CN118153919A - Equipment maintenance arrangement method and related equipment - Google Patents
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Abstract
The application provides an equipment maintenance scheduling method and related equipment, wherein the equipment maintenance scheduling method comprises the following steps: acquiring a maintenance period of each of a plurality of devices; acquiring the fault interval time of each device, and determining the average fault interval time of each device; acquiring monitoring operation data of each device in real time; when the distance between any one device and the last shutdown time reaches the corresponding average fault interval time, inputting the monitoring operation data corresponding to the any one device to a fault prediction model trained to a convergence state in advance, and obtaining the predicted fault interval time of the any one device; determining the fault probability of each device according to the predicted fault interval time; and determining the priority of maintaining each device according to the fault probability, and distributing the maintenance sequence of each device according to the priority. The application relates to the technical field of equipment operation and maintenance, and can improve the efficiency of the equipment operation and maintenance.
Description
Technical Field
The application relates to the technical field of intelligent manufacturing, in particular to the technical field of equipment operation and maintenance, and especially relates to an equipment maintenance arrangement method and device and electronic equipment.
Background
With the development of industrialization, more and more enterprises and organizations tend to implement production and manufacture by using mechanized or electrified equipment, so as to improve productivity and production efficiency. However, the current part of industries have the phenomenon of heavy production and light maintenance. And because the equipment management system is not sound, the lack of real-time supervision causes some equipment abnormal damage, reduces the availability factor, even in influencing construction or output, has the risk of equipment major accident.
At present, maintenance planning is usually made regularly by maintenance personnel, and equipment is maintained according to the maintenance planning. However, because the equipment maintenance management system is not standard, maintenance personnel forget to maintain or excessively maintain the equipment, the equipment failure may be further deteriorated, and the maintenance and maintenance schedule of the equipment cannot be adjusted in real time according to the actual working condition in this way, so that the equipment maintenance efficiency is reduced.
Disclosure of Invention
In view of the foregoing, it is necessary to provide an equipment maintenance scheduling method and related equipment, so as to solve the technical problem of low efficiency of equipment maintenance scheduling. The related equipment comprises an equipment maintenance arrangement device and electronic equipment.
The application provides a device maintenance scheduling method, which is applied to electronic devices, and comprises the following steps: acquiring a maintenance period of each of a plurality of devices; acquiring the fault interval time of each device, and determining the average fault interval time of each device; acquiring monitoring operation data of each device in real time; when the distance between any one device and the last shutdown time reaches the corresponding average fault interval time, inputting the monitoring operation data corresponding to the any one device to a fault prediction model trained to a convergence state in advance, and obtaining the predicted fault interval time of the any one device; determining the fault probability of each device according to the predicted fault interval time; and determining the priority of maintaining each device according to the fault probability, and distributing the maintenance sequence of each device according to the priority.
In some embodiments, the method further comprises training the fault prediction model, the training the fault prediction model comprising: acquiring a plurality of historical fault interval times of each device; determining historical operating data of each device within each historical fault interval time; constructing sample data according to the historical operation data of each device in each historical fault interval time; inputting the sample data to a pre-constructed initial prediction model to obtain prediction time data; determining a first loss value of the initial predictive model from the historical inter-fault time and the predicted time data; and when the first loss value is larger than a preset termination threshold value, updating the initial prediction model according to a back propagation algorithm until the first loss value is smaller than or equal to the termination threshold value, stopping updating the initial prediction model, and obtaining the fault prediction model trained to a convergence state.
In some embodiments, said constructing sample data from historical operational data of said each device over said historical inter-fault time comprises: constructing covariance matrixes of multiple dimensions of the operation data according to the operation data; determining a characteristic value of each dimension according to the covariance matrix; normalizing the characteristic values to obtain normalized characteristic values of each dimension; and determining the product of the normalized characteristic value and the corresponding dimension as the sample data.
In some embodiments, the determining the first loss value of the initial predictive model from the historical inter-fault time and the predicted time data comprises: determining the correlation between the historical fault interval time and corresponding historical operation data; determining the first loss value from the correlation, the historical inter-fault time, and the predicted time data, comprising:
，
Wherein Loss represents a first Loss value of the initial prediction model, n represents the number of the plurality of devices, i represents an index of each device, w i represents a correlation between a historical fault interval time corresponding to an ith device and historical operation data, x i represents a historical fault interval time corresponding to the ith device, and t i represents predicted time data of the ith device.
In some embodiments, the determining the correlation of the historical interfailure time with the corresponding historical operating data comprises: sampling each dimension in the historical operation data based on a preset sampling frequency to obtain first sequence data corresponding to each dimension; arranging a plurality of historical fault interval times according to the sequence from the early to the late of the historical fault interval time to obtain second sequence data; calculating a similarity between each of the first sequence data and the second sequence data; and determining the average value of the similarity as the correlation.
In some embodiments, said determining the probability of failure of each device from said predicted inter-failure time comprises: determining an absolute value of a difference value between the current time and the predicted fault interval time corresponding to each device; and inputting the absolute value to a preset probability calculation function to obtain the fault probability of each device.
In some embodiments, the probability calculation function satisfies the following relationship:
，
Wherein P represents the fault probability corresponding to any one device; d represents the current time, m represents the predicted fault interval time, |d-m| represents the absolute value, and e represents a natural constant; z 1 and z 2 represent harmonic parameters, where when d > m, z 1 is 1 and z 2 is 0, when d At m, z 1 is 0 and z 2 is 1.
In some embodiments, when the any one device does not perform device maintenance within the corresponding maintenance period, determining that the priority of the any one device is highest.
The embodiment of the application also provides a device for equipment maintenance arrangement, which comprises: an acquisition module for acquiring a maintenance period of each of a plurality of devices; the acquisition module is further configured to acquire a fault interval time of each device, and determine an average fault interval time of each device; the acquisition module is also used for acquiring the monitoring operation data of each device in real time; the determining module is used for inputting the monitoring operation data corresponding to any one device to a fault prediction model trained to a convergence state in advance when the distance between any one device and the last shutdown time reaches the corresponding average fault interval time, so as to obtain the predicted fault interval time of any one device; the determining module is further configured to determine a fault probability of each device according to the predicted fault interval time; the determining module is further configured to determine a priority of maintenance for each device according to the fault probability, and arrange a maintenance order of each device according to the priority.
The embodiment of the application also provides electronic equipment, which comprises: a memory storing at least one instruction; and the processor executes the instructions stored in the memory to realize the equipment maintenance scheduling method.
The embodiment of the application also provides a computer readable storage medium, wherein at least one instruction is stored in the computer readable storage medium, and the at least one instruction is executed by a processor in electronic equipment to realize the equipment maintenance scheduling method.
According to the technical scheme, the maintenance period of each device in the plurality of devices is firstly obtained, so that the periodic maintenance plan of each device is determined, and the stability of the arrangement of the maintenance plans of the devices can be improved. And determining the average fault interval time of each device, acquiring monitoring operation data of each device in real time, predicting the predicted fault interval time of the device by using a fault prediction model when the distance between any one device and the last shutdown time reaches the corresponding average fault interval time, and determining the fault probability of each device according to the predicted fault interval time. Therefore, the working condition of the equipment in the real-time operation process can be represented according to the monitoring operation data, and the probability of each equipment failure is determined according to the quantized data of the monitoring operation data, so that the priority of equipment maintenance can be evaluated by using the quantized data of the failure probability, and the maintenance sequence of each equipment can be arranged according to the priority. Therefore, maintenance plans of all the equipment can be adjusted in real time according to the actual working conditions of the equipment, and the equipment maintenance efficiency can be improved.
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Fig. 1 is an application scenario diagram of an apparatus maintenance scheduling method according to an embodiment of the present application.
Fig. 2 is a flowchart of a device maintenance scheduling method according to an embodiment of the present application.
Fig. 3 is a functional block diagram of an apparatus maintenance arrangement device according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The application will be described in detail below with reference to the drawings and the specific embodiments thereof in order to more clearly understand the objects, features and advantages of the application. It should be noted that, without conflict, embodiments of the present application and features in the embodiments may be combined with each other. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, the described embodiments are merely some, rather than all, embodiments of the present application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
The embodiment of the application provides a device maintenance scheduling method, which can be applied to one or more electronic devices, wherein the electronic devices are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware comprises, but is not limited to, a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), a Programmable gate array (Field-Programmable GATE ARRAY, FPGA), a digital Processor (DIGITAL SIGNAL Processor, DSP), an embedded device and the like.
The electronic device may be any electronic product that can interact with a customer in a human-computer manner, such as a Personal computer, a tablet computer, a smart phone, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), a game console, an interactive internet protocol television (Internet Protocol Television, IPTV), a smart wearable device, etc.
The electronic device may also include a network device and/or a client device. Wherein the network device includes, but is not limited to, a single network server, a server group composed of a plurality of network servers, or a Cloud based Cloud Computing (Cloud Computing) composed of a large number of hosts or network servers.
The network in which the electronic device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), and the like.
As shown in fig. 1, the device maintenance scheduling method provided by the present application can be applied to an electronic device 100, where the electronic device 100 is communicatively connected to a server 200. The electronic device 100 is configured to obtain a maintenance period of each of a plurality of devices from the server 200; acquiring the fault interval time of each device, and determining the average fault interval time of each device; acquiring monitoring operation data of each device in real time; when the distance between any one device and the last shutdown time reaches the corresponding average fault interval time, inputting the monitoring operation data corresponding to the any one device to a fault prediction model trained to a convergence state in advance, and obtaining the predicted fault interval time of the any one device; determining the fault probability of each device according to the predicted fault interval time; and determining the priority of maintaining each device according to the fault probability, and distributing the maintenance sequence of each device according to the priority.
Fig. 2 is a flowchart of an apparatus maintenance scheduling method according to an embodiment of the present application. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The equipment maintenance scheduling method provided by the embodiment of the application comprises the following steps.
S20, acquiring a maintenance period of each of the plurality of devices.
In an embodiment of the present application, in order to ensure that a plurality of devices to be maintained can be maintained at regular time, a maintenance period of each device may be acquired first, and then whether to maintain the device may be determined according to the maintenance period corresponding to the device. The equipment to be maintained may be electrical equipment, mechanical equipment, etc., which is not limited in the present application. By way of example, the maintenance period may be three months, six months, or one year, as the application is not limited in this regard.
S21, acquiring the fault interval time of each device, and determining the average fault interval time of each device.
In one embodiment of the present application, the failure interval time may be an interval between times when any one device occurs every two adjacent failures. Wherein the fault interval time can reflect the quality of each maintenance performed on the equipment. Specifically, when the fault interval time is shorter, the quality of the last maintenance of the equipment is lower, and when the fault interval time is longer, the quality of the last maintenance of the equipment is higher.
In an embodiment of the present application, in order to determine a corresponding time at which a fault may occur according to a fault interval time of each device, an average fault interval time of each device may be determined, where the average fault interval time may be an average value of the fault interval time of each device. Therefore, when the fault probability of the equipment is predicted according to the fault interval time in the follow-up, the error when the maintenance of the equipment is arranged by the maximum value or the minimum value in the fault interval time corresponding to each equipment is avoided.
S22, acquiring monitoring operation data of each device in real time.
In an embodiment of the application, in order to flexibly arrange maintenance plans corresponding to a plurality of devices, each device is ensured to be maintained in time, the efficiency of maintaining the device is further improved, and monitoring operation data of each device can be obtained in real time. The monitoring operation data can be data collected by each sensor of the equipment in real time in the operation process of the equipment. Specifically, the monitoring operation data can be used to characterize whether various indexes in the operation process of the device are in a normal range, and when the monitoring operation data are not in a stable state or the monitoring operation data are not in the normal range, the device may fail. Thus, acquiring the monitoring data for each device in real time can provide data support for the subsequent prediction of the time at which the device failed and the real-time adjustment of the maintenance schedule of the device.
By way of example, the monitored operating data of the device may be an indicator of the power, temperature, vibration frequency, etc. of the device as it is operating, as the application is not limited in this regard.
S23, when the distance between any one device and the last downtime reaches the corresponding average fault interval time, the monitoring operation data corresponding to any one device is input to a fault prediction model trained to a convergence state in advance, and the predicted fault interval time of any one device is obtained.
In an embodiment of the application, in order to determine the probability of any one device failure in real time, and further adjust the device maintenance plan in time, monitoring operation data of the device can be analyzed when the last downtime of any one device reaches the corresponding average failure interval time. Therefore, the running condition of the equipment can be monitored in real time, the maintenance efficiency of the equipment can be improved, and the maintenance efficiency of the equipment can be ensured. Specifically, when the distance between any one device and the last shutdown time reaches the corresponding average fault interval time, the fact that the distance between the device and the last fault time reaches the historical average level is indicated, and the probability of subsequent faults is higher, so that monitoring operation data corresponding to the current time after the last fault of the device can be determined, and the monitoring operation data is input into a pre-trained fault prediction model to obtain the predicted fault interval time of the device. Specifically, when the predicted fault interval time is larger, the probability of the equipment fault is smaller, and the corresponding maintenance plan of the equipment can be delayed to be executed; when the predicted fault interval time is smaller, the probability of the equipment fault is larger, and the maintenance plan corresponding to the equipment needs to be executed preferentially. Therefore, the probability of the equipment failure can be predicted in real time according to the operation data of each equipment in the corresponding average failure interval time, and the maintenance plan of each equipment can be adjusted in real time according to the failure probability.
In one embodiment of the present application, to ensure the accuracy of the predicted failure time interval predicted by the failure prediction model, the failure prediction model needs to be trained in advance to a converged state. Specifically, training the fault prediction model includes: acquiring a plurality of historical fault interval times of each device; determining historical operating data of each device within each historical fault interval time; constructing sample data according to the historical operation data of each device in each historical fault interval time; inputting the sample data to a pre-constructed initial prediction model to obtain prediction time data; determining a first loss value of the initial predictive model from the historical inter-fault time and the predicted time data; and when the first loss value is larger than a preset termination threshold value, updating the initial prediction model according to a back propagation algorithm until the first loss value is smaller than or equal to the termination threshold value, stopping updating the initial prediction model, and obtaining the fault prediction model trained to a convergence state.
The first loss value is used for representing the difference between the predicted time data predicted by the initial prediction model and the historical fault interval time, and when the first loss is larger, the difference between the predicted time data and the historical fault interval time is larger, the performance of the initial prediction model is poorer, and the accuracy of the predicted time data is lower. The initial predictive model may thus be updated according to a back-propagation algorithm until the first loss value is less than or equal to the termination threshold, stopping updating the initial predictive model. The termination threshold may be a preset value, and exemplary, the termination threshold may be 0.1, 0.01 or 0.001, which is not limited in the present application. And when the first loss value is smaller than or equal to the termination threshold value, indicating that the performance of the initial prediction model is accurate, stopping updating the initial prediction model to obtain the fault prediction model trained to a convergence state.
In an embodiment of the present application, since the operation data of each device includes multiple dimensions, for example, each dimension may be data of power, temperature, vibration frequency, etc. when the device operates. In order to improve the quality of the construction of the sample data according to the operation data, firstly, the importance of the data of each dimension in the operation data can be determined, and the construction of the sample data according to the importance and the operation data, specifically, the construction of the sample data according to the operation data of each device in the fault interval time comprises the following steps: constructing covariance matrixes of multiple dimensions of the operation data according to the operation data; determining a characteristic value of each dimension according to the covariance matrix; normalizing the characteristic values to obtain normalized characteristic values of each dimension; and determining the product of the normalized characteristic value and the corresponding dimension as the sample data.
In an embodiment of the present application, in order to prevent the occurrence of the overfitting phenomenon during the training of the fault prediction model, a correlation between the historical fault interval time corresponding to each historical device and the historical operation data may be determined first, and the first loss value of the initial prediction model may be regularized according to the correlation. Wherein the correlation is used to characterize the extent to which historical operating data affects the inter-fault time. Specifically, when the correlation is higher, the influence degree of the historical operation data on the fault interval time is higher, the confidence of the difference degree between the predicted time data predicted according to the historical operation data and the historical fault interval time is higher; when the correlation is lower, the influence degree of the historical operation data on the fault interval time is lower, and the confidence of the difference degree between the predicted time data predicted according to the historical operation data and the historical fault interval time is lower.
In an embodiment of the present application, determining the correlation of the historical fault interval time and the corresponding historical operating data includes: sampling each dimension in the historical operation data based on a preset sampling frequency to obtain first sequence data corresponding to each dimension; arranging a plurality of historical fault interval times according to the sequence from the early to the late of the historical fault interval time to obtain second sequence data; calculating a similarity between each of the first sequence data and the second sequence data; and determining the average value of the similarity as the correlation. Wherein a cosine similarity between each of the first sequence data and the second sequence data is determined as the similarity; the reciprocal of the euclidean distance between each first sequence data and the second sequence data may also be determined as the similarity; the inverse of the DTW distance between each of the first sequence data and the second sequence data may also be determined as the similarity, and the specific method of determining the similarity is not limited by the present application.
In one embodiment of the present application, the determining the first loss value of the initial predictive model according to the historical inter-fault time and the predicted time data includes: determining the correlation between the historical fault interval time and corresponding historical operation data; determining the first loss value from the correlation, the historical inter-fault time, and the predicted time data, comprising:
，
Wherein Loss represents a first Loss value of the initial prediction model, n represents the number of the plurality of devices, i represents an index of each device, w i represents a correlation between a historical fault interval time corresponding to an ith device and historical operation data, x i represents a historical fault interval time corresponding to the ith device, and t i represents predicted time data of the ith device.
S24, determining the fault probability of each device according to the predicted fault interval time.
In one embodiment of the present application, the predicted inter-fault time is used to characterize the time interval from the last fault to the next fault of any one device predicted by the fault prediction model. If the current time exceeds the predicted fault interval time predicted by the fault prediction model, the larger the difference between the current time and the predicted fault interval time is, the higher the fault probability is; if the current time does not exceed the predicted fault interval time, the smaller the difference between the current time and the predicted fault interval time, the higher the fault probability. Specifically, the determining the fault probability of each device according to the predicted fault interval time includes: determining an absolute value of a difference value between the current time and the predicted fault interval time corresponding to each device; and inputting the absolute value to a preset probability calculation function to obtain the fault probability of each device.
In one embodiment of the present application, the probability calculation function satisfies the following relation:
Wherein P represents the fault probability corresponding to any one device; d represents the current time, m represents the predicted fault interval time, |d-m| represents the absolute value, and e represents a natural constant; z 1 and z 2 represent harmonic parameters, where when d > m, z 1 is 1 and z 2 is 0, when d At m, z 1 is 0 and z 2 is 1.
Thus, if the current time exceeds the predicted fault interval time predicted by the fault prediction model, the larger the difference between the current time and the predicted fault interval time is, the higher the fault probability is; if the current time does not exceed the predicted fault interval time predicted by the fault prediction model, the smaller the difference between the current time and the predicted fault interval time is, the higher the fault probability is.
And S25, determining the priority of maintaining each device according to the fault probability, and distributing the maintenance sequence of each device according to the priority.
In an embodiment of the present application, in order to flexibly arrange the order of maintaining each device, the priority of maintaining each device may be determined according to the quantized data of the failure probability calculated in real time, and the order of maintaining each device may be arranged according to the order of the priority from high to low. Specifically, the value of the fault probability may be used as a priority, and the fault probability may be normalized to obtain the priority, which is not limited in the present application.
For example, when the failure probabilities corresponding to the three devices are respectively 0.2 corresponding to the a device, 0.9 corresponding to the B device, and 0.7 corresponding to the C device, the priority of a is determined to be 0.2, the priority of B is determined to be 0.9, and the priority of C is determined to be 0.7 according to the failure probabilities, and the maintenance order is B, C and a are arranged according to the order of the priorities from large to small.
And S26, when the equipment maintenance is not carried out on any one of the equipment within the corresponding maintenance period, determining that the priority of the any one of the equipment is highest.
In an embodiment of the present application, in order to periodically force maintenance on the devices, the priority of maintenance on each device may also be updated according to the maintenance period of the device. Specifically, when any one device does not perform device maintenance in the corresponding maintenance period, it may be determined that the priority of the any one device is highest. In this way, the periodic maintenance plan of each device can be determined according to the maintenance period, so that the stability of device maintenance is improved.
According to the technical scheme, the maintenance period of each device in the plurality of devices is firstly obtained, so that the periodic maintenance plan of each device is determined, and the stability of the arrangement of the maintenance plans of the devices can be improved. And determining the average fault interval time of each device, acquiring monitoring operation data of each device in real time, predicting the predicted fault interval time of the device by using a fault prediction model when the distance between any one device and the last shutdown time reaches the corresponding average fault interval time, and determining the fault probability of each device according to the predicted fault interval time. Therefore, the working condition of the equipment in the real-time operation process can be represented according to the monitoring operation data, and the probability of each equipment failure is determined according to the quantized data of the monitoring operation data, so that the priority of equipment maintenance can be evaluated by using the quantized data of the failure probability, and the maintenance sequence of each equipment can be arranged according to the priority. Therefore, maintenance plans of all the equipment can be adjusted in real time according to the actual working conditions of the equipment, and the equipment maintenance efficiency can be improved.
Referring to fig. 3, fig. 3 is a functional block diagram of an equipment maintenance arrangement device according to an embodiment of the application. The equipment maintenance scheduling apparatus 31 includes an acquisition module 310 and a determination module 311. The module/unit referred to herein is a series of computer readable instructions capable of being executed by the processor 13 and of performing a fixed function, stored in the memory 12. In the present embodiment, the functions of the respective modules/units will be described in detail in the following embodiments.
The acquiring module 310 is configured to acquire a maintenance period of each of the plurality of devices.
The obtaining module 310 is further configured to obtain a time between failures of each device, and determine an average time between failures of each device.
The acquiring module 310 is further configured to acquire the monitored operation data of each device in real time.
The determining module 311 is configured to input the monitored operation data corresponding to any one device to a failure prediction model trained in advance to a convergence state when the distance between any one device and the last shutdown time reaches the corresponding average failure interval time, and obtain a predicted failure interval time of any one device.
The determining module 311 is further configured to determine a failure probability of each device according to the predicted failure interval time.
The determining module 311 is further configured to determine a priority for maintaining each device according to the failure probability, and arrange a maintenance order of each device according to the priority.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 100 comprises a memory 12 and a processor 13. The memory 12 is used for storing computer readable instructions, and the processor 13 is used to execute the computer readable instructions stored in the memory to implement the device maintenance scheduling method according to any of the embodiments described above.
In an embodiment of the application, the electronic device 100 further comprises a bus, a computer program stored in said memory 12 and executable on said processor 13, such as a device maintenance scheduling program.
Fig. 4 shows only an electronic device 100 having a memory 12 and a processor 13, it will be understood by those skilled in the art that the configuration shown in fig. 4 is not limiting of the electronic device 100 and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
In connection with fig. 2, the memory 12 in the electronic device 100 stores a plurality of computer readable instructions to implement a device maintenance scheduling method, the processor 13 being executable to implement: acquiring a maintenance period of each of a plurality of devices; acquiring the fault interval time of each device, and determining the average fault interval time of each device; acquiring monitoring operation data of each device in real time; when the distance between any one device and the last shutdown time reaches the corresponding average fault interval time, inputting the monitoring operation data corresponding to the any one device to a fault prediction model trained to a convergence state in advance, and obtaining the predicted fault interval time of the any one device; determining the fault probability of each device according to the predicted fault interval time; and determining the priority of maintaining each device according to the fault probability, and distributing the maintenance sequence of each device according to the priority.
Specifically, the specific implementation method of the above instructions by the processor 13 may refer to the description of the relevant steps in the corresponding embodiment of fig. 2, which is not repeated herein.
Those skilled in the art will appreciate that the schematic diagram is merely an example of the electronic device 100, and is not meant to limit the electronic device 100, and the electronic device 100 may be a bus-type structure, a star-type structure, other hardware or software, or a different arrangement of components than illustrated, where the electronic device 100 may include more or less hardware or software, and where the electronic device 100 may include an input/output device, a network access device, etc.
It should be noted that the electronic device 100 is only an example, and other electronic products that may be present in the present application or may be present in the future are also included in the scope of the present application by way of reference.
The memory 12 includes at least one type of readable storage medium, which may be non-volatile or volatile. The readable storage medium includes flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 12 may in some embodiments be an internal storage unit of the electronic device 100, such as a removable hard disk of the electronic device 100. The memory 12 may also be an external storage device of the electronic device 100 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), etc. that are provided on the electronic device 100. The memory 12 may be used not only for storing application software installed in the electronic device 100 and various types of data, such as a code of a device maintenance scheduling program, etc., but also for temporarily storing data that has been output or is to be output.
The processor 13 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, various control chips, and the like. The processor 13 is a Control Unit (Control Unit) of the electronic device 100, connects the respective components of the entire electronic device 100 using various interfaces and lines, and executes various functions of the electronic device 100 and processes data by running or executing programs or modules stored in the memory 12 (for example, executing a device maintenance scheduling program, etc.), and calling data stored in the memory 12.
The processor 13 executes the operating system of the electronic device 100 and various types of applications installed. The processor 13 executes the application program to implement the steps of each of the above-described embodiments of a device maintenance scheduling method, such as the steps shown in fig. 2.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules/units may be a series of computer readable instruction segments capable of performing particular functions for describing the execution of the computer program in the electronic device 100. For example, the computer program may be split into an acquisition module 310 and a determination module 311.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a Processor (Processor) to perform portions of a device maintenance scheduling method according to various embodiments of the present application.
The modules/units integrated with the electronic device 100 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on this understanding, the present application may also be implemented by a computer program for instructing a relevant hardware device to implement all or part of the procedures of the above-mentioned embodiment method, where the computer program may be stored in a computer readable storage medium and the computer program may be executed by a processor to implement the steps of each of the above-mentioned method embodiments.
Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory, other memories, and the like.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one arrow is shown in FIG. 4, but only one bus or one type of bus is not shown. The bus is arranged to enable a connection communication between the memory 12 and at least one processor 13 or the like.
The embodiment of the present application further provides a computer readable storage medium (not shown), where computer readable instructions are stored, where the computer readable instructions are executed by a processor in an electronic device to implement an apparatus maintenance scheduling method according to any one of the foregoing embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module 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 can be realized in a form of hardware or a form of hardware and a form of software functional modules.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Several of the elements or devices described in the specification may be embodied by one and the same item of software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present application without departing from the spirit and scope of the technical solution of the present application.
Claims (10)
1. A device maintenance scheduling method applied to an electronic device, the method comprising:
acquiring a maintenance period of each of a plurality of devices;
acquiring the fault interval time of each device, and determining the average fault interval time of each device;
acquiring monitoring operation data of each device in real time;
When the distance between any one device and the last shutdown time reaches the corresponding average fault interval time, inputting the monitoring operation data corresponding to the any one device to a fault prediction model trained to a convergence state in advance, and obtaining the predicted fault interval time of the any one device;
determining the fault probability of each device according to the predicted fault interval time;
And determining the priority of maintaining each device according to the fault probability, and distributing the maintenance sequence of each device according to the priority.
2. The equipment maintenance scheduling method of claim 1, wherein the method further comprises training the fault prediction model, the training the fault prediction model comprising:
acquiring a plurality of historical fault interval times of each device;
Determining historical operating data of each device within each historical fault interval time;
constructing sample data according to the historical operation data of each device in each historical fault interval time;
inputting the sample data to a pre-constructed initial prediction model to obtain prediction time data;
Determining a first loss value of the initial predictive model from the historical inter-fault time and the predicted time data;
And when the first loss value is larger than a preset termination threshold value, updating the initial prediction model according to a back propagation algorithm until the first loss value is smaller than or equal to the termination threshold value, stopping updating the initial prediction model, and obtaining the fault prediction model trained to a convergence state.
3. The equipment maintenance scheduling method of claim 2, wherein said constructing sample data from said historical operational data for each of said equipment during said historical inter-fault time comprises:
constructing covariance matrixes of multiple dimensions of the operation data according to the operation data;
determining a characteristic value of each dimension according to the covariance matrix;
normalizing the characteristic values to obtain normalized characteristic values of each dimension;
And determining the product of the normalized characteristic value and the corresponding dimension as the sample data.
4. The equipment maintenance scheduling method of claim 2, wherein said determining a first loss value of the initial predictive model from the historical inter-fault time and the predicted time data comprises:
Determining the correlation between the historical fault interval time and corresponding historical operation data;
Determining the first loss value from the correlation, the historical inter-fault time, and the predicted time data, comprising:
，
Wherein Loss represents a first Loss value of the initial prediction model, n represents the number of the plurality of devices, i represents an index of each device, w i represents a correlation between a historical fault interval time corresponding to an ith device and historical operation data, x i represents a historical fault interval time corresponding to the ith device, and t i represents predicted time data of the ith device.
5. The equipment maintenance scheduling method of claim 4, wherein said determining a correlation of the historical inter-fault time with corresponding historical operational data comprises:
sampling each dimension in the historical operation data based on a preset sampling frequency to obtain first sequence data corresponding to each dimension;
arranging a plurality of historical fault interval times according to the sequence from the early to the late of the historical fault interval time to obtain second sequence data;
calculating a similarity between each of the first sequence data and the second sequence data;
And determining the average value of the similarity as the correlation.
6. The equipment maintenance scheduling method of claim 1, wherein said determining the failure probability of each of the equipment according to the predicted inter-failure time includes:
Determining an absolute value of a difference value between the current time and the predicted fault interval time corresponding to each device;
and inputting the absolute value to a preset probability calculation function to obtain the fault probability of each device.
7. The equipment maintenance scheduling method of claim 6, wherein the probability calculation function satisfies the following relation:
，
Wherein P represents the fault probability corresponding to any one device; d represents the current time, m represents the predicted fault interval time, |d-m| represents the absolute value, and e represents a natural constant; z 1 and z 2 represent harmonic parameters, where when d > m, z 1 is 1 and z 2 is 0, when d At m, z 1 is 0 and z 2 is 1.
8. The equipment maintenance scheduling method according to claim 1, wherein when the equipment maintenance is not performed by any one of the equipment within the corresponding maintenance period, the priority of the any one of the equipment is determined to be highest.
9. A device maintenance scheduling apparatus, characterized in that the apparatus comprises a module implementing a device maintenance scheduling method according to any one of claims 1 to 8, the apparatus comprising:
an acquisition module for acquiring a maintenance period of each of a plurality of devices;
The acquisition module is further configured to acquire a fault interval time of each device, and determine an average fault interval time of each device;
the acquisition module is also used for acquiring the monitoring operation data of each device in real time;
The determining module is used for inputting the monitoring operation data corresponding to any one device to a fault prediction model trained to a convergence state in advance when the distance between any one device and the last shutdown time reaches the corresponding average fault interval time, so as to obtain the predicted fault interval time of any one device;
The determining module is further configured to determine a fault probability of each device according to the predicted fault interval time;
the determining module is further configured to determine a priority of maintenance for each device according to the fault probability, and arrange a maintenance order of each device according to the priority.
10. An electronic device comprising a processor and a memory, wherein the processor is configured to implement the device maintenance scheduling method of any one of claims 1 to 8 when executing a computer program stored in the memory.
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