Smart home multi-device control system and method based on Internet of Things

Abstract

The invention discloses a smart home multi-device control system and method based on the Internet of Things, and relates to the field of smart home, and the method comprises the steps: obtaining the multi-dimensional data of a plurality of smart home devices through the Internet of Things, calculating the multi-dimensional features, and constructing a multi-dimensional feature vector; calculating a multi-dimensional coincidence index according to the multi-dimensional feature vector, and calculating a behavior connectivity according to historical user behavior data; a comprehensive association score is calculated according to the multi-dimensional fit index and the behavior connectivity, and when the state of any smart home device changes, a user behavior with the comprehensive association score larger than a threshold value is executed; and when the redundancy control conflict exists, performing priority ranking on the user behaviors needing to be executed according to a priority regulation and control algorithm, generating an execution control sequence, and performing multi-device control. Through multi-dimensional data analysis and intelligent integrating degree calculation, automatic linkage of intelligent household equipment is realized, a control strategy is learned and optimized based on behavior connectivity, user demands are accurately matched, and household comfort is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home, and specifically provides a multi-device control system and method for smart home based on the Internet of Things. Background Art

[0002] In recent years, with the rapid development of Internet of Things (IoT) technology, the types of smart home devices have been continuously enriched, including smart lights, smart air conditioners, smart door locks, smart security systems, etc. These devices are usually interconnected through wireless communication protocols such as Wi-Fi, Bluetooth, ZigBee, or NB-IoT, and can be remotely controlled through a mobile phone APP, voice assistant, or smart central control device. However, the existing smart home device control systems still have the following problems: The devices operate independently and lack intelligent linkage. Most of the current smart home devices operate independently and can only respond to a single instruction from the user. For example, the user can remotely turn on the air conditioner or adjust the light brightness, but there is no intelligent coordination between the devices, and they cannot be automatically adjusted according to the user's habits and environmental changes. The device control logic is single and cannot adapt to complex scenarios. The control rules of the existing systems are usually based on preset simple rules, such as "IF-THEN" logic. When the user returns home (IF the device detects the user's location), the lights are automatically turned on (THEN). Such rules are difficult to adapt to complex actual situations. The utilization of user behavior data is insufficient, and personalized control cannot be achieved. Although some smart home systems can record the user's operation logs, most of them lack data analysis and intelligent decision-making capabilities, resulting in the inability to automatically learn the user's habits and the lack of intelligent recommendation and prediction capabilities.

[0003] In summary, the existing multi-device control methods for smart homes have the following main deficiencies: The devices operate in isolation and lack intelligent linkage: They cannot achieve adaptive regulation according to the environmental state and device state; The control rules are single and cannot handle complex scenarios: The existing smart home control mainly relies on fixed logic and is difficult to automatically optimize the control strategy according to the user's behavior pattern; The lack of user behavior data analysis makes it difficult to adapt personalized settings: The existing smart home devices have limited learning ability for user behavior and fail to use big data to optimize the control strategy. Summary of the Invention

[0004] Based on the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a multi-device control system and method for smart home based on the Internet of Things to solve the above technical problems.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A multi-device control method for smart home based on the Internet of Things, including:

[0006] Obtain multi-dimensional data of multiple smart home devices through the Internet of Things, calculate multi-dimensional features, and construct a multi-dimensional feature vector based on the multi-dimensional data and multi-dimensional features;

[0007] Calculate a multi-dimensional matching index based on the multi-dimensional feature vector, and calculate the behavior connection degree according to the historical user behavior data;

[0008] Calculate a comprehensive correlation score based on the multi-dimensional matching index and the behavior connection degree. When the state of any smart home device changes, execute the user behavior with a comprehensive correlation score greater than the threshold;

[0009] When there is a redundant control conflict in the user behavior to be executed, sort the priorities of the user behavior to be executed according to the preset priority regulation algorithm, generate an execution control sequence, and perform multi-device control.

[0010] The present invention is further configured such that the multi-dimensional data includes device status data, environmental status data, and user behavior data. The device status data includes switch status, device health status, operating load, and the number of device error occurrences; the environmental status data includes temperature, humidity, ambient light intensity, noise level, and oxygen concentration; the user behavior data includes behavior time, behavior device, behavior device parameters, behavior device parameter values, and execution time interval. Specifically, in the device status data, the switch status indicates the current switch status of the device. If the device is in the working state, it is "on", denoted as 1; if the device is not working, it is "off", denoted as 0, and the current switch status of the device is obtained through the built-in status monitoring system of the device; the device health status reflects the current operating state of the device, whether there are faults or abnormalities. The health status is quantified by the usage time and the number of faults, which is prior art and will not be elaborated here; the operating load represents the workload of the device when executing tasks, and the load of the device is obtained through power metering devices, sensors, or software interfaces; the number of device error occurrences indicates the number of errors that occur in the device within a certain period of time, reflecting the reliability and stability of the device, and the error log of the device will record device faults, abnormalities, or warning messages.Through the device's logging system, the error count of each device can be obtained; in the environmental status data, temperature represents the temperature in the environment, and temperature data is collected in real time by temperature sensors installed indoors or in the device, and the data is sent to the system for processing via the Internet of Things protocol; humidity represents the humidity level in the environment, indicating the content of water vapor in the air, and humidity data is collected by humidity sensors and sent to the system for real-time monitoring via the network protocol; environmental light intensity represents the intensity of light in the environment, which affects the working mode of the intelligent lighting system, and the light intensity is monitored by installing light sensors, and the data is transmitted to the central control system via the Internet of Things protocol for processing; noise level represents the noise intensity in the environment, reflecting the quietness of the environment, and the environmental noise intensity is collected in real time by noise sensors, and the data is sent to the system via the network for real-time analysis; oxygen concentration represents the oxygen concentration in the environment, which is used in the air quality monitoring system, and a low oxygen concentration will trigger air purifiers or ventilation equipment, and the oxygen concentration is detected in real time by oxygen sensors and the data is sent to the system via the network; in the user behavior data, behavior time represents the specific time when the user performs a certain behavior, reflecting the user's operation habits and helping with the automatic adjustment of the device, and the timestamp of the user's operation is obtained through the device control system or application interface; behavior device represents the name or type of the device operated by the user, and by the user's operation log or interface interaction record, it can be determined which devices the user is operating, and the device name or ID will be recorded by the smart home system or control platform; behavior device parameter represents the parameters related to the device, and the specific parameter settings of the device are obtained through the device's API or control system; behavior device parameter value represents the specific value of the device parameter, such as the set temperature of the air conditioner and the brightness value of the light at a specific moment, and the device control system will record the value of each parameter through the interface or device feedback data; execution time interval represents the execution duration of the user behavior, indicating the time interval from the start to the completion of the task, and the start time and end time of the task are calculated through the timestamps in the user operation log.

[0011] The present invention is further configured such that the multi-dimensional features include device stability features, resource coordination features, and flexible response features;

[0012] The calculation logic of the device stability feature is as follows: D hh is the device stability feature, F(t) is the number of error reports within the time window t, and F max is the maximum allowable number of error reports; specifically, the device stability feature is used to measure the stability of the device over a period of time, and is specifically evaluated by the ratio of the number of error reports of the device within the time window to the maximum allowable number of error reports. If the number of error reports of the device is too high within a certain period of time, its stability is poor. By comparing the number of error reports of the device with the maximum allowable number of error reports, the stability of the device can be evaluated more accurately;

[0013] The calculation logic of the resource coordination feature is as follows: S ld is the resource coordination feature, γ is the adjustment coefficient, and L is the device operation load; specifically, the resource coordination feature is an index used to measure the load distribution efficiency of the device during multitask or multi-device collaborative control, helping to evaluate how the device reasonably adjusts its working state under different load conditions to ensure the smooth completion of tasks and avoid overload or inefficient operation; the adjustment coefficient γ is a coefficient that controls the influence degree of the load on the resource coordination, and its value range is [1, 10]; by adjusting the load and the adjustment coefficient, the resource coordination ability of the device under different loads can be dynamically evaluated;

[0014] The calculation logic of the flexible response feature is as follows: S st is the flexible response feature, η is the device response sensitivity coefficient, n is the number of environmental factors, λ i is the weight coefficient of the i-th environmental factor, α i is the target response value under the i-th environmental factor, Δx i is the deviation value under the i-th environmental factor. Specifically, the flexible response feature is used to measure the response ability of the device when facing environmental changes, reflecting how the device adjusts its working state according to environmental changes to cope with external environmental changes. The larger the flexible response feature value, the stronger the device's adaptability to environmental changes and the more flexible it can adjust its working state; the smaller the value, the weaker the device's adaptability, and there may be problems such as response delay or poor adaptability. The response ability of the device to the external environment is affected by multiple factors, such as temperature, humidity, light, air pressure, etc. Each environmental factor will affect the working efficiency and adaptability of the device. For each environmental factor, the deviation of the device's response to this environmental factor is calculated through the target response value α i of the device and the actual response Δx i of the device, indicating the gap between the device's current working efficiency and the expected target under specific environmental conditions. Calculate the deviation of the flexible response: the deviation value Δx i reflects the difference between the actual response and the target response of the device. A larger deviation indicates that the device has poor adaptability to this environmental factor, slow response or inability to achieve the expected effect. In order to strengthen the penalty effect on the deviation, the square of the deviation (α i -Δx i ) 2 is used in the calculation, making the influence of a larger deviation on the final response feature more significant. Each environmental factor has a different influence on the flexible response feature, and the weight coefficient λ i is used to adjust the importance of each environmental factor. The weight coefficient λ iAdjustments are made according to the characteristics of different devices and environments so that the impact of each environmental factor on the device response characteristics meets actual needs. The sensitivity coefficient η controls the device's response speed to environmental changes. If the device is extremely sensitive to environmental changes and responds quickly, the sensitivity coefficient is small; if the device responds slowly and has low sensitivity, the coefficient is large. By adjusting η, the device's response capability is more in line with actual application requirements. Finally, the weighted sum of environmental factor deviations is divided by the sensitivity coefficient to obtain the device's flexible response characteristics under environmental changes. If the device's response under all environmental factors is close to the expected value (i.e., the deviation is small), the value of the flexible response characteristic will be large, indicating that the device has a strong flexible response capability.

[0015] The present invention is further configured to set the device health status, the number of device error reports and the device stability characteristics as device status characteristics;

[0016] Setting the switch state, operation load and resource coordination characteristics as the device operation characteristics;

[0017] Set temperature, humidity, ambient light intensity, noise level, oxygen concentration, and dynamic response characteristics as environmental response characteristics.

[0018] The present invention is further configured that the multi-dimensional fit index includes a device fit index, a functional fit index and an environmental fit index;

[0019] The calculation logic of the device fit index is: ICI is the equipment fit index, θ i is the weight of the i-th dimension of the device status feature, S 1i and S 2i is the parameter value of device 1 and 2 in the i-th dimension; specifically, the device fit index is used to measure the fit of two devices in the multi-dimensional feature space, indicating the similarity of the devices in their respective state feature dimensions. The higher the device fit index, the greater the similarity of the two devices in each feature dimension, and vice versa, it means that the devices are quite different in some aspects. The device fit index is an indicator that comprehensively evaluates the similarity of devices in multiple dimensional features. By calculating the differences in each feature dimension of the device, weighting processing, and standardization, the fit between devices can be obtained to help optimize the selection and coordination of devices. Through this index, the efficiency of multi-device collaboration can be improved, and the stability and reliability of the system can be improved;

[0020] The calculation logic of the functional fit index is: FAI is the functional fit index, δ k is the weight of the kth dimension of the device operation feature, D 1k and D 2k is the parameter value of device 1 and 2 in the kth dimension, σ kis the adjustment index; specifically, the functional fit index is used to measure the functional fit degree of two devices in multiple dimensions, especially the degree of difference in device operation characteristics. By comparing the characteristic differences of devices in different dimensions, the functional fit index can help evaluate the similarity of two devices when performing similar tasks. If the devices have small differences in multiple key characteristic dimensions, their functional fit degree is high; otherwise, the functional fit degree is low. The functional fit index evaluates the functional matching degree of devices by calculating the differences and weights of devices in multiple characteristic dimensions. By adjusting the weight coefficient and the adjustment index, the influence of each characteristic can be flexibly controlled, so that this index can be widely applied to scenarios such as device matching, collaborative work, and multi-device management, thereby improving the overall efficiency, stability, and collaborative work ability of devices;

[0021] The calculation logic of the environmental fit index is as follows: ERI is the environmental fit index, and λ j is the weight of the j-th dimension of the environmental response characteristic, and E 1j and E 2j are the parameter values of Device 1 and Device 2 in the j-th dimension, and ω E is the sensitive parameter; specifically, the environmental fit index is used to measure the fit degree of two devices in the environmental characteristic dimension, indicating the similarity of Device 1 and Device 2 in multiple environmental response characteristic dimensions. By calculating the performance differences of devices under different environmental factors, the environmental fit index helps evaluate whether the devices can effectively perform the same task under similar environmental conditions. If the two devices have similar performances in multiple environmental dimensions, the environmental fit degree is high; if the differences are large, the fit degree is low. The environmental fit index helps evaluate the adaptability of devices under the same environmental conditions by calculating the differences and weights of devices in multiple environmental characteristic dimensions. By adjusting the weight coefficient and the sensitive parameter, the influence of environmental differences on the fit degree calculation can be flexibly controlled, so that this index is applicable to various devices and environmental conditions, thereby improving the efficiency, stability, and collaborative work ability of devices.

[0022] The present invention is further set such that the calculation logic of the behavior connection degree is as follows: D coact(O1, O2) is the behavior connection degree of behaviors O1 and O2, Count(O1→O2) is the number of times behavior O2 is executed after executing behavior O1, and Count(O1) is the number of times behavior O1 is executed. Specifically, the behavior connection degree is used to measure the correlation or dependence between a certain behavior and subsequent behaviors. Specifically, it represents the frequency of executing behavior O2 after executing behavior O1, thereby revealing the strength of the correlation between behaviors. The higher the behavior connection degree, the stronger the correlation between these two behaviors, and there may be a certain dependence relationship or pattern. The behavior connection degree is an important indicator for measuring the correlation or dependence between two behaviors. By calculating the logarithmic transformation of the frequency of executing behavior O2 after executing behavior O1, the strength of the connection between behaviors can be revealed. This calculation logic provides a tool for behavior analysis and optimization for intelligent systems, which helps to optimize device collaboration, personalized services, and system scheduling.

[0023] The present invention is further set as follows. The calculation logic of the comprehensive correlation score is: S(O1, O2) = λ1·(β1·ICI + β2·FAI + β3·ERI) + λ2·D coact (O1, O2), S(O1, O2) is the comprehensive correlation score, λ1 and λ2 are weighting coefficients, and their sum is 1. β1, β2, and β3 are the weight coefficients of the device fit index, function fit index, and environment fit index, and their sum is 1.

[0024] The present invention is further set as follows. The construction logic of the priority control algorithm is:

[0025] When there is a redundant control conflict, calculate the task priority according to the device importance of the device corresponding to the user behavior and the behavior urgency of the executed behavior;

[0026] Sort the control sequence in ascending order according to the task priority and perform multi-device control.

[0027] The present invention is further set as follows. The calculation logic of the task priority is: Drg is the task priority, P i is the device importance of the device corresponding to the i-th user behavior, is the weight coefficient for adjusting the influence of behavior urgency on the execution order, T i is the behavior urgency of the executed behavior corresponding to the i-th user behavior.

[0028] The present invention also provides an Internet of Things-based intelligent home multi-device control system for implementing the above-mentioned Internet of Things-based intelligent home multi-device control method, including:

[0029] Feature construction module: Obtain multi-dimensional data of multiple intelligent home devices through the Internet of Things, calculate multi-dimensional features, and construct a multi-dimensional feature vector according to the multi-dimensional data and multi-dimensional features;

[0030] The first calculation module: calculates a multi-dimensional fit index based on the multi-dimensional feature vector, and calculates a behavior connection degree based on historical user behavior data;

[0031] The second calculation module: calculates a comprehensive correlation score based on the multi-dimensional fit index and the behavior connection degree, and executes user behaviors with a comprehensive correlation score greater than the threshold when the state of any smart home device changes;

[0032] The control module: when there are redundant control conflicts in the user behaviors to be executed, sorts the priorities of the user behaviors to be executed according to a preset priority regulation algorithm, generates an execution control sequence, and performs multi-device control.

[0033] The present invention provides a multi-device control system and method for a smart home based on the Internet of Things. The method obtains multi-dimensional data of multiple smart home devices through the Internet of Things, calculates multi-dimensional features, and constructs a multi-dimensional feature vector based on the multi-dimensional data and multi-dimensional features; calculates a multi-dimensional fit index based on the multi-dimensional feature vector, and calculates a behavior connection degree based on historical user behavior data; calculates a comprehensive correlation score based on the multi-dimensional fit index and the behavior connection degree, and executes user behaviors with a comprehensive correlation score greater than the threshold when the state of any smart home device changes; when there are redundant control conflicts in the user behaviors to be executed, sorts the priorities of the user behaviors to be executed according to a preset priority regulation algorithm, generates an execution control sequence, and performs multi-device control. The beneficial effects generated include:

[0034] 1. Realize the automatic linkage of smart home devices: Through multi-dimensional data analysis, construct a multi-dimensional feature vector, realize the intelligent linkage between devices, improve the intelligent level, reduce user manual operations, and improve home comfort;

[0035] 2. Improve the accuracy of device control: Use multi-dimensional fit index calculation, including device fit index, function fit index, and environment fit index, to evaluate the collaborative adaptation degree between devices, and ensure that the executed control strategy meets user needs;

[0036] 3. Intelligently learn user habits and optimize control strategies: Use behavior connection degree calculation to learn the historical operation patterns of users, construct personalized control strategies, and automatically adjust the device operation mode according to user usage habits, improving the personalization and convenience of the user experience.

[0037] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically exemplified below. Brief Description of the Drawings

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. In the drawings:

[0039] Figure 1 It is a flowchart of a method for controlling multiple smart home devices based on the Internet of Things shown in an exemplary embodiment of the present invention;

[0040] Figure 2 It is a schematic structural diagram of a system for controlling multiple smart home devices based on the Internet of Things shown in an exemplary embodiment of the present invention. Detailed implementation manners

[0041] The following will illustrate the implementation manners of the present invention with reference to the drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention, rather than for limiting the protection scope of the present invention.

[0042] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and proportion of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0043] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0044] Embodiment 1

[0045] A method for controlling multiple smart home devices based on the Internet of Things, as Figure 1 shown, includes:

[0046] Obtaining multi-dimensional data of multiple smart home devices through the Internet of Things, calculating multi-dimensional features, and constructing a multi-dimensional feature vector based on the multi-dimensional data and multi-dimensional features;

[0047] Calculate the multi-dimensional fit index based on the multi-dimensional feature vector, and calculate the behavior connection degree according to the historical user behavior data;

[0048] Calculate the comprehensive correlation score according to the multi-dimensional fit index and the behavior connection degree. When the state of any smart home device changes, execute the user behavior with the comprehensive correlation score greater than the threshold;

[0049] When there is a redundant control conflict in the user behavior to be executed, sort the priorities of the user behavior to be executed according to the preset priority control algorithm, generate an execution control sequence, and perform multi-device control.

[0050] The present invention is further configured that the multi-dimensional data includes device status data, environmental status data, and user behavior data. The device status data includes switch status, device health status, operating load, and the number of device error reports; the environmental status data includes temperature, humidity, environmental light intensity, noise level, and oxygen concentration; the user behavior data includes behavior time, behavior device, behavior device parameters, behavior device parameter values, and execution time interval.

[0051] The present invention is further configured that the multi-dimensional features include device stability features, resource coordination features, and flexible response features;

[0052] The calculation logic of the device stability feature is: D hh is the device stability feature, F(t) is the number of error reports within the time window t, and F max is the maximum allowable number of error reports;

[0053] The calculation logic of the resource coordination feature is: S ld is the resource coordination feature, γ is the adjustment coefficient, and L is the device operating load;

[0054] The calculation logic of the flexible response feature is: S st is the flexible response feature, η is the device response sensitivity coefficient, n is the number of environmental factors, and λ i is the weight coefficient of the i-th environmental factor, and α i is the target response value for the i-th environmental factor, and Δx i is the deviation value for the i-th environmental factor.

[0055] The present invention is further configured to set the device health status, the number of device error reports, and the device stability feature as device status features;

[0056] Set the switch status, operating load, and resource coordination feature as device operation features;

[0057] Set temperature, humidity, ambient light intensity, noise level, oxygen concentration, and flexible response characteristics as environmental response characteristics.

[0058] The present invention is further configured such that the multi-dimensional fit index includes a device fit index, a function fit index, and an environment fit index;

[0059] The calculation logic of the device fit index is: ICI is the device fit index, θ i is the weight of the i-th dimension of the device state characteristics, S 1i and S 2i are the parameter values of devices 1 and 2 in the i-th dimension;

[0060] The calculation logic of the function fit index is: FAI is the function fit index, δ k is the weight of the k-th dimension of the device operation characteristics, D 1k and D 2k are the parameter values of devices 1 and 2 in the k-th dimension, σ k is the adjustment index;

[0061] The calculation logic of the environment fit index is: ERI is the environment fit index, λ j is the weight of the j-th dimension of the environmental response characteristics, E 1j and E 2j are the parameter values of devices 1 and 2 in the j-th dimension, ω E is the sensitive parameter.

[0062] The present invention is further configured such that the calculation logic of the behavior connection degree is: D coact (O1, O2) is the behavior connection degree of behaviors O1 and O2, Count(O1→O2) is the number of times behavior O2 is executed after executing behavior O1, and Count(O1) is the number of times behavior O1 is executed.

[0063] The present invention is further configured such that the calculation logic of the comprehensive correlation score is: S(O1, O2 ) = λ1·(β1·ICI + β2·FAI + β3·ERI) + λ2·D coact (O1, O2), S(O1, O2) is the comprehensive correlation score, λ1 and λ2 are weighting coefficients, and their sum is 1, and β1, β2, and β3 are the weight coefficients of the device fit index, the function fit index, and the environment fit index, and their sum is 1.

[0064] The present invention is further configured such that the construction logic of the priority regulation algorithm is:

[0065] When there is a redundant control conflict, calculate the task priority according to the device importance of the device corresponding to the user behavior and the behavior urgency of the execution behavior;

[0066] Sort in ascending order according to the task priority, execute the control sequence, and perform multi-device control.

[0067] The present invention is further configured that the calculation logic of the task priority is: Drg is the task priority, P i is the device importance of the device corresponding to the i-th user behavior, is the weight for adjusting the influence of the behavior urgency on the execution order, T i is the behavior urgency of the execution behavior corresponding to the i-th user behavior.

[0068] Embodiment 2

[0069] Please refer to Figure 2 , an exemplary multi-device control system for smart home based on the Internet of Things, which is used to implement the above-mentioned multi-device control method for smart home based on the Internet of Things, including:

[0070] Feature construction module: Obtain multi-dimensional data of multiple smart home devices through the Internet of Things, calculate multi-dimensional features, and construct a multi-dimensional feature vector according to the multi-dimensional data and multi-dimensional features;

[0071] First calculation module: Calculate the multi-dimensional fit index according to the multi-dimensional feature vector, and calculate the behavior connection degree according to the historical user behavior data;

[0072] Second calculation module: Calculate the comprehensive correlation score according to the multi-dimensional fit index and the behavior connection degree, and execute the user behavior with a comprehensive correlation score greater than the threshold when the state of any smart home device changes;

[0073] Control module: When there is a redundant control conflict in the user behavior to be executed, perform priority sorting on the user behavior to be executed according to the preset priority regulation algorithm, generate an execution control sequence, and perform multi-device control.

[0074] It should be noted that the above-mentioned multi-device control system for smart home based on the Internet of Things provided by the above embodiment and the above-mentioned multi-device control method for smart home based on the Internet of Things belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiment, and will not be repeated here. In practical applications, the above-mentioned multi-device control system for smart home based on the Internet of Things provided by the above embodiment can, according to needs, allocate the above functions to different functional modules, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above. This is not limited here either.

[0075] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0076] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context before and after.

[0077] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following items" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0078] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0079] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0080] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0081] In several embodiments provided in this application, it should be understood that the disclosed systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be electrical, mechanical, or other forms.

[0082] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0084] When the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0085] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A smart home multi-device control method based on the Internet of Things, characterized in that: include: Obtain multidimensional data of multiple smart home devices through the Internet of Things, calculate multidimensional features, and construct a multidimensional feature vector based on the multidimensional data and multidimensional features; Calculate the multidimensional fit index based on the multidimensional feature vector and calculate the behavioral connection degree based on the historical user behavior data; The comprehensive association score is calculated based on the multi-dimensional fit index and the behavioral connection degree. When the state of any smart home device changes, the user behavior with a comprehensive association score greater than the threshold is executed; When there is a redundant control conflict in the user behavior that needs to be executed, the user behavior that needs to be executed is prioritized according to the preset priority control algorithm, and an execution control sequence is generated to perform multi-device control.

2. According to the method for controlling multiple devices in a smart home based on the Internet of Things according to claim 1, it is characterized in that: Multidimensional data includes device status data, environmental status data and user behavior data. Device status data includes switch status, device health status, operating load and number of device errors; environmental status data includes temperature, humidity, ambient light intensity, noise level and oxygen concentration; user behavior data includes behavior time, behavior device, behavior device parameters, behavior device parameter values ​​and execution time interval.

3. According to claim 2, a smart home multi-device control method based on the Internet of Things is characterized in that: The multi-dimensional features include equipment stability, resource coordination, and flexible response. The calculation logic of the device stability feature is: D hh is the stability characteristic of the device, F9t) is the number of errors in the time window t, and F max The maximum number of allowed error reports; The calculation logic of resource coordination features is: S ld is the resource coordination feature, γ is the adjustment coefficient, and L is the equipment operation load; The calculation logic of the smart response feature is: S st is the dynamic response characteristic, η is the equipment response sensitivity coefficient, n is the number of environmental factors, λ i is the weight coefficient of the i-th environmental factor, α i is the target response value of the i-th environmental factor, Δx i is the deviation value of the i-th environmental factor.

4. The method for controlling multiple devices in a smart home based on the Internet of Things according to claim 3, characterized in that: Set the device health status, device error count and device stability characteristics as device status characteristics; Setting the switch state, operation load and resource coordination characteristics as the device operation characteristics; Set temperature, humidity, ambient light intensity, noise level, oxygen concentration, and dynamic response characteristics as environmental response characteristics.

5. The method for controlling multiple devices in a smart home based on the Internet of Things according to claim 1, characterized in that: The multi-dimensional fit index includes the equipment fit index, the functional fit index, and the environmental fit index; The calculation logic of the device fit index is: ICI is the equipment fit index, θ i is the weight of the i-th dimension of the device status feature, S 1i and S 2i are the parameter values ​​of devices 1 and 2 in the i-th dimension; The calculation logic of the functional fit index is: FAI is the functional fit index, δ k is the weight of the kth dimension of the device operation feature, D 1k and D 2k is the parameter value of device 1 and 2 in the kth dimension, σ k is the adjustment index; The calculation logic of the environmental fit index is: ERI is the environmental fit index, λ j is the weight of the jth dimension of the environmental response feature, E 1j and E 2j The parameter values ​​of devices 1 and 2 in the jth dimension, ω E is a sensitive parameter.

6. The method for controlling multiple devices in a smart home based on the Internet of Things according to claim 5, characterized in that: The calculation logic of behavior connection is: D coact 9O1,O2) is the behavior connection degree of behaviors O1 and O2, Count(O1→O2) is the number of times behavior O2 is executed after behavior O1 is executed, and Count(O1) is the number of times behavior O1 is executed.

7. The method for controlling multiple devices in a smart home based on the Internet of Things according to claim 6, characterized in that: The calculation logic of the comprehensive correlation score is: S(O1,O2)=λ1·(β1·ICI+β2·FAI+β3·ERI)+λ2·D coact 9O1,O2), S(O1,O2) is the comprehensive correlation score, λ1 and λ2 are weighting coefficients, and the sum is 1, β1, β2 and β3 are the weighting coefficients of the device fit index, function fit index and environment fit index, and the sum is 1.

8. The method for controlling multiple devices in a smart home based on the Internet of Things according to claim 1, characterized in that: The construction logic of the priority control algorithm is: When there is a redundant control conflict, the task priority is calculated based on the device importance of the device corresponding to the user behavior and the behavior urgency of the execution behavior; Sort tasks in ascending order of priority, execute control sequences, and control multiple devices.

9. The method for controlling multiple devices in a smart home based on the Internet of Things according to claim 8, characterized in that: The calculation logic of task priority is: Drg is the task priority, P i is the device importance of the device corresponding to the i-th user behavior, To adjust the weight of the impact of behavior urgency on the execution order, T i is the behavior urgency of the execution behavior corresponding to the i-th user behavior.

10. A smart home multi-device control system based on the Internet of Things, used to implement a smart home multi-device control method based on the Internet of Things as described in any one of claims 1 to 9, characterized in that: include: Feature construction module: obtain multidimensional data of multiple smart home devices through the Internet of Things, calculate multidimensional features, and construct multidimensional feature vectors based on the multidimensional data and multidimensional features; The first calculation module: calculates the multidimensional fit index based on the multidimensional feature vector and calculates the behavior connection degree based on the historical user behavior data; The second calculation module: calculates the comprehensive association score based on the multi-dimensional fit index and the behavior connection degree. When the state of any smart home device changes, executes the user behavior with a comprehensive association score greater than the threshold; Control module: When there is a redundant control conflict in the user behavior that needs to be executed, the user behavior that needs to be executed is prioritized according to the preset priority control algorithm, and an execution control sequence is generated to perform multi-device control.

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