CN115001943B - Equipment fault identification method, equipment and storage medium based on big data - Google Patents
Equipment fault identification method, equipment and storage medium based on big data Download PDFInfo
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- CN115001943B CN115001943B CN202210584449.1A CN202210584449A CN115001943B CN 115001943 B CN115001943 B CN 115001943B CN 202210584449 A CN202210584449 A CN 202210584449A CN 115001943 B CN115001943 B CN 115001943B
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
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- H—ELECTRICITY
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
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Abstract
The application discloses a device fault identification method, device and storage medium based on big data, wherein the device fault identification method based on 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 carrying out equipment fault identification according to the equipment running state data and the weight value information to obtain an equipment fault identification result. The technical problem that equipment fault recognition effect is poor among the prior art has been solved to this application.
Description
Technical Field
The present disclosure relates to the field of big data technologies, and in particular, to a method, an apparatus, and a storage medium for identifying a device failure based on big data.
Background
Along with the improvement of safety awareness and privacy awareness, more families and enterprises begin to use monitoring equipment to monitor surrounding environments, but people neglect to cause problems of the monitoring equipment when relying on the monitoring equipment to bring safe, reliable and high-quality convenience life, and the current monitoring equipment can remind faults generated by the current monitoring equipment, but has poor recognition effect on the faults.
Disclosure of Invention
The main purpose of the application is to provide a device fault identification method, device and storage medium based on big data, and aims to solve the technical problem of poor device fault identification effect in the prior art.
In order to achieve the above object, the present application provides a device failure recognition method based on big data, the device failure recognition method based on big data 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 carrying out equipment fault identification according to the equipment running state data and the weight value information to obtain an equipment fault identification result.
Optionally, the step of performing equipment fault identification according to the equipment running state data and the weight value information to obtain an equipment fault identification result includes:
dividing the equipment running state data into user alarm data and user off-line data;
and carrying out equipment fault identification according to the weight value information, the user alarm data and the user off-line 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 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 possibility of equipment fault;
if the fault discrimination value is larger than a preset equipment fault threshold value, detecting whether equipment is normally on line 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 is not in 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 is that the equipment does not have a fault.
Optionally, the step of determining the 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 the equipment operation state data corresponding to the equipment to be detected and acquiring the weight value information determined by the historical equipment operation state data, the equipment fault identification method based on big data further includes:
collecting historical equipment operation state data which corresponds to all similar equipment corresponding to the equipment to be detected together;
according to the equipment identification codes, the geographic positions 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 includes a first data classification result and a second data classification result,
the step of carrying out 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:
classifying the historical equipment operation state data according to the geographic positions 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 information according to the data classification result comprises the following steps:
determining a first weight value according to the data volume ratio 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 a device for identifying a device failure based on big data, the device for identifying a device failure based on big data comprising:
the data acquisition module is used for acquiring equipment operation state data corresponding to equipment to be detected and acquiring weight value information determined by historical equipment operation state data;
and the fault identification module is used for carrying out equipment fault identification 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 off-line data;
and carrying out equipment fault identification according to the weight value information, the user alarm data and the user off-line 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 possibility of equipment fault;
if the fault discrimination value is larger than a preset equipment fault threshold value, detecting whether equipment is normally on line 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 is not in 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 is that the equipment does not have a 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 device fault recognition device based on big data is further configured to:
collecting historical equipment operation state data which corresponds to all similar equipment corresponding to the equipment to be detected together;
according to the equipment identification codes, the geographic positions 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 device fault recognition device based on big data is further configured to:
classifying the historical equipment operation state data according to the geographic positions 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 device fault recognition device based on big data is further configured to:
determining a first weight value according to the data volume ratio 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 application also provides an electronic device comprising: the device fault identification method 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 is stored in the memory and can run on the processor, and 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 equipment failure recognition method, which when executed by a processor implements the steps of the big data based equipment failure recognition method as described above.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of a big data based device fault identification method as described above.
The application provides a device fault identification method, device and storage medium based on big data, wherein the method acquires device running state data corresponding to a device to be detected and acquires weight value information determined by historical device running state data; and carrying out equipment fault identification according to the equipment running state data and the weight value information to obtain an equipment fault identification result. That is, the method and the device acquire the equipment running state data, acquire the weight value information of the historical equipment running state data, and obtain the fault discrimination value for judging whether the user equipment fails according to the preset calculation mode, so that the error reminding of the normal equipment is filtered to find the abnormal equipment, the problem equipment can be accurately positioned, and the equipment fault identification effect is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the 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 that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a first embodiment of an equipment fault recognition method based on big data in the present application;
FIG. 2 is a schematic flow chart of a second embodiment of the apparatus fault recognition method based on big data in the present application;
fig. 3 is a schematic device structure diagram of a hardware operating environment related to a device fault identification method based on big data in an embodiment of the present application.
The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings in conjunction with the embodiments.
Detailed Description
In order to make the above objects, features and advantages of the present invention more comprehensible, the following description of the embodiments accompanied with the accompanying drawings will be given in detail. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
In a first embodiment of the present application, referring to fig. 1, the method for identifying a device failure based on big data includes:
Step S10, equipment operation state data corresponding to equipment to be detected are collected, and weight value information determined by historical equipment operation state data is obtained;
and step S20, carrying out equipment fault identification 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 in a current preset fixed time period, if the user device alarms once, one piece of alarm data is added, if the user device is offline once, one piece of offline data is added, and the acquisition process can be implemented by Kafka; the weight value information determined by the historical equipment running state data at least comprises a first weight value and a second weight value, wherein the first weight value can be the data volume duty ratio in a target time period in a first data classification result, and the second weight value can be the data volume duty ratio in the target time period in a second data classification result, and a minimum value and a maximum value can be preset for the ratio; when the equipment is identified to be faulty, the equipment is notified and alarmed, and when the equipment is abnormal but the user equipment is identified to be not faulty, the abnormality is stored for subsequent checking, and the classification and calculation process in the method can be realized through Spark.
As an example, step S10 to step S20 include:
acquiring equipment running state data corresponding to equipment to be detected within 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 through classification, the weight value information, the alarm data and the offline data are calculated in a preset calculation mode to obtain a fault discrimination value, if the fault discrimination value is larger than a preset equipment fault threshold value and the equipment is always in an offline state within a follow-up fixed time period, the equipment is identified to be faulty, notification and alarm can be carried out, if the fault discrimination value is larger than the preset equipment fault threshold value but the equipment is online within the follow-up fixed time period, the equipment is identified to be faulty, but an abnormal result is stored for subsequent abnormal check, and if the fault discrimination value is not larger than the preset equipment fault threshold value, the user equipment is identified to be faulty.
The step of performing equipment fault identification according to the equipment running state data and the weight value information to obtain an equipment fault identification result comprises the following steps:
S21, dividing the equipment operation state data into user alarm data and user off-line data;
and S22, carrying out equipment fault identification according to the weight value information, the user alarm data and the user off-line data to obtain a fault identification result.
In this embodiment, it should be noted that the device operation state data is data collected during a preset period of time of the device to be detected, for example, the device operation state data may be data collected during an hour before a current time point, and the device operation state data may be at least two data including user alarm data and user offline data.
As an example, 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 through a preset calculation method to obtain a fault discrimination value, if the fault discrimination value is larger than a preset equipment fault threshold value and the equipment is always in an offline state within a follow-up fixed time period, identifying that the equipment is faulty, notifying and alarming, if the fault discrimination value is larger than the preset equipment fault threshold value but the equipment is online within the follow-up fixed time period, identifying that the equipment is not faulty, and storing an abnormal result for subsequent abnormal checking, and if the fault discrimination value is not larger than the equipment fault threshold value, identifying that the equipment is not faulty.
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 the following steps:
step S221, 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 possibility of equipment fault occurrence;
step S222, if the fault discrimination value is larger than a preset equipment fault threshold value, detecting whether equipment is normally on line 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 is not in fault;
step S224, if the device is not normally on line within the preset time range, the fault identification result is that the device is faulty;
step S225, if the fault discrimination value is not greater than the preset equipment fault threshold, the fault identification result is that the equipment is not faulty.
In this embodiment, the fault determination value is a value of the degree of possibility of the device being faulty, and the greater the fault determination value is, the higher the possibility of the device being faulty is.
As an example, step S221 to step S225 include:
obtaining a fault discrimination value through the weight value information, the user alarm data and the user offline data in the preset calculation mode; if the fault discrimination value is larger than a preset equipment fault threshold value and the equipment is always in an offline state within a follow-up preset time range, identifying that the equipment is faulty; if the fault discrimination value is larger than a preset equipment fault threshold value but is online within a subsequent preset time range and no other alarm data exist, the equipment is identified to be free from faults, the abnormal result is stored for subsequent checking, and if the fault discrimination value is smaller than or equal to the preset equipment fault threshold value, the equipment is identified to be free from faults.
Wherein, the step of determining the fault discrimination value according to the weight value information, the user alarm data and the user offline data comprises the following steps:
step S2211, a first judgment value is determined according to the user alarm data, and a first behavior value is determined according to the first judgment value and a preset alarm base;
step S2212, a second judgment value is determined according to the user offline data, and a second behavior value is determined according to the second judgment value and the weight value information;
Step S2213, generating the fault discrimination 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 is one base in the preset calculation mode, and the initial value of the preset alarm base is 2, which can be adjusted according to the requirement; the weight value information at least comprises a first weight value and a second weight value.
As an example, steps S2211 to S2213 include:
calculating to obtain a first behavior value according to the first judgment value and the preset alarm base number; calculating to obtain a second behavior value according to the second judgment value and the weight value information; 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 comprises a first weight value and a second weight value, the first weight value is marked as m1, the second weight value is marked as m2, the first judgment value is marked as n1, the second judgment value is marked as n2, the preset first weight base number is marked as a1, the preset second weight base number is marked as a2, the preset alarm base number is marked as b, if the data volume ratio in a target time period in a data classification result is set as c, the calculation mode of the first weight value and the second weight is generally 1/c, the first judgment value and the second judgment value are the values for judging whether data exists in the user alarm data and the user downlink data, if the data exists, the value is 1, if the data does not exist, the value is 0, the initial value of the first weight base number is 1.56, the initial value of the second weight is 0.44, the initial value of the weight is set as the base number 2, and the first weight base number and the second weight base number can be set according to the preset alarm base number and the first self-alarming requirement.
The fault discrimination value is a degree value of the possibility of the equipment fault, 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 offline state within a follow-up preset time range, identifying that the equipment is faulty; if the fault discrimination value is larger than a preset equipment fault threshold value but is online within a subsequent preset time range and no other alarm data exist, the equipment is identified to be free from faults, the abnormal result is stored for subsequent checking, and if the fault discrimination value is smaller than or equal to the preset equipment fault threshold value, the equipment is identified to be free from faults. According to the error reminding method and device, error reminding of most normal devices can be filtered according to a preset calculation mode, so that the problem devices can be more accurately located, and the early warning effect is improved.
The application provides a device fault identification method, device and storage medium based on big data, wherein the method acquires device running state data corresponding to a device to be detected and acquires weight value information determined by historical device running state data; and carrying out equipment fault identification according to the equipment running state data and the weight value information to obtain an equipment fault identification result. That is, the method and the device acquire the equipment running state data, acquire the weight value information of the historical equipment running state data, and obtain the fault discrimination value for judging whether the user equipment fails according to the preset calculation mode, so that the error reminding of the normal equipment is filtered to find the abnormal equipment, the problem equipment can be accurately positioned, and the equipment fault identification effect is improved.
Example two
Further, referring to fig. 2, in another embodiment of the present application, the same or similar content as the first embodiment may be referred to the description above, and will not be repeated herein. On the basis, before the step of acquiring the equipment running state data corresponding to the equipment to be detected and acquiring the weight value information determined by the historical equipment running state data, the equipment fault identification method based on big data further comprises the following steps:
Step A10, collecting historical equipment operation state data which corresponds to all similar equipment corresponding to the equipment to be detected;
step A20, carrying out 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;
and step A30, determining the weight value information according to the data classification result.
In this embodiment, it should be noted that, the historical equipment operation state data corresponding to all the similar equipment corresponding to the equipment to be detected is alarm data and offline data of all the equipment in a period of time continuously received, the period of 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 result classified according to the geographic location corresponding to the similar equipment and the data collection time corresponding to the historical equipment operation state data, and the second data classification result may be a data result classified according to the equipment identification code and the data collection time corresponding to the historical equipment operation state data.
As an example, steps a10 to a30 include:
continuously collecting historical equipment operation state data in a period of time; classifying the historical equipment operation state data according to the geographic positions corresponding to the similar equipment, classifying the historical equipment operation state data according to the data acquisition time corresponding to the historical equipment operation state data to obtain a first data classification result, classifying the historical equipment operation state data according to the equipment identification code, and obtaining a second data classification result according to the data acquisition time corresponding to the historical equipment operation state data; 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 carrying out 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;
And 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 collection time corresponding to the operation state data of the historical device may be a specific time period, and the data collection 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, steps a21 to a22 include:
classifying the historical equipment operation state data according to the cities in which the equipment is located, arranging the classified data according to time sequences, 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 classifying the historical equipment operation state data according to the equipment identification number to obtain the historical operation data of the single user, and classifying according to the working days or the holidays to obtain a second data classification result.
It should be noted that, because the operations and the rest are different between different cities, the historical equipment operation state data is classified according to the cities to obtain the historical operation data of the 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 used as nodes, one year is divided into a plurality of sections, the period from the beginning to the beginning is one section, and the period from the beginning to the beginning is one section, so that first data classification results according to different cities and different dates are obtained, the historical equipment classification state data can be Beijing beginning to the beginning, shanghai beginning to the beginning, and likewise, the data in the second data classification results can be data of the working days of a certain user equipment or data of the vacation of another user equipment.
Wherein the weight information includes a first weight value and a second weight value,
the step of determining the weight information according to the data classification result comprises the following steps:
step A31, determining a first weight value according to the data volume ratio in the target time period in the first data classification result;
And 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, steps a31 to a32 include:
for example, in the calculation method of the first weight, the data amount from 0 to 1 in beijing Lichun to summer accounts for 0.2 of the total data amount from beijing Lichun to summer, the first weight is calculated to be 1/0.2=5, note that the minimum weight defaults to 1, the maximum weight defaults to 8, if the calculated first weight is out of the range from the minimum weight to the maximum weight, the maximum weight or the minimum weight is taken as the first weight, the calculation method of the second weight is consistent with the calculation method of the first weight, for example, the data amount at 3 pm on the working day of a certain user equipment accounts for 0.5 of the total data amount on the working day of the certain user equipment, and the second weight is calculated to be 1/0.5=2.
In this embodiment, a method for determining weight value information is provided, which can reduce erroneous judgment of an equipment failure recognition algorithm.
Example III
The embodiment of the application also provides a device fault recognition device based on big data, which comprises:
The data acquisition module is used for acquiring equipment operation state data corresponding to equipment to be detected and acquiring weight value information determined by historical equipment operation state data;
and the fault identification module is used for carrying out equipment fault identification 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 off-line data;
and carrying out equipment fault identification according to the weight value information, the user alarm data and the user off-line 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 possibility of equipment fault;
if the fault discrimination value is larger than a preset equipment fault threshold value, detecting whether equipment is normally on line 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 is not in 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 is that the equipment does not have a 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 device fault recognition device based on big data is further configured to:
collecting historical equipment operation state data which corresponds to all similar equipment corresponding to the equipment to be detected together;
according to the equipment identification codes, the geographic positions 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 device fault recognition device based on big data is further configured to:
classifying the historical equipment operation state data according to the geographic positions 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 device fault recognition device based on big data is further configured to:
determining a first weight value according to the data volume ratio 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 equipment fault recognition device based on the big data provided by the invention solves the technical problem of poor equipment fault recognition effect by adopting the equipment fault recognition method based on the big data in the embodiment. Compared with the prior art, the device for identifying equipment failure based on big data provided by the embodiment of the invention has the same beneficial effects as the device for identifying equipment failure based on big data provided by the embodiment, and other technical features in the device for identifying equipment failure based on big data are the same as the features disclosed by the method of the embodiment, and are not repeated herein.
Example IV
The embodiment of the invention provides electronic equipment, which comprises: 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, so that the at least one processor can execute the equipment fault identification method based on big data in the first embodiment.
Referring now to fig. 3, a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure is shown. 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., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 3, the electronic device may include a processing means (e.g., a central processing unit, a graphic processor, etc.) that may 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 required for the operation of the electronic device are also stored. The processing device, ROM and RAM are connected to each other via a bus. An input/output (I/O) interface is also connected to the bus.
In general, 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, etc.; output devices including, for example, liquid Crystal Displays (LCDs), speakers, vibrators, etc.; storage devices including, for example, magnetic tape, hard disk, etc.; a communication device. The communication means may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While electronic devices having various systems are shown in the figures, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to embodiments of the present disclosure, the processes described above with reference to 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 shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from ROM. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by a processing device.
The electronic equipment provided by the invention adopts the equipment fault identification method based on big data in the embodiment, and solves the technical problem of poor equipment fault identification effect. Compared with the prior art, the electronic equipment provided by the embodiment of the invention has the same beneficial effects as the equipment fault identification method based on big data provided by the embodiment, and other technical features in the electronic equipment are the same as the features disclosed by the method of the embodiment, and 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 description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Example five
The present embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon for performing the method of big data based device failure recognition in the above embodiment.
The computer readable storage medium according to the embodiments of the present invention may be, for example, a usb disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. 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 this 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, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The above-described computer-readable storage medium may be contained in an electronic device; or may exist alone without being assembled into an electronic device.
The computer-readable storage medium carries one or more programs that, 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 carrying out equipment fault identification 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 of the present disclosure may be written in 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts 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 in software or hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.
The computer readable storage medium provided by the invention stores the computer readable program instructions for executing the equipment fault identification method based on big data, 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 invention are the same as those of the equipment fault identification method based on big data provided by the above embodiment, and are not described in detail herein.
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 a big data based device fault identification method as described above.
The computer program product provided by the application solves the technical problem of poor equipment fault recognition effect. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the invention are the same as those of the equipment fault identification method based on big data provided by the embodiment, and are not repeated here.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims.
Claims (6)
1. The equipment fault identification method based on the big data 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;
performing equipment fault identification according to the equipment running state data and the weight value information to obtain an equipment fault identification result;
the step of performing equipment fault identification according to the equipment running state data and the weight value information to obtain an equipment fault identification result comprises the following steps:
dividing the equipment operation state data into alarm data of the user equipment and off-line data of the user equipment;
performing equipment fault identification according to the weight value information, the alarm data of the user equipment and the offline data of the user equipment to obtain a fault identification result;
the step of performing equipment fault identification according to the weight value information, the alarm data of the user equipment and the offline data of the user equipment to obtain a fault identification result comprises the following steps:
determining a fault discrimination value according to the weight value information, the alarm data of the user equipment and the offline data of the user equipment, wherein the fault discrimination value is used for representing the degree of possibility of equipment fault;
If the fault discrimination value is larger than a preset equipment fault threshold value, detecting whether equipment is normally on line 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 is not in 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;
if the fault discrimination value is not greater than the preset equipment fault threshold value, the fault identification result is that the equipment does not have a fault;
wherein, the step of determining the fault discrimination value according to the weight value information, the alarm data of the user equipment and the offline data of the user equipment includes:
determining a first judgment value according to alarm data of the user equipment, 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 offline data of the user equipment, 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.
2. The big data based equipment fault identification method as claimed in claim 1, wherein before the step of acquiring the weight value information determined by the historical equipment operation state data and corresponding equipment operation state data of the equipment to be detected, the big data based equipment fault identification method further comprises:
Collecting historical equipment operation state data which corresponds to all similar equipment corresponding to the equipment to be detected together;
according to the equipment identification codes, the geographic positions 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.
3. The method for large data based equipment failure recognition of claim 2, wherein the data classification result includes a first data classification result and a second data classification result,
the step of carrying out 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:
classifying the historical equipment operation state data according to the geographic positions 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.
4. The method for identifying a failure of a device based on big data according to claim 3, wherein the weight information includes a first weight value and a second weight value,
the step of determining the weight information according to the data classification result comprises the following steps:
determining a first weight value according to the data volume ratio 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.
5. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the big data based device fault identification method of any of claims 1 to 4.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program that implements the big data based device failure recognition method, the program that implements the big data based device failure recognition method 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 4.
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