Household appliance abnormity management and control system and method based on artificial intelligence

Abstract

The invention discloses a household appliance abnormity management and control system and method based on artificial intelligence, and belongs to the technical field of household appliance management and control. The method comprises the following steps: extracting factory equipment parameters of each household appliance from a database, and collecting historical equipment parameters of each household appliance and historical line parameters of a household circuit in historical work; generating a parameter change curve according to the historical equipment parameters and the historical line parameters; based on the historical equipment parameter change curve, the historical line parameter change curve and the factory equipment parameters, state analysis is carried out on each household electrical appliance, and the abnormal state and the abnormal degree of each household electrical appliance are identified and judged; and according to the abnormal state data of each household electrical appliance, constructing a household electrical appliance abnormal state prediction model, monitoring real-time equipment parameters and real-time line parameters of the household electrical appliance through the prediction model, and giving a corresponding early warning prompt to a user. The abnormal condition of the household electrical appliance can be timely warned, and the damage probability of the household electrical appliance is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of home appliance control, and particularly to an abnormal control system and method for home appliances based on artificial intelligence. Background Art

[0002] Household appliances mainly refer to various electrical and electronic appliances used in households and similar places. Household appliances have liberated people from heavy, trivial, and time-consuming housework, created a more comfortable, beautiful, and healthier living and working environment for humans, provided rich and colorful cultural and entertainment conditions, and have become necessities for modern family life.

[0003] With the continuous improvement of people's living quality, the types and models of household appliances have been increasing continuously. The working hours and frequencies of household appliances remain high, which is likely to cause damage to household appliances. Abnormalities in household appliances may lead to safety problems such as fires and electric shocks, threatening the lives of family members; electrical abnormalities may cause equipment damage, and then result in property losses. Abnormalities in household appliances may affect daily life.

[0004] Therefore, the present invention discloses an abnormal control system and method for home appliances based on artificial intelligence to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an abnormal control system and method for home appliances based on artificial intelligence to solve the problems raised in the above background art.

[0006] To solve the above technical problems, the present invention provides the following technical solution: An abnormal control method for home appliances based on artificial intelligence, characterized in that the method includes the following steps: S1: Extract the factory equipment parameters of each household appliance from the database, and collect the historical equipment parameters of each household appliance and the historical line parameters of the household circuit during historical operation; S2: Generate historical equipment parameter change curves and historical line parameter change curves according to the historical equipment parameters of each household appliance and the historical line parameters of the household circuit during historical operation; S3: Based on the historical equipment parameter change curves, historical line parameter change curves, and factory equipment parameters, perform status analysis on each household appliance to identify and judge the abnormal status and degree of each household appliance; S4: Construct an abnormal status prediction model for household appliances according to the abnormal status data of each household appliance, monitor the real-time equipment parameters and real-time line parameters of household appliances through the prediction model, and give corresponding warning prompts to users.

[0007] According to the above solution, in step S1, the historical device parameters of each household electrical appliance device include historical device voltage, historical device current, historical device temperature, and historical device battery capacity; the factory device parameters of each household electrical appliance include factory device voltage, factory device current, factory device temperature, and factory device battery capacity; the historical line parameters of the household circuit include historical line voltage; the data acquisition time nodes of the historical device parameters of each household electrical appliance device and the historical line parameters of the household circuit are fixed and the same.

[0008] According to the above solution, in step S2, the historical device parameter change curves include a historical device voltage change curve, a historical device current change curve, a historical device temperature change curve, and a historical device battery capacity change curve; the historical line parameter change curve includes a historical line voltage change curve; the historical device parameter change curve is a curve of historical device parameters changing with time; the historical line parameter change curve is a curve of historical line parameters changing with time.

[0009] According to the above solution, in step S3, the household electrical appliances to be analyzed in a user's home form a set , where represents the total number of household electrical appliances to be analyzed in a user's home, and the th household electrical appliance to be analyzed is denoted as , where ; for the household electrical appliance to be analyzed , subtract the corresponding factory device parameters from the respective parameter values of the historical device parameter change curve to form the historical device parameter absolute change parameter values; form the historical device parameter absolute change curve according to the historical device parameter absolute change parameter values; the historical device parameter absolute change curve is a curve of the historical device parameter absolute change parameter values changing with time; the historical device parameter absolute change curve includes a historical device voltage absolute change curve, a historical device current absolute change curve, a historical device temperature absolute change curve, and a historical device battery capacity absolute change curve; Subtract the corresponding factory device voltage from the respective parameter values of the historical device voltage change curve to form the historical device voltage absolute change parameter values; form the historical device voltage absolute change curve according to the historical device voltage absolute change parameter values; the historical device voltage absolute change curve is a curve of the historical device voltage absolute change parameter values changing with time; the generation methods of the other historical device parameter absolute change curves are the same as above by analogy; Translate the historical device voltage absolute change curve along the direction of the parameter value axis, and the translation amount is equal to the factory device voltage of the household electrical appliance to be analyzed ; the acquisition methods of the other historical device parameter absolute change curves are the same as the above method by analogy; By subtracting the factory equipment parameters from the equipment parameters in historical work, the real-time change amount of the equipment parameters of the household appliance to be analyzed can be clearly shown, reducing the influence of the factory equipment parameters on the actual data.

[0010] For the household appliance to be analyzed Multiply the parameter value of the absolute change curve of the historical equipment voltage of the household appliance to be analyzed by the parameter value of the absolute change curve of the historical equipment current to obtain the absolute change parameter value of the historical equipment power, and generate an absolute change curve of the historical equipment power from the absolute change parameter value of the historical equipment power; the absolute change curve of the historical equipment power is a curve showing how the absolute change parameter value of the historical equipment power changes over time; Normalize the above absolute change curve of the historical equipment power, the absolute change curve of the historical equipment temperature, and the absolute change curve of the battery capacity; assign weights to the normalized absolute change curve of the historical equipment power, the absolute change curve of the historical equipment temperature, and the absolute change curve of the battery capacity respectively to obtain the real-time status value curve of the household appliance to be analyzed ; The above-mentioned real-time status value curve of the household appliance to be analyzed is obtained by assigning weights to the absolute change curve of the historical equipment power , the absolute change curve of the historical equipment temperature and the absolute change curve of the battery capacity R respectively, where the real-time status value R, where ,, and and represent the weights of the absolute change of equipment power, the absolute change of equipment temperature, and the absolute change of battery capacity respectively; Set a threshold for the real-time status value . If the real-time status value of the household appliance to be analyzed is greater than the real-time status value threshold , then the household appliance to be analyzed is an abnormal household appliance; if the real-time status value of the household appliance to be analyzed is less than or equal to the real-time status value threshold , then the household appliance to be analyzed is a normal household appliance; The system preset detection period . Extract all the real-time status values of abnormal household appliances in the detection period where the real-time status value is greater than the real-time status value threshold For the abnormal state time interval, extract the corresponding historical line voltage change curve according to each abnormal state time interval; extract the voltage qualified time interval from the corresponding historical line voltage change curve, and form a set of voltage qualified time intervals in the abnormal state time interval ; where represents the total number of voltage qualified time intervals in the abnormal state time interval; denote the voltage qualified time interval in the th abnormal state time interval as , where represents the start time node of the voltage qualified time interval in the abnormal state time interval, represents the end time node of the voltage qualified time interval in the abnormal state time interval, ; calculate the total abnormal state time of the abnormal household appliances, and the specific calculation formula is as follows: , By extracting the corresponding historical line voltage change curve through the above abnormal state time interval, extracting the voltage qualified time interval from the corresponding historical line voltage change curve, and forming a set of voltage qualified time intervals in the abnormal state time interval; it can effectively eliminate the situation that the abnormal household appliances are caused by the unstable household line, and can more accurately judge the actual situation of the household appliances.

[0011] Assign weights to the total abnormal state time and the real-time state value, and calculate the abnormal state degree score , and the specific calculation formula is as follows: , where is the weight of the real-time state value, is the weight of the total abnormal state time, ; Judge the abnormal degree of the household appliances according to the abnormal state degree score; update the abnormal state degree of the household appliances in the database.

[0012] Updating the abnormal state degree of the household appliances in the database can clearly find the household appliances with abnormal states in the historical work in the background data, and remind the user that such household appliances are of certain danger.

[0013] According to the above scheme, for the household appliance abnormal state prediction model in step S4, extract the start time nodes in the set of voltage qualified time intervals in the abnormal state time interval to form a new set ; perform prediction calculation on the start time node difference; the specific calculation formula is as follows: , Similarly, perform predictive calculations on the difference in end time nodes; the specific calculation formula is as follows: , Predict that the voltage qualified time interval in the next abnormal state time interval is ; Advance the start time node of the voltage qualified time interval in the next abnormal state time interval by time period to give a warning prompt to the user; The warning prompt includes the name of the abnormal household appliance, the voltage qualified time interval in the next abnormal state time interval, a prompt to turn on the voltage stabilizer, and a list of household appliances recommended to be turned off; The list of household appliances recommended to be turned off is arranged in descending order according to the absolute change parameter value of the historical equipment power.

[0014] Through the above prediction of the voltage qualified time interval in the next abnormal state time interval, a warning prompt is given to the user before the arrival of this time interval, so as to minimize the user's losses.

[0015] Another aspect of the present application provides an artificial intelligence-based home appliance anomaly control system. The system is applied to implement the above artificial intelligence-based home appliance anomaly control method, and is characterized in that the system includes a database, a data collection and extraction module, a parameter curve generation module, a home appliance status analysis module, and an anomaly prediction and control module; The database is used to store the data information of household appliances; the data collection and extraction module is used to extract the factory equipment parameters of each household appliance from the database, and collect the historical equipment parameters of each household appliance during historical work and the historical line parameters of the household circuit; the parameter curve generation module generates historical equipment parameter change curves and historical line parameter change curves according to the historical equipment parameters of each household appliance during historical work and the historical line parameters of the household circuit; the home appliance status analysis module performs status analysis on each household appliance based on the historical equipment parameter change curves, historical line parameter change curves, and factory equipment parameters, and identifies and judges the abnormal status and degree of each household appliance; the abnormal status prediction module constructs an artificial intelligence home appliance abnormal status prediction model according to the abnormal status data of each household appliance, monitors the real-time equipment parameters and real-time line parameters of the household appliances through the prediction model, and gives corresponding warning prompts to the user.

[0016] According to the above solution, the data collection and extraction module includes a data collection unit and a data extraction unit; The data collection unit is used to collect the historical equipment parameters of each household appliance during historical work and the historical line parameters of the household circuit; the data extraction unit is used to extract the factory equipment parameters of each household appliance from the database; The historical device parameters of each household electrical appliance device include historical device voltage, historical device current, historical device temperature, and historical device battery capacity; the factory device parameters of each household electrical appliance include factory device voltage, factory device current, factory device temperature, and factory device battery capacity; the historical line parameters of the household circuit include historical line voltage.

[0017] According to the above solution, the parameter curve generation module includes a household electrical appliance device parameter curve generation unit and a household circuit line parameter curve generation unit; The household electrical appliance device parameter curve generation unit is used to generate a change curve of the historical device parameters of the household electrical appliance; the change curve of the historical device parameters includes a change curve of the historical device voltage, a change curve of the historical device current, a change curve of the historical device temperature, and a change curve of the historical device battery capacity; the household circuit line parameter curve generation unit is used to generate a change curve of the historical line parameters of the household circuit; the change curve of the historical line parameters includes a change curve of the historical line voltage.

[0018] According to the above solution, the household electrical appliance status analysis module includes an abnormal status analysis unit and an abnormal degree analysis unit; The abnormal status analysis unit calculates the real-time status value of the household electrical appliance to be analyzed based on the change curve of the historical device parameters, the change curve of the historical line parameters, and the factory device parameters, and judges the status of the household electrical appliance to be analyzed; the abnormal degree analysis unit calculates the abnormal degree score of the household electrical appliance to be analyzed based on the real-time status value of the household electrical appliance to be analyzed and the voltage qualified time interval in the abnormal status time interval.

[0019] According to the above solution, the abnormal prediction and control module includes an abnormal time prediction unit and a monitoring and prompting unit; The abnormal time prediction unit predicts the voltage qualified time interval in the abnormal status time interval of the next household electrical appliance based on the abnormal status data of the household electrical appliance; the monitoring and prompting unit monitors the device parameters of the household electrical appliance in real time based on the abnormal time prediction unit and gives corresponding early warning prompts to the user.

[0020] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By subtracting the factory equipment parameters from the equipment parameters in historical operations, the real-time change amount of the equipment parameters of the household appliances to be analyzed can be clearly displayed, reducing the influence of the factory equipment parameters on the actual data; By extracting the corresponding historical line voltage change curve for the abnormal state time interval, and extracting the voltage qualified time interval from the corresponding historical line voltage change curve, a set of voltage qualified time intervals in the abnormal state time interval is formed; It can effectively eliminate the situation where the household appliances are abnormal due to unstable household circuits, and can more accurately judge the actual situation of the household appliances; Updating the abnormal state degree of the household appliances in the database can clearly find the household appliances that have had abnormal states in historical operations in the background data, reminding users that such household appliances have a certain degree of danger; By predicting the voltage qualified time interval in the next abnormal state time interval, a warning prompt is given to the user before the arrival of this time interval, minimizing the user's losses as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the accompanying drawings: Figure 1 is a schematic flow chart of a method for controlling abnormal household appliances based on artificial intelligence according to the present invention; Figure 2 is a schematic structural diagram of a system for controlling abnormal household appliances based on artificial intelligence according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1 , the present invention provides a technical solution: A method for controlling abnormal household appliances based on artificial intelligence, characterized in that the method includes the following steps: S1: Extract the factory equipment parameters of each household appliance from the database, and collect the historical equipment parameters of each household appliance in historical operations and the historical line parameters of the household circuit; In step S1, the historical device parameters of each household electrical appliance include historical device voltage, historical device current, historical device temperature, and historical device battery capacity; the factory device parameters of each household electrical appliance include factory device voltage, factory device current, factory device temperature, and factory device battery capacity; the historical line parameters of the household circuit include historical line voltage; the data acquisition time nodes of the historical device parameters of each household electrical appliance and the historical line parameters of the household circuit are fixed and the same.

[0024] S2: Generate historical device parameter change curves and historical line parameter change curves based on the historical device parameters of each household electrical appliance and the historical line parameters of the household circuit during historical operation; In step S2, the historical device parameter change curves include historical device voltage change curve, historical device current change curve, historical device temperature change curve, and historical device battery capacity change curve; the historical line parameter change curve includes historical line voltage change curve; the historical device parameter change curve is a curve of historical device parameters changing with time; the historical line parameter change curve is a curve of historical line parameters changing with time.

[0025] S3: Based on the historical device parameter change curves, historical line parameter change curves, and factory device parameters, perform status analysis on each household electrical appliance to identify and judge the abnormal status and degree of abnormality of each household electrical appliance; In step S3, the household electrical appliances to be analyzed in a user's home form a set , where represents the total number of household electrical appliances to be analyzed in a user's home, and the th household electrical appliance to be analyzed is denoted as , where ; for the household electrical appliance to be analyzed , subtract the corresponding factory device parameters from each parameter value of the historical device parameter change curve to form the historical device parameter absolute change parameter value; form a historical device parameter absolute change curve based on the historical device parameter absolute change parameter value; the historical device parameter absolute change curve is a curve of the historical device parameter absolute change parameter value changing with time; The historical device parameter absolute change curve includes historical device voltage absolute change curve, historical device current absolute change curve, historical device temperature absolute change curve, and historical device battery capacity absolute change curve; Subtract the corresponding factory equipment voltage from each parameter value of the historical equipment voltage change curve to form the historical equipment voltage absolute change parameter value; form the historical equipment voltage absolute change curve based on the historical equipment voltage absolute change parameter value; the historical equipment voltage absolute change curve is a curve of the historical equipment voltage absolute change parameter value changing with time; the generation methods of other historical equipment parameter absolute change curves are the same by analogy; The household appliance to be analyzed Multiply the parameter value of the historical equipment voltage absolute change curve of the household appliance to be analyzed by the parameter value of the historical equipment current absolute change curve to obtain the historical equipment power absolute change parameter value, and generate the historical equipment power absolute change curve from the historical equipment power absolute change parameter value; the historical equipment power absolute change curve is a curve of the historical equipment power absolute change parameter value changing with time; Normalize the above historical equipment power absolute change curve, historical equipment temperature absolute change curve, and battery capacity absolute change curve; assign weights to the normalized historical equipment power absolute change curve, historical equipment temperature absolute change curve, and battery capacity absolute change curve respectively to obtain the real-time status value curve of the household appliance to be analyzed ; The above-mentioned historical equipment power absolute change curve 、historical equipment temperature absolute change curve and battery capacity absolute change curve R are assigned weights respectively to obtain the real-time status value curve of the household appliance to be analyzed , and the real-time status value R, where 、 and represent the weights of the absolute change of equipment power, the absolute change of equipment temperature, and the absolute change of battery capacity respectively; Set a threshold for the real-time status value , if the real-time status value of the household appliance to be analyzed is greater than the real-time status value threshold , then the household appliance to be analyzed is an abnormal household appliance; if the real-time status value of the household appliance to be analyzed is less than or equal to the real-time status value threshold , then the household appliance to be analyzed is a normal household appliance; In this embodiment, the system preset detection period is 24 hours, and extract all the real-time status values of abnormal household appliances within the 24-hour detection period that are greater than the real-time status value threshold For the abnormal state time interval, extract the corresponding historical line voltage change curve according to each abnormal state time interval; extract the voltage qualified time interval from the corresponding historical line voltage change curve, and form a set of the voltage qualified time intervals in the abnormal state time interval ; If the total number of voltage qualified time intervals in the abnormal state time interval is 10; take the voltage qualified time interval in the th abnormal state time interval as , where represents the start time node of the voltage qualified time interval in the abnormal state time interval, represents the end time node of the voltage qualified time interval in the abnormal state time interval, ; Calculate the total abnormal state time of the abnormal household appliances, and the specific calculation formula is as follows: , Assign weights to the total abnormal state time and the real-time state value, and calculate the abnormal state degree score , and the specific calculation formula is as follows: , where is the weight of the real-time state value, is the weight of the total abnormal state time, ; Judge the abnormal degree of the household appliances according to the abnormal state degree score; update the abnormal state degree of the household appliances in the database.

[0026] S4: Construct an abnormal state prediction model for household appliances based on the abnormal state data of each household appliance, monitor the real-time device parameters and real-time line parameters of the household appliances through the prediction model, and give corresponding warning prompts to the user.

[0027] According to the above solution, for the abnormal state prediction model of household appliances in step S4, extract the start time nodes in the set of the voltage qualified time intervals in the abnormal state time interval to form a new set ; Perform prediction calculation on the difference of the start time nodes; the specific calculation formula is as follows: , Similarly, perform prediction calculation on the difference of the end time nodes; the specific calculation formula is as follows: , Predict that the voltage qualified time interval in the next abnormal state time interval is ; Advance the start time node of the voltage qualified time interval in the next abnormal state time interval During a time period, a warning prompt is given to the user; The warning prompt includes the name of the abnormal household appliance, the voltage qualified time interval in the next abnormal state time interval, a prompt to turn on the voltage stabilizer, and a list of household appliances recommended to be turned off; The list of household appliances recommended to be turned off is arranged in descending order according to the absolute change parameter value of the historical equipment power.

[0028] Please refer to Figure 2 , the present invention provides a technical solution: an artificial intelligence-based household appliance abnormal control system, which is applied to implement the above artificial intelligence-based household appliance abnormal control method, and is characterized in that the system includes a database, a data collection and extraction module, a parameter curve generation module, a household appliance status analysis module, and an abnormal prediction and control module; The database is used to store the data information of household appliances; the data collection and extraction module is used to extract the factory equipment parameters of each household appliance from the database, and collect the historical equipment parameters of each household appliance in historical operation and the historical line parameters of the household circuit; the parameter curve generation module generates a historical equipment parameter change curve and a historical line parameter change curve according to the historical equipment parameters of each household appliance in historical operation and the historical line parameters of the household circuit; the household appliance status analysis module performs status analysis on each household appliance based on the historical equipment parameter change curve, the historical line parameter change curve, and the factory equipment parameters, and identifies and judges the abnormal status and abnormal degree of each household appliance; the abnormal status prediction module constructs an artificial intelligence-based household appliance abnormal status prediction model according to the abnormal status data of each household appliance, monitors the real-time equipment parameters and real-time line parameters of the household appliance through the prediction model, and gives corresponding warning prompts to the user.

[0029] According to the above solution, the data collection and extraction module includes a data collection unit and a data extraction unit; The data collection unit is used to collect the historical equipment parameters of each household appliance in historical operation and the historical line parameters of the household circuit; the data extraction unit is used to extract the factory equipment parameters of each household appliance from the database; The historical equipment parameters of each household appliance device include historical equipment voltage, historical equipment current, historical equipment temperature, and historical equipment battery capacity; the factory equipment parameters of each household appliance include factory equipment voltage, factory equipment current, factory equipment temperature, and factory equipment battery capacity; the historical line parameters of the household circuit include historical line voltage.

[0030] According to the above solution, the parameter curve generation module includes a household appliance equipment parameter curve generation unit and a household circuit line parameter curve generation unit; The household appliance device parameter curve generation unit is used to generate the historical device parameter change curves of household appliances; the historical device parameter change curves include the historical device voltage change curve, the historical device current change curve, the historical device temperature change curve, and the historical device battery capacity change curve; the household circuit line parameter curve generation unit is used to generate the historical line parameter curves of the household circuit; the historical line parameter curves include the historical line voltage change curve.

[0031] According to the above solution, the household appliance status analysis module includes an abnormal status analysis unit and an abnormal degree analysis unit; The abnormal status analysis unit calculates the real-time status value of the household appliance to be analyzed based on the historical device parameter change curves, the historical line parameter change curves, and the factory device parameters, and judges the status of the household appliance to be analyzed; the abnormal degree analysis unit calculates the abnormal degree score of the household appliance to be analyzed based on the real-time status value of the household appliance to be analyzed and the voltage qualified time interval in the abnormal status time interval.

[0032] According to the above solution, the abnormal prediction and control module includes an abnormal time prediction unit and a monitoring and prompting unit; The abnormal time prediction unit predicts the voltage qualified time interval in the abnormal status time interval of the next household appliance based on the abnormal status data of the household appliance; the monitoring and prompting unit monitors the device parameters of the household appliance in real time based on the abnormal time prediction unit and gives corresponding warning prompts to the user.

[0033] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0034] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for abnormal control of household appliances based on artificial intelligence, characterized in that: The method comprises the following steps: S1: extracting factory equipment parameters of each household appliance from the database, collecting historical equipment parameters of each household appliance in historical work and historical line parameters of household circuits; S2: Generate a historical equipment parameter change curve and a historical line parameter change curve according to the historical equipment parameters of each household appliance and the historical line parameters of the household circuit in the historical work; S3: Based on the historical equipment parameter change curve, the historical line parameter change curve and the factory equipment parameters, the real-time status value of the household appliances is calculated and the abnormal status time interval of the household appliances is analyzed to identify and judge the abnormal status and degree of abnormality of each household appliance; S4: construct a household appliance abnormal state prediction model according to the abnormal state and abnormal degree of each household appliance, monitor the real-time device parameters and real-time line parameters of the household appliance through the prediction model, and give corresponding early warning prompts to the user; In step S3, the voltage qualified time interval is extracted from the historical line voltage change curve, and the voltage qualified time interval in the abnormal state time interval is formed into a set ; Assign weights to the total abnormal state time and real-time state value, and score the degree of abnormal state calculate; In step S4, a voltage qualified time interval set in the abnormal state time interval is extracted. The start time nodes in the ,form a new set and build a prediction model for abnormal status of household appliances.

2. According to the artificial intelligence-based household appliance abnormality management method of claim 1, it is characterized by: In step S1, the historical equipment parameters of each household electrical appliance include the historical equipment voltage, the historical equipment current, the historical equipment temperature and the historical equipment battery capacity; the factory equipment parameters of each household appliance include the factory equipment voltage, the factory equipment current, the factory equipment temperature and the factory equipment battery capacity; the historical line parameters of the household circuit include the historical line voltage; the data acquisition time nodes of the historical equipment parameters of each household electrical appliance and the historical line parameters of the household circuit are fixed and the same.

3. The method for abnormal control of household appliances based on artificial intelligence according to claim 2 is characterized in that: In step S2, the historical device parameter change curve includes a historical device voltage change curve, a historical device current change curve, a historical device temperature change curve and a historical device battery capacity change curve; the historical line parameter change curve includes a historical line voltage change curve; the historical device parameter change curve is a curve of historical device parameter changes over time; the historical line parameter change curve is a curve of historical line parameter changes over time.

4. The method for abnormal control of household appliances based on artificial intelligence according to claim 3 is characterized in that: In step S3, the household appliances to be analyzed in a user's home are grouped into ,in Indicates the total number of household appliances to be analyzed in a user's home. The household appliances to be analyzed are recorded as ,in ; For household appliances to be analyzed , subtracting the corresponding factory equipment parameters from each parameter value of the historical equipment parameter change curve to form the historical equipment parameter absolute change parameter value; forming the historical equipment parameter absolute change curve according to the historical equipment parameter absolute change parameter value; the historical equipment parameter absolute change curve is a curve showing the absolute change parameter values ​​of the historical equipment parameters changing with time; Home appliances to be analyzed The parameter value of the historical device voltage absolute change curve is multiplied by the parameter value of the historical device current absolute change curve to obtain the historical device power absolute change parameter value, and the historical device power absolute change parameter value is used to generate a historical device power absolute change curve; the historical device power absolute change curve is a curve of the historical device power absolute change parameter value changing over time; The above historical equipment power absolute change curve, historical equipment temperature absolute change curve and battery capacity absolute change curve are normalized; weights are assigned to the normalized historical equipment power absolute change curve, historical equipment temperature absolute change curve and battery capacity absolute change curve respectively to obtain the household appliances to be analyzed. Real-time status value curve; Set thresholds for real-time status values , if the household appliances to be analyzed Real-time status value Greater than the real-time status value threshold , then the household appliances to be analyzed For abnormal household appliances; if the household appliances to be analyzed Real-time status value Less than or equal to the real-time status value threshold , then the household appliances to be analyzed For normal household appliances.

5. The method for abnormal control of household appliances based on artificial intelligence according to claim 4 is characterized in that: System preset detection cycle , extract in the detection cycle Real-time status values ​​of all abnormal household appliances Greater than the real-time status value threshold The abnormal state time interval is extracted according to each abnormal state time interval, and the corresponding historical line voltage change curve is extracted from the corresponding historical line voltage change curve. The voltage qualified time interval in the abnormal state time interval is formed into a set ;in Indicates the total number of voltage qualified time intervals in the abnormal state time interval; The first The voltage qualified time interval in the abnormal state time interval is recorded as ,in Indicates the start time node of the voltage qualified time interval in the abnormal state time interval, Indicates the end time node of the voltage qualified time interval in the abnormal state time interval, ; Total abnormal state time of abnormal household appliances The specific calculation formula is as follows: ， Assign weights to the total abnormal state time and real-time state value, and score the degree of abnormal state Calculation, the specific calculation formula is as follows: ， in is the weight of the real-time status value, is the weight of the total abnormal state time, ; The abnormality degree of the household appliance is judged according to the abnormality degree score; and the abnormality degree of the household appliance is updated in the database.

6. The method for abnormal control of household appliances based on artificial intelligence according to claim 5 is characterized in that: In step S4, the household appliance abnormal state prediction model extracts the voltage qualified time interval set in the abnormal state time interval The starting time node in forms a new set ; Predict and calculate the start time node difference; the specific calculation formula is as follows: ， Similarly, the end time node difference is predicted and calculated; the specific calculation formula is as follows: ， The voltage qualified time interval in the next abnormal state time interval is predicted to be ; The start time node of the voltage qualified time interval in the next abnormal state time interval is advanced Time period, to give warning prompts to users; The warning prompt includes the name of the abnormal household appliance, the voltage qualified time interval in the next abnormal state time interval, the prompt to turn on the voltage stabilizer and the list of household appliances recommended to be turned off; The list of household appliances recommended to be turned off is arranged from large to small according to the historical absolute change parameter values ​​of the equipment power.

7. A household appliance abnormality control system based on artificial intelligence, the system is applied to the household appliance abnormality control method based on artificial intelligence as described in any one of claims 1-6, characterized in that: The system includes a database, a data acquisition and extraction module, a parameter curve generation module, a household appliance status analysis module, and an abnormality prediction and control module; The database is used to store data information of household appliances; the data acquisition and extraction module is used to extract the factory equipment parameters of each household appliance from the database, and collect the historical equipment parameters of each household appliance and the historical line parameters of the household circuit in the historical work; the parameter curve generation module generates a historical equipment parameter change curve and a historical line parameter change curve according to the historical equipment parameters of each household appliance and the historical line parameters of the household circuit in the historical work; the household appliance state analysis module performs state analysis on each household appliance based on the historical equipment parameter change curve, the historical line parameter change curve and the factory equipment parameters, and identifies and determines the abnormal state and degree of abnormality of each household appliance; the abnormal state prediction module constructs a household appliance abnormal state prediction model according to the abnormal state data of each household appliance, monitors the real-time equipment parameters and real-time line parameters of the household appliance through the prediction model, and makes corresponding early warning prompts to the user.

8. The artificial intelligence-based household appliance abnormality management and control system according to claim 7, characterized in that: The data acquisition and extraction module includes a data acquisition unit and a data extraction unit; The data collection unit is used to collect historical equipment parameters of each household appliance and historical line parameters of household circuits in historical work; the data extraction unit is used to extract factory equipment parameters of each household appliance from the database; The historical equipment parameters of each household appliance include the historical equipment voltage, the historical equipment current, the historical equipment temperature and the historical equipment battery capacity; the factory equipment parameters of each household appliance include the factory equipment voltage, the factory equipment current, the factory equipment temperature and the factory equipment battery capacity; the historical line parameters of the household circuit include the historical line voltage.

9. The artificial intelligence-based household appliance abnormality management and control system according to claim 7, characterized in that: The parameter curve generation module includes a household electrical appliance parameter curve generation unit and a household circuit line parameter curve generation unit; The household appliance device parameter curve generation unit is used to generate a household appliance historical device parameter change curve; the historical device parameter change curve includes a historical device voltage change curve, a historical device current change curve, a historical device temperature change curve and a historical device battery capacity change curve; the household circuit line parameter curve generation unit is used to generate a household circuit historical line parameter curve; the historical line parameter curve includes a historical line voltage change curve.

10. The home appliance abnormality management and control system based on artificial intelligence according to claim 7, characterized in that: The household appliance state analysis module includes an abnormal state analysis unit and an abnormal degree analysis unit; The abnormal state analysis unit calculates the real-time state value of the household appliance to be analyzed based on the historical equipment parameter change curve, the historical line parameter change curve and the factory equipment parameters, and determines the state of the household appliance to be analyzed; the abnormal degree analysis unit calculates the abnormal degree score of the household appliance to be analyzed based on the real-time state value of the household appliance to be analyzed and the voltage qualified time interval in the abnormal state time interval; The abnormal prediction and control module includes an abnormal time prediction unit and a monitoring prompt unit; The abnormal time prediction unit predicts a voltage qualified time interval in the next abnormal state time interval of the household appliance based on the abnormal state data of the household appliance; The monitoring and prompting unit monitors the equipment parameters of the household appliances in real time based on the abnormal time prediction unit, and makes corresponding early warning prompts to the user.

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