CN112888007A - Method and device for diagnosing off-line reason of device and storage medium - Google Patents
Method and device for diagnosing off-line reason of device and storage medium Download PDFInfo
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- CN112888007A CN112888007A CN202011615628.4A CN202011615628A CN112888007A CN 112888007 A CN112888007 A CN 112888007A CN 202011615628 A CN202011615628 A CN 202011615628A CN 112888007 A CN112888007 A CN 112888007A
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Abstract
The application discloses a method and a device for diagnosing offline reasons of the device and a storage medium. The off-line reason diagnosis method of the equipment comprises the following steps: acquiring an equipment offline log; counting the off-line times of the equipment, the on-line rate of the equipment, the single off-line time of the equipment and the login condition of an equipment module in preset time; in response to the fact that the online rate of the equipment is smaller than the average online rate of all the equipment, fault diagnosis is conducted on the offline reason of the equipment by means of the running characteristic value of the equipment; responding to the fact that the single offline time of the equipment exceeds the preset time, and performing fault diagnosis on the offline reason of the equipment by combining the historical offline log and the online condition of the same routing equipment; and responding to successful login of the equipment module, acquiring an equipment operation environment log, and performing fault diagnosis on the equipment offline reason. According to the method for diagnosing the off-line reason of the equipment, the off-line log of the equipment is uploaded, the data log is uploaded and downloaded by a module of the server, the off-line reason of the user equipment is comprehensively analyzed, the 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 a method and equipment for diagnosing equipment offline reasons and a storage medium.
Background
With the development of the technology of the internet of things, the Wi-Fi wireless network technology can be used only after being tested and authorized by a Wi-Fi alliance, while most domestic wireless networks based on 802.11 are not tested by the Wi-Fi alliance, so that the quality of the wireless routers in the market is uneven. Meanwhile, due to the fact that the wireless network environment of the user is complex, the equipment is abnormally off-line, the off-line reason is very strong in speciality, the user often cannot judge whether the route reason or the equipment reason is caused, the fault cannot be solved, and therefore user experience is reduced. Therefore, a method for diagnosing the off-line reason of the device is needed to guide the user to solve the wireless network failure.
Disclosure of Invention
The application provides a method, equipment and a storage medium for diagnosing equipment offline reasons, and aims to solve the technical problem that the equipment offline reasons cannot be judged.
In order to solve the technical problem, the application adopts a technical scheme that: an apparatus offline cause diagnosis method, comprising: acquiring an equipment offline log; counting the off-line times of the equipment in preset time, the on-line rate of the equipment in preset time, the single off-line time of the equipment and the login condition of an equipment module; responding to the fact that the online rate of the equipment in the preset time is smaller than the average online rate of all the equipment, and utilizing the running characteristic value of the equipment in the preset time to carry out fault diagnosis on the offline reason of the equipment; responding to the fact that the single offline time of the equipment exceeds the preset time, and performing fault diagnosis on the offline reason of the equipment by combining a historical offline log and the online condition of the same-route equipment; and responding to the successful login of the equipment module, acquiring an equipment operation environment log, and performing fault diagnosis on the equipment offline reason.
According to an embodiment of the present application, 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 running characteristic value of the equipment in the preset time, and carrying out fault diagnosis on the offline reason of the equipment by combining the running characteristic value of historical equipment.
According to an embodiment of the present application, the performing fault diagnosis on the offline reason of the device by combining the historical offline log and the online condition of the device on the same route includes: acquiring a historical offline log, and judging whether the probability of offline of the equipment due to the same reason in historical time is greater than or equal to a first preset value; analyzing whether the devices connected with the same router are offline at the same time or not in response to the fact that the probability that the devices are offline for the same reason at the historical time is smaller than the first preset value; responding to the condition that the equipment connected with the same router is not offline at the same time, and analyzing whether the equipment connected with the same router and adopting the same module firmware is offline at the same time; responding to the condition that equipment which is connected with the same router and adopts the same module firmware is not offline at the same time; and carrying out fault diagnosis on the equipment offline reason by using the equipment operation characteristic value and combining with the historical equipment operation characteristic value to obtain an offline reason diagnosis result.
According to an embodiment of the present application, the performing fault diagnosis on the offline reason of the device by combining the historical offline log and the online condition of the device on the same route includes: responding to the fact that the probability of off-line due to the same reason is larger than or equal to a first preset value, or responding to the fact that equipment connected with the same router is off-line at the same time, or responding to the fact that equipment connected with the same router and adopting the same module firmware is off-line at the same time; and carrying out big data analysis on the offline log to obtain an offline reason diagnosis result.
According to an embodiment of the present application, the obtaining an equipment operation characteristic value in the predetermined time in response to that the number of times of offline equipment in the predetermined time is greater than the predetermined number of times, and performing fault diagnosis on an offline reason of the equipment in combination with a historical equipment operation characteristic value includes: responding to the fact that the number of times of the equipment offline in the preset time is larger than the preset number of times; acquiring an equipment operation characteristic value in the preset time, wherein the equipment operation characteristic value comprises statistical information of operation data of equipment in the preset time; and carrying out fault diagnosis by using the equipment operation characteristic value and combining the historical equipment operation characteristic value in a preset historical time period to obtain the offline reason diagnosis result.
According to an embodiment of the present application, the obtaining an equipment operating environment log in response to successful login of the equipment module, and performing fault diagnosis on the offline reason of the equipment includes: after responding to the offline of the equipment, 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 a disconnection reason; and (5) carrying out microscopic off-line log analysis to obtain an off-line reason diagnosis result.
According to an embodiment of the present application, the method further comprises: sending the offline reason diagnosis result to a user or an after-sales engineer; and forming a solution guidance scheme according to the offline reason diagnosis result.
According to an embodiment of the present application, the method comprises: and carrying out fault diagnosis on the equipment in a preset time period, and recording a diagnosis result.
In order to solve the above technical problem, the present application adopts another technical solution: an apparatus for diagnosing off-line causes of equipment, comprising: the equipment network operation environment log system records the equipment offline log when the equipment is offline, and counts the equipment offline times in preset time, the equipment online rate in preset time, the equipment single offline time length and the equipment module login condition; the equipment fault diagnosis system is used for responding to the fact that the online rate of the equipment in the preset time is smaller than the average online rate of all the equipment, and diagnosing the fault of the equipment offline reason by utilizing the running characteristic value of the equipment in the preset time; the equipment big data analysis system is used for responding to the fact that the single off-line time of the equipment exceeds the preset time, and fault diagnosis is carried out on the off-line reason of the equipment by combining a historical off-line log and the on-line condition of the same routing 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 reason.
According to an embodiment of the present application, the method includes: and applying a push system, sending the diagnosis result of the off-line reason to a user or a technician, and forming a solution guidance scheme according to the diagnosis result of the off-line reason.
In order to solve the above technical problem, the present application adopts another technical solution: a computer readable storage medium storing program data executable to implement a method as any one of the above.
The beneficial effect of this application is: 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, uploading and downloading the data log and transmitting the data log by the module of the server and through comprehensive analysis. And then can guide the user to correctly use thing networking device, guide after sale to solve user's problem, guide research and development to restore equipment network compatibility problem and optimize equipment wireless connection function, promote user experience.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of an off-line cause diagnosis method for a device according to the present application;
FIG. 2 is a schematic flow chart illustrating offline log analysis in an embodiment of the apparatus offline cause diagnosis method according to the present application;
FIG. 3 is a schematic flow chart illustrating fault diagnosis in an embodiment of the apparatus offline cause diagnosis method according to the present application;
FIG. 4 is a schematic flow chart illustrating a combination of a historical offline log and online condition analysis of a routing device in an embodiment of the device offline reason diagnosis method according to the present application;
FIG. 5 is a schematic diagram of a framework of an embodiment of an apparatus offline cause diagnosis apparatus according to the present application;
FIG. 6 is a block diagram of an embodiment of an electronic device of the present application;
FIG. 7 is a block diagram of an embodiment of a computer-readable storage medium of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1 to 4, the present application provides an apparatus offline cause diagnosis method, including the following steps:
s101: and acquiring an equipment offline log.
And acquiring an equipment offline log. The offline log includes the reason for the offline network at the time the device was 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, channel, etc.), module network information (e.g., local IP, user network operation, reason for module reboot, etc.), operator network and server connection information (e.g., server heartbeat, server outage, operator public network IP, etc.), outage reason (connection route loss, heartbeat timeout, user distribution network, 802.11 protocol error (outage replay code), etc.).
It should be noted that the device may be an intelligent household appliance, such as an intelligent sound box, an intelligent air conditioner, an intelligent electric cooker, an intelligent floor sweeping robot, or other intelligent household appliances connected to the user network. The device may also be an office device connected to the user network in an office, or an intelligent mechanical device connected to the user network in a factory, which is not limited herein.
S102: and counting the off-line times of the equipment, the on-line rate of the equipment, the single off-line time of the equipment and the login condition of the equipment module in preset time.
And counting the off-line times of the equipment, the on-line rate of the equipment, the single off-line time of the equipment and the login condition of the equipment module in 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 operation environment log, and performing fault diagnosis on the equipment offline reason.
S1031: and responding to successful login again after the equipment is offline.
And responding to successful login of the equipment module after the equipment is offline, namely successful login of the Wi-Fi module of the equipment, and performing data transmission between the cloud and 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 a disconnection reason.
The equipment module logs in again successfully, the cloud module can acquire an equipment operation environment log, and 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 reason diagnosis result.
And performing offline log analysis on the offline reason of the equipment, namely specifically analyzing the offline reason of the equipment to obtain an offline reason diagnosis result. The offline reasons of the equipment can be specifically analyzed through a 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 equipment each time, a model is built for judging the specific offline reasons by running environmental logs of the equipment network, and the microscopic offline log analysis script management system can be used for updating and increasing new offline reason judgment as more offline reasons are found.
In some embodiments, if the offline log analysis finds that the offline cause diagnosis result is a poor signal, the user may be advised to change the router to another location. If the offline reason diagnosis result is found to be incompatible through offline log analysis, a user can be advised 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 equipment in a preset time period, and recording the diagnosis result. The preset time period is generally a time period in which the device is used less frequently at night. For example, with 24 hours as unit time, the offline log can be analyzed in real time, fault diagnosis is performed within a preset time period at night, a diagnosis result is stored in a system database, and when the device is offline again, the data can be inquired, so that problems can be found and solved in time, and the occurrence of the offline condition of the device is reduced. And fault diagnosis is carried out in a preset time period, so that the calculation 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 online rate of the equipment in the preset time is smaller than the average online rate of all the equipment, and diagnosing the fault of the equipment offline reason by utilizing the characteristic values of the equipment operation 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 for all devices within a predetermined time, it is indicated that the device may be present multiple times and that some off-line reason is regularly present.
It can be understood that, if the predetermined time is 24 hours, in response to that the online rate of the statistical equipment in 24 hours is less than the average online rate of all the equipment, the fault diagnosis is performed on the reason of the offline equipment by using the characteristic values of the operation of the equipment in the predetermined time. The device operation characteristic values comprise a signal intensity average value, a device online time length average value, a device offline time length average value, an internal and external network IP address and the like.
The method comprises the following steps of utilizing the equipment operation characteristic values in the preset time to carry out fault diagnosis, and obtaining an offline reason diagnosis result, wherein the offline reason diagnosis result comprises the following steps:
the offline reason of the equipment can be specifically analyzed through a macroscopic fault diagnosis model script management system. When the equipment presents for multiple times and a certain off-line reason appears regularly, the equipment operation characteristic value and the off-line reason in the preset time are utilized to carry out fault diagnosis analysis according to the specific equipment operation characteristic value and the off-line reason, and as more equipment network faults are found, a new fault diagnosis model can be updated and added by utilizing the script management system.
Specifically, by performing statistical analysis on the characteristic values of the operation of the equipment in the predetermined time and performing fault diagnosis analysis, it is possible to obtain the diagnosis result of the off-line reason that cannot be found by microscopic off-line log analysis, for example, it is found by fault diagnosis analysis that the equipment fault is due to the equipment identification code string numbers of two different equipments.
It should be noted that, during a preset time period, the fault diagnosis may be performed on the device, and the diagnosis result may be recorded. For example, the predetermined time is 24 hours, and the predetermined time period is usually a time period in which the use frequency of the device is low at night. The running characteristic values of the equipment are counted and analyzed within the preset time period at night, fault diagnosis is carried out, the diagnosis result is stored in the system database, and when the equipment is off-line again, the data can be inquired, so that problems can be found and solved in time, and the occurrence of the off-line condition of the equipment is reduced. And fault diagnosis is carried out in a preset time period, so that the calculation amount of equipment with high operation frequency can be reduced, and the influence on the operation of the equipment is avoided.
Further, the method also comprises the following steps:
s105: and responding to the fact that the device offline times in the preset time are larger than the preset times, acquiring the device operation characteristic value in the preset time, and carrying out fault diagnosis on the device offline reason by combining the historical device operation characteristic value.
In response to the device being offline more than the predetermined number of times within the predetermined time, it is indicated that the device may be present multiple times and that a certain reason for offline occurs regularly.
The method specifically comprises the following steps:
s1051: and responding to the fact that the device offline times in the preset time are larger than the preset times.
And responding to the fact that the device offline times in the preset time are larger than the preset times. For example, the number of times the device is offline is greater than 25 times in 24 hours, and may be 15, 20, 30, and so on in other embodiments. Indicating that the device may be present multiple times and that some off-line reason occurs regularly. A macroscopic analysis of its off-line causes is required.
S1052: and acquiring a device operation characteristic value in preset time, wherein the device operation characteristic value comprises statistical information of operation data of the device in the preset time.
And acquiring equipment operation characteristic values, wherein 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.
S1053: and carrying out fault diagnosis by using the equipment operation characteristic value and combining the historical equipment operation characteristic value in a preset historical time period to obtain an offline reason diagnosis result.
It should be noted that, in the method, the predetermined time is taken as unit time, and when the online rate of the equipment in the predetermined time is smaller than the average online rate of all the equipment, the statistical analysis is performed on the operation characteristic value of the equipment in the predetermined time, and the fault diagnosis is performed on the offline reason of the equipment, and the fault diagnosis is recorded. The method is equivalent to that fault diagnosis is carried out on the off-line reason of the equipment every unit time, and the fault diagnosis is recorded in a system as a historical equipment operation characteristic value and the off-line reason.
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 using the running characteristic value of the equipment in the preset time and combining the historical running characteristic value of the historical equipment recorded in the preset historical time period (for example, in 30 days) in the system, and an offline reason diagnosis result is obtained. For example, if the device operation characteristic value in the predetermined time is close to the historical device operation characteristic value, the offline reason of the device at that time may 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 performing fault diagnosis on the offline reason of the equipment by combining the historical offline log and the online condition of the same-route equipment.
And responding to the fact that the single offline time of the equipment exceeds the preset time, which shows that the equipment module can not successfully log in, and the cloud end can not acquire the running environment log of the equipment, so that specific offline reasons are analyzed. Therefore, at this time, the fault diagnosis needs to be performed on the offline reason of the device by combining the historical offline log and the online situation of the same-route device.
The method specifically comprises the following steps:
s1061: and acquiring a historical offline log, and judging whether the probability of offline of the equipment due to the same reason in historical time is greater than or equal to a first preset value.
And acquiring a historical offline log, and analyzing whether the equipment is frequently offline for the same offline reason at the historical time. If the probability that the equipment is offline for the same reason in the historical time is larger than or equal to the first preset value, the fact that the equipment is offline possibly caused by the same reason is indicated, and therefore big data analysis is conducted on the offline logs, 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 with the same router are off-line at the same time or not in response to the probability that the devices are off-line for the same reason at the historical time being less than a first preset value.
And analyzing whether a plurality of devices connected with the same router are off-line simultaneously or not in response to the probability that the devices are off-line for the same reason in the historical time being less than a first preset value. And if the probability of responding to the offline for the same reason is smaller than a first preset value, analyzing whether a plurality of devices connected with 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 the offline logs are subjected to big data analysis to obtain offline reason diagnosis results.
S1063: and analyzing whether the devices which are connected with the same router and adopt the same module firmware are offline at the same time or not in response to the fact that the devices which are connected with the same router are not offline at the same time.
And responding to the condition that the equipment of the same router is not offline at the same time, and analyzing whether the equipment which is connected with the same router and adopts the same module firmware is offline at the same time. If the same router is connected and the equipment adopting the same module firmware is off-line at the same time, the problem of the module firmware version is possible, and the off-line log can be subjected to big data analysis to obtain an off-line reason diagnosis result.
S1064: responding to the condition that the equipment which is connected with the same router and adopts the same module firmware is not offline at the same time; and carrying out fault diagnosis on the equipment offline reason by using the equipment operation characteristic value and combining with the historical equipment operation characteristic value to obtain an offline reason diagnosis result.
And responding to the situation that the equipment which is connected with the same router and adopts the same module firmware is not offline at the same time, and performing fault diagnosis on the offline reason of the equipment by using the equipment operation characteristic value and combining with the historical equipment operation characteristic value to obtain an offline reason diagnosis result. Specifically, refer to step S1053.
S107: and sending the diagnosis result of the offline reason to a user or a technician.
And sending the diagnosis result of the offline reason to a user or a technician. Thereby enabling the user and technician to understand why the device is offline. The technician may be an after-sales engineer who is responsible for solving the after-sales problem for the equipment manufacturer, or may be another technician who can solve the technical problem, such as a third party.
S108: and forming a solution guidance scheme according to the offline reason diagnosis result.
A solution guidance scheme is formed according to an offline reason diagnosis result, a user is guided to repair, parameters of an operation environment can be sent to technical personnel aiming at faults which cannot be solved by the user, the technical personnel can reproduce the environment and solve problems, compatibility of a module and a route is improved, meanwhile, accuracy of judgment can be improved through various new fault types, fault characteristic value calculation and a fault diagnosis model are updated, and accordingly online stability of equipment is improved.
It should be noted that, after the device is offline every time and the module is successfully logged, the offline reason of the device is offline log analyzed in step S103 of the present application, so as to obtain an offline reason diagnosis result. When the device is offline for multiple times, the offline reason of the device is diagnosed by adopting the steps S104 and S105, and an offline reason diagnosis result is obtained. And when the offline duration of the equipment is overtime, the offline reason of the equipment is subjected to fault diagnosis by adopting the step S106 and combining the historical offline log and the online condition of the same-route equipment. The combined judgment method can judge the offline reason of the equipment in time, guide the user to solve the problem and improve the user experience.
In a specific implementation, in an embodiment, after the device offline log is obtained, it may be first determined whether the device online rate in a predetermined time is less than the average online rate of all devices; if not, judging that the off-line times of the equipment in the preset time are more than the preset times; if the number of times does not exceed the preset number, further judging that the single off-line time of the equipment exceeds the preset time; if the preset time is not exceeded, the equipment module is judged to be successfully logged in. Or in other embodiments, other sequential determination manners may be adopted, so as to implement the above-described combined determination method.
According to the equipment offline reason diagnosis method, the offline reason of the user equipment is judged through uploading the offline log of the equipment, uploading and downloading the data log of the module of the server and transmitting the data log, and through comprehensive analysis. The device stores the wireless environment information during offline, including connection routing information, IP information, the reason for restarting the device and the like, analyzes and distinguishes user behaviors, network operator behaviors, routing behaviors and device behaviors through various offline characteristics, judges the specific reason causing offline, and uploads records to the cloud when a server is successfully logged in next time. When the device is not on-line, the reason of the off-line can be inferred through historical data analysis. And guides the user to correctly use the Internet of things equipment, guides the after-sale solution of user problems, guides the research and development of repairing equipment network compatibility problems and optimizes the wireless connection function of the equipment. The method has a remarkable application effect in the household appliances of the Internet of things.
Referring to fig. 5, another embodiment of the present application provides an apparatus 20 for diagnosing an offline reason of a device, which includes a device network operating 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 is offline, the device network operating environment log system 21 records the device offline log, and counts the device offline times in a predetermined time, the device online rate in a predetermined time, the single device offline duration, and the device module login condition. The offline log includes the reason for the offline network at the time the device was 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, channel, etc.), module network information (e.g., local IP, user network operation, reason for module reboot, etc.), operator network and server connection information (e.g., server heartbeat, server outage, operator public network IP, etc.), outage reason (connection route loss, heartbeat timeout, user distribution network, 802.11 protocol error (outage replay code), etc.).
It should be noted that the device may be an intelligent household appliance, such as an intelligent sound box, an intelligent air conditioner, an intelligent electric cooker, an intelligent floor sweeping robot, or other intelligent household appliances connected to the user network. The device may also be an office device connected to the user network at the office, or an intelligent mechanical device connected to the user network at the factory.
In response to the online rate of the device being less than the average online rate of all devices within the predetermined time, the device fault diagnosis system 22 performs fault diagnosis on the offline cause of the device using the operating characteristic values of the device within the predetermined time. The equipment failure diagnosis system 22 includes an equipment operation characteristic value calculation script management system, a macro failure diagnosis model script management system, and a macro failure 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 as more faults are found, the fault diagnosis model is continuously improved, so that the equipment operation characteristic value is continuously increased and improved, the equipment operation characteristic value is calculated in the script form, and the equipment operation characteristic value is easily updated and increased. The macro fault diagnosis model script management system stores the fault diagnosis model, when the equipment presents for many times and a certain off-line reason appears regularly, the equipment diagnoses the fault according to the macro fault by using the operation characteristic value and the off-line reason, and the macro fault diagnosis model script management system can be used for updating and adding a new fault diagnosis model as more equipment network faults are found. The macro fault diagnosis processor realizes the operation action of fault diagnosis.
In response to successful login of the equipment module, the equipment offline log analysis system 24 obtains an equipment operating environment log, and performs fault diagnosis on the equipment offline reason. The device offline log analysis system 24 includes a microscopic offline log analysis script management system and a microscopic offline log analysis processor, the microscopic offline log analysis script management system is used for specifically judging each offline reason, and it depends on the device network operating environment log to establish a model to judge the specific offline reason, and as more offline reasons are found, the script management system can be used to update and add new offline reason judgment. The microscopic off-line log analysis processor realizes the operation action of off-line reason 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 conditions permit.
And in response to the fact that the single offline time of the equipment exceeds the preset time, the equipment big data analysis system 23 performs fault diagnosis on the offline reason of the equipment by combining the historical offline log and the online condition of the same routing equipment. Because the judgment of the microscopic off-line reason has hysteresis (because the equipment module is off-line, the log cannot be uploaded to the server, and the log can be uploaded only after the next successful login), when the off-line time length exceeds the preset time length, the equipment big data analysis system 23 analyzes the historical log and deduces the off-line reason. Meanwhile, the online conditions of the devices on the same route are compared transversely for judging external influence factors.
Further, the device offline cause diagnosis apparatus 20 further includes an application pushing system 25, where the application pushing system 25 sends the offline cause diagnosis result to a user or an after-market engineer, and forms a solution guidance scheme according to the offline cause diagnosis result.
The device offline reason diagnosis device 20 comprehensively analyzes and judges the offline reason of the user module by uploading the offline log of the device, uploading and downloading the data log of the module of the server and transmitting the data log, and calculating a script management system, a microscopic offline log analysis script management system and a macroscopic fault diagnosis model script management system according to a configurable device operation characteristic value. The device stores the wireless environment information during offline, including connection routing information, IP information, the reason for restarting the device and the like, analyzes and distinguishes user behaviors, network operator behaviors, routing behaviors and device behaviors through various offline characteristics, judges the specific reason causing offline, and uploads records to the cloud when a server is successfully logged in next time. When the device is not on-line, the reason of the off-line can be inferred through historical data analysis. And guiding a user to correctly use the Internet of things equipment, guiding after-sale user problem solving, guiding research and development of repairing equipment network compatibility problems and optimizing the wireless connection function of the equipment.
Referring to fig. 6, fig. 6 is a schematic diagram of a frame of an embodiment of an electronic device according to the present application.
The present application further provides an electronic device 30, which includes a memory 31 and a processor 32 coupled to each other, wherein 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, the electronic device 30 may include, but is not limited to: a microcomputer, a server, and the electronic device 30 may also include a mobile device such as a notebook computer, a tablet computer, and the like, which is not limited herein.
In particular, the processor 32 is configured to control itself and the memory 31 to implement the steps in any of the above-described embodiments of the image exposure adjustment method. 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 (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, 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 block diagram illustrating an embodiment of a computer-readable storage medium according to the present application.
Yet another embodiment of the present application provides a computer-readable storage medium 40, on which program data 41 is stored, and when executed by a processor, the program data 41 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 method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a module or a unit is merely one type of logical division, and an actual implementation may have another division, for example, a unit or a component may be combined or integrated with another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some interfaces, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on network elements. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium 40. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium 40 and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method 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), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are intended to be included within the scope of the present application.
Claims (11)
1. An off-line cause diagnosis method for equipment, comprising:
acquiring an equipment offline log;
counting the off-line times of the equipment in preset time, the on-line rate of the equipment in preset time, the single off-line time of the equipment and the login condition of an equipment module;
responding to the fact that the online rate of the equipment in the preset time is smaller than the average online rate of all the equipment, and utilizing the running characteristic value of the equipment in the preset time to carry out fault diagnosis on the offline reason of the equipment;
responding to the fact that the single offline time of the equipment exceeds the preset time, and performing fault diagnosis on the offline reason of the equipment by combining a historical offline log and the online condition of the same-route equipment;
and responding to the successful login of the equipment module, acquiring an equipment operation environment log, and performing fault diagnosis on the equipment offline reason.
2. The method according to claim 1, characterized in that it 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 running characteristic value of the equipment in the preset time, and carrying out fault diagnosis on the offline reason of the equipment by combining the running characteristic value of historical equipment.
3. The method of claim 1, wherein the fault diagnosis of the offline reason of the device in combination with the historical offline log and the online condition of the device on the same route comprises:
acquiring a historical offline log, and judging whether the probability of offline of the equipment due to the same reason in historical time is greater than or equal to a first preset value;
analyzing whether the devices connected with the same router are offline at the same time or not in response to the fact that the probability that the devices are offline for the same reason at the historical time is smaller than the first preset value;
responding to the condition that the equipment connected with the same router is not offline at the same time, and analyzing whether the equipment connected with the same router and adopting the same module firmware is offline at the same time;
responding to the condition that equipment which is connected with the same router and adopts the same module firmware is not offline at the same time;
and carrying out fault diagnosis on the equipment offline reason by using the equipment operation characteristic value and combining with the historical equipment operation characteristic value to obtain an offline reason diagnosis result.
4. The method of claim 3, wherein the fault diagnosing the offline reason of the device by combining the historical offline log and the online condition of the device on the same route comprises:
responding to the fact that the probability of offline is larger than or equal to the first preset value due to the same reason, or responding to the fact that equipment connected with the same router is offline at the same time, or responding to the fact that equipment connected with the same router and adopting the same module firmware is offline at the same time;
and carrying out big data analysis on the offline log to obtain an offline reason diagnosis result.
5. The method according to claim 2, wherein the step of obtaining the operation characteristic value of the equipment in the predetermined time in response to the number of times of offline of the equipment in the predetermined time being greater than the predetermined number of times, and performing fault diagnosis on the reason of offline of the equipment by combining the operation characteristic value of the historical equipment comprises the steps of:
responding to the fact that the number of times of the equipment offline in the preset time is larger than the preset number of times;
acquiring an equipment operation characteristic value in the preset time, wherein the equipment operation characteristic value comprises statistical information of operation data of equipment in the preset time;
and carrying out fault diagnosis by using the equipment operation characteristic value and combining the historical equipment operation characteristic value in a preset historical time period to obtain the offline reason diagnosis result.
6. The method of claim 1, wherein the obtaining an equipment operating environment log in response to successful login of the equipment module, and performing fault diagnosis on the offline reason of the equipment comprises:
after responding to the offline of the equipment, the equipment module successfully logs in again;
acquiring the equipment operation environment log, wherein the equipment operation environment log comprises routing information, module network information, operator network and server connection information and a disconnection reason;
and performing offline log analysis to obtain an offline reason diagnosis result.
7. The method according to any one of claims 1-6, further comprising:
sending the offline reason diagnosis result to a user or an after-sales engineer;
and forming a solution guidance scheme according to the offline reason diagnosis result.
8. The method according to claim 1, characterized in that it comprises:
and carrying out fault diagnosis on the equipment in a preset time period, and recording a diagnosis result.
9. An apparatus for diagnosing a cause of an offline device, comprising:
the equipment network operation environment log system records the equipment offline log when the equipment is offline, and counts the equipment offline times in preset time, the equipment online rate in preset time, the equipment single offline time length and the equipment module login condition;
the equipment fault diagnosis system is used for responding to the fact that the online rate of the equipment in the preset time is smaller than the average online rate of all the equipment, and diagnosing the fault of the equipment offline reason by utilizing the running characteristic value of the equipment in the preset time;
the equipment big data analysis system is used for responding to the fact that the single off-line time of the equipment exceeds the preset time, and fault diagnosis is carried out on the off-line reason of the equipment by combining a historical off-line log and the on-line condition of the same routing 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 reason.
10. The apparatus of claim 9, comprising:
and applying a push system, sending the diagnosis result of the off-line reason to a user or a technician, and forming a solution guidance scheme according to the diagnosis result of the off-line reason.
11. A computer-readable storage medium, characterized in that the storage medium stores program data that can be executed to implement the method according to any one of claims 1-8.
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| CN202011615628.4A CN112888007B (en) | 2020-12-30 | 2020-12-30 | Method, device and storage medium for diagnosing offline reasons of device |
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| CN202011615628.4A CN112888007B (en) | 2020-12-30 | 2020-12-30 | Method, device and storage medium for diagnosing offline reasons of device |
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| CN112888007B (en) | 2023-05-05 |
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