Intelligent household electrical appliance interaction control system based on embedded software

Abstract

The invention discloses an intelligent household electrical appliance interaction control system based on embedded software. A system operation process specifically comprises the following steps: acquiring a user input signal; analyzing the user input signal based on an embedded software architecture to generate a standardized control instruction; establishing communication connection with a plurality of intelligent household electrical appliances through an Internet of Things protocol, and obtaining real-time environment data and real-time operation states of the intelligent household electrical appliances; performing conjoint analysis on the user historical behavior data and the user input signal; adjusting operation parameters of the intelligent household electrical appliance; and returning interaction response information to the user through at least one mode of a voice feedback module, a touch interface, a gesture recognition interface or a mobile phone APP. The method has the following advantages and effects: multi-mode instructions can be adaptively fused, the equipment capability and the user intention can be dynamically matched, and cross-equipment and cross-protocol efficient and accurate control is realized through standardized instruction generation, real-time load sensing and priority dynamic calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of household appliances, and in particular to an intelligent household appliance interactive control system based on embedded software. Background Art

[0002] With the rapid development of the Internet of Things (IoT), the interaction methods for smart home appliances are becoming increasingly diverse. Users can control home appliances through various channels, including voice, gestures, touch screens, and mobile apps. However, existing systems have fundamental flaws in the deep integration of multimodal commands and dynamic device adaptation, resulting in inefficient control command generation and execution, which seriously restricts the user experience.

[0003] The flaw in the deep integration of multimodal commands is that the current system lacks a unified command parsing mechanism when receiving diverse user inputs. The signal formats generated by different input methods vary greatly, making it difficult to standardize control commands. This is especially true in scenarios where cross-brand, cross-protocol devices coexist, as the system cannot efficiently match unique device identifiers. For example, when a user controls a device through fuzzy descriptions, traditional methods rely on precise device name matching. If device registration information is incomplete or location identifiers conflict, command failure or erroneous operation are likely to occur. Furthermore, existing parsing logic fails to consider the real-time load of the device and historical user behavior, making it difficult to dynamically optimize command execution priorities. This can lead to response delays or increased energy consumption in high-load devices.

[0004] The flaw of dynamic device adaptation is that when multiple devices execute instructions concurrently, the fixed priority configuration cannot cope with dynamic load changes, which may cause critical tasks to be blocked by low-priority instructions; in addition, there is a lack of closed-loop verification after parameter adjustment.

[0005] Therefore, there is an urgent need for an interactive control system that can adaptively integrate multimodal instructions, dynamically match device capabilities with user intentions, and achieve efficient and precise control across devices and protocols through standardized instruction generation, real-time load perception, and dynamic priority calculation. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent household appliance interactive control system based on embedded software to solve the problems raised in the background technology.

[0007] The above technical objectives of the present invention are achieved through the following technical solutions:

[0008] The intelligent home appliance interactive control system based on embedded software, the system operation process specifically includes the following steps:

[0009] S100: Acquire a user input signal, wherein the user input signal includes at least one of a voice command, a touch operation, a gesture action, or a mobile phone APP control command;

[0010] S200, parsing the user input signal based on the embedded software architecture to generate standardized control instructions;

[0011] S300, establishing communication connections with multiple smart home appliances through the Internet of Things protocol, and obtaining real-time environmental data and real-time operating status of the smart home appliances;

[0012] S400: Jointly analyzing user historical behavior data and user input signals; including:

[0013] S410, performing cluster analysis on user historical operation data using a neural network model to identify user preference patterns and device usage habits;

[0014] S420, combining real-time environmental data with equipment operating status to generate a control strategy;

[0015] S500, adjusting the operating parameters of the smart home appliance;

[0016] S600: Return interactive response information to the user through at least one of a voice feedback module, a touch interface, a gesture recognition interface, or a mobile phone APP.

[0017] By adopting the above technical solutions, the interaction dimension between users and smart home appliances is significantly expanded by supporting multiple input methods such as voice commands, touch operations, gestures and mobile phone APP control commands. In addition, the integrated design of multimodal input can cover the needs of different user groups and enhance the universality of the system. The original input signal is parsed and standardized control commands are generated through the embedded software architecture, which solves the problem of incompatible command formats between smart home appliances of different brands or protocols. The standardized command format serves as an intermediate layer abstraction, shielding the heterogeneity of the underlying devices, enabling the system to uniformly manage multiple devices and reduce integration complexity. The system obtains real-time data through the Internet of Things protocol. Real-time environmental data and equipment operating status provide a data basis for the dynamic adjustment of control strategies; this data-driven decision-making mechanism improves the response efficiency and reliability of the system; cluster analysis of user historical behavior data can explore user preference patterns and generate personalized equipment control strategies based on this, which can adjust equipment parameters in advance, reduce the number of user manual interventions, and improve the level of intelligence; in addition, the system dynamically adjusts equipment parameters based on priority algorithms and energy consumption constraint models to ensure that key tasks are executed first while optimizing energy utilization efficiency; in addition, through the feedback module, users can obtain operation results or abnormal alarms in real time, forming an interactive closed loop and enhancing users' trust in the system.

[0018] Further configuration is that the S200 specifically includes the following steps:

[0019] S210, receiving the original input signal of the voice command, touch operation, gesture action or mobile phone APP control command, and storing the original input signal in a temporary buffer area according to type classification;

[0020] S220, calling a corresponding parsing module according to the type of the original input signal to generate an intermediate instruction;

[0021] S230: Mapping the intermediate instruction to a preset standardized control instruction format to generate a standardized control instruction; specifically, the process includes the following sub-steps:

[0022] S230.1. Extract the operation type, device description information, and expected target parameter value from the intermediate instruction, where the operation type includes a switch instruction, a parameter adjustment instruction, or a mode switching instruction, and the device description information includes a device name, location identifier, or function category;

[0023] S230.2. Match the device description information in the device registry:

[0024] If the device name is exactly the same as the device identifier in the device registry, the corresponding device unique code is directly extracted;

[0025] If the device description information is a location identifier, the identifiers of all devices under the location are retrieved from the device registry, and the target devices are filtered according to the functional category;

[0026] If the match fails, the fuzzy search algorithm is called to rematch based on the similarity of device functions;

[0027] S230.3. Select a standardized control instruction template based on the operation type:

[0028] If it is a switch instruction, the basic control template is called, and the basic control template includes a device identifier and an operation type; if it is a parameter adjustment instruction, the numerical template is called, and the numerical template includes a filled device identifier, a target parameter value, and an adjustment step; if it is a mode switching instruction, the mode template is called, and the mode template includes a filled device identifier and a preset mode code;

[0029] S230.4. Filling in the fields in the basic control template, numerical template, or pattern template includes:

[0030] Fill the device unique code matched from the device registry in S230.2 into the device identifier field of the corresponding standardized control instruction template; if the device description information is a location identifier, select the device code with the highest priority under the location based on the functional category;

[0031] If the basic control template is called, fill in the Boolean status parameter according to the on / off status of the intermediate instruction; if the numerical template is called, fill in the corresponding field with the target parameter value parsed from the intermediate instruction; if the mode template is called, match the preset mode code according to the mode name of the intermediate instruction and fill it in; if the numerical template or mode template is called, fill in the corresponding field with the expected target parameter value obtained by parsing the intermediate instruction as the reference value for device adjustment;

[0032] The priority value is calculated using a priority algorithm based on the device's real-time load rate and the user's historical operation frequency;

[0033] S230.5. Perform syntax check on the filled corresponding standardized control instruction template;

[0034] S230.6. Confirm the basic control template, numerical template, or pattern template that has passed verification as a standardized control instruction.

[0035] By adopting the above technical solution, by classifying the original input signals by type and storing them in a temporary buffer area, the system can process instructions from different input sources in parallel, avoiding parsing conflicts caused by the mixing of multimodal signals; calling a dedicated parsing module according to the input type to generate intermediate instructions, realizing the conversion of original signals to intermediate semantics; the intermediate instruction layer provides a unified semantic expression for different input sources, simplifying the standardized instruction generation process; accurately matching device description information through the device registry to ensure that control instructions are accurately associated with the target device. This mechanism effectively avoids misoperation caused by device aliases or ambiguous descriptions; performing syntax checking on the generated standardized instructions can prevent device abnormalities due to parsing errors or out-of-bounds values, thereby avoiding device damage or safety hazards.

[0036] It is further provided that the process of calculating the priority value by the priority algorithm includes:

[0037] If the real-time load rate of the device is greater than or equal to the first threshold, the priority value is set to the lowest level;

[0038] If the real-time load rate is less than the second threshold and the user's historical operation frequency exceeds the third threshold, the priority value is set to the highest level;

[0039] If the real-time load rate is less than the second threshold and the user's historical operation frequency does not exceed the third threshold, calculating the priority value;

[0040] If the priority value triggers the lowest level, a load alarm prompt will be sent to the user terminal, and a delayed execution or device switching solution will be recommended in the mobile APP.

[0041] By adopting the above technical solution, the order of instruction execution is dynamically adjusted through a priority algorithm to ensure that instructions for high-load devices are downgraded, while instructions for high-frequency operation devices are given priority execution; this mechanism balances the problem of device resource competition and avoids the overall system performance degradation due to local overload; the user's historical operation frequency is introduced into the priority calculation, so that instructions for high-frequency operation devices receive a higher execution weight; for example, the humidifier instruction that the user turns on at a scheduled time every day will be processed first, while devices that are occasionally used will be scheduled according to the default priority. This design fits user habits and improves the perception of response speed.

[0042] Further configuration is that the S410 is specifically:

[0043] Perform data preprocessing, including:

[0044] Extract historical operation data from the user behavior database, including operation timestamp, device identifier, operation type, and operation parameters;

[0045] Perform normalization processing to convert the operation timestamp into a time period label, and linearly scale the operation parameters to the [0, 1] interval according to the device's allowable range;

[0046] User behavior grouping based on the K-means clustering algorithm, including:

[0047] Combining the pre-processed historical operation data into a user operation sequence according to the device identifier and the time period label, wherein the user operation sequence is composed of operation records within a continuous time window;

[0048] Determine the optimal number of clusters by using the elbow rule to generate user behavior cluster groups, each of which is associated with a specific device usage habit;

[0049] Build a user-device association matrix, including:

[0050] Based on the user behavior clustering groups, the operation frequency and parameter distribution of the devices in each group are counted to generate a user preference pattern matrix. The user preference pattern matrix includes the parameter distribution range P of the target device. range ; Store the user preference pattern matrix in the user behavior analysis module.

[0051] By adopting the above technical solution, by converting operation timestamps into time period labels and scaling parameter values to the interval [0,1], the impact of dimensional differences in different device parameters on clustering results is eliminated, allowing accurate identification of user preference combinations. Based on the elbow rule to determine the optimal number of clusters, the system can automatically discover potential behavior patterns without pre-setting user group classifications, helping the system preload device parameter configurations for different scenarios. The generated user preference pattern matrix provides data support for the control strategy. For example, if the matrix shows that users often set the air conditioning temperature to 25-26℃, the system can set this interval as the default adjustment range, reducing the number of manual inputs by users.

[0052] Further configuration is that the S420 is specifically:

[0053] Extract the real-time environmental data and the real-time operating status of the smart home appliance obtained in S300, and integrate the parameter distribution range P of the target device in the user preference pattern matrix range , integrated to include the ambient temperature E temp 、Ambient humidity E humidity , Equipment load rate D load and the task queue length Q length The environmental state vector S t ;

[0054] Generate control strategies based on rule engines, including;

[0055] If the operating parameters of the current standardized control instruction are within the parameter distribution range P range In S200, the standardized control instruction generated in S200 is directly called and added to the execution queue;

[0056] If the operating parameters deviate from the parameter distribution range P range However, if it is less than the preset threshold, a gradual adjustment instruction is generated, and according to S230.4, the basic control template, the numerical template or the pattern template is refilled to generate a new standardized control instruction;

[0057] If the operating parameters deviate from the parameter distribution range P range If the preset threshold is exceeded, the user confirmation process is triggered, and a prompt message is pushed through voice or APP. After the user confirms, standardized control instructions are generated according to the confirmation parameters;

[0058] If the equipment load rate D load If the load rate is greater than or equal to the device load rate threshold, non-emergency commands will be suspended and only security commands will be executed.

[0059] By adopting the above technical solution, a complete environmental state vector is formed by integrating ambient temperature, humidity, device load rate, task queue length, and user preference parameter distribution. This enables control decisions to be no longer based on a single user command, but rather a comprehensive assessment of the real-time environment and user habits, achieving dynamic adaptive control. This significantly improves the system's intelligence and adaptability to complex scenarios. Incorporating the user preference matrix into the environmental state vector ensures that control commands are intelligently adjusted within a parameter range that matches user habits. Even without explicit user instructions, the system can make appropriate inferences based on preferences, resulting in a more natural device response that aligns with user expectations. If operating parameters deviate significantly from the user's preferred range, the system implements a gradual adjustment or triggers a user confirmation process to effectively prevent misoperation, ensuring device safety and user experience. This flexible response mechanism strikes a balance between intelligent automation and user control. When device load exceeds the specified limit, non-urgent control commands are prioritized, retaining only security tasks, avoiding system overload, extending device life, and improving overall energy efficiency. By standardizing the environmental state vector, the system can simultaneously coordinate the responses of multiple devices, improving the consistency and comfort of the overall home environment, and possessing good scalability and integration.

[0060] Further configuration is that the S500 specifically includes the following steps:

[0061] S510, sorting the devices according to their priorities, inserting the control instructions of the high-priority devices into the front of the execution queue;

[0062] S520: Calculate the device operating energy consumption threshold based on the energy consumption constraint model. If the adjusted parameter exceeds the threshold, trigger an alarm and recommend an energy-saving alternative.

[0063] S530: After the equipment parameters are adjusted, the equipment feedback data is collected in real time through sensors to verify the adjustment effect. If the adjustment effect is not as expected, the control strategy is regenerated.

[0064] By adopting the above technical solution, a priority sorting mechanism places high-priority device instructions such as security and environmental control at the front of the execution queue, ensuring that critical tasks are completed first, effectively improving the timeliness and reliability of the overall system response. Combined with an energy consumption constraint model, the adjusted energy consumption forecast value of the device is calculated in real time. If it exceeds the threshold, an active alarm is issued and an energy-saving solution is promoted, significantly improving the energy efficiency management level of the smart home appliance system and meeting the needs of green and low-carbon development. Sensors collect the actual operating parameters of the adjusted device and compare and verify them with the target values to achieve precise adjustment and anomaly identification, forming a complete self-feedback closed loop, greatly improving the system control accuracy and reliability. If the adjustment effect is not ideal, the system can adaptively regenerate the control strategy rather than simply reporting an error and interrupting, demonstrating the system's robustness and continuous optimization capabilities, reducing the need for manual intervention, and improving the user experience. Through real-time energy consumption monitoring and prediction mechanisms, the system not only supports total energy consumption control, but also dynamically adjusts the operating mode according to the time-of-use electricity price strategy, saving energy expenses for users while reducing peak pressure on the power grid, with significant benefits.

[0065] Further configuration is that the S510 is specifically:

[0066] Extracting the device identifier and operation type from the standardized control instruction and matching them with the device priority configuration table stored in the device registry; wherein the priority configuration table is predefined based on the device function category, with security devices taking priority over environmental control devices, which in turn take priority over entertainment devices;

[0067] If multiple devices have the same priority, they are sorted again based on the priority value calculated in S230.4, with the device with the larger value being ranked higher.

[0068] According to the sorting results, the control instructions of the high-priority devices are dynamically inserted into the front position of the execution queue, and the task execution order is updated through the queue management module.

[0069] By adopting the above technical solution, a preset priority configuration table based on the device function category can enable the orderly allocation of execution resources when multiple devices operate collaboratively, avoiding response delays or system freezes caused by resource competition; when multiple devices have the same priority, a dynamic priority value calculated based on user frequency and load conditions is further introduced to flexibly adjust the sorting to ensure that the most urgent and important device control needs are responded to in different usage scenarios; through dynamic command queuing and queue management, important command delays caused by traditional FIFO queues are avoided, especially when multiple tasks are concurrent in a home environment, the system response is smoother and more efficient; the priority value can change dynamically according to the operating habits of different users, and the system can learn and adapt to the different preferences of family members, reflecting a highly personalized intelligent experience.

[0070] Further configuration is that the S520 is specifically:

[0071] Extract the rated power, historical average energy consumption, and current operating mode parameters of the target device from the device registry to build an energy consumption prediction function;

[0072] Calculate the real-time energy consumption threshold allowed by the device based on the total energy consumption limit and time-of-use electricity price strategy set by the user;

[0073] Substitute the expected target parameter value recorded in the standardized control instruction into the energy consumption prediction function. If the predicted energy consumption value exceeds the real-time energy consumption threshold, an energy consumption alarm signal is triggered;

[0074] Retrieve energy-saving alternatives based on device functional similarity, including:

[0075] Filter low-power device identifiers of similar functions in the device registry;

[0076] Or generate parameter adjustment suggestions, reduce the expected target parameter value by a preset ratio and recalculate the energy consumption value;

[0077] The energy-saving alternative solution is pushed through a mobile phone APP or voice module, and the user is waited for confirmation of the instruction.

[0078] By adopting the above technical solution, before the device is executed, the energy consumption value is predicted based on parameter adjustment. If it exceeds the user-set threshold, an early alarm will be issued, effectively avoiding overload operation or unreasonable energy waste, and ensuring the safe and stable operation of the system; the system can not only adjust parameters within the device to reduce energy consumption, but also recommend low-power devices with similar functions, providing users with diversified and personalized energy-saving options; energy-saving suggestions are pushed through APP or voice, not only allowing users to understand the energy consumption of the device, but also encouraging users to actively participate in energy consumption management, thereby improving the green sustainability of the overall home smart energy use.

[0079] Further configuration is that the S530 is specifically:

[0080] Obtain real-time operating parameters from sensor nodes of target devices through IoT protocols;

[0081] Compare the real-time operating parameters with the expected target parameter values stored in the standardized control instructions:

[0082] If the deviation between the two is less than or equal to the set percentage, the adjustment is confirmed to be valid and recorded in the equipment operation log;

[0083] If the deviation between the two is greater than the set percentage, an abnormal flag is triggered and the deviation type is extracted;

[0084] Call the adaptive correction algorithm according to the deviation type:

[0085] If it is a parameter adjustment deviation, the target parameter value is recalculated according to the gradient descent method and a new standardized control instruction is generated;

[0086] If the device response is delayed, the retry interval of the instruction in the execution queue is extended;

[0087] Add the modified standardized control instructions to the execution queue and perform the following operations:

[0088] Check whether the current number of retries exceeds the preset maximum retry threshold; if it does not exceed the threshold, repeat the process of S530; if it exceeds the threshold, trigger the abnormal termination process, which includes sending an adjustment failure alarm message to the user terminal and recommending manual intervention.

[0089] By adopting the above technical solution, the adjusted device status and target parameters are compared in real time to ensure the actual effect of each control instruction, avoid misadjustment or failure, and improve the consistency and reliability of system control; the correction algorithm is called according to the deviation type classification, and targeted adaptive adjustments are made instead of simple retries or interruptions, which greatly enhances the system's self-healing ability and problem-handling efficiency; a maximum retry limit mechanism is designed to avoid the waste of resources caused by infinite retries, and ensure that the control task is completed to the greatest extent possible within a reasonable number of times, taking into account both efficiency and stability; when the anomaly cannot be corrected automatically, alarms and manual intervention suggestions are pushed to the user in a timely manner to ensure the user's ultimate control over the system operation, thereby improving the transparency and trust of the system; finally, based on the adaptive correction mechanism, anomaly processing data can be continuously accumulated to train a more efficient self-learning model.

[0090] In summary, the present invention has the following beneficial effects:

[0091] It can adaptively integrate multi-modal instructions, dynamically match device capabilities with user intentions, and achieve efficient and precise control across devices and protocols through standardized instruction generation, real-time load perception, and dynamic priority calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a schematic diagram of the overall process of the embodiment;

[0093] Figure 2 Schematic diagram of the process of S200 in the embodiment;

[0094] Figure 3 Schematic diagram of the process of S410 in the embodiment;

[0095] Figure 4 Schematic diagram of the process of S500 in the embodiment. DETAILED DESCRIPTION

[0096] The present invention will be further described in detail below with reference to the accompanying drawings.

[0097] As attached Figure 1 As shown;

[0098] This embodiment discloses an intelligent home appliance interactive control system based on embedded software. The system operation process specifically includes the following steps:

[0099] S100, obtaining a user input signal, wherein the user input signal includes at least one of a voice command, a touch operation, a gesture action, or a mobile phone APP control command;

[0100] S200, analyzing user input signals based on the embedded software architecture and generating standardized control instructions;

[0101] Obtaining user input signals includes:

[0102] The voice recognition module receives voice commands, performs noise reduction and semantic analysis on the voice commands, and extracts key control keywords;

[0103] The touch sensor acquires the coordinates and pressure data of the user's touch operation, and matches the corresponding control intention according to the preset touch pattern library;

[0104] Capture user gestures through a camera, extract and classify gesture trajectories using image recognition algorithms, and generate gesture control instructions.

[0105] Receive remote control commands through the mobile phone APP and encrypt and verify the commands to ensure communication security.

[0106] S300: Establishing communication connections with multiple smart home appliances through the Internet of Things protocol and obtaining real-time environmental data and the real-time operating status of the smart home appliances; specifically, dynamically selecting one of the Zigbee, Wi-Fi, or Bluetooth protocols as the primary communication channel based on the device type;

[0107] S400: Jointly analyzing user historical behavior data and user input signals; including:

[0108] S410, performing cluster analysis on user historical operation data using a neural network model to identify user preference patterns and device usage habits;

[0109] S420, combining real-time environmental data with equipment operating status to generate a control strategy;

[0110] S500, adjusting the operating parameters of the smart home appliance;

[0111] S600: Return interactive response information to the user through at least one of a voice feedback module, a touch interface, a gesture recognition interface, or a mobile phone APP.

[0112] Example 1

[0113] In S100, the user sends a "turn on the living room air conditioner" command through the mobile phone APP, and the system receives and verifies the legitimacy of the command through HTTPS encryption;

[0114] The voice module receives the voice command "Raise the temperature to 25°C" and extracts the keywords "temperature" and "25°C" after noise reduction processing;

[0115] The touch screen detects the user's sliding operation (coordinate range X: 100-200, Y: 50-150) and matches the preset "wind speed adjustment" mode;

[0116] The camera captures the user's waving motion and classifies it as a "turn off device" gesture through image recognition.

[0117] In S200, the speech analysis module maps "25°C" to a numerical intermediate instruction, including the operation type as parameter adjustment, the device as air conditioning, and the target value as 25;

[0118] The touch analysis module converts the sliding coordinates into the intermediate instruction of "wind speed + 2 gear";

[0119] The protocol parsing module decrypts the APP command and generates an intermediate command, which includes the operation type as switch, the device as air conditioner, and the status as on.

[0120] In S300, the system selects Wi-Fi as the main communication protocol for the air conditioner, and obtains its real-time temperature of 23°C and load rate of 60%. It connects to the temperature and humidity sensor through the Zigbee protocol and collects the environmental data of temperature of 24°C and humidity of 50%.

[0121] In S400, the neural network clustering found that the user is accustomed to setting the air conditioner to 26℃ at night, and generated a preference pattern matrix, in which the parameter distribution range P range The rule engine determines that the current instruction 25℃ is within the parameter distribution range P range Directly generate control strategies.

[0122] In S500 and S600, the system sends standardized control instructions to the air conditioner, including the device code AC_001 and the target temperature of 25°C. After the air conditioner is adjusted, the feedback information "the temperature has been set to 25°C" is pushed through the APP.

[0123] As attached Figure 2 As shown;

[0124] S200 specifically includes the following steps:

[0125] S210, receiving original input signals of voice commands, touch operations, gestures, or mobile phone APP control commands, and storing the original input signals in a temporary buffer area according to the classification of types;

[0126] S220, calling a corresponding parsing module according to the type of the original input signal to generate an intermediate instruction;

[0127] Specifically: if the input signal is a voice command, the voice analysis module is called to perform semantic segmentation and keyword extraction on the voice command to generate an intermediate command;

[0128] If the input signal is a touch operation, the touch analysis module is called to perform pattern matching on the touch coordinates and pressure data, and mapped into intermediate instructions according to the preset touch pattern library;

[0129] If the input signal is a gesture, the gesture parsing module is called to track the trajectory of the continuous image frames captured by the camera and classify them into intermediate instructions through the convolutional neural network;

[0130] If the input signal is a mobile phone APP control command, the protocol parsing module is called to decrypt the encrypted command and convert it into an intermediate command;

[0131] S230: Mapping the intermediate instruction to a preset standardized control instruction format to generate a standardized control instruction; specifically, the process includes the following sub-steps:

[0132] S230.1. Extract the operation type, device description information, and expected target parameter value from the intermediate instruction, where the operation type includes a switch instruction, a parameter adjustment instruction, or a mode switching instruction, and the device description information includes the device name, location identifier, or function category;

[0133] S230.2. Match the device description information in the device registry:

[0134] If the device name is exactly the same as the device identifier in the device registry, the corresponding device unique code is directly extracted;

[0135] If the device description information is a location identifier, the identifiers of all devices under the location are retrieved from the device registry, and the target devices are filtered according to the functional category;

[0136] If the match fails, the fuzzy search algorithm is called to rematch based on the similarity of device functions;

[0137] S230.3. Select a standardized control instruction template based on the operation type:

[0138] If it is a switch instruction, the basic control template is called, which includes the device identifier and operation type; if it is a parameter adjustment instruction, the numerical template is called, which includes the filled device identifier, target parameter value and adjustment step; if it is a mode switching instruction, the mode template is called, which includes the filled device identifier and preset mode code;

[0139] S230.4. Fill in the fields in the basic control template, numerical template, or pattern template, including:

[0140] Fill the device unique code matched from the device registry in S230.2 into the device identifier field of the corresponding standardized control instruction template; if the device description information is a location identifier, select the device code with the highest priority under the location based on the functional category;

[0141] If the basic control template is called, fill in the Boolean status parameter according to the on / off status of the intermediate instruction; if the numerical template is called, fill in the corresponding field with the target parameter value parsed from the intermediate instruction; if the mode template is called, match the preset mode code according to the mode name of the intermediate instruction and fill it in; if the numerical template or mode template is called, fill in the corresponding field with the expected target parameter value obtained by parsing the intermediate instruction as the reference value for device adjustment;

[0142] The priority value is calculated using a priority algorithm based on the device's real-time load rate and the user's historical operation frequency;

[0143] S230.5. Perform syntax check on the corresponding standardized control instruction template after filling in; including:

[0144] Check whether the device identifier is a valid code bound in the device registry;

[0145] Verify whether the target parameter value is within the threshold range allowed by the device;

[0146] S230.6. Confirm the basic control template, numerical template, or pattern template that has passed verification as a standardized control instruction.

[0147] Example 2

[0148] In S210 and S220, the voice command "turn off the bedroom light" is stored in the buffer area, and the voice analysis module is called to segment the keywords "turn off" and "bedroom light";

[0149] In S230 , the intermediate instruction “bedroom light” matches the code “Light_02” in the device registration table.

[0150] Select the basic control template and fill in the device tag "Light_02" and the status parameter "Off".

[0151] When verifying the template, it is found that the device code is valid and the status parameters are legal, and a standardized control instruction is generated.

[0152] If the user describes "lamp in the living room" but there is no such name in the registry, a fuzzy search is called to match devices with the location "living room" and the function "lighting" (such as ceiling lamps and wall lamps), and the ceiling lamp is selected according to priority.

[0153] Specifically, the process of calculating the priority value by the priority algorithm includes:

[0154] If the real-time load rate of the device is greater than or equal to the first threshold, the priority value is set to the lowest level;

[0155] If the real-time load rate is less than the second threshold and the user's historical operation frequency exceeds the third threshold, the priority value is set to the highest level;

[0156] If the real-time load rate is less than the second threshold and the user's historical operation frequency does not exceed the third threshold, the priority value is calculated according to the following formula:

[0157] P=(f user / f max ×W1)+(W2-l real / C)

[0158] Among them, P is the priority value, f user is the user's historical operation frequency, f max is the preset maximum allowed frequency, l real is the real-time load rate of the device, W1, W1, and C are configurable weight coefficients and normalization constants;

[0159] If the priority value triggers the lowest level, a load alarm prompt will be sent to the user terminal, and a delayed execution or device switching solution will be recommended in the mobile APP.

[0160] Example 3

[0161] Scenario A

[0162] The system detects that the load rate of the home theater system with the device ID CHANG HT_001 exceeds the first threshold of 80%, and sets its priority to the lowest level.

[0163] The load rate of the sweeping robot with device ID Cleaner_02 is 20%, which is less than the second threshold of 50%. The user's historical operation frequency is 2 times / day, which is less than the third threshold of 5 times / day. According to the formula, the priority value P = 0.44, where W1 = 0.6, W2 = 0.4, and C = 100; its priority value is lower than the air conditioner's priority value P = 0.07.

[0164] The system adds the sweeping robot command to the low-priority queue and pushes it to the APP: "A high load on the home theater is detected, and the sweeping task will be delayed for 30 minutes. Do you want to force it to start immediately?"

[0165] The technical effect is to avoid overloading high-load equipment while providing flexible user intervention options.

[0166] Scenario B

[0167] The smoke sensor triggers an alarm. Its device ID is Smoke_01. At the same time, the user sends the command "Close all curtains" through the app.

[0168] The adjustment process is as follows: the security equipment priority is configured to the highest level, and the environmental control type is configured to level 3; the system immediately interrupts the currently executing curtain closing instruction and gives priority to the smoke alarm; the execution queue is forcibly inserted into the security instruction: "Open all windows for ventilation and close the gas valve."

[0169] If the curtain motor is in a half-open state due to a command interruption, the system records the interruption position and automatically resumes operation after the security task is completed.

[0170] As attached Figure 3 As shown;

[0171] S410 is specifically:

[0172] Perform data preprocessing, including:

[0173] Extract historical operation data from the user behavior database, including operation timestamp, device identifier, operation type, and operation parameters;

[0174] Perform normalization processing to convert the operation timestamp into a time period label, and linearly scale the operation parameters to the [0, 1] interval according to the device's allowable range;

[0175] User behavior grouping based on the K-means clustering algorithm, including:

[0176] The pre-processed historical operation data is combined into a user operation sequence according to the device identifier and time period label. The user operation sequence consists of operation records within a continuous time window.

[0177] The optimal number of clusters is determined by the elbow rule to generate user behavior cluster groups, each of which is associated with specific device usage habits;

[0178] Build a user-device association matrix, including:

[0179] Based on user behavior clustering groups, the operation frequency and parameter distribution of the devices in each group are counted to generate a user preference pattern matrix. The user preference pattern matrix contains the parameter distribution range P of the target device.range ; Store the user preference pattern matrix in the user behavior analysis module.

[0180] Example 4

[0181] Historical operation data (such as “18:30, air conditioning, set temperature 26°C”) is converted into the time period label “evening” and the normalization parameter 0.7.

[0182] K-means divides user operations into three categories: environmental adjustment after get off work on weekdays, high-frequency use of entertainment equipment on weekends, and automatic activation of security equipment at night.

[0183] Generate a user-device matrix and calculate the distribution range P of the air conditioner parameters at night. range 24-28℃.

[0184] Specifically, S420 is:

[0185] Extract the real-time environmental data and real-time operating status of smart home appliances obtained in S300, and integrate the parameter distribution range P of the target device in the user preference pattern matrix range , integrated into the encoding of the environment state vector S t =[E temp ,E humidity ,D load ,Q length, P range ]; among them, E temp is the ambient temperature, E humidity is the ambient humidity, D load is the equipment load factor, Q length is the length of the task queue;

[0186] Generate control strategies based on rule engines, including;

[0187] If the operating parameters of the current standardized control instruction are within the parameter distribution range P range In S200, the standardized control instruction generated in S200 is directly called and added to the execution queue;

[0188] If the operating parameters deviate from the parameter distribution range P range But if it is less than 10% of the preset threshold, a gradual adjustment instruction is generated, and according to S230.4, the basic control template, numerical template or pattern template is refilled to generate a new standardized control instruction;

[0189] If the operating parameters deviate from the parameter distribution range P range If the threshold exceeds 10%, the user confirmation process is triggered, prompting information is pushed through voice or APP, and after the user confirms, standardized control instructions are generated according to the confirmation parameters;

[0190] If the equipment load rate Dload If the load rate is greater than or equal to the device load rate threshold, non-emergency commands will be suspended and only security commands will be executed.

[0191] Example 5

[0192] Scenario A

[0193] Environmental state vector S t =[24℃,50%,60%,3,24-28℃];

[0194] The user instruction "set the air conditioner to 22℃" deviates from the parameter distribution range P range If the threshold exceeds 10%, the APP confirmation prompt will be triggered.

[0195] If the user confirms, a standardization instruction is generated; if the user refuses, a gradual adjustment to 24°C is recommended.

[0196] Scenario B

[0197] Environmental state vector S t =[28℃,65%,45%,2,26-30℃];

[0198] The user command is detected as "turn on the living room device";

[0199] Matches "living room devices" include: air conditioners with device ID AC_001, dehumidifiers with device ID Dehum_01, and fans with device ID Fan_03;

[0200] The target humidity of the dehumidifier is 55%, and the parameter distribution range is P range Automatic correction; the initial setting of the air conditioner is air supply mode, and after the humidity drops to 60%, it switches to cooling mode with a temperature of 28℃.

[0201] If the dehumidifier fails, the system will automatically increase the fan speed to the highest level to assist ventilation and push a maintenance notification.

[0202] As attached Figure 4 As shown;

[0203] S500 specifically includes the following steps:

[0204] S510, sorting the devices according to their priorities, inserting the control instructions of the high-priority devices into the front of the execution queue;

[0205] S520: Calculate the device operating energy consumption threshold based on the energy consumption constraint model. If the adjusted parameter exceeds the threshold, trigger an alarm and recommend an energy-saving alternative.

[0206] S530: After the equipment parameters are adjusted, the equipment feedback data is collected in real time through sensors to verify the adjustment effect. If the adjustment effect is not as expected, the control strategy is regenerated.

[0207] Specifically, S510 is:

[0208] Extracting device identifiers and operation types from standardized control instructions and matching them with the device priority configuration table stored in the device registry; the priority configuration table is predefined based on device functional categories, with security devices taking priority over environmental control devices, which in turn take priority over entertainment devices;

[0209] If multiple devices have the same priority, they are sorted again based on the priority value calculated in S230.4, with the device with the larger value being ranked higher.

[0210] According to the sorting results, the control instructions of the high-priority devices are dynamically inserted into the front position of the execution queue, and the task execution order is updated through the queue management module.

[0211] Specifically, S520 is:

[0212] Extract the rated power, historical average energy consumption and current operation mode parameters of the target device from the device registry and construct the energy consumption prediction function E=f(P rated ,t,M), where P rated is the rated power, t is the expected operating time, and M is the operating mode code;

[0213] Calculate the real-time energy consumption threshold allowed by the device based on the total energy consumption limit and time-of-use electricity price strategy set by the user;

[0214] Substitute the expected target parameter value recorded in the standardized control instruction into the energy consumption prediction function. If the predicted energy consumption value exceeds the real-time energy consumption threshold, an energy consumption alarm signal is triggered;

[0215] Retrieve energy-saving alternatives based on device functional similarity, including:

[0216] Filter low-power device identifiers of similar functions in the device registry;

[0217] Or generate parameter adjustment suggestions, reduce the expected target parameter value by a preset ratio and recalculate the energy consumption value;

[0218] Push energy-saving alternatives through mobile phone APP or voice module and wait for user confirmation instructions.

[0219] Specifically, S530 is:

[0220] Obtain real-time operating parameters from the sensor nodes of the target device through the Internet of Things protocol; the real-time operating parameters include power, temperature, speed or status flags;

[0221] Compare the real-time operating parameters with the expected target parameter values stored in the standardized control instructions:

[0222] If the deviation between the two is less than or equal to the set percentage, the adjustment is confirmed to be valid and recorded in the equipment operation log;

[0223] If the deviation between the two is greater than the set percentage, an abnormal flag is triggered and the deviation type is extracted;

[0224] Call the adaptive correction algorithm according to the deviation type:

[0225] If it is a parameter adjustment deviation, the target parameter value is recalculated according to the gradient descent method and a new standardized control instruction is generated;

[0226] If the device response is delayed, the retry interval of the instruction in the execution queue is extended;

[0227] Add the modified standardized control instructions to the execution queue and perform the following operations:

[0228] Check whether the current number of retries exceeds the preset maximum retry threshold; if it does not exceed the threshold, repeat the process of S530; if it exceeds the threshold, trigger the abnormal termination process, which includes sending an adjustment failure alarm message to the user terminal and recommending manual intervention.

[0229] Example 6

[0230] Scenario A

[0231] The user uses the app to set the oven temperature of the device with ID Oven_01 to 200°C and the running time to 1 hour.

[0232] The oven equipment has a rated power of Prated = 3kW, operating mode M = high-temperature baking, and an energy consumption function of E = 3kW × 1h = 3kWh. The user's daily energy consumption limit is 2.5kWh.

[0233] The system triggers an alarm and recommends alternative solutions:

[0234] Solution 1: Switch to "fast baking" mode, operating mode M = 180°C, energy consumption function E = 2.2 kWh.

[0235] Option 2: Shorten the duration to 50 minutes, energy consumption function E = 2.5kWh.

[0236] APP push: "Energy consumption exceeds the limit, it is recommended to use fast baking mode (save 0.8kWh)."

[0237] Scenario B

[0238] The air conditioner with device ID AC_001 receives a command setting of 25°C, but the sensor feedback temperature is 23°C. The deviation of the two is 8%, exceeding the set threshold of 5%.

[0239] First adjustment: The target value was adjusted to 24°C. After retrying, the sensor detected a temperature of 23.5°C. The actual deviation was 2.08%, which was less than the set threshold of 5%. The deviation met the standard, and the adjustment was successful, without further correction.

[0240] If the actual temperature is 22.5°C after the first adjustment, the actual deviation is 6.25%, triggering a second adjustment, and the target value is further reduced to 23°C until the deviation meets the threshold.

[0241] This specific embodiment is merely an explanation of the present invention and is not intended to limit the present invention. After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed. However, as long as such modifications are within the scope of the claims of the present invention, they are protected by patent law.

Claims

1. Intelligent home appliance interactive control system based on embedded software, characterized by: The system operation process specifically includes the following steps: S100: Acquire a user input signal, wherein the user input signal includes at least one of a voice command, a touch operation, a gesture action, or a mobile phone APP control command; S200, parsing the user input signal based on the embedded software architecture to generate standardized control instructions; S300, establishing communication connections with multiple smart home appliances through the Internet of Things protocol, and obtaining real-time environmental data and real-time operating status of the smart home appliances; S400: Jointly analyzing user historical behavior data and user input signals; including: S410, performing cluster analysis on user historical operation data using a neural network model to identify user preference patterns and device usage habits; S420, combining real-time environmental data with equipment operating status to generate a control strategy; S500, adjusting the operating parameters of the smart home appliance; S600: Return interactive response information to the user through at least one of a voice feedback module, a touch interface, a gesture recognition interface, or a mobile phone APP.

2. The embedded software-based intelligent home appliance interactive control system according to claim 1, characterized in that: The S200 specifically includes the following steps: S210, receiving the original input signal of the voice command, touch operation, gesture action or mobile phone APP control command, and storing the original input signal in a temporary buffer area according to type classification; S220, calling a corresponding parsing module according to the type of the original input signal to generate an intermediate instruction; S230: Mapping the intermediate instruction to a preset standardized control instruction format to generate a standardized control instruction; specifically, the process includes the following sub-steps: S230.

1. Extract the operation type, device description information, and expected target parameter value from the intermediate instruction, where the operation type includes a switch instruction, a parameter adjustment instruction, or a mode switching instruction, and the device description information includes a device name, location identifier, or function category; S230.

2. Match the device description information in the device registry: If the device name is exactly the same as the device identifier in the device registry, the corresponding device unique code is directly extracted; If the device description information is a location identifier, the identifiers of all devices under the location are retrieved from the device registry, and the target devices are filtered according to the functional category; If the match fails, the fuzzy search algorithm is called to rematch based on the similarity of device functions; S230.

3. Select a standardized control instruction template based on the operation type: If it is a switch instruction, the basic control template is called, and the basic control template includes a device identifier and an operation type; if it is a parameter adjustment instruction, the numerical template is called, and the numerical template includes a filled device identifier, a target parameter value, and an adjustment step; if it is a mode switching instruction, the mode template is called, and the mode template includes a filled device identifier and a preset mode code; S230.

4. Filling in the fields in the basic control template, numerical template, or pattern template includes: Fill the device unique code matched from the device registry in S230.2 into the device identifier field of the corresponding standardized control instruction template; if the device description information is a location identifier, select the device code with the highest priority under the location based on the functional category; If the basic control template is called, fill in the Boolean status parameter according to the on / off status of the intermediate instruction; if the numerical template is called, fill in the corresponding field with the target parameter value parsed from the intermediate instruction; if the mode template is called, match the preset mode code according to the mode name of the intermediate instruction and fill it in; if the numerical template or mode template is called, fill in the corresponding field with the expected target parameter value obtained by parsing the intermediate instruction as the reference value for device adjustment; Based on the real-time load rate of the device and the user's historical operation frequency, the priority value is calculated through the priority algorithm; S230.

5. Perform syntax check on the corresponding standardized control instruction template after filling in; S230.

6. Confirm the basic control template, numerical template, or pattern template that has passed verification as a standardized control instruction.

3. The embedded software-based intelligent home appliance interactive control system according to claim 2, characterized in that: The process of calculating the priority value by the priority algorithm includes: If the real-time load rate of the device is greater than or equal to the first threshold, the priority value is set to the lowest level; If the real-time load rate is less than the second threshold and the user's historical operation frequency exceeds the third threshold, the priority value is set to the highest level; If the real-time load rate is less than the second threshold and the user's historical operation frequency does not exceed the third threshold, calculating the priority value; If the priority value triggers the lowest level, a load alarm prompt will be sent to the user terminal, and a delayed execution or device switching solution will be recommended in the mobile APP.

4. The embedded software-based intelligent home appliance interactive control system according to claim 1, characterized in that: The S410 specifically includes: Perform data preprocessing, including: Extract historical operation data from the user behavior database, including operation timestamp, device identifier, operation type, and operation parameters; Perform normalization processing to convert the operation timestamp into a time period label, and linearly scale the operation parameters to the [0, 1] interval according to the device's allowable range; User behavior grouping based on the K-means clustering algorithm, including: Combining the pre-processed historical operation data into a user operation sequence according to the device identifier and the time period label, wherein the user operation sequence is composed of operation records within a continuous time window; Determine the optimal number of clusters by using the elbow rule to generate user behavior cluster groups, each of which is associated with a specific device usage habit; Build a user-device association matrix, including: Based on the user behavior clustering groups, the operation frequency and parameter distribution of the devices in each group are counted to generate a user preference pattern matrix. The user preference pattern matrix includes the parameter distribution range P of the target device. range ; Store the user preference pattern matrix in the user behavior analysis module.

5. The embedded software-based intelligent home appliance interactive control system according to claim 4, characterized in that: The S420 is specifically as follows: Extract the real-time environmental data and the real-time operating status of the smart home appliance obtained in S300, and integrate the parameter distribution range P of the target device in the user preference pattern matrix range , integrated to include the ambient temperature E temp 、Ambient humidity E humidity , Equipment load rate D load and the task queue length Q length The environmental state vector S t ; Generate control strategies based on rule engines, including; If the operating parameters of the current standardized control instruction are within the parameter distribution range P range In S200, the standardized control instruction generated in S200 is directly called and added to the execution queue; If the operating parameters deviate from the parameter distribution range P range However, if it is less than the preset threshold, a gradual adjustment instruction is generated, and according to S230.4, the basic control template, the numerical template or the pattern template is refilled to generate a new standardized control instruction; If the operating parameters deviate from the parameter distribution range P range If the preset threshold is exceeded, the user confirmation process is triggered, and a prompt message is pushed through voice or APP. After the user confirms, standardized control instructions are generated according to the confirmation parameters; If the equipment load rate D load If the load rate is greater than or equal to the device load rate threshold, non-emergency commands will be suspended and only security commands will be executed.

6. The embedded software-based intelligent home appliance interactive control system according to claim 1, characterized in that: The S500 specifically includes the following steps: S510, sorting by device priority, inserting control instructions of high-priority devices into the front end of the execution queue; S520: Calculate the device operating energy consumption threshold based on the energy consumption constraint model. If the adjusted parameter exceeds the threshold, trigger an alarm and recommend an energy-saving alternative. S530: After the equipment parameters are adjusted, the equipment feedback data is collected in real time through sensors to verify the adjustment effect. If the adjustment effect is not as expected, the control strategy is regenerated.

7. The embedded software-based intelligent home appliance interactive control system according to claim 6, characterized in that: The S510 specifically includes: Extracting the device identifier and operation type from the standardized control instruction and matching them with the device priority configuration table stored in the device registry; wherein the priority configuration table is predefined based on the device function category, with security devices taking priority over environmental control devices, which in turn take priority over entertainment devices; If multiple devices have the same priority, they are sorted again based on the priority value calculated in S230.4, with the device with the larger value being ranked higher. According to the sorting results, the control instructions of the high-priority devices are dynamically inserted into the front position of the execution queue, and the task execution order is updated through the queue management module.

8. The embedded software-based intelligent home appliance interactive control system according to claim 6, characterized in that: The S520 is specifically as follows: Extract the rated power, historical average energy consumption, and current operating mode parameters of the target device from the device registry to build an energy consumption prediction function; Calculate the real-time energy consumption threshold allowed by the device based on the total energy consumption limit and time-of-use electricity price strategy set by the user; Substitute the expected target parameter value recorded in the standardized control instruction into the energy consumption prediction function. If the predicted energy consumption value exceeds the real-time energy consumption threshold, an energy consumption alarm signal is triggered; Retrieve energy-saving alternatives based on device functional similarity, including: Filter low-power device identifiers of similar functions in the device registry; Or generate parameter adjustment suggestions, reduce the expected target parameter value by a preset ratio and recalculate the energy consumption value; The energy-saving alternative solution is pushed through a mobile phone APP or voice module, and the user is waited for confirmation of the instruction.

9. The embedded software-based intelligent home appliance interactive control system according to claim 6, characterized in that: The S530 is specifically as follows: Obtain real-time operating parameters from sensor nodes of target devices through IoT protocols; Compare the real-time operating parameters with the expected target parameter values stored in the standardized control instructions: If the deviation between the two is less than or equal to the set percentage, the adjustment is confirmed to be valid and recorded in the equipment operation log; If the deviation between the two is greater than the set percentage, an abnormal flag is triggered and the deviation type is extracted; Call the adaptive correction algorithm according to the deviation type: If it is a parameter adjustment deviation, the target parameter value is recalculated according to the gradient descent method and a new standardized control instruction is generated; If the device response is delayed, the retry interval of the instruction in the execution queue is extended; Add the modified standardized control instructions to the execution queue and perform the following operations: Check whether the current number of retries exceeds the preset maximum retry threshold; if it does not exceed the threshold, repeat the process of S530; if it exceeds the threshold, trigger the abnormal termination process, which includes sending an adjustment failure alarm message to the user terminal and recommending manual intervention.

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