform a method according to any one of the above-mentioned aspects of the application.
In summary, one or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. The method comprises the steps of receiving a prediction request sent by a user side, actively monitoring any one (first equipment) of a plurality of equipment to be monitored, acquiring first monitoring data of the equipment to be monitored in real time, determining preset first monitoring data according to the model of the first equipment, comparing the first monitoring data acquired in real time with the preset first monitoring data based on known parameters when the equipment is in normal operation, and timely finding out data abnormality to judge whether the first equipment has faults. When the first equipment has a first fault, the association degree between the first equipment and the second equipment is also obtained, if the target association degree between the first equipment and the second equipment is larger than or equal to the preset association degree, the second equipment can be deduced that the second equipment also has potential risks, the association analysis is helpful for identifying potential cascading faults, the accuracy and the comprehensiveness of fault early warning are further improved, the problem that the current intelligent operation and maintenance system can only analyze faults of single equipment and the analysis is mostly limited to passive response after the faults occur is successfully solved, and once the faults or the potential faults are detected, early warning can be sent out before or at an initial stage of the faults, so that operation and maintenance personnel have enough time to intervene and repair, and unnecessary loss caused by fault expansion is avoided.
2. And acquiring second monitoring data of the second equipment, and comparing the second monitoring data with preset second monitoring data determined according to the equipment model (second model), so that whether the second equipment has a fault (second fault) can be accurately detected. The customized monitoring data comparison based on the equipment model improves the accuracy and pertinence of fault detection. Once the second equipment is confirmed to have faults, the first faults and the second faults are immediately input into a preset operation and maintenance database for inquiring, and the rapid fault information integration and inquiry are helpful for operation and maintenance teams to rapidly acquire corresponding operation and maintenance measures, so that timely response and treatment of the faults are realized, time for manual intervention and decision is reduced, and operation and maintenance work efficiency is improved.
Drawings
FIG. 1 is a schematic flow chart of a fault prediction method for an intelligent operation and maintenance system according to an embodiment of the present application;
Fig. 2 is a schematic structural diagram of a fault prediction device for an intelligent operation and maintenance system according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Reference numerals describe 201, receiving unit, 202, processing unit, 203, determining unit, 300, electronic device, 301, processor, 302, memory, 303, user interface, 304, network interface, 305, communication bus.
Detailed Description
In order that those skilled in the art will better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments.
In describing embodiments of the present application, words such as "for example" or "for example" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "such as" or "for example" in embodiments of the application should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "or" for example "is intended to present related concepts in a concrete fashion.
In the description of embodiments of the application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Along with the full deepening of the industrial age, industrial enterprises increasingly depend on various devices to improve the production efficiency. However, the equipment inevitably fails during operation, and these failures not only reduce the production efficiency, but also may cause serious safety accidents. Therefore, the fault monitoring of the equipment is particularly critical, and the abnormal condition of the equipment can be found and processed in an early stage, so that the occurrence of major accidents can be effectively prevented.
Currently, to more effectively implement equipment fault monitoring, industrial enterprises tend to employ intelligent operation and maintenance systems. The intelligent operation and maintenance system is characterized in that the front edge technologies such as big data, machine learning, artificial intelligence and the like are comprehensively utilized, and deep mining and fine analysis are carried out on a large amount of data generated in the daily operation and maintenance process so as to accurately identify hidden problems and faults in the equipment operation process. However, current intelligent operations and maintenance systems still rely on manual periodic inspection to collect monitoring data. The collected monitoring data is analyzed, and the analysis is mostly limited to passive response after the fault occurs, but not early warning of potential faults, so that unnecessary loss is caused due to untimely maintenance.
Therefore, how to solve the problem that the current intelligent operation and maintenance system depends on manual periodic inspection and passive response after failure. The fault prediction method for the intelligent operation and maintenance system is applied to a server. The server of the present application may be a platform for providing a fault prediction service for an intelligent operation and maintenance system, and fig. 1 is a schematic flow chart of a fault prediction method for an intelligent operation and maintenance system according to an embodiment of the present application, and referring to fig. 1, the method includes the following steps S101 to S107.
S101, receiving a prediction request sent by a user terminal, and determining first equipment according to the prediction request.
In S101, a user sends a prediction request to a server through a user terminal, the user refers to a staff responsible for daily operation and maintenance of the device, and the server receives the prediction request sent by the user terminal. When a prediction request is received, the content of the prediction request needs to be analyzed, and the prediction request is identified. And extracting device information from the parsed prediction request, wherein the device information comprises a device ID, a device name or a device type. And searching and determining the first equipment from the plurality of equipment to be monitored according to the extracted equipment information.
S102, determining preset first monitoring data according to a first model corresponding to the first equipment.
In S102, after determining the first device, a connection is established with the first device through a communication protocol (e.g., modbus, OPC UA, etc.). And installing a sensor at the first equipment in advance, and acquiring data of the first equipment through the sensor to obtain first monitoring data, wherein the first monitoring data comprises operation data, electric data, vibration data and environment data, the operation data refers to operation time, operation speed and load condition, and the electric data refers to current, voltage, power and resistance. The vibration data refers to the vibration amplitude, vibration frequency, and vibration phase. The environmental data is a temperature condition, a humidity condition, an air pressure, and an illumination representing the environment surrounding the first device. And after the first monitoring data are acquired, acquiring model information of the first equipment from a system database, and inquiring corresponding preset first monitoring data in a preset data standard database according to the equipment model. The preset first monitoring data at this time refer to a threshold value corresponding to each parameter when the first device is in normal operation, and the preset first monitoring data at this time includes preset operation data, preset electrical data, preset vibration data and preset environmental data.
S103, judging whether the first monitoring data are consistent with preset first monitoring data or not.
In S103, the collected first monitoring data is compared with the preset first monitoring data, and whether the first device is in a fault state is determined according to the comparison result.
And S104, when the first monitoring data are inconsistent with the preset first monitoring data, determining that the first equipment has a first fault.
In S104, each data in the first monitoring data is compared with each data in the preset first monitoring data in sequence, if a certain data in the first monitoring data is not within the range of the preset monitoring data, the first monitoring data is inconsistent with the preset first monitoring data, and it can be determined that the first device has a fault. In particular what faults are present, a determination may be made based on inconsistent monitoring data, which is not explained here too much, at which point the first device may be marked as a fault state.
S105, acquiring the target association degree between the first equipment and the second equipment.
In S105, after determining that the first device has a fault, other devices in the multiple devices to be monitored need to be analyzed, whether the first device has a fault and affects the other devices is analyzed, so that a potential fault is identified, and early warning is performed in time. Because the plurality of devices to be monitored are connected through networking, the association degree between the first device and other devices is required to be obtained, and potential faults of the other devices are identified according to the association degree.
In addition, the target association degree between the first device and the second device is obtained, specifically comprising obtaining a first monitoring value corresponding to the first device, wherein the first monitoring value comprises a first temperature, a first pressure value, a first vibration value, a first current value, a first voltage value and a first rotation speed value, obtaining a second monitoring value corresponding to the second device, wherein the second monitoring value comprises a second temperature, a second pressure value, a second vibration value, a second current value, a second voltage value and a second rotation speed value, calculating the first monitoring value and the second monitoring value by using a preset formula to obtain a plurality of correlation values, determining a plurality of weights according to the plurality of correlation values, multiplying each correlation value by the weight corresponding to each correlation value to obtain a plurality of target values, and adding the plurality of target values to obtain the target association degree.
Specifically, a second device is selected from the devices to be monitored, wherein the second device is any one device except the first device in the devices to be monitored, and first operation parameters corresponding to the first device can be obtained in real time through a sensor connected with the first device, and the first operation parameters comprise a first temperature, a first pressure value, a first vibration value, a first current value, a first voltage value and a first rotation speed value. And when the first equipment is acquired, the operation parameters corresponding to the different time points of the first equipment can be acquired, and the first operation parameters are recorded to obtain first monitoring values, wherein each first monitoring value comprises the operation parameters corresponding to the different time points of the first equipment. And acquiring a second operating parameter of the second device through a sensor connected with the second device. The second operating parameters include a second temperature, a second pressure value, a second vibration value, a second current value, a second voltage value, and a second rotational speed value. After the acquisition process, similar to the first device, the operation parameters of the second device corresponding to different time points are acquired, and it is ensured that the time point of the second device for acquiring the operation parameters is consistent with the time point of the first device, namely the first device and the second device acquire the operation parameters corresponding to the first device at the same time point and record the operation parameters. For example, if temperatures of the first device and the second device are monitored, 5 time points are selected for recording, the 5 time points are respectively time point 1, time point 2, time point 3, time point 4, and time point 5, the first temperature corresponding to the first device is 22 ℃ and the second temperature corresponding to the second device is 24 ℃ at the time point 1, the first temperature corresponding to the first device is 23 ℃ and the second temperature corresponding to the second device is 25 ℃ at the time point 2, the first temperature corresponding to the first device is 25 ℃ and the second temperature corresponding to the second device is 26 ℃ at the time point 3, the first temperature corresponding to the first device is 27 ℃ and the second temperature corresponding to the second device is 28 ℃ at the time point 4, the first temperature corresponding to the first device is 29 ℃ and the second temperature corresponding to the second device is 30 ℃ at the time point 5, and the first temperatures of the different time points are summed to obtain the first temperature. And summarizing the second temperatures of the second equipment at different time points to obtain the second temperature. And monitoring the pressure, vibration, current and voltage continuously according to the mode of monitoring the temperature so as to obtain respective first pressure value, second pressure value and the like. After a first monitoring value corresponding to the first equipment and a second monitoring value corresponding to the second equipment are obtained.
Further, a first monitoring value and a second monitoring value are calculated by using a preset formula to obtain a plurality of correlation values, and the method specifically comprises the steps of obtaining a first parameter from the first monitoring value, wherein the first parameter comprises a first temperature, a first pressure value, a first vibration value, a first current value, a first voltage value and a first rotation speed value, obtaining a second parameter from the second monitoring value, wherein the second parameter comprises a second temperature, a second pressure value, a second vibration value, a second current value, a second voltage value and a second rotation speed value, and calculating the first parameter and the second parameter by using the preset formula to obtain a plurality of correlation values, wherein the preset formula is as follows: Wherein r represents a correlation value, n represents a maximum value corresponding to the observation times, T1 ij represents a j-th first parameter in a first monitoring value of a first device at an i-th observation time, T2 ij represents a j-th second parameter in a second monitoring value of a second device at the i-th observation time, T1 j represents an average value of the j-th first parameter of the n-th observation times in the first device, and T2 j represents an average value of the j-th second parameter of the n-th observation times in the second device.
In the preset formula, n represents the total times of acquiring a certain parameter (a first parameter or a second parameter) at different time points, one time point corresponds to one observation time, one observation time corresponds to one observation time, and if the temperature at 5 time points is selected to be acquired, n is 5. t1 ij represents a j-th first parameter in the first monitoring values of the first device at the i-th observation time, and since each first parameter includes a plurality of monitoring values, if j is 1, according to the sorting result of each parameter in the current first parameter, the first parameter is confirmed to be the first temperature, and when the first parameter is the first temperature, 5 monitoring values of the first device at 5 time points are obtained, and one monitoring value corresponds to one time point. t2 ij represents a j second parameter in a second monitoring value of the second device at the i observation time, if j is 1, according to the sorting result of each parameter in the current second parameter, the second parameter is a second temperature, and the first parameter and the second parameter are calculated, namely, the first temperature in the first device and the second temperature in the second device are subjected to correlation calculation, and when the correlation calculation is performed, the temperature and the temperature are required to be ensured to be subjected to correlation calculation, and the pressure are required to be subjected to correlation calculation. When j is 2, the first parameter is a first pressure value according to the sequencing result of each parameter in the first parameter, and the second parameter is a second pressure value according to the sequencing result of each parameter in the second parameter. T1 j represents the average value of the j-th first parameter of n observation times in the first device, T1 j can be understood as obtaining the average value of the j-th first parameter of the first device at a plurality of time points, and as each first parameter comprises a plurality of monitoring values, the monitoring values are overlapped to obtain a total monitoring value, and then divided by the monitoring number, wherein the monitoring number refers to the total number of the monitoring values to obtain the average value of the first device. T2 j represents the average value of the j-th second parameter of the n observation times in the second device, T2 j is understood as obtaining the average value of the j-th second parameter of the second device at a plurality of time points, and since each second parameter comprises a plurality of second monitoring values, the second monitoring values are overlapped to obtain a second total monitoring value, and then divided by the second monitoring number, wherein the second monitoring number is the total number of the second monitoring values to obtain the average value of the second device. Calculating a plurality of first temperature values in the first temperature, wherein t1 j = (22+23+25+27+29)/5=25.2 of the first device, calculating a second temperature value in the second temperature, and calculating t2 j = (24+25+26+28+30)/5=26.6 of the second device.
And then starting to calculate a difference value, calculating t1 ij-T1j and t2 ij-T2j for each observation time i and each parameter j, calculating a numerator part, calculating the sum of (t 1 ij-T1j）（t2ij-T2j) for all observation time i and parameters j, respectively calculating a denominator part, if the numerator part is 27 and the denominator part is 26, dividing the numerator part by the denominator part to obtain a coherence value r, wherein the coherence value represents the degree of correlation of the first device and the second device for the 1 st first parameter and the second parameter, and the 1 st first parameter and the second parameter represent the temperatures of the two devices. And repeating the steps, and calculating other parameters in the first equipment and the second equipment to obtain the correlation value of each parameter. And determining the weight according to the condition of each parameter, and distributing the weight according to the importance of the monitoring value to the equipment state evaluation. For example, if temperature is a key indicator to determine if a device is overheated, a higher weight is given. The correlation value itself may be used as a basis for the weights, but typically normalization or normalization is required to ensure that the sum of all weights is 1. Multiplying each correlation value by the corresponding weight to obtain a target value. And adding all the target values to obtain the target association degree between the two devices.
For example, according to the above calculation method, the correlation calculation is sequentially performed on each parameter of the first device and the second device to obtain a plurality of correlation values, and weights of temperature, pressure, vibration, current, voltage, and rotation speed may be given to be 0.3, 0.2, 0.1, 0.15, and 0.1, respectively. And multiplying each correlation value by the weight of the correlation value, and then summing to obtain the target correlation degree.
S106, judging whether the target association degree is larger than or equal to a preset association degree.
In S106, after the target association degree between the first device and the second device is obtained by calculation, the calculated target association degree is compared with a preset association degree, where the preset association degree is set by determining whether the two devices will affect each other.
And S107, if the target association degree is greater than or equal to the preset association degree, determining that the first equipment and the second equipment have fault states, and sending the fault states to the user side so that a user corresponding to the user side can check the fault states.
In S107, if the target association degree is greater than or equal to the preset association degree, it is indicated that there is a strong association between the first device and the second device, and when the first device has a fault, the second device is affected by the fault of the first device, so that it is determined that the second device also has a fault state.
In addition, after determining that the second equipment is affected by the fault of the first equipment, the operation data of the second equipment needs to be monitored in real time to realize the monitoring of the potential fault of the second equipment, and the method specifically comprises the steps of obtaining second monitoring data corresponding to the second equipment; the method comprises the steps of determining preset second monitoring data according to a second model, wherein the second model is a device model corresponding to second equipment, confirming that the second equipment has a second fault when the second monitoring data is inconsistent with the preset second monitoring data, inputting the first fault and the second fault into a preset operation and maintenance database to inquire to obtain operation and maintenance measures, and generating an operation and maintenance assessment report according to the operation and maintenance measures so as to send the operation and maintenance assessment report to a user side. Specifically, a sensor or other monitoring device is deployed on the second device for monitoring the operating state of the second device in real time. The sensor may comprise a temperature sensor, a pressure sensor, a vibration sensor, etc., depending on the type of second device and the parameters to be monitored. Second monitoring data of the second device is acquired in real time by the sensor, which data may include temperature, pressure, vibration amplitude, current, voltage, etc. The frequency and accuracy of the data acquisition should be set according to the characteristics of the second device and the monitoring requirements. The collected second monitoring data is sent to the server to obtain the model information of the second device, which can be obtained through a device tag, a device specification or a record in a device management system. And searching corresponding preset second monitoring data from a preset monitoring data table according to the model of the second equipment. The preset data is typically set according to the normal operating state of the device, manufacturer's recommendations or industry standards. If the operating state or operating environment of the second device changes, or the device is repaired or parts replaced, the preset monitoring data may need to be updated. And comparing the second monitoring data acquired in real time with preset second monitoring data. And if the second monitoring data exceeds a preset range or threshold value or is obviously inconsistent with the preset data, judging that the second equipment has faults. The type of fault may be determined based on the parameters and extent of the excess, such as overheating, overpressure, excessive vibration, etc. A preset operation and maintenance database is established in advance, wherein the operation and maintenance database contains information such as various equipment fault types, fault reasons, operation and maintenance measures, solutions and the like. The database may be built based on historical operational data, manufacturer recommendations, industry standards, or expert experience. The types, descriptions, and related information of the first fault and the second fault are entered into the operation data base. And searching corresponding operation and maintenance measures and solutions according to the input fault information by using a database query function. And generating an operation and maintenance assessment report according to the queried operation and maintenance measures and solutions. The report should contain key information on the type of fault, the cause, recommended action and maintenance, expected effect, time of implementation, etc. And selecting a proper sending mode to send the operation and maintenance assessment report to a user side, such as an email, a short message notification, APP pushing and the like. And receiving feedback of the user side to the operation and maintenance evaluation report, and knowing the acceptance degree and implementation effect of the user to the operation and maintenance measures and the solutions. Follow-up tracking and optimization can be performed according to user feedback to improve operation and maintenance efficiency and equipment reliability. If the second monitoring data is consistent with the preset second monitoring data, the second equipment is required to be marked so as to monitor the marked equipment in real time.
Further, when the target association degree is smaller than the preset association degree, a target device set with a connection relation with the first device is obtained, whether second devices exist in the target device set is judged, and when the second devices do not exist in the target device set, the second devices are confirmed to have no fault state. Specifically, the correlation between the first device and the current suspected faulty device (second device) is evaluated using the correlation value calculated previously. The preset association degree is a preset threshold value, and is used for judging whether the target association degree is high enough or not so as to confirm that significant association exists between the first equipment and the suspected fault equipment. And comparing the calculated target association degree with a preset association degree, and when the target association degree is smaller than the preset association degree, acquiring all devices which have connection relation with the first device to form a target device set. The connection relationship may be based on physical connection (e.g., wires, pipes, etc.), communication connection (e.g., network, signals, etc.), or logical connection (e.g., procedure call, data sharing, etc.), depending on the scene requirements of the actual device. For each device in the target device set, it is necessary to collect its basic information (e.g., device type, model number, location, etc.) and operation state information (e.g., temperature, pressure, vibration, current, voltage, rotational speed, etc.). Such information may be obtained by sensors, data acquisition systems, or remote services, depending on the geographic location of the device and the communication conditions. After the target device set is acquired, it is necessary to check whether the second device is included therein. By comparing the unique identifier of the second device (e.g., device ID, MAC address, etc.) or fuzzy matching based on information of device name, model, etc. If the second device does not exist in the target device set, the second device can be initially confirmed not to be in the current suspected fault state. Meaning that the degree of association between the second device and the first device is not sufficient to treat it as part of a malfunctioning device, or that the second device itself is not affected by the malfunction. Whether the second equipment is in a fault state or not can be effectively judged, and powerful support is provided for subsequent fault checking and repairing work. In addition, if the second device exists in the target device set, determining that the first device and the second device have fault states. In the set of constructed target devices, the inclusion of the second device has been confirmed by comparing the device identifier, name or other relevant information. Since the second device is contained in the target set of devices, this indicates that there is some degree of association between it and the first device. Such association may be based on physical connections, communication connections, data sharing or logical dependencies, etc. From the fact that the second device is included in the set of target devices, and the association between the first device and the second device, it can be deduced that there may be some common failure mode or cause of failure between the first device and the second device. It may be confirmed that both the first device and the second device are in a fault state, at which point a potential fault is predicted for the second device.
Still further, the image is analyzed through real-time monitoring of the image corresponding to the first equipment to determine the fault type of the equipment, and the method specifically comprises the steps of obtaining a target image corresponding to the first equipment when the first monitoring data are consistent with preset first monitoring data, processing the target image to obtain target feature data, comparing the target feature data with the preset feature data, determining that the first equipment has a damage fault state when the target feature data are inconsistent with the preset feature data, obtaining a target temperature corresponding to the first equipment according to the damage fault state, and determining that the first equipment has an overheat fault if the target temperature is greater than the preset temperature. Specifically, the acquired first monitoring data is compared with preset first monitoring data. The preset first monitoring data is a threshold value or range set according to the normal operation state of the equipment. When the elevator monitoring data are consistent with the preset first monitoring data, the corresponding equipment is preliminarily judged to be free from faults, and at the moment, the image of the equipment is required to be acquired for further analysis. Image acquisition equipment such as a camera and a thermal infrared imager can be used, and a proper image acquisition mode and parameters can be selected according to the actual condition and monitoring requirement of the equipment. And preprocessing the obtained target image, including denoising, enhancing, filtering and the like, so as to improve the quality and definition of the image. Suitable image processing algorithms and tools, such as image filtering, edge detection, binarization, etc., are selected according to the requirements and objectives of the image processing. In the preprocessed image, feature data related to the target analysis is extracted. Such feature data may include texture, shape, color, brightness, etc. of the image. The feature data may be extracted using conventional image processing methods such as image filtering, edge detection, etc., or using machine learning methods such as support vector machines, decision trees, etc., or deep learning methods such as Convolutional Neural Networks (CNNs), etc. And comparing the extracted target characteristic data with preset characteristic data. The preset characteristic data is set according to the normal operation state or the known fault state of the equipment. And judging whether the target characteristic data is consistent with the preset characteristic data or not according to the comparison result. If they are consistent, it is indicated that the equipment may be in a normal operation state, and if they are inconsistent, it is indicated that the equipment may have some kind of fault or abnormality. And when the target characteristic data are inconsistent with the preset characteristic data, determining the damage fault state of the equipment. The fault status may include information of the type of fault, the degree of fault, etc. And selecting a proper temperature sensor or temperature measuring equipment to obtain the target temperature corresponding to the equipment according to the determined damage fault state. The target temperature may include a surface temperature, an internal temperature, etc. of the device. Temperature measuring equipment such as an infrared thermometer, a thermocouple, a resistance thermometer and the like can be used, and a proper temperature measuring mode and parameters can be selected according to the actual conditions and temperature measuring requirements of the equipment. And comparing the obtained target temperature with a preset temperature. The preset temperature is set according to the normal operating state of the apparatus and the maximum allowable temperature. And if the target temperature is greater than the preset temperature, indicating that the equipment has overheat fault. Overheating faults may lead to reduced equipment performance, reduced life and even damage. And (3) according to the actual condition of the equipment and the reason of the overheat fault, adopting corresponding measures to repair and maintain so as to ensure the normal operation and the safety of the equipment. If the target temperature is less than or equal to the preset temperature, the first equipment is indicated to have no overheat fault, and the first equipment is in a normal running state.
The embodiment of the application also provides a fault prediction device for an intelligent operation and maintenance system, and fig. 2 is a schematic structural diagram of the fault prediction device for an intelligent operation and maintenance system according to the embodiment of the application, and referring to fig. 2, the device includes a receiving unit 201, a processing unit 202 and a determining unit 203.
The receiving unit 201 receives a prediction request sent by a user side, determines a first device according to the prediction request, wherein the first device is any one of a plurality of devices to be monitored, and acquires first monitoring data corresponding to the first device, wherein the first monitoring data comprises operation data, electric data, vibration data and environment data.
The processing unit 202 determines preset first monitoring data according to a first model corresponding to the first equipment, judges whether the first monitoring data are consistent with the preset first monitoring data, determines that the first equipment has a first fault when the first monitoring data are inconsistent with the preset first monitoring data, acquires a target association degree between the first equipment and the second equipment, and judges whether the target association degree is greater than or equal to the preset association degree.
And the determining unit 203 determines that the first device and the second device have fault states if the target association degree is greater than or equal to the preset association degree, and sends the fault states to the user side so that a user corresponding to the user side can check the fault states.
In a possible implementation manner, the receiving unit 201 is configured to obtain a first monitoring value corresponding to the first device, where the first monitoring value includes a first temperature, a first pressure value, a first vibration value, a first current value, a first voltage value, and a first rotation speed value, obtain a second monitoring value corresponding to the second device, where the second monitoring value includes a second temperature, a second pressure value, a second vibration value, a second current value, a second voltage value, and a second rotation speed value, calculate the first monitoring value and the second monitoring value by using a preset formula to obtain a plurality of correlation values, determine a plurality of weights according to the plurality of correlation values, multiply each correlation value with the weight corresponding to each correlation value to obtain a plurality of target values, and add the plurality of target values to obtain the target correlation.
In one possible implementation, the processing unit 202 is configured to obtain a first parameter from the first monitored value, where the first parameter includes a first temperature, a first pressure value, a first vibration value, a first current value, a first voltage value, and a first rotation speed value, obtain a second parameter from the second monitored value, where the second parameter includes a second temperature, a second pressure value, a second vibration value, a second current value, a second voltage value, and a second rotation speed value, and calculate the first parameter and the second parameter using a preset formula to obtain a plurality of correlation values, where the preset formula is as follows:;
Wherein r represents a correlation value, n represents a maximum value corresponding to the observation times, T1 ij represents a j-th first parameter in a first monitoring value of a first device at an i-th observation time, T2 ij represents a j-th second parameter in a second monitoring value of a second device at the i-th observation time, T1 j represents an average value of the j-th first parameter of the n-th observation times in the first device, and T2 j represents an average value of the j-th second parameter of the n-th observation times in the second device.
In a possible implementation manner, the receiving unit 201 is configured to obtain, when the target association degree is less than the preset association degree, a target device set having a connection relationship with the first device, the processing unit 202 is configured to determine whether a second device exists in the target device set, and the determining unit 203 is configured to confirm that the second device does not have a fault state when the second device does not exist in the target device and the target device.
In a possible implementation manner, the determining unit 203 is configured to determine that the first device and the second device both have a fault state if the second device exists in the target device set.
In a possible implementation manner, the receiving unit 201 is configured to obtain a target image corresponding to the first device when the first monitoring data is consistent with the preset first monitoring data, the processing unit 202 is configured to process the target image to obtain target feature data, compare the target feature data with the preset feature data, determine that a damage fault state exists in the first device when the target feature data is inconsistent with the preset feature data, obtain a target temperature corresponding to the first device according to the damage fault state, and determine that an overheat fault exists in the first device if the target temperature is greater than the preset temperature.
In a possible implementation manner, the receiving unit 201 is configured to obtain second monitoring data corresponding to the second device, the processing unit 202 is configured to determine preset second monitoring data according to a second model, where the second model is a device model corresponding to the second device, the determining unit 203 is configured to confirm that a second fault exists in the second device when the second monitoring data is inconsistent with the preset second monitoring data, input the first fault and the second fault into a preset operation and maintenance database, query to obtain operation and maintenance measures, generate an operation and maintenance evaluation report according to the operation and maintenance measures, and send the operation and maintenance evaluation report to the user side so that the user can maintain according to the operation and maintenance evaluation report.
It should be noted that, when the apparatus provided in the foregoing embodiment implements the functions thereof, only the division of the foregoing functional modules is used as an example, in practical application, the foregoing functional allocation may be implemented by different functional modules, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the embodiments of the apparatus and the method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the embodiments of the method are detailed in the method embodiments, which are not repeated herein.
The application also discloses electronic equipment. Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 300 may include at least one processor 301, at least one network interface 304, a user interface 303, a memory 302, and at least one communication bus 305.
Wherein a communication bus 305 is used to enable connected communications between these components.
The user interface 303 may include a Display screen (Display), a Camera (Camera), and the optional user interface 303 may further include a standard wired interface, and a wireless interface.
The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and lines to connect various portions of the overall server, perform various functions of the server and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 302, and invoking data stored in the memory 302. Alternatively, the processor 301 may be implemented in at least one hardware form of digital signal Processing (DIGITAL SIGNAL Processing, DSP), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 301 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU is mainly used for processing an operating system, a user interface, an application request and the like, the GPU is used for rendering and drawing contents required to be displayed by the display screen, and the modem is used for processing wireless communication. It will be appreciated that the modem may not be integrated into the processor 301 and may be implemented by a single chip.
The Memory 302 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 302 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 302 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 302 may include a stored program area and a stored data area, wherein the program area is stored. Instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc., may be stored, and the storage data area may store data and the like involved in the above-described respective method embodiments. The memory 302 may also optionally be at least one storage device located remotely from the aforementioned processor 301.
As shown in fig. 3, an operating system, a network communication module, a user interface module, and an application program for failure prediction of the intelligent operation and maintenance system may be included in the memory 302 as one type of computer storage medium.
In the electronic device 300 shown in fig. 3, the user interface 303 is primarily used to provide an input interface for a user to obtain data entered by the user, while the processor 301 may be used to invoke an application stored in the memory 302 for fault prediction of the intelligent operation and maintenance system, which when executed by one or more processors, causes the electronic device to perform the method as described in one or more of the embodiments above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or 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 service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network 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 memory. Based on this understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory, and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present application. The memory includes various media capable of storing program codes, such as a USB flash disk, a mobile hard disk, a magnetic disk or an optical disk.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains.
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