Whole house household appliance simulation method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of smart home, in particular to a whole-house household appliance simulation method, which comprises the following steps of: acquiring a preset whole-house house type image, loading a corresponding household appliance intelligent agent at a reserved position of a household appliance in the whole-house house type image, loading an environment sensor at a preset spatial position in the whole-house house type image, and constructing a meta intelligent agent in the whole-house house type image. The element intelligent agent communicates with all the household appliance intelligent agents and all the environment sensors, according to response conditions of the household appliance intelligent agents, simulation of the household appliance intelligent agents and the environment sensors is started through preset simulation data, operation data of the household appliance intelligent agents in the simulation process are collected, and the household appliance intelligent agents are adjusted according to the operation data; and obtaining the adjusted household appliance intelligent agent. The simulated household appliance intelligent body and the environment sensor are controlled through the element intelligent body, the simulated operation data of the household appliance intelligent body are obtained, and the operation mode of the household appliance intelligent body is adjusted, so that more complex and changeable whole house scenes are simulated to optimize the intelligent mode of the household appliance.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a whole-house appliance simulation method, device, computer equipment and storage medium. Background Art

[0002] The whole house automation system can be understood as a problem of human-machine intelligent collaboration, that is, serving people as the center and coordinating various types of home appliances. In order to build a whole house automation system, we must first build a whole house intelligent physical experimental environment composed of various home appliances and sensors, and then provide better and more intelligent control algorithms for home appliances.

[0003] At present, the construction of a real environment needs to be built according to a specific house design drawing, the corresponding household appliances need to be installed, and the corresponding power and communication networks need to be built. Once the construction is finalized, it is difficult to change it, resulting in a single scene for the entire house.

[0004] Therefore, how to use simulation to enrich the whole-house scene to avoid a single whole-house scene being unable to optimize the operating mode of home appliances has become an urgent problem to be solved. Summary of the invention

[0005] Based on this, the present invention provides a whole-house appliance simulation method, device, computer equipment and storage medium to solve how to use simulation to enrich the whole-house scene, so as to avoid the problem of a single whole-house scene caused by using real equipment for testing.

[0006] In a first aspect, an embodiment of the present invention provides a whole-house appliance simulation method, comprising: Obtain a preset whole-house floor plan, load a corresponding home appliance intelligent body in a reserved position of a home appliance in the whole-house floor plan, and load an environmental sensor in a preset spatial position in the whole-house floor plan; Constructing a meta-agent in the whole house floor plan, wherein the meta-agent communicates with all home appliance agents and all environmental sensors; According to the response condition of the home appliance intelligent body, the simulation of the home appliance intelligent body and the environmental sensor is started through preset simulation data, and the operation data of the home appliance intelligent body during the simulation process is collected; The home appliance intelligent body is adjusted according to the operation data to obtain an adjusted home appliance intelligent body.

[0007] In a second aspect, an embodiment of the present invention provides a device for simulating whole-house appliances, comprising: A whole house floor plan construction module is used to obtain a preset whole house floor plan, load corresponding home appliance intelligent bodies in the reserved positions of home appliances in the whole house floor plan, and load environmental sensors in the preset spatial positions in the whole house floor plan; A meta-agent building module, used to build a meta-agent in the whole house floor plan, wherein the meta-agent communicates with all home appliance agents and all environmental sensors; A simulation module, used to start the simulation of the home appliance intelligent body and the environmental sensor according to the response condition of the home appliance intelligent body through preset simulation data, and collect the operation data of the home appliance intelligent body during the simulation process; The analysis module is used to adjust the home appliance intelligent body according to the operation data to obtain the adjusted home appliance intelligent body.

[0008] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the whole-house appliance simulation method described in the first aspect when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the whole-house appliance simulation method described in the first aspect is implemented.

[0010] Compared with the prior art, the present invention has the following beneficial effects: by obtaining a preset whole-house floor plan, loading the corresponding home appliance intelligent body in the reserved position of the home appliance in the whole-house floor plan, and loading the environmental sensor in the preset spatial position in the whole-house floor plan, a meta-intelligent body is constructed in the whole-house floor plan, the meta-intelligent body communicates with all home appliance intelligent bodies and all environmental sensors, and according to the response conditions of the home appliance intelligent body, the simulation of the home appliance intelligent body and the environmental sensor is started through the preset simulation data, and the operation data of the home appliance intelligent body during the simulation is collected, and the home appliance intelligent body is adjusted according to the operation data to obtain the adjusted home appliance intelligent body. The simulated home appliance intelligent body and environmental sensor are controlled by the meta-intelligent body, and the operation data of the home appliance intelligent body simulation is obtained to adjust the operation mode of the home appliance intelligent body, so as to simulate more complex and changeable whole-house scenes to optimize the intelligent mode of home appliances. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0012] Figure 1 Schematic diagram of an application environment of a whole-house appliance simulation method provided by Embodiment 1 of the present invention; Figure 2It is a flowchart of a whole-house appliance simulation method provided by Embodiment 2 of the present invention; Figure 3 It is a flowchart of a whole-house appliance simulation method provided by Embodiment 3 of the present invention; Figure 4 It is a flowchart of a whole-house appliance simulation method provided by Embodiment 4 of the present invention; Figure 5 It is a flowchart of a whole-house appliance simulation method provided by Embodiment 5 of the present invention; Figure 6 It is a flowchart of a whole-house appliance simulation method provided by Embodiment 6 of the present invention; Figure 7 It is a flowchart of a whole-house appliance simulation method provided by Embodiment 7 of the present invention; Figure 8 It is a flowchart of a whole-house appliance simulation method provided by Embodiment 8 of the present invention; Fig. 9 It is a structural schematic diagram of a whole-house appliance simulation device provided by Embodiment 9 of the present invention; Fig.10 It is a structural diagram of a computer device provided in Embodiment 10 of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] like Figure 1 As shown, it is a schematic diagram of the application environment of a whole-house appliance simulation method provided in the first embodiment of the present invention, wherein the client and the server are connected to communicate, and the user can provide the server with conditions, requirements and operation instructions for whole-house appliance simulation by operating the client, and the server is used to execute the whole-house appliance simulation method of the present invention according to the relevant content sent by the client. Among them, the client includes but is not limited to various personal computers, laptops, smart phones, tablet computers, portable wearable devices and other computer devices. The computer device corresponding to the server can be implemented with an independent server or a server cluster composed of multiple servers.

[0015] like Figure 2 FIG. 1 is a flow chart of a whole-house appliance simulation method provided by Embodiment 2 of the present invention, wherein the whole-house appliance simulation method is applied in Figure 1The whole-house appliance simulation method may include the following steps: Step S201, obtain a preset whole house floor plan, load corresponding home appliance intelligent bodies in the reserved positions of home appliances in the whole house floor plan, and load environmental sensors in the preset spatial positions in the whole house floor plan.

[0016] Among them, a real residential environment can be simulated according to the whole house floor plan to complete the conversion from physical environment to virtual environment. For example, single-story apartments, single-family villas and other types should reflect elements such as house type, layout, and orientation, and can also be manually drawn. For possible residential environments, the whole house floor plan can usually be obtained through house developers or trading websites to obtain standard floor plan and floor plan files of the house and then modeled. It is provided by an architectural design company or interior designer. The whole house floor plan shows in detail the floor layout of the house, the location of each room, the distribution of doors and windows, and the reserved positions of home appliances. The floor plan can be drawn through the information of the whole house floor plan. The floor plan can be provided in a variety of formats, such as manual drawing, CAD, PDF, pictures, etc., to ensure that the obtained floor plan is clear and accurate.

[0017] The home appliance intelligent agent is a logical abstraction of home appliances. It refers to an intelligent agent that simulates the functions and electrical characteristics of home appliances, makes autonomous perception decisions and executes them. It includes basic characteristics such as agent autonomy, interactivity, responsiveness and adaptability. It can complete tasks in a complex and changeable home environment. The simulation system represents a virtual model of actual home appliances, creates a corresponding home appliance intelligent agent model for each reserved position, loads it into the simulation system, and initializes the state and parameters of the intelligent agent according to the specifications and functions of the actual home appliances.

[0018] The smart home appliance has the ability to operate adaptively, respond to services proactively, and learn autonomously based on user habits and environmental changes. It can also reflect the working status of the smart home appliance in the actual environment through digital twin technology. For example, for a TV smart home, it can include functions such as power on / off, screen brightness, saturation, grayscale, sound volume, network protocol, etc. For an air conditioner smart home, it includes wind speed, wind direction, target temperature, cooling / heating, etc.

[0019] When building home appliance intelligence, a variety of different algorithms can be used to build it. The algorithms of home appliance intelligence include but are not limited to decision trees, finite state machines, hierarchical finite state machines, behavior trees, imitation learning, reinforcement learning, collaborative multi-agents, goal-oriented behavior planning, and generative artificial intelligence methods based on large language models. For example, air conditioners and refrigerators have complex adaptive frequency conversion functions based on environmental changes, and ordinary kettles may only have overheating protection functions, which can be controlled by simple if-then control rules.

[0020] According to actual needs, select appropriate spatial locations in the house layout to load environmental sensors, such as the center of the room, near the window, air-conditioning outlet, etc. Create environmental sensor models for each selected location and load them into the simulation system. For example, place a temperature sensor near the center of the room to monitor temperature changes and feedback changes in the environment in the house, so that when the temperature is lower than the expected value, the floor heating or air conditioning and other home appliances can be automatically turned on.

[0021] Step S202: construct a meta-agent in the whole house floor plan, and the meta-agent communicates with all home appliance agents and all environmental sensors.

[0022] Among them, the meta-agent model is loaded in a location that is easy to communicate in the whole house floor plan, and the meta-agent model is loaded into the simulation system and initialized. The meta-agent refers to an agent that can command, dispatch, train and discover agents. It is mainly divided into two functions. One is responsible for commanding and dispatching other agents in the home environment, acting as a smart housekeeper, like JARVISin Iron Man, and the other is to automatically discover new behavior patterns or agents through the activity log records of the agent. For example, [Automated Design of Agentic Systems] designed the automated design of agentic systems (ADAS), which aims to automatically create powerful agent system designs, including inventing new building blocks and / or combining them in new ways. As a central control and coordination center, it is responsible for integrating and managing the data of all home appliance agents and environmental sensors to achieve automated control of home appliances throughout the house.

[0023] The meta-agent is equivalent to the central control unit in the smart home system, which can be specifically manifested as a central integrated control panel that can communicate with all smart appliances and other smart devices in the house. Of course, the central integrated control panel can also be connected to the user's mobile phones, computers and other mobile terminals.

[0024] Step S203 , according to the response condition of the home appliance intelligent body, the simulation of the home appliance intelligent body and the environmental sensor is started through preset simulation data, and the operation data of the home appliance intelligent body during the simulation process is collected.

[0025] Among them, simulation data is the simulation task, which is a series of specific goals, scenarios and operations pre-set in the simulation process to simulate the behavior and performance of a system, process or phenomenon. The simulation task can include multiple subtasks, each of which corresponds to one or a group of operations to control home appliance intelligent bodies and environmental sensors. For example, to evaluate the cooling performance of an air conditioner under extreme high temperature conditions, set the initial indoor temperature to 35°C and the outdoor temperature to 40°C, control the air conditioner to operate in high-efficiency mode, set the temperature to 25°C, enable the temperature sensor, and monitor the indoor temperature changes in real time, so as to measure the time required to reach the target temperature, the peak energy consumption, and the temperature fluctuation range.

[0026] According to the preset simulation tasks, meta-agents are used to control and coordinate the interaction between the home appliance agent and the environmental sensor, and then perform simulation operations, and collect and analyze the operation data of the home appliance agent in the process. The preset simulation tasks can be set, for example, to automatically adjust the light brightness and switch status according to the indoor light intensity and user activities, or to test the alarm and response mechanism of the system by simulating abnormal situations (such as illegal opening of windows, indoor smoke, etc.). During the simulation process, the operation data of the home appliance agent needs to be collected in real time. These data include but are not limited to: the switch status and working mode of the home appliance agent, the cooling / heating efficiency of the air conditioner, the washing time of the washing machine, etc.

[0027] Step S204: adjust the home appliance intelligent body according to the operation data to obtain an adjusted home appliance intelligent body.

[0028] After completing the simulation task, the collected operation data must be collated and prepared, which may include the state data of the home appliance intelligent body and the performance data of the home appliance intelligent body.

[0029] Among them, the status data of the smart home appliance may refer to the switch status, operation mode, working time, etc., and the performance data of the smart home appliance may refer to the temperature control accuracy of the air conditioner, the washing effect of the washing machine, etc.

[0030] The collected operation data is used for detailed analysis and evaluation, and different algorithms can be used to adjust the data. According to the simulation results, the performance of the home appliance intelligent body in different scenarios is evaluated, such as response time, accuracy, energy consumption, etc. Based on the analysis results of the operation data, the home appliance intelligent body that needs to be adjusted is determined, and the corresponding adjustment strategy is formulated. The adjustment goals may include: optimizing the control algorithm, reducing the response time, adjusting the working mode or optimizing the operation strategy, etc. Through multiple iterations, the operation mode of the home appliance intelligent body is gradually optimized. After each iteration, the simulation task is re-executed, new operation data is collected, and evaluation and adjustment are performed.

[0031] In the embodiment of the present application, a preset whole-house floor plan is obtained, the corresponding home appliance intelligent body is loaded in the reserved position of the home appliance in the whole-house floor plan, and the environmental sensor is loaded in the preset spatial position in the whole-house floor plan, and a meta-intelligent body is constructed in the whole-house floor plan. The meta-intelligent body communicates with all home appliance intelligent bodies and all environmental sensors, and according to the response conditions of the home appliance intelligent body, the simulation of the home appliance intelligent body and the environmental sensor is started through the preset simulation data, and the operation data of the home appliance intelligent body during the simulation is collected. According to the operation data, the home appliance intelligent body is adjusted to obtain the adjusted home appliance intelligent body. The simulated home appliance intelligent body and environmental sensor are controlled by the meta-intelligent body, and the operation data of the home appliance intelligent body simulation is obtained to adjust the operation mode of the home appliance intelligent body, so as to simulate more complex and changeable whole-house scenes to optimize the intelligent mode of home appliances.

[0032] like Figure 3 FIG. 2 is a flow chart of a whole-house appliance simulation method provided in Embodiment 3 of the present invention. After adjusting the home appliance intelligent body according to the operation data in step S204 to obtain the adjusted home appliance intelligent body, the whole-house appliance simulation method may further include the following steps: Step S301, loading the adjusted home appliance agent into the whole house floor plan, and establishing communication between the meta-agent and the adjusted home appliance agent.

[0033] The adjusted home appliance intelligent body is loaded into the whole house floor plan, and the control logic and parameter settings of the home appliance intelligent body are updated according to the adjusted home appliance intelligent body. For example, the floor heating before adjustment will be turned on when the temperature is lower than 10℃, and the floor heating after adjustment will start when the temperature is lower than 10℃ and turn off when it is higher than 25℃.

[0034] Establish communication between the meta-agent and these home appliance agents to ensure that the meta-agent can control and monitor the adjusted home appliance agents in real time to achieve automated management of home appliances throughout the house. Configure the connection between the meta-agent and each adjusted home appliance agent in the simulation system to ensure smooth communication. Set communication parameters (such as data transmission rate, communication frequency, etc.) according to the adjusted operating mode.

[0035] Step S302, returning to execute, according to the response condition of the home appliance intelligent body, starting the simulation of the home appliance intelligent body and the environmental sensor through the preset simulation data, and collecting the operation data of the home appliance intelligent body during the simulation process.

[0036] Among them, the purpose of automatically executing the system operation in a loop is achieved by executing the simulation of the home appliance intelligent body again according to the response conditions of the home appliance intelligent body.

[0037] Step S303: Evaluate the adjusted home appliance intelligent body according to the operation data to obtain an evaluation result.

[0038] Among them, after completing the simulation task, the collected operation data is sorted, new feature patterns or new agents are analyzed, and the degree of improvement of the adjusted home appliance agent in various indicators is analyzed. The valuable feature patterns are reported to the developers to provide reference for product optimization and improvement. The discovered agent is added to the constructed simulation environment and connected to the corresponding drive data for response. The developer observes whether the behavior characteristics of the agent have certain practical value. If so, the characteristics of the agent are recorded and used in the research and development of new home appliance agents or control algorithms. If not, the agent can be specially recorded and then deleted from the constructed simulation environment. Common evaluation indicators include but are not limited to: the reaction speed of the home appliance agent to external instructions or environmental changes, the ratio of the power consumption of the equipment to its output efficiency, reflecting the energy saving degree of the home appliance, based on user feedback or standardized measurement methods (such as comprehensive evaluation of temperature and humidity suitability), etc.

[0039] Based on the evaluation results, it is determined whether the performance of the home appliance intelligent body after adjustment has reached the expected goal, and the equipment with excellent performance and those that need improvement are identified. After the evaluation is completed, the evaluation results are fed back to the system for continuous optimization. For example, according to the set evaluation indicators, a comprehensive evaluation model (such as weighted scoring method, hierarchical analysis method, etc.) is constructed to assign different weights to each indicator. Or through model calculation, the comprehensive evaluation score of each home appliance intelligent body is obtained to help judge its overall performance.

[0040] In this embodiment, the meta-agent is connected to the whole house floor plan, the adjusted home appliance agent is loaded, and the simulation task is performed again, so as to collect the operation data of the home appliance agent during the simulation again, and then the adjusted home appliance agent is evaluated to obtain the evaluation result. In this way, more new operation data of the home appliance agent is collected, and then new behavior patterns are evaluated to further optimize the home appliance agent.

[0041] like Figure 4 FIG. 2 is a flow chart of a whole-house appliance simulation method provided by Embodiment 4 of the present invention. After obtaining a preset whole-house floor plan in step S201, the whole-house appliance simulation method may further include: Step S401, loading at least one bionic agent in the whole house floor plan, and establishing communication between the meta-agent and each bionic agent.

[0042] Among them, simulated intelligent agents refer to intelligent agents similar to biological characteristics, which are generated by using generative artificial intelligence technology, including biological information characteristics, physiological characteristics and psychological characteristics, and can simulate the activities of organisms in the home environment. Bionic intelligent agents are used to simulate the activities of humans or pets in the home environment or the needs of triggering the control of home appliances due to changes in environmental conditions. They can better make up for the shortcomings of insufficient biological information, physiological characteristics and human-computer interaction characteristics in the simulation process of previous home automation systems. For example, bionic intelligent agents can be constructed to simulate users of different genders and ages, and to simulate different types of pets such as cats and dogs, and simulate their movement and activities in the home environment.

[0043] Developers can select suitable agent models from the bionic agent library to ensure that they can meet the needs of the home environment. According to the layout of the whole house floor plan, the bionic agent is placed in the appropriate position. The bionic agent simulates real home behavior and can self-adjust according to environmental changes and user needs. After loading the bionic agent, connection configuration is required to ensure that the meta-agent can effectively control and monitor these bionic agents. For example, by sending test instructions to each bionic agent and observing its response, it is ensured that the bionic agent can timely feedback its state changes (such as working status, environmental perception, etc.) to the meta-agent.

[0044] Step S402, for any bionic intelligent agent, when executing simulation, the bionic intelligent agent generates a control instruction for any home appliance intelligent agent, and the control instruction is used to instruct the corresponding home appliance intelligent agent to perform corresponding actions.

[0045] Among them, during the simulation process, the bionic agent generates control instructions according to the environmental state and preset logic. The bionic agent first obtains the current environmental state through environmental sensors (such as temperature sensors, light sensors, etc.), analyzes the acquired data, determines whether the current environment meets the preset conditions, and generates specific control instructions based on the decision results. For example, a control instruction may be: "Air conditioning agent, set the mode to cooling, the target temperature is 24℃". And the control instruction can be transmitted to the corresponding home appliance agent through the preset communication protocol, so that the home appliance agent performs the corresponding operation.

[0046] In this embodiment, the behavior of real people is simulated by loading a bionic agent into the whole house layout, and the control of the meta-agent is accepted by establishing a connection with the meta-agent; during the simulation, the control instructions generated by the bionic agent to the home appliance agent are used to instruct the home appliance agent to perform corresponding actions. This reduces the need for human intervention and makes the home appliance simulation system more intelligent.

[0047] like Figure 5FIG. 1 is a flow chart of a whole-house appliance simulation method provided in Embodiment 5 of the present invention. Based on the above Embodiment 4, the whole-house appliance simulation method may further include: Step S501, the meta-agent is also used to collect control instructions generated by all bionic agents during the simulation process, and form associated data with the operating data of the home appliance agent after the control instructions.

[0048] Among them, the meta-agent monitors and directs the control instructions generated by the bionic agent in real time. These instructions contain information such as the target home appliance agent, operation type and parameters. Each generated control instruction will be recorded by the meta-agent, and the control instruction will be associated with its corresponding operation data to form a set of associated data structures, which associates each control instruction with its operation status data after execution. For example, the control instruction to turn off the lamp is associated with the lamp status (off) after its execution and energy consumption data (such as the change in power consumption before and after turning it off).

[0049] Step S502, adjusting the home appliance intelligent body according to the operation data to obtain the adjusted home appliance intelligent body, including: According to the operation data and the associated data, the home appliance intelligent body is adjusted to obtain the adjusted home appliance intelligent body.

[0050] Before adjusting the home appliance agent, it is necessary to first collect and analyze the operating data and related data. The operating data includes but is not limited to the on / off status of the device, working mode, temperature, humidity, light intensity, etc. The related data includes but is not limited to recording the control instructions generated by the bionic agent, including the instruction content, execution time, target home appliance agent, etc., and recording the operating state changes and energy consumption changes of the home appliance agent after executing the instructions.

[0051] Based on the results of data analysis, specific adjustment strategies are formulated to optimize the intelligent body of home appliances. For example, the operation mode of the device can be dynamically adjusted according to environmental data (such as temperature, humidity, light, etc.). For example, smart curtains can automatically adjust the opening and closing angle according to the light intensity; by analyzing the state changes of the device, the mode can be adjusted to reduce frequent start and stop, thereby improving the stability of the device operation.

[0052] In this embodiment, the control instructions generated by the bionic agent are collected by the meta-agent and combined with the data of the home appliance agent running after the control instructions to form associated data, and the home appliance agent is adjusted through the associated data to obtain a new home appliance agent. In this way, the device operation mode is dynamically adjusted according to the real-time needs of users and environmental changes, and different modes are adjusted to adapt to different application scenarios.

[0053] like Figure 6FIG. 4 is a flow chart of a whole-house appliance simulation method provided by Embodiment 6 of the present invention. The step S402 of generating a control instruction for any home appliance agent by a bionic agent may include the following steps: Step S601, obtaining the historical operation behavior of the preset user, as well as the current operating parameters of the home appliance intelligent body and the current monitoring parameters of the environmental sensor.

[0054] Among them, by collecting the historical operation records of users in the smart home system. Including but not limited to: the specific time of the operation, the home appliance intelligent body being operated, the operation instructions executed by the user, such as switch, adjustment, mode switching, etc., the specific parameters of the operation, such as temperature setting, brightness adjustment, etc., and collecting environmental sensors to monitor the current operating status of the home appliance intelligent body in real time. The operating parameters of the home appliance intelligent body include but are not limited to: whether the device is turned on, the current working mode of the device, such as standard mode, energy-saving mode, fast mode, etc. The current monitoring parameters of the environmental sensor include but are not limited to: current indoor temperature, current indoor humidity, current indoor and outdoor light intensity, current indoor air quality index (such as PM2.5, CO2 concentration, etc.).

[0055] Step S602: Generate predicted operating behavior based on historical operating behavior in combination with current operating parameters and monitoring parameters.

[0056] Among them, through time series analysis, the typical operation behaviors of users in different time periods are identified. For example, in the morning, curtains are usually closed and lights are turned on; through scene mode analysis, the operation preferences of users in different scenarios are identified. For example, in an entertainment scenario, users may turn on the speakers and adjust the lights. The user's operation habits and preferences are extracted as input features of the prediction model, for example, whether the user tends to use a specific device at a specific time.

[0057] Extract the current operating status and parameters of the device as input features of the prediction model, such as the current temperature setting and operating mode of the device, and extract the current monitoring parameters of the environment as input features of the prediction model, such as indoor temperature and light intensity.

[0058] Use the trained classification model to predict the user's next possible operation type, output the probability distribution of the operation type, and provide multiple possible operation options. For example, the probability of predicting that the user turns on the air conditioner is 80%, and the probability of adjusting the light is 20%.

[0059] Step S603, using the natural language model of the bionic intelligent agent, converting the predicted operation behavior into voice control instructions, the voice control instructions including control instructions for any home appliance intelligent agent.

[0060] The predicted operation behavior information is input into the trained natural language generation model, and the model generates natural language instructions based on the input information and selects a suitable speech synthesis model, such as a TTS model, a WaveNet model, etc. Through a suitable communication protocol (such as Wi-Fi, Zigbee, etc.), the parsed operation instructions are sent to the corresponding home appliance intelligent body to form control.

[0061] In this embodiment, by obtaining the historical operation behavior of the preset user, combined with the operating parameters of the home appliance intelligent body and the environmental monitoring parameters, the predicted operation behavior is generated, and the natural language model is used to convert the predicted operation behavior into voice control instructions for controlling the response of the home appliance intelligent body. By converting the predicted operation behavior into natural language instructions, the user can interact with the smart home system more intuitively and naturally, improving the intelligence level of the system.

[0062] like Figure 7 FIG. 2 is a flow chart of a whole-house home appliance simulation method provided by Embodiment 7 of the present invention. In step S203, according to the response condition of the home appliance intelligent body, the simulation of the home appliance intelligent body and the environmental sensor is started by using preset simulation data, and the operation data of the home appliance intelligent body during the simulation is collected, which may include the following steps: Step S701, for any smart home appliance, set the environmental sensor inside the smart home appliance to a first preset temperature for simulation, wherein the first preset temperature is greater than a second preset temperature. When the temperature inside the smart home appliance is greater than the second preset temperature, cooling begins.

[0063] Among them, according to specific needs, the value of the first preset temperature and the value of the second preset temperature are determined, and the second preset temperature is lower than the first preset temperature to ensure that the cooling operation can be triggered during the simulation process, and the current temperature value of the environmental sensor is read, and the read current temperature is compared with the second preset temperature. If the current temperature is greater than the second preset temperature, the cooling operation is triggered. For example, the first preset temperature of the environmental sensor in the air conditioner is set to 25°C, and the second preset temperature (i.e., the temperature in the environment) is set to 20°C, so that the air conditioner starts cooling during the simulation.

[0064] Step S702, collecting the refrigeration start-up data and refrigeration effect data of the home appliance intelligent body during the simulation process, and using the refrigeration start-up data and refrigeration effect data as the operation data of the home appliance intelligent body.

[0065] Among them, the startup time, temperature value, timestamp and other information of each cooling operation are recorded, and the parameters of the cooling effect, such as indoor temperature changes, cooling duration, etc., are recorded. The collected cooling startup data and cooling effect data are sorted out for subsequent analysis. For example, the cooling performance of the home appliance intelligent body is evaluated, such as the average cooling time, temperature changes, etc. In this embodiment, by simulating the preset temperature of any home appliance intelligent body, the refrigeration effect is achieved, and the refrigeration pneumatic data and refrigeration effect data are collected, which are used as the operation data of the home appliance intelligent body. By analyzing the refrigeration startup data and effect data, the performance of the home appliance intelligent body can be evaluated, which helps to determine whether the intelligent body can meet the needs of users.

[0066] like Figure 8 FIG. 2 is a flow chart of a whole-house appliance simulation method provided by Embodiment 8 of the present invention. The step S201 of obtaining a preset whole-house floor plan may include the following steps: Step S801, obtaining a floor plan, and obtaining house layout information based on the floor plan file.

[0067] Developers can obtain standard floor plan files of houses from housing developers or trading websites and then build a simulation environment. Floor plans can be two-dimensional representations of the internal structure of a house, showing the layout, size and relative position of each room. They can be drawn manually or by using image recognition algorithms to identify the house type and furniture. For example, CAD (computer-aided design) software or other drawing tools can be used to manually draw floor plans.

[0068] Developers can use image processing technology to automatically identify and extract house layout information or directly read geometric information in files through CAD parsing libraries. For example, grayscale and binarization of floor plans can be performed to identify room boundaries and walls, edge detection algorithms (such as the Canny operator) can be used to identify the boundaries of walls, doors and windows, and contour detection algorithms can be used to identify the shape and size of each room.

[0069] Step S802, drawing a whole house floor plan based on the house layout information, combined with house space parameters and reserved positions for home appliances.

[0070] Among them, choose a suitable drawing tool, such as Matplotlib in Python or CAD software, draw the floor plan of the whole house according to the layout information of the house, and refer to the spatial parameters of the house and the reserved positions of home appliances. After the drawing is completed, the floor plan can be saved as an image file or other format for subsequent use.

[0071] In this embodiment, the house layout information is obtained through the house plan file, and the whole house plan is drawn according to the house layout information combined with the house space parameters and the reserved positions of the home appliances. The whole house layout is displayed graphically to ensure the accuracy of the simulation data and provide reliable basic data for subsequent simulation analysis.

[0072] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0073] like Fig. 9 As shown, it is a schematic diagram of a whole-house appliance simulation device provided in Embodiment 9 of the present invention, and the whole-house appliance simulation device corresponds to the whole-house appliance simulation method in the above-mentioned embodiment. The whole-house appliance simulation device includes a whole-house floor plan construction module 91, a meta-agent construction module 92, a simulation module 93 and an analysis module 94. The functional modules are described in detail as follows: The whole house floor plan construction module 91 is used to obtain a preset whole house floor plan, load the corresponding home appliance intelligent body in the reserved position of the home appliance in the whole house floor plan, and load the environmental sensor in the preset space position in the whole house floor plan; A meta-agent building module 92 is used to build a meta-agent in the whole house floor plan, and the meta-agent communicates with all home appliance agents and all environmental sensors; The simulation module 93 is used to start the simulation of the home appliance intelligent body and the environmental sensor according to the response conditions of the home appliance intelligent body through the preset simulation data, and collect the operation data of the home appliance intelligent body during the simulation process; The analysis module 94 is used to adjust the home appliance intelligent body according to the operation data to obtain the adjusted home appliance intelligent body.

[0074] Optionally, the whole-house appliance simulation device further includes: The adjustment agent access module is used to adjust the home appliance agent according to the operation data, and after obtaining the adjusted home appliance agent, load the home appliance agent after the mode adjustment in the whole house floor plan, and establish a connection between the meta-agent and the home appliance agent after the mode adjustment; The return execution module is used to return to the execution and start the simulation of the home appliance intelligent body and the environmental sensor through the preset simulation data according to the response condition of the home appliance intelligent body, and collect the operation data of the home appliance intelligent body during the simulation; The evaluation module is used to evaluate the home appliance intelligent body after mode adjustment according to the operation data to obtain the evaluation result.

[0075] Optionally, the whole-house appliance simulation device further includes: A bionic agent loading module is used to load at least one bionic agent into the whole house floor plan after obtaining a preset whole house floor plan, and establish a connection between the meta-agent and each bionic agent; The control instruction generation module is used to generate control instructions for any home appliance intelligent body through the bionic intelligent body when performing simulation. The control instructions are used to instruct the corresponding home appliance intelligent body to perform corresponding actions.

[0076] Optionally, the whole-house appliance simulation device further includes: Agent data association module, the meta-agent is also used to collect the control instructions generated by all bionic agents during the simulation process, and form associated data with the operation data of the home appliance agent after the control instructions; The mode adjustment module is used to adjust the home appliance intelligent body according to the operation data to obtain the adjusted home appliance intelligent body.

[0077] Optionally, the mode adjustment module includes: The joint data adjustment unit is used to adjust the home appliance intelligent body according to the operation data and the associated data to obtain the adjusted home appliance intelligent body.

[0078] Optionally, the control instruction generating module includes: A data acquisition unit, used to acquire the historical operation behavior of a preset user, as well as the current operation parameters of the home appliance intelligent body and the current monitoring parameters of the environmental sensor; The behavior generation unit generates predicted operation behaviors based on historical operation behaviors combined with current operation parameters and monitoring parameters; The voice conversion unit is used to convert the predicted operation behavior into voice control instructions using the natural language model of the bionic intelligent body, and the voice control instructions include control instructions for any home appliance intelligent body.

[0079] Optionally, the return execution module includes: The temperature simulation unit is used to set the environment sensor inside the home appliance intelligent body to a first preset temperature for simulation for any home appliance intelligent body, wherein the first preset temperature is greater than a second preset temperature, and when the temperature inside the home appliance intelligent body is greater than the second preset temperature, cooling is started; The data acquisition unit is used to collect the refrigeration startup data and refrigeration effect data of the home appliance intelligent body during the simulation process, and use the refrigeration startup data and refrigeration effect data as the operation data of the home appliance intelligent body.

[0080] Optionally, the whole house floor plan construction module 91 includes: A floor plan acquisition unit, used to acquire a floor plan and obtain house layout information based on the floor plan file; The whole house drawing unit is used to draw the whole house floor plan according to the house layout information, combined with the house space parameters and the reserved positions of household appliances.

[0081] For the specific definition of the whole-house appliance simulation device, please refer to the definition of the whole-house appliance simulation method above, which will not be repeated here. Each module in the above-mentioned whole-house appliance simulation device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0082] like Fig.10 As shown, it is a schematic diagram of the structure of a computer device provided in Embodiment 10 of the present invention. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a whole-house home appliance simulation method is implemented.

[0083] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the whole-house appliance simulation method in the above embodiment is implemented, for example Figures 2 to 8 Alternatively, when the processor executes the computer program, the functions of each module / unit in the embodiment of the whole-house appliance simulation device are realized, for example Fig. 9 The functions of the meta-agent building module 92 shown are not described here in detail to avoid repetition.

[0084] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the whole-house appliance simulation method in the above embodiment is implemented. Figures 2 to 8 Alternatively, when the computer program is executed by the processor, the functions of each module / unit in the embodiment of the whole-house appliance simulation device are realized, for example Fig. 9 The functions of the analysis module 94 are not described here in detail to avoid repetition. The computer-readable storage medium may be non-volatile or volatile.

[0085] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0086] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0087] The embodiments described above 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 aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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, and should all be included in the protection scope of the present invention.

Claims

1. A whole-house appliance simulation method, characterized in that: include: Obtain a preset whole-house floor plan, load a corresponding home appliance intelligent body in a reserved position of a home appliance in the whole-house floor plan, and load an environmental sensor in a preset spatial position in the whole-house floor plan; Constructing a meta-agent in the whole house floor plan, wherein the meta-agent communicates with all home appliance agents and all environmental sensors; According to the response condition of the home appliance intelligent body, the simulation of the home appliance intelligent body and the environmental sensor is started through preset simulation data, and the operation data of the home appliance intelligent body during the simulation process is collected; The home appliance intelligent body is adjusted according to the operation data to obtain an adjusted home appliance intelligent body.

2. The whole-house appliance simulation method according to claim 1, characterized in that: After adjusting the home appliance intelligent body according to the operation data to obtain the adjusted home appliance intelligent body, the method further includes: Loading the adjusted home appliance agent in the whole house floor plan, and establishing communication between the meta-agent and the adjusted home appliance agent; Returning to execute the response condition of the home appliance intelligent body, starting the simulation of the home appliance intelligent body and the environmental sensor through preset simulation data, and collecting the operation data of the home appliance intelligent body during the simulation; The adjusted home appliance intelligent body is evaluated according to the operating data to obtain an evaluation result.

3. The whole-house appliance simulation method according to claim 1, characterized in that: After obtaining the preset whole house floor plan, the method further includes: Load at least one bionic intelligent agent in the whole house floor plan, and establish communication between the meta-agent and each bionic intelligent agent; For any bionic intelligent agent, when executing simulation, the bionic intelligent agent generates control instructions for any home appliance intelligent agent, and the control instructions are used to instruct the corresponding home appliance intelligent agent to perform corresponding actions.

4. The whole-house appliance simulation method according to claim 3, characterized in that: The whole-house appliance simulation method further comprises: The meta-agent is also used to collect control instructions generated by all bionic agents during the simulation process, and form associated data with the operation data of the home appliance agent after the control instructions; The step of adjusting the home appliance intelligent body according to the operation data to obtain an adjusted home appliance intelligent body includes: The home appliance intelligent body is adjusted according to the operation data and the associated data to obtain an adjusted home appliance intelligent body.

5. The whole-house appliance simulation method according to claim 3, characterized in that: The step of generating control instructions for any household appliance intelligent body through the bionic intelligent body includes: Acquire the historical operation behavior of the preset user, as well as the current operating parameters of the home appliance intelligent body and the current monitoring parameters of the environmental sensor; Generate a predicted operating behavior based on the historical operating behavior, combined with the current operating parameters and the monitoring parameters; The predicted operation behavior is converted into voice control instructions using the natural language model of the bionic intelligent body, and the voice control instructions include control instructions for any home appliance intelligent body.

6. The whole-house appliance simulation method according to claim 1, characterized in that: The step of starting the simulation of the home appliance intelligent body and the environmental sensor according to the response condition of the home appliance intelligent body through preset simulation data, and collecting the operation data of the home appliance intelligent body during the simulation process includes: For any smart home appliance, setting the environment sensor inside the smart home appliance to a first preset temperature for simulation, wherein the first preset temperature is greater than a second preset temperature, and when the temperature inside the smart home appliance is greater than the second preset temperature, cooling is started; The refrigeration startup data and refrigeration effect data of the home appliance intelligent body are collected during the simulation process, and the refrigeration startup data and the refrigeration effect data are used as the operation data of the home appliance intelligent body.

7. The whole-house appliance simulation method according to claim 1, characterized in that: The obtaining of a preset whole house floor plan includes: Obtaining a floor plan, and obtaining house layout information based on the floor plan file; The whole house floor plan is drawn according to the house layout information, combined with house space parameters and reserved positions of home appliances.

8. A device for simulating household appliances in a whole house, characterized in that: include: A whole house floor plan construction module is used to obtain a preset whole house floor plan, load corresponding home appliance intelligent bodies in the reserved positions of home appliances in the whole house floor plan, and load environmental sensors in the preset spatial positions in the whole house floor plan; A meta-agent building module, used to build a meta-agent in the whole house floor plan, wherein the meta-agent communicates with all home appliance agents and all environmental sensors; A simulation module, used to start the simulation of the home appliance intelligent body and the environmental sensor according to the response condition of the home appliance intelligent body through preset simulation data, and collect the operation data of the home appliance intelligent body during the simulation process; The analysis module is used to adjust the home appliance intelligent body according to the operation data to obtain an adjusted home appliance intelligent body.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the whole-house appliance simulation method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the whole-house appliance simulation method as described in any one of claims 1 to 7 is implemented.

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