CN117499216B

CN117499216B - State early warning method, device, equipment and medium of Internet of things equipment

Info

Publication number: CN117499216B
Application number: CN202311856980.0A
Authority: CN
Inventors: 符佳俊; 陈道远; 张忠敏; 巫锦辉; 钟仙凤
Original assignee: Gree Electric Appliances Inc of Zhuhai; Zhuhai Lianyun Technology Co Ltd
Current assignee: Gree Electric Appliances Inc of Zhuhai; Zhuhai Lianyun Technology Co Ltd
Priority date: 2023-12-29
Filing date: 2023-12-29
Publication date: 2024-04-12
Anticipated expiration: 2043-12-29
Also published as: CN117499216A

Abstract

The application discloses a state early warning method, device, electronic equipment and computer readable storage medium of internet of things equipment, belongs to the internet of things field, and includes: responding to a control instruction received by the Internet of things equipment, and acquiring the running state of the Internet of things equipment through a state acquisition device; recording the running state and a control instruction corresponding to the running state into a running log of the Internet of things equipment; counting the number of control instructions and running states in the running log, and determining the running times of the Internet of things equipment according to the number; under the condition that the operation times meet the abnormality judgment conditions, generating abnormal statistical data according to the equipment information and the operation times of the equipment of the Internet of things; the abnormal statistical data is used for representing that the running times of the Internet of things equipment exceeds the limit; and determining the operation hidden danger of the equipment of the Internet of things according to the abnormal statistical data, and displaying the operation hidden danger. The problem that in the related art, in the use process of the Internet of things equipment, the hidden danger of equipment operation cannot be confirmed in time is solved.

Description

State early warning method, device, equipment and medium of Internet of things equipment

Technical Field

The application belongs to the technical field of the Internet of things, and particularly relates to a state early warning method and device of Internet of things equipment, electronic equipment and a computer readable storage medium.

Background

With the development of the internet of things technology, more and more intelligent devices are applied to various fields, such as home, industry, medical treatment, traffic and the like, and convenience and efficiency are brought to life and work of people. However, the operation state of the smart device may also be affected by various factors, such as usage environment, load change, aging loss, malfunction occurrence, etc., which may cause performance degradation, life reduction, and even dangerous accidents of the device. Therefore, the operation state of the intelligent equipment is monitored and early-warned in time, and the intelligent equipment is an important means for ensuring the safe and reliable operation of the equipment.

Currently, the inquiry of the hidden trouble of the operation of the equipment mainly depends on periodic maintenance inspection, namely, the equipment is periodically inspected and tested by manual or special instruments so as to find the abnormality or damage of the equipment. The method has the advantages that the equipment can be comprehensively and deeply detected, potential problems are found and timely processed.

However, these methods have some drawbacks, such as a long maintenance period, failure to reflect the running state of the device in real time, and missing some critical abnormal signals or fault development processes; the maintenance cost is high, the manpower, material resources and time resources are required to be consumed, and certain interference is caused to the normal operation of the equipment; the dimension inspection result depends on the level and accuracy of dimension inspection personnel or instruments, and subjective errors or measurement errors can exist to influence the quality of judgment and decision.

Disclosure of Invention

The application aims to provide a state early warning method and device of Internet of things equipment, electronic equipment and a computer readable storage medium, and at least solves the problem that the hidden danger of equipment operation cannot be confirmed in time in the use process of the Internet of things equipment.

In a first aspect, an embodiment of the present application discloses a status early warning method for an internet of things device, where a status collector is provided on the internet of things device, the method includes:

responding to a control instruction received by the Internet of things equipment, and acquiring the running state of the Internet of things equipment through the state acquisition device;

recording the running state and a control instruction corresponding to the running state into a running log of the Internet of things equipment;

counting the number of control instructions and running states in the running log, and determining the running times of the Internet of things equipment according to the number;

generating abnormal statistical data according to the equipment information of the Internet of things equipment and the operation times under the condition that the operation times meet the abnormal judgment conditions; the abnormal statistical data is used for representing that the running times of the Internet of things equipment exceeds a limit;

and determining the operation hidden danger of the Internet of things equipment according to the abnormal statistical data, and displaying the operation hidden danger.

In a second aspect, the embodiment of the application also discloses a state early warning device of an internet of things device, a state collector is arranged on the internet of things device, and the device comprises:

the state module is used for responding to the control instruction received by the Internet of things equipment and acquiring the running state of the Internet of things equipment through the state acquisition device;

the log module is used for inputting the running state and the control instruction corresponding to the running state into the running log of the Internet of things equipment;

the statistics module is used for counting the quantity of control instructions and running states in the running log and determining the running times of the Internet of things equipment according to the quantity;

the judging module is used for generating abnormal statistical data according to the equipment information of the Internet of things equipment and the operation times under the condition that the operation times meet the abnormal judging conditions; the abnormal statistical data is used for representing that the running times of the Internet of things equipment exceeds a limit;

and the display module is used for determining the operation hidden danger of the Internet of things equipment according to the abnormal statistical data and displaying the operation hidden danger.

In a third aspect, an embodiment of the present application further discloses an electronic device, including a processor and a memory, where the memory stores a program or instructions executable on the processor, the program or instructions implementing the steps of the method according to the first aspect when executed by the processor.

In a fourth aspect, embodiments of the present application also disclose a readable storage medium having stored thereon a program or instructions which, when executed by a processor, implement the steps of the method as described in the first aspect.

In summary, in the embodiment of the application, by setting the state collector on the internet of things device, real-time state monitoring and recording of the internet of things device can be achieved, so that visibility and traceability of the device state are improved. Based on the operation log of the equipment, abnormal statistical data of the operation of the equipment can be analyzed, and further the hidden danger of the operation of the equipment is displayed. When the hidden operating trouble of the equipment is displayed, corresponding information such as early warning level, suggested measures, maintenance scheme and the like can be given according to the type, function, fault reason and the like of the equipment, so that users or engineering personnel can take effective maintenance or repair measures. Therefore, according to the method, in the running process of the equipment, a user or engineering personnel is not required to go to the door to detect the running state of the equipment, and the problems of equipment damage, safety accidents, energy waste and the like caused by imperfect equipment alarming function or untimely inspection of the user in the related technology are solved. The method of the embodiment of the application not only saves time and cost of users and engineering personnel, but also improves the early warning capability and the intelligent level of the equipment state.

Drawings

In the drawings:

fig. 1 is a step flowchart of a status early warning method of an internet of things device provided in an embodiment of the present application;

fig. 2 is a flowchart of steps of another method for early warning of a state of an internet of things device according to an embodiment of the present application;

fig. 3 is a device network connection block diagram of a state early warning method of an internet of things device according to an embodiment of the present application;

fig. 4 is a specific implementation process of a state early warning method of an internet of things device in the present application in a home internet of things environment;

FIG. 5 is another implementation process of the state early warning method of an IOT device of the present application in a home IOT environment;

fig. 6 is a block diagram of a state early warning device of an internet of things device according to an embodiment of the present application;

FIG. 7 is a block diagram of an electronic device of one embodiment provided by embodiments of the present application;

fig. 8 is a block diagram of an electronic device of another embodiment provided by an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

The terms first, second and the like in the description and in the claims, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged, as appropriate, such that embodiments of the present application may be implemented in sequences other than those illustrated or described herein, and that the objects identified by "first," "second," etc. are generally of a type and not limited to the number of objects, e.g., the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/", generally means that the associated object is an "or" relationship.

Fig. 1 is a state early warning method of an internet of things device provided in this embodiment, where a state collector is disposed on the internet of things device.

The method may comprise the steps of:

step 101, responding to a control instruction received by the internet of things equipment, and acquiring the running state of the internet of things equipment through the state acquisition device.

The purpose of this step is that through the state collector, obtain the running state of thing networking equipment. The state collector is a device capable of sensing and collecting various state parameters of the internet of things equipment, such as electric quantity, temperature, switching times, fault codes and the like. The state collector can adopt different sensors and interfaces according to different types and functions of the Internet of things equipment so as to realize effective monitoring of equipment states. When the control instruction is received by the internet of things equipment, for example, opening, closing, adjusting, inquiring and the like, the state collector obtains the corresponding running state of the equipment according to the content of the instruction and transmits the corresponding running state to the control module of the internet of things equipment.

For example, the intelligent curtain is used as an internet of things device, and can be remotely controlled through a mobile phone terminal or intelligent voice central control. The intelligent curtain is provided with a state collector which can comprise a current sensor and a counter. When the intelligent curtain receives a control instruction of opening or closing, the state collector can acquire a current value of the curtain motor through the current sensor so as to judge whether the curtain is normally opened or closed. Meanwhile, the state collector can record the opening and closing times of the curtain through the counter so as to count the service life of the curtain. The state collector transmits the current value and the opening and closing times to a control module of the intelligent curtain so as to facilitate subsequent data processing and analysis.

And 102, recording the running state and a control instruction corresponding to the running state into a running log of the Internet of things equipment.

The method comprises the steps of recording the running state and control instructions of the Internet of things equipment into the running log of the equipment. A log is a data file for recording the operation of a device and may contain information such as status parameters, control instructions, time stamps, error codes, etc. of the device. The travel log may be stored in an internal memory or an external storage device of the internet of things device for subsequent data analysis and troubleshooting. When the equipment of the Internet of things acquires the running state of the equipment through the state acquisition device, the running state and a control instruction corresponding to the running state are recorded into a running log of the equipment according to a certain format and sequence.

For example, the intelligent air conditioner is used as an internet of things device, and can be remotely controlled through a mobile phone terminal or intelligent voice central control. The intelligent air conditioner is provided with a state collector which comprises a temperature sensor, a humidity sensor and a power meter. When the intelligent air conditioner receives a control instruction for adjusting temperature or humidity, the state collector can acquire the current temperature and humidity value of the air conditioner through the temperature sensor and the humidity sensor, and acquire the current power value of the air conditioner through the power meter. The state collector records the values and the content of the control instruction into an operation log of the intelligent air conditioner.

And step 103, counting the number of control instructions and running states in the running log, and determining the running times of the Internet of things equipment according to the number.

The method comprises the step of determining the operation times of the Internet of things equipment according to the control instructions in the operation log and the number of operation states. The running times refer to the times of executing control instructions in a certain time of the Internet of things equipment, and can reflect the use frequency and service life of the equipment. The running times can adopt different statistical methods and standards according to different device types and functions. After the running state and the control command are recorded into the running log by the Internet of things equipment, the running log can be subjected to statistical analysis, so that the running times of the equipment are obtained.

For example, the intelligent door lock is used as an internet of things device, and can be remotely controlled through a mobile phone terminal or intelligent voice central control. The intelligent door lock is provided with a state collector which comprises a switch lock sensor and an electricity meter. When the intelligent door lock receives an unlocking or locking control instruction, the state collector can acquire the unlocking state of the door lock through the unlocking and locking sensor and acquire the electric quantity value of the door lock through the electric quantity meter. The state collector records the values and the content of the control instruction into the operation log of the intelligent door lock. The number of times of operation of the intelligent door lock can be determined according to the control instructions and the number of switch states in the operation log. The number of runs can be used to determine the use and life of the door lock and predict the maintenance requirements and replacement time of the door lock.

Step 104, generating abnormal statistical data according to the equipment information of the Internet of things equipment and the operation times under the condition that the operation times meet the abnormal judgment conditions; the abnormal statistical data is used for representing that the running times of the Internet of things equipment exceeds a limit.

The method comprises the steps of generating abnormal statistical data according to equipment information and operation times of equipment of the Internet of things. The abnormal statistical data is data used for representing that the running times of the equipment of the Internet of things exceeds the limit, and can comprise information such as the type, the function, the service life, the use frequency, the abnormal reason and the like of the equipment. The anomaly statistics may be used to determine whether the equipment needs maintenance or replacement, and to pre-warn of potential risks of the equipment. When the operation times of the equipment of the internet of things meet the abnormality judgment conditions, for example, the design life of the equipment is exceeded or the performance limit of the equipment is reached, abnormal statistical data are generated according to the equipment information and the operation times of the equipment.

For example, the intelligent socket is used as an internet of things device, and can be remotely controlled through a mobile phone terminal or intelligent voice central control. The intelligent socket is provided with a state collector which comprises a key sensor and a ammeter. When the intelligent socket receives an on or off control instruction, the state collector can acquire the key times of the socket through the key sensor and acquire the current value of the socket through the ammeter. The state collector records the values and the content of the control instruction into the operation log of the intelligent socket. The operation times of the intelligent socket can be determined according to the control instructions in the operation log and the number of key times. Assuming that the design life of the smart jack is 10 ten thousand times, when the number of operations reaches or exceeds 10 ten thousand times, the abnormality judgment condition is satisfied. At the moment, abnormal statistical data are generated according to the equipment information and the operation times of the intelligent socket, and the abnormal statistical data can be used for prompting a user to replace the intelligent socket in time so as to avoid equipment failure or faults.

And 105, determining the operation hidden danger of the Internet of things equipment according to the abnormal statistical data, and displaying the operation hidden danger.

The method comprises the steps of determining operation hidden danger of the Internet of things equipment according to abnormal statistical data and displaying the operation hidden danger. The operation hidden trouble refers to the problems of equipment failure, performance reduction, safety risk and the like possibly caused by the fact that the operation times of the equipment of the Internet of things exceed the limit. The operation hidden danger can be used for reminding a user to maintain or replace equipment in time so as to ensure the normal operation of the equipment and the benefit of the user. When the operation times of the Internet of things equipment meet the abnormal judgment conditions, for example, the operation times exceed the design life of the equipment or reach the performance limit of the equipment, the operation hidden danger of the Internet of things equipment is determined according to the abnormal statistical data, and the operation hidden danger is displayed.

For example, an intelligent kettle is used as an internet of things device, and can be remotely controlled through a mobile phone terminal or intelligent voice central control. The intelligent kettle is provided with a state collector which comprises a temperature sensor and a water level gauge. When the intelligent kettle receives a control instruction for boiling water or preserving heat, the state collector can acquire the current temperature value of the kettle through the temperature sensor and acquire the current water level value of the kettle through the water level gauge. The state collector records the values and the content of the control instruction into the operation log of the intelligent kettle. According to the control instructions and the number of temperature values in the operation log, the operation times of the intelligent kettle can be determined. Assuming that the design life of the intelligent kettle is 5000 times, when the running times reach or exceed 5000 times, the abnormal judgment condition is met. At this time, according to the equipment information and the operation times of the intelligent kettle, abnormal statistical data are generated, and according to the abnormal statistical data, the hidden danger of operation of the intelligent kettle can be determined to be aging of the heating element, which may cause the heating efficiency of the kettle to be reduced, and even dangerous situations such as electric leakage or fire occur. In order to show the operation hidden danger, can use and utilize the warning lamp of predetermineeing on the intelligent kettle, the relation of the number of times of operation and heating efficiency of intelligent kettle is visualized through the scintillation of warning lamp.

Fig. 2 is a schematic diagram of another state early warning method of an internet of things device according to an embodiment of the present application, where a state collector is disposed on the internet of things device.

The method may comprise the steps of:

step 201, responding to a control instruction received by the internet of things device, and acquiring an operation state of the internet of things device through the state acquisition device.

The method shown in this step is already described in step 101, and will not be described here again.

Step 202, recording the running state and a control instruction corresponding to the running state into a running log of the internet of things device.

The method shown in this step is already described in step 102, and will not be described here again.

Optionally, the internet of things device includes a plurality of sub-devices, and step 202 includes the following sub-steps:

sub-step 2021, determining the number of sub-devices that fail to execute the control instruction in the internet of things device within a preset time period.

The aim of this sub-step is to determine the number of sub-devices in the internet of things device that failed execution of the control instruction within a preset time period. The failure execution refers to that the internet of things equipment cannot execute the operation of the control instruction correctly after receiving the control instruction, or cannot return the execution result correctly after executing the operation of the control instruction. The number of sub-devices failing to execute the control instruction refers to the number of sub-devices failing to execute the control instruction in the internet of things device within a preset time period, such as one day, one week or one month. The number of sub-devices that failed to execute may be used to evaluate the reliability and stability of the internet of things device.

For example, the intelligent lamp is an internet of things device, and can be remotely controlled through a mobile phone terminal or intelligent voice central control. The intelligent light comprises a plurality of sub-devices, such as a plurality of light bulbs. When a user issues a control instruction to the intelligent lamp through the internet of things platform, for example, the intelligent lamp is turned on, a plurality of bulbs of the intelligent lamp need to completely complete the operation of the control instruction, and an execution result is returned to the internet of things platform. If some bulbs cannot complete the operation of the control instruction or cannot return the execution result, the bulbs are considered to fail to execute the control instruction. Assuming that the preset time period is one day, it is necessary to count the number of bulbs in the intelligent lamp that failed to execute the control command in one day. And querying all device logs generated by the intelligent lamp in one day and corresponding log levels through the device log function of the Internet of things platform. If the log level is error, warning, etc., it indicates that the sub-equipment fails to execute the control instruction, the number or name of the sub-equipment is recorded, and the number of bulbs failing to execute is accumulated.

Sub-step 2022, marking the operation state of the internet of things device as an abnormal operation state if the number of sub-devices of the control instruction for which the execution of the control instruction fails exceeds a preset sub-device number threshold.

The purpose of this substep is to mark the running state of the internet of things device as an abnormal running state in the case that the number of the pieces of the child devices failing to execute the control instruction exceeds a preset threshold value of the number of the child devices. The abnormal operation state refers to a state that the operation state of the internet of things equipment has obvious difference or deviation from the normal operation state, such as offline equipment, equipment failure, equipment performance reduction and the like. The abnormal operation state may be used to remind a user to check or repair the equipment in time to prevent the equipment from being damaged or to influence the business. When the number of the sub-devices failing to execute the control instruction exceeds a preset sub-device number threshold, for example, 10% of the total number of the devices or 50% of the key components of the devices, it is indicated that the running state of the internet of things device is abnormal, and the running state of the internet of things device needs to be marked as an abnormal running state.

For example, in the example of substep 2021, assuming that the preset number of sub-devices threshold is 10% of the total number of devices, if the number of bulbs in the intelligent light that failed to execute the control instruction exceeds the threshold during a day, it is indicated that there is an abnormality in the operation state of the intelligent light, and the operation state of the intelligent light needs to be marked as an abnormal operation state.

Sub-step 2023, recording the abnormal running state and the control instruction of the execution failure into a running log of the internet of things device.

The purpose of this substep is to log the abnormal running state and the control command of the execution failure into the running log of the internet of things equipment. The travel log may be stored in an internal memory or an external storage device of the internet of things device for subsequent data analysis and troubleshooting. When the operation state of the internet of things equipment is marked as an abnormal operation state, such as equipment offline, equipment failure, equipment performance reduction and the like, the abnormal operation state and a control instruction of failure execution are required to be recorded into an operation log of the equipment according to a certain format and sequence.

For example, in the example of substep 2021, the fault condition of the intelligent light may be recorded in a running log, such as in a device log interface of the internet of things platform, and an exception tag, such as an abnormal running state, is added to the device log of the intelligent light to record the abnormal condition of the device. The device alarm interface of the internet of things platform can also add an abnormal level, such as high risk or emergency, to the device alarm of the intelligent lamp so as to remind a user of timely processing the abnormal condition of the device and prevent the burning of a household circuit caused by the flash of the bulb.

Optionally, the operation state has corresponding operation parameters, and step 202 includes the following substeps:

sub-step 2024, determining, according to the control instruction, an operation index corresponding to the control instruction.

The purpose of this substep is to determine, based on the control command, an operation index corresponding to the control command. The operation index refers to a desired or standard value of the operation state of the internet of things equipment, such as electric quantity, temperature, switching times, fault codes and the like. The operation index may be used to measure the operation effect and quality of the device. When the internet of things device receives the control instruction, for example, adjusts the temperature or the humidity, it is necessary to determine an operation index, for example, a temperature value or a humidity value, corresponding to the control instruction according to the content of the control instruction.

For example, an intelligent air purifier is an internet of things device, which can be remotely controlled through a mobile phone terminal or an intelligent voice central control. The intelligent air purifier is provided with a state collector which comprises an air speed sensor, an electricity meter and an air quality sensor. When a user issues a control instruction to the intelligent air purifier through the internet of things platform, for example, the wind speed or the air quality is adjusted, an operation index corresponding to the control instruction needs to be determined according to the content of the control instruction, for example, if the control instruction is to adjust the wind speed, for example, the wind speed is adjusted to be 2, the operation index corresponding to the control instruction is a wind speed value, for example, the wind speed value is 2. If the control command is to adjust the air quality, for example, to an optimum air quality, the operation index corresponding to the control command is an air quality value, for example, an optimum air quality value.

Sub-step 2025, marking the operation state of the internet of things device as an abnormal operation state if the value of the parameter corresponding to the operation state does not conform to the operation index.

The purpose of this substep is to mark the running state of the internet of things device as an abnormal running state if the value of the parameter corresponding to the running state does not meet the running index. The abnormal operation state refers to a state that the operation state of the internet of things equipment has obvious difference or deviation from the normal operation state, such as offline equipment, equipment failure, equipment performance reduction and the like. The abnormal operation state may be used to remind a user to check or repair the equipment in time to prevent the equipment from being damaged or to influence the business. When the value of the parameter corresponding to the running state does not accord with the running index, for example, the difference between the temperature value or the humidity value and the set value of the control instruction is too large, the running state of the internet of things equipment is abnormal, and the running state of the internet of things equipment needs to be marked as an abnormal running state.

For example, in the example of sub-step 2024, after the internet of things platform receives the operation state of the intelligent air purifier, it is necessary to compare the value of the parameter corresponding to the operation state, such as the wind speed value or the air quality value, with the operation index to determine whether there is an abnormality. If the value of the parameter corresponding to the operation state is too different from the operation index, for example, the wind speed value is lower than 50% of the set value or the air quality value is higher than 50% of the set value, it is indicated that the operation state of the intelligent air purifier is abnormal, and the operation state of the intelligent air purifier needs to be marked as an abnormal operation state.

Sub-step 2026, logging the abnormal operation state and the control instruction into an operation log of the internet of things device.

The sub-step aims to record the abnormal operation state and the control instruction into the operation log of the Internet of things equipment. A log is a data file for recording the operation of a device and may contain information such as status parameters, control instructions, time stamps, error codes, etc. of the device. The travel log may be stored in an internal memory or an external storage device of the internet of things device for subsequent data analysis and troubleshooting. When the operation state of the internet of things equipment is marked as an abnormal operation state, such as equipment offline, equipment failure, equipment performance reduction and the like, the abnormal operation state and a control command are required to be recorded into an operation log of the equipment according to a certain format and sequence.

For example, in the example of substep 2024, the abnormal operating conditions and control instructions are entered into an operating log of the intelligent air purifier. The operation log can be used for recording abnormal operation states and control instructions of the intelligent air purifier so that a user can know the operation condition and problem reasons of the equipment.

And 203, counting the number of control instructions and running states in the running log, and determining the running times of the Internet of things equipment according to the number.

The method shown in this step is already described in step 103, and will not be described here again.

Step 204, generating abnormal statistical data according to the equipment information of the internet of things equipment and the operation times under the condition that the operation times meet the abnormal judgment conditions; the abnormal statistical data is used for representing that the running times of the Internet of things equipment exceeds a limit.

The method shown in this step is already described in step 104, and will not be described here again.

Optionally, the operation number includes a total operation number of the internet of things device, and step 204 includes the following substeps:

a sub-step 2041 of generating first abnormal statistical data according to the device information of the internet of things device and the total operation times under the condition that the total operation times exceed a preset first time number threshold; the first anomaly statistics are data used to characterize overrun of use of the internet of things device.

The method comprises the following substep of generating first abnormal statistical data according to equipment information and running total times of the equipment of the Internet of things under the condition that the running total times exceed a preset first time number threshold value. The first anomaly statistics are data for characterizing the overrun of the use of the internet of things device, such as device type, device function, device lifetime, frequency of use, anomaly cause, etc. The use overrun refers to that the running times of the internet of things equipment exceed the design life of the equipment or reach the performance limit of the equipment, which may cause problems of equipment failure, performance degradation, safety risk and the like. The data of overrun can be used for reminding a user to maintain or replace equipment in time so as to ensure normal operation of the equipment and benefits of the user. When the operation times of the internet of things equipment reach or exceed a preset first time number threshold, for example, the operation times exceed 80% of the design life of the equipment or reach 90% of the performance limit of the equipment, the fact that the use of the internet of things equipment is overrun is indicated, and first abnormal statistical data are required to be generated according to the equipment information and the total operation times of the internet of things equipment.

For example, the smart bracelet is an internet of things device, and can be remotely controlled through a mobile phone terminal or an intelligent voice central control. The intelligent bracelet is provided with a state collector which comprises a heart rate sensor, a blood pressure sensor, an electricity meter and the like. When the intelligent bracelet receives a control command, for example, heart rate or blood pressure is measured, the state collector acquires the current state value of the bracelet through the corresponding sensor, and the values and the content of the control command are recorded into the operation log of the intelligent bracelet. According to the control instructions and the quantity of the state values in the operation log, the operation times of the intelligent bracelet can be determined. Assuming that the design life of the smart band is 10,000 times, the abnormality judgment condition is satisfied when the number of runs reaches or exceeds 10,000 times. At this time, first abnormal statistical data is generated according to the equipment information and the operation times of the intelligent bracelet.

Optionally, the number of operations includes an abnormal number of operations, and step 204 includes the substeps of:

sub-step 2042, under the condition that the abnormal operation times exceeds a preset second time threshold, generating second abnormal statistical data according to the equipment information of the internet of things equipment and the abnormal operation times; the second anomaly statistics are data that are used to characterize an equipment failure of the internet of things equipment.

The aim of the substep is to generate second abnormal statistical data according to the equipment information and the abnormal operation times of the equipment of the internet of things under the condition that the abnormal operation times exceed a preset second time threshold. The second anomaly statistics are data characterizing device faults of the internet of things device, such as fault type, fault frequency, fault cause, fault impact, and the like. The equipment fault refers to a state that the running state of the equipment of the internet of things is obviously different or deviated from the normal running state, such as offline equipment, equipment damage, equipment function loss and the like. The data of the equipment faults can be used for analyzing the rules and the characteristics of the faults, providing a diagnosis and removal scheme of the faults and improving the reliability and the stability of the equipment. When the abnormal operation times of the internet of things equipment reach or exceed a preset second time threshold, for example, the abnormal operation times of the internet of things equipment exceed 10% of the total operation times of the equipment or exceed 20% of the normal operation times of the equipment, the equipment failure of the internet of things equipment is indicated, and second abnormal statistical data are required to be generated according to the equipment information and the abnormal operation times of the internet of things equipment.

For example, a smart porch light is an internet of things device that can be remotely controlled through a cell phone terminal or intelligent voice center control. The intelligent corridor lamp is provided with a state collector which comprises a sound sensor, a current sensor, a voltage sensor and the like. When the intelligent gallery lamp receives a control command, such as a sound signal, the state collector acquires the current state values of the intelligent gallery lamp through the corresponding sensors, and records the values and the content of the control command into the operation log of the intelligent gallery lamp. The number of intelligent gallery lamp runs may be determined based on the number of control instructions and status values in the travel log. A utility fault is considered to occur in a smart porch lamp if an abnormal operating condition occurs during operation, such as an illumination value of 0 and a current value other than 0, or a voltage value of 0 and a current value other than 0. If the number of the corridor lamps with equipment faults exceeds the threshold value, the equipment faults of the intelligent corridor lamps are indicated, second abnormal statistical data are required to be generated according to the equipment information and the abnormal operation times of the intelligent corridor lamps, the second abnormal statistical data can be used for analyzing rules and characteristics of the equipment faults of the intelligent corridor lamps, a fault diagnosis and removal scheme is provided, and reliability and stability of the intelligent corridor lamps are improved.

Optionally, based on sub-step 2042, the present solution further comprises the following branching steps:

step 20421, according to the second abnormal statistical data, querying a preset fault lookup table for a fault reason of the internet of things device; the fault lookup table is used for recording the corresponding relation between the abnormal statistical data and the fault reasons.

The purpose of this step is to query the fault cause of the internet of things device in a preset fault lookup table according to the second abnormal statistical data. The fault reasons refer to root causes of equipment faults, such as circuit faults, sensor faults, software faults and the like, of the equipment of the Internet of things. The fault lookup table is a data table for recording the correspondence between the abnormal statistical data and the fault reasons, and can be classified and screened according to the dimensions of equipment type, fault frequency and the like. The fault lookup table can be used for rapidly locating fault reasons and providing a diagnosis and removal scheme for faults. When the abnormal operation times of the internet of things equipment reach or exceed a preset second time threshold, for example, the abnormal operation times of the internet of things equipment exceed 10% of the total operation times of the equipment or exceed 20% of the normal operation times of the equipment, the equipment failure of the internet of things equipment is indicated, and the failure reason of the internet of things equipment is required to be inquired in a preset failure inquiry table according to the second abnormal statistical data of the internet of things equipment.

For example, in the example of sub-step 2042, assuming that the preset second count threshold is 1% of the total number of operations of the device, if the number of intelligent porch lamps in which the device failure occurs exceeds the threshold in one year, it is indicated that the device failure of the intelligent porch lamps requires generating second abnormal statistical data according to the device information and the abnormal number of operations of the intelligent porch lamps, and according to the second abnormal statistical data of the intelligent porch lamps, the failure cause of the intelligent porch lamps is queried in the preset failure lookup table, and by means of the failure lookup table, the failure cause of the intelligent porch lamps, for example, the cause of the bulb being unlit is a program error, can be rapidly located. Meanwhile, corresponding maintenance operation can be performed according to the fault diagnosis and fault removal scheme provided by the fault lookup table, and the normal operation of the intelligent corridor lamp is restored.

Step 20422, uploading the fault cause to a processing end; the processing end is used for feeding back the corresponding repair data packet according to the fault cause; the repair data packet is used for repairing the fault of the Internet of things equipment.

The purpose of this step is to upload the fault cause to the processing end, and the processing end is used for feeding back corresponding repair data packet according to the fault cause, and repair data packet is used for repairing the fault of the internet of things equipment. The fault reasons refer to root causes of equipment faults, such as circuit faults, sensor faults, software faults and the like, of the equipment of the Internet of things. The processing end is a server or cloud platform for receiving the fault reasons, analyzing the fault reasons, generating the repair data packet and sending the repair data packet. The repair data packet is a data file for repairing the fault of the internet of things equipment, and can contain information such as firmware, configuration, parameters, instructions and the like of the equipment. The repair data packet can be used for updating the software or hardware of the device and restoring the normal operation of the device. When the abnormal operation times of the internet of things equipment reach or exceed a preset second time threshold, for example, the abnormal operation times of the internet of things equipment exceed 10% of the total operation times of the equipment or exceed 20% of the normal operation times of the equipment, the equipment failure of the internet of things equipment is indicated, the failure cause needs to be uploaded to a processing end, the processing end is used for feeding back corresponding repair data packets according to the failure cause, and the repair data packets are used for repairing the failure of the internet of things equipment.

For example, in the example of substep 2042, where it has been queried that the failure cause of the intelligent gallery lamp is a program error, an update packet may be downloaded from the processing side, the update packet being a program initialization packet.

And 20423, acquiring the repair data packet, and repairing the fault of the Internet of things equipment through the repair data packet.

The method aims at acquiring the repair data packet and repairing the fault of the Internet of things equipment. The repair data packet is a data file for repairing the fault of the internet of things equipment, and can contain information such as firmware, configuration, parameters, instructions and the like of the equipment. The repair data packet can be used for updating the software or hardware of the device and restoring the normal operation of the device. When the abnormal operation times of the internet of things equipment reach or exceed a preset second time threshold, for example, the abnormal operation times of the internet of things equipment exceed 10% of the total operation times of the equipment or exceed 20% of the normal operation times of the equipment, the equipment failure of the internet of things equipment is indicated, a repair data packet needs to be acquired, and the failure of the internet of things equipment is repaired.

For example, in the example of substep 2042, where the failure cause of the intelligent porch lamp has been queried for a program error, an update packet may be downloaded from the processing side, the update packet being a program initialization packet, and the initialization packet being loaded into the control system of the intelligent porch lamp to initialize the control system of the intelligent porch lamp to solve the problem of a system error of the intelligent porch lamp.

Step 205, determining the operation hidden danger of the internet of things equipment according to the abnormal statistical data, and displaying the operation hidden danger.

The method shown in this step is already described in step 105, and will not be described here again.

Optionally, step 205 includes sub-step 2051:

sub-step 2051, inquiring operation hidden danger of the internet of things equipment in a preset hidden danger inquiry table according to the abnormal statistical data; the hidden danger lookup table is used for recording the corresponding relation between the abnormal statistical data and the operation hidden danger.

The method comprises the steps of inquiring operation hidden danger of the Internet of things equipment in a preset hidden danger inquiring table according to abnormal statistical data. The operation hidden trouble is the potential problem that influences equipment performance, stability, security that thing networking equipment probably appears in the operation in-process, for example battery ageing, signal interference, equipment overheat etc.. The hidden danger lookup table is a data table for recording the corresponding relation between abnormal statistical data and operation hidden danger, and can be classified and screened according to the dimensions of equipment type, hidden danger level and the like. The hidden danger lookup table can be used for predicting and preventing operation hidden danger of the equipment of the Internet of things, and reliability and safety of the equipment are improved.

For example, if an abnormal operation state occurs in the operation process of the intelligent bracelet, for example, the heart rate value or the blood pressure value does not accord with the normal range, or the temperature value is too high or too low, the bracelet is considered to have operation hidden trouble. Through hidden danger lookup table, can predict and prevent intelligent bracelet's operation hidden danger, for example heart rate abnormality and abnormal hidden danger reason of blood pressure are the sensor ageing, and hidden danger level is moderate, and hidden danger influence is the accuracy that influences heart rate and blood pressure measurement, and hidden danger prevention is periodic replacement heart rate and blood pressure sensor. Meanwhile, the emergency degree of the hidden danger can be determined according to the hidden danger level provided by the hidden danger lookup table, and the hidden danger with high level is processed preferentially, so that the reliability and the safety of the intelligent bracelet are improved.

Optionally, step 205 includes sub-step 2052:

sub-step 2052, responding to a query instruction of a user, selecting a first target internet of things device from all the internet of things devices, and displaying operation hidden danger of the first target internet of things device.

The aim of the substep is to respond to the inquiry instruction of the user, select the first target Internet of things equipment from all the Internet of things equipment, and display the operation hidden trouble of the first target Internet of things equipment. The query instruction refers to an instruction for querying the running condition of the device, such as a state, a parameter, a log, hidden danger and the like of the query device, which is sent to the internet of things device by a user through the internet of things platform or other terminals. The first target internet of things device refers to an internet of things device to be queried, which is designated by a user through a query instruction, and the user can select the first target internet of things device from all the internet of things devices through information such as type, name, number, position and the like of the device. The operation hidden trouble is the potential problem that influences equipment performance, stability, security that thing networking equipment probably appears in the operation in-process, for example battery ageing, signal interference, equipment overheat etc.. The step of displaying the operation hidden trouble refers to displaying the operation hidden trouble of the first target internet of things equipment in the form of characters, charts, sounds and the like on an interface of an internet of things platform or other terminals so that a user can know the operation condition and problem reason of the equipment. When a user sends a query instruction through an internet of things platform or other terminals, the first target internet of things device needs to be selected from all the internet of things devices, and the operation hidden danger of the first target internet of things device is displayed.

For example, if an abnormal operation state occurs in the intelligent refrigerator during operation, for example, a temperature value or a humidity value is excessively different from a set value of a control command, the refrigerator is considered to have an operation hidden trouble. When a user sends a query instruction, for example, query the operation hidden danger of the intelligent refrigerator, through the mobile phone terminal, the first target internet of things device, namely the intelligent refrigerator, needs to be selected from all the internet of things devices, and the operation hidden danger of the intelligent refrigerator is displayed. There are various methods for exhibiting the operation hidden trouble, for example: and adding a hidden danger mark, such as a yellow question mark or an exclamation mark, to the equipment icon or name of the intelligent refrigerator at the equipment management interface of the mobile phone terminal so as to prompt a user to pay attention to the hidden danger condition of the equipment. And adding a hidden danger label, such as an operation hidden danger, to the equipment log of the intelligent refrigerator at an equipment log interface of the mobile phone terminal so as to record hidden danger conditions of equipment. And adding a hidden danger level, such as medium risk or high risk, to the equipment alarm of the intelligent refrigerator at the equipment alarm interface of the mobile phone terminal so as to remind a user to timely process hidden danger conditions of the equipment. Through the show operation hidden danger, can let the user know intelligent refrigerator's operational aspect and problem reason, for example temperature anomaly and the unusual hidden danger reason of humidity be that the sensor ages, hidden danger level is moderate, and hidden danger influence is the cold-stored and fresh-keeping effect of influence, and hidden danger prevention is periodic replacement temperature and humidity sensor.

Optionally, step 205 includes sub-step 2053:

sub-step 2053, selecting a second target internet of things device from all the internet of things devices, and displaying the operation hidden trouble of the second target internet of things device; the second target internet of things device is an internet of things device with the running times in the abnormal statistical data not in a preset active early warning threshold range.

The purpose of this step is to select the second target internet of things device from among all the internet of things devices, and show the operation hidden danger of the second target internet of things device. The second target internet of things device is an internet of things device with the running times in the abnormal statistical data not in a preset active early warning threshold range. The active early warning threshold is a preset threshold for reminding a user of the running state of the equipment according to factors such as the design life, the use frequency and the abnormal reason of the equipment. When the running times of the equipment reach or exceed the active early warning threshold value, the Internet of things platform can send early warning information to the user in a short message mode, an automatic mail mode and the like. The operation hidden trouble is the potential problem that influences equipment performance, stability, security that thing networking equipment probably appears in the operation in-process, for example battery ageing, signal interference, equipment overheat etc.. The data of the operation hidden trouble can be used for predicting and preventing the operation hidden trouble of the equipment of the Internet of things, and the reliability and the safety of the equipment are improved.

For example, when the operation of the intelligent refrigerator has operation hidden danger, the operation hidden danger of the second target internet of things equipment can be displayed in an automatic mail mode, and the alarm lamp installed on the intelligent refrigerator can continuously flash to remind a user of paying attention to the operation state of the equipment, and corresponding precautions can be taken in time, so that the equipment is prevented from being failed or damaged.

And step 206, responding to the selection of the target equipment by the user, and displaying the running state of the target equipment on a screen under the condition that the selection of the target equipment does not contain the selection of time.

The purpose of this step is to show the running state of the target device on the screen after the user selects the target device. The running state refers to the current state parameters of the internet of things equipment, such as electric quantity, temperature, switching times, fault codes and the like. The operating state may be used to display real-time conditions and performance metrics of the device. When a user selects a target device, such as clicking on an icon or name of the device, an operation to show the operational status of the device is triggered. If the user's selection does not include a selection of time, e.g., does not specify querying the device status for a certain period of time, then the latest operating status of the target device is presented.

For example, the intelligent fan is used as an internet of things device, and can be remotely controlled through a mobile phone terminal or intelligent voice central control. The intelligent fan is provided with a state collector which comprises a wind speed sensor and an electricity meter. When a user selects an icon of the intelligent fan on the mobile phone terminal, the running state of the intelligent fan is displayed on a screen, and the running state can be used for displaying the current wind speed and the current electric quantity of the intelligent fan so that the user can adjust or charge the intelligent fan according to the needs.

Step 207, in response to a user selection of a target device, acquiring a running log of the target device at a target time when the selection of the target device includes a selection of the target time, and displaying a running state of the target device at the target time according to the running log.

The purpose of this step is to show the running state of the target device at the target time on the screen after the user selects the target device. The running state refers to the current state parameters of the internet of things equipment, such as electric quantity, temperature, switching times, fault codes and the like. The operating state may be used to display real-time conditions and performance metrics of the device. When a user selects a target device, such as clicking on an icon or name of the device, an operation to show the operational status of the device is triggered. If the user selection includes a selection of time, for example, the device state of a certain time period or a certain time point is appointed to be queried, the running log of the target device at the target time is acquired according to the user selection, and the running state of the target device at the target time is displayed according to the running log.

For example, an intelligent sweeper is an internet of things device, which can be remotely controlled through a mobile phone terminal or intelligent voice central control. The intelligent sweeper is provided with a state collector which comprises a sweeping area meter and an electricity meter. When a user selects an icon of the intelligent sweeper on the mobile phone terminal, the running state of the intelligent sweeper is displayed on a screen, and the running state can be used for displaying the current sweeping area and the electric quantity of the intelligent sweeper so that the user can adjust or charge the intelligent sweeper as required. If the user selects a target time, for example 2023-1-1 day 18:00, when selecting the icon of the intelligent sweeper, the user will obtain the running log of the intelligent sweeper at the target time according to the selection of the user, and display the running state of the intelligent sweeper at the target time according to the running log, where the running state can be used for displaying the sweeping area and the electric quantity of the intelligent sweeper at the target time, so that the user can know the history condition and the using effect of the equipment.

As shown in fig. 3, in a device network connection block diagram of an internet of things device status pre-warning method provided in the embodiments of the present Application in a home environment, various home intelligent devices (i.e., internet of things devices in the present Application) are connected under the same wireless fidelity protocol (WiFi, wireless Fidelity), such as an intelligent curtain, an intelligent socket, an intelligent camera, an intelligent door lock, an intelligent air conditioner, an intelligent sweeping robot, an intelligent range hood, and the like, and control devices such as a host computer/terminal program (APP) and an intelligent voice central control are connected through a terminal applet so as to realize integrated management of the internet of things device.

Based on the architecture of fig. 3, as shown in fig. 4, a specific implementation process of the state early warning method for implementing the state early warning method for the internet of things equipment in the home internet of things environment is shown:

s1: presetting reporting area time of each piece of sub-equipment to report own data:

the method comprises the steps that judgment logic of uploading abnormal conditions of each device is preset through terminal APP or intelligent voice central control; writing a program corresponding to the method into corresponding equipment, or writing preset logic into a file packet for importing each equipment;

s2: issuing a region reporting instruction received by the sub-equipment;

the step of issuing and confirming the preset reporting mode to each device; for example, the file package in the step S1 is issued to equipment and loaded;

s3: receiving and transmitting an instruction to transmit own real-time data back to a terminal applet or intelligent voice central control;

this step represents uploading an abnormal situation when the device satisfies a preset condition, that is, executing steps S201 to S202 in the first embodiment of the present application;

s4: the terminal applet or intelligent voice central control processes and analyzes the special instruction;

the step may be to perform data analysis according to the abnormal situation, that is, execute step S203 in the first embodiment of the present application;

S5: obtaining intelligent single product instruction analysis self data all information;

the step may perform data statistics according to the abnormal situation, that is, step S204 in the first embodiment of the present application is executed;

s6: obtaining real-time data of the terminal and presenting the real-time data to a terminal applet or a central control device;

the method comprises the steps that display is performed through terminal APP or intelligent voice central control under the early warning condition; i.e. to perform sub-step S2051 in the first embodiment of the present application;

s7: according to different equipment, further judgment of early warning conditions can be carried out according to abnormal conditions; i.e. executing step S204 and its sub-steps in the first embodiment of the present application; if the curtain is opened and closed for 100 times (the opening and closing life is 10 ten thousand times), or the socket button is opened and closed for 50 times (the button life is 10 ten thousand times), the switch button is opened and closed for 30 times (the button life is 20 ten thousand times), or the intelligent door lock is opened and closed for 20 times, the battery power is 50 percent (the switch lock life is 20 ten thousand times and is lower than 10 percent for alarming), or the intelligent air conditioner is opened and closed for 10 times, the opening degree of a filter screen is 96 percent (lower than 15 percent for pre-cleaning), or the residual power of the intelligent sweeper is 75 percent, and the whole sweeper is cleaned for 50 percent (the power is lower than 10 for early warning); the automatic control device can also be used for displaying the average wind speed and automatic adjustment of gears of the intelligent fan and the like.

S8: the threshold is exceeded triggering a feedback alarm.

The method comprises the steps of displaying through alarm equipment under the early warning condition; i.e. to perform step S2052 in the first embodiment of the present application; in the process, according to the presented data, when the data of the sub-equipment is abnormal, the system can automatically save the error log and upload the error log to the error log information base of the manufacturer, and the problem processing at the first time is convenient, so that the problem maintenance of the related sub-equipment is performed.

As shown in fig. 5, in another embodiment of the present application, in a home internet of things environment, an early warning may be performed on a device by using a manner of flashing regional time light, so that a homeowner may find a hidden operating trouble of the device, and further perform troubleshooting in advance: wherein step R1 is performed from step S201 to step S202 in the first embodiment of the present application; step R2 is performed from step S203 to step S204 in the first embodiment of the present application; step R3 is performed in step S2052 in the first embodiment of the present application; when the related sub-equipment exceeds the self use range or is about to reach the preset value of the original factory, the central control system recognizes and issues instructions to the lamps controllable in the home, for example, regional light can be set to flash to red and blue lamps alternately to indicate that the service life times of intelligent single products in the interval are close or the service life times of intelligent single products are about to be inquired by a user to be reminded of problems.

Referring to fig. 6, a state early warning device 30 of an internet of things device provided in an embodiment of the present application is shown, a state collector is set on the internet of things device, and the device 30 includes:

A state module 301, configured to obtain, by using the state collector, an operation state of the internet of things device in response to a control instruction received by the internet of things device;

the log module 302 is configured to record the running state and a control instruction corresponding to the running state into a running log of the internet of things device;

the statistics module 303 is configured to count the number of control instructions and running states in the running log, and determine the running times of the internet of things device according to the number;

a judging module 304, configured to generate abnormal statistical data according to the device information of the internet of things device and the operation times when the operation times meet an abnormal judging condition; the abnormal statistical data is used for representing that the running times of the Internet of things equipment exceeds a limit;

and the display module 305 is configured to determine an operation hidden danger of the internet of things device according to the abnormal statistical data, and display the operation hidden danger.

Optionally, the internet of things device includes a plurality of sub-devices, and the log module 302 includes:

and the sub-equipment confirming sub-module is used for determining the number of sub-equipment which fails to execute the control instruction in the Internet of things equipment in a preset time period.

The first sub-equipment abnormality recording sub-module is used for marking the running state of the Internet of things equipment as an abnormal running state under the condition that the number of the sub-equipment of the control instruction which fails to execute the control instruction exceeds a preset sub-equipment number threshold value.

And the second sub-equipment abnormal recording sub-module is used for recording the abnormal operation state and the control instruction of the execution failure into the operation log of the Internet of things equipment.

Optionally, the operation state has a corresponding operation parameter, and the log module 302 includes:

and the index confirmation sub-module is used for determining an operation index corresponding to the control instruction according to the control instruction.

The first index abnormal recording sub-module is used for marking the running state of the Internet of things equipment as an abnormal running state under the condition that the value of the parameter corresponding to the running state does not accord with the running index.

And the second index abnormal recording sub-module is used for recording the abnormal running state and the control instruction into the running log of the Internet of things equipment.

Optionally, the operation times include a total operation times of the internet of things device, and the judging module 304 includes:

The first anomaly statistics sub-module is used for generating first anomaly statistics data according to the equipment information of the Internet of things equipment and the total running times under the condition that the total running times exceed a preset first time number threshold value; the first anomaly statistics are data used to characterize overrun of use of the internet of things device.

Optionally, the operation number includes an abnormal operation number, and the judging module 304 includes:

the second abnormal statistics sub-module is used for generating second abnormal statistics data according to the equipment information of the Internet of things equipment and the abnormal operation times under the condition that the abnormal operation times exceed a preset second time threshold; the second anomaly statistics are data that are used to characterize an equipment failure of the internet of things equipment.

Optionally, when the judging module 304 includes a second anomaly statistics sub-module, the apparatus 30 further includes:

the fault query module is used for querying the fault reason of the Internet of things equipment in a preset fault query table according to the second abnormal statistical data; the fault lookup table is used for recording the corresponding relation between the abnormal statistical data and the fault reasons.

The fault uploading module is used for uploading the fault reasons to the processing end; the processing end is used for feeding back the corresponding repair data packet according to the fault cause; the repair data packet is used for repairing the fault of the Internet of things equipment.

And the fault repairing module is used for acquiring the repairing data packet and repairing the fault of the Internet of things equipment through the repairing data packet.

Optionally, the display module 305 includes:

the active query sub-module is used for querying the operation hidden danger of the Internet of things equipment in a preset hidden danger query table according to the abnormal statistical data; the hidden danger lookup table is used for recording the corresponding relation between the abnormal statistical data and the operation hidden danger.

Optionally, the display module 305 includes:

the first display sub-module is used for responding to a query instruction of a user, selecting first target Internet of things equipment from all Internet of things equipment, and displaying operation hidden danger of the first target Internet of things equipment.

Optionally, the display module 305 includes:

the second display sub-module is used for selecting second target internet of things equipment from all the internet of things equipment and displaying operation hidden danger of the second target internet of things equipment; the second target internet of things device is an internet of things device with the running times in the abnormal statistical data not in a preset active early warning threshold range.

Optionally, the apparatus 30 further includes:

and the real-time display module is used for responding to the selection of the target equipment by the user, and displaying the running state of the target equipment on a screen under the condition that the selection of the target equipment does not contain the selection of time.

The history display module is used for responding to the selection of the target equipment by the user, acquiring the running log of the target equipment at the target time under the condition that the selection of the target equipment comprises the selection of the target time, and displaying the running state of the target equipment at the target time according to the running log.

Referring to fig. 7, an electronic device 500 may include one or more of the following components: a processing component 502, a memory 504, a power supply component 506, a multimedia component 508, an audio component 510, an input/output (I/O) interface 512, a sensor component 514, and a communication component 516.

The processing component 502 generally controls overall operation of the electronic device 500, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 502 may include one or more processors 520 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 502 can include one or more modules that facilitate interactions between the processing component 502 and other components. For example, the processing component 502 can include a multimedia module to facilitate interaction between the multimedia component 508 and the processing component 502.

The memory 504 is used to store various types of data to support operations at the electronic device 500. Examples of such data include instructions for any application or method operating on the electronic device 500, contact data, phonebook data, messages, pictures, multimedia, and so forth. The memory 504 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

The power supply component 506 provides power to the various components of the electronic device 500. The power components 506 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 500.

The multimedia component 508 includes an interface between the electronic device 500 and a user that provides an output interface. In some embodiments, the interface may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the interface includes a touch panel, the interface may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may not only sense demarcations of touch or sliding actions, but also detect durations and pressures associated with touch or sliding operations. In some embodiments, the multimedia component 508 includes a front-facing camera and/or a rear-facing camera. When the electronic device 500 is in an operational mode, such as a shooting mode or a multimedia mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 510 is for outputting and/or inputting audio signals. For example, the audio component 510 includes a Microphone (MIC) for receiving external audio signals when the electronic device 500 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 504 or transmitted via the communication component 516. In some embodiments, the audio component 510 further comprises a speaker for outputting audio signals.

Input/output I/O interface 512 provides an interface between processing component 502 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.

The sensor assembly 514 includes one or more sensors for providing status assessment of various aspects of the electronic device 500. For example, the sensor assembly 515 may detect an on/off state of the electronic device 500, a relative positioning of the components, such as a display and keypad of the electronic device 500, the sensor assembly 514 may also detect a change in position of the electronic device 500 or a component of the electronic device 500, the presence or absence of a user's contact with the electronic device 500, an orientation or acceleration/deceleration of the electronic device 500, and a change in temperature of the electronic device 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 515 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 516 is employed to facilitate communication between the electronic device 500 and other devices, either in a wired or wireless manner. The electronic device 500 may access a wireless network based on a communication standard, such as WiFi, an operator network (e.g., 2G, 3G, 4G, or 5G), or a combination thereof. In one exemplary embodiment, the communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 500 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for implementing a state pre-warning method for an internet of things device provided in embodiments of the present application.

In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 504, including instructions executable by processor 520 of electronic device 500 to perform the above-described method. For example, the non-transitory storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

Fig. 8 is a block diagram of an electronic device 600 in accordance with another embodiment of the invention. For example, the electronic device 600 may be provided as a server. Referring to fig. 8, the electronic device 600 includes a processing component 622 that further includes one or more processors and memory resources represented by a memory 632 for storing instructions, such as application programs, executable by the processing component 622. The application programs stored in memory 632 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 622 is configured to execute instructions to perform a state early warning method of the internet of things device provided in the embodiments of the present application.

The electronic device 600 may also include a power component 626 configured to perform power management of the electronic device 600, a wired or wireless network interface 650 configured to connect the electronic device 600 to a network, and an input/output (I/O) interface 658. The electronic device 600 may operate based on an operating system stored in the memory 632, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. The state early warning method of the Internet of things equipment is characterized in that a state collector is arranged on the Internet of things equipment, and the method comprises the following steps:

generating abnormal statistical data according to the equipment information of the Internet of things equipment and the operation times under the condition that the operation times meet the abnormal judgment conditions;

determining operation hidden danger of the Internet of things equipment according to the abnormal statistical data, and displaying the operation hidden danger;

the operation times comprise abnormal operation times and total operation times, and the generating abnormal statistical data according to the equipment information of the internet of things equipment and the operation times under the condition that the operation times meet the abnormal judgment conditions comprises the following steps:

generating first abnormal statistical data according to the equipment information of the Internet of things equipment and the total operation times under the condition that the total operation times exceed a preset first time number threshold value; the first abnormal statistical data is data used for representing that the use of the Internet of things equipment exceeds the limit;

generating second abnormal statistical data according to the equipment information of the Internet of things equipment and the abnormal operation times under the condition that the abnormal operation times exceed a preset second time threshold; the second anomaly statistics are data used for characterizing equipment faults of the internet of things equipment;

The method further comprises the steps of: inquiring the fault reason of the Internet of things equipment in a preset fault inquiry table according to the second abnormal statistical data; the fault lookup table is used for recording the corresponding relation between the abnormal statistical data and fault reasons;

uploading the fault reasons to a processing end; the processing end is used for feeding back the corresponding repair data packet according to the fault cause; the repair data packet is used for repairing the fault of the Internet of things equipment;

and acquiring the repair data packet, and repairing the fault of the Internet of things equipment through the repair data packet.

2. The method of claim 1, wherein the internet of things device comprises a plurality of sub-devices, the logging the operation state and the control instruction corresponding to the operation state into the operation log of the internet of things device comprises:

determining the number of pieces of sub-equipment which fail to execute the control instruction in the Internet of things equipment in a preset time period;

marking the running state of the Internet of things equipment as an abnormal running state under the condition that the number of the pieces of equipment of the control instruction failing to execute the control instruction exceeds a preset number threshold of pieces of equipment;

And recording the abnormal operation state and the control instruction of the execution failure into an operation log of the Internet of things equipment.

3. The method of claim 1, wherein the operating state has a corresponding operating parameter, and the logging the operating state and a control instruction corresponding to the operating state into an operating log of the internet of things device comprises:

determining an operation index corresponding to the control instruction according to the control instruction;

marking the running state of the Internet of things equipment as an abnormal running state under the condition that the value of the parameter corresponding to the running state does not accord with the running index;

and recording the abnormal operation state and the control instruction into an operation log of the Internet of things equipment.

4. The method of claim 1, wherein the determining the operational hidden danger of the internet of things device based on the anomaly statistics comprises:

inquiring operation hidden danger of the Internet of things equipment in a preset hidden danger inquiry table according to the abnormal statistical data; the hidden danger lookup table is used for recording the corresponding relation between the abnormal statistical data and the operation hidden danger.

5. The method of claim 1, wherein said presenting said operational risk comprises:

Responding to a query instruction of a user, selecting a first target Internet of things device from all Internet of things devices, and displaying operation hidden danger of the first target Internet of things device.

6. The method of claim 1, wherein said presenting said operational risk comprises:

selecting second target Internet of things equipment from all the Internet of things equipment, and displaying operation hidden danger of the second target Internet of things equipment; the second target internet of things device is an internet of things device with the running times in the abnormal statistical data not in a preset active early warning threshold range.

7. The method of claim 1, wherein the method further comprises:

responding to the selection of the target equipment by a user, and displaying the running state of the target equipment on a screen under the condition that the selection of the target equipment does not contain the selection of time;

and responding to the selection of the target equipment by a user, acquiring the operation log of the target equipment at the target time under the condition that the selection of the target equipment comprises the selection of the target time, and displaying the operation state of the target equipment at the target time according to the operation log.

8. The utility model provides a state early warning device of thing networking equipment, its characterized in that sets up the state collector on the thing networking equipment, the device includes:

the judging module is used for generating abnormal statistical data according to the equipment information of the Internet of things equipment and the operation times under the condition that the operation times meet the abnormal judging conditions;

the display module is used for determining the operation hidden danger of the Internet of things equipment according to the abnormal statistical data and displaying the operation hidden danger;

the operation times comprise abnormal operation times and total operation times, and the judging module comprises:

the first anomaly statistics sub-module is used for generating first anomaly statistics data according to the equipment information of the Internet of things equipment and the total operation times under the condition that the total operation times exceed a preset first time number threshold value; the first abnormal statistical data is data used for representing that the use of the Internet of things equipment exceeds the limit;

The second abnormal statistics sub-module is used for generating second abnormal statistics data according to the equipment information of the Internet of things equipment and the abnormal operation times under the condition that the abnormal operation times exceed a preset second time threshold; the second anomaly statistics are data used for characterizing equipment faults of the internet of things equipment;

the apparatus further comprises:

the fault query module is used for querying the fault reason of the Internet of things equipment in a preset fault query table according to the second abnormal statistical data; the fault lookup table is used for recording the corresponding relation between the abnormal statistical data and fault reasons;

the fault uploading module is used for uploading the fault reasons to the processing end; the processing end is used for feeding back the corresponding repair data packet according to the fault cause; the repair data packet is used for repairing the fault of the Internet of things equipment;

and the fault repair module is used for acquiring the repair data packet and repairing the fault of the Internet of things equipment through the repair data packet.

9. An electronic device, comprising: a processor, a memory for storing instructions executable by the processor;

wherein the processor is configured to execute the instructions to implement the method of any one of claims 1 to 7.

10. A computer readable storage medium, characterized in that instructions in the computer readable storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method of any one of claims 1 to 7.