Automatic remote diagnosis method and device

Abstract

The invention discloses an automatic remote diagnosis method and device, relates to the technical field of intelligent household appliances, and solves the technical problem that the problems of operation efficiency and operation cost cannot be solved because the specific fault type of the intelligent household appliance is not automatically analyzed in the prior art. According to the invention, after-sales data of a user is collected, a test signal is selected according to an error type in the after-sales data, and a data transmission channel between a cloud Internet of Things platform and an intelligent device corresponding to a device serial number is constructed through a WiFi module for data transmission; the cloud internet-of-things platform and the intelligent device perform identity verification through the verification signal and the serial port data, the cloud internet-of-things platform sends a test signal to the intelligent device after verification is passed, and the intelligent device sends index data to the cloud internet-of-things platform according to the test signal; and comparing the index data with preset data to obtain a quantity state of the index data, and performing different fault diagnosis on the intelligent equipment according to different quantity states.

Description

Technical Field

[0001] The present invention belongs to the field of smart home appliances and relates to Internet of Things technology, in particular to an automated remote diagnosis method and device. Background Art

[0002] With the rapid development of smart home appliances, smart home appliances are becoming more and more popular. Users may encounter some unknown problems when using smart home appliances. For example, when using a mobile terminal APP to remotely control the air conditioner, there is a probability that the control will fail. Users will then report the problem to after-sales service. Although manual door-to-door processing can effectively solve the problem, the labor cost is high and the work efficiency problem needs to be solved.

[0003] The invention patent with application number CN202310277611X discloses a remote monitoring method, system, device and storage medium for smart home appliances. The invention presets data collection items to be collected, and obtains status data of the smart home appliances based on the networking status of the smart home appliances. The processing personnel diagnose faults of the smart home appliances based on the status data; when diagnosing faults of smart home appliances, the invention only obtains the preset status data of the smart home appliances, and is unable to collect data according to the specific fault type of the smart home appliances, and automatically diagnose the faults that occur, resulting in the inability to effectively solve the problems of operational efficiency and operating costs.

[0004] The present invention provides an automated remote diagnosis method and device to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a method and device for automated remote diagnosis, which is used to solve the technical problem that the prior art does not perform automated analysis on specific fault types of smart home appliances, resulting in the inability to solve operational efficiency and operational cost problems.

[0006] To achieve the above object, a first aspect of the present invention provides a method for automated remote diagnosis, comprising:

[0007] Step 1: Collect the user's after-sales data; select the test signal according to the after-sales data and match the corresponding data transmission channel;

[0008] Step 2: The cloud IoT platform sends a verification signal to the smart device through the data transmission channel. After receiving the verification signal, the smart device sends serial port data to the cloud IoT platform for confirmation. After confirmation, the cloud IoT platform sends a test signal to the smart device.

[0009] Step 3: After receiving the test signal, the smart device sends the indicator data to the cloud IoT platform at intervals of n seconds according to the test signal; where n is a positive number;

[0010] Step 4: The cloud IoT platform completes remote fault diagnosis based on indicator data.

[0011] Preferably, the step of selecting a test signal according to after-sales data and matching a corresponding data transmission channel includes:

[0012] Extracting after-sales data; the after-sales data includes the error type and device serial number of the user's failure to control the device;

[0013] Match the error type with the existing test signal type to determine whether the error type is the same as the existing test signal type; if yes, select the test signal corresponding to the existing test signal type; if not, manually judge and select the test signal

[0014] According to the device serial number, the corresponding smart device is matched, and the cloud IoT platform sends the test signal to the WiFi module of the smart device, and builds a data transmission channel between the smart device and the cloud IoT platform through the WiFi module.

[0015] Preferably, the smart device sends the index data to the cloud IoT platform at intervals of n seconds according to the test signal, including:

[0016] Extracting a test signal; the test signal includes preset data and a test time; the preset data includes preset requirements and a preset sending quantity;

[0017] A timer is set in the smart device, and a corresponding data collector is selected according to the preset requirements of the test signal; after receiving the test signal, the timer is started, and k groups of corresponding test data are collected through the data collector every n seconds;

[0018] The mth test data collected, the corresponding mth test time and the device status data at this time are bound to obtain bound test data, and the bound test data are sent to the cloud IoT platform according to the preset data, and the bound test data combination received by the cloud IoT platform is marked as indicator data; wherein m=1, 2, 3…k, k is a positive integer, and the device status data includes the battery status, network connection status, device working status, sensor status and version configuration of the device.

[0019] Preferably, the cloud IoT platform completes remote fault diagnosis based on the indicator data, including:

[0020] Extract indicator data;

[0021] Determine whether the number of bound test data in the indicator data is the same as the preset sending number; if yes, mark the corresponding indicator data as complete data; if not, mark the corresponding indicator data as missing data and count the number of missing data;

[0022] The complete data and the missing data are marked as the quantity status of the indicator data, and the fault diagnosis of the intelligent device is performed according to the quantity status of the indicator data.

[0023] Preferably, the fault diagnosis of the smart device according to the quantity status of the indicator data includes:

[0024] B1: Extract the quantity status of indicator data;

[0025] B2: Determine whether the quantity status is complete data; if yes, jump to B3; if no, detect the missing control range of the missing data, and perform fault diagnosis on the intelligent device according to the missing control range, missing times and preset data of the missing data;

[0026] B3: Determine whether the complete control amplitude of the complete data is the same as the preset requirement; if yes, mark the diagnostic fault as a controller fault; if no, mark the corresponding complete data as difference data and jump to B4;

[0027] B4: Determine whether the sensor state corresponding to the difference data is normal; if yes, mark the diagnostic fault as a data calculation fault; if no, mark the diagnostic fault state as a sensor fault.

[0028] Preferably, the fault diagnosis of the smart device according to the missing control amplitude, the missing times and the preset data of the missing data includes:

[0029] S1: Extract missing control amplitude, missing times and preset data;

[0030] S2: Detect the missing frequency of missing data; the missing frequency includes missing order and missing pattern;

[0031] S3: Detect device status data corresponding to missing data;

[0032] S4: Determine whether an abnormal state occurs in the device status data; if yes, mark the device status data sub-data in which the abnormal state occurs as the cause of the fault; if no, jump to S5;

[0033] S5: Determine whether the missing control amplitude in the missing data is zero; if yes, mark the missing data as normal missing data; if no, calculate the out-of-control level according to the missing control amplitude, the number of missing times and preset data, and perform fault diagnosis on the intelligent device according to the out-of-control level;

[0034] S6: Determine whether the missing pattern and missing order of the missing data are missing according to the presentation pattern; if yes, jump to S7; if no, mark the diagnosis fault as a network fluctuation fault;

[0035] S7: Determine whether the equipment is in a load state; if yes, mark the diagnostic fault as a transmission load fault; if no, mark the diagnostic fault as a sensor detection fault.

[0036] Preferably, the out-of-control level is calculated according to the missing control amplitude, the missing times and preset data, including:

[0037] C1: Extract missing control amplitude, missing times and preset data;

[0038] C2: Mark the difference between the missing control range and the preset requirement in the preset data as the control difference;

[0039] C3: By formula The out-of-control coefficient SKY is calculated; where αm is the control coefficient of the mth test data, which is set according to the preset number of times. KZi represents the control difference of the i-th missing data, i = 1, 2, 3…j, j is a positive integer;

[0040] C4: Setting a first out-of-control threshold and a second out-of-control threshold; the first out-of-control threshold and the second out-of-control threshold are set based on manual setting experience of different intelligent devices;

[0041] C5: Determine whether the out-of-control coefficient is greater than the first out-of-control threshold; if yes, mark the out-of-control level of the missing data as the first out-of-control level; if no, jump to C6;

[0042] C6: Determine whether the out-of-control coefficient is greater than the second out-of-control threshold; if yes, mark the out-of-control level of the missing data as the second out-of-control level; if no, mark the out-of-control level of the missing data as the third out-of-control level.

[0043] A second aspect of the present invention provides an automated remote diagnosis device, comprising: a WIFI module and a cloud Internet of Things platform;

[0044] WIFI module: used to build a data transmission channel between smart devices and the cloud IoT platform, and the data transmission channel is used for data interaction between smart devices and the cloud IoT platform;

[0045] Cloud IoT platform: used to perform remote diagnosis on the smart device based on the data transmitted by the WiFi module.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The present invention collects after-sales data from users, selects a test signal according to the error type in the after-sales data, and constructs a data transmission channel between a cloud-based Internet of Things platform and a smart device corresponding to a device serial number through a WiFi module for data transmission, so that a more targeted test signal can be selected to improve the accuracy and efficiency of fault diagnosis; the cloud-based Internet of Things platform sends a verification signal to the smart device, and the smart device sends serial port data to the cloud-based Internet of Things platform after receiving the verification signal, and the cloud-based Internet of Things platform sends the test signal to the smart device after receiving the serial port data, and the verification process can confirm whether the data transmission is smooth and the correctness of the channel connection, thereby ensuring the safety and speed of data transmission; after receiving the test signal, the smart device sends indicator data to the cloud-based Internet of Things platform according to the test signal, and the cloud-based Internet of Things platform performs remote fault diagnosis on the smart device according to the indicator data, thereby reducing the need for on-site service by technical personnel, reducing operating costs, and more efficiently utilizing human resources and technical resources to improve overall operating efficiency.

[0048] 2. The present invention obtains the quantity status of the indicator data by comparing the indicator data with the preset data, and performs different fault diagnoses on the smart device for different quantity statuses; obtains the number of missing times corresponding to the indicator data by comparing the indicator data with the preset sending quantity in the preset data, and detects the missing control amplitude and the complete control amplitude, and performs fault diagnosis on the smart device according to the missing control amplitude, the number of missing times and the preset data of the missing data, which can more accurately locate the cause of the fault, reduce misdiagnosis and missed diagnosis, and improve the accuracy of fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 The following is a flowchart of an embodiment of the present invention.

[0051] Figure 2 FIG. 1 is a schematic diagram of the working process of a diagnostic device in one embodiment of the present invention.

[0052] Figure 3 The complete flowchart of performing fault diagnosis on complete data according to quantity status in one embodiment of the present invention. DETAILED DESCRIPTION

[0053] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1-Figure 3 The first aspect of the present invention provides a method for automated remote diagnosis, comprising:

[0055] Step 1: Collect the user's after-sales data; select the test signal according to the after-sales data and match the corresponding data transmission channel.

[0056] Exemplarily, the user's error type and device serial number for the air conditioner currently in use are collected through the user feedback system. The error type in this embodiment is abnormal air conditioner temperature control, and the device serial number is 001. The existing test signal type is extracted from the database to determine whether the error type is the same as the existing test signal type. It is determined that the existing test signal type includes abnormal air conditioner temperature control, and temperature control is selected as the test signal.

[0057] The smart device in this embodiment is a smart air conditioner. The device serial number received by the cloud IoT platform is 001, so the smart air conditioner No. 001 is matched as the test device; the WiFi module builds a data transmission channel between the smart air conditioner No. 001 and the cloud IoT platform.

[0058] Step 2: The cloud IoT platform sends a verification signal to the smart device through the data transmission channel. After receiving the verification signal, the smart device sends serial port data to the cloud IoT platform for confirmation; after confirmation, the cloud IoT platform sends a test signal to the smart device.

[0059] Exemplarily, the cloud-based IoT platform sends a verification signal to the smart air conditioner through a data transmission channel. After receiving the verification signal, the smart air conditioner sends serial port data to the cloud-based IoT platform to confirm that the channel connection is correct. After receiving the serial port data sent by the smart device, the cloud-based IoT platform sends a temperature control test signal to the smart air conditioner.

[0060] It should be noted that the verification signal sent by the cloud IoT platform and the serial port data sent by the smart device are identity authentication data. In order to set up in advance whether the verification channel can be used normally and confirm the correctness of the channel connection, the data sending channel is used for data interaction and transmission between the smart device and the cloud IoT platform; to ensure the transmission speed, the identity authentication data is generally set as a small data packet, which only needs to be received and recognized by the cloud IoT platform and the smart device.

[0061] Step 3: After receiving the test signal, the smart device sends the indicator data to the cloud IoT platform at intervals of n seconds according to the test signal.

[0062] Exemplarily, the test signal in this embodiment includes a preset requirement of lowering the temperature by 2 degrees each time, a preset number of transmissions of 5 times, a test time of 10:00, and a set interval time of 1 minute; the time when the smart air conditioner receives the test signal is 10:01, the set display temperature is 28 degrees, the timer is set to 1 minute, the temperature sensor and the timer are started to collect the current display temperature of the smart air conditioner every 1 minute, and the current battery status, network connection status, device working status and sensor status of the smart air conditioner are collected, and the version configuration is configured. A total of five groups of test data and device status data of the smart air conditioner are collected, and the five groups of test data and the corresponding test times are bound. The test time is the sum of the corresponding interval times after the smart air conditioner receives the test signal, which are 10:02, 10:03, 10:04, 10:05 and 10:06 respectively. The test time and the corresponding test data are bound to obtain five groups of bound test data, and the five groups of test data and the device status data are sent to the cloud IoT platform, and all bound test data combinations received by the cloud IoT platform are marked as indicator data.

[0063] It should be noted that by binding the test time and the bound test data, when there is a data transmission error or data loss due to other reasons during the data transmission process, the corresponding order of the corresponding bound test data can also be determined according to the test time.

[0064] Step 4: The cloud IoT platform completes remote fault diagnosis based on indicator data.

[0065] Exemplarily, it is determined whether the number of bound test data in the indicator data is the same as the preset sending number. In this embodiment, the number of bound test data collected by the cloud-based Internet of Things platform is 4, and the preset sending number is 5. The indicator data of smart air conditioner No. 001 is marked as missing data, and the number of missing data is statistically 1, and the quantity status of smart air conditioner No. 001 is missing data.

[0066] Determine whether the quantity status is complete data. In this embodiment, if the quantity status of the smart device is missing data, the missing control amplitude of the missing data is detected; the missing control amplitude is the difference between the bound test data of the smart device and the preset requirements. For example, the test signal in this embodiment is temperature control, and the missing control amplitude is the difference between the displayed temperature of each group of missing data and the preset required temperature.

[0067] In this embodiment, the preset requirements are that the first group displays a temperature of 26 degrees, the second group displays a temperature of 24 degrees, the third group displays a temperature of 22 degrees, the fourth group displays a temperature of 20 degrees, and the fifth group displays a temperature of 18 degrees; and the indicator data received by the cloud IoT platform is that the first group displays a temperature of 26 degrees, the second group displays a temperature of 25 degrees, the third group has no data, the fourth group displays a temperature of 24 degrees, and the fifth group displays a temperature of 22 degrees; the missing frequency of missing data is detected, and it is detected that there is no missing pattern of the missing data in this embodiment, and there is no fixed order; detect whether there is an abnormal state in all sub-data in the device status data, detect whether the battery of the smart air conditioner is working normally, whether the network connection has a delay or an unstable connection, whether the smart device is working in a load state, and whether the version configuration is updated to the latest version. When an abnormal state occurs in the sub-data in the device status data, the corresponding abnormal state is marked as the cause of the failure of the corresponding indicator data; if there are multiple abnormal states, all multiple abnormal states are regarded as the cause of the failure.

[0068] In another embodiment, if the quantity status of the indicator data is complete data, the complete control range of the complete data is detected, and the complete control range is the difference between the bound test data in the complete data and the preset requirements. If there is no complete control range, the diagnostic fault is marked as a controller fault, indicating that there is no problem with the adjustment of the smart device, but there is a problem with the controller and the smart device cannot be controlled; if there is a complete control range, the complete data is marked as difference data, and it is determined whether the sensor status corresponding to the difference data is normal. When the sensor status is normal, it means that the sensor is working normally and there is no data missing. It is a display fault caused by a calculation error in the internal calculation software. If the sensor works abnormally, it means that the display fault is a display fault caused by a sensor fault.

[0069] In other embodiments, if the device status data sub-data corresponding to the missing data has no abnormal state and is in normal working state, it is further determined whether the missing control amplitude in the missing data is zero. If the missing control amplitude is zero, it means that there is only a data transmission problem, the network fluctuation state is detected, and the corresponding missing data is marked as normal missing data; if the missing control amplitude is not zero, it means that the smart device has data acquisition and display problems, then the difference between the missing control amplitude and the preset requirement in the preset data is marked as the control difference, and the formula is used. The out-of-control coefficient SKY is calculated, where αm is the control coefficient of the mth test data, which is set according to the preset number of times. KZi represents the control difference of the i-th missing data, i=1, 2, 3…j, j is a positive integer; the out-of-control coefficient is compared with the set first out-of-control threshold and the second out-of-control threshold to obtain the corresponding out-of-control level. For different out-of-control levels, different levels of fault levels are performed on the smart device. Different fault levels correspond to different software or hardware faults, which require the above maintenance; continue to judge whether the missing pattern and missing sequence of the missing data are missing according to the regularity of presentation. In this embodiment, the missing pattern and missing sequence of the missing data do not show regular missing, which indicates a network fluctuation fault. If there is a regular missing, continue to judge whether the working state of the equipment is in a load state. If it is in a load state, it indicates that the data is missing due to resource occupation. If it is not in a load state, it indicates that there is no resource occupation problem and the sensor detection has a fault.

[0070] The second embodiment of the present invention discloses an automated remote diagnosis device, including a WIFI module and a cloud Internet of Things platform;

[0071] WIFI module: used to build a data transmission channel between smart devices and the cloud IoT platform, and the data transmission channel is used for data interaction between smart devices and the cloud IoT platform;

[0072] Cloud IoT platform: used to perform remote diagnosis on the smart device based on the data transmitted by the WiFi module.

[0073] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0074] Working principle of the present invention:

[0075] The present invention collects after-sales data of users, selects a test signal according to the error type in the after-sales data, and constructs a data transmission channel between a cloud-based Internet of Things platform and a smart device corresponding to a device serial number through a WiFi module for data transmission; the cloud-based Internet of Things platform and the smart device perform identity authentication through a verification signal and serial port data, and after the verification is passed, the cloud-based Internet of Things platform sends the test signal to the smart device, and the smart device sends index data to the cloud-based Internet of Things platform according to the test signal, and the cloud-based Internet of Things platform compares the index data with preset data, analyzes the fault state of the smart device according to the comparison result, and performs fault diagnosis on the smart device.

[0076] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for automated remote diagnosis, characterized in that: include: Step 1: Collect the user's after-sales data; select the test signal according to the after-sales data and match the corresponding data transmission channel; Step 2: The cloud IoT platform sends a verification signal to the smart device through the data transmission channel. After receiving the verification signal, the smart device sends serial port data to the cloud IoT platform for confirmation. After confirmation, the cloud IoT platform sends a test signal to the smart device. Step 3: After receiving the test signal, the smart device sends the indicator data to the cloud IoT platform at intervals of n seconds according to the test signal; Step 4: The cloud IoT platform completes remote fault diagnosis based on indicator data.

2. The method of automated remote diagnosis according to claim 1, characterized in that: The selecting of the test signal according to the after-sales data and matching the corresponding data transmission channel comprises: Extracting after-sales data; the after-sales data includes the error type and device serial number of the user's failure to control the device; Match the error type with the existing test signal type to determine whether the error type is the same as the existing test signal type; if yes, select the test signal corresponding to the existing test signal type; if not, manually judge and select the test signal; According to the device serial number, the corresponding smart device is matched, and the cloud IoT platform sends the test signal to the WiFi module of the smart device, and builds a data transmission channel between the smart device and the cloud IoT platform through the WiFi module.

3. The method of automated remote diagnosis according to claim 1, characterized in that: The smart device sends the index data to the cloud IoT platform at intervals of n seconds according to the test signal, including: Extracting a test signal; the test signal includes preset data and a test time; the preset data includes preset requirements and a preset sending quantity; A timer is set in the smart device, and a corresponding data collector is selected according to the preset requirements of the test signal; after receiving the test signal, the timer is started, and k groups of corresponding test data are collected through the data collector every n seconds; The mth test data collected and the corresponding mth test time are bound to obtain bound test data, and the bound test data and device status data are sent to the cloud IoT platform according to preset data respectively, and the bound test data combination received by the cloud IoT platform is marked as indicator data; wherein m=1, 2, 3…k, k is a positive integer, and the device status data includes the battery status, network connection status, device working status, sensor status and version configuration of the device.

4. The method of automated remote diagnosis according to claim 1, characterized in that: The cloud IoT platform completes remote fault diagnosis based on indicator data, including: Extract indicator data; Determine whether the number of bound test data in the indicator data is the same as the preset sending number; if yes, mark the corresponding indicator data as complete data; if not, mark the corresponding indicator data as missing data and count the number of missing data; The complete data and the missing data are marked as the quantity status of the indicator data, and the fault diagnosis of the intelligent device is performed according to the quantity status of the indicator data.

5. The method of automated remote diagnosis according to claim 4, characterized in that: The fault diagnosis of the intelligent device according to the quantity status of the indicator data includes: B1: Extract the quantity status of indicator data; B2: Determine whether the quantity status is complete data; if yes, jump to B3; if no, detect the missing control range of the missing data, and perform fault diagnosis on the intelligent device according to the missing control range, missing times and preset data of the missing data; B3: Determine whether the complete data is the same as the preset requirements; if yes, mark the diagnostic fault as a controller fault; if no, mark the corresponding complete data as difference data and jump to B4; B4: Determine whether the sensor state corresponding to the difference data is normal; if yes, mark the diagnostic fault as a data calculation fault; if no, mark the diagnostic fault state as a sensor fault.

6. The method of automated remote diagnosis according to claim 5, characterized in that: The method of performing fault diagnosis on the smart device according to the missing control amplitude, missing times and preset data of the missing data includes: S1: Extract missing control amplitude, missing times and preset data; S2: Detect the missing frequency of missing data; the missing frequency includes missing order and missing pattern; S3: Detect device status data corresponding to missing data; S4: Determine whether an abnormal state occurs in the device status data; if yes, mark the device status data sub-data in which the abnormal state occurs as the cause of the fault; if no, jump to S5; S5: Determine whether the missing control amplitude in the missing data is zero; if yes, mark the missing data as normal missing data; if no, calculate the out-of-control level according to the missing control amplitude, the number of missing times and preset data, and perform fault diagnosis on the intelligent device according to the out-of-control level; S6: Determine whether the missing pattern and missing order of the missing data are missing according to the presentation pattern; if yes, jump to S7; if no, mark the diagnosis fault as a network fluctuation fault; S7: Determine whether the equipment is in a load state; if yes, mark the diagnostic fault as a transmission load fault; if no, mark the diagnostic fault as a sensor detection fault.

7. The method of automated remote diagnosis according to claim 5, characterized in that: The out-of-control level is calculated based on the missing control amplitude, the missing times and the preset data, including: C1: Extract missing control amplitude, missing times and preset data; C2: Mark the difference between the missing control range and the preset requirement in the preset data as the control difference; C3: By formula The out-of-control coefficient SKY is calculated; where αm is the control coefficient of the mth test data, which is set according to the preset number of times. KZi represents the control difference of the i-th missing data, i = 1, 2, 3…j, j is a positive integer; C4: Setting a first out-of-control threshold and a second out-of-control threshold; the first out-of-control threshold and the second out-of-control threshold are set based on manual setting experience of different intelligent devices; C5: Determine whether the out-of-control coefficient is greater than the first out-of-control threshold; if yes, mark the out-of-control level of the missing data as the first out-of-control level; if no, jump to C6; C6: Determine whether the out-of-control coefficient is greater than the second out-of-control threshold; if yes, mark the out-of-control level of the missing data as the second out-of-control level; if no, mark the out-of-control level of the missing data as the third out-of-control level.

8. An automated remote diagnosis device, applied to an automated remote diagnosis method as claimed in any one of claims 1 to 7, characterized in that: include: WIFI module and cloud IoT platform; WIFI module: used to build a data transmission channel between smart devices and the cloud IoT platform, and the data transmission channel is used for data interaction between smart devices and the cloud IoT platform; Cloud IoT platform: used to perform remote diagnosis on the smart device based on the data transmitted by the WiFi module.

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