False alarm detection method and device, electronic equipment and storage medium

Abstract

The invention provides a false alarm detection method and device, electronic equipment and a storage medium, and relates to the technical field of Internet of Things. The method comprises the following steps: acquiring space state data reported by each intelligent device in a target space and acquiring target detection data reported by a target intelligent device; based on the spatial state indicated by the spatial state data, performing false alarm identification on the target detection data to obtain a false alarm detection result; the false alarm detection result is used for indicating whether the target intelligent equipment has a false alarm condition or not; and if the false alarm detection result indicates that a false alarm condition exists, adjusting equipment parameters of the target intelligent equipment according to the false alarm condition so as to indicate the target intelligent equipment to continuously detect the target space according to the adjusted equipment parameters. The problem that a control scheme cannot be executed according to expectation due to the fact that detection of intelligent equipment is not accurate enough in the related technology is solved.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and more specifically, to a false alarm detection method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the development of IoT technology, various smart devices have entered people's lives. Smart sensors can be used to detect the status of a space in real time, such as whether there are people, temperature, humidity, etc., so that users can better understand the occurrence of various space conditions.

[0003] After detecting spatial conditions, smart sensors can trigger corresponding target control schemes, such as turning lights on or off, based on the detection results. However, due to the inaccuracy of smart sensor detection, the detection results are prone to errors, which affects the user's understanding of the spatial conditions. Furthermore, the susceptibility of detection results to errors can also affect the erroneous execution of automated behaviors, leading to malfunctions of smart devices and impacting the user experience.

[0004] As can be seen from the above, the problem that the detection of intelligent devices is not accurate enough, resulting in the control scheme failing to perform as expected, still needs to be solved. Summary of the Invention

[0005] This application provides a false alarm detection method, apparatus, electronic device, and storage medium, which can solve the problem in related technologies where the detection of intelligent devices is not accurate enough, causing the target control scheme to fail to execute as expected. The technical solutions are as follows:

[0006] According to one aspect of this application, a false alarm detection method includes: acquiring spatial state data reported by each smart device in a target space and acquiring target detection data reported by a target smart device; identifying false alarms in the target detection data based on the spatial state indicated by the spatial state data to obtain a false alarm detection result; the false alarm detection result is used to indicate whether the target smart device has a false alarm; if the false alarm detection result indicates that a false alarm exists, adjusting the device parameters of the target smart device according to the false alarm to instruct the target smart device to continue detecting the target space according to the adjusted device parameters.

[0007] According to one aspect of this application, a false alarm detection device includes: an acquisition module, configured to acquire spatial state data reported by each smart device in a target space, and acquire target detection data reported by a target smart device; an identification module, configured to identify false alarms in the target detection data based on the spatial state indicated by the spatial state data, and obtain a false alarm detection result; the false alarm detection result is used to indicate whether the target smart device has a false alarm situation; and an adjustment module, configured to adjust the device parameters of the target smart device according to the false alarm situation if the false alarm detection result indicates that a false alarm situation exists, so as to instruct the target smart device to continue to detect the target space according to the adjusted device parameters.

[0008] In an exemplary embodiment, the acquisition module is further configured to acquire device data of each of the smart devices in the target space; the device data is data fed back by each of the smart devices and related to the target space; and to analyze the state of the target space based on the device data to obtain the space state data.

[0009] In an exemplary embodiment, the acquisition module is further configured to perform feature extraction based on the data of each of the devices to obtain target features; the target features are used to indicate the spatial state characteristics of the target space; perform state analysis on the target space according to the target features to determine the spatial state of the target space; and obtain the spatial state data based on the spatial state of the target space.

[0010] In an exemplary embodiment, the acquisition module is further configured to perform feature extraction based on the data of each of the devices to obtain target features; the target features are used to indicate the spatial characteristics of the target space; perform state analysis on the target space according to the target features to determine the spatial state of the target space; and obtain the spatial state data based on the spatial state of the target space.

[0011] In an exemplary embodiment, the identification module is further configured to determine whether the target detection data conforms to the spatial state indicated by the spatial state data; if it does not conform, it is determined that the target smart device has a false alarm and a corresponding false alarm detection result is generated.

[0012] In an exemplary embodiment, the spatial state data further includes user feedback data; the user feedback data is data related to the target space provided by the user; the identification module is further configured to determine that the target smart device has a false alarm when the target detection data does not conform to the user feedback data, and generate a corresponding false alarm detection result.

[0013] In an exemplary embodiment, the adjustment module is further configured to determine the target device parameters that need to be adjusted from the device parameters of the target smart device based on the false alarm situation; modify the target device parameters according to a set rule to instruct the target smart device to continue detecting the target space according to the adjusted device parameters; the target device parameters include at least one or more of sensing sensitivity and trigger threshold.

[0014] In an exemplary embodiment, the adjustment module is further configured to acquire user adjustment data; the user adjustment data is data generated by the user adjusting the device parameters of the target smart device; and adjust the corresponding device parameters of the target smart device according to the user adjustment data, so as to instruct the target smart device to continue to detect the target space according to the adjusted device parameters.

[0015] In an exemplary embodiment, the false alarm detection device is further configured to request false alarm identification of the target detection data if the target detection data is detected to meet the triggering conditions corresponding to the target control scheme; wherein, the target control scheme is configured to control the controlled device to perform corresponding automated operations by monitoring the target detection data reported by the target intelligent device.

[0016] In an exemplary embodiment, the false alarm detection device is further configured to, if the false alarm occurs, control the controlled device associated with the target intelligent device in the target control scheme to perform the correct automated operation based on the spatial state indicated by the spatial state data.

[0017] According to one aspect of this application, an electronic device includes at least one processor and at least one memory, wherein a computer program is stored on the memory; the computer program is executed by one or more of the processors, causing the electronic device to implement the false alarm detection method as described above.

[0018] According to one aspect of this application, a storage medium having a computer program stored thereon, the computer program being executed by one or more processors to implement the false alarm detection method as described above.

[0019] According to one aspect of this application, a computer program product includes a computer program stored in a storage medium, wherein one or more processors of an electronic device read the computer program from the storage medium, load and execute the computer program, causing the electronic device to implement the false alarm detection method as described above.

[0020] The beneficial effects of the technical solution provided in this application are:

[0021] In the above technical solution, false alarms are identified based on the spatial state indicated by the spatial state data reported by each intelligent device in the target space and the spatial state indicated by the target detection data reported by the target intelligent device, so as to determine whether the target intelligent device has made a false alarm. Based on the false alarm, the device parameters of the target intelligent device are adjusted, thereby avoiding the situation where the target control scheme associated with the target intelligent device cannot be executed as expected due to the unsuitable precision or accuracy of the target intelligent device. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram based on the implementation environment involved in this application;

[0024] Figure 2 This is a flowchart illustrating a false alarm detection method according to an exemplary embodiment;

[0025] Figure 3 yes Figure 2 A flowchart of step 310 in one embodiment corresponds to the following example;

[0026] Figure 4 yes Figure 3 A flowchart of step 313 in one embodiment corresponds to the following example;

[0027] Figure 5 yes Figure 2 A flowchart of step 330 in one embodiment corresponds to the following example;

[0028] Figure 6 This is a flowchart illustrating another false alarm detection method according to an exemplary embodiment;

[0029] Figure 7 This is a schematic diagram illustrating the specific implementation of a false alarm detection method in an application scenario;

[0030] Figure 8 This is a structural block diagram of a false alarm detection device according to an exemplary embodiment;

[0031] Figure 9 This is a hardware structure diagram of an electronic device according to an exemplary embodiment;

[0032] Figure 10 This is a structural block diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0034] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this disclosure means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0035] As mentioned earlier, the detection accuracy of smart sensors is not high enough, and the detection results are prone to errors.

[0036] It is understandable that due to the differences in the precision and accuracy of smart sensors, detection results are prone to errors. Errors in detection results can also affect the erroneous execution of automated behaviors, leading to equipment malfunctions.

[0037] Specifically, various intelligent sensors, such as temperature sensors, humidity sensors, light sensors, and human body sensors, are often configured in the target space to detect the target space and obtain its spatial status, such as temperature, humidity, light intensity, and whether anyone is present.

[0038] Of course, different target control schemes can be set for different spatial states. When the smart sensor detects a change in the spatial state, it can upload the device data to the server. Based on the device data, the server controls the corresponding smart device to perform operations according to the preset target control scheme.

[0039] However, due to differences in the precision and accuracy of smart sensors, the reported device data may be incorrect. In other words, the spatial state corresponding to the device data may not match the actual spatial state. For example, if the precision of the human body sensor varies, and the target space is empty, the human body sensor may mistakenly detect the wind blowing through the green vegetation as human activity. In this case, the reported device data will indicate that there is someone in the target space, thus triggering the target control scheme corresponding to "someone".

[0040] As can be seen from the above, there is still a defect in the relevant technologies: the detection of intelligent devices is not accurate enough, which leads to the target control scheme failing to perform as expected.

[0041] Therefore, the false alarm detection method provided in this application can effectively improve the accuracy of equipment control. Accordingly, this false alarm detection method is applicable to false alarm detection devices, which can be deployed on electronic devices. These electronic devices can be computer devices configured with a von Neumann architecture, such as desktop computers, laptops, servers, etc.; they can also be electronic devices with central control functions, such as gateways; and they can also be...

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0043] Figure 1 This is a schematic diagram of an implementation environment involved in a device control method. The implementation environment includes at least a user terminal 110, a smart device 130, a server 170, and network equipment. Figure 1 In this context, network devices include gateway 150 and router 190, but this is not intended to be a specific limitation.

[0044] The user terminal 110, which can also be considered as a user terminal or terminal, can deploy (or install) the client associated with the smart device 130. This user terminal 110 can be an electronic device such as a smartphone, tablet, laptop, desktop computer, smart control panel, or other device with display and control functions, and is not limited here.

[0045] The client, associated with the smart device 130, is essentially where the user registers an account and configures the smart device 130. For example, the configuration includes adding a device identifier to the smart device 130, so that when the client runs on the user terminal 110, it can provide the user with functions such as device display and device control of the smart device 130. This client can be in the form of an application or a web page. Correspondingly, the interface for displaying the device on the client can be in the form of a program window or a web page, and there is no limitation here.

[0046] Smart device 130 is deployed in gateway 150 and communicates with gateway 150 through its own configured communication module, thereby being controlled by gateway 150. It should be understood that smart device 130 generally refers to one of multiple smart devices 130. This application embodiment only uses smart device 130 as an example; that is, this application embodiment does not limit the number or type of smart devices deployed in gateway 150. In one application scenario, smart device 130 is deployed in gateway 150 by accessing it through a local area network. The process of smart device 130 accessing gateway 150 through a local area network includes: gateway 150 first establishes a local area network, and smart device 130 joins the local area network established by gateway 150 by connecting to it. This local area network includes, but is not limited to, ZIGBEE or Bluetooth. Among them, the smart device 130 can be a smart printer, smart fax machine, smart camera, smart air conditioner, smart door lock, smart light, or a human body sensor, door and window sensor, temperature and humidity sensor, water immersion sensor, natural gas alarm, smoke alarm, wall switch, wall socket, wireless switch, wireless wall sticker switch, cube controller, curtain motor, millimeter wave radar, etc., equipped with a communication module.

[0047] The interaction between user terminal 110 and smart device 130 can be achieved through a local area network (LAN) or a wide area network (WAN). In one application scenario, user terminal 110 establishes a wired or wireless communication connection with gateway 150 via router 190, such as Wi-Fi, allowing user terminal 110 and gateway 150 to be deployed on the same LAN, thus enabling user terminal 110 to interact with smart device 130 via the LAN path. In another application scenario, user terminal 110 establishes a wired or wireless communication connection with gateway 150 via server 170, such as 2G, 3G, 4G, 5G, or Wi-Fi, allowing user terminal 110 and gateway 150 to be deployed on the same WAN, thus enabling user terminal 110 to interact with smart device 130 via the WAN path.

[0048] The server-side 170 can also be considered as the cloud, cloud platform, platform side, server side, etc. This server-side 170 can be a single server, a server cluster consisting of multiple servers, or a cloud computing center consisting of multiple servers, in order to better provide backend services to a massive number of user terminals 110. For example, backend services include device control services.

[0049] In one application scenario, the smart device 130 includes a light sensor, a sound sensor, a human body sensor, and a smart light. These smart devices are deployed within a target space, where a pre-set target control scheme for the smart light is configured. This scheme controls the smart light to perform corresponding automated operations by monitoring target detection data reported by the human body sensor. If the target control scheme is triggered, the server 170 acquires spatial state data describing the spatial state of the target space. Further, after acquiring the spatial state data, the server 170 performs operations based on the spatial state data... The target detection data reported by the human body sensor is used for false alarm detection to obtain a false alarm detection result. For example, if the target detection data reported by the human body sensor indicates that the target space is occupied, but the space status data indicates that the target space is actually empty, the false alarm detection result will indicate that the human body sensor has made a false alarm. It can be understood that because of the false alarm, the target detection data reported by the human body sensor indicates that the target space is occupied, but the target space is actually empty. Therefore, based on the false alarm detection result, the smart lights associated with the human body sensor under the target control scheme can be controlled to perform automated operations that conform to the unoccupied state.

[0050] Of course, in other application scenarios, the device control process completed by the server 170 can also be implemented by the gateway 150. In this case, after the server 170 generates the false alarm detection result, it sends the false alarm detection result to the gateway 150 through the wide area network path, so that the gateway 150 can control the smart light associated with the human body sensor under the target control scheme based on the false alarm detection result and perform automated operations that conform to the unmanned state.

[0051] Please see Figure 2 This application provides a false alarm detection method, which is applicable to electronic devices, such as... Figure 1 The server 170 shown in the implementation environment can also be a gateway 150.

[0052] In the following method embodiments, for ease of description, the execution subject of each step of the method is an electronic device, but this does not constitute a specific limitation.

[0053] like Figure 2 As shown, the method may include the following steps:

[0054] Step 310: Obtain spatial status data reported by each intelligent device in the target space and target detection data reported by the target intelligent device.

[0055] Spatial state data is used to indicate the spatial state of the target space. Specifically, spatial state data can be used to describe the physical or environmental state of the target space, such as temperature, humidity, light intensity, noise level, and human activity.

[0056] In one possible implementation, the spatial state data is obtained by fusing and analyzing device data obtained from monitoring the target space by multiple intelligent devices in the target space. The intelligent devices deployed in the target space may include intelligent sensors, such as human body sensors, sound sensors, light sensors, etc., or other devices that integrate sensing functions, which are not limited here.

[0057] In another possible implementation, the spatial state data includes user feedback data, which is data related to the spatial state of the target space reported by the user. For example, if the target intelligent device reports a false alarm, or if the controlled device associated with the target intelligent device under the target control scheme incorrectly executes an automated operation, and the user performs an additional device control operation on the controlled device, then corresponding user feedback data will be obtained based on this additional device control operation. Alternatively, if the user views the target detection data reported by the target intelligent device through a user terminal, and finds that the target detection data does not match the actual spatial state of the target space, then the user can report the above situation through the user terminal, thereby generating user feedback data. It should be noted that the above solutions are only illustrative examples and do not constitute a limitation of the present invention.

[0058] Regarding the target space, it can refer to the space where the Internet of Things (IoT) is deployed. If the IoT is deployed in the living room, then the living room is the target space. If the IoT is deployed throughout the entire building, then the entire building is the target space. There is no limitation here.

[0059] Various smart devices are deployed in the target space and can connect to the Internet of Things (IoT) via local area networks or wide area networks. Then, the server can obtain the spatial status data reported by each smart device through the IoT.

[0060] Regarding the target intelligent device, it refers to the intelligent device for which false alarm identification is required. The target intelligent device can be any device used to trigger the execution of the target control scheme, and there is no limitation here. Specifically, the target control scheme can include triggering conditions, the controlled device and its set operations. If the server detects that the target detection data reported by the target intelligent device meets the triggering conditions, then it will instruct the controlled device to execute the set operations.

[0061] To further clarify, the target intelligent device can be an intelligent device capable of detecting the spatial state of the target space. Different target intelligent devices can detect different types of spatial states. For example, if the target intelligent device is a temperature sensor, then the target intelligent device can detect the temperature of the target space.

[0062] Based on this, target detection data is the data obtained by the target intelligent device from the target space. It can be used to indicate the spatial state determined by the target intelligent device from the target space detection. For example, if the target intelligent device is a temperature sensor, then the target intelligent device can be used to indicate the temperature determined by the temperature sensor from the target space detection.

[0063] Step 330: Based on the spatial state indicated by the spatial state data, perform false alarm identification on the target detection data to obtain false alarm detection results.

[0064] First, it should be noted that if the precision or accuracy of the target intelligent device is not appropriate, the target intelligent device will report the erroneous target detection data to the control end (such as the server end), which may cause the target control scheme to fail to execute or be executed incorrectly. If the above situation occurs, it is caused by a false alarm by the target intelligent device.

[0065] For example, the target smart device is a human body sensor, and the target control scheme is to control the smart light to turn on when the human body sensor detects that someone is in the target space. If the human body sensor malfunctions and cannot detect someone when someone is in the target space, then the smart light will not be able to successfully turn on. Conversely, if someone is detected when no one is in the target space, then the smart light will incorrectly turn on.

[0066] Based on this, false alarm identification can be used to detect whether the precision or accuracy of the target smart device is appropriate, and the false alarm detection results are used to indicate whether the target smart device has made a false alarm.

[0067] False alarm identification refers to determining whether the accuracy or precision of a target intelligent device is appropriate based on the spatial state indicated by the spatial state data and the target detection data. In other words, the accuracy or precision of a target intelligent device can be determined by comparing whether the spatial state indicated by the target detection data matches the spatial state indicated by the spatial state data.

[0068] It should be noted that since spatial state data can be obtained by fusing and analyzing device data from multiple smart devices in the target space, the spatial state indicated by the spatial state data should be closer to the actual spatial state of the target space. Therefore, the spatial state indicated by the spatial state data can be taken as the actual spatial state of the target space. Furthermore, by identifying false alarms in the target detection data based on the spatial state data, the corresponding false alarm detection results can be obtained.

[0069] Furthermore, based on the target detection data obtained by the target intelligent device in detecting the spatial state of the target space, the spatial state indicated by the target detection data can be determined. For example, if the target intelligent device is a human body sensor, and the human body sensor detects the presence of a person in the target space, then it will generate corresponding target detection data. Furthermore, it can be determined that the spatial state indicated by the target detection data is a person-occupied state. As another example, if the target intelligent device is a light sensor, and the light sensor detects a high light intensity in the target space, then it will generate corresponding target detection data. Accordingly, it can be determined that the spatial state indicated by the light sensor is a high light intensity state.

[0070] If the spatial state indicated by the target detection data matches the spatial state indicated by the spatial state data, it indicates that the precision or accuracy of the target intelligent device is appropriate and the target intelligent device has not made any false alarms; otherwise, it indicates that the precision or accuracy of the target intelligent device is inappropriate and the target intelligent device has made false alarms.

[0071] Based on this, by identifying false alarms in target detection data, we can avoid detection errors by intelligent target devices and prevent the target control scheme from failing to perform as expected.

[0072] Step 350: If the false alarm detection result indicates that there is a false alarm, the device parameters of the target smart device are adjusted according to the false alarm, so as to instruct the target smart device to continue to detect the target space according to the adjusted device parameters.

[0073] The target control scheme is used to monitor the target detection data reported by the target intelligent device, and when the spatial state indicated by the target detection data meets the triggering conditions, control the controlled device to perform corresponding automated operations. The controlled device is the device associated with the target intelligent device in the target control scheme.

[0074] As mentioned above, the spatial state indicated by the target detection data is the triggering condition for the target control scheme. If there are false alarms in the target detection data reported by the target intelligent device, it may cause the target control scheme to fail to execute or be executed incorrectly.

[0075] It should be noted that adjusting the device parameters of the target smart device based on the aforementioned false alarms can help analyze the possible causes of false alarms. For example, if the target smart device malfunctions, it can be replaced with a smart device of the same type that has the same functions as the target smart device and can continue to detect the spatial state of the desired target space, thereby avoiding subsequent false alarms.

[0076] In addition, improper installation of the target smart device may also cause false alarms. In this case, the target smart device needs to be installed in the target space in the correct way so that the target smart device can better detect the target space and thus avoid false alarms.

[0077] Furthermore, false alarms from target intelligent devices could also be caused by environmental interference. Specifically, environmental interference can refer to the presence of multiple interference sources in the target space, such as electromagnetic interference, noise interference, temperature and humidity changes, and light fluctuations. It can also refer to the presence of multiple targets in the target space, in which case the targets may be very close to each other or frequently intersect. Understandably, all of the above-mentioned environmental interference situations may prevent the target intelligent device from accurately detecting the target space, thus resulting in false alarms.

[0078] When environmental interference exists in the target space, irrelevant signals can be filtered out through anti-interference algorithms. In addition, environmental interference is closely related to the device parameter settings of the target intelligent device. In particular, device parameters such as sensitivity and accuracy play a key role in the performance and environmental adaptability of the target intelligent device. For example, the sensitivity of the target intelligent device can affect its response to small changes or signals in the target space. The higher the sensitivity, the easier it is for the target intelligent device to detect subtle environmental changes. If the sensitivity of the target intelligent device is set too high in a scenario with a lot of environmental interference, the target intelligent device will be very susceptible to slight interference (such as electromagnetic interference, changes in light, swaying of objects in the wind, etc.), leading to an increase in false alarms.

[0079] Therefore, if the device parameter settings of the target intelligent device are not adapted to the complex environment of the target space, the target intelligent device will be unable to detect the target space as expected, thus failing to obtain accurate target detection data.

[0080] It is understandable that if the target detection data is inaccurate, the execution of the corresponding target control scheme may occur in two ways. One is that the target control scheme is not executed when it is required. For example, the spatial state indicated by the target detection data has met the triggering conditions of the target control scheme, but the actual spatial state of the target space has not. The other is that the target control scheme is executed when it is not required. For example, the actual spatial state of the target space has met the triggering conditions of the target control scheme, but the spatial state indicated by the target detection data has not.

[0081] To avoid the aforementioned situation, when the false alarm detection results indicate that the target intelligent device has made a false alarm, that is, when the spatial state indicated by the target detection data does not match the spatial state indicated by the spatial state data, the device parameters of the target intelligent device can be adjusted so that the device parameter settings can adapt to the complex environment of the target space.

[0082] Before adjusting the device parameters of the target smart device, it is necessary to first determine the possible causes of false alarms. Specifically, the difference between the spatial state indicated by the target detection data and the spatial state indicated by the spatial state data can be determined to identify which device parameter of the target smart device is incorrectly set, thus causing the difference. The determined device parameters can then be adjusted accordingly to make the target smart device adapt to the target space after the device parameters are adjusted.

[0083] For example, target detection data indicates that the target intelligent device has detected the presence of a target (e.g., a person) in the target space, but the spatial state data indicates that there is actually no activity in the target space. In this case, it may be due to the target intelligent device's sensitivity being set too high, causing false detection. In this case, the sensitivity can be adjusted to reduce the sensitivity to avoid the target intelligent device mistakenly identifying interfering targets (e.g., wind blowing green plants) in the target space as the activity of a target (e.g., a person).

[0084] Therefore, by adjusting the device parameters of the target intelligent device according to the false alarm situation, the target intelligent device can more effectively adapt to the complex environment of the target space, thereby improving the accuracy of detection and avoiding the generation of inaccurate target detection data, thus avoiding the occurrence of false alarms.

[0085] Through the above process, false alarms are identified based on the spatial state data reported by each intelligent device in the target space and the spatial state indicated by the target detection data reported by the target intelligent device. This is to determine whether the target intelligent device has made a false alarm, and to adjust the device parameters of the target intelligent device according to the false alarm situation. This avoids the situation where the target control scheme associated with the target intelligent device cannot be executed as expected due to the unsuitable accuracy or precision of the target intelligent device.

[0086] Please see Figure 3 In one exemplary embodiment, step 310 may further include the following steps:

[0087] Step 311: Obtain device data for each smart device in the target space.

[0088] Among them, device data refers to data related to the target space fed back by various intelligent devices.

[0089] Regarding intelligent devices, these are devices distinct from the target intelligent device. It can be understood that if the target intelligent device reports a false alarm, then the precision or accuracy of the target intelligent device may also be flawed. In other words, the spatial state indicated by the target detection data it reports may also be incorrect.

[0090] Based on this, by providing more objective and accurate device data from intelligent devices that are different from the target intelligent devices, interference from target detection data can be avoided. Furthermore, the device data provided by each intelligent device can provide an independent reference for determining the spatial state of the target space, thereby verifying the target detection data of the target intelligent devices and correcting erroneous execution of the target control scheme.

[0091] Among them, intelligent devices can be intelligent devices with sensing functions that can continuously monitor the spatial status of the target space, such as human body sensors, light sensors, sound sensors and other intelligent devices.

[0092] Regarding device data, it can include device location, device status, environmental parameters, etc. Among them, different smart devices can monitor different types of environmental parameters, such as temperature, humidity, light intensity, noise level, and human activity. The type of environmental parameter is related to the sensing function of the smart device. For example, a light sensor has a light intensity sensing function, and its environmental parameter type is light intensity.

[0093] It is understandable that intelligent devices with different sensing functions are configured in the target space, and each intelligent device can continuously monitor the spatial status of the target space to obtain more comprehensive device data.

[0094] Step 313: Based on the data from each device, perform a state analysis on the target space to obtain spatial state data.

[0095] First, it should be noted that intelligent devices with different sensing functions can be deployed in the target space to monitor different types of environmental parameters. Similarly, multiple intelligent devices with the same sensing function can be deployed in different locations in the target space to obtain more comprehensive and accurate environmental parameters.

[0096] Therefore, based on the data from each device, state analysis can be performed on the target space. In one possible implementation, the state analysis of the target space is achieved through a state analysis model. The state analysis model is a machine learning model that has been trained and has the ability to perform state analysis on the target space. For example, the machine learning model can be a convolutional neural network (CNN), a recurrent neural network (RNN), or a support vector machine (SVM), a random forest, etc., without any limitation here.

[0097] It should be noted that when performing state analysis on the target space based on different types of environmental parameters in the equipment data, the correlation between different types of environmental parameters in each equipment data can also be analyzed to obtain more accurate spatial state data. For example, the relationship between light intensity and human activity: when the light intensity of the target space suddenly increases, it may be caused by human activity in the target space.

[0098] For example, by collecting data related to the target space, such as device data of smart devices in the target space, target detection data reported by the target smart devices, execution status of the target control scheme, and spatial state of the target space, the above data can be used to train until a false alarm detection model is obtained. The false alarm detection model can then be used to automatically determine whether the target smart device has made a false alarm.

[0099] Under the above embodiments, the spatial state of the target space can be analyzed based on the device data of each smart device, which can avoid interference from the target smart device and maintain the objectivity of each smart device. Furthermore, by performing state analysis through the device data of each smart device, the spatial state of the target space can be obtained comprehensively and accurately, thereby verifying the target detection data reported by the target smart device and correcting the erroneous execution of the target control scheme.

[0100] Please see Figure 4 In one exemplary embodiment, step 313 may further include the following steps:

[0101] Step 3131: Extract features based on data from each device to obtain target features.

[0102] Among them, target features are used to indicate the spatial state characteristics of the target space. Spatial state characteristics can be environmental state characteristics, such as temperature, humidity, light intensity, etc., or physical state characteristics, such as the intensity of human activity, etc., without limitation.

[0103] First, it should be noted that before extracting features from the data of each device, the data of each device can be preprocessed. For example, missing values, outliers and noise in the device data can be processed to ensure the data quality of the device data. Another example is to synchronize the device data of different smart devices according to timestamps to ensure the time consistency of the data of each device.

[0104] The target features can include statistical features, such as mean, variance, and maximum value; time-series features, such as trends and periodicity; and spatial features, such as distribution differences.

[0105] Furthermore, since the data from each device includes various types of environmental parameters, the target features can also include multi-dimensional features obtained by integrating different types of environmental parameters, so that the correlation between different types of environmental parameters can be considered during subsequent state analysis.

[0106] Step 3133: Perform state analysis on the target space based on the target characteristics to determine the spatial state of the target space.

[0107] State analysis can be achieved using joint feature analysis methods, such as random forests, support vector machines, and neural networks; it can also be achieved using spatiotemporal joint analysis methods, such as LSTM (Long Short-Term Memory Network) and spatiotemporal regression; or it can be achieved using correlation analysis methods, which are not limited here.

[0108] For example, the equipment data includes the temperature, humidity, and light intensity of the target space. First, the temperature, humidity, and light intensity are synchronized over time, and missing and outlier values ​​are processed. Then, the temperature, humidity, and light intensity are integrated into a multi-dimensional feature to obtain the target feature. Finally, state analysis is performed based on the target feature, including using a state analysis model (such as random forest) to perform joint analysis on the target feature, predict the spatial state of the target space, and analyze the correlation between temperature, humidity, and light intensity to identify potential spatial state relationships, thereby determining the spatial state of the target space.

[0109] Step 3135: Obtain spatial state data based on the spatial state of the target space.

[0110] It is understandable that, since the data from each device corresponds to different smart devices, and these smart devices can have different sensing functions, the device data can include different types of environmental parameters. In other words, by analyzing the target space based on the target features extracted from the data from each device, we can determine the different physical or environmental states of the target space, such as the temperature, humidity, light intensity, noise level, and human activities of the target space.

[0111] Furthermore, the device data also includes the device location of smart devices. Therefore, by extracting target features from the data of each device and performing state analysis on the target space, it is also possible to determine the spatial state of different locations within the target space, such as the temperature of the bedroom or the temperature of the living room.

[0112] In summary, spatial state data is generated based on different physical or environmental states of the target space and the spatial states of different locations within the target space.

[0113] Under the above embodiments, target features are extracted based on data from each device, and the spatial state of the target space is determined based on the target features. Multiple state analysis methods can be used to perform detailed state analysis of the target space, thereby obtaining more comprehensive and accurate spatial state data.

[0114] Please see Figure 5 In one exemplary embodiment, step 330 may further include the following steps:

[0115] Step 3301: Determine whether the target detection data conforms to the spatial state indicated by the spatial state data.

[0116] It is understandable that if the precision or accuracy of the target intelligent device is not erroneous, the spatial state indicated by the target detection data it reports should conform to the actual spatial state of the target space. In other words, if the precision or accuracy of the target intelligent device is erroneous, the spatial state indicated by the target detection data it reports will also be erroneous, thus not conforming to the actual spatial state of the target space.

[0117] As mentioned earlier, spatial state data can be obtained by fusing and analyzing data from multiple smart devices. It is accurate, comprehensive, and objective. Therefore, the spatial state indicated by the spatial state data can be regarded as the actual spatial state of the target space.

[0118] For example, if the target intelligent device is a human body sensor, and the spatial state data indicates that the spatial state is unmanned, then if the accuracy or precision of the human body sensor is not problematic, the target detection data reported by the human body sensor will match the spatial state indicated by the spatial state data, and the spatial state indicated by the target detection data should be unmanned. However, if the accuracy or precision of the human body sensor is problematic, then the target detection data reported by the human body sensor may not match the spatial state indicated by the spatial state data, and the spatial state indicated by the target detection data may be occupied.

[0119] Based on this, if the spatial state indicated by each target intelligent device matches the spatial state indicated by the spatial state data, then the accuracy and precision of the target intelligent devices are not problematic, and there is no need to correct the target intelligent devices or the target control scheme.

[0120] Conversely, if the condition is not met, proceed to step 3303. If the condition is not met, it is determined that the target smart device has a false alarm, and a corresponding false alarm detection result is generated.

[0121] It is understandable that, based on the spatial state indicated by the spatial state data, it is possible to determine whether the target detection data reported by the target intelligent device is accurate, thereby generating false alarm detection results.

[0122] Taking the previous example again, if the spatial state indicated by the spatial state data is an unmanned state, then it can be determined whether the spatial state indicated by the target detection data reported by the human body sensor is correct, thereby generating the corresponding false alarm detection result.

[0123] For example, if the target smart device is a millimeter-wave radar, the millimeter-wave radar detects that the target space is unmanned, but the light sensor detects a significant change in light from bright to dark and then back to bright within a short period of time (e.g., within 2 seconds), it determines that the target space is actually occupied, but the millimeter-wave radar has made a false alarm and mistakenly judged that the target space is unmanned.

[0124] Through the above process, it is determined whether the target detection data reported by the target intelligent device is accurate, thereby avoiding the target control scheme associated with the target intelligent device from failing to execute as expected due to inaccurate target detection data. In addition, if the spatial state indicated by the target detection data is incorrect, the correct spatial state of the target detection data can be determined based on the spatial state indicated by the spatial state data, and a false alarm detection result can be generated. Based on the false alarm detection result, the target control scheme associated with the target intelligent device can be controlled to operate as expected.

[0125] In an exemplary embodiment, step 330 may further include the following steps: if the target detection data does not conform to the spatial state indicated by the user feedback data, determine that the target smart device has made a false alarm, and generate a corresponding false alarm detection result.

[0126] The spatial status data may include user feedback data, which is data provided by users that is related to the target space.

[0127] It should be noted that if the target control scheme corresponding to the target space fails to perform as expected by the user, the user can provide feedback on the target control scheme, and the control terminal (gateway or server) can obtain the user feedback data accordingly.

[0128] For example, if a user stops a triggered target control scheme through the user terminal, the user terminal can upload user feedback data (controlling the stopped target control scheme) to the control terminal; or, for another example, if a user directly controls the controlled device corresponding to the target control scheme, the controlled device can generate and upload user feedback data to the control terminal.

[0129] To further explain, user feedback data can indicate the spatial state of the target space, which can be regarded as the actual spatial state of the target space. Therefore, if the target smart device reports a false alarm, the target detection data it reports is not accurate enough. In other words, the spatial state indicated by the target detection data does not match the spatial state indicated by the user feedback data. Based on this, it can be confirmed that the target smart device has reported a false alarm, thus obtaining the false alarm detection result.

[0130] For example, the target smart device is a human body sensor, and the corresponding controlled device is a smart light. The target control scheme is that when the target detection data reported by the human body sensor indicates that someone is present, the smart light is turned on. However, if the target space is unoccupied, the target control scheme is executed incorrectly, causing the smart light to be turned on erroneously. In this case, the user can report the erroneous execution of the target control scheme through their client, thereby sending user feedback data to the server. The server then controls the smart light to turn off via the Internet of Things (IoT). From the user feedback data indicating that the target control scheme was executed incorrectly (the smart light was turned on erroneously), we know that the actual spatial state of the target space should be unoccupied. Therefore, the target detection data (present) reported by the human body sensor does not match the actual spatial state of the target space, thus indicating that the human body sensor has given a false alarm.

[0131] In addition, one possible implementation is to detect whether the user has performed additional device control operations on the controlled device; if so, it is determined that the target smart device has a false alarm and a corresponding false alarm detection result is generated.

[0132] Among them, additional control operations are automated operations that are distinct from the target control scheme control execution. They are device control operations performed by the user on the controlled device. Specifically, they can be device control operations in which the user directly controls the controlled device, or they can be device control operations in which the user indirectly controls the controlled device through the control terminal (gateway or server) using the user terminal. No limitation is made here.

[0133] It is understandable that if a user performs additional device control operations on the controlled device of the target control scheme, it indicates that the user is not satisfied with the execution of the target control scheme. This may be due to errors in the precision or accuracy of the target intelligent device, or inaccurate target detection data reported by the target intelligent device, resulting in false alarms by the target intelligent device, thus leading to the user's dissatisfaction with the execution of the target control scheme.

[0134] For example, the target smart device is a human body sensor, and the corresponding controlled device is a smart light. The target control scheme is to control the smart light to turn on when the human body sensor indicates that someone is present. In other words, the automated operation corresponding to the target control scheme is to control the smart light to turn on. If the target space is unoccupied, but the target detection data indicates that the target space is occupied, causing the smart light to be turned on incorrectly, then the user can manually control the smart light to turn off (an additional device control operation). In this way, the additional device control operation performed by the user on the controlled device can be detected.

[0135] Based on this, when it is detected that the user has performed additional device control operations on the controlled device, it can be determined that the target smart device has a false alarm and generate the corresponding false alarm detection result.

[0136] Through the above process, the system learns and improves its false alarm identification capabilities by determining whether the user performs additional device control operations on the controlled device.

[0137] In conjunction with the above embodiments, when the target control scheme fails to perform as expected, the user can obtain user feedback data by reporting that the target control scheme has failed to perform as expected. Based on the user feedback data, it can be determined that the target smart device has a false alarm, and then the false alarm detection result can be obtained, thereby controlling the target control scheme to perform as expected.

[0138] It should be noted that if the target intelligent device experiences false alarms, meaning its accuracy or precision is incorrect, it may be due to a malfunction of the device. In this case, repairing or replacing the target intelligent device is necessary to resolve the false alarm issue and ensure the target control scheme executes as expected. Additionally, incorrect parameter settings on the target intelligent device could also cause errors in its accuracy or precision.

[0139] In an exemplary embodiment, after step 350, the following steps may be included: determining the target device parameters that need to be adjusted in the device parameters of the target smart device based on the false alarm situation; modifying the target device parameters according to the set rules to instruct the target smart device to continue to detect the target space according to the adjusted device parameters.

[0140] The target device parameters include at least one or more of the following: sensing sensitivity and trigger threshold.

[0141] The rules are used to adjust the target device parameters to a suitable range so that the target control scheme can be executed as expected.

[0142] In one possible implementation, data related to the target space, such as the spatial state of the target space, the execution status of the target control scheme, and the device parameters of the target intelligent device, can be collected. Machine learning methods can then be used to train the aforementioned data until a trained parameter adjustment model is obtained. The parameter adjustment model can then be used to automatically adjust the target device parameters.

[0143] Regarding target device parameters, these refer to the device parameters in the target intelligent device that need to be adjusted. Adjusting the target device parameters not only enables the target control scheme to execute as expected, but also avoids false alarms in subsequent detection results. Target device parameters include at least one or more of sensing sensitivity and trigger threshold. Sensing sensitivity refers to the sensitivity of the target intelligent device to sensing changes in the spatial state of the target space, while trigger threshold refers to the threshold for determining whether the spatial state of the target space has changed.

[0144] It is understandable that if the target intelligent device's sensing sensitivity is too low and / or its trigger threshold is too high, it may fail to detect changes in the spatial state of the target space when those changes have already occurred. Conversely, if the target intelligent device's sensing sensitivity is too high and / or its trigger threshold is too low, and there is environmental interference in the target space, the target intelligent device may interpret the environmental interference as a change in the spatial state of the target space (i.e., a change that could trigger the execution of the target control scheme) even when the spatial state of the target space has not yet changed, thus causing the target control scheme to be executed incorrectly.

[0145] Specifically, if the sensitivity of the human body sensor is too low and / or the trigger threshold for detecting human activity is too high, then the human body sensor may not be able to detect that there is someone in the target space if human activity is not significant (e.g., a user is lying on the sofa resting). If the sensitivity of the human body sensor is too high and / or the trigger threshold for detecting human activity is too low, then the human body sensor may mistakenly identify the environmental interference in the target space as a person in the target space if there is environmental interference (e.g., the wind blowing the curtains).

[0146] Therefore, the sensing sensitivity and / or trigger threshold of the target intelligent device must be set within an appropriate range to accurately identify the spatial state of the target space. Based on this, if the target control scheme fails to execute as expected due to inappropriate settings of the sensing sensitivity and / or trigger threshold of the target intelligent device, adjustments to the sensing sensitivity and / or trigger threshold are necessary. This ensures that the target intelligent device can promptly detect changes in the spatial state of the target space, and that it will not incorrectly determine a change in the spatial state of the target space if the spatial state remains unchanged.

[0147] Through the above process, when the target device parameters of the target intelligent device are not set appropriately, the target device parameters are adjusted to a suitable range by setting rules, and the sensitivity and trigger threshold of the sensor are adjusted in real time to adapt to different environments and user needs. This enables the target intelligent device to identify the spatial state of the target space in a timely and accurate manner, thereby avoiding the situation where the target control scheme fails to execute as expected.

[0148] Furthermore, if the target smart device's parameters are unsuitable, causing the target control scheme to fail to execute as expected, the user can manually adjust the target smart device's parameters.

[0149] In an exemplary embodiment, after step 350, the following steps may be included: acquiring user adjustment data; adjusting the corresponding device parameters of the target smart device according to the user adjustment data, so as to instruct the target smart device to continue to detect the target space according to the adjusted device parameters.

[0150] Among them, user adjustment data is data generated by users adjusting the device parameters of the target smart device, and is used to indicate the user's specific device parameter adjustment method.

[0151] For example, if a user adjusts the sensitivity of a target smart device by reducing it by 5%, the user's adjustment data will instruct the first smart sensor to reduce its sensitivity by 5%.

[0152] It should be noted that users can adjust the device parameters of the target smart device through the user terminal or directly, and there is no limitation on this.

[0153] Therefore, after obtaining the user's adjustment data, the target smart device can be adjusted accordingly based on the adjustment method of the target smart device and its parameters as indicated by the user's adjustment data.

[0154] Through the above process, users can adjust the device parameters of the target smart device, increasing the flexibility of device parameter adjustment and further reducing the probability that the target control scheme fails to perform as expected.

[0155] In an exemplary embodiment, before step 330, the following steps may be included: if the target detection data is detected to meet the triggering conditions corresponding to the target control scheme, then a request is made to identify false alarms in the target detection data.

[0156] The target control scheme is used to control the controlled equipment to perform corresponding automated operations by monitoring the target detection data reported by the target intelligent device.

[0157] It is understandable that the target control scheme will be executed after the target detection data meets the triggering conditions. Therefore, in order to avoid the target control scheme being executed incorrectly due to inaccurate target detection data, a false alarm identification can be requested for the target detection data when the target detection data meets the triggering conditions corresponding to the target control scheme. This is to determine whether the target detection data is accurate, thereby avoiding the target control scheme being executed incorrectly due to false alarms from the target intelligent device.

[0158] The above embodiments avoid executing the target control scheme without false alarm identification, thereby reducing malfunctions caused by external interference and improving system stability and robustness.

[0159] In an exemplary embodiment, after step 350, the following step may also be included: if a false alarm occurs, control the controlled device associated with the target intelligent device in the target control scheme to perform the correct automated operation according to the spatial state indicated by the spatial state data.

[0160] It is understandable that if the target intelligent device reports a false alarm, the target control scheme will be executed incorrectly, causing the controlled device to perform incorrect automated operations. Therefore, it is necessary to correct it.

[0161] As mentioned earlier, the spatial state indicated by the spatial state data can be regarded as the actual spatial state of the target space. Therefore, the controlled equipment can be corrected based on the spatial state indicated by the spatial state data, and the controlled equipment can be controlled to perform the correct automated operation.

[0162] For example, the target control scheme is to turn on the smart light when someone is in the living room. If the space status data indicates that no one is in the living room, then the human body sensor will give a false alarm, and the target detection data will indicate that someone is in the living room, and the smart light will be turned on incorrectly. At this time, the action of the smart light can be corrected according to the space status data indicating that no one is in the living room, and the smart light can be controlled to perform the correct automated operation - turn off.

[0163] Through the above process, when the target intelligent device reports a false alarm, the spatial state indicated by the spatial state data can effectively correct the erroneous automated operation of the controlled device, ensuring that the controlled device performs the correct and appropriate automated operation, improving the accuracy of the target control scheme, optimizing the user experience, reducing interference caused by the erroneous operation of the controlled device, and better meeting user needs.

[0164] Please see Figure 6 This application provides a false alarm detection method, which is applicable to electronic devices, such as... Figure 1 The user terminal 110 in the implementation environment is shown.

[0165] In the following method embodiments, for ease of description, the execution subject of each step of the method is an electronic device, but this does not constitute a specific limitation.

[0166] like Figure 6 As shown, the method may include the following steps:

[0167] Step 510: Display the target page.

[0168] The target page is used to display the target control scheme. The target control scheme is used to control the controlled device to perform corresponding automated operations by monitoring the reported target detection data. Specifically, when the target detection data reported by the target intelligent device meets the triggering conditions of the target control scheme, the controlled device will perform the corresponding automated operations.

[0169] Step 530: Display the detection results of the target smart device on the target page.

[0170] First, it should be noted that users can view the detection results of the target smart device through the user terminal. The detection results are obtained by the target smart device in detecting the target space, and the results can be determined based on the target detection data reported by the target smart device. It can be understood that if the actual situation in the target space is inconsistent with the detection results of the target smart device, it indicates that the target smart device has made a false alarm.

[0171] Furthermore, if the detection result of the target smart device is the trigger condition of the target control scheme, then when the detection result meets the trigger condition of the target control scheme, the target control scheme will be executed. Then, the user can view the execution status of the target control scheme through the user terminal. If the execution status does not meet the user's expectations, it indicates that the target smart device has given a false alarm.

[0172] In one possible implementation, a trigger message is pushed to the user's device for the user to view. The trigger message indicates that the target control scheme has been triggered. By viewing the trigger message, the user can understand the triggering status of the target control scheme and make timely adjustments to the target control scheme if it fails to perform as expected.

[0173] Step 550, in response to the corrective operation for the target control scheme, causes the control terminal to control the controlled device associated with the target intelligent device in the target control scheme to perform the correct automation operation.

[0174] The target control scheme is used to control the controlled equipment to perform corresponding automated operations by monitoring the target detection data reported by the target intelligent device.

[0175] Regarding the corrective operation, it is used to ensure that the target control scheme can be executed as expected, that is, to enable the control terminal to control the controlled equipment associated with the target intelligent device under the target control scheme to perform automated operations that conform to the actual spatial state of the target space.

[0176] In one possible implementation, users can generate user feedback data through corrective actions. This user feedback data is data related to the target space provided by the user. If the target control scheme corresponding to the target space fails to execute as expected by the user, the user can provide feedback on the target control scheme. Accordingly, the control end (gateway or server) can obtain the user feedback data and control the target control scheme based on the user feedback data, so that the target control scheme can execute as expected.

[0177] For example, if a user stops the execution of a triggered target control scheme through the user terminal, the user terminal can then upload the user feedback data (the control scheme that has been triggered has stopped executing) to the control terminal.

[0178] In another possible implementation, the user can also generate user adjustment data through correction operations. This user adjustment data indicates the method for adjusting specific device parameters and is generated by the user adjusting the device parameters of the target smart device. Specifically, the user can adjust the target device parameters of the target smart device through the user terminal and generate corresponding user adjustment data. Then, the control terminal can obtain the user adjustment data and make corresponding adjustments to the target smart device according to the method for adjusting the target smart device and its parameters indicated by the user adjustment data.

[0179] It should be noted that users can also correct the detection results of the target smart device through the correction operation. It can be understood that when the target smart device gives a false alarm, the detection result does not match the actual situation. Therefore, the detection result can be corrected directly.

[0180] Through the above embodiments, users can understand the triggering status of the target control scheme on the target page. Users can interact with the system through smartphone applications or other interfaces, providing feedback to help the system learn and improve. When the target control scheme does not execute as expected, users can perform corrective operations on the target control scheme through the user terminal, thereby enabling the target control scheme to execute as expected. In addition, users can manually confirm or correct false alarms and manually adjust device parameters through the user terminal.

[0181] Figure 7 This is a schematic diagram illustrating the specific implementation of a false alarm detection method in an application scenario. Taking a smart home scenario as an example, this application scenario can be applied to... Figure 1 The server-side 170 shown in the implementation environment can also be applied to Figure 1 Gateway 150 is shown in the implementation environment.

[0182] Step 801 involves using millimeter-wave radar to monitor the target space.

[0183] Specifically, millimeter-wave radar periodically scans the target space to detect the presence of human beings and controls relevant target control schemes. For example, if the millimeter-wave radar detects that no one is in the room, it sends target detection data indicating the vacancy status to a server or gateway, thereby controlling the automatic shutdown of smart lights.

[0184] If, in step 803, the target detection data reported by the millimeter-wave radar indicates that the target space is unmanned, the target control scheme is triggered to control the smart light to turn off.

[0185] In step 805, spatial state data is obtained.

[0186] The spatial status data includes user feedback data, which can be used to indicate whether the user should turn the smart light back on; it also includes device data from the light sensor, which indicates that the light value increases from 20 lux to 100 lux, indicating that the target space is occupied; it may also include device data from the sound sensor, which indicates that the target space is occupied if the sound sensor detects human voices.

[0187] In step 807, false alarm identification is performed on the target detection data reported by the millimeter-wave radar based on the spatial state data to obtain the false alarm detection result.

[0188] For example, if a millimeter-wave radar falsely reports that the target space is unoccupied, but a sound sensor detects a significant change in sound in the target space, it is determined that there is actually human activity in the target space. The millimeter-wave radar may be too insensitive to detect the micro-motion energy in the target space, thus determining that the millimeter-wave radar has made a false alarm.

[0189] If, in step 809, the false alarm detection result indicates that the millimeter-wave radar has issued a false alarm, then the smart light is controlled to turn on based on the presence of people indicated by the spatial status data.

[0190] In step 811, the equipment parameters of the millimeter-wave radar are adjusted according to the set rules so that the target control scheme can be executed as expected.

[0191] The rules can be set using intelligent learning algorithms.

[0192] For example, if a person sits on a sofa for a long time, the millimeter-wave radar may mistakenly report that the room is empty and automatically turn off the smart light. However, the target detection data reported by the millimeter-wave radar will be corrected based on the spatial status data indicating that someone is present. In this case, under the "lights on when someone is present" target control scheme, the smart light will turn back on because someone is present. At the same time, the false alarm will increase the sensitivity of the millimeter-wave radar to detect people, enabling it to detect more subtle movements and changes in heat sources, and thus avoid easily triggering the empty state.

[0193] For example, if the wind blows the curtains and the green plants sway, the millimeter-wave radar might mistakenly detect a person and automatically turn on the smart lights. In this case, the target detection data reported by the millimeter-wave radar should be adjusted back to an unoccupied state. Under the "lights off when no one is around" target control scheme, the smart lights will turn off again due to the unoccupied state. At the same time, after a false alarm of a person being present, the trigger threshold for the person being present on the millimeter-wave radar should be increased to enable it to adapt in real time and avoid interference from environmental energy noise.

[0194] In this application scenario, a multi-sensor combination is employed, integrating intelligent devices such as millimeter-wave radar sensors, light sensors, and acoustic sensors. This allows for the fusion and analysis of data from multiple devices, improving the accuracy of false alarm detection. Furthermore, it incorporates intelligent learning algorithms to learn environmental patterns and user behavior within the target space. Through long-term data analysis, it gradually optimizes false alarm detection capabilities, enabling false alarm feedback and further enhancing accuracy. In addition, when a false alarm is detected by the human body sensor, it can be adjusted in real time to adapt to different spaces and meet user needs.

[0195] Compared with related technologies, this application can identify false alarms based on the spatial state indicated by the spatial state data reported by each intelligent device in the target space and the spatial state indicated by the target detection data reported by the target intelligent device, so as to determine whether the target intelligent device has a false alarm and analyze the cause of the false alarm. Based on the cause, the device parameters of the target intelligent device can be adjusted, thereby avoiding the situation where the target control scheme associated with the target intelligent device cannot be executed as expected due to the unsuitable precision or accuracy of the target intelligent device.

[0196] Furthermore, analyzing the spatial state of the target space based on device data from each intelligent device can avoid interference from the target intelligent devices and maintain their objectivity. Moreover, state analysis using device data from each intelligent device can comprehensively and accurately obtain the spatial state of the target space, thereby verifying the target detection data reported by the target intelligent devices and correcting erroneous execution of the target control scheme. Of course, extracting target features based on data from each device and determining the spatial state of the target space based on these features allows for detailed state analysis using various methods, resulting in more comprehensive and accurate spatial state data.

[0197] Furthermore, when the target control scheme fails to perform as expected, the user can provide feedback that the target control scheme has failed to perform as expected, obtain user feedback data, and then determine the false alarm situation of the target smart device based on the user feedback data, thereby obtaining the false alarm detection result, and thus controlling the target control scheme to perform as expected.

[0198] Furthermore, when the device parameters of the target intelligent device are not set appropriately, rules can be set to adjust the device parameters to a suitable range, and the sensitivity and trigger threshold of the sensors can be adjusted in real time to adapt to different environments and user needs. This enables the target intelligent device to identify the spatial state of the target space in a timely and accurate manner, thereby avoiding situations where the target control scheme fails to execute as expected. In addition, users can also adjust the device parameters of the target intelligent device, increasing the flexibility of device parameter adjustment and further reducing the probability of the target control scheme failing to execute as expected.

[0199] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0200] The following are embodiments of the apparatus described in this application, which can be used to execute the false alarm detection method involved in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the method embodiments of the false alarm detection method involved in this application.

[0201] Please see Figure 8 This application provides a false alarm detection device 900, including but not limited to: an acquisition module 910, an identification module 930, and a control module 950.

[0202] Among them, the acquisition module 910 acquires the spatial status data reported by each intelligent device in the target space, and acquires the target detection data reported by the target intelligent device.

[0203] The identification module 930 is used to identify false alarms in target detection data based on the spatial state indicated by the spatial state data, and obtain false alarm detection results; the false alarm detection results are used to indicate whether the target intelligent device has made a false alarm.

[0204] The control module 950 is configured to adjust the device parameters of the target smart device according to the false alarm situation if the false alarm detection result indicates that a false alarm exists, so as to instruct the target smart device to continue to detect the target space according to the adjusted device parameters.

[0205] In an exemplary embodiment, the acquisition module 910 is further configured to acquire device data of each smart device in the target space; the device data is data fed back by each smart device and related to the target space, so as to instruct the target smart device to continue to detect the target space according to the adjusted device parameters; and to analyze the state of the target space based on the device data to obtain space state data.

[0206] In an exemplary embodiment, the acquisition module 910 is further configured to perform feature extraction based on data from each device to obtain target features; the target features are used to indicate the spatial state characteristics of the target space; the target space is analyzed for state based on the target features to determine the spatial state of the target space; and spatial state data is obtained based on the spatial state of the target space.

[0207] In an exemplary embodiment, the acquisition module 910 is further configured to perform feature extraction based on data from each device to obtain target features; the target features are used to indicate the spatial characteristics of the target space; the target space is analyzed for state based on the target features to determine the spatial state of the target space; and spatial state data is obtained based on the spatial state of the target space.

[0208] In an exemplary embodiment, the identification module 930 is further configured to determine whether the target detection data conforms to the spatial state indicated by the spatial state data; if it does not conform, it is determined that the target smart device has a false alarm and a corresponding false alarm detection result is generated.

[0209] In an exemplary embodiment, the spatial state data further includes user feedback data; the user feedback data is data related to the target space provided by the user; the identification module 930 is further configured to determine that the target smart device has made a false alarm when the target detection data does not match the user feedback data, and generate a corresponding false alarm detection result.

[0210] In an exemplary embodiment, the adjustment module 950 is further configured to determine the target device parameters that need to be adjusted from the device parameters of the target smart device based on the false alarm situation; and modify the target device parameters according to the set rules, wherein the target device parameters include at least one or more of sensing sensitivity and trigger threshold.

[0211] In an exemplary embodiment, the adjustment module 950 is further configured to acquire user adjustment data; the user adjustment data is data generated by the user adjusting the device parameters of the target smart device; the corresponding device parameters of the target smart device are adjusted according to the user adjustment data, so as to instruct the target smart device to continue to detect the target space according to the adjusted device parameters.

[0212] In an exemplary embodiment, the false alarm detection device 900 is further configured to request false alarm identification of the target detection data if the detected target detection data meets the triggering conditions corresponding to the target control scheme; wherein, the target control scheme is configured to control the controlled device to perform corresponding automated operations by monitoring the target detection data reported by the target intelligent device.

[0213] In an exemplary embodiment, the false alarm detection device 900 is further configured to, if the false alarm occurs, control the controlled device associated with the target intelligent device in the target control scheme to perform the correct automated operation based on the spatial state indicated by the spatial state data.

[0214] It should be noted that the false alarm detection device provided in the above embodiments is only illustrated by the division of the above functional modules when performing false alarm detection. In actual applications, the above functions can be assigned to different functional modules as needed. That is, the internal structure of the false alarm detection device will be divided into different functional modules to complete all or part of the functions described above.

[0215] Furthermore, the false alarm detection device and false alarm detection method embodiments provided in the above embodiments belong to the same concept, and the specific way in which each module performs its operation has been described in detail in the method embodiments, and will not be repeated here.

[0216] Figure 9 A schematic diagram of the structure of an electronic device is shown according to an exemplary embodiment. This electronic device is suitable for... Figure 1 The server-side configuration 170 in the implementation environment is shown.

[0217] It should be noted that this electronic device is merely an example adapted to this application and should not be construed as providing any limitation on the scope of use of this application. Furthermore, this electronic device should not be interpreted as requiring or depending on any specific feature. Figure 9 One or more components of the exemplary electronic device 2000 shown.

[0218] The hardware structure of electronic devices 2000 can vary significantly due to differences in configuration or performance, such as... Figure 9 As shown, the electronic device 2000 includes: a power supply 210, an interface 230, at least one memory 250, and at least one central processing unit (CPU) 270.

[0219] Specifically, power supply 210 is used to provide operating voltage for various hardware devices on electronic device 2000.

[0220] Interface 230 includes at least one wired or wireless network interface 231 for interacting with external devices. For example, to perform... Figure 1 The diagram illustrates the interaction between electronic device 170 and smart device 130 in the implementation environment.

[0221] Of course, in other examples adapted in this application, interface 230 may further include at least one serial-to-parallel conversion interface 233, at least one input / output interface 235, and at least one USB interface 237, etc. Figure 9As shown, this does not constitute a specific limitation.

[0222] The memory 250 serves as a carrier for resource storage and can be a read-only memory, random access memory, disk, or optical disk, etc. The resources stored on it include the operating system 251, application programs 253, and data 255, etc., and the storage method can be temporary storage or permanent storage.

[0223] The operating system 251 is used to manage and control the various hardware devices and application programs 253 on the electronic device 2000, so as to enable the central processing unit 270 to perform calculations and processing on the massive data 255 in the memory 250. It can be Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0224] Application 253 is a computer program that performs at least one specific task based on operating system 251, and may include at least one module ( Figure 9 (Not shown), each module may contain a computer program for the electronic device 2000. For example, the false alarm detection device may be considered as an application program 253 deployed on the electronic device 2000.

[0225] Data 255 can be photos, pictures, etc. stored on a disk, or spatial status data, etc., stored in memory 250.

[0226] The central processing unit 270 may include one or more processors and is configured to communicate with the memory 250 via at least one communication bus to read computer programs stored in the memory 250, thereby performing operations and processing on massive amounts of data 255 stored in the memory 250. For example, a false alarm detection method may be implemented by the central processing unit 270 reading a series of computer programs stored in the memory 250.

[0227] Furthermore, this application can also be implemented through hardware circuits or a combination of hardware circuits and software. Therefore, the implementation of this application is not limited to any specific hardware circuit, software, or combination thereof.

[0228] Please see Figure 10 This application provides an electronic device 4000, which may include: a desktop computer, a laptop computer, a server, a gateway, etc.

[0229] exist Figure 10 In this context, the electronic device 4000 includes at least one processor 4001 and at least one memory 4003.

[0230] The data interaction between the processor 4001 and the memory 4003 can be achieved through at least one communication bus 4002. This communication bus 4002 may include a path for transmitting data between the processor 4001 and the memory 4003. The communication bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0231] Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this application.

[0232] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0233] The memory 4003 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program instructions or code in the form of instructions or data structures and accessible by the electronic device 400, but not limited thereto.

[0234] The memory 4003 stores a computer program, and the processor 4001 can read the computer program stored in the memory 4003 through the communication bus 4002.

[0235] The computer program is executed by one or more processors 4001 to implement the false alarm detection methods in the above embodiments.

[0236] Furthermore, this application provides a storage medium storing a computer program, which is executed by one or more processors to implement the false alarm detection method described above.

[0237] This application provides a computer program product, which includes a computer program stored in a storage medium. One or more processors of an electronic device read the computer program from the storage medium, load and execute the computer program, so that the electronic device implements the false alarm detection method as described above.

[0238] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A false alarm detection method, characterized in that, include: Acquire spatial status data reported by each intelligent device in the target space and acquire target detection data reported by the target intelligent device; Based on the spatial state indicated by the spatial state data, false alarm identification is performed on the target detection data to obtain a false alarm detection result; the false alarm detection result is used to indicate whether the target intelligent device has a false alarm situation. If the false alarm detection result indicates that there is a false alarm, the device parameters of the target smart device are adjusted according to the false alarm, so as to instruct the target smart device to continue to detect the target space according to the adjusted device parameters.

2. The method as described in claim 1, characterized in that, The acquisition of spatial status data reported by each intelligent device in the target space includes: Obtain device data of each of the intelligent devices in the target space; the device data is data fed back by each of the intelligent devices and is related to the target space; The state of the target space is analyzed based on the data from each of the devices to obtain the space state data.

3. The method as described in claim 2, characterized in that, The process of analyzing the state of the target space based on the data from each of the devices to obtain the space state data includes: Based on the data from each of the aforementioned devices, feature extraction is performed to obtain target features; the target features are used to indicate the spatial state characteristics of the target space. Based on the target characteristics, the target space is analyzed to determine the spatial state of the target space; Based on the spatial state of the target space, the spatial state data is obtained.

4. The method as described in claim 1, characterized in that, The step of identifying false alarms in the target detection data based on the spatial state indicated by the spatial state data, and obtaining false alarm detection results, includes: Determine whether the target detection data conforms to the spatial state indicated by the spatial state data; If the condition is not met, the target smart device is determined to have a false alarm, and a corresponding false alarm detection result is generated.

5. The method as described in claim 1, characterized in that, The spatial status data also includes user feedback data; the user feedback data is data provided by users that is related to the spatial status of the target space. The step of identifying false alarms in the target detection data based on the spatial state indicated by the spatial state data, and obtaining false alarm detection results, includes: If the target detection data does not match the user feedback data, it is determined that the target smart device has made a false alarm, and a corresponding false alarm detection result is generated.

6. The method as described in claim 1, characterized in that, The step involves adjusting the device parameters of the target intelligent device based on the false alarm situation, to instruct the target intelligent device to continue detecting the target space according to the adjusted device parameters, including: Based on the false alarm situation, determine the target device parameters that need to be adjusted from the device parameters of the target smart device; The target device parameters are modified according to the set rules to instruct the target smart device to continue detecting the target space based on the adjusted device parameters; the target device parameters include at least one or more of sensing sensitivity and trigger threshold.

7. The method as described in claim 1, characterized in that, The step involves adjusting the device parameters of the target intelligent device based on the false alarm situation, to instruct the target intelligent device to continue detecting the target space according to the adjusted device parameters, including: Acquire user adjustment data; the user adjustment data is data generated by the user adjusting the device parameters of the target smart device; The corresponding device parameters of the target smart device are adjusted based on the user adjustment data, so as to instruct the target smart device to continue to detect the target space according to the adjusted device parameters.

8. The method as described in claims 1 to 7, characterized in that, Before obtaining the false alarm detection result by performing false alarm identification on the target detection data based on the spatial state data, the method further includes: If the target detection data is detected to meet the triggering conditions corresponding to the target control scheme, then a request is made to identify false alarms in the target detection data. The target control scheme is used to control the controlled device to perform corresponding automated operations by monitoring the target detection data reported by the target intelligent device.

9. The method as described in claim 8, characterized in that, After adjusting the device parameters of the target intelligent device based on the false alarm situation to instruct the target intelligent device to continue detecting the target space according to the adjusted device parameters, the method further includes: If the false alarm occurs, the controlled device associated with the target intelligent device in the target control scheme shall perform the correct automated operation according to the spatial state indicated by the spatial state data.

10. A false alarm detection device, characterized in that, include: The acquisition module is used to acquire spatial status data reported by each intelligent device in the target space, as well as target detection data reported by the target intelligent device. The identification module is used to identify false alarms in the target detection data based on the spatial state indicated by the spatial state data, and to obtain false alarm detection results; The false alarm detection result is used to indicate whether the target smart device has generated a false alarm. An adjustment module is used to adjust the device parameters of the target smart device according to the false alarm situation if the false alarm detection result indicates that there is a false alarm, so as to instruct the target smart device to continue to detect the target space according to the adjusted device parameters.

11. An electronic device, characterized in that, include: At least one processor and at least one memory, wherein, The memory stores computer programs; The computer program is executed by one or more of the processors, causing the electronic device to implement the false alarm detection method as described in any one of claims 1 to 9.

12. A storage medium having a computer program stored thereon, characterized in that, The computer program is executed by one or more processors to implement the false alarm detection method as described in any one of claims 1 to 9.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

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