Ambient light control system based on WiFi and Bluetooth dual protocols

Abstract

The invention discloses an atmosphere lamp control system based on WiFi and Bluetooth dual protocols, and relates to the technical field of lamp control. A monitoring module obtains an abnormal summary database matching the application scenario through a crawler network based on the atmosphere lamp application scenario. During the operation of the atmosphere lamp, multi-source data related to the abnormal summary database in the application scenario is monitored in real time. A set division module substitutes the multi-source data into a regression analysis model for comprehensive analysis to obtain the abnormal coefficient of the application scenario. The application scenario is divided into different sets according to the comparison result of the abnormal coefficient and the gradient threshold. The control module generates a corresponding control strategy based on fuzzy reasoning according to the set division result of the application scenario, and controls the operation of the atmosphere lamp according to the control strategy. The control system can automatically control the operation of the atmosphere lamp to warn the user according to the abnormal situation changes in different scenarios, thereby increasing the practicality and functionality of the atmosphere lamp.

Description

Technical Field

[0001] The present invention relates to the technical field of lamp control, and in particular to an atmosphere lamp control system based on WiFi and Bluetooth dual protocols. Background Art

[0002] Ambient lighting refers to lamps that can create a specific environmental atmosphere by adjusting color, brightness, and dynamic effects. Initially, such lamps were mainly used for decoration and stage effects. With the advancement of technology and the popularization of smart homes, ambient lighting has gradually entered people's daily lives.

[0003] The existing ambient light control system usually controls the color and brightness of the ambient light according to the user's request. In the actual application process, we found that when the ambient light is used in different scenes, it should also be able to control the ambient light to make corresponding operation prompts in combination with the abnormal conditions of different scenes, so as to increase the practicality and functionality of the ambient light;

[0004] Based on this, the present invention proposes an atmosphere light control system based on WiFi and Bluetooth dual protocols, which can automatically control the operation of the atmosphere light to alert the user according to abnormal changes in different scenes, thereby increasing the practicality and functionality of the atmosphere light. Summary of the invention

[0005] The purpose of the present invention is to provide an atmosphere light control system based on WiFi and Bluetooth dual protocols to solve the shortcomings of the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an atmosphere light control system based on WiFi and Bluetooth dual protocols, including a data acquisition module, a monitoring module, a set division module, and a control module;

[0007] Data acquisition module: After the ambient light is installed, the operator inputs the current application scenario of the ambient light, and the user uses the mobile terminal to send the demand instructions based on WiFi or Bluetooth protocol. The demand instructions include the color of the ambient light and the total brightness of the ambient light, and the operation of the ambient light is controlled according to the demand;

[0008] Monitoring module: Based on the application scenario of the atmosphere light, the abnormal summary database matching the application scenario is obtained through the crawler network. During the operation of the atmosphere light, multi-source data related to the abnormal summary database in the application scenario is monitored in real time;

[0009] Set division module: Substitute multi-source data into the regression analysis model for comprehensive analysis to obtain the abnormal coefficient of the application scenario, and divide the application scenario into different sets based on the comparison result between the abnormal coefficient and the gradient threshold;

[0010] Control module: Generate corresponding control strategies based on fuzzy reasoning according to the set classification results of application scenarios, and control the operation of the atmosphere light according to the control strategy.

[0011] In a preferred embodiment, after the set partitioning module obtains the real-time multi-source data of the application scenario, the multi-source data is set to a set x, and each multi-source data in the set x is represented as {x1, x2, ..., x n}, n is the number of multi-source data in the set x, and the abnormal coefficient is calculated by the regression analysis model. The exponential expression is:

[0012]

[0013] Where A_coefficient is the abnormal coefficient, {x1, x2, ..., x n} are variables, {w1, w2, ..., w n} is the regression coefficient of each variable, and the value of each regression coefficient is greater than 0.

[0014] In a preferred embodiment, the set partitioning module generates an abnormality coefficient by comprehensively analyzing multi-source data in the application scenario through a regression analysis model, the gradient threshold includes a first abnormality threshold and a second abnormality threshold, and the first abnormality threshold is less than the second abnormality threshold, the first abnormality threshold is used to determine whether the application scenario has an abnormality, and the second abnormality threshold is used to determine the severity of the abnormality of the application scenario;

[0015] If the abnormality coefficient of the application scenario is less than or equal to the first abnormality threshold, it is judged that there is no abnormality in the application scenario and it is included in the non-abnormality set. If the abnormality coefficient is greater than the first abnormality threshold and the abnormality coefficient is less than or equal to the second abnormality threshold, it is judged that there is a slight abnormality in the application scenario and it is included in the slight abnormality set. If the abnormality coefficient is greater than the second abnormality threshold, it is judged that there is a serious abnormality in the application scenario and it is included in the serious abnormality set.

[0016] In a preferred embodiment, after receiving the set division results of the application scenarios, the control module defines the set division results of the application scenarios as input variables, defines the control strategy as output variables, performs fuzzy reasoning according to fuzzy rules, and outputs the corresponding control strategy.

[0017] In a preferred embodiment, the application scenario acquired by the monitoring module is a home. In the home application scenario, an anomaly summary database matching the home is obtained through a crawler network. The anomaly summary database includes crawling data from power companies to monitor home electricity usage. Anomalies in electricity usage include power overload and short circuit. Home security data is crawled through security cameras and door and window sensor devices. Anomaly security data includes intrusion and fire. The monitoring module monitors multi-source data of the home based on the anomaly summary database. The multi-source data includes power overload deviation, current rise rate, length of stay of unknown persons, and duration of flames.

[0018] In a preferred embodiment, the application scenario acquired by the monitoring module is a car. In the car application scenario, an abnormality summary database matching the car is acquired through a crawler network to acquire car diagnostic data, the diagnosis including data of engine failure and brake system abnormality; vehicle driving data is acquired, the driving abnormality data includes whether the vehicle speed, steering, and braking operation are abnormal; in-car environment data is acquired, the environmental abnormality data includes whether the air-conditioning system and odor sensor equipment are abnormal; driver behavior data is acquired, the behavior abnormality data includes whether the driving behavior is abnormal; the monitoring module monitors multi-source data of the car based on the abnormality summary database, the multi-source data includes engine noise, brake fluid deviation, speeding deviation, in-car temperature deviation, and fatigue driving time.

[0019] In a preferred embodiment, the monitoring module monitors multi-source data of the car based on the abnormal summary database, and the multi-source data includes engine noise, brake fluid deviation, speeding deviation, in-car temperature deviation and fatigue driving time. The engine noise, brake fluid deviation, speeding deviation, in-car temperature deviation and fatigue driving time are set as the x set, and the abnormal coefficient of the car scene is calculated by the regression analysis model. The exponential expression is:

[0020]

[0021] Where A_coefficient is the abnormal coefficient, {x1, x2, x3, x4, x5} are engine noise, brake fluid deviation, speeding deviation, interior temperature deviation and fatigue driving time, respectively, {w1, w2, w3, w4, w5} are the regression coefficients of engine noise, brake fluid deviation, speeding deviation, interior temperature deviation and fatigue driving time, respectively, and {w1, w2, w3, w4, w5} are all greater than 0.

[0022] The ambient light control method based on WiFi and Bluetooth dual protocols includes the following steps:

[0023] The user uses the mobile terminal to send demand instructions to the control system based on WiFi or Bluetooth protocol. The demand instructions include the color of the ambient light and the total brightness of the ambient light, and the operation of the ambient light is controlled according to the demand;

[0024] Based on the application scenario of the atmosphere light, the control system obtains the abnormal summary database matching the application scenario through the crawler network. During the operation of the atmosphere light, the multi-source data related to the abnormal summary database in the application scenario is monitored in real time;

[0025] After comprehensive analysis of multi-source data in the regression analysis model, the abnormal coefficient of the application scenario is obtained. The application scenarios are divided into different sets based on the comparison results of the abnormal coefficient and the gradient threshold. The corresponding control strategy is generated based on fuzzy reasoning according to the set classification results of the application scenarios. The control system controls the operation of the atmosphere light according to the control strategy.

[0026] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0027] 1. The present invention uses a monitoring module to obtain an abnormal summary database that matches the application scenario through a crawler network based on the atmosphere light application scenario. During the operation of the atmosphere light, the multi-source data related to the abnormal summary database in the application scenario is monitored in real time. The set division module substitutes the multi-source data into the regression analysis model for comprehensive analysis to obtain the abnormal coefficient of the application scenario. The application scenario is divided into different sets based on the comparison result of the abnormal coefficient and the gradient threshold. The control module generates a corresponding control strategy based on fuzzy reasoning based on the set division result of the application scenario, and controls the operation of the atmosphere light according to the control strategy. The control system can automatically control the operation of the atmosphere light to alert the user according to the abnormal situation changes in different scenes, thereby increasing the practicality and functionality of the atmosphere light. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0029] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Example 1: Please refer to Figure 1 As shown, the atmosphere light control system based on WiFi and Bluetooth dual protocols described in this embodiment includes a data acquisition module, a monitoring module, a set division module, and a control module;

[0032] Data acquisition module: After the ambient light is installed, the operator inputs the current application scenario of the ambient light. The user uses the mobile terminal to send the demand instruction based on WiFi or Bluetooth protocol. The demand instruction includes the color of the ambient light and the total brightness of the ambient light. The ambient light is controlled according to the demand, and the application scenario information is sent to the monitoring module;

[0033] Monitoring module: Based on the application scenario of the atmosphere light, the abnormal summary database matching the application scenario is obtained through the crawler network. During the operation of the atmosphere light, the multi-source data related to the abnormal summary database in the application scenario is monitored in real time, and the multi-source data is sent to the set division module;

[0034] Set division module: Substitute multi-source data into the regression analysis model for comprehensive analysis to obtain the abnormal coefficient of the application scenario, and divide the application scenario into different sets based on the comparison result between the abnormal coefficient and the gradient threshold. The set division result is sent to the control module;

[0035] Control module: Generate corresponding control strategies based on fuzzy reasoning according to the set classification results of application scenarios, and control the operation of the atmosphere light according to the control strategy.

[0036] This application uses a monitoring module to obtain an abnormal summary database that matches the application scenario through a crawler network based on the atmosphere light application scenario. During the operation of the atmosphere light, the multi-source data related to the abnormal summary database in the application scenario is monitored in real time. The set division module substitutes the multi-source data into the regression analysis model for comprehensive analysis to obtain the abnormal coefficient of the application scenario. The application scenario is divided into different sets based on the comparison result of the abnormal coefficient and the gradient threshold. The control module generates a corresponding control strategy based on fuzzy reasoning based on the set division result of the application scenario, and controls the operation of the atmosphere light according to the control strategy. The control system can automatically control the operation of the atmosphere light to alert the user according to the abnormal situation changes in different scenarios, thereby increasing the practicality and functionality of the atmosphere light.

[0037] The specific working process of the control system is as follows:

[0038] After the atmosphere light is installed, the operator inputs the current application scenario of the atmosphere light into the control system. The user uses the mobile terminal to send demand instructions to the control system based on WiFi or Bluetooth protocol. The demand instructions include the color of the atmosphere light and the total brightness of the atmosphere light. The operation of the atmosphere light is controlled according to the demand. The control system obtains the abnormal summary database matching the application scenario through the crawler network based on the atmosphere light application scenario. During the operation of the atmosphere light, the multi-source data related to the abnormal summary database in the application scenario is monitored in real time, and the multi-source data is substituted into the regression analysis model for comprehensive analysis to obtain the abnormal coefficient of the application scenario. The application scenario is divided into different sets according to the comparison result of the abnormal coefficient and the gradient threshold, and the corresponding control strategy is generated based on fuzzy reasoning according to the set classification result of the application scenario. The control system controls the operation of the atmosphere light according to the control strategy.

[0039] Embodiment 2: After the atmosphere light is installed, the operator inputs the current application scenario of the atmosphere light into the data acquisition module. The user uses the mobile terminal to send a demand instruction based on WiFi or Bluetooth protocol. The demand instruction includes the color of the atmosphere light and the total brightness of the atmosphere light. The atmosphere light is controlled according to the demand, and the application scenario information is sent to the monitoring module.

[0040] The data acquisition module receives the demand instruction from the mobile terminal, extracts the color information in the instruction, such as RGB value or preset color name, converts the color information into the format required for ambient light control, extracts the total brightness information in the instruction, usually expressed as a percentage or a specific value, converts the brightness information into the format required for light control, applies the parsed color information to the ambient light, so that its luminous color meets the instruction requirements, adjusts the total brightness of the ambient light according to the parsed brightness information, and controls the driving current or PWM signal of the light to achieve brightness adjustment;

[0041] In this application, the atmosphere lights controlled by the control system may be used in the following application scenarios, including but not limited to homes, stages, cars, hotel rooms, shopping malls, cafes / restaurants, offices, exhibition halls / museums, gyms / yoga studios, cinemas, etc.

[0042] Embodiment 3: The monitoring module obtains an abnormal summary database matching the application scenario through a crawler network based on the atmosphere light application scenario. During the operation of the atmosphere light, the multi-source data related to the abnormal summary database in the application scenario is monitored in real time, and the multi-source data is sent to the set partitioning module;

[0043] This embodiment takes two application scenarios, home and car, as examples:

[0044] The monitoring module acquires application scenarios for families. In the family application scenario, the crawler network is used to obtain an abnormal summary database that matches the family. The abnormal summary database includes crawling data from the power company to monitor the family's electricity consumption. The abnormal electricity consumption includes power overload and short circuit. The family security data is crawled through security cameras and door and window sensor devices. The security abnormality data includes intrusion and fire.

[0045] Among them, the monitoring module monitors the multi-source data of the household based on the abnormal summary database. The multi-source data includes power overload deviation, current rise rate, length of stay of unknown persons and duration of flame.

[0046] The application scenario acquired by the monitoring module is automobile. In the automobile application scenario, the abnormal summary database matching the automobile is obtained through the crawler network, and the automobile diagnostic data is obtained. The diagnosis includes data engine failure and brake system abnormality. The vehicle driving data is obtained. The driving abnormality data includes whether the speed, steering, and brake operation are abnormal. The in-vehicle environment data is obtained. The environmental abnormality data includes whether the air conditioning system and odor sensor equipment are abnormal. The driver behavior data is obtained. The behavior abnormality data includes whether the driving behavior is abnormal.

[0047] Among them, the monitoring module monitors the multi-source data of the car based on the abnormal summary database. The multi-source data includes engine noise, brake fluid deviation, speeding deviation, in-vehicle temperature deviation and fatigue driving time.

[0048] Embodiment 4: The set division module substitutes the multi-source data into the regression analysis model for comprehensive analysis to obtain the abnormal coefficient of the application scenario, and divides the application scenario into different sets according to the comparison result between the abnormal coefficient and the gradient threshold;

[0049] After the set partitioning module obtains the real-time multi-source data of the application scenario, it sets the multi-source data to the x set. Each multi-source data in the set x is represented as {x1, x2, ..., x n}, n is the number of multi-source data in the set x, and the abnormal coefficient is calculated by the regression analysis model. The exponential expression is:

[0050]

[0051] Where A_coefficient is the abnormal coefficient, {x1, x2, ..., x n} is a variable (the number of multi-source data), {w1, w2, ..., w n} is the regression coefficient of each variable, and the value of each regression coefficient is greater than 0;

[0052] The logical factors of the abnormality coefficient when used in the present invention are as follows: taking the impact of multi-source data on energy consumption as an example, the first is the indicator, that is, the factor that causes the application scenario to be abnormal (the present invention refers to the impact of multi-source data on the safety of the application scenario); the second is the weight of these indicators, that is, the proportion of each type of multi-source data when it is generated; the third is the operation equation, that is, what kind of mathematical operation process is used to obtain the result, and the abnormality coefficient is obtained by calculating the indicators with their respective weights through the operation equation.

[0053] The multi-source data obtained from the sample were transformed and processed into a data language recognized by computer software; secondly, these evaluation factors were analyzed by Logistic regression using SPSS software to screen out factors and their weights that were significantly correlated with the results; thirdly, the evaluation factors and weights were brought into the Logistic regression equation for calculation to obtain the results, which are as follows:

[0054] First, ensure the integrity of multi-source data, handle missing values ​​and outliers, and convert the data into a format that SPSS software can recognize. Usually, the data is stored in csv, xlsx and other formats, and then imported into SPSS. Open SPSS software, import the processed data file, and transform the variables as needed. For example, for continuous variables, standardize or normalize them. Select the "Analyze" menu, then select the "Binary Logistic" option under "Regression". In the dialog box, add the dependent variable (outcome) and the independent variable (multi-source data) to the corresponding boxes. SPSS will fit the Logisti according to the selected variables. c regression analysis model. In the output results, you will see the coefficients, standard errors, p-values ​​and other information of the model. Check the coefficients and p-values ​​in the output results to determine which variables have a significant correlation with the results. Usually, a p-value less than 0.05 is considered significant. While fitting the model, use variable selection methods, such as stepwise regression, to help screen the most relevant factors. According to the coefficients of the Logistic regression analysis model, the size of the coefficients reflects the degree of influence of each factor on the result. After obtaining the significant factors and their coefficients, the Logistic regression equation is obtained. This equation is used to calculate the probability of each sample and then predict the results.

[0055] The set division module divides the application scenarios into different sets based on the comparison results between the anomaly coefficient and the gradient threshold;

[0056] After the abnormal coefficient is generated by comprehensively analyzing the multi-source data in the application scenario through the regression analysis model, the larger the abnormal coefficient is, the more abnormal the application scenario is. The gradient threshold includes a first abnormal threshold and a second abnormal threshold, and the first abnormal threshold is less than the second abnormal threshold. The first abnormal threshold is used to determine whether the application scenario is abnormal, and the second abnormal threshold is used to determine the severity of the abnormality of the application scenario;

[0057] If the abnormality coefficient of the application scenario is less than or equal to the first abnormality threshold, it is judged that there is no abnormality in the application scenario and it is included in the non-abnormality set. If the abnormality coefficient is greater than the first abnormality threshold and the abnormality coefficient is less than or equal to the second abnormality threshold, it is judged that there is a slight abnormality in the application scenario and it is included in the slight abnormality set. If the abnormality coefficient is greater than the second abnormality threshold, it is judged that there is a serious abnormality in the application scenario and it is included in the serious abnormality set.

[0058] The control module generates corresponding control strategies based on fuzzy reasoning according to the set classification results of application scenarios, and controls the operation of the atmosphere lights according to the control strategies;

[0059] After receiving the set division result of the application scenario, the control module defines the set differentiation result of the application scenario as an input variable, and performs the following definition: the application scenario divided into the non-abnormal set is defined as No abnormality, the application scenario divided into the mild abnormal set is defined as Mild abnormality, and the application scenario divided into the severe abnormal set is defined as Serious abnormality;

[0060] Define the control strategies as output variables with the following definitions: “Unregulated ambient lighting”, “The ambient light slowly flashes and emits yellow light”, “The ambient light flashes rapidly and emits red light”;

[0061] Mark the set differentiation result of the application scenario as M and the control strategy as K, then we can define the following fuzzy rules:

[0062] Rule 1:IF(M is No abnormality)THEN(K is Unregulated ambientlighting);

[0063] Rule 2:IF(M is Mild abnormality)THEN(K is The ambient light slowly flashes and emits yellow light);

[0064] Rule 3:IF(M is Serious abnormality)THEN(K is The ambient lightflashes rapidly and emits red light);

[0065] Perform fuzzy reasoning based on fuzzy rules and output the corresponding control strategy.

[0066] Embodiment 5: The monitoring module monitors the multi-source data of the family based on the abnormal summary database. The multi-source data includes power overload deviation, current rise rate, unknown person stay time and flame duration. The power overload deviation, current rise rate, unknown person stay time and flame duration are set as x set. The abnormal coefficient of the family scene is calculated by the regression analysis model. The exponential expression is:

[0067]

[0068] Where A_coeffictient is the abnormal coefficient, {x1, x2, x3, x4} are the power overload deviation, current rise rate, unknown personnel stay time and flame duration, respectively, {w1, w2, w3, w4} are the regression coefficients of power overload deviation, current rise rate, unknown personnel stay time and flame duration, respectively, and {w1, w2, w3, w4} are all greater than 0;

[0069] The logic for obtaining power overload deviation is: use smart meters and current sensors to collect real-time current data, compare actual power consumption with rated power consumption, and calculate overload deviation. The larger the power overload deviation, the more likely it is to have the following effects:

[0070] Damage to electrical equipment: Overload current can cause electrical equipment to overheat and become damaged, potentially leading to equipment failure or even fire;

[0071] Electrical fires: Long-term electrical overloads may cause the insulation of wires to age and become damaged, leading to electrical fires;

[0072] Circuit breaker tripping: In order to protect the circuit, the household circuit breaker will trip under overload conditions, causing power outages;

[0073] Electrical safety hazards: Continuous power overload will cause electrical safety hazards and increase the risks of household electricity use.

[0074] Current rise rate: The current sensor installed on the main circuit or equipment power line can detect the real-time current and collect current data within a certain time interval. The current difference is obtained by subtracting the current value at the previous time point from the current value at the next time point. The current rise rate is obtained by dividing the current difference by the monitoring time. The greater the current rise rate, the following effects may be caused:

[0075] Equipment startup shock: If the current rise rate is too high, it may be due to excessive shock current when the equipment is started, which may easily damage the electrical equipment;

[0076] Short Circuit Fault: An abnormally high rate of current rise may be a sign of a short circuit, requiring immediate action to prevent further damage;

[0077] Unstable power supply: Rapid current changes can cause unstable power supply and affect the normal operation of other electrical equipment in the home;

[0078] Grid load fluctuations: Rapid current changes will cause load fluctuations in the home power grid, which may affect the overall power safety.

[0079] Duration of stay of unknown persons: The camera at the front door of the home is used to record whether there are unknown persons at the door and the duration of stay of unknown persons. The longer the unknown persons stay, the following problems may exist:

[0080] Security risks: Strangers staying in the home for too long may pose a security risk and increase the risk of theft, intrusion, etc.

[0081] Privacy breach: Unknown people staying for a long time may threaten the privacy of family members;

[0082] Unusual activity: If there is no confirmation from a family member, a person who stays for a long time may be engaging in unusual or inappropriate activities and needs to be addressed promptly.

[0083] Flame duration: The flame sensor installed in the home area monitors whether there is a flame in the area. When a flame appears, the flame duration is recorded. The longer the flame duration is, the more likely the following problems are:

[0084] Fire spread: The longer the flame lasts, the greater the risk of fire spreading, which may lead to more serious property damage and casualties;

[0085] Spread of toxic smoke: Continuous fire will produce a large amount of toxic smoke, endangering the lives of family members;

[0086] Damage to building structures: Prolonged fire can cause serious damage to building structures and even cause the building to collapse;

[0087] Delayed emergency response: The longer the fire lasts, the more serious the consequences will be if the emergency response is delayed, increasing the difficulty of extinguishing the fire.

[0088] Embodiment 6: The monitoring module monitors the multi-source data of the car based on the abnormal summary database. The multi-source data includes engine noise, brake fluid deviation, speeding deviation, in-car temperature deviation and fatigue driving time. The engine noise, brake fluid deviation, speeding deviation, in-car temperature deviation and fatigue driving time are set as the x set. The abnormal coefficient of the car scene is calculated by the regression analysis model. The exponential expression is:

[0089]

[0090] Where A_coefficient is the abnormal coefficient, {x1, x2, x3, x4, x5} are the regression coefficients of engine noise, brake fluid deviation, overspeed deviation, in-vehicle temperature deviation and fatigue driving time, respectively; {w1, w2, w3, w4, w5} are the regression coefficients of engine noise, brake fluid deviation, overspeed deviation, in-vehicle temperature deviation and fatigue driving time, respectively; and {w1, w2, w3, w4, w5} are all greater than 0;

[0091] Engine noise: The noise sensor installed in the engine compartment can collect the sound data of the engine in real time. The louder the engine noise, the more abnormal the following conditions may be:

[0092] Engine malfunction: Unusual noises may indicate internal engine malfunctions, such as worn cylinders, pistons, or bearings;

[0093] Exhaust system problems: Exhaust system failures or leaks may cause increased noise;

[0094] Transmission system problems: Wear or failure of gears, bearings and other components in the transmission system may cause abnormal noise;

[0095] Increased vehicle vibrations: Increased noise levels can lead to reduced driving comfort and may be accompanied by increased vibrations.

[0096] Brake fluid deviation: The brake fluid level can be monitored in real time by installing a level sensor in the brake fluid tank. The greater the brake fluid deviation, the following abnormal conditions may exist:

[0097] Brake failure: Low brake fluid level or insufficient pressure may cause brake failure, increasing the risk of an accident;

[0098] Reduced braking performance: Brake fluid quality problems or insufficient fluid level can lead to reduced braking performance and longer braking distance;

[0099] System Leaks: Brake fluid deviation may indicate a brake system leak that requires prompt repair.

[0100] Speeding deviation: The vehicle's speed and location data are acquired in real time through the vehicle's GPS module. The larger the speeding deviation, the more abnormal the situation may be:

[0101] Increased risk of traffic accidents: Speeding greatly increases the risk of traffic accidents, especially in complex road conditions or bad weather conditions;

[0102] Fines and Demerit Points: Speeding can result in traffic fines and demerit points on your driver's license.

[0103] Difficulty in vehicle control: High speed deviation will make the vehicle more difficult to control, making driving more difficult;

[0104] Increased fuel consumption and wear: Speeding will increase vehicle fuel consumption and wear of mechanical parts, shortening the life of the vehicle.

[0105] Temperature deviation in the car: The temperature in the car is monitored in real time by temperature sensors installed in different locations in the car. The greater the temperature deviation in the car, the following abnormal conditions may exist:

[0106] Driver and passenger discomfort: Excessive high or low temperatures in the car can cause discomfort to the driver and passengers, affecting the driver's concentration and reaction speed;

[0107] Health issues: prolonged exposure to extreme temperatures may have adverse effects on occupant health, such as heat stroke or hypothermia;

[0108] Electronic equipment failure: Excessively high temperatures inside the vehicle may cause electronic equipment to overheat and fail, affecting the normal operation of the vehicle's electronic systems.

[0109] Fatigue driving duration: The camera and infrared sensor installed in the car monitor the driver's facial expression and eye state in real time. The longer the fatigue driving duration, the more likely the following abnormal conditions are:

[0110] Delayed reaction time: Fatigue driving can cause delayed driver reaction time, increasing the risk of traffic accidents;

[0111] Impaired judgment: Driving for long periods of time can cause the driver's judgment to decline, increasing the possibility of misoperation;

[0112] Inattention: Fatigue can cause drivers to lose focus and easily ignore changes in road conditions and traffic signs.

[0113] In summary:

[0114] Louder engine noise: Can cause engine failure, exhaust system problems, driveline issues, and increased vehicle vibrations.

[0115] The greater the deviation of brake fluid: it may cause brake failure, reduced braking performance and system leakage.

[0116] The greater the speeding deviation: it may lead to an increased risk of traffic accidents, fines and penalty points, difficulty in vehicle control, and increased fuel consumption and wear.

[0117] Larger temperature deviations in the car: May cause discomfort for the driver and passengers, health problems and malfunctions of electronic devices.

[0118] The longer you drive while fatigued, the more likely it is to lead to delayed reaction time, decreased judgment, lack of concentration, and physical health problems.

[0119] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0120] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0121] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0122] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0123] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The atmosphere light control system based on WiFi and Bluetooth dual protocols is characterized by: It includes data acquisition module, monitoring module, set division module and control module; Data acquisition module: After the ambient light is installed, the operator inputs the current application scenario of the ambient light, and the user uses the mobile terminal to send demand instructions based on WiFi or Bluetooth protocol. The demand instructions include the color of the ambient light and the total brightness of the ambient light, and the operation of the ambient light is controlled according to the demand; Monitoring module: Based on the application scenario of the ambient light, the abnormal summary database matching the application scenario is obtained through the crawler network. The application scenarios include home scenarios and car scenarios. During the operation of the ambient light, multi-source data related to the abnormal summary database in the application scenario is monitored in real time. The multi-source data includes engine noise, brake fluid deviation, speeding deviation, in-vehicle temperature deviation, and fatigue driving time; Set partitioning module: Substitute multi-source data into the regression analysis model for comprehensive analysis to obtain the abnormal coefficient of the application scenario. The expression is: , where is the abnormal coefficient, They are engine noise, brake fluid deviation, speeding deviation, in-vehicle temperature deviation and fatigue driving time. are the regression coefficients of engine noise, brake fluid deviation, speeding deviation, interior temperature deviation and fatigue driving time, respectively, and The values ​​are all greater than 0, and the application scenarios are divided into different sets based on the comparison results between the anomaly coefficient and the gradient threshold; Control module: Generate corresponding control strategies based on fuzzy reasoning according to the set classification results of application scenarios, and control the operation of the atmosphere light according to the control strategy.

2. The atmosphere light control system based on WiFi and Bluetooth dual protocols according to claim 1 is characterized in that: The set partitioning module generates an abnormality coefficient by comprehensively analyzing multi-source data in the application scenario through a regression analysis model, wherein the gradient threshold includes a first abnormality threshold and a second abnormality threshold, and the first abnormality threshold is less than the second abnormality threshold, the first abnormality threshold is used to determine whether the application scenario has an abnormality, and the second abnormality threshold is used to determine the severity of the abnormality of the application scenario; If the abnormality coefficient of the application scenario is less than or equal to the first abnormality threshold, it is judged that there is no abnormality in the application scenario and it is included in the non-abnormality set. If the abnormality coefficient is greater than the first abnormality threshold and the abnormality coefficient is less than or equal to the second abnormality threshold, it is judged that there is a slight abnormality in the application scenario and it is included in the slight abnormality set. If the abnormality coefficient is greater than the second abnormality threshold, it is judged that there is a serious abnormality in the application scenario and it is included in the serious abnormality set.

3. The atmosphere light control system based on WiFi and Bluetooth dual protocols according to claim 2 is characterized in that: After receiving the set division result of the application scenario, the control module defines the set division result of the application scenario as an input variable, defines the control strategy as an output variable, performs fuzzy reasoning according to fuzzy rules, and outputs the corresponding control strategy.

4. The atmosphere light control system based on WiFi and Bluetooth dual protocols according to claim 3 is characterized in that: The application scenario acquired by the monitoring module is home. In the home application scenario, an abnormal summary database matching the home is obtained through the crawler network. The abnormal summary database includes crawling data from the power company to monitor the home's electricity consumption. The abnormal electricity consumption includes power overload and short circuit. The home security data is crawled through security cameras and door and window sensor devices. The security abnormality data includes intrusion and fire. The monitoring module monitors the multi-source data of the home based on the abnormal summary database. The multi-source data includes power overload deviation, current rise rate, length of stay of unknown persons, and duration of flame.

5. The atmosphere light control system based on WiFi and Bluetooth dual protocols according to claim 4 is characterized in that: The application scenario acquired by the monitoring module is automobile. In the automobile application scenario, an abnormal summary database matching the automobile is obtained through a crawler network, and automobile diagnostic data is obtained. The diagnosis includes data on engine failure and brake system abnormality. Vehicle driving data is obtained. The driving abnormality data includes whether the vehicle speed, steering, and brake operation are abnormal. The in-vehicle environment data is obtained. The environmental abnormality data includes whether the air-conditioning system and odor sensor equipment are abnormal. The driver behavior data is obtained. The behavior abnormality data includes whether the driving behavior is abnormal.

6. The atmosphere light control system based on WiFi and Bluetooth dual protocols according to claim 5 is characterized in that: The monitoring module monitors the multi-source data of the car according to the abnormal summary database, and the multi-source data includes engine noise, brake fluid deviation, overspeed deviation, vehicle temperature deviation and fatigue driving time, and sets the engine noise, brake fluid deviation, overspeed deviation, vehicle temperature deviation and fatigue driving time as gather.

7. The atmosphere light control method based on WiFi and Bluetooth dual protocols is implemented by the control system according to any one of claims 1 to 6, characterized in that: The control method comprises the following steps: The user uses the mobile terminal to send demand instructions to the control system based on WiFi or Bluetooth protocol. The demand instructions include the color of the ambient light and the total brightness of the ambient light, and the operation of the ambient light is controlled according to the demand; Based on the application scenario of the atmosphere light, the control system obtains the abnormal summary database matching the application scenario through the crawler network. During the operation of the atmosphere light, the control system monitors the multi-source data related to the abnormal summary database in the application scenario in real time. After comprehensive analysis of multi-source data in the regression analysis model, the abnormal coefficient of the application scenario is obtained. The application scenarios are divided into different sets based on the comparison results of the abnormal coefficient and the gradient threshold. The corresponding control strategy is generated based on fuzzy reasoning according to the set classification results of the application scenarios. The control system controls the operation of the atmosphere light according to the control strategy.

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