Household equipment control method and system based on multi-grid high-precision sensing

Abstract

The invention discloses a household equipment control method and system based on multi-grid high-precision sensing. The method comprises the following steps: acquiring a plurality of echo data acquired by radar equipment arranged in a plurality of grid areas of a household area; according to the echo data, user track distribution in each grid region is determined; according to the user track distribution, determining an equipment control demand corresponding to at least one grid region; and on the basis of a preset corresponding relationship between a control demand and a control strategy, according to the equipment control demand, determining a control instruction of the home equipment in the at least one grid region. Therefore, accurate user behavior driving equipment control based on gridding radar perception can be realized, home intelligent responsiveness and regional management efficiency are improved, and the equipment adjustment lag risk caused by the fact that user activities are not perceived is reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a home appliance control method and system based on multi-grid high-precision sensing. Background Technology

[0002] With the rapid popularization of smart home systems in multi-zone management, users are increasingly valuing device-driven control and regionalized response through precise user behavior perception. Existing technologies typically collect echo data by setting up single or a small number of radar devices in the home area, using simple motion detection or fixed rules to determine user activity, and generating control commands for home devices based on standard triggering mechanisms to support basic intelligent responses. However, existing solutions lack trajectory distribution analysis of echo data from multiple grid areas and dynamic prediction of the corresponding control needs, making it difficult to achieve precise regionalized device control driven by user behavior. Commonly used global or delayed perception strategies cannot adapt to complex home layouts, resulting in insufficient intelligent responsiveness and management efficiency. Undetected user activity can easily lead to delayed or ineffective device adjustments, limiting the actual convenience and energy efficiency of the home system. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a home device control method and system based on multi-grid high-precision sensing, which can realize precise user behavior-driven device control based on gridded radar perception, improve the smart responsiveness and regional management efficiency of the home, and reduce the risk of device adjustment lag caused by unsensed user activities.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a home appliance control method based on multi-grid high-precision sensing, the method comprising: Acquire multiple echo data points from radar devices located in multiple grid zones within a residential area; Based on the echo data, determine the distribution of user trajectories in each of the grid regions; Based on the user trajectory distribution, determine at least one device control requirement corresponding to the grid area; Based on the pre-defined correspondence between control requirements and control strategies, control instructions for at least one home appliance in the grid area are determined according to the device control requirements.

[0005] As an optional implementation, in the first aspect of the invention, the radar device is a millimeter-wave device that collects echo data of the grid area of ​​0.1-1 square meters at 1-10 frames every 0.1-10 seconds.

[0006] As an optional implementation, in the first aspect of the invention, determining the user trajectory distribution in each of the grid regions based on the echo data includes: For each grid region, a set of echo data associated with that grid region is determined from all the echo data; The echo data set is input into the user trajectory analysis model corresponding to the grid area to obtain the user trajectory distribution in the grid area.

[0007] As an optional implementation, in the first aspect of the present invention, the user trajectory analysis model is a neural network model, which is trained using a training dataset that includes training echo data corresponding to multiple grid regions and corresponding user trajectory annotations.

[0008] As an optional implementation, in a first aspect of the invention, determining the set of echo data associated with the grid region from all the echo data includes: For each echo data, determine whether the location of the radar device corresponding to the echo data is within the grid area to obtain a first determination result; When the first determination result is yes, the echo data is included in the echo data set related to the grid area; If the first judgment result is negative, determine whether the distance between the device location and the center point of the grid area is less than a preset distance threshold to obtain a second judgment result; If the second judgment result is negative, the echo data will not be included in the echo data set related to the grid area; When the second judgment result is yes, it is determined whether the echo data conforms to the reflection echo rule corresponding to the grid area, and a third judgment result is obtained; When the third determination result is yes, the echo data is included in the echo data set related to the grid area; If the third determination result is negative, the echo data will not be included in the echo data set related to the grid area.

[0009] As an optional implementation, in the first aspect of the present invention, the step of determining whether the echo data conforms to the reflection echo rule corresponding to the grid area to obtain a third determination result includes: Calculate the average data similarity between the echo data and multiple historical reflected echo data corresponding to the grid area to obtain the probability of the reflected echo; Determine whether the probability of the reflected echo is greater than a preset threshold to obtain a third determination result.

[0010] As an optional implementation, in the first aspect of the present invention, determining the device control requirements corresponding to at least one of the grid regions based on the user trajectory distribution includes: For each grid region, the corresponding region parameters and the user trajectory distribution are input into the trained user equipment control demand prediction model to obtain the output control demand intensity and control demand content; the control demand content includes control equipment and control commands. Identify at least one related grid region from the other grid regions that is associated with this grid region; Calculate the average value of the control demand intensity corresponding to the grid region and all the related grid regions to obtain the control necessity parameter; If the control necessity parameter is greater than a preset parameter threshold, the grid area is determined as the grid area that needs to be controlled.

[0011] As an optional implementation, in the first aspect of the invention, the relevant grid region satisfies one or more of the following rules: The echo data set corresponding to this grid area contains echo data of the radar equipment installed within the relevant grid area; The echo data set corresponding to the relevant grid area contains echo data of the radar equipment set in that grid area; The data similarity between the echo data set corresponding to the relevant grid region and the echo data set corresponding to the grid region is greater than a preset similarity threshold.

[0012] A second aspect of this invention discloses a home appliance control system based on multi-grid high-precision sensing, the system comprising: The acquisition module is used to acquire multiple echo data obtained by radar devices located in multiple grid areas within the home area; The first determining module is used to determine the distribution of user trajectories in each of the grid regions based on the echo data; The second determining module is used to determine at least one device control requirement corresponding to the grid area based on the user trajectory distribution. The control module is used to determine control commands for at least one home appliance in the grid area based on the correspondence between preset control requirements and control strategies, and according to the device control requirements.

[0013] As an optional implementation, in a second aspect of the invention, the radar device is a millimeter-wave device that collects echo data of a grid area of ​​0.1-1 square meters at 1-10 frames every 0.1-10 seconds.

[0014] As an optional implementation, in a second aspect of the invention, the first determining module determines the specific method of user trajectory distribution in each of the grid regions based on the echo data, including: For each grid region, a set of echo data associated with that grid region is determined from all the echo data; The echo data set is input into the user trajectory analysis model corresponding to the grid area to obtain the user trajectory distribution in the grid area.

[0015] As an optional implementation, in the second aspect of the present invention, the user trajectory analysis model is a neural network model, which is trained using a training dataset that includes training echo data corresponding to multiple grid regions and corresponding user trajectory annotations.

[0016] As an optional implementation, in a second aspect of the invention, the specific method by which the first determining module determines the set of echo data related to the grid region from all the echo data includes: For each echo data, determine whether the location of the radar device corresponding to the echo data is within the grid area to obtain a first determination result; When the first determination result is yes, the echo data is included in the echo data set related to the grid area; If the first judgment result is negative, determine whether the distance between the device location and the center point of the grid area is less than a preset distance threshold to obtain a second judgment result; If the second judgment result is negative, the echo data will not be included in the echo data set related to the grid area; When the second judgment result is yes, it is determined whether the echo data conforms to the reflection echo rule corresponding to the grid area, and a third judgment result is obtained; When the third determination result is yes, the echo data is included in the echo data set related to the grid area; If the third determination result is negative, the echo data will not be included in the echo data set related to the grid area.

[0017] As an optional implementation, in a second aspect of the present invention, the specific method by which the first determining module determines whether the echo data conforms to the reflection echo rule corresponding to the grid region and obtains the third determination result includes: Calculate the average data similarity between the echo data and multiple historical reflected echo data corresponding to the grid area to obtain the probability of the reflected echo; Determine whether the probability of the reflected echo is greater than a preset threshold to obtain a third determination result.

[0018] As an optional implementation, in a second aspect of the invention, the first determining module determines the specific method by which it determines the device control requirements corresponding to at least one of the grid areas based on the user trajectory distribution, including: For each grid region, the corresponding region parameters and the user trajectory distribution are input into the trained user equipment control demand prediction model to obtain the output control demand intensity and control demand content; the control demand content includes control equipment and control commands. Identify at least one related grid region from the other grid regions that is associated with this grid region; Calculate the average value of the control demand intensity corresponding to the grid region and all the related grid regions to obtain the control necessity parameter; If the control necessity parameter is greater than a preset parameter threshold, the grid area is determined as the grid area that needs to be controlled.

[0019] As an optional implementation, in a second aspect of the invention, the relevant grid region satisfies one or more of the following rules: The echo data set corresponding to this grid area contains echo data of the radar equipment installed within the relevant grid area; The echo data set corresponding to the relevant grid area contains echo data of the radar equipment set in that grid area; The data similarity between the echo data set corresponding to the relevant grid region and the echo data set corresponding to the grid region is greater than a preset similarity threshold.

[0020] A third aspect of this invention discloses another home appliance control system based on multi-grid high-precision sensing, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the home device control method based on multi-grid high-precision sensing disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the home appliance control method based on multi-grid high-precision sensing disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention acquires echo data and determines the user trajectory distribution in each grid area by setting up radar devices in multiple grid areas of a home. Based on the trajectory distribution, it predicts the device control needs and generates home device control commands based on the corresponding relationships. This enables precise user behavior-driven device control based on gridded radar perception, improves the responsiveness of home intelligence and the efficiency of regional management, and reduces the risk of device adjustment lag caused by undetected user activities. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a home appliance control method based on multi-grid high-precision sensing disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of a home appliance control system based on multi-grid high-precision sensing disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another home appliance control system based on multi-grid high-precision sensing disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention discloses a home appliance control method and system based on multi-grid high-precision sensing. By using radar devices positioned in multiple grid areas within a home, echo data is acquired, and the user trajectory distribution within each grid is determined. Based on the trajectory distribution, device control needs are predicted, and control commands for home appliances are generated based on corresponding relationships. This enables precise user behavior-driven device control based on gridded radar perception, improving the responsiveness and regional management efficiency of smart homes, and reducing the risk of device adjustment lag due to undetected user activity. Detailed explanations follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a home appliance control method based on multi-grid high-precision sensing disclosed in an embodiment of the present invention. Wherein, Figure 1 The described home appliance control method based on multi-grid high-precision sensing can be applied to data processing systems / data processing devices / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 1 As shown, the home appliance control method based on multi-grid high-precision sensing can include the following operations: 101. Acquire multiple echo data from radar devices located in multiple grid areas within the home area.

[0032] Optionally, the radar device is a millimeter-wave device, which collects echo data of the grid area of ​​0.1-1 square meters at 1-10 frames every 0.1-10 seconds, which can effectively realize user action recognition and trajectory recognition within the grid with low data and high precision.

[0033] Optionally, the radar device can be a 60GHz millimeter-wave radar, a 24GHz ultra-wideband radar, or a multi-input multi-output radar array, supporting centimeter-level positioning accuracy; this invention does not impose any limitations.

[0034] Optionally, the multiple grid areas can divide the home space into regular grids of 2 meters × 2 meters or 1.5 meters × 1.5 meters, with a total number of grids of 20-80, which is not limited in this invention.

[0035] 102. Based on the echo data, determine the distribution of user trajectories in each grid area. Optionally, the user trajectory distribution may include user dwell time, movement path, action type (such as walking, sitting or lying down), or number of people distribution, which is not limited in this invention.

[0036] 103. Based on the distribution of user trajectories, determine the device control requirements corresponding to at least one grid area. Optionally, the control requirements for this device can be "brightening the living room lights", "cooling the bedroom air conditioner", or "turning on the kitchen range hood", and this invention does not limit these requirements.

[0037] 104. Based on the pre-defined correspondence between control requirements and control strategies, determine the control instructions for home appliances in at least one grid area according to the device control requirements.

[0038] Optionally, the control command can be a ZigBee, Wi-Fi, or infrared command, which can be directly sent to the motors of lights, air conditioners, or curtains. This invention does not limit the scope of the control command.

[0039] As can be seen, the above-mentioned embodiments of the invention acquire echo data and determine the user trajectory distribution in each grid area by setting radar devices in multiple grid areas of the home, predict device control needs based on trajectory distribution, and generate home device control commands based on the corresponding relationship. This enables precise user behavior-driven device control based on gridded radar perception, improves the responsiveness of home intelligence and regional management efficiency, and reduces the risk of device adjustment lag caused by undetected user activities.

[0040] As an optional embodiment, the step of determining the user trajectory distribution in each grid area based on the echo data in the above steps includes: For each grid region, determine the set of echo data associated with that grid region from all echo data; The echo data set is input into the user trajectory analysis model corresponding to the grid area to obtain the user trajectory distribution in the grid area.

[0041] Optionally, the echo data set may include radar echoes that are directly overlaid and indirectly reflected, which is not limited in this invention.

[0042] Optionally, the user trajectory analysis model is a neural network model, which is trained using a training dataset that includes training echo data corresponding to multiple grid regions and corresponding user trajectory annotations.

[0043] Optionally, the user trajectory analysis model can be a point cloud-based Transformer time series model trained on 500,000 home radar labeled data, and this invention does not limit it.

[0044] As can be seen, through the above optional embodiments, by selecting relevant echo datasets for each grid area and merging them into the user trajectory analysis model to obtain the user trajectory distribution, accurate grid user activity trajectory identification based on multi-source echo fusion is achieved, improving the accuracy and regional coverage of trajectory distribution analysis, and reducing the risk of trajectory misjudgment caused by mixed echo data.

[0045] As an optional embodiment, the step of determining the set of echo data related to the grid area from all echo data in the above steps includes: For each echo data, determine whether the location of the radar device corresponding to the echo data is within the grid area to obtain the first judgment result; If the first judgment result is yes, the echo data is included in the echo data set related to the grid area; If the first judgment result is negative, determine whether the distance between the device location and the center point of the grid area is less than a preset distance threshold to obtain the second judgment result; If the second judgment result is negative, the echo data will not be included in the echo data set related to this grid area; If the second judgment result is yes, determine whether the echo data conforms to the reflection echo rule corresponding to the grid area, and obtain the third judgment result; If the third judgment result is yes, the echo data is included in the echo data set related to the grid area; If the third judgment result is negative, the echo data will not be included in the echo data set related to that grid area.

[0046] Optionally, the reflected echo rule can be a multipath propagation characteristic, an intensity attenuation model, or a direction angle constraint; this invention does not limit this.

[0047] As can be seen, through the above optional embodiments, by determining whether the radar device's location is within the grid or within a nearby distance threshold and combining the reflection echo rules to filter echo data into the set, accurate echo data allocation based on dual filtering of location and reflection characteristics is achieved, improving the purity and reliability of the grid-related echo set and reducing the risk of trajectory analysis deviation caused by interference from echoes outside the grid.

[0048] As an optional embodiment, the step of determining whether the echo data conforms to the reflection echo rule corresponding to the grid area to obtain a third determination result includes: Calculate the average data similarity between the echo data and multiple historical reflected echo data corresponding to the grid area to obtain the probability of the reflected echo; The third judgment result is obtained by determining whether the probability of the reflected echo is greater than a preset threshold.

[0049] Optionally, the data similarity can be waveform cosine similarity or feature vector Euclidean distance; this invention does not impose any limitation.

[0050] As can be seen, through the above optional embodiments, the probability of a reflected echo is determined by calculating the average similarity between the echo data and the historical reflected echo data of the grid, thereby achieving accurate reflected echo identification based on historical pattern matching, improving the accuracy of determining the attribution of nearby radar echoes, and reducing the risk of echo data mismatch caused by reflection misjudgment.

[0051] As an optional embodiment, the step above, determining the device control requirements corresponding to at least one grid area based on the user trajectory distribution, includes: For each grid region, the corresponding region parameters and user trajectory distribution are input into the trained user equipment control demand prediction model to obtain the output control demand intensity and control demand content; at least one related grid region is determined from other grid regions. Calculate the average value of the control demand intensity corresponding to the grid region and all related grid regions to obtain the control necessity parameter; If the control necessity parameter is greater than the preset parameter threshold, the grid area is determined as the grid area that needs to be controlled.

[0052] Optionally, the control requirements include control devices and control commands.

[0053] Optionally, the user equipment control demand prediction model can be a 4-layer Transformer encoder (8 attention heads, 512 hidden dimensions) + multi-task output head, trained for 120 training epochs on 300,000 household activity-device demand labeled data, with a demand prediction accuracy of 95.6%. This invention does not limit the model.

[0054] Optionally, the average value of the control demand intensity can be a weighted average, with the weights related to the grid adjacency relationship; however, this invention does not impose any limitations on this.

[0055] Optionally, in subsequent steps, corresponding device control commands are generated only for the grid areas that need to be controlled.

[0056] As can be seen, through the above optional embodiments, by inputting grid area parameters and user trajectory distribution into the user equipment control demand prediction model to output the intensity and content of control demand, and combining the average value of relevant grid control demand to determine the necessity of control and the grids that need to be controlled, accurate control demand assessment based on model prediction and regional linkage is achieved, improving the pertinence and coordination of equipment control, and reducing the risk of unreasonable control caused by the isolation of single grid demand.

[0057] As an optional embodiment, in the above steps, the relevant grid area satisfies one or more of the following rules: The echo data set corresponding to this grid area contains echo data of the radar equipment set up in the relevant grid area; The echo data set corresponding to the relevant grid area contains echo data of the radar equipment set up in that grid area; The data similarity between the echo data set corresponding to the relevant grid area and the echo data set corresponding to that grid area is greater than the preset similarity threshold.

[0058] As can be seen, through the above optional embodiments, relevant grid areas are determined based on rules such as echo data cross-reference and data similarity, thereby achieving accurate inter-grid linkage identification based on perception association, improving the scientificity and comprehensiveness of the calculation of control necessity parameters, and reducing the risk of misjudgment of control requirements due to neglect of grid association.

[0059] Let's illustrate the implementation of the above technical solution with a specific example: As Mr. Wang entered the apartment through the entrance hall, the lights in the living room and kitchen automatically brightened.

[0060] The apartment was divided into 36 2m x 2m grids, and 8 60GHz millimeter-wave radars were deployed.

[0061] The radar real-time echo showed that Mr. Wang moved from grid G1 (entrance hall) to G12 (living room sofa), staying for a total of 45 seconds, and the action was judged as "sitting down to rest".

[0062] The system determines that the G12 grid is directly covered by radar, and the first judgment result is "yes". All echoes are then assigned to the G12 set.

[0063] The radar location in the neighboring grid G11 (coffee table area) is not within G12, but it is 1.8 meters away from the center point, which is less than the threshold of 2.5 meters. The echo conforms to the reflection rules (the intensity attenuation conforms to the wall reflection model). The reflection probability is 0.85, which is greater than the threshold of 0.78. The third judgment is "yes", so it is classified into set G12.

[0064] The predicted user trajectory distribution for G12 is: 45-second stay, seated, single person.

[0065] Input the Transformer demand forecasting model and output the control demand: intensity 0.92, content "Adjust the living room main light to warm light 70% brightness + turn on the air conditioner to 26℃ heating".

[0066] Relevant grids: G11, G13 (TV area), and G7 (restaurant) are determined to require control due to echo crossover or similarity >0.82 and average demand intensity 0.89 > threshold 0.7.

[0067] The system issues the following instructions: Set the living room ceiling light to 70% warm light, the air conditioner to 26℃ for heating, and play soft background music.

[0068] Mr. Wang can enjoy a comfortable environment as soon as he sits down without any manual operation. The system accurately identifies his rest needs and links multiple devices.

[0069] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a home appliance control system based on multi-grid high-precision sensing, as disclosed in an embodiment of the present invention. Figure 2 The described home appliance control system based on multi-grid high-precision sensing can be applied to data processing systems / data processing devices / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 2 As shown, the home appliance control system based on multi-grid high-precision sensing may include: The acquisition module 201 is used to acquire multiple echo data obtained by radar devices set in multiple grid areas of the home area.

[0070] The first determining module 202 is used to determine the distribution of user trajectories in each grid area based on the echo data. The second determining module 203 is used to determine the device control requirements corresponding to at least one grid area based on the user trajectory distribution. The control module 204 is used to determine the control instructions for home appliances in at least one grid area based on the correspondence between preset control requirements and control strategies and the device control requirements.

[0071] As can be seen, the above-mentioned embodiments of the invention acquire echo data and determine the user trajectory distribution in each grid area by setting radar devices in multiple grid areas of the home, predict device control needs based on trajectory distribution, and generate home device control commands based on the corresponding relationship. This enables precise user behavior-driven device control based on gridded radar perception, improves the responsiveness of home intelligence and regional management efficiency, and reduces the risk of device adjustment lag caused by undetected user activities.

[0072] As an optional embodiment, the radar device is a millimeter-wave device that acquires echo data of the grid area of ​​0.1-1 square meters at 1-10 frames every 0.1-10 seconds.

[0073] As can be seen, the above optional embodiments limit the details of the radar equipment to achieve lower power and lower data volume echo data acquisition, assisting in the realization of precise user behavior-driven device control based on gridded radar perception, and improving the responsiveness and regional management efficiency of smart homes.

[0074] As an optional embodiment, the first determining module determines the specific method of user trajectory distribution in each grid area based on the echo data, including: For each grid region, determine the set of echo data associated with that grid region from all echo data; The echo data set is input into the user trajectory analysis model corresponding to the grid area to obtain the user trajectory distribution in the grid area.

[0075] As can be seen, through the above optional embodiments, by selecting relevant echo datasets for each grid area and merging them into the user trajectory analysis model to obtain the user trajectory distribution, accurate grid user activity trajectory identification based on multi-source echo fusion is achieved, improving the accuracy and regional coverage of trajectory distribution analysis, and reducing the risk of trajectory misjudgment caused by mixed echo data.

[0076] As an optional embodiment, the user trajectory analysis model is a neural network model, which is trained using a training dataset that includes training echo data corresponding to multiple grid regions and corresponding user trajectory annotations.

[0077] As can be seen, the above optional embodiments limit the details of the user trajectory analysis model to achieve more accurate user trajectory prediction, assist in the realization of precise user behavior-driven device control based on gridded radar perception, and improve the responsiveness and regional management efficiency of smart homes.

[0078] As an optional embodiment, the first determining module determines the specific method by which it determines the set of echo data related to the grid region from all echo data, including: For each echo data, determine whether the location of the radar device corresponding to the echo data is within the grid area to obtain the first judgment result; If the first judgment result is yes, the echo data is included in the echo data set related to the grid area; If the first judgment result is negative, determine whether the distance between the device location and the center point of the grid area is less than a preset distance threshold to obtain the second judgment result; If the second judgment result is negative, the echo data will not be included in the echo data set related to this grid area; If the second judgment result is yes, determine whether the echo data conforms to the reflection echo rule corresponding to the grid area, and obtain the third judgment result; If the third judgment result is yes, the echo data is included in the echo data set related to the grid area; If the third judgment result is negative, the echo data will not be included in the echo data set related to that grid area.

[0079] As can be seen, through the above optional embodiments, by determining whether the radar device's location is within the grid or within a nearby distance threshold and combining the reflection echo rules to filter echo data into the set, accurate echo data allocation based on dual filtering of location and reflection characteristics is achieved, improving the purity and reliability of the grid-related echo set and reducing the risk of trajectory analysis deviation caused by interference from echoes outside the grid.

[0080] As an optional embodiment, the specific method by which the first determining module determines whether the echo data conforms to the reflection echo rule corresponding to the grid area, and obtains the third determination result, includes: Calculate the average data similarity between the echo data and multiple historical reflected echo data corresponding to the grid area to obtain the probability of the reflected echo; The third judgment result is obtained by determining whether the probability of the reflected echo is greater than a preset threshold.

[0081] As can be seen, through the above optional embodiments, the probability of a reflected echo is determined by calculating the average similarity between the echo data and the historical reflected echo data of the grid, thereby achieving accurate reflected echo identification based on historical pattern matching, improving the accuracy of determining the attribution of nearby radar echoes, and reducing the risk of echo data mismatch caused by reflection misjudgment.

[0082] As an optional embodiment, the first determining module determines the specific method by which it determines the device control requirements corresponding to at least one grid area based on the user trajectory distribution, including: For each grid region, the corresponding region parameters and user trajectory distribution are input into the trained user equipment control demand prediction model to obtain the output control demand intensity and control demand content; optionally, the control demand content includes control equipment and control commands. Identify at least one related grid region from other grid regions that is associated with this grid region; Calculate the average value of the control demand intensity corresponding to the grid region and all related grid regions to obtain the control necessity parameter; If the control necessity parameter is greater than the preset parameter threshold, the grid area is determined as the grid area that needs to be controlled.

[0083] As can be seen, through the above optional embodiments, by inputting grid area parameters and user trajectory distribution into the user equipment control demand prediction model to output the intensity and content of control demand, and combining the average value of relevant grid control demand to determine the necessity of control and the grids that need to be controlled, accurate control demand assessment based on model prediction and regional linkage is achieved, improving the pertinence and coordination of equipment control, and reducing the risk of unreasonable control caused by the isolation of single grid demand.

[0084] As an optional embodiment, the relevant grid area satisfies one or more of the following rules: The echo data set corresponding to this grid area contains echo data of the radar equipment set up in the relevant grid area; The echo data set corresponding to the relevant grid area contains echo data of the radar equipment set up in that grid area; The data similarity between the echo data set corresponding to the relevant grid area and the echo data set corresponding to that grid area is greater than the preset similarity threshold.

[0085] As can be seen, through the above optional embodiments, relevant grid areas are determined based on rules such as echo data cross-reference and data similarity, thereby achieving accurate inter-grid linkage identification based on perception association, improving the scientificity and comprehensiveness of the calculation of control necessity parameters, and reducing the risk of misjudgment of control requirements due to neglect of grid association.

[0086] Example 3 Please see Figure 3 , Figure 3 This is another home appliance control system based on multi-grid high-precision sensing disclosed in the embodiments of the present invention. Figure 3 The described home appliance control system based on multi-grid high-precision sensing is applied in a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the home appliance control system based on multi-grid high-precision sensing may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the home device control method based on multi-grid high-precision sensing described in Embodiment 1.

[0087] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of the home appliance control method based on multi-grid high-precision sensing described in Embodiment 1.

[0088] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the home appliance control method based on multi-grid high-precision sensing described in Embodiment 1.

[0089] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0091] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0092] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0097] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0098] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0099] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0100] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0101] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0102] Finally, it should be noted that the home appliance control method and system based on multi-grid high-precision sensing disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling home appliances based on multi-grid high-precision sensing, characterized in that, The method includes: Acquire multiple echo data points from radar devices located in multiple grid zones within a residential area; Based on the echo data, determine the distribution of user trajectories in each of the grid regions; Based on the user trajectory distribution, determine at least one device control requirement corresponding to the grid area; Based on the pre-defined correspondence between control requirements and control strategies, control instructions for at least one home appliance in the grid area are determined according to the device control requirements.

2. The home appliance control method based on multi-grid high-precision sensing according to claim 1, characterized in that, The radar device is a millimeter-wave device, which collects echo data of the grid area of ​​0.1-1 square meters at 1-10 frames every 0.1-10 seconds.

3. The home appliance control method based on multi-grid high-precision sensing according to claim 1, characterized in that, Determining the user trajectory distribution in each grid region based on the echo data includes: For each grid region, a set of echo data associated with that grid region is determined from all the echo data; The echo data set is input into the user trajectory analysis model corresponding to the grid area to obtain the user trajectory distribution in the grid area.

4. The home appliance control method based on multi-grid high-precision sensing according to claim 3, characterized in that, The user trajectory analysis model is a neural network model, which is trained using a training dataset that includes training echo data corresponding to multiple grid regions and corresponding user trajectory annotations.

5. The home appliance control method based on multi-grid high-precision sensing according to claim 3, characterized in that, The step of determining the set of echo data associated with the grid region from all the echo data includes: For each echo data, determine whether the location of the radar device corresponding to the echo data is within the grid area to obtain a first determination result; When the first determination result is yes, the echo data is included in the echo data set related to the grid area; If the first judgment result is negative, determine whether the distance between the device location and the center point of the grid area is less than a preset distance threshold to obtain a second judgment result; If the second judgment result is negative, the echo data will not be included in the echo data set related to the grid area; When the second judgment result is yes, it is determined whether the echo data conforms to the reflection echo rule corresponding to the grid area, and a third judgment result is obtained; When the third determination result is yes, the echo data is included in the echo data set related to the grid area; If the third determination result is negative, the echo data will not be included in the echo data set related to the grid area.

6. The home appliance control method based on multi-grid high-precision sensing according to claim 5, characterized in that, The process of determining whether the echo data conforms to the reflection echo rule corresponding to the grid area to obtain a third determination result includes: Calculate the average data similarity between the echo data and multiple historical reflected echo data corresponding to the grid area to obtain the probability of the reflected echo; Determine whether the probability of the reflected echo is greater than a preset threshold to obtain a third determination result.

7. The home appliance control method based on multi-grid high-precision sensing according to claim 1, characterized in that, The step of determining the device control requirements corresponding to at least one of the grid areas based on the user trajectory distribution includes: For each grid region, the corresponding region parameters and the user trajectory distribution are input into the trained user equipment control demand prediction model to obtain the output control demand intensity and control demand content; the control demand content includes control equipment and control commands. Identify at least one related grid region from the other grid regions that is associated with this grid region; Calculate the average value of the control demand intensity corresponding to the grid region and all the related grid regions to obtain the control necessity parameter; If the control necessity parameter is greater than a preset parameter threshold, the grid area is determined as the grid area that needs to be controlled.

8. The home appliance control method based on multi-grid high-precision sensing according to claim 7, characterized in that, The relevant grid region satisfies one or more of the following rules: The echo data set corresponding to this grid area contains echo data of the radar equipment installed within the relevant grid area; The echo data set corresponding to the relevant grid area contains echo data of the radar equipment installed in that grid area; The data similarity between the echo data set corresponding to the relevant grid region and the echo data set corresponding to the grid region is greater than a preset similarity threshold.

9. A home appliance control system based on multi-grid high-precision sensing, characterized in that, The system includes: The acquisition module is used to acquire multiple echo data obtained by radar devices located in multiple grid areas within the home area; The first determining module is used to determine the distribution of user trajectories in each of the grid regions based on the echo data; The second determining module is used to determine at least one device control requirement corresponding to the grid area based on the user trajectory distribution. The control module is used to determine control commands for at least one home appliance in the grid area based on the correspondence between preset control requirements and control strategies, and according to the device control requirements.

10. A home appliance control system based on multi-grid high-precision sensing, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the home device control method based on multi-grid high-precision sensing as described in any one of claims 1-8.

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