CN118154170A - Fault maintenance optimization method and related equipment - Google Patents
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Abstract
The application provides a fault maintenance optimization method and related equipment, wherein the method comprises the following steps: acquiring attribute data of a plurality of historical worksheets, wherein the attribute data at least comprises fault interval time; constructing sequence data corresponding to each dimension according to the attribute data, wherein the sequence data at least comprises a fault interval time sequence; determining a quality score of the corresponding historical worksheets according to the fault interval time and the downtime of each historical worksheet, wherein the quality score is used for representing the confidence coefficient of the attribute data corresponding to the historical worksheets; determining initial correlations of the sequence data for each dimension with the time sequence of the fault interval; determining a correlation score of the sequence data of each dimension and the time sequence of the fault interval according to the quality score and the initial correlation; and determining the sequence data with the highest correlation score as a target dimension, and outputting a work order optimization suggestion according to the target dimension. The application can improve the efficiency of equipment operation and maintenance.
Description
Technical Field
The application relates to the technical field of intelligent manufacturing, in particular to the technical field of fault maintenance, and especially relates to a fault maintenance optimization method and related equipment.
Background
With the development of the intelligent manufacturing field, more and more manufacturers or enterprises tend to manage work orders of equipment faults by using a quantification means during equipment maintenance, so that the convenience of equipment maintenance is improved. The work order is used for recording various attribute data related to equipment faults.
At present, the traditional work order processing method faces the problem of lack of mining on historical data, so that equipment fault maintenance planning is inaccurate and low in efficiency.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a fault maintenance optimization method and related devices, so as to solve the technical problem of low efficiency of maintaining faults of the devices. The related equipment comprises a fault maintenance optimizing device and electronic equipment.
The application provides a fault maintenance optimization method which is applied to electronic equipment, and comprises the following steps: acquiring attribute data of a plurality of historical worksheets, wherein the attribute data are used for representing data of a plurality of dimensions corresponding to fault maintenance, and the attribute data comprise worksheet time, whole worksheet time, response time, maintenance time, downtime and fault interval time; constructing sequence data corresponding to each dimension according to the attribute data, wherein the sequence data comprises an integral time sequence, a response time sequence, a maintenance time sequence, a shutdown time sequence and a fault interval time sequence; determining a quality score of each historical work order according to the fault interval time and the downtime of each historical work order, wherein the quality score is used for representing the confidence coefficient of attribute data corresponding to the historical work order; determining initial correlations of the sequence data for each dimension with the time sequence of the fault interval; determining a correlation score of the sequence data of each dimension and the fault interval time sequence according to the quality score and the initial correlation; and determining a target dimension corresponding to the sequence data with the highest correlation score, and outputting a work order optimization suggestion according to the target dimension.
In some embodiments, the constructing the sequence data corresponding to each dimension according to the attribute data includes: sequencing the plurality of historical worksheets according to the order from the early to the late of the worksheet time corresponding to each historical worksheet; and combining the data in each historical work order according to the sequence of the historical work orders.
In some embodiments, the determining the quality score for each historical worksheet based on the inter-fault time and the downtime of the corresponding historical worksheet comprises: normalizing the downtime of the plurality of historical worksheets to obtain normalized downtime; normalizing the fault interval time of the plurality of historical worksheets to obtain normalized fault interval time; determining the quality score from a normalized downtime and a normalized failure time; wherein the manner in which the quality score is determined satisfies the following relationship: Wherein i represents an index of the historical worksheet; z i represents the quality score of the ith historical worksheet; a i represents the normalized fault interval time corresponding to the ith historical worksheet; b i represents the normalized downtime corresponding to the ith historical work order; e represents a natural constant.
In some embodiments, determining the initial correlation of sequence data of any one dimension with the inter-fault time sequence comprises: determining outliers in the sequence data of any one dimension and the number of the outliers; determining initial confidence of the sequence data of any one dimension according to the outlier and the quantity of the outlier; determining a correlation coefficient between the sequence data of any one dimension and the fault interval time sequence; and determining the product of the initial confidence coefficient and the correlation coefficient as initial correlation.
In some embodiments, the determining the initial confidence of the sequence data for the arbitrary dimension based on the outliers and the number of outliers comprises: determining a dispersion of the outliers; the manner of determining the initial confidence of the sequence data of any one dimension according to the dispersion degree and the quantity of the dispersion values satisfies the following relation: Wherein y represents the initial confidence of the sequence data of any one dimension; c represents the dispersion of the outliers; d represents the number of outliers; e represents a natural constant.
In some embodiments, the determining the dispersion of the outliers comprises: determining data except the outlier in the sequence data of any dimension as basic data; determining a first difference between every two base data; determining the average value of the first difference values to obtain a first average value; determining a second difference between each two outliers; determining the average value of the second difference values to obtain a second average value; and determining the ratio of the second mean value to the first mean value as the dispersion.
In some embodiments, determining a correlation score for any one of the sequence data and the time series of fault intervals based on the quality score and the initial correlation comprises: determining the maximum time delay between any one sequence data and the fault interval time sequence; determining a correlation score according to the quality score, the maximum delay and the initial correlation, wherein the correlation score satisfies the following relation: Wherein r represents the relevance score; z represents the quality score; t represents the maximum delay; s represents the initial correlation.
In some embodiments, the determining the maximum delay comprises: shifting any one sequence data back for one unit time for a plurality of times; calculating the dot product of any sequence data after each backward shift and the fault interval time sequence; and determining the unit time corresponding to the maximum dot product as the maximum time delay.
The embodiment of the application also provides a fault maintenance optimization device, which comprises: the acquisition module is used for acquiring attribute data of a plurality of historical worksheets; the attribute data are used for representing data of a plurality of dimensions corresponding to fault maintenance; wherein the attribute data includes work order time, whole order time, response time, maintenance time, downtime, and fault interval time; the determining module is used for constructing sequence data corresponding to each dimension according to the attribute data, wherein the sequence data comprises an integer single time sequence, a response time sequence, a maintenance time sequence, a shutdown time sequence and a fault interval time sequence; the determining module is further configured to determine a quality score of each historical work order according to the fault interval time and the downtime of each historical work order, where the quality score is used to characterize the confidence level of the attribute data corresponding to the historical work order; the determining module is further used for determining initial correlation of the sequence data of each dimension and the fault interval time sequence respectively; the determining module is further configured to determine a relevance score of the sequence data of each dimension and the time sequence of the fault interval according to the quality score and the initial relevance; the determining module is further configured to determine that the sequence data with the highest relevance score is a target dimension, and output a worksheet optimization suggestion according to the target dimension.
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 fault maintenance optimization method.
According to the technical scheme, the attribute data of a plurality of historical worksheets at least comprising the fault interval time are acquired; constructing sequence data which at least comprises a fault interval time sequence and corresponds to each dimension according to the attribute data; determining a quality score of the corresponding historical worksheets according to the fault interval time and the downtime of each historical worksheet, wherein the quality score is used for representing the confidence coefficient of the attribute data corresponding to the historical worksheets; determining initial correlations of the sequence data for each dimension with the time sequence of the fault interval; determining a correlation score of the sequence data of each dimension and the time sequence of the fault interval according to the quality score and the initial correlation; and determining the sequence data with the highest correlation score as a target dimension, and outputting a work order optimization suggestion according to the target dimension. The dimension to be optimized is determined according to the correlation between the sequences, the downtime and the fault interval time, so that the efficiency of equipment operation and maintenance can be improved.
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Fig. 1 is an application scenario diagram of a fault maintenance optimization method according to an embodiment of the present application.
Fig. 2 is a flowchart of a fault maintenance optimization method according to an embodiment of the present application.
Fig. 3 is a functional block diagram of a fault maintenance optimization apparatus 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 fault maintenance optimization 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 fault maintenance optimization 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 attribute data of a plurality of historical worksheets from the server 200; wherein the attribute data includes at least a fault interval time; constructing sequence data corresponding to each dimension according to the attribute data, wherein the sequence data at least comprises a fault interval time sequence; determining a quality score of the corresponding historical worksheets according to the fault interval time and the downtime of each historical worksheet, wherein the quality score is used for representing the confidence coefficient of the attribute data corresponding to the historical worksheets; determining initial correlations of the sequence data for each dimension with the time sequence of the fault interval; determining a correlation score of the sequence data of each dimension and the time sequence of the fault interval according to the quality score and the initial correlation; and determining the sequence data with the highest correlation score as a target dimension, and outputting a work order optimization suggestion according to the target dimension. The application can improve the efficiency of equipment operation and maintenance.
Fig. 2 is a flowchart of a fault maintenance optimization 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 fault maintenance optimization method provided by the embodiment of the application comprises the following steps of.
S20, acquiring attribute data of a plurality of historical worksheets.
In an embodiment of the present application, the attribute data is used to characterize data of multiple dimensions corresponding to fault maintenance; wherein the attribute data includes work order time, whole order time, response time, maintenance time, downtime, and inter-fault time. Specifically, the work order time is used for representing the time of generating a historical work order, namely the time of equipment failure; the whole time is used for representing the time consumed by the fault corresponding to the historical work order after maintenance is finished; the response time is used for representing the time interval from the reception of the historical work order to the start of maintenance by the maintenance personnel; the maintenance time is used for representing the time spent by maintenance personnel from the start of maintenance to the completion of maintenance; the downtime is used for representing the downtime of equipment caused by the fault corresponding to the historical work order; the fault interval time is used for representing the interval between the event of fault occurrence corresponding to any one historical work order and the time of the last fault occurrence.
S21, constructing sequence data corresponding to each dimension according to the attribute data, wherein the sequence data comprises an integer time sequence, a response time sequence, a maintenance time sequence, a shutdown time sequence and a fault interval time sequence.
In an embodiment of the present application, the constructing the sequence data corresponding to each dimension according to the attribute data includes: sequencing the plurality of historical worksheets according to the order from the early to the late of the worksheet time corresponding to each historical worksheet; and combining the data in each historical work order according to the sequence of the historical work orders.
S22, determining quality scores of the corresponding historical worksheets according to the fault interval time and the downtime of each historical worksheet, wherein the quality scores are used for representing the confidence degrees of the attribute data corresponding to the historical worksheets.
In an embodiment of the present application, the smaller the fault interval time, the higher the maintenance quality corresponding to the historical work order, the better the maintenance plan corresponding to the historical work order has, and thus the higher the quality score of the historical work order. The higher the quality score, the higher the referenceability of the historical worksheet, and the higher the confidence. The larger the downtime is, the longer the time spent in processing the fault corresponding to the historical work order is, the more serious the fault corresponding to the historical work order is, and the higher the referenceability of the historical work order is, the higher the confidence is.
In an embodiment of the present application, the determining the quality score of each historical worksheet according to the failure interval time and the downtime of each historical worksheet includes: normalizing the downtime of the plurality of historical worksheets to obtain normalized downtime; normalizing the fault interval time of the plurality of historical worksheets to obtain normalized fault interval time; determining the quality score from a normalized downtime and a normalized failure time; wherein the manner in which the quality score is determined satisfies the following relationship:
Wherein i represents an index of the history worksheet; z i represents the quality score of the ith historical worksheet; a i represents the normalized fault interval time corresponding to the ith historical worksheet; b i represents the normalized downtime corresponding to the ith historical work order; e represents a natural constant.
S23, initial correlation of the sequence data of each dimension and the fault interval time sequence is respectively determined.
In one embodiment of the present application, to determine a target dimension from sequence data of multiple dimensions, an initial correlation of sequence data of each dimension with a time sequence of failure intervals may be first determined. Wherein the initial correlation is used for representing the influence degree of the sequence data of each dimension on the fault interval time sequence, and when the initial correlation is larger, determining the initial correlation of the sequence data of any dimension and the fault interval time sequence comprises: determining outliers in the sequence data of any one dimension and the number of the outliers; determining initial confidence of the sequence data of any one dimension according to the outlier and the quantity of the outlier; determining a correlation coefficient between the sequence data of any one dimension and the fault interval time sequence; and determining the product of the initial confidence coefficient and the correlation coefficient as initial correlation.
In an embodiment of the present application, the determining the initial confidence of the sequence data of the arbitrary dimension according to the outlier and the number of outliers includes: determining a dispersion of the outliers; the manner of determining the initial confidence of the sequence data of any one dimension according to the dispersion degree and the quantity of the dispersion values satisfies the following relation:
Wherein y represents the initial confidence of the sequence data of any one dimension; c represents the dispersion of the outliers; d represents the number of outliers; e represents a natural constant. The degree of dispersion is used for representing the degree of dispersion of the outliers, and when the degree of dispersion is larger, the degree of dispersion of the outliers in the sequence data of any dimension is more discrete, so that the initial confidence of the sequence data of the dimension is lower; when the number of outliers is larger, it is indicated that the more outliers in the sequence data, the lower the initial confidence of the sequence data.
In an embodiment of the present application, the determining the dispersion of the outliers includes: determining data except the outlier in the sequence data of any dimension as basic data; determining a first difference between every two base data; determining the average value of the first difference values to obtain a first average value; determining a second difference between each two outliers; determining the average value of the second difference values to obtain a second average value; and determining the ratio of the second mean value to the first mean value as the dispersion.
The first difference value is used for representing the difference degree between any two basic data, and when the difference degree is smaller, the two basic data are similar; thus, when the first average value is larger, the average distribution degree of the whole basic data is more concentrated; the second difference value is used for representing the difference degree between any two outliers, and when the difference degree is smaller, the two outliers are similar; thus, when the second mean value is larger, the average distribution degree of the outlier whole is more concentrated; thus, when the ratio of the second mean value to the first mean value is larger, it is indicated that the degree of distribution of the outliers is more discrete with respect to the degree of distribution of the base data, the degree of dispersion of the outliers is larger.
And S24, determining a correlation score of the sequence data of each dimension and the fault interval time sequence according to the quality score and the initial correlation.
In an embodiment of the present application, the determining the correlation score of any one of the sequence data and the time series between failures according to the quality score and the initial correlation includes: determining the maximum time delay between any one sequence data and the fault interval time sequence; determining a correlation score according to the quality score, the maximum delay and the initial correlation, wherein the correlation score satisfies the following relation:
Wherein r represents the relevance score; z represents the quality score; t represents the maximum delay; s represents the initial correlation. When the maximum time delay between any one sequence data and the fault interval time sequence is smaller, the influence of the sequence data on the fault interval time sequence is indicated to be corresponding to smaller time hysteresis, and the correlation score between the sequence data and the fault interval time sequence is smaller; when the quality score of any one sequence data is higher, the confidence of the sequence data is higher, and the correlation score between the sequence data and the fault interval time sequence is higher.
In one embodiment of the present application, the determining the maximum delay includes: shifting any one sequence data back for one unit time for a plurality of times; calculating the dot product of any sequence data after each backward shift and the fault interval time sequence; and determining the unit time corresponding to the maximum dot product as the maximum time delay.
And S25, determining the sequence data with the highest correlation score as a target dimension, and outputting a work order optimization suggestion according to the target dimension.
In an embodiment of the present application, the target dimension is used to represent the sequence data with higher correlation with the time sequence between failures, that is, the influence of the sequence data corresponding to the target dimension on the time sequence between failures is larger, and the time lag of the sequence data corresponding to the target dimension when affecting the time sequence between failures is smaller. In order to optimize the worksheet according to the target dimension, further improve the efficiency of fault maintenance, worksheet optimization suggestions can be output according to the target dimension, and further accurate equipment maintenance suggestions can be provided for maintenance personnel in time. For example, when the target dimension is the response time sequence, the response time sequence is indicated to have the strongest influence on the fault interval time sequence, and the work order optimization suggestion can be output according to the response time sequence. The worksheet optimization suggestion is used for representing a suggestion for optimizing data of a certain dimension in a worksheet to improve equipment maintenance efficiency, and the worksheet optimization suggestion can be text data, voice data and the like, and the worksheet optimization suggestion is not limited in this regard. For example, the work order optimization suggestion may be: "optimize response time in worksheets to promote efficiency of equipment maintenance".
According to the technical scheme, the attribute data of a plurality of historical worksheets at least comprising the fault interval time are acquired; constructing sequence data which at least comprises a fault interval time sequence and corresponds to each dimension according to the attribute data; determining a quality score of the corresponding historical worksheets according to the fault interval time and the downtime of each historical worksheet, wherein the quality score is used for representing the confidence coefficient of the attribute data corresponding to the historical worksheets; determining initial correlations of the sequence data for each dimension with the time sequence of the fault interval; determining a correlation score of the sequence data of each dimension and the time sequence of the fault interval according to the quality score and the initial correlation; and determining the sequence data with the highest correlation score as a target dimension, and outputting a work order optimization suggestion according to the target dimension. The dimension to be optimized is determined according to the correlation between the sequences, the downtime and the fault interval time, so that the efficiency of equipment operation and maintenance can be improved.
Referring to fig. 3, fig. 3 is a functional block diagram of a fault maintenance optimization apparatus according to an embodiment of the present application. The fault maintenance optimization 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 attribute data of a plurality of historical worksheets; the attribute data are used for representing data of a plurality of dimensions corresponding to fault maintenance; wherein the attribute data includes work order time, whole order time, response time, maintenance time, downtime, and inter-fault time.
The determining module 311 is configured to construct, according to the attribute data, sequence data corresponding to each dimension, where the sequence data includes an integer time sequence, a response time sequence, a maintenance time sequence, a downtime time sequence, and a fault interval time sequence.
The determining module 311 is further configured to determine a quality score of each historical work order according to the failure interval time and the downtime of each historical work order, where the quality score is used to characterize the confidence level of the attribute data corresponding to the historical work order.
The determining module 311 is further configured to determine an initial correlation between the sequence data of each dimension and the time sequence of the fault interval, respectively.
The determining module 311 is further configured to determine a correlation score of the sequence data of each dimension and the time sequence of the fault interval according to the quality score and the initial correlation.
The determining module 311 is further configured to determine that the sequence data with the highest relevance score is a target dimension, and output a worksheet optimization suggestion according to the target dimension.
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 a fault maintenance optimization method according to any of the above embodiments.
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, for example a fault maintenance optimization 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 fault maintenance optimization method, the processor 13 being executable to implement: acquiring attribute data of a plurality of historical worksheets; the attribute data are used for representing data of a plurality of dimensions corresponding to fault maintenance; wherein the attribute data includes work order time, whole order time, response time, maintenance time, downtime, and fault interval time; constructing sequence data corresponding to each dimension according to the attribute data, wherein the sequence data comprises an integral time sequence, a response time sequence, a maintenance time sequence, a shutdown time sequence and a fault interval time sequence; determining a quality score of each historical work order according to the fault interval time and the downtime of each historical work order, wherein the quality score is used for representing the confidence coefficient of attribute data corresponding to the historical work order; determining initial correlations of the sequence data for each dimension with the time sequence of the fault interval; determining a correlation score of the sequence data of each dimension and the fault interval time sequence according to the quality score and the initial correlation; and determining the sequence data with the highest correlation score as a target dimension, and outputting a work order optimization suggestion according to the target dimension.
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 kind of malfunction maintenance optimization 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 (for example, executing a kind of malfunction maintenance optimization program, etc.) stored in the memory 12, 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 fault maintenance optimization 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 described above 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 fault maintenance optimization 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 a fault maintenance optimization 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 fault maintenance optimization method applied to an electronic device, the method comprising:
Acquiring attribute data of a plurality of historical worksheets, wherein the attribute data are used for representing data of a plurality of dimensions corresponding to fault maintenance, and the attribute data comprise worksheet time, whole worksheet time, response time, maintenance time, downtime and fault interval time;
constructing sequence data corresponding to each dimension according to the attribute data, wherein the sequence data comprises an integral time sequence, a response time sequence, a maintenance time sequence, a shutdown time sequence and a fault interval time sequence;
determining a quality score of each historical work order according to the fault interval time and the downtime of each historical work order, wherein the quality score is used for representing the confidence coefficient of attribute data corresponding to the historical work order;
Determining initial correlations of the sequence data for each dimension with the time sequence of the fault interval;
determining a correlation score of the sequence data of each dimension and the fault interval time sequence according to the quality score and the initial correlation;
And determining a target dimension corresponding to the sequence data with the highest correlation score, and outputting a work order optimization suggestion according to the target dimension.
2. The fault maintenance optimization method of claim 1, wherein constructing sequence data corresponding to each dimension according to the attribute data comprises:
Sequencing the plurality of historical worksheets according to the order from the early to the late of the worksheet time corresponding to each historical worksheet;
and combining the data in each historical work order according to the sequence of the historical work orders.
3. The method of claim 1, wherein determining a quality score for each historical worksheet based on the time between failures and the downtime of the corresponding historical worksheet comprises:
Normalizing the downtime of the plurality of historical worksheets to obtain normalized downtime;
Normalizing the fault interval time of the plurality of historical worksheets to obtain normalized fault interval time;
Determining the quality score from a normalized downtime and a normalized failure time;
wherein the manner in which the quality score is determined satisfies the following relationship:
，
Wherein i represents an index of the history worksheet; z i represents the quality score of the ith historical worksheet; a i represents the normalized fault interval time corresponding to the ith historical worksheet; b i represents the normalized downtime corresponding to the ith historical work order; e represents a natural constant.
4. The method of claim 1, wherein determining an initial correlation of sequence data of any one dimension with the time series of inter-fault intervals comprises:
Determining outliers in the sequence data of any one dimension and the number of the outliers;
determining initial confidence of the sequence data of any one dimension according to the outlier and the quantity of the outlier;
Determining a correlation coefficient between the sequence data of any one dimension and the fault interval time sequence;
and determining the product of the initial confidence coefficient and the correlation coefficient as initial correlation.
5. The fault maintenance optimization method of claim 4, wherein said determining initial confidence of sequence data for said any one dimension based on said outliers and the number of outliers comprises:
determining a dispersion of the outliers;
the manner of determining the initial confidence of the sequence data of any one dimension according to the dispersion degree and the quantity of the dispersion values satisfies the following relation:
，
Wherein y represents the initial confidence of the sequence data of any one dimension; c represents the dispersion of the outliers; d represents the number of outliers; e represents a natural constant.
6. The method of claim 5, wherein said determining the dispersion of said outliers comprises:
Determining data except the outlier in the sequence data of any dimension as basic data;
Determining a first difference between every two base data;
Determining the average value of the first difference values to obtain a first average value;
Determining a second difference between each two outliers;
Determining the average value of the second difference values to obtain a second average value;
and determining the ratio of the second mean value to the first mean value as the dispersion.
7. The method of claim 1, wherein said determining a correlation score for any one of the sequence data and the time-between-failure sequence based on the quality score and the initial correlation comprises:
Determining the maximum time delay between any one sequence data and the fault interval time sequence;
Determining a correlation score according to the quality score, the maximum delay and the initial correlation, wherein the correlation score satisfies the following relation:
，
wherein r represents the relevance score; z represents the quality score; t represents the maximum delay; s represents the initial correlation.
8. The method of fault maintenance optimization of claim 7, wherein said determining a maximum time delay comprises:
Shifting any one sequence data back for one unit time for a plurality of times;
calculating the dot product of any sequence data after each backward shift and the fault interval time sequence;
And determining the unit time corresponding to the maximum dot product as the maximum time delay.
9. A fault maintenance optimisation apparatus, the apparatus comprising means for implementing a fault maintenance optimisation method as claimed in any one of claims 1 to 8, the apparatus comprising:
The acquisition module is used for acquiring attribute data of a plurality of historical worksheets; the attribute data are used for representing data of a plurality of dimensions corresponding to fault maintenance; wherein the attribute data includes work order time, whole order time, response time, maintenance time, downtime, and fault interval time;
The determining module is used for constructing sequence data corresponding to each dimension according to the attribute data, wherein the sequence data comprises an integer single time sequence, a response time sequence, a maintenance time sequence, a shutdown time sequence and a fault interval time sequence;
The determining module is further configured to determine a quality score of each historical work order according to the fault interval time and the downtime of each historical work order, where the quality score is used to characterize the confidence level of the attribute data corresponding to the historical work order;
The determining module is further used for determining initial correlation of the sequence data of each dimension and the fault interval time sequence respectively;
The determining module is further configured to determine a relevance score of the sequence data of each dimension and the time sequence of the fault interval according to the quality score and the initial relevance;
the determining module is further configured to determine that the sequence data with the highest relevance score is a target dimension, and output a worksheet optimization suggestion according to the target dimension.
10. An electronic device comprising a processor and a memory, wherein the processor is configured to implement a fault maintenance optimization method according to any one of claims 1 to 8 when executing a computer program stored in the memory.
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