CN115001943A - Equipment fault identification method and equipment based on big data and storage medium - Google Patents
Equipment fault identification method and equipment based on big data and storage medium Download PDFInfo
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract
The application discloses a method, equipment and a storage medium for identifying equipment faults based on big data, wherein the method for identifying the equipment faults based on the big data comprises the following steps: acquiring equipment running state data corresponding to equipment to be detected, and acquiring weight value information determined by historical equipment running state data; and identifying equipment faults according to the equipment running state data and the weight value information to obtain an equipment fault identification result. The application solves the technical problem of poor equipment fault identification effect in the prior art.
Description
Technical Field
The present application relates to the field of big data technologies, and in particular, to a method, a device, and a storage medium for identifying a device fault based on big data.
Background
Along with the promotion of safety consciousness and privacy consciousness, more and more families and enterprises begin to use monitoring equipment to monitor surrounding environment, but people rely on monitoring equipment to bring safe and reliable high-quality convenient life, but neglected monitoring equipment self and can go wrong, and present monitoring equipment is nevertheless poor to the discernment effect of trouble although can remind the trouble that self produced.
Disclosure of Invention
The application mainly aims to provide a method, equipment and a storage medium for identifying equipment faults based on big data, and aims to solve the technical problem that in the prior art, the equipment fault identification effect is poor.
In order to achieve the above object, the present application provides a big data-based device failure identification method, where the big data-based device failure identification method includes:
acquiring equipment running state data corresponding to equipment to be detected, and acquiring weight value information determined by historical equipment running state data;
and identifying equipment faults according to the equipment running state data and the weight value information to obtain an equipment fault identification result.
Optionally, the step of performing device fault identification according to the device operating state data and the weight value information to obtain a device fault identification result includes:
dividing the equipment running state data into user alarm data and user offline data;
and identifying equipment faults according to the weight value information, the user alarm data and the user offline data to obtain a fault identification result.
Optionally, the step of performing equipment fault identification according to the weight value information, the user alarm data, and the user offline data to obtain a fault identification result includes:
determining a fault judgment value according to the weight value information, the user alarm data and the user offline data, wherein the fault judgment value is used for representing the degree of the fault possibility of the equipment;
if the fault discrimination value is larger than a preset equipment fault threshold value, detecting whether the equipment is on line normally within a preset time range;
if the equipment is normally on line within the preset time range, the fault identification result is that the equipment does not have a fault;
if the equipment is not normally on line within the preset time range, the fault identification result is that the equipment is in fault;
and if the fault discrimination value is not greater than the preset equipment fault threshold value, the fault identification result indicates that the equipment is not in fault.
Optionally, the step of determining a fault discrimination value according to the weight value information, the user alarm data, and the user offline data includes:
determining a first judgment value according to the user alarm data, and determining a first behavior value according to the first judgment value and a preset alarm base number;
determining a second judgment value according to the user offline data, and determining a second behavior value according to the second judgment value and the weight value information;
and generating the fault discrimination value according to the first behavior value and the second behavior value.
Optionally, before the step of acquiring device operating state data corresponding to the device to be detected and acquiring weight value information determined by historical device operating state data, the method for identifying the device fault based on the big data further includes:
acquiring historical equipment operation state data corresponding to all similar equipment corresponding to the equipment to be detected;
according to the equipment identification code, the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data, carrying out data classification on the historical equipment operation state data to obtain a data classification result;
and determining the weight value information according to the data classification result.
Optionally, the data classification result comprises a first data classification result and a second data classification result,
the step of classifying the data of the operation state of the historical device according to the device identification code, the geographic position corresponding to the similar device and the data acquisition time corresponding to the operation state data of the historical device to obtain a data classification result comprises the following steps:
classifying the historical equipment operation state data according to the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data to obtain a first data classification result;
and classifying the historical equipment operation state data according to the equipment identification code and the data acquisition time corresponding to the historical equipment operation state data to obtain a second data classification result.
Optionally, the weight value information includes a first weight value and a second weight value,
the step of determining the weight value information according to the data classification result includes:
determining a first weight value according to the data volume proportion in the target time period in the first data classification result;
and determining a second weight value according to the data volume ratio in the target time period in the second data classification result.
In order to achieve the above object, the present application further provides an apparatus for identifying an equipment failure based on big data, where the apparatus for identifying an equipment failure based on big data includes:
the data acquisition module is used for acquiring equipment running state data corresponding to the equipment to be detected and acquiring weight value information determined by historical equipment running state data;
and the fault identification module is used for identifying equipment faults according to the equipment running state data and the weight value information to obtain an equipment fault identification result.
Optionally, the fault identification module is further configured to:
dividing the equipment running state data into user alarm data and user offline data;
and identifying equipment faults according to the weight value information, the user alarm data and the user offline data to obtain a fault identification result.
Optionally, the fault identification module is further configured to:
determining a fault discrimination value according to the weight value information, the user alarm data and the user offline data, wherein the fault discrimination value is used for representing the degree of the fault possibility of the equipment;
if the fault discrimination value is larger than a preset equipment fault threshold value, detecting whether the equipment is on line normally within a preset time range;
if the equipment is normally on line within the preset time range, the fault identification result is that the equipment does not have a fault;
if the equipment is not normally on line within the preset time range, the fault identification result is that the equipment is in fault;
and if the fault discrimination value is not greater than the preset equipment fault threshold value, the fault identification result indicates that the equipment is not in fault.
Optionally, the fault identification module is further configured to:
determining a first judgment value according to the user alarm data, and determining a first behavior value according to the first judgment value and a preset alarm base number;
determining a second judgment value according to the user offline data, and determining a second behavior value according to the second judgment value and the weight value information;
and generating the fault discrimination value according to the first behavior value and the second behavior value.
Optionally, the big-data-based device failure identification apparatus is further configured to:
acquiring historical equipment operation state data corresponding to all similar equipment corresponding to the equipment to be detected;
according to the equipment identification code, the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data, carrying out data classification on the historical equipment operation state data to obtain a data classification result;
and determining the weight value information according to the data classification result.
Optionally, the big-data-based device fault identifying apparatus is further configured to:
classifying the historical equipment operation state data according to the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data to obtain a first data classification result;
and classifying the historical equipment operation state data according to the equipment identification code and the data acquisition time corresponding to the historical equipment operation state data to obtain a second data classification result.
Optionally, the big-data-based device failure identification apparatus is further configured to:
determining a first weight value according to the data volume proportion in the target time period in the first data classification result;
and determining a second weight value according to the data volume ratio in the target time period in the second data classification result.
The present application further provides an electronic device, including: the device fault identification method based on big data comprises a memory, a processor and a program of the device fault identification method based on big data, wherein the program of the device fault identification method based on big data can realize the steps of the device fault identification method based on big data when being executed by the processor.
The present application also provides a computer-readable storage medium having stored thereon a program for implementing the big-data based device failure recognition method, the program implementing the big-data based device failure recognition method as described above when executed by a processor.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the big data based device failure identification method as described above.
The application provides an equipment fault identification method, equipment and a storage medium based on big data, and the method, the equipment and the storage medium are used for acquiring equipment running state data corresponding to equipment to be detected and acquiring weight value information determined by historical equipment running state data; and identifying equipment faults according to the equipment running state data and the weight value information to obtain an equipment fault identification result. That is, according to the method and the device for identifying the fault of the user equipment, the running state data of the equipment is collected, the weight value information of the running state data of the historical equipment is obtained, and the fault judgment value for judging whether the user equipment is in fault or not is obtained according to the preset calculation mode, so that the error prompt of normal equipment is filtered to find abnormal equipment, the problem equipment can be accurately positioned, and the effect of equipment fault identification is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a first embodiment of a big data-based device fault identification method according to the present application;
FIG. 2 is a schematic flow chart illustrating a second embodiment of the big data-based device failure identification method according to the present application;
fig. 3 is a schematic device structure diagram of a hardware operating environment related to a big data-based device fault identification method in an embodiment of the present application.
The implementation of the objectives, functional features, and advantages of the present application will be further described with reference to the accompanying drawings.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures are described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
In a first embodiment of the big data-based device fault identification method of the present application, referring to fig. 1, the big data-based device fault identification method includes:
step S10, collecting equipment running state data corresponding to the equipment to be detected, and acquiring weight value information determined by historical equipment running state data;
and step S20, identifying equipment faults according to the equipment running state data and the weight value information to obtain an equipment fault identification result.
In this embodiment, it should be noted that the device running state data corresponding to the device to be detected at least includes alarm data and offline data of the user device within a preset fixed time period, if the user device alarms once, the number of the alarm data is increased by one, and if the user device is offline once, the number of the offline data is increased by one, and the acquisition process may be implemented by Kafka; the weight value information determined by the historical device running state data at least comprises a first weight value and a second weight value, the first weight value can be a data volume proportion in a target time period in a first data classification result, the second weight value can be a data volume proportion in a target time period in a second data classification result, and a minimum value and a maximum value can be preset for the proportion; when the fault of the equipment is identified, the equipment is informed and alarmed, and when the fault of the user equipment is identified but the equipment is abnormal, the abnormality is stored for subsequent checking, and the classification and calculation process in the application can be realized through Spark.
As one example, steps S10 to S20 include:
acquiring equipment running state data corresponding to equipment to be detected in a preset fixed time period, and acquiring weight value information of historical equipment running state data; according to the equipment running state data, alarm data of the equipment and offline data of the equipment are obtained in a classified mode, the weight value information, the alarm data and the offline data are calculated in a preset calculation mode to obtain a fault judgment value, if the fault judgment value is larger than a preset equipment fault threshold value and the equipment is always in an offline state in a follow-up fixed time period, the equipment is identified to be in fault, notification and alarm can be carried out, if the fault judgment value is larger than the preset equipment fault threshold value but the equipment is on line in follow-up fixed time, the equipment is identified not to be in fault, but an abnormal result is stored to be used for follow-up abnormal check, and if the fault judgment value is not larger than the preset equipment fault threshold value, the user equipment is identified not to be in fault.
The step of identifying the equipment fault according to the equipment running state data and the weight value information to obtain an equipment fault identification result comprises the following steps:
step S21, dividing the equipment running state data into user alarm data and user off-line data;
and step S22, identifying equipment faults according to the weight value information, the user alarm data and the user offline data to obtain fault identification results.
In this embodiment, it should be noted that the device operation state data is data collected by the device to be detected within a preset period of time, for example, the device operation state data may be data collected in an hour before a current time point, and the device operation state data may be at least divided into two types of data, namely user alarm data and user offline data.
As an example, the steps S21 to S22 include:
acquiring user alarm data and user offline data from the equipment running state data, and if the two data do not exist, indicating that the user equipment does not generate alarm and offline actions; and calculating the weight value information, the user alarm data and the user offline data by a preset calculation method to obtain a fault judgment value, if the fault judgment value is larger than a preset equipment fault threshold value and the equipment is always in an offline state in a subsequent fixed time period, identifying that the equipment has a fault, notifying and alarming, if the fault judgment value is larger than the preset equipment fault threshold value but the equipment is online in the subsequent fixed time period, identifying that the equipment has no fault, but storing an abnormal result for subsequent abnormal check, and if the fault judgment value is not larger than the equipment fault threshold value, identifying that the equipment has no fault.
The step of identifying equipment faults according to the weight value information, the user alarm data and the user offline data to obtain a fault identification result comprises the following steps:
step S221, determining a fault judgment value according to the weight value information, the user alarm data and the user offline data, wherein the fault judgment value is used for representing the degree of the possibility of equipment failure;
step S222, if the fault discrimination value is larger than a preset equipment fault threshold value, detecting whether the equipment is on line normally within a preset time range;
step S223, if the equipment is normally on line within the preset time range, the fault identification result is that the equipment does not have a fault;
step S224, if the equipment is not on line normally within the preset time range, the fault identification result is that the equipment is in fault;
step S225, if the fault judgment value is not greater than the preset device fault threshold, the fault identification result indicates that the device has not failed.
In this embodiment, it should be noted that the fault determination value is a numerical value of a degree of possibility of the equipment failing, and the higher the fault determination value is, the higher the possibility of the equipment failing is.
As an example, steps S221 to S225 include:
obtaining a fault discrimination value by the preset calculation mode according to the weight value information, the user alarm data and the user offline data; if the fault discrimination value is larger than a preset equipment fault threshold value and the equipment is always in an off-line state within a subsequent preset time range, identifying that the equipment has a fault; if the fault discrimination value is larger than a preset equipment fault threshold value, but the equipment is on line within a subsequent preset time range and no other alarm data exists, identifying that the equipment does not have a fault, but storing the abnormal result for subsequent checking, and if the fault discrimination value is smaller than or equal to the preset equipment fault threshold value, identifying that the equipment does not have a fault.
Wherein, the step of determining a fault discrimination value according to the weight value information, the user alarm data and the user offline data comprises:
step S2211, determining a first judgment value according to the user alarm data, and determining a first behavior value according to the first judgment value and a preset alarm base number;
step S2212, determining a second decision value according to the offline data of the user, and determining a second behavior value according to the second decision value and the weight value information;
step S2213, generating the fault determination value according to the first behavior value and the second behavior value.
In this embodiment, it should be noted that the first judgment value and the second judgment value are values for judging whether data exists in the user alarm data and the user offline data, if the user alarm data exists, the first judgment value is 1, if the user alarm data does not exist, the first judgment value is 0, if the user offline data exists, the second judgment value is 1, if the user offline data does not exist, the second judgment value is 0, the preset alarm base number is a base number in the preset calculation mode, and an initial value of the preset alarm base number is 2, which can be adjusted according to needs; the weight value information at least includes a first weight value and a second weight value.
As an example, steps S2211 to S2213 include:
calculating according to the first judgment value and the preset alarm base number to obtain a first behavior value; calculating according to the second judgment value and the weight value information to obtain a second behavior value; and calculating the sum of the first behavior value and the second behavior value to obtain a fault discrimination value.
As an embodiment, the preset calculation manner of the fault discrimination value may be:
the weight value information at least includes a first weight value and a second weight value, the first weight value is m1, the second weight value is m2, the first judgment value is n1, the second judgment value is n2, the preset first weight base number is a1, the preset second weight base number is a2, the preset alarm base number is b, if the data amount ratio in the target time period in the data classification result is set as c, the calculation manner of the first weight value and the second weight is usually 1/c, the first judgment value and the second judgment value are values for judging whether data exists in the user alarm data and the user offline data, if data exists, the value is 1, if data does not exist, the value is 0, the initial value of the first weight base number is 1.56, and the initial value of the second base number is 0.44, the initial numerical value of the preset alarm base number is 2, and the first weight base number, the second weight base number and the preset alarm base number can be set according to requirements.
The fault discrimination value is a degree value of the fault possibility of the equipment, the initial value of the preset equipment fault threshold value is 3.4, the initial range of the preset time range is 3 hours, and the preset equipment fault threshold value and the preset time range can be automatically adjusted according to requirements; if the fault discrimination value is larger than a preset equipment fault threshold value and the equipment is always in an off-line state within a subsequent preset time range, identifying that the equipment has a fault; if the fault discrimination value is larger than a preset equipment fault threshold value but is on line within a subsequent preset time range and no other alarm data exists, identifying that the equipment does not have a fault, but storing the abnormal result for subsequent checking, and if the fault discrimination value is smaller than or equal to the preset equipment fault threshold value, identifying that the equipment does not have a fault. Error reminding of most normal equipment can be filtered according to a preset calculation mode, so that the problem equipment can be positioned more accurately, and the early warning effect is improved.
The application provides an equipment fault identification method, equipment and a storage medium based on big data, and the method, the equipment and the storage medium are used for acquiring equipment running state data corresponding to equipment to be detected and acquiring weight value information determined by historical equipment running state data; and identifying equipment faults according to the equipment running state data and the weight value information to obtain an equipment fault identification result. That is, according to the method and the device for identifying the fault of the user equipment, the running state data of the equipment is collected, the weight value information of the running state data of the historical equipment is obtained, and the fault judgment value for judging whether the user equipment is in fault or not is obtained according to the preset calculation mode, so that the error prompt of normal equipment is filtered to find abnormal equipment, the problem equipment can be accurately positioned, and the effect of equipment fault identification is improved.
Example two
Further, referring to fig. 2, based on the first embodiment of the present application, in another embodiment of the present application, the same or similar contents to the first embodiment described above may be referred to the above description, and are not repeated again in the following. On this basis, before the step of acquiring the device running state data corresponding to the device to be detected and acquiring the weight value information determined by the historical device running state data, the device fault identification method based on big data further includes:
step A10, collecting historical equipment operation state data corresponding to all similar equipment corresponding to the equipment to be detected;
step A20, performing data classification on the historical equipment operation state data according to the equipment identification code, the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data to obtain a data classification result;
step a30, determining the weight value information according to the data classification result.
In this embodiment, it should be noted that the historical device operating state data, which is commonly corresponding to all similar devices corresponding to the device to be detected, is alarm data and offline data of all devices within a period of time that are continuously received, where the time is usually one year, the data classification result includes a first data classification result and a second data classification result, the first data classification result may be a data classification result classified according to a geographic location corresponding to the similar device and data acquisition time corresponding to the historical device operating state data, and the second data classification result may be a data classification result classified according to the device identification code and data acquisition time corresponding to the historical device operating state data.
As an example, the steps a10 to a30 include:
continuously collecting historical equipment running state data within a period of time; classifying the data of the operation state of the historical equipment, classifying the data of the operation state of the historical equipment according to the corresponding geographical position of the similar equipment, classifying the data of the operation state of the historical equipment according to the corresponding data acquisition time of the data of the operation state of the historical equipment to obtain a first data classification result, classifying the data of the operation state of the historical equipment according to the equipment identification code, and obtaining a second data classification result according to the corresponding data acquisition time of the data of the operation state of the historical equipment; and determining the weight value information according to the data classification result.
Wherein the data classification result comprises a first data classification result and a second data classification result,
the step of performing data classification on the historical equipment operation state data according to the equipment identification code, the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data to obtain a data classification result comprises the following steps:
step A21, classifying the historical equipment operation state data according to the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data to obtain a first data classification result;
step A22, classifying the historical equipment operation state data according to the equipment identification code and the data acquisition time corresponding to the historical equipment operation state data to obtain a second data classification result.
In this embodiment, it should be noted that the geographic location corresponding to the similar device may be a city, the data acquisition time corresponding to the historical device operating state data may be a specific time period, and the data acquisition time may be twenty-four solar terms with seasonality; the equipment identification code is an identification code for identifying specific equipment, and the equipment identification code of the equipment is unique.
As an example, step a21 through step a22 include:
classifying the data of the historical equipment running state, classifying according to the city where the equipment is located, arranging the classified data according to a time sequence, classifying according to different time periods in one year, and classifying according to twenty-four solar terms to obtain a first data classification result; and performing data classification on the historical equipment operation state data, classifying according to the equipment identification number to obtain historical operation data of a single user, and classifying according to a working day or a holiday to obtain a second data classification result.
It should be noted that, because work and rest are different between different cities, the historical equipment operation state data is classified according to the cities to obtain historical city operation data of different cities, such as beijing, hangzhou, Changsha, and the like; the city historical operation data is classified according to dates, for example, twenty-four solar terms are taken as nodes, one year is divided into multiple sections, one section is from spring to summer, one section is from summer to autumn, one section is from autumn to winter, and one section is from winter to spring, so that first data classification results classified according to different cities and different dates are obtained, the historical equipment classification state data can be data from Beijing spring to summer, and can also be data from Shanghai autumn to winter, and similarly, the data in the second data classification result can be data of a working day of a certain user equipment, and can also be data of a holiday of another user equipment.
Wherein the weight value information includes a first weight value and a second weight value,
the step of determining the weight value information according to the data classification result includes:
step A31, determining a first weight value according to the data volume proportion in the target time period in the first data classification result;
step a32, determining a second weight value according to the data volume ratio in the target time period in the second data classification result.
As an example, the steps a31 to a32 include:
for example, if the data amount from 0 o ' clock to 1 o ' clock in beijing beginning of spring to beginning of summer is 0.2 of the total data amount from 0 o ' clock in beijing beginning of spring to beginning of summer, the first weight value is calculated to be 1/0.2 ═ 5, it should be noted that the minimum weight value defaults to 1 and the maximum weight value defaults to 8, if the calculated first weight value is out of the range from the minimum weight value to the maximum weight value, the maximum weight value or the minimum weight value is taken as the first weight value, and the calculation method of the second weight value is consistent with the calculation method of the first weight value, for example, if the data amount at 3 pm on a certain user equipment working day is 0.5 of the total data amount on a certain user equipment working day, the second weight value is calculated to be 1/0.5 ═ 2.
In this embodiment, a method for determining weight value information is provided, which can reduce misjudgment of an equipment fault identification algorithm.
EXAMPLE III
The embodiment of the present application further provides an apparatus for identifying an equipment failure based on big data, where the apparatus for identifying an equipment failure based on big data includes:
the data acquisition module is used for acquiring equipment running state data corresponding to equipment to be detected and acquiring weight value information determined by historical equipment running state data;
and the fault identification module is used for identifying equipment faults according to the equipment running state data and the weight value information to obtain an equipment fault identification result.
Optionally, the fault identification module is further configured to:
dividing the equipment running state data into user alarm data and user offline data;
and identifying equipment faults according to the weight value information, the user alarm data and the user offline data to obtain a fault identification result.
Optionally, the fault identification module is further configured to:
determining a fault judgment value according to the weight value information, the user alarm data and the user offline data, wherein the fault judgment value is used for representing the degree of the fault possibility of the equipment;
if the fault discrimination value is larger than a preset equipment fault threshold value, detecting whether the equipment is on line normally within a preset time range;
if the equipment is normally on line within the preset time range, the fault identification result is that the equipment does not have a fault;
if the equipment is not normally on line within the preset time range, the fault identification result is that the equipment is in fault;
and if the fault discrimination value is not greater than the preset equipment fault threshold value, the fault identification result indicates that the equipment is not in fault.
Optionally, the fault identification module is further configured to:
determining a first judgment value according to the user alarm data, and determining a first behavior value according to the first judgment value and a preset alarm base number;
determining a second judgment value according to the user offline data, and determining a second behavior value according to the second judgment value and the weight value information;
and generating the fault discrimination value according to the first behavior value and the second behavior value.
Optionally, the big-data-based device failure identification apparatus is further configured to:
acquiring historical equipment operation state data corresponding to all similar equipment corresponding to the equipment to be detected;
according to the equipment identification code, the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data, carrying out data classification on the historical equipment operation state data to obtain a data classification result;
and determining the weight value information according to the data classification result.
Optionally, the big-data-based device fault identifying apparatus is further configured to:
classifying the historical equipment operation state data according to the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data to obtain a first data classification result;
and classifying the historical equipment operation state data according to the equipment identification code and the data acquisition time corresponding to the historical equipment operation state data to obtain a second data classification result.
Optionally, the big-data-based device failure identification apparatus is further configured to:
determining a first weight value according to the data volume proportion in the target time period in the first data classification result;
and determining a second weight value according to the data volume ratio in the target time period in the second data classification result.
The device fault identification device based on big data provided by the invention adopts the device fault identification method based on big data in the embodiment, so that the technical problem of poor device fault identification effect is solved. Compared with the prior art, the beneficial effects of the device fault identification apparatus based on big data provided by the embodiment of the present invention are the same as those of the device fault identification method based on big data provided by the above embodiment, and other technical features of the device fault identification apparatus based on big data are the same as those disclosed in the above embodiment method, and are not repeated herein.
Example four
An embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the big data based device failure identification method in the first embodiment.
Referring now to FIG. 3, shown is a block diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 3, the electronic device may include a processing means (e.g., a central processing unit, a graphic processor, etc.) that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage means into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device, the ROM, and the RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
Generally, the following systems may be connected to the I/O interface: input devices including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, and the like; output devices including, for example, Liquid Crystal Displays (LCDs), speakers, vibrators, and the like; storage devices including, for example, magnetic tape, hard disk, etc.; and a communication device. The communication means may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device with various systems, it is to be understood that not all illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from a storage means, or installed from a ROM. The computer program, when executed by a processing device, performs the functions defined in the methods of the embodiments of the present disclosure.
The electronic equipment provided by the invention adopts the equipment fault identification method based on the big data in the embodiment, and the technical problem of poor equipment fault identification effect is solved. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiment of the invention are the same as the beneficial effects of the device fault identification method based on big data provided by the embodiment, and other technical features of the electronic device are the same as those disclosed by the embodiment method, which are not repeated herein.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
EXAMPLE five
The present embodiment provides a computer-readable storage medium having stored thereon computer-readable program instructions for performing the method for big data based device failure identification in the first embodiment.
The computer readable storage medium provided by the embodiments of the present invention may be, for example, a USB flash disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination thereof. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer-readable storage medium may be embodied in an electronic device; or may be present alone without being incorporated into the electronic device.
The computer-readable storage medium carries one or more programs which, when executed by an electronic device, cause the electronic device to: acquiring equipment running state data corresponding to equipment to be detected, and acquiring weight value information determined by historical equipment running state data; and identifying equipment faults according to the equipment running state data and the weight value information to obtain an equipment fault identification result.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the names of the modules do not in some cases constitute a limitation of the unit itself.
The computer-readable storage medium provided by the invention stores computer-readable program instructions for executing the big data-based equipment fault identification method, and solves the technical problem of poor equipment fault identification effect. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment of the present invention are the same as the beneficial effects of the big data based device failure identification method provided by the above embodiment, and are not described herein again.
EXAMPLE six
The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the big-data based device failure identification method as described above.
The computer program product provided by the application solves the technical problem of poor equipment fault identification effect. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present invention are the same as those of the device fault identification method based on big data provided by the above embodiment, and are not described herein again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (9)
1. The big data-based equipment fault identification method is characterized by comprising the following steps of:
acquiring equipment running state data corresponding to equipment to be detected, and acquiring weight value information determined by historical equipment running state data;
and identifying equipment faults according to the equipment running state data and the weight value information to obtain an equipment fault identification result.
2. The big data-based equipment fault identification method according to claim 1, wherein the step of performing equipment fault identification according to the equipment operating state data and the weight value information to obtain an equipment fault identification result comprises:
dividing the equipment running state data into user alarm data and user offline data;
and identifying equipment faults according to the weight value information, the user alarm data and the user offline data to obtain a fault identification result.
3. The big-data-based equipment fault identification method according to claim 2, wherein the step of performing equipment fault identification according to the weight value information, the user alarm data and the user offline data to obtain a fault identification result comprises:
determining a fault discrimination value according to the weight value information, the user alarm data and the user offline data, wherein the fault discrimination value is used for representing the degree of the fault possibility of the equipment;
if the fault discrimination value is larger than a preset equipment fault threshold value, detecting whether the equipment is on line normally within a preset time range;
if the equipment is normally on line within the preset time range, the fault identification result is that the equipment does not have a fault;
if the equipment is not normally on line within the preset time range, the fault identification result is that the equipment is in fault;
and if the fault discrimination value is not greater than the preset equipment fault threshold value, the fault identification result indicates that the equipment is not in fault.
4. The big-data-based equipment fault identification method according to claim 3, wherein the step of determining a fault discrimination value according to the weight value information, the user alarm data and the user offline data comprises:
determining a first judgment value according to the user alarm data, and determining a first behavior value according to the first judgment value and a preset alarm base number;
determining a second judgment value according to the user offline data, and determining a second behavior value according to the second judgment value and the weight value information;
and generating the fault discrimination value according to the first behavior value and the second behavior value.
5. The big-data-based equipment fault identification method according to claim 1, wherein before the step of acquiring equipment operation state data corresponding to equipment to be detected and obtaining weight value information determined by historical equipment operation state data, the big-data-based equipment fault identification method further comprises:
acquiring historical equipment running state data corresponding to all similar equipment corresponding to the equipment to be detected;
according to the equipment identification code, the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data, carrying out data classification on the historical equipment operation state data to obtain a data classification result;
and determining the weight value information according to the data classification result.
6. The big-data based equipment failure identification method according to claim 5, wherein the data classification result comprises a first data classification result and a second data classification result,
the step of classifying the data of the operation state of the historical device according to the device identification code, the geographic position corresponding to the similar device and the data acquisition time corresponding to the operation state data of the historical device to obtain a data classification result comprises the following steps:
classifying the historical equipment operation state data according to the geographic position corresponding to the similar equipment and the data acquisition time corresponding to the historical equipment operation state data to obtain a first data classification result;
and classifying the historical equipment operation state data according to the equipment identification code and the data acquisition time corresponding to the historical equipment operation state data to obtain a second data classification result.
7. The big-data based apparatus failure recognition method of claim 5, wherein the weight value information includes a first weight value and a second weight value,
the step of determining the weight value information according to the data classification result comprises:
determining a first weight value according to the data volume proportion in the target time period in the first data classification result;
and determining a second weight value according to the data volume ratio in the target time period in the second data classification result.
8. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the big data based device failure identification method of any of claims 1 to 7.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for implementing a big-data based device failure recognition method, the program being executed by a processor to implement the steps of the big-data based device failure recognition method according to any one of claims 1 to 7.
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