CN112888007B - Method, device and storage medium for diagnosing offline reasons of device - Google Patents
Method, device and storage medium for diagnosing offline reasons of device Download PDFInfo
- Publication number
- CN112888007B CN112888007B CN202011615628.4A CN202011615628A CN112888007B CN 112888007 B CN112888007 B CN 112888007B CN 202011615628 A CN202011615628 A CN 202011615628A CN 112888007 B CN112888007 B CN 112888007B
- Authority
- CN
- China
- Prior art keywords
- offline
- equipment
- log
- same
- historical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/04—Arrangements for maintaining operational condition
Abstract
The application discloses a method, equipment and a storage medium for diagnosing offline reasons of equipment. The equipment offline reason diagnosis method comprises the following steps: acquiring an equipment offline log; counting the offline times, the online rate, the single offline time length and the login condition of the equipment module within a preset time; responding to the fact that the equipment online rate is smaller than the average online rate of all the equipment, and performing fault diagnosis on the offline reasons of the equipment by utilizing the equipment operation characteristic values; responding to the fact that the single offline time of the equipment exceeds the preset time, and carrying out fault diagnosis on the offline reasons of the equipment by combining the historical offline logs and the online conditions of the same-route equipment; and responding to successful login of the equipment module, acquiring an equipment running environment log, and performing fault diagnosis on the offline reasons of the equipment. According to the equipment offline reason diagnosis method, the offline log of the equipment is uploaded, the module of the server is used for uploading and downloading the data log, the offline reason of the user equipment is comprehensively analyzed, a user is guided to solve the problem, and the user experience is improved.
Description
Technical Field
The application belongs to the technical field of big data, and particularly relates to an off-line reason diagnosis method, equipment and a storage medium for equipment.
Background
With the development of the internet of things technology, the Wi-Fi wireless network technology can be used only after being tested and authorized by the Wi-Fi alliance, but most of wireless networks based on 802.11 in China do not pass the Wi-Fi alliance test, so that quality of wireless routers in the market is uneven. Meanwhile, because the wireless network environment of the user is complex, the equipment is abnormally offline, the offline reasons are very professional, the user often cannot judge whether the equipment is the route reason or the equipment reason, the failure cannot be solved, and therefore the user experience is reduced. There is a need for an offline cause diagnosis method for a device to guide a user in solving a wireless network failure.
Disclosure of Invention
The application provides a method, equipment and a storage medium for diagnosing offline reasons of equipment, so as to solve the technical problem that the offline reasons of the equipment cannot be judged.
In order to solve the technical problems, one technical scheme adopted by the application is as follows: a method of device offline cause diagnosis, comprising: acquiring an equipment offline log; counting the offline times of the equipment in a preset time, the online rate of the equipment in the preset time, the single offline time length of the equipment and the login condition of the equipment module; responding to the fact that the equipment online rate is smaller than the average online rate of all the equipment in the preset time, and performing fault diagnosis on equipment offline reasons by utilizing the equipment operation characteristic values in the preset time; responding to the fact that the single offline time of the equipment exceeds a preset time, and carrying out fault diagnosis on the offline reasons of the equipment by combining a historical offline log and online conditions of the same-route equipment; and responding to successful login of the equipment module, acquiring an equipment operation environment log, and performing fault diagnosis on the equipment offline reasons.
According to an embodiment of the present application, the method includes: and responding to the fact that the offline times of the equipment in the preset time are larger than the preset times, acquiring the equipment operation characteristic values in the preset time, and carrying out fault diagnosis on the offline reasons of the equipment by combining the historical equipment operation characteristic values.
According to an embodiment of the present application, the performing fault diagnosis on the offline cause of the device by combining the historical offline log and the online condition of the co-routed device includes: acquiring a historical offline log, and judging whether the probability of the equipment offline for the same reason in the historical time is greater than or equal to a first preset value; analyzing whether the devices connected with the same router are offline simultaneously or not in response to the probability that the devices are offline for the same reason at the historical time being smaller than the first preset value; analyzing whether the devices connected with the same router and adopting the same module firmware are offline at the same time or not in response to the devices connected with the same router are offline at the same time; responsive to connecting the same router and devices employing the same module firmware not being offline at the same time; and performing fault diagnosis on the offline reasons of the equipment by utilizing the equipment operation characteristic values and combining the historical equipment operation characteristic values to obtain offline reason diagnosis results.
According to an embodiment of the present application, the performing fault diagnosis on the offline cause of the device by combining the historical offline log and the online condition of the co-routed device includes: responding to the probability of offline for the same reason being greater than or equal to a first preset value, or responding to the equipment connected with the same router being offline simultaneously, or responding to the equipment connected with the same router and adopting the same module firmware being offline simultaneously; and carrying out big data analysis on the offline log to obtain an offline cause diagnosis result.
According to an embodiment of the present application, the responding to the device offline times in the predetermined time being greater than the predetermined times, obtaining the device operation characteristic value in the predetermined time, and performing fault diagnosis on the device offline reasons in combination with the historical device operation characteristic value, includes: responsive to the device offline times being greater than the predetermined times within the predetermined time; acquiring a device operation characteristic value in the preset time, wherein the device operation characteristic value comprises statistical information of operation data of the device in the preset time; and performing fault diagnosis by utilizing the equipment operation characteristic value and combining the historical equipment operation characteristic value in a preset historical period to obtain the offline cause diagnosis result.
According to an embodiment of the present application, the responding to the successful login of the device module, obtaining a device running environment log, and performing fault diagnosis on the offline reason of the device includes: after the equipment is offline, the equipment module successfully logs in again; acquiring an equipment operation environment log, wherein the equipment operation environment log comprises routing information, module network information, operator network and server connection information and disconnection reasons; and performing microcosmic offline log analysis to obtain an offline cause diagnosis result.
According to an embodiment of the present application, the method further comprises: transmitting the offline cause diagnosis result to a user or an after-market engineer; and forming a solution guiding solution according to the offline reason diagnosis result.
According to an embodiment of the present application, the method includes: and performing fault diagnosis on the equipment in a preset time period, and recording a diagnosis result.
In order to solve the technical problem, another technical scheme adopted by the application is as follows: an apparatus offline cause diagnosis device, comprising: the equipment network operation environment log system records equipment offline logs when equipment is offline, and counts equipment offline times in preset time, equipment online rate in preset time, single offline time length of the equipment and equipment module login conditions; the equipment fault diagnosis system is used for carrying out fault diagnosis on equipment offline reasons by utilizing equipment operation characteristic values in preset time in response to the fact that the equipment online rate in the preset time is smaller than the average online rate of all the equipment; the equipment big data analysis system is used for responding to the fact that the single offline time of the equipment exceeds a preset time, and performing fault diagnosis on the offline reasons of the equipment by combining the historical offline logs and online conditions of the same-route equipment; and the equipment offline log analysis system is used for responding to successful login of the equipment module, acquiring an equipment operation environment log and carrying out fault diagnosis on the equipment offline reasons.
According to an embodiment of the present application, there is provided: and sending the offline reason diagnosis result to a user or a technician by using the pushing system, and forming a solution guiding scheme according to the offline reason diagnosis result.
In order to solve the technical problem, another technical scheme adopted by the application is as follows: a computer readable storage medium storing program data executable to implement a method as any one of the above.
The beneficial effects of this application are: according to the equipment offline reason diagnosis method, the offline reason of the user equipment can be judged through uploading the offline log of the equipment, and the module of the server is connected with the offline and transmission data log through comprehensive analysis. And the Internet of things equipment can be guided to be used correctly by a user, the problem of the user is guided to be solved after sales, the problem of network compatibility of the repair equipment is guided to be researched and developed, the wireless connection function of the equipment is optimized, and the user experience is improved.
Drawings
For a clearer description of the technical solutions in the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art, wherein:
FIG. 1 is a flow chart of an embodiment of an off-line cause diagnosis method for a device of the present application;
FIG. 2 is a flow chart of offline log analysis in an embodiment of an offline cause diagnosis method for a device of the present application;
FIG. 3 is a schematic flow chart of fault diagnosis in an embodiment of an off-line cause diagnosis method of the apparatus of the present application;
FIG. 4 is a flow chart of an embodiment of an offline cause diagnosis method for a device according to the present application, which combines a historical offline log with online condition analysis of a co-routed device;
FIG. 5 is a schematic diagram of a framework of one embodiment of an off-line cause diagnosis device of the present application;
FIG. 6 is a schematic diagram of a frame of an embodiment of an electronic device of the present application;
FIG. 7 is a schematic diagram of a framework of one embodiment of a computer readable storage medium of the present application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Referring to fig. 1 to 4, the present application provides an off-line equipment reason diagnosis method, which includes the following steps:
s101: and obtaining an offline log of the equipment.
And obtaining an offline log of the equipment. The offline log includes network offline reasons at the time the device is offline and the operating environment of the network. Specifically, the device offline log includes, but is not limited to, routing information (e.g., RSSI, BSSID, SSID, channels, etc.), module network information (e.g., local IP, user network operation, module restart reasons, etc.), operator network and server connection information (e.g., server heartbeat, server disconnection, operator public network IP, etc.), disconnection reasons (connection route loss, heartbeat timeout, user distribution network, 802.11 protocol error (disconnection code), etc.
It should be noted that the device may be an intelligent home appliance, such as an intelligent sound box, an intelligent air conditioner, an intelligent electric cooker, an intelligent sweeping robot, or the like, which is connected to a user network. The device may also be an office device with an office connected to a user network, or an intelligent mechanical device with a user network connected to a factory, without limitation.
S102: and counting the offline times of the equipment, the online rate of the equipment, the single offline time of the equipment and the login condition of the equipment module within a preset time.
And counting the offline times of the equipment, the online rate of the equipment, the single offline time of the equipment and the login condition of the equipment module within a preset time. The predetermined time may be 12 hours, 24 hours, 36 hours, etc.
S103: and responding to successful login of the equipment module, acquiring an equipment running environment log, and performing fault diagnosis on the offline reasons of the equipment.
S1031: and responding to successful login of the equipment module again after the equipment is offline.
And responding to successful login of the equipment module again after the equipment is offline, namely successful login of the Wi-Fi module of the equipment, and the cloud can perform data transmission with the equipment.
S1032: and acquiring an equipment operation environment log, wherein the equipment operation environment log comprises routing information, module network information, operator network and server connection information and disconnection reasons.
And after the equipment module is successfully logged in again, the cloud module can acquire an equipment operation environment log, wherein the equipment operation environment log comprises routing information, module network information, operator network and server connection information, disconnection reasons and the like.
S1033: and performing offline log analysis to obtain an offline cause diagnosis result.
And carrying out offline log analysis on the offline reasons of the equipment, namely specifically analyzing the offline reasons of the equipment to obtain an offline reason diagnosis result. The device offline reasons can be specifically analyzed through the microscopic offline log analysis script management system, the microscopic offline log analysis script management system is used for specifically judging the offline reasons of the device each time, a model is built by means of the device network operation environment log to judge the specific offline reasons, and as more offline reasons are found, the microscopic offline log analysis script management system can be used for updating and adding new offline reason judgment.
In some embodiments, if the offline cause diagnosis is found to be poor signal by offline log analysis, the user may be advised to swap the router for a location. If the offline reason diagnosis result is not compatible through the offline log analysis, the user can be recommended to replace the router or an engineer can solve the firmware problem so as to realize compatibility.
In an embodiment, the method further includes performing fault diagnosis on the device during a preset time period, and recording a diagnosis result. The preset period of time is typically a period of time in which the device is used less frequently at night. For example, the offline log can be analyzed in real time by taking 24 hours as a unit time, fault diagnosis can be carried out within a preset time period at night, and diagnosis results are stored in a system database, so that when the equipment is offline again, the data can be queried, thereby being convenient for finding problems in time and solving the problems, and reducing the occurrence of the offline condition of the equipment. And fault diagnosis is carried out within a preset time period, so that the operation amount of equipment with high operation frequency can be reduced, and the influence on the operation of the equipment is avoided.
S104: and responding to the fact that the equipment online rate is smaller than the average online rate of all the equipment in the preset time, and performing fault diagnosis on the offline reasons of the equipment by utilizing the equipment operation characteristic values in the preset time.
The device online rate is the device online rate of the current analysis device, and the average online rate of all devices is the average online rate of all devices in the same route. In response to the device presence rate being less than the average presence rate of all devices for a predetermined time, it is indicated that the devices may be presented multiple times and that some offline cause regularly occurs.
It will be appreciated that if the predetermined time is 24 hours, in response to the statistical device online rate being less than the average online rate of all devices within 24 hours, the device offline cause is diagnosed using the device operation characteristic value within the predetermined time. The equipment operation characteristic values comprise a signal intensity average value, an equipment online time length average value, an equipment offline time length average value, an internal and external network IP address and the like.
Performing fault diagnosis by using the equipment operation characteristic value in a preset time, and obtaining an offline cause diagnosis result comprises the following steps:
the equipment offline reasons can be specifically analyzed through the macroscopic fault diagnosis model script management system. When the equipment is presented for a plurality of times and a certain offline reason appears regularly, the equipment operation characteristic value and the offline reason within the preset time are utilized to carry out fault diagnosis analysis according to the specific equipment operation characteristic value and the offline reason, and as more equipment network faults are discovered, a new fault diagnosis model can be updated and added by utilizing the script management system.
Specifically, by performing statistical analysis on the operating characteristic values of the equipment within a predetermined time and performing fault diagnosis analysis, an offline cause diagnosis result which cannot be found by microscopic offline log analysis can be obtained, for example, the problem that the equipment fault is due to the equipment identification code serial numbers of two different equipment is found by the fault diagnosis analysis.
It should be noted that, the fault diagnosis can be performed on the device in a preset time period, and the diagnosis result is recorded. For example, the predetermined time is 24 hours, and the preset time period is typically a period in which the device is used less frequently at night. And the operation characteristic value of the equipment is statistically analyzed within a preset time period at night, fault diagnosis is carried out, the diagnosis result is stored in a system database, and when the equipment is offline again, the data can be inquired, so that the problems can be found out in time and solved, and the occurrence of the offline condition of the equipment is reduced. And fault diagnosis is carried out within a preset time period, so that the operation amount of equipment with high operation frequency can be reduced, and the influence on the operation of the equipment is avoided.
Further, the method comprises the following steps:
s105: and responding to the fact that the offline times of the equipment in the preset time are larger than the preset times, acquiring the equipment operation characteristic value in the preset time, and carrying out fault diagnosis on the offline reasons of the equipment by combining the historical equipment operation characteristic value.
In response to the device being offline more than a predetermined number of times within a predetermined time, it is indicated that the device may be presented multiple times and that some offline cause regularly occurs.
The method specifically comprises the following steps:
s1051: in response to the device offline times being greater than the predetermined times within the predetermined time.
In response to the device offline times being greater than the predetermined times within the predetermined time. For example, the number of device offline times is greater than 25 times in 24 hours, and may be 15, 20, 30, etc. other times in other embodiments. Indicating that the device may be presented multiple times and that some offline cause occurs regularly. Macroscopic analysis of its offline cause is required.
S1052: and acquiring an equipment operation characteristic value in a preset time, wherein the equipment operation characteristic value comprises statistical information of operation data of equipment in the preset time.
And acquiring an equipment operation characteristic value, wherein the equipment operation characteristic value comprises a signal intensity average value, an equipment online time length average value, an equipment offline time length average value, an internal and external network IP address and the like.
S1053: and performing fault diagnosis by using the equipment operation characteristic value and combining the historical equipment operation characteristic value in a preset historical period to obtain an offline cause diagnosis result.
The method is characterized in that the preset time is taken as unit time, and when the equipment online rate is smaller than the average online rate of all the equipment in the preset time, the equipment operation characteristic value in the preset time is subjected to statistical analysis, and the equipment offline reason is subjected to fault diagnosis and recorded. The fault diagnosis is carried out on the offline reasons of the equipment in unit time, and the fault diagnosis is recorded in the system and used as the historical equipment operation characteristic value and the offline reasons.
If the offline times of the equipment in the preset time are larger than the preset times, the equipment is subjected to fault diagnosis by utilizing the equipment operation characteristic value in the preset time and combining the historical equipment operation characteristic value recorded in a historical record in a preset historical period (for example, in 30 days). For example, if the device operation characteristic value is similar to the historical device operation characteristic value in the preset time, the offline reason of the secondary device can be diagnosed as the corresponding historical offline reason.
S106: and responding to the fact that the single offline time of the equipment exceeds the preset time, and carrying out fault diagnosis on the offline reasons of the equipment by combining the historical offline log and the online conditions of the same-route equipment.
And responding to the fact that the single offline time of the equipment exceeds the preset time, indicating that the equipment module cannot log in successfully, and the cloud end cannot acquire the equipment operation environment log, so that the specific offline reason is analyzed. Therefore, the off-line reasons of the equipment are required to be subjected to fault diagnosis by combining the historical off-line log and the on-line condition of the same-route equipment.
The method specifically comprises the following steps:
s1061: and acquiring a historical offline log, and judging whether the probability of the equipment offline for the same reason in the historical time is larger than or equal to a first preset value.
A historical offline log is obtained and an analysis is made as to whether the device is often offline for the same offline reason at historical time. If the probability of the equipment offline for the same reason in the historical time is larger than or equal to a first preset value, the fact that the equipment offline for the same reason is possibly caused by the same reason is indicated, so that big data analysis is carried out on the offline log, and an offline reason diagnosis result is obtained. The historical time may be one week, two weeks, or one month of history, etc.
S1062: and analyzing whether the devices connected to the same router are offline simultaneously or not in response to the probability that the devices are offline for the same reason at the historical time being less than a first predetermined value.
And analyzing whether a plurality of devices connected to the same router are offline simultaneously or not in response to the probability that the devices are offline for the same reason in the historical time being less than a first predetermined value. If the probability of offline for the same reason is less than a first predetermined value, then analyzing whether multiple devices connected to the same router are offline at the same time. If a plurality of devices connected with the same router are offline at the same time, the router may have problems, and big data analysis is performed on the offline log to obtain an offline reason diagnosis result.
S1063: in response to devices connected to the same router not being offline at the same time, analyzing whether devices connected to the same router and employing the same module firmware are offline at the same time.
In response to devices of the same router not being offline at the same time, analyzing whether devices connected to the same router and employing the same module firmware are offline at the same time. If the same router is connected and the equipment adopting the same module firmware is offline at the same time, the problem of the version of the module firmware is likely, and big data analysis can be performed on the offline log to obtain the offline cause diagnosis result.
S1064: in response to connecting the same router, and the devices employing the same module firmware are not offline at the same time; and performing fault diagnosis on the offline reasons of the equipment by utilizing the equipment operation characteristic values and combining the historical equipment operation characteristic values to obtain offline reason diagnosis results.
And responding to the fact that the equipment connected with the same router and adopting the same module firmware is not offline at the same time, performing fault diagnosis on offline reasons of the equipment by utilizing the equipment operation characteristic value and combining the historical equipment operation characteristic value to obtain offline reason diagnosis results. Reference is specifically made to step S1053.
S107: and sending the offline reason diagnosis result to a user or a technician.
And sending the offline reason diagnosis result to a user or a technician. Thereby allowing the user and technician to be aware of the reason why the device is offline. The technician can be an after-sales engineer responsible for solving the after-sales problem for equipment manufacturers, or can be a third party or other technicians capable of solving the technical problem.
S108: and forming a solution guiding scheme according to the offline reason diagnosis result.
According to the offline cause diagnosis result, a solution guiding scheme is formed, a user is guided to repair, and according to faults which cannot be solved by the user, parameters of an operation environment can be sent to a technician, the technician reproduces the environment and solves the problems, compatibility of a module and a route is improved, meanwhile, fault characteristic value calculation and fault diagnosis models can be updated through various newly-appearing fault types, and accuracy of judgment is improved, so that on-line stability of equipment is improved.
It should be noted that, after the offline condition occurs to the device each time and the final module is successfully logged in, the offline log analysis is performed on the offline reason of the device by adopting step S103 of the present application, so as to obtain the offline reason diagnosis result. And when the equipment is offline for a plurality of times, performing fault diagnosis on the offline reasons of the equipment by adopting the step S104 and the step S105 to obtain offline reason diagnosis results. And when the offline time of the equipment is overtime, performing fault diagnosis on the offline reasons of the equipment by adopting the step S106 and combining the historical offline log and the online conditions of the same-route equipment. The combination judging method can judge the offline reason of the equipment in time, guide the user to solve the problem and promote the user experience.
In a specific implementation, in an embodiment, after obtaining an offline log of a device, it may first determine whether the device online rate is less than an average online rate of all devices in a predetermined time; if not, judging that the offline times of the equipment in the preset time are more than the preset times; if the preset times are not exceeded, further judging that the single offline time of the equipment exceeds the preset time; if the preset time is not exceeded, judging that the equipment module is successfully logged in. Or in other embodiments, other judging modes may be adopted, so as to achieve the above combination judging method.
According to the equipment offline reason diagnosis method, the offline reason of the user equipment is judged through comprehensive analysis by uploading the offline log of the equipment, uploading and downloading the data log and transmitting the data log on the module of the server. The device stores the wireless environment information including connection route information, IP information, device restarting reasons and the like when offline, analyzes and distinguishes user behaviors, network operator behaviors, route behaviors and device behaviors through various offline characteristics, judges the specific reasons causing offline, and uploads records to the cloud when the server is successfully logged in next time. When the device is not online, the reason for this offline can be inferred through historical data analysis. And guiding the user to correctly use the Internet of things equipment, guiding the after-sale solution of the user problem, guiding the research and development of the network compatibility problem of the repair equipment and optimizing the wireless connection function of the equipment. The method has obvious application effect in the household appliances of the Internet of things.
Referring to fig. 5, a device offline cause diagnosis apparatus 20 is provided according to another embodiment of the present application, which includes a device network operation environment log system 21, a device fault diagnosis system 22, a device big data analysis system 23, and a device offline log analysis system 24.
When the device goes offline, the device network running environment log system 21 records the offline log of the device, and counts the offline times of the device in a preset time, the online rate of the device in the preset time, the single offline time of the device and the login condition of the device module. The offline log includes network offline reasons at the time the device is offline and the operating environment of the network. Specifically, the device offline log includes, but is not limited to, routing information (e.g., RSSI, BSSID, SSID, channels, etc.), module network information (e.g., local IP, user network operation, module restart reasons, etc.), operator network and server connection information (e.g., server heartbeat, server disconnection, operator public network IP, etc.), disconnection reasons (connection route loss, heartbeat timeout, user distribution network, 802.11 protocol error (disconnection code), etc.
It should be noted that the device may be an intelligent home appliance, such as an intelligent sound box, an intelligent air conditioner, an intelligent electric cooker, an intelligent sweeping robot, or the like, which is connected to a user network. The device may also be an office device with an office connected to a user network or an intelligent mechanical device with a user network in a factory.
In response to the device online rate being less than the average online rate of all devices for the predetermined time, the device fault diagnosis system 22 uses the device operation characteristic value for the predetermined time to perform fault diagnosis on the offline reason of the device. The device fault diagnosis system 22 includes a device operation characteristic value calculation script management system, a macro fault diagnosis model script management system, and a macro fault diagnosis processor. The equipment operation characteristic value calculation script management system is an equipment operation characteristic value calculation model stored in a script form, and the fault diagnosis model is continuously perfected along with the discovery of more faults, so that the equipment operation characteristic value can be continuously increased and perfected, the equipment operation characteristic value calculation is realized in the script form, and the equipment operation characteristic value calculation is easy to upgrade, update and increase. The macro fault diagnosis model script management system stores a fault diagnosis model, when equipment is presented for a plurality of times and a certain offline reason appears regularly, the equipment uses the operation characteristic value and the offline reason to diagnose the fault according to specific macro faults, and as more equipment network faults are discovered, a new fault diagnosis model can be updated and added by the macro fault diagnosis model script management system. The macro fault diagnosis processor realizes the operation action of fault diagnosis.
In response to successful login of the device module, the device offline log analysis system 24 obtains a device running environment log, and performs fault diagnosis on the offline reasons of the device. The device offline log analysis system 24 includes a microscopic offline log analysis script management system and a microscopic offline log analysis processor, where the microscopic offline log analysis script management system is used to determine offline reasons each time specifically, and it relies on the device network running environment log to build a model to determine specific offline reasons, and as more offline reasons are found, the script management system can be used to update and add new offline reason determinations. The microcosmic offline log analysis processor realizes the operation action of offline cause analysis. The microscopic offline log analysis processor and the macroscopic fault diagnosis processor may be the same processor, or different processors may be employed if the conditions allow.
In response to the single offline time of the device exceeding the predetermined time, the device big data analysis system 23 performs fault diagnosis on the offline reason of the device by combining the historical offline log and the online condition of the same-route device. Because of the hysteresis of the judgment of the microscopic offline reason (because the device module is offline, the log cannot be uploaded to the server and can be uploaded after the next successful login), when the offline time period exceeds the preset time period, the device big data analysis system 23 analyzes the historical log and deduces the offline reason. Meanwhile, the on-line condition of the equipment in the same route is required to be transversely compared for judging external influence factors.
Further, the device offline cause diagnosis apparatus 20 further includes an application push system 25, and the application push system 25 sends the offline cause diagnosis result to the user or the after-market engineer, and forms a solution guide solution according to the offline cause diagnosis result.
The device offline cause diagnosis device 20 of the present application, through uploading the offline log of the device, and the module on-line and off-line and transmission data log of the server, and through a configurable device operation characteristic value calculation script management system, a microcosmic offline log analysis script management system and a macroscopic fault diagnosis model script management system, comprehensively analyzes and judges the offline cause of the user module. The device stores the wireless environment information including connection route information, IP information, device restarting reasons and the like when offline, analyzes and distinguishes user behaviors, network operator behaviors, route behaviors and device behaviors through various offline characteristics, judges the specific reasons causing offline, and uploads records to the cloud when the server is successfully logged in next time. When the device is not online, the reason for this offline can be inferred through historical data analysis. The method comprises the steps of guiding a user to correctly use the Internet of things equipment, guiding after-sale to solve the user problem, guiding research and development of the network compatibility problem of the repair equipment and optimizing the wireless connection function of the equipment.
Referring to fig. 6, fig. 6 is a schematic frame diagram of an embodiment of an electronic device of the present application.
A further embodiment of the present application provides an electronic device 30, including a memory 31 and a processor 32 coupled to each other, where the processor 32 is configured to execute program instructions stored in the memory 31 to implement the device offline cause diagnosis method of any of the above embodiments. In one particular implementation scenario, electronic device 30 may include, but is not limited to: the microcomputer and the server, and the electronic device 30 may also include a mobile device such as a notebook computer and a tablet computer, which is not limited herein.
Specifically, the processor 32 is configured to control itself and the memory 31 to implement the steps of any of the image exposure adjustment method embodiments described above. The processor 32 may also be referred to as a CPU (Central Processing Unit ). The processor 32 may be an integrated circuit chip having signal processing capabilities. The processor 32 may also be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. In addition, the processor 32 may be commonly implemented by an integrated circuit chip.
Referring to FIG. 7, FIG. 7 is a schematic diagram illustrating an embodiment of a computer readable storage medium of the present application.
Yet another embodiment of the present application provides a computer readable storage medium 40 having stored thereon program data 41, which when executed by a processor, implements the device offline cause diagnosis method of any of the above embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed methods and apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical, or other forms.
The elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over network elements. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium 40. Based on such understanding, the technical solution of the present application, or a part contributing to the prior art or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium 40, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium 40 includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description is only exemplary embodiments of the present application and is not intended to limit the scope of the present application, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the present application.
Claims (10)
1. A method for off-line cause diagnosis of a device, comprising:
acquiring an equipment offline log;
counting the offline times of the equipment in a preset time, the online rate of the equipment in the preset time, the single offline time length of the equipment and the login condition of the equipment module;
responding to the fact that the equipment online rate is smaller than the average online rate of all the equipment in the preset time, and performing fault diagnosis on equipment offline reasons by utilizing the equipment operation characteristic values in the preset time;
responding to the fact that the single offline time of the equipment exceeds a preset time, and carrying out fault diagnosis on the offline reasons of the equipment by combining a historical offline log and online conditions of the same-route equipment;
responding to successful login of the equipment module, acquiring an equipment operation environment log, and performing fault diagnosis on the equipment offline reason;
the method for diagnosing the fault of the offline reasons of the equipment by combining the historical offline log and the online condition of the same-route equipment comprises the following steps:
acquiring a historical offline log, and judging whether the probability of the equipment offline for the same reason in the historical time is greater than or equal to a first preset value;
analyzing whether the devices connected with the same router are offline simultaneously or not in response to the probability that the devices are offline for the same reason at the historical time being smaller than the first preset value;
analyzing whether the devices connected with the same router and adopting the same module firmware are offline at the same time or not in response to the devices connected with the same router are offline at the same time;
responsive to connecting the same router and devices employing the same module firmware not being offline at the same time;
and performing fault diagnosis on the offline reasons of the equipment by utilizing the equipment operation characteristic values and combining the historical equipment operation characteristic values to obtain offline reason diagnosis results.
2. The method according to claim 1, characterized in that the method comprises:
and responding to the fact that the offline times of the equipment in the preset time are larger than the preset times, acquiring the equipment operation characteristic values in the preset time, and carrying out fault diagnosis on the offline reasons of the equipment by combining the historical equipment operation characteristic values.
3. The method of claim 1, wherein the combining the historical offline log and the online status of the co-routed device for fault diagnosis of the offline cause of the device comprises:
responding to the probability of offline for the same reason being greater than or equal to the first preset value, or responding to the equipment connected with the same router being offline simultaneously, or responding to the equipment connected with the same router and adopting the same module firmware being offline simultaneously;
and carrying out big data analysis on the offline log to obtain the offline cause diagnosis result.
4. The method of claim 2, wherein the responding to the device offline times in the preset time being greater than the preset times, obtaining the device operation characteristic value in the preset time, and performing fault diagnosis on the device offline reasons in combination with the historical device operation characteristic value, comprises:
responsive to the device offline times being greater than the predetermined times within the predetermined time;
acquiring a device operation characteristic value in the preset time, wherein the device operation characteristic value comprises statistical information of operation data of the device in the preset time;
and performing fault diagnosis by utilizing the equipment operation characteristic value and combining the historical equipment operation characteristic value in a preset historical period to obtain the offline cause diagnosis result.
5. The method of claim 1, wherein the obtaining a log of an operating environment of the device in response to the device module logging successfully, performing fault diagnosis on offline causes of the device, comprises:
after the equipment is offline, the equipment module successfully logs in again;
the method comprises the steps that the equipment operation environment log is obtained, wherein the equipment operation environment log comprises routing information, module network information, operator network and server connection information and disconnection reasons;
and performing offline log analysis to obtain an offline cause diagnosis result.
6. The method according to any one of claims 1-5, further comprising:
transmitting the offline cause diagnosis result to a user or an after-market engineer;
and forming a solution guiding solution according to the offline reason diagnosis result.
7. The method according to claim 1, characterized in that the method comprises:
and performing fault diagnosis on the equipment in a preset time period, and recording a diagnosis result.
8. An apparatus offline cause diagnosis device, comprising:
the equipment network operation environment log system records equipment offline logs when equipment is offline, and counts equipment offline times in preset time, equipment online rate in preset time, single offline time length of the equipment and equipment module login conditions;
the equipment fault diagnosis system is used for carrying out fault diagnosis on equipment offline reasons by utilizing equipment operation characteristic values in preset time in response to the fact that the equipment online rate in the preset time is smaller than the average online rate of all the equipment;
the equipment big data analysis system is used for responding to the fact that the single offline time of the equipment exceeds a preset time, and performing fault diagnosis on the offline reasons of the equipment by combining the historical offline logs and online conditions of the same-route equipment;
the equipment offline log analysis system is used for responding to successful login of the equipment module, acquiring an equipment operation environment log and carrying out fault diagnosis on the equipment offline reasons;
the equipment big data analysis system combines the historical offline log and the online condition of the same-route equipment to carry out fault diagnosis on the offline reasons of the equipment, and the equipment big data analysis system comprises the following steps:
acquiring a historical offline log, and judging whether the probability of the equipment offline for the same reason in the historical time is greater than or equal to a first preset value;
analyzing whether the devices connected with the same router are offline simultaneously or not in response to the probability that the devices are offline for the same reason at the historical time being smaller than the first preset value;
analyzing whether the devices connected with the same router and adopting the same module firmware are offline at the same time or not in response to the devices connected with the same router are offline at the same time;
responsive to connecting the same router and devices employing the same module firmware not being offline at the same time;
and performing fault diagnosis on the offline reasons of the equipment by utilizing the equipment operation characteristic values and combining the historical equipment operation characteristic values to obtain offline reason diagnosis results.
9. The apparatus as claimed in claim 8, comprising:
and sending the offline reason diagnosis result to a user or a technician by using the pushing system, and forming a solution guiding scheme according to the offline reason diagnosis result.
10. A computer readable storage medium, characterized in that the storage medium stores program data, which is executable to implement the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011615628.4A CN112888007B (en) | 2020-12-30 | 2020-12-30 | Method, device and storage medium for diagnosing offline reasons of device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011615628.4A CN112888007B (en) | 2020-12-30 | 2020-12-30 | Method, device and storage medium for diagnosing offline reasons of device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112888007A CN112888007A (en) | 2021-06-01 |
| CN112888007B true CN112888007B (en) | 2023-05-05 |
Family
ID=76046428
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011615628.4A Active CN112888007B (en) | 2020-12-30 | 2020-12-30 | Method, device and storage medium for diagnosing offline reasons of device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112888007B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114244681B (en) * | 2021-12-21 | 2023-08-01 | 深圳Tcl新技术有限公司 | Equipment connection fault early warning method and device, storage medium and electronic equipment |
| CN117544985B (en) * | 2024-01-09 | 2024-03-19 | 成都趣点科技有限公司 | Equipment offline communication management method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105606390A (en) * | 2016-02-29 | 2016-05-25 | 美的集团股份有限公司 | Terminal fault diagnosis method and terminal fault diagnosis device |
| CN108010305A (en) * | 2017-12-14 | 2018-05-08 | 深圳市科陆电子科技股份有限公司 | A kind of self-diagnosing method of comprehensive energy management platform data acquisition failure |
| CN109474494A (en) * | 2018-12-05 | 2019-03-15 | 深圳绿米联创科技有限公司 | Equipment detection method, device, server and storage medium |
| CN110401949A (en) * | 2019-06-19 | 2019-11-01 | 北京友宝在线科技股份有限公司 | Terminal device running log method for uploading and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090182533A1 (en) * | 2008-01-14 | 2009-07-16 | Apple Inc. | Remote diagnostic service |
-
2020
- 2020-12-30 CN CN202011615628.4A patent/CN112888007B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105606390A (en) * | 2016-02-29 | 2016-05-25 | 美的集团股份有限公司 | Terminal fault diagnosis method and terminal fault diagnosis device |
| CN108010305A (en) * | 2017-12-14 | 2018-05-08 | 深圳市科陆电子科技股份有限公司 | A kind of self-diagnosing method of comprehensive energy management platform data acquisition failure |
| CN109474494A (en) * | 2018-12-05 | 2019-03-15 | 深圳绿米联创科技有限公司 | Equipment detection method, device, server and storage medium |
| CN110401949A (en) * | 2019-06-19 | 2019-11-01 | 北京友宝在线科技股份有限公司 | Terminal device running log method for uploading and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112888007A (en) | 2021-06-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111600781B (en) | Firewall system stability testing method based on tester | |
| CN112888007B (en) | Method, device and storage medium for diagnosing offline reasons of device | |
| WO2019169743A1 (en) | Server failure detection method and system | |
| EP3222004B1 (en) | Diagnostic testing in networks | |
| WO2018201997A1 (en) | Method and device for calculating household appliance faults | |
| CN104067599A (en) | Network state monitoring system | |
| CN111787570B (en) | Data transmission method and device of Internet of things equipment and computer equipment | |
| CN112188535A (en) | Internet of things end-to-end fault delimiting method and device | |
| CN110647417A (en) | Energy internet abnormal data processing method, device and system | |
| EP1622310A2 (en) | Administration system for network management systems | |
| CN113452576A (en) | Network environment monitoring method and device, storage medium and electronic device | |
| US10397065B2 (en) | Systems and methods for characterization of transient network conditions in wireless local area networks | |
| US20230089918A1 (en) | Method and apparatus for controlling charging, based on monitored communication signals associated with a charging session | |
| EP4099643A1 (en) | A method, a system and a computer program product for monitoring an industrial ethernet protocol type network | |
| JP2007228421A (en) | Ip network route diagnosis apparatus and ip network route diagnosis system | |
| JP5876169B2 (en) | Method and server for determining the quality of a home network | |
| CN107306213A (en) | Diagnostic method and diagnostic device for network | |
| CN115118619A (en) | Network monitoring method, network monitoring device, electronic device, network monitoring medium, and program product | |
| CA2932392C (en) | Improved network management | |
| CN117061335A (en) | Cloud platform equipment health management and control method and device, storage medium and electronic equipment | |
| CN114266368A (en) | Equipment fault processing method and device, storage medium and electronic device | |
| US20200244524A1 (en) | Network device monitors | |
| CN115460630A (en) | Node management system, method, equipment and readable storage medium | |
| WO2024042346A1 (en) | Unified service quality model for mobile networks | |
| US11777827B1 (en) | Vendor-agnostic clientless speed measurement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |