A smart home data analysis and optimization system

Abstract

The present invention relates to the field of device control technology, specifically to a smart home data analysis and optimization system, the system comprising an environment perception adjustment module, a user preference analysis module, a preference matching adjustment module and a device status analysis module. The present invention can respond to environmental changes by automatically adjusting the operating parameters of home appliances, extract user preferences by comparing the user's operation records with the automatic adjustment results, accurately identify user needs, gradually adjust the device settings, and dynamically adjust according to the user's operation mode, so that each device adjustment is more in line with the user's actual needs, enhances the intelligence and personalization of device control, optimizes the comfort of the home environment, and timely discovers and adjusts faulty equipment through real-time monitoring of device status, avoiding damage or energy efficiency reduction caused by failure or abnormality of the equipment, and ensuring the stability and long-term service life of the equipment.

Description

Technical Field

[0001] The present invention relates to the field of device control technology, and in particular to a smart home data analysis and optimization system. Background Art

[0002] The field of equipment control technology mainly involves the effective management, adjustment and optimization of equipment through automation and intelligent means to improve equipment operation efficiency, save energy and enhance user experience. This field includes applications such as smart homes, industrial automation, smart buildings, and energy management. The core of equipment control technology includes sensor data acquisition, real-time data processing, control algorithm design, equipment adaptive adjustment, and remote monitoring. Using artificial intelligence, machine learning, and data analysis technology, equipment control systems can achieve efficient automatic adjustment and fault prevention, thereby improving overall system performance, reducing energy consumption, and enhancing equipment stability and reliability.

[0003] Among them, the smart home data analysis and optimization system aims to collect, analyze and optimize the data of various devices in the smart home environment, collect home environment data in real time, such as temperature and humidity, light intensity, motion information, etc., and process it through data analysis algorithms to optimize the control strategy of home devices. Its core functions include automatic adjustment of air conditioning, lighting, security and other equipment to improve energy efficiency, comfort and safety. The system can also learn user preferences and gradually adjust the control plan to achieve the best user experience and energy saving effect.

[0004] Traditional home systems ignore the dynamic combination of real-time environmental changes and user preferences, and only make adjustments based on environmental data, ignoring the user's operating behavior and preference changes in different time periods, resulting in the inability of device control to meet the actual needs of different users, affecting the home experience and energy saving effects. For example, when the user's temperature control preferences change, the traditional system fails to learn and adjust the control strategy in a timely manner, causing user dissatisfaction or waste of device energy. The traditional system relies on static threshold judgment to monitor the status of the device, and cannot achieve efficient identification and dynamic adjustment of the real-time operating status of the device, resulting in the failure to identify equipment failures in the early stage, increasing the risk of equipment damage. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a smart home data analysis and optimization system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a smart home data analysis and optimization system, the system comprising:

[0007] The environment perception adjustment module collects the temperature, humidity and light intensity of the current environment based on the smart home environment, standardizes and scales the data, and adjusts the operating parameters of the smart home devices to match the real-time environment, including adjusting the air conditioner output power, curtain opening and closing ratio and light brightness intensity, and obtains automatic adjustment parameter information;

[0008] The user preference analysis module extracts the adjustment parameters of the smart home devices by the user in different time periods based on the automatic adjustment parameter information and the user operation records of the smart home devices, analyzes the deviation between the user adjustment parameters and the automatic adjustment parameters, evaluates the deviation ratio, and identifies the user adjustment preference according to the user adjustment frequency to obtain the user preference identification result;

[0009] The preference matching adjustment module adjusts the current automatic adjustment parameters based on the user preference identification result and the automatic adjustment parameter information according to the current time period and the user preference mode, matches the user preference, and obtains the user matching parameter adjustment information;

[0010] The device status analysis module detects the operating parameters of smart home devices in real time based on the user matching parameter adjustment information, including current, voltage and device operating temperature, and compares them with the preset safety range to determine whether the device has any operating abnormalities. If the device fails, the device operating parameters are adjusted immediately, and a maintenance prompt is issued to the user to obtain a fault warning result.

[0011] The present invention is improved in that the method for performing standardization and scaling processing on data is:

[0012] Based on the smart home environment, the temperature, humidity and light intensity data of the current environment are collected through temperature sensors, humidity sensors and light sensors to obtain basic environmental data;

[0013] Based on the basic environmental data, by comparing with a preset data range, removing abnormal data values, and obtaining filtered environmental data;

[0014] Based on the filtered environmental data, the formula:

[0015]

[0016] Calculate the data after standardization and scaling to obtain uniformly processed environmental data;

[0017] in, To standardize the scaled data, is the original data, is the minimum value of the data, is the maximum value of the data, is the scaling factor.

[0018] The present invention is improved in that the step of obtaining the automatic adjustment parameter information is:

[0019] Based on the uniformly processed environmental data, ideal environmental standard values ​​of differentiated smart home devices are extracted to obtain benchmark environmental parameters;

[0020] Based on the baseline environmental parameters, compared with the current environmental parameters, combined with the operating parameters of the device in the baseline environment, the formula is:

[0021]

[0022] Calculate the automatically adjusted operating parameters of the device to obtain automatically adjusted parameter information;

[0023] in, For equipment Automatically adjusted operating parameters, For equipment The operating parameters under the benchmark environment, Yes Equipment The adjustment coefficient, , and Yes Equipment Response weight coefficients to temperature, humidity and light, The current ambient temperature and equipment The difference between the reference ambient temperature, The current ambient humidity and equipment The difference in the reference ambient humidity, For the current environment lighting and equipment The difference from the baseline ambient lighting.

[0024] The present invention is improved in that the method for evaluating the deviation ratio is:

[0025] Based on the automatic adjustment parameter information, according to the user operation record of the smart home device, extract the user's adjustment parameter record of the smart home device in the current time period, including the device parameters adjusted by the user, the corresponding device automatically adjusted operating parameters and the corresponding timestamp, to obtain the user adjustment related data;

[0026] Based on the user adjustment associated data, by the formula:

[0027]

[0028] Calculating a user adjustment deviation ratio of the device to obtain a user adjustment deviation evaluation result;

[0029] in, Yes Equipment User-adjustable parameters, For equipment The type adjustment coefficient, For equipment Automatically adjusted operating parameters, For equipment The user adjusts the deviation ratio.

[0030] The present invention is improved in that the steps of obtaining the user preference identification result are:

[0031] Based on the user adjustment deviation evaluation result, extract the user's adjustment deviation record in the current period, count the number of occurrences of each deviation, and obtain preference association data;

[0032] Based on the preference association data, by formula:

[0033]

[0034] Calculate the user preference deviation ratio of the device to obtain a user preference recognition result;

[0035] in, For equipment The proportion of user preference deviation, is the adjustment factor for the deviation ratio, is the total number of deviation proportion types, Yes Equipment No. The deviation ratio, Yes Equipment No. The number of times the deviation ratio occurs, is the total number of adjustments made by the user.

[0036] The present invention is improved in that the step of obtaining the user matching parameter adjustment information is as follows:

[0037] Based on the user preference identification result and the automatic adjustment parameter information, extract the user preference deviation ratio of the device in the current time period and the operating parameters of the device after automatic adjustment to obtain matching adjustment associated data;

[0038] The associated data is adjusted based on the matching, by the formula:

[0039]

[0040] Real-time adjustment parameters of the computing device to match user preferences and obtain user matching parameter adjustment information;

[0041] in, For equipment Real-time adjustment parameters, For equipment Automatically adjusted operating parameters, For equipment The user preference deviation ratio.

[0042] The present invention is improved in that the method for determining whether the device has an abnormal operation is:

[0043] Based on the user matching parameter adjustment information, the operating parameters of the smart home device are detected in real time, including current, voltage and device operating temperature, to obtain real-time operating data;

[0044] Based on the real-time operating data, compared with the preset safe operating range, the formula is:

[0045]

[0046] Fault deviation of computing equipment;

[0047] in, For equipment The fault deviation, For equipment The current value, For equipment The preset maximum current value, For equipment The current voltage value, For equipment The preset maximum voltage value, For equipment The current temperature value, For equipment The preset maximum temperature value, , and Equipment The weight coefficients of current, voltage and temperature;

[0048] Based on the fault deviation of the device, a comparison is made with a preset fault threshold. When the fault deviation of the device is greater than the preset fault threshold, it is determined that the device has an operation abnormality, and an equipment operation abnormality identification result is obtained.

[0049] The present invention is improved in that the steps of obtaining the fault warning result are:

[0050] Based on the equipment operation abnormality identification result and the user matching parameter adjustment information, extract the equipment fault deviation and the equipment real-time adjustment parameter to obtain abnormal adjustment related data;

[0051] Adjust the associated data based on the anomaly, by the formula:

[0052]

[0053] Calculate the operating parameters of abnormal equipment status;

[0054] in, Yes Equipment Real-time adjustment parameters, Yes Equipment The regulating factor, Yes Equipment The fault deviation, For equipment Operation parameters of abnormal status.

[0055] Based on the operating parameters of the abnormal state of the equipment, maintenance reminders are immediately issued to the user to obtain fault warning results.

[0056] Compared with the prior art, the advantages and positive effects of the present invention are:

[0057] In the present invention, by automatically adjusting the operating parameters of household appliances, it is possible to respond to environmental changes. By comparing the user's operation records with the automatic adjustment results, the user's preferences can be extracted, the user's needs can be accurately identified, and the equipment settings can be gradually adjusted to improve comfort and energy saving. The air conditioner, curtains and lighting equipment can be adjusted according to real-time environmental changes, and dynamic adjustments can be made according to the user's operation mode, so that each equipment adjustment is more in line with the user's actual needs, thereby enhancing the intelligence and personalization of equipment control, optimizing the comfort of the home environment, and timely discovering and adjusting faulty equipment through real-time monitoring of equipment status, avoiding damage or energy efficiency reduction caused by equipment failure or abnormality, and ensuring the stability and long-term service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a system flow chart of the present invention;

[0059] Figure 2 A flow chart of the present invention for performing normalization and scaling processing on data;

[0060] Figure 3 A flow chart of obtaining automatic adjustment parameter information of the present invention;

[0061] Figure 4 A flow chart for evaluating the deviation ratio for the present invention;

[0062] Figure 5 A flowchart of obtaining user preference identification results of the present invention;

[0063] Figure 6 A flow chart of obtaining user matching parameter adjustment information according to the present invention;

[0064] Figure 7 This is a flow chart of the present invention for determining whether a device has an abnormal operation;

[0065] Figure 8 The present invention is a flow chart for obtaining fault warning results. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0067] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0068] See also Figure 1 The present invention provides a technical solution: a smart home data analysis and optimization system, the system comprising:

[0069] The environment perception adjustment module collects the temperature, humidity and light intensity of the current environment based on the smart home environment, standardizes and scales the data, and adjusts the operating parameters of the smart home devices to match the real-time environment, including adjusting the air conditioner output power, curtain opening and closing ratio and light brightness intensity, and obtains automatic adjustment parameter information;

[0070] The user preference analysis module extracts the adjustment parameters of the smart home devices by the user in different time periods based on the automatic adjustment parameter information and the user operation records of the smart home devices, analyzes the deviation between the user adjustment parameters and the automatic adjustment parameters, evaluates the deviation ratio, and identifies the user adjustment preference according to the user adjustment frequency to obtain the user preference identification result;

[0071] The preference matching adjustment module adjusts the current automatic adjustment parameters based on the user preference identification result and the automatic adjustment parameter information according to the current time period and the user preference mode, matches the user preference, and obtains the user matching parameter adjustment information;

[0072] The device status analysis module adjusts the parameter information based on the user's matching information, and detects the operating parameters of smart home devices in real time, including current, voltage and device operating temperature. It compares the parameters with the preset safety range to determine whether the device has any abnormal operation. If the device fails, it adjusts the device operating parameters immediately to prevent damage to the device, and sends maintenance prompts to the user to obtain fault warning results.

[0073] The automatically adjusted parameter information includes the temperature level, humidity level and light intensity information of the current environment. The user preference recognition results include the air conditioner temperature setting value, wind speed setting value, curtain opening and closing angle and lighting brightness level. The user matching parameter adjustment information includes the optimized air conditioner temperature setting value, wind speed mode, curtain opening and closing ratio and light brightness. The fault warning results include abnormal amplitude marks of current and voltage, fluctuation frequency of operating temperature and corresponding warning information.

[0074] See also Figure 2 , the method for standardizing and scaling the data is:

[0075] Based on the smart home environment, the temperature, humidity and light intensity data of the current environment are collected through temperature sensors, humidity sensors and light sensors to obtain basic environmental data;

[0076] Based on the basic environmental data, by comparing the preset data range, removing abnormal data values, and obtaining the filtered environmental data;

[0077] Based on the filtered environmental data, the formula:

[0078]

[0079] Calculate the data after standardization and scaling to obtain uniformly processed environmental data;

[0080] in, To standardize the scaled data, is the original data, is the minimum value of the data, is the maximum value of the data, is the scaling factor.

[0081] formula:

[0082]

[0083] The meaning and acquisition method of parameters:

[0084] Raw data : Raw data represents environmental data collected by sensors or devices. For example, measurements of temperature, humidity, and light intensity.

[0085] Minimum : represents the minimum observed value of the data. When the data is standardized, the minimum value is used as the basis for scaling calculations.

[0086] Maximum : Represents the maximum observed value in the data set and is used as the basis for the denominator in scaling calculations to ensure that the data is mapped to the target interval.

[0087] Scaling factor :The scaling factor is used to adjust the range of the standardized data so that the standardized data can be mapped to the target range and is preset according to needs.

[0088] Calculation example:

[0089] Set a set of temperature data , we hope to standardize the data to the interval [0,1]. The original temperature data is ; Minimum ; Maximum value ; Set the zoom factor to .

[0090] Calculation process:

[0091] For temperature :

[0092]

[0093] For temperature :

[0094]

[0095] For temperature :

[0096]

[0097] For temperature :

[0098]

[0099] For temperature :

[0100]

[0101] After normalization, the data is mapped to the range of [0,1], and the minimum value becomes , the maximum value becomes , other temperature values ​​are standardized according to their positions relative to the maximum and minimum values. This helps to eliminate the dimensional differences between data and avoid unnecessary influence of certain features on model training.

[0102] See also Figure 3 , the steps to obtain automatic adjustment parameter information are:

[0103] Based on the uniformly processed environmental data, the ideal environmental standard values ​​of differentiated smart home devices are extracted to obtain the benchmark environmental parameters;

[0104] Based on the baseline environmental parameters, compared with the current environmental parameters, combined with the operating parameters of the device in the baseline environment, the formula is:

[0105]

[0106] Calculate the automatically adjusted operating parameters of the device to obtain automatically adjusted parameter information;

[0107] in, For equipment Automatically adjusted operating parameters, For equipment The operating parameters under the benchmark environment, Yes Equipment The adjustment coefficient, , and Yes Equipment Response weight coefficients to temperature, humidity and light, The current ambient temperature and equipment The difference between the reference ambient temperature, The current ambient humidity and equipment The difference in the reference ambient humidity, For the current environment lighting and equipment The difference from the baseline ambient lighting.

[0108] formula:

[0109]

[0110] The meaning and acquisition method of parameters:

[0111] :equipment The operating parameters under the reference environment refer to the operating status of the equipment under ideal or preset environmental conditions, which are preset by the manufacturer or design standards of the equipment and are usually the values ​​given in the equipment specification sheet.

[0112] :equipment The adjustment coefficient reflects the response strength of the device to environmental differences. The response strength of the device can be adjusted by this coefficient. The larger the coefficient, the more sensitive the device is to environmental changes. It is set according to the device type and actual usage, and is usually measured experimentally or determined by empirical values.

[0113] Environmental parameter difference , , : The current ambient temperature and equipment The difference between the reference ambient temperature, The current ambient humidity and equipment The difference in the reference ambient humidity, For the current environment lighting and equipment The difference from the baseline ambient lighting.

[0114] Weight coefficient , and : The weight coefficient indicates the degree of response of the device to differences in environmental parameters (temperature, humidity, light). Each device will respond differently to different environmental parameters according to its design characteristics and usage environment. The specific value is obtained through the device's design documents or actual testing, and reflects the sensitivity of the device to various environmental factors.

[0115] Calculation example:

[0116] Set up an air conditioner (device index ), whose benchmark operating parameters are , adjustment coefficient , and its response weights to temperature, humidity, and light are , , . Set the current environment parameters as: temperature ,humidity ,illumination ; The benchmark environmental parameters are: temperature ,humidity ,illumination ;

[0117] Calculate the difference in environmental parameters: ; ; ;

[0118] Substitute the difference into the device adjustment formula:

[0119]

[0120] Through the above calculation, the adjusted output of the equipment air conditioner under the current environmental conditions is 2079.5W. The adjustment is calculated based on the difference between the current temperature, humidity and light and the baseline environment.

[0121] See also Figure 4 , the method to evaluate the deviation ratio is:

[0122] Based on the automatic adjustment parameter information, according to the user operation record of the smart home device, extract the user's adjustment parameter record of the smart home device in the current time period, including the device parameters adjusted by the user, the corresponding device automatically adjusted operating parameters and the corresponding timestamp, to obtain the user adjustment related data;

[0123] Adjust the associated data based on the user, through the formula:

[0124]

[0125] Calculating a user adjustment deviation ratio of the device to obtain a user adjustment deviation evaluation result;

[0126] in, Yes Equipment User-adjustable parameters, For equipment The type adjustment coefficient, For equipment Automatically adjusted operating parameters, For equipment The user adjusts the deviation ratio.

[0127] formula:

[0128]

[0129] Parameter details and how to obtain them:

[0130] :equipment User-adjusted parameters are device parameters that are actively adjusted by the user during a certain period of time. They can be obtained from the operation records of smart home devices, such as manual settings made by the user in the control panel or smart device app. Usually these parameters represent the operating status of the device, such as temperature, humidity, light intensity, power, etc. The specific values ​​depend on the adjustments input by the user.

[0131] :equipment Automatically adjusted operating parameters obtained through the previous steps.

[0132] :equipment The coefficient reflects the sensitivity of different devices to deviations during the adjustment process. Some devices may respond more sensitively to environmental changes, while other devices may be less sensitive to changes. The coefficient is obtained through empirical testing.

[0133] Calculation example:

[0134] Set up analysis of air conditioning equipment (equipment ) to adjust the deviation ratio, set the following parameters: User-set temperature parameters , temperature parameters after automatic adjustment , the adjustment factor of the device type (Set the air conditioning equipment to be more sensitive to temperature changes).

[0135] Calculate the deviation: ;

[0136] Calculate the deviation ratio :

[0137]

[0138] The calculated deviation ratio is 0.1636, which means that the deviation between the user and the automatic adjustment is about 16.36%. This ratio shows that there is a certain deviation between the user's adjustment and the system's automatic adjustment, and the air conditioner is more sensitive to this deviation.

[0139] See also Figure 5 , the steps to obtain the user preference identification results are:

[0140] Based on the user adjustment deviation evaluation results, extract the user's adjustment deviation records in the current period, count the number of occurrences of each deviation, and obtain preference association data;

[0141] Based on the preference association data, through the formula:

[0142]

[0143] Calculate the user preference deviation ratio of the device to obtain a user preference recognition result;

[0144] in, For equipment The proportion of user preference deviation, is the adjustment factor for the deviation ratio, is the total number of deviation proportion types, Yes Equipment No. The deviation ratio, Yes Equipment No. The number of times the deviation ratio occurs, is the total number of adjustments made by the user.

[0145] formula:

[0146]

[0147] Parameter details and how to obtain them:

[0148] :equipment No. The deviation ratio is calculated by the previous step.

[0149] :equipment No. The number of occurrences of the deviation ratio indicates the number of times the user adjusts the deviation ratio in the record, which is obtained through statistics.

[0150] : Total number of user adjustments, indicating the total number of all records of user adjustments to the device within the statistical time range.

[0151] : The adjustment coefficient of the deviation ratio is used to adjust the impact of each deviation ratio on the end-user preference pattern, usually adjusted according to the sensitivity range of device characteristics and user behavior.

[0152] Calculation example:

[0153] The deviation ratio data of the set device is as follows: Deviation ratio , number of occurrences , deviation ratio , number of occurrences , deviation ratio , number of occurrences , the total number of user adjustments is , deviation ratio adjustment coefficient .

[0154] Calculation process:

[0155]

[0156] By calculating the user preference deviation ratio of the device The value integrates the user's adjustment behavior under different deviation types, reflects the user's preference trend for the deviation ratio in the overall adjustment, and takes into account the impact of the adjustment coefficient on the deviation.

[0157] See also Figure 6 , the steps for obtaining user matching parameter adjustment information are:

[0158] Based on the user preference recognition results and the automatic adjustment parameter information, the user preference deviation ratio of the device in the current period and the operating parameters of the device after automatic adjustment are extracted to obtain matching adjustment associated data;

[0159] Adjust the associated data based on the match, through the formula:

[0160]

[0161] Real-time adjustment parameters of the computing device to match user preferences and obtain user matching parameter adjustment information;

[0162] in, For equipment Real-time adjustment parameters, For equipment Automatically adjusted operating parameters, For equipment The user preference deviation ratio.

[0163] The meaning and acquisition method of parameters:

[0164] :equipment The automatically adjusted operating parameters are calculated in the previous steps.

[0165] :equipment The user preference deviation ratio is obtained through the previous steps.

[0166] Specific acquisition method: Through the record of user operation data, combined with the historical record of automatic adjustment, the deviation between the two is counted. For example, if the user frequently modifies the device settings, causing the actual value to deviate significantly from the automatic adjustment value, the deviation ratio value will be higher.

[0167] Paragraph 2: Calculation Example

[0168] Set the device to a lighting device , whose current automatic adjustment parameters (unit: watt), user preference deviation ratio , indicating that the user-adjusted deviation is 10%.

[0169] Calculation process:

[0170]

[0171] The calculation shows that the new parameter value of the device is 165 watts after taking into account the user's preference adjustment. This means that the original 150 watt parameter value recommended by the automatic adjustment system has increased by 10% under the influence of the user. This adjustment reflects the user's manual adjustment preference. The system corrects the automatic adjustment value through this deviation ratio to make it more in line with the user's actual needs.

[0172] See also Figure 7 , the method to determine whether the device is operating abnormally is:

[0173] Based on the user matching parameter adjustment information, the operating parameters of smart home devices are detected in real time, including current, voltage and device operating temperature, to obtain real-time operating data;

[0174] Based on the real-time operation data, compared with the preset safe operation range, the formula is:

[0175]

[0176] Fault deviation of computing equipment;

[0177] in, For equipment The fault deviation, For equipment The current value, For equipment The preset maximum current value, For equipment The current voltage value, For equipment The preset maximum voltage value, For equipment The current temperature value, For equipment The preset maximum temperature value, , and Equipment The weight coefficients of current, voltage and temperature;

[0178] The fault deviation of the equipment is compared with the preset fault threshold. When the fault deviation of the equipment is greater than the preset fault threshold, it is determined that the equipment has an operation abnormality, and an equipment operation abnormality identification result is obtained.

[0179] formula:

[0180]

[0181] :equipment The current value, usually measured by current sensors installed on the equipment. When the equipment is running in real time, each sensor continuously monitors the current changes and sends this data to the control system. This value fluctuates as the equipment load changes.

[0182] :equipment The preset maximum current value is the upper limit of the safe working current of the equipment, which is usually determined by the design standard of the equipment or given by the equipment manufacturer in the equipment manual. In the control system, this maximum value is fixed and used for comparison with the real-time current.

[0183] :equipment The current voltage value can be monitored and obtained in real time through a voltage sensor. Voltage data is usually provided by a voltage sensor connected to the power supply of the device.

[0184] :equipment The preset maximum voltage value is the rated maximum voltage of the device, which is usually provided by the device's design standards or manufacturer's documents. The system uses this maximum value as a standard value to compare with the actual voltage.

[0185] :equipment The current temperature value, as measured by a temperature sensor on the equipment. Temperature data is usually fed back into the control system, where it is updated regularly and used to compare with the set safe operating range.

[0186] :equipment The preset maximum temperature value is usually specified by the equipment manufacturer, and the control system of the equipment itself will use it as a safety reference point when working.

[0187] Weight coefficient , and :The coefficients are used to adjust the degree of influence of current, voltage and temperature deviation on the final fault judgment. Since different devices may have different sensitivities to current, voltage and temperature, these weight coefficients will be adjusted according to the characteristics of the device. For example, some devices may be more sensitive to current deviation, while others are more concerned about temperature. Therefore, the weight coefficients need to be set according to the actual working characteristics of the device.

[0188] Calculation examples and derivation process

[0189] Setting up the device , its real-time parameters and safety range are as follows: Current in amperes (real-time value), Maximum current in amperes (safe range), Voltage Volts (real-time value), Maximum voltage volts (safe range), Temperature in degrees Celsius (real-time value), Maximum temperature in degrees Celsius (safety range). The weighting factor of the device is: (Current Weight) (voltage weight) (Temperature weight).

[0190] Calculation process:

[0191] Calculate the current deviation: ;

[0192] Calculate the voltage deviation:

[0193] ;

[0194] Calculate the temperature deviation:

[0195] ;

[0196] Calculate the total degree of deviation:

[0197]

[0198] The calculation results show that the device has obvious temperature deviation, which exceeds the set safety range. If the set deviation threshold is 5, , the equipment is judged to be faulty and an alarm should be triggered or adjustments should be made to prevent equipment damage.

[0199] See also Figure 8 , the steps to obtain the fault warning results are:

[0200] Based on the equipment operation abnormality identification results and the user matching parameter adjustment information, the equipment fault deviation and the equipment real-time adjustment parameters are extracted to obtain abnormal adjustment related data;

[0201] Adjust the associated data based on anomalies, through the formula:

[0202]

[0203] Calculate the operating parameters of abnormal equipment status;

[0204] in, Yes Equipment Real-time adjustment parameters, Yes Equipment The regulating factor, Yes Equipment The fault deviation, For equipment Operation parameters of abnormal status.

[0205] Based on the operating parameters of the abnormal status of the equipment, maintenance reminders are immediately issued to users to obtain fault warning results.

[0206] formula:

[0207]

[0208] Parameter meaning and acquisition method

[0209] :equipment The real-time adjustment parameters are calculated in the previous steps.

[0210] :equipment The fault deviation is calculated by the previous step.

[0211] :equipment The adjustment factor is used to control the changes in equipment operating parameters. It is set according to the equipment's sensitivity to failures. The higher the equipment value and the more likely it is to be severely damaged, the higher the adjustment factor value.

[0212] Calculation example:

[0213] Set the following parameters: Set the fault deviation , adjust parameters in real time , the adjustment factor is set to , a parameter used to control the adjustment range, reflecting the influence of deviation on parameter adjustment.

[0214] Calculate the adjusted operating parameters :

[0215]

[0216] The adjusted operating parameters of the device are 89.75 W. This means that after a fault deviation of the device (such as an over-limit of current, voltage, or temperature) causes a deviation value, the system calculates a new operating parameter through an adjustment factor and adjusts the power of the device from 100 W to 89.75 W to avoid overloading the device and keep it within a safe range.

[0217] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A smart home data analysis and optimization system, characterized in that: The system comprises: The environment perception adjustment module collects the temperature, humidity and light intensity of the current environment based on the smart home environment, standardizes and scales the data, and adjusts the operating parameters of the smart home devices to match the real-time environment, including adjusting the air conditioner output power, curtain opening and closing ratio and light brightness intensity, and obtains automatic adjustment parameter information; The user preference analysis module extracts the adjustment parameters of the smart home devices by the user in different time periods based on the automatic adjustment parameter information and the user operation records of the smart home devices, analyzes the deviation between the user adjustment parameters and the automatic adjustment parameters, evaluates the deviation ratio, and identifies the user adjustment preference according to the user adjustment frequency to obtain the user preference identification result; The method for evaluating the deviation ratio is: Based on the automatic adjustment parameter information, according to the user operation record of the smart home device, extract the user's adjustment parameter record of the smart home device in the current time period, including the device parameters adjusted by the user, the corresponding device automatically adjusted operating parameters and the corresponding timestamp, to obtain the user adjustment related data; Based on the user adjustment associated data, by the formula: Calculate the user adjustment deviation ratio of the device to obtain a user adjustment deviation evaluation result; in, Yes Equipment User-adjustable parameters, For equipment The type adjustment coefficient, For equipment Automatically adjusted operating parameters, For equipment User-adjusted deviation ratio; The steps for obtaining the user preference identification result are: Based on the user adjustment deviation evaluation result, extract the user's adjustment deviation record in the current period, count the number of occurrences of each deviation, and obtain preference association data; Based on the preference association data, by formula: Calculate the user preference deviation ratio of the device to obtain a user preference recognition result; in, For equipment The proportion of user preference deviation, is the adjustment factor for the deviation ratio, is the total number of deviation proportion types, Yes Equipment No. The deviation ratio, Yes Equipment No. The number of times the deviation ratio occurs, is the total number of adjustments made by the user; The preference matching adjustment module adjusts the current automatic adjustment parameters based on the user preference identification result and the automatic adjustment parameter information according to the current time period and the user preference mode, matches the user preference, and obtains the user matching parameter adjustment information; The steps for obtaining the user matching parameter adjustment information are as follows: Based on the user preference identification result and the automatic adjustment parameter information, extract the user preference deviation ratio of the device in the current time period and the operating parameters of the device after automatic adjustment to obtain matching adjustment associated data; The associated data is adjusted based on the matching, by the formula: Real-time adjustment parameters of the computing device to match user preferences and obtain user matching parameter adjustment information; in, For equipment Real-time adjustment parameters, For equipment Automatically adjusted operating parameters, For equipment The proportion of user preference deviation; The device status analysis module detects the operating parameters of smart home devices in real time based on the user matching parameter adjustment information, including current, voltage and device operating temperature, and compares them with the preset safety range to determine whether the device has any operating abnormalities. If the device fails, the device operating parameters are adjusted immediately, and a maintenance prompt is issued to the user to obtain a fault warning result.

2. The smart home data analysis and optimization system according to claim 1, characterized in that: The method for performing standardization and scaling processing on data is: Based on the smart home environment, the temperature, humidity and light intensity data of the current environment are collected through temperature sensors, humidity sensors and light sensors to obtain basic environmental data; Based on the basic environmental data, by comparing with a preset data range, removing abnormal data values, and obtaining filtered environmental data; Based on the filtered environmental data, the formula: Calculate the data after standardization and scaling to obtain uniformly processed environmental data; in, To standardize the scaled data, is the original data, is the minimum value of the data, is the maximum value of the data, is the scaling factor.

3. The smart home data analysis and optimization system according to claim 2, characterized in that: The steps for obtaining the automatic adjustment parameter information are as follows: Based on the uniformly processed environmental data, ideal environmental standard values ​​of differentiated smart home devices are extracted to obtain benchmark environmental parameters; Based on the baseline environmental parameters, compared with the current environmental parameters, combined with the operating parameters of the device in the baseline environment, the formula is: Calculate the automatically adjusted operating parameters of the device to obtain automatically adjusted parameter information; in, For equipment Automatically adjusted operating parameters, For equipment The operating parameters under the benchmark environment, Yes Equipment The adjustment coefficient, , and Yes Equipment Response weight coefficients to temperature, humidity and light, The current ambient temperature and equipment The difference between the reference ambient temperature, The current ambient humidity and equipment The difference in the reference ambient humidity, For the current environment lighting and equipment The difference from the baseline ambient lighting.

4. The smart home data analysis and optimization system according to claim 1, characterized in that: The method for determining whether the device has an abnormal operation is as follows: Based on the user matching parameter adjustment information, the operating parameters of the smart home device are detected in real time, including current, voltage and device operating temperature, to obtain real-time operating data; Based on the real-time operating data, compared with the preset safe operating range, the formula is: Fault deviation of computing equipment; in, For equipment The fault deviation, For equipment The current value, For equipment The preset maximum current value, For equipment The current voltage value, For equipment The preset maximum voltage value, For equipment The current temperature value, For equipment The preset maximum temperature value, , and Equipment The weight coefficients of current, voltage and temperature; Based on the fault deviation of the device, a comparison is made with a preset fault threshold. When the fault deviation of the device is greater than the preset fault threshold, it is determined that the device has an operation abnormality, and an equipment operation abnormality identification result is obtained.

5. The smart home data analysis and optimization system according to claim 4, characterized in that: The steps for obtaining the fault warning result are: Based on the equipment operation abnormality identification result and the user matching parameter adjustment information, extract the equipment fault deviation and the equipment real-time adjustment parameter to obtain abnormal adjustment related data; Adjust the associated data based on the anomaly, by the formula: Calculate the operating parameters of abnormal equipment status; in, Yes Equipment Real-time adjustment parameters, Yes Equipment The regulating factor, Yes Equipment The fault deviation, For equipment Operation parameters of abnormal status; Based on the operating parameters of the abnormal state of the equipment, maintenance reminders are immediately issued to the user to obtain fault warning results.

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