Control method and system of intelligent household electrical appliance

Abstract

The invention discloses a control method and system for an intelligent household electrical appliance, and relates to the technical field of household electrical appliance control, and the method comprises the steps: receiving a composite trigger signal which comprises a first function instruction transmitted by a user through a terminal device and an environment parameter collected by an environment monitoring device in real time; according to the composite trigger signal, dynamically determining a first function of a first intelligent household electrical appliance and a second function of at least one second intelligent household electrical appliance associated with the target scene, and obtaining operation states of the first intelligent household electrical appliance and the second intelligent household electrical appliance in real time; and generating an equipment control sequence through a pre-trained scene decision model based on the running state and the environmental parameters, and opening the first function and the second function in a linkage manner. According to the invention, more accurate scene-based linkage control is realized by utilizing the composite trigger signal and the environmental parameters and dynamically deciding through the intelligent model. Therefore, the technical problem that the control operation efficiency of the intelligent household electrical appliance is low is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of home appliance control, and in particular to a control method and system for intelligent home appliance. Background Art

[0002] With the continuous advancement of science and technology and the improvement of people's living standards, smart home appliances are widely used in the home field. Users can bind smart home appliances to the cloud platform to achieve remote control of smart home appliances and improve the interaction efficiency between users and smart home appliances.

[0003] In the existing technology, the way users remotely control and operate smart home appliances is relatively fixed. They send operation instructions to the cloud platform through terminal devices to enable specific functions of the smart home appliances bound to the cloud platform. However, when it is necessary to enable application scenarios of multiple smart home appliances, users can only send operation instructions multiple times to enable specific functions of different smart home appliances in turn, resulting in low control operation efficiency of smart home appliances, which in turn affects the user experience. Summary of the Invention

[0004] The purpose of the present invention is to provide a control method and system for smart home appliances, which solve the technical problem of low control operation efficiency of smart home appliances in the prior art.

[0005] To achieve this object, the present invention adopts the following technical solutions: According to a first aspect, the present invention provides a method for controlling a smart home appliance, comprising: Step S1, receiving a composite trigger signal, wherein the composite trigger signal includes a first function instruction sent by a user through a terminal device and environmental parameters collected in real time by an environmental monitoring device; Step S2: dynamically determining a first function of a first smart home appliance and a second function of at least one second smart home appliance associated with the target scene according to the composite trigger signal, and obtaining the operating status of the first smart home appliance and the second smart home appliance in real time; Step S3, based on the operating status and the environmental parameters, generate a device control sequence through a pre-trained scenario decision model, and start the first function and the second function in a linked manner; wherein the scenario decision model dynamically adjusts the device startup sequence, power parameters and linkage delay time according to the mapping relationship between historical operation data and environmental parameters.

[0006] Optionally, step S1 includes: Step S11, receiving a first function instruction sent by a user through a terminal device, and parsing a device identifier and a target function code in the first function instruction; Step S12, performing multi-dimensional real-time collection of the home environment through environmental monitoring equipment to obtain environmental parameters of the home environment, wherein the environmental parameters include at least two of temperature, humidity, light intensity, human motion status, and sound decibel value; Step S13, performing weighted fusion processing on the first functional instruction and the environmental parameter to generate a composite trigger signal; wherein, when the environmental parameter exceeds a preset safety threshold, the decision weight of the environmental parameter is increased to more than 1.5 times that of the first functional instruction.

[0007] Optionally, step S1 further includes: Step S14: When a logical conflict is detected between the first functional instruction and the environmental parameter, an abnormal arbitration mechanism is triggered; wherein the abnormal arbitration mechanism includes: Send conflict warnings and suggested operation plans to terminal devices; If no user response is received within the preset time, the security policy triggered by the environmental parameters will be executed first.

[0008] Optionally, step S13 includes: Step S131, calculating a weight value corresponding to each environmental parameter based on the type and value of the environmental parameter; wherein the initial weight of the environmental parameter is assigned according to a preset priority, and the priority is pre-set based on the degree of impact on safety and comfort during the home appliance control process; Step S132: Determine whether the environmental parameter exceeds a preset safety threshold; if so, dynamically amplify the weight of the environmental parameter so that its proportion in the weighted fusion is increased to more than 1.5 times the weight of the first functional instruction; if not, maintain the original weight; Step S133: normalizing the weighted values of the first functional instruction and the environmental parameters to form a fusion vector of unified dimension; Step S134, generating a composite trigger signal based on the fusion vector; the composite trigger signal is used as an input basis for scene recognition and device linkage control, and is used for inference processing of the scene decision model.

[0009] Optionally, step S2 includes: Step S21: preliminarily matching a corresponding first smart home appliance based on the device identifier and the function instruction in the composite trigger signal, and extracting a candidate scene template associated with the first smart home appliance; Step S22: determining a target scene corresponding to a current trigger condition based on the environmental parameters and the candidate scene template, and determining a first function of a first smart home appliance corresponding to the target scene; Step S23, in the target scenario, based on the functional collaboration rules and the device linkage strategy, screening at least one second smart home appliance from the preset set of smart home appliances, and determining its second function in the current scenario; Step S24: obtaining the operating status information of the first smart home appliance and the second smart home appliance in real time.

[0010] Optionally, step S22 includes: Step S221, performing a matching calculation on the environmental parameter set in the composite trigger signal and a preset environmental condition threshold of the candidate scene template to obtain a matching calculation result; Step S222, activating the target scene according to the matching degree calculation result; Step S223: Determine a first function to be performed by the first smart home appliance according to the device function mapping library of the target scenario; wherein the first function includes a combination of an operation mode and a parameter configuration.

[0011] Optionally, step S23 includes: Step S231: Analyze the core functional requirements of the target scenario and, based on functional collaboration rules, split the core functional requirements into required functional units and optional functional units; wherein the required functional units are implemented by the first function of the first smart home appliance, and the optional functional units need to be supplemented by the second smart home appliance; Step S232: Screen devices that support optional functional units from the smart home appliance set as candidate devices and sort them by device status priority. Step S233: If a function conflict between the candidate device and the first smart home appliance is detected, a conflict warning and an alternative solution are pushed to the terminal; if there is no conflict, the candidate device with the highest priority is selected as the second smart home appliance; Step S234: configure the second function of the second smart home appliance according to the operating parameters of the target scenario.

[0012] Optionally, step S3 includes: Step S31: performing feature encoding and spatiotemporal alignment processing on the operating state and the environmental parameters to generate a multidimensional state vector; wherein the operating state includes the real-time load rate of the equipment, the fault flag, and the remaining working time, and the environmental parameters are associated and matched with the acquisition timestamp according to the spatial position; Step S32: Input the multi-dimensional state vector into a pre-trained scenario decision model, and generate an initial device control sequence through the temporal reasoning layer and device collaborative network in the model; the initial device control sequence includes a device startup sequence, basic power parameters, and a default linkage delay time; Step S33, based on the dynamic mapping relationship between current environmental parameters and historical operation data, the initial equipment control sequence is optimized in real time; Step S34, execute linkage operations according to the optimized device control sequence: send a first function execution instruction to the first smart home appliance device, and synchronously send an asynchronous trigger instruction with a delay parameter to at least one second smart home appliance device to achieve collaborative function activation; the execution result is fed back to the scene decision model for parameter calibration.

[0013] Optionally, step S33 includes: Step S331 , when it is detected that the environmental parameter deviates from the historical reference value by more than a preset tolerance, the power parameter is dynamically adjusted according to the deviation ratio; Step S332: recalculate the linkage delay time according to the real-time load rate difference between the first smart home appliance and the second smart home appliance; Step S333: If the fault flag of the smart home appliance is activated, the candidate device replacement strategy is started and the startup sequence of the smart home appliance is updated.

[0014] According to a second aspect, the present invention provides a control system for a smart home appliance, which is controlled by the control method for the smart home appliance according to the first aspect, including: a signal receiving module, configured to receive a composite trigger signal, the composite trigger signal including a first function instruction sent by a user through a terminal device and environmental parameters collected in real time by an environmental monitoring device; a function determination module, configured to dynamically determine a first function of a first smart home appliance and a second function of at least one second smart home appliance associated with a target scenario based on the composite trigger signal, and to obtain in real time the operating status of the first smart home appliance and the second smart home appliance; A linkage control module is used to generate a device control sequence based on the operating status and the environmental parameters through a pre-trained scenario decision model, and to jointly enable the first function and the second function; wherein the scenario decision model dynamically adjusts the device startup sequence, power parameters and linkage delay time according to the mapping relationship between historical operation data and environmental parameters.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a control method and system for smart home appliances. This system simultaneously receives user-initiated commands (first function commands) and real-time environmental parameters collected by environmental monitoring equipment to form a composite trigger signal, significantly enhancing the accuracy and reliability of scene recognition. The composite trigger signal combines the user's subjective intent with the objective environmental state, providing a more comprehensive and reliable basis for subsequent determination of the target scene and effectively reducing false triggering or scene mismatches. Based on the composite trigger signal, the system dynamically determines the first function of a first smart home appliance and the second function of at least one second smart home appliance associated with the target scene, and obtains their operating status in real time, enabling the construction of intelligent scenarios with flexible binding and on-demand collaboration. Based on the device operating status and environmental parameters, a pre-trained scenario decision model is used to generate a device control sequence, enabling the coordinated activation of device functions. This intelligently optimizes device startup sequence, power parameters, and linkage delay time, thereby improving control operation efficiency and device lifespan. Therefore, the present invention addresses the prior art problem of low control operation efficiency for smart home appliances. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] The structures, proportions, sizes, etc. depicted in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not intended to limit the conditions under which the present invention can be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportional relationships, or adjustments in size should still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and objectives that can be achieved by the present invention.

[0018] Figure 1 This is a flow chart of a method for controlling a smart home appliance provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a control system for a smart home appliance provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions 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 embodiments described below are only 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 making creative work are within the scope of protection of the present invention.

[0020] In the description of the present invention, it should be understood that the terms "upper," "lower," "top," "bottom," "inner," "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of the present invention and simplify the description. They are not intended to indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. It should be noted that when a component is considered to be "connected" to another component, it may be directly connected to the other component or there may be a centrally located component.

[0021] The technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods. Example

[0022] An embodiment of the present invention provides a method for controlling a smart home appliance, including: Step S1: receiving a composite trigger signal, where the composite trigger signal includes a first function instruction sent by a user through a terminal device and environmental parameters collected in real time by an environmental monitoring device.

[0023] In one embodiment, step S1 includes: Step S11: Receive a first function instruction sent by a user via a terminal device and parse the device identifier and target function code contained in the first function instruction. In this embodiment, the user sends a command (e.g., "Turn on sleep mode") via a mobile app or voice assistant. The system uses the natural language processing (NLP) module to parse the key elements of the instruction: device identifiers such as "master bedroom air conditioner" and "living room curtains," and target function codes such as "cooling mode" for the air conditioner and "close" for the curtains. The parsed structured data is stored in a command queue, awaiting environmental parameter matching.

[0024] Step S12, using environmental monitoring equipment to perform multi-dimensional real-time collection of the home environment to obtain environmental parameters of the home environment, which include at least two of temperature, humidity, light intensity, human motion status, and sound decibel value; in this embodiment, a multi-sensor network is deployed for real-time monitoring: temperature and humidity sensors collect real-time temperature and humidity in the bedroom, light sensors detect the light transmittance of curtains, human infrared sensors identify the stationary and moving states of the human body, and microphone arrays monitor the decibel value of environmental noise.

[0025] Step S13 performs a weighted fusion process on the first functional instruction and the environmental parameters to generate a composite trigger signal. When the environmental parameters exceed a preset safety threshold, the decision weight of the environmental parameters is increased to at least 1.5 times that of the first functional instruction. In this embodiment, when a temperature > 60°C or a carbon monoxide concentration > 100 ppm is detected, the fire sensor issues an alarm, forcibly triggering safety mode. This weighted fusion process is well known in the art and will not be further described here.

[0026] In step S14, when a logical conflict is detected between the first function instruction and the environmental parameters, an exception arbitration mechanism is triggered. The exception arbitration mechanism includes sending a conflict alert and a suggested action plan to the terminal device. If no user response is received within a preset time, the safety policy triggered by the environmental parameters is prioritized. For example, if the user instruction "open windows for ventilation" conflicts with PM2.5 levels > 150 μg / m³, a pop-up window alert is sent to the user: "Severe air pollution detected. Recommendation to cancel window opening. Do you want to enforce this?" If no response is received within 30 seconds, an alternative action is automatically executed: starting the air purifier (maximum setting) or shutting down the fresh air system (to prevent pollution intrusion).

[0027] It's important to note that step S11 accurately extracts user intent, avoiding command ambiguity (e.g., distinguishing between "cooling" and "dehumidification" modes), providing structured command data for step S13 and supporting weight allocation. Step S12 implements multi-dimensional environmental perception covering physical and biological indicators (e.g., light and human status), improving data completeness. This provides a basis for conflict resolution in steps S13 and S14 and provides real-time input to the scenario-based decision model in step S3. Step S13 implements a safety-first mechanism: environmental risks (e.g., gas leaks) automatically override user commands to ensure home safety. Dynamic weights are output to step S2, ensuring that scenario-based decisions prioritize safety. Step S14's dual conflict resolution safeguards: prioritizing user interaction and automatic timeout protection balance user-friendliness with reliability, avoiding dangerous operations (e.g., opening windows to contaminate the skylight) in step S3 and reducing system fault tolerance costs.

[0028] Specifically, step S13 includes: Step S131: Calculate the weight value corresponding to each environmental parameter based on the type and value of the environmental parameter. The initial weight value of the environmental parameter is assigned based on a preset priority, which is pre-set based on the degree of impact of the environmental parameter on safety and comfort during the home appliance control process. The initial weight value of the environmental parameter is assigned based on the preset priority. The priority is pre-defined based on the degree of impact of the environmental parameter on safety and comfort during the home appliance control process.

[0029] For example, an environment parameter priority mapping table is established, as shown in Table 1 below: Table 1 Environmental parameter priority mapping table: Parameter Type Initial weight Priority basis Harmful gas concentration 0.9 The highest safety risk (e.g. CO > 50 ppm can be fatal) temperature 0.7 Directly affects physical comfort Human body movement state 0.6 Presence detection (key for energy saving) Light intensity 0.5 Visual comfort adjustment Sound decibel value 0.4 Noise reduction needs (affecting sleep quality) This step S131 uniformly quantifies a variety of heterogeneous environmental parameters through preset priorities, so that the system can respond dynamically according to the importance of the scene, which helps to form a perception fusion model with user experience as the core.

[0030] The weight adjustment formula used for temperature weight is as follows: ; Where: W1 is the temperature weight, W0 is the initial weight, and α is the sensitivity coefficient (safety parameter α = 0.8, comfort parameter α = 0.3). For example: when the measured room temperature value V1 = 35°C and the temperature threshold V0 = 30°C, the temperature weight increases to 0.7×(1+0.8×(35-30) / 30)=0.793.

[0031] Step S132: Determine whether the environmental parameter exceeds a preset safety threshold; if so, dynamically amplify the weight of the environmental parameter so that its proportion in the weighted fusion is increased to more than 1.5 times the weight of the first functional instruction; if not, maintain the original weight; For example, when PM2.5>150μg / m 3 (safety threshold), its weight increased from the initial 0.9 to 0.9×2.0=1.8, while the user instruction weight was fixed at 1.0; environmental parameters accounted for 64% of the fusion signal and dominated subsequent decision-making.

[0032] In step S133 , the weighted values of the first functional instruction and the environmental parameters are normalized to form a fusion vector of unified dimension. In this embodiment, the normalization process is well known in the art and will not be described in detail here.

[0033] Step S134, generating a composite trigger signal based on the fusion vector; the composite trigger signal is used as the input basis for scene recognition and device linkage control, and is used for inference processing of the scene decision model.

[0034] It should be noted that step S131 avoids subjective weighting bias by presetting priorities for safety and comfort. The dynamic calculation formula adapts to different home scenarios (e.g., automatically reducing humidity weight in winter), solving the problem of quantifying the contribution of multiple environmental parameters. Step S132 implements proactive security risk prevention. The dynamic weight amplification mechanism makes safety parameters the dominant decision-making factor when exceeding standards. The 1.5x threshold design balances user intent with safety assurance. Step S133 eliminates dimensional differences in multi-source data. Normalization makes heterogeneous data such as temperature (°C) and decibel level (dB) comparable, and the fusion vector is compressed to a range of 0–1. Step S134 constructs a machine-readable decision basis. Because the structured data packet carries the dominant factor identifier, it accelerates scenario matching in step S2. Because the structured data packet carries the security level label, it drives the scenario decision model in step S3 to switch to the security policy mode.

[0035] Step S2, dynamically determining the first function of the first smart home appliance and the second function of at least one second smart home appliance associated with the target scene according to the composite trigger signal, and obtaining the operating status of the first smart home appliance and the second smart home appliance in real time.

[0036] In one embodiment, step S2 includes: In step S21, based on the device identifier and function instruction in the composite trigger signal, a preliminary match is performed on the corresponding first smart home appliance, and candidate scene templates associated with the first smart home appliance are extracted. In this embodiment, the device identifier (e.g., "AC001") in the composite trigger signal is parsed, the first smart home appliance (e.g., the master bedroom air conditioner) is matched in the device registry, and the function instruction set supported by the device (e.g., cooling / heating / dehumidification) is extracted. Based on the device function code (e.g., "COOL"), the scene association rule library is searched, and candidate scene templates (e.g., "sleep scene," "energy-saving scene," and "rapid cooling scene") are output. Candidate scenes are sorted according to historical trigger frequency (e.g., 80% of the user's cooling instructions at night are associated with the sleep scene).

[0037] In step S22, based on the environmental parameters and the candidate scene templates, the target scene corresponding to the current trigger condition is determined, and the first function of the first smart home appliance corresponding to the target scene is determined. In this embodiment, the environmental parameters collected in step S12 (e.g., temperature 32°C, light 0 lux) are matched with the trigger conditions of the candidate scenes. For the sleep scene, light <5 lux, noise <40dB, with a 92% match; for the rapid cooling scene, temperature >35°C, no movement, with a 45% match. The scene with the highest match (the sleep scene) is selected as the target scene, and the user's original command (cooling mode) is associated with the target scene, converting it into a scenario-based command (low-speed cooling at 26°C in sleep mode).

[0038] Step S23: In the target scenario, at least one second smart home appliance is selected from the preset set of smart home appliances according to the functional collaboration rules and device linkage strategy, and its second function in the current scenario is determined. In this embodiment, if the ambient humidity is greater than 70%, the humidifier is eliminated (to avoid excessive humidity); the light brightness is automatically reduced to 30% according to the current time (23:00); and a second device list (curtains + humidifier) and its scenario-based functions (off + night mode) are output.

[0039] Step S24: obtaining the operating status information of the first smart home appliance and the second smart home appliance in real time.

[0040] It should be noted that step S21 infers possible scenarios based on device function codes (e.g., "cooling" could mean sleep or rapid cooling). This candidate template narrows the scope of subsequent matching, improving efficiency. Step S22 quantifies the degree of match between environmental parameters and scene conditions (e.g., 0 lux illumination matches the sleep scene 92% of the time). User commands are converted into scenario-based functions (e.g., low-speed cooling at 26°C), avoiding mechanical execution and achieving environmentally adaptive scene matching. Step S23 implements real-time device selection based on physical rules (e.g., disabling humidifiers when humidity exceeds 70%) and automatically adjusts parameters based on a time factor (e.g., reducing brightness at night), reducing manual user configuration. Detection of device conflicts in step S23 (e.g., a user command to open a window and the air conditioner to cool) triggers the arbitration mechanism in step S14. Step S24 obtains the device's operating status (e.g., the air conditioner is already in cooling mode), enabling the scenario decision model in step S3 to skip redundant commands.

[0041] Specifically, step S22 includes: In step S221, the matching degree of the environmental parameter set in the composite trigger signal is calculated with the preset environmental condition threshold of the candidate scene template to obtain a matching degree calculation result; in this embodiment, the weighted sum is performed: total matching degree = Σ(parameter weight × single parameter matching degree); example: the current ambient temperature is 28°C, the matching degree is 0.7; the illumination is 2lux, the matching degree is 1.0, and the calculated comprehensive matching degree is 0.85.

[0042] Step S222: activating the target scene based on the matching calculation result. In this embodiment, the activation condition is determined as follows: Hard conditions: If safety parameters (such as CO concentration) exceed the standard, the security scene is activated forcibly; Soft conditions: Immediate activation for comprehensive matching degree >85%, 60%~85% requires secondary confirmation.

[0043] Step S223: Determine a first function to be performed by the first smart home appliance according to the device function mapping library of the target scenario; wherein the first function includes a combination of an operation mode and a parameter configuration.

[0044] For example, the structure of the device function mapping library is shown in Table 2 below: Table 2 Device function mapping library: Scene ID Device ID Operation Mode Parameter configuration Priority SC_102 AC001 Sleep mode {temp:26, fan:low speed} 1 SC_102 AC001 Energy saving mode {temp:28, fan:auto} 2 In step S3, based on the operating status and environmental parameters, a device control sequence is generated through a pre-trained scenario decision model, and the first function and the second function are activated in a linked manner; wherein the scenario decision model dynamically adjusts the device startup sequence, power parameters and linkage delay time according to the mapping relationship between historical operation data and environmental parameters.

[0045] In one embodiment, step S3 includes: In step S31, the operating status and environmental parameters are feature-encoded and time-space aligned to generate a multi-dimensional state vector; wherein the operating status includes the real-time load rate of the equipment, the fault flag and the remaining working time, and the environmental parameters are associated and matched with the acquisition timestamp according to the spatial position; in this embodiment, the time-space alignment algorithm known in the art is used for data processing, and the environmental data within 10 seconds is slidingly averaged based on the control trigger moment; a home space topology map is established, and the parameters of adjacent areas are weighted and fused.

[0046] Step S32: Input the multi-dimensional state vector into the pre-trained scenario decision model, and generate an initial device control sequence through the temporal reasoning layer and device collaborative network in the model; the initial device control sequence includes the device startup sequence, basic power parameters, and default linkage delay time; Step S33, based on the dynamic mapping relationship between current environmental parameters and historical operation data, the initial equipment control sequence is optimized in real time; Step S34, execute linkage operations according to the optimized device control sequence: send a first function execution instruction to the first smart home appliance device, and synchronously send an asynchronous trigger instruction with a delay parameter to at least one second smart home appliance device to achieve collaborative function activation; the execution result is fed back to the scene decision model for parameter calibration.

[0047] It should be noted that the spatiotemporal alignment in step S31 eliminates regional monitoring bias, and load rate encoding prevents equipment from overloading. The composite signal safety tag in step S13 is linked to the model switching safety policy in step S32 for safety coordination (e.g., shutting down all non-essential equipment in the event of a fire). The scenario-based function parameters output in step S22 are then used to adapt to the optimized base power values in step S33 (e.g., in sleep mode, power is automatically reduced to 70%).

[0048] Specifically, step S33 includes: Step S331 , when it is detected that the environmental parameter deviates from the historical reference value by more than a preset tolerance, the power parameter is dynamically adjusted according to the deviation ratio; Use the following formula to adjust the power parameters; ; Where P1 is the power after adjustment, P0 is the power before adjustment, k = 1.2 (temperature) / 0.8 (humidity), and n is the offset ratio; For example, in the temperature scenario, a sudden rise of 5°C in the room temperature in summer (a deviation of 12% from the baseline value) results in a 14.4% increase in air conditioning power. In the humidity scenario, a heavy rain with humidity reaching 90% (a deviation of 50% from the baseline value) results in a 40% increase in dehumidifier power.

[0049] Step S332: recalculate the linkage delay time according to the real-time load rate difference between the first smart home appliance and the second smart home appliance; The dynamic delay table is shown below: Step S333: If the fault flag of the smart home appliance is activated, the candidate device replacement strategy is started and the startup sequence of the smart home appliance is updated.

[0050] Exemplary candidate device selection rules: Function priority: same function > degraded function > compensatory function; Space priority: same area > adjacent area > global device.

[0051] Example of sequential reconstruction: original sequence: [air conditioning cooling → fresh air start]; when the air conditioning fails: [fresh air strong mode → fan-assisted cooling].

[0052] It should be noted that the safety threshold data in step S13 and the power adjustment boundary constraints in step S331 can be linked for safety (e.g., disabling gas equipment when CO exceeds the standard). The scene parameters output in step S22 and the criteria for selecting alternative devices in step S333 can achieve scene adaptation (e.g., prioritizing projectors over TVs in cinema mode).

[0053] Specifically, the scenario is a movie viewing mode in hot weather. Initial parameters: the air conditioner base power is 2000W (26°C), and the default projector startup delay is 3s. A sudden environmental change occurs: the room temperature rises from 28°C to 35°C (a 25% deviation). In step S331, the power is increased to 2000*(1+1.2*0.25)=2600W. The difference between the air conditioner load factor of 0.85 and the projector load factor of 0.25 is 0.6. In step S332, the projector delay is adjusted to 3*(1+0.3*0.6 / 0.85)=3.64s.

[0054] Working Principle: The present invention provides a control method for smart home appliances. By simultaneously receiving active user instructions (first function instructions) and real-time environmental parameters collected by environmental monitoring equipment, a composite trigger signal is formed, significantly enhancing the accuracy and reliability of scene recognition. The composite trigger signal combines the user's subjective intentions with the objective environmental conditions, providing a more comprehensive and reliable basis for the subsequent determination of the target scene, effectively reducing false triggering or scene mismatch. Based on the composite trigger signal, the first function of the first smart home appliance and the second function of at least one second smart home appliance associated with the target scene are dynamically determined, and their operating status is obtained in real time, realizing the construction of non-fixed binding, on-demand collaborative smart scenes. Based on the device operating status and environmental parameters, a pre-trained scenario decision model is used to generate a device control sequence, and the device functions are activated in a linked manner. This can intelligently optimize the device startup sequence, power parameters, and linkage delay time, thereby improving control operation efficiency and device life. Therefore, the present invention solves the technical problem of low control operation efficiency of smart home appliances in the prior art.

[0055] Example 2: An embodiment of the present invention provides a control system for a smart home appliance, which is controlled by the control method for the smart home appliance according to the first aspect, including: The signal receiving module 101 is configured to receive a composite trigger signal, the composite trigger signal including a first function instruction sent by a user through a terminal device and environmental parameters collected in real time by an environmental monitoring device; The function determination module 102 is configured to dynamically determine a first function of a first smart home appliance and a second function of at least one second smart home appliance associated with a target scenario based on the composite trigger signal, and obtain the operating status of the first smart home appliance and the second smart home appliance in real time; The linkage control module 103 is used to generate a device control sequence based on the operating status and environmental parameters through a pre-trained scenario decision model, and to activate the first function and the second function in a linked manner; wherein the scenario decision model dynamically adjusts the device startup sequence, power parameters and linkage delay time according to the mapping relationship between historical operation data and environmental parameters.

[0056] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling a smart home appliance, characterized in that: include: Step S1, receiving a composite trigger signal, wherein the composite trigger signal includes a first function instruction sent by a user through a terminal device and environmental parameters collected in real time by an environmental monitoring device; Step S2: dynamically determining a first function of a first smart home appliance and a second function of at least one second smart home appliance associated with the target scene according to the composite trigger signal, and obtaining the operating status of the first smart home appliance and the second smart home appliance in real time; Step S3, based on the operating status and the environmental parameters, generate a device control sequence through a pre-trained scenario decision model, and start the first function and the second function in a linked manner; wherein the scenario decision model dynamically adjusts the device startup sequence, power parameters and linkage delay time according to the mapping relationship between historical operation data and environmental parameters.

2. The control method of the smart home appliance according to claim 1, characterized in that: The step S1 comprises: Step S11, receiving a first function instruction sent by a user through a terminal device, and parsing a device identifier and a target function code in the first function instruction; Step S12, performing multi-dimensional real-time collection of the home environment through environmental monitoring equipment to obtain environmental parameters of the home environment, wherein the environmental parameters include at least two of temperature, humidity, light intensity, human motion status, and sound decibel value; Step S13, performing weighted fusion processing on the first functional instruction and the environmental parameter to generate a composite trigger signal; wherein, when the environmental parameter exceeds a preset safety threshold, the decision weight of the environmental parameter is increased to more than 1.5 times that of the first functional instruction.

3. The control method of the smart home appliance according to claim 2, characterized in that: The step S1 further includes: Step S14: When a logical conflict is detected between the first functional instruction and the environmental parameter, an abnormal arbitration mechanism is triggered; wherein the abnormal arbitration mechanism includes: Send conflict warnings and suggested operation plans to terminal devices; If no user response is received within the preset time, the security policy triggered by the environmental parameters will be executed first.

4. The control method of the smart home appliance according to claim 2, characterized in that: The step S13 includes: Step S131, calculating a weight value corresponding to each environmental parameter based on the type and value of the environmental parameter; wherein the initial weight of the environmental parameter is assigned according to a preset priority, and the priority is pre-set based on the degree of impact on safety and comfort during the home appliance control process; Step S132: Determine whether the environmental parameter exceeds a preset safety threshold; if so, dynamically amplify the weight of the environmental parameter so that its proportion in the weighted fusion is increased to more than 1.5 times the weight of the first functional instruction; if not, maintain the original weight; Step S133: normalizing the weighted values of the first functional instruction and the environmental parameters to form a fusion vector of unified dimension; Step S134, generating a composite trigger signal based on the fusion vector; the composite trigger signal is used as an input basis for scene recognition and device linkage control, and is used for inference processing of the scene decision model.

5. The control method of the smart home appliance according to any one of claims 1 to 4, characterized in that: The step S2 comprises: Step S21: preliminarily matching a corresponding first smart home appliance based on the device identifier and the function instruction in the composite trigger signal, and extracting a candidate scene template associated with the first smart home appliance; Step S22: determining a target scene corresponding to a current trigger condition based on the environmental parameters and the candidate scene template, and determining a first function of a first smart home appliance corresponding to the target scene; Step S23, in the target scenario, based on the functional collaboration rules and the device linkage strategy, screening at least one second smart home appliance from the preset set of smart home appliances, and determining its second function in the current scenario; Step S24: obtaining the operating status information of the first smart home appliance and the second smart home appliance in real time.

6. The control method of the smart home appliance according to claim 5, characterized in that: The step S22 includes: Step S221, performing a matching calculation on the environmental parameter set in the composite trigger signal and a preset environmental condition threshold of the candidate scene template to obtain a matching calculation result; Step S222, activating the target scene according to the matching degree calculation result; Step S223: Determine a first function to be performed by the first smart home appliance according to the device function mapping library of the target scenario; wherein the first function includes a combination of an operation mode and a parameter configuration.

7. The control method of the smart home appliance according to claim 5, characterized in that: The step S23 includes: Step S231: Analyze the core functional requirements of the target scenario and, based on functional collaboration rules, split the core functional requirements into required functional units and optional functional units; wherein the required functional units are implemented by the first function of the first smart home appliance, and the optional functional units need to be supplemented by the second smart home appliance; Step S232: Screen devices that support optional functional units from the smart home appliance set as candidate devices and sort them by device status priority. Step S233: If a function conflict between the candidate device and the first smart home appliance is detected, a conflict warning and an alternative solution are pushed to the terminal; if there is no conflict, the candidate device with the highest priority is selected as the second smart home appliance; Step S234: configure the second function of the second smart home appliance according to the operating parameters of the target scenario.

8. The control method of the smart home appliance according to claim 1, characterized in that: The step S3 comprises: Step S31: performing feature encoding and spatiotemporal alignment processing on the operating state and the environmental parameters to generate a multidimensional state vector; wherein the operating state includes the real-time load rate of the equipment, the fault flag, and the remaining working time, and the environmental parameters are associated and matched with the acquisition timestamp according to the spatial position; Step S32: Input the multi-dimensional state vector into a pre-trained scenario decision model, and generate an initial device control sequence through the temporal reasoning layer and device collaborative network in the model; the initial device control sequence includes a device startup sequence, basic power parameters, and a default linkage delay time; Step S33, based on the dynamic mapping relationship between current environmental parameters and historical operation data, the initial equipment control sequence is optimized in real time; Step S34, execute linkage operations according to the optimized device control sequence: send a first function execution instruction to the first smart home appliance device, and synchronously send an asynchronous trigger instruction with a delay parameter to at least one second smart home appliance device to achieve collaborative function activation; the execution result is fed back to the scene decision model for parameter calibration.

9. The control method of the smart home appliance according to claim 8, characterized in that: The step S33 includes: Step S331 , when it is detected that the environmental parameter deviates from the historical reference value by more than a preset tolerance, the power parameter is dynamically adjusted according to the deviation ratio; Step S332: recalculate the linkage delay time according to the real-time load rate difference between the first smart home appliance and the second smart home appliance; Step S333: If the fault flag of the smart home appliance is activated, the candidate device replacement strategy is started and the startup sequence of the smart home appliance is updated.

10. A control system for a smart home appliance, which is controlled by the control method for a smart home appliance according to any one of claims 1 to 9, characterized in that: include: a signal receiving module, configured to receive a composite trigger signal, the composite trigger signal including a first function instruction sent by a user through a terminal device and environmental parameters collected in real time by an environmental monitoring device; a function determination module, configured to dynamically determine a first function of a first smart home appliance and a second function of at least one second smart home appliance associated with a target scenario based on the composite trigger signal, and to obtain in real time the operating status of the first smart home appliance and the second smart home appliance; A linkage control module is used to generate a device control sequence based on the operating status and the environmental parameters through a pre-trained scenario decision model, and to jointly enable the first function and the second function; wherein the scenario decision model dynamically adjusts the device startup sequence, power parameters and linkage delay time according to the mapping relationship between historical operation data and environmental parameters.

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