Biological body monitoring method and system

Abstract

The embodiment of the invention provides an organism monitoring method and system. The organism monitoring method comprises the steps that at least one first signal emitted by at least one emission source is acquired based on at least one terminal device in a target room; extracting at least one second signal from the at least one first signal; determining whether there is an organism in the target room based on the at least one second signal; in response to the existence of the living body in the target room, determining the biological species of the living body based on the at least one second signal; and determining a management scheme for the target room based on the biological species.

Description

Divisional Statement

[0001] This application is a divisional application of the Chinese application with the application number 202211604940.2 and the application date of December 14, 2022, and the invention name of "a biological monitoring management method and system". TECHNICAL FIELD

[0002] The present specification relates to the field of room management, in particular to a biological monitoring method and system. BACKGROUND

[0003] In practical applications, existing biological monitoring often needs hardware support such as cameras and infrared sensors, which has high installation and wiring costs. Due to privacy factors and other reasons, its application in, for example, hotel room management is limited. Moreover, in the prior art, when the personnel in the room have service needs, they usually need to actively call the business to initiate a service request, which reduces service efficiency. If the business initiates a telephone inquiry or knocks on the door to inquire whether the customer needs service, there may be a situation where the customer is not in the room, resulting in a waste of service personnel's energy and time, and even causing disturbance to the customer, reducing the customer's stay experience.

[0004] Therefore, it is necessary to provide a biological monitoring method and system to achieve more efficient and accurate, low-cost biological monitoring management, to avoid wasting service personnel, improve service quality and efficiency, and thus improve customer experience. SUMMARY

[0005] One of the embodiments of the present specification provides a biological monitoring method. The biological monitoring method comprises: acquiring at least one first signal emitted by at least one emission source based on at least one terminal device in a target room; extracting at least one second signal from the at least one first signal; determining whether a biological body exists in the target room based on the at least one second signal; in response to the existence of the biological body in the target room, determining the biological species of the biological body based on the at least one second signal; and determining a management scheme for the target room based on the biological species.

[0006] One of the embodiments of the present specification provides a biological monitoring system, comprising: an acquisition module configured to acquire at least one first signal emitted by at least one emission source based on at least one terminal device in a target room; an extraction module configured to extract at least one second signal from the at least one first signal; a determination module configured to determine whether a biological body exists in the target room based on the at least one second signal; in response to the existence of the biological body in the target room, determine the biological species of the biological body based on the at least one second signal; and determine a management scheme for the target room based on the biological species.

[0007] One of the embodiments of the present specification provides a computer readable storage medium, the storage medium stores computer instructions, when the computer reads the computer instructions in the storage medium, the computer executes the above-mentioned biological body monitoring management method. BRIEF DESCRIPTION OF DRAWINGS

[0008] The present specification will be further described in the manner of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers refer to the same structures or operations, in which: Figure 1 is a schematic diagram of an application scenario of a biological body monitoring management system according to some embodiments of the present specification; Figure 2 is an exemplary block diagram of a biological body monitoring management system according to some embodiments of the present specification; Figure 3 is an exemplary flowchart of a biological body monitoring management method according to some embodiments of the present specification; Figure 4A is an exemplary schematic diagram of a first prediction model according to some embodiments of the present specification; Figure 4B is an exemplary schematic diagram of a second prediction model according to some embodiments of the present specification; Figure 5 is an exemplary schematic diagram of a third prediction model according to some embodiments of the present specification; Figure 6 is an exemplary schematic diagram of a fourth prediction model according to some embodiments of the present specification; Figure 7 is an exemplary schematic diagram of a fifth prediction model according to some embodiments of the present specification; DETAILED DESCRIPTION

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings needed to be used in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, the present specification can also be applied to other similar scenarios without creative labor, unless it is obvious from the language environment or otherwise stated. The same numbers in the drawings represent the same structures or operations.

[0010] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0011] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0012] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0013] Figure 1 This is a schematic diagram illustrating the application scenarios of a biological monitoring and management system based on some embodiments of this specification.

[0014] In some embodiments, the bio-monitoring management system can be used in various locations for bio-monitoring, such as service management of apartments, houses, or hotel rooms, store monitoring, intrusion detection, elderly care, and smart shopping. The bio-monitoring management system can achieve bio-monitoring and management by implementing the bio-monitoring management methods disclosed in this application.

[0015] In some embodiments, such as Figure 1 As shown, the organism monitoring and management system 100 may include a transmitter 110, an organism 120, a terminal device 130, a processing device 140, a network 150, and a storage device 160.

[0016] The transmitter 110 can be a device for transmitting signals. For example, the transmitter 110 can be used to transmit a first signal. In some embodiments, the signal may include a Wi-Fi signal, etc. In some embodiments, the transmitter 110 may include a Wi-Fi signal amplifier, a router, etc. In some embodiments, an area may include multiple transmitters, which may be arranged at multiple locations within the area. For example, a transmitter may be arranged at the front desk, warehouse, etc., in a store.

[0017] The organism 120 can be any living individual or object. In some embodiments, the organism 120 may include a mouse 120-1, a human 120-2, or any combination thereof. In some embodiments, the organism 120 may also include other organisms, such as cats, dogs, birds, snakes, etc.

[0018] The terminal device 130 refers to one or more devices that can receive the signal transmitted by the transmission source 110. For example, the terminal device 130 can receive the first signal transmitted by the transmission source 110. In some embodiments, the terminal device 130 can include one or any combination of a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, a desktop computer 130-4, and other devices with input and / or output functions. In some embodiments, the mobile device 130-1 can include a wearable device and a smart mobile device, or any combination thereof. In some embodiments, the smart mobile device can include a smart phone, a personal digital assistant (PDA), a game device, a navigation device, a handheld terminal (POS), or any combination thereof. In some embodiments, the terminal device 130 can also include a smart home appliance (not shown in the figure). For example, a smart refrigerator, a smart washing machine, a smart air conditioner, a smart sound, a smart projector, and other devices with wireless signal transmission functions.

[0019] It should be noted that the above examples are only used to illustrate the generality of the terminal device 130, but not to limit the scope thereof.

[0020] The processing device 140 can be used to process information and / or data related to the organism monitoring management system 100. The processing device 140 can execute program instructions based on the data, information, and / or processing results to perform one or more functions described in this specification. In some embodiments, the processing device 140 can obtain information and / or data sent by the terminal device 130 via the network 150. For example, the processing device 140 can obtain the first signal received by the terminal device 130 through the network 150. For another example, the processing device 140 can process the first signal to extract the second signal.

[0021] In some embodiments, the processing device 140 can include one or more sub-processing devices (e.g., single-core processing devices or multi-core multi-core processing devices). In some embodiments, the processing device 140 can be a single server or a group of servers. The group of servers can be a centralized group of servers connected to the network 150 via an access point, or a distributed group of servers connected to the network 150 via one or more access points, respectively.

[0022] The network 150 can connect the components of the system and / or connect the system with external resource parts. The network 150 can enable communication between the components and between other parts outside the system, facilitating exchange of data and / or information. In some embodiments, one or more components in the organism monitoring management system 100 (e.g., the terminal device 130 and the processing device 140) can exchange information and / or data through the network 150. For example, the terminal device 130 can send the first signal to the processing device 140 through the network 140.

[0023] In some embodiments, the network 150 can be any one or more of a wired network or a wireless network. In some embodiments, the network can be of various topologies or combinations of topologies, such as point-to-point, shared, hub-and-spoke, etc. In some embodiments, the network 150 can include one or more network access points. For example, the network 150 can include wired or wireless network access points, such as base stations and / or network switching points, through which one or more components of the organism monitoring management system 100 can connect to the network 150 to exchange data and / or information.

[0024] The storage device 160 can be used to store data and / or instructions. In some embodiments, the storage device 160 can store information and / or data acquired by the terminal device 130. For example, the storage device 160 can store the first signal acquired from the terminal device 130. In some embodiments, the storage device 160 can store data and / or instructions used by the processing device 140 to perform or use to complete the exemplary organism monitoring management method described in this specification. The storage device 160 can include one or more storage components, each of which can be a separate device or part of other devices. In some embodiments, the storage device 160 can include random access memory (RAM), read-only memory (ROM), mass storage, removable storage, volatile read-write memory, etc. or any combination thereof. In some embodiments, the storage device 160 can be implemented on a cloud platform.

[0025] It should be noted that the application scenarios are provided only for illustrative purposes and are not intended to limit the scope of this specification. Those of ordinary skill in the art can make various modifications or changes based on the description of this specification. For example, the application scenarios can also include storage devices. For another example, the application scenarios can be implemented on other devices to achieve similar or different functions. However, changes and modifications will not depart from the scope of this specification.

[0026] Figure 2 is an exemplary block diagram of an organism monitoring management system according to some embodiments of the present specification. In some embodiments, the organism monitoring management system 200 can include an acquisition module 210, an extraction module 220, and a determination module 230.

[0027] The acquisition module 210 is configured to acquire at least one first signal emitted by at least one emission source based on at least one terminal device in a target room.

[0028] The extraction module 220 is configured to extract at least one second signal from the at least one first signal.

[0029] More information about the first signal and the second signal can be found inFigure 3 and related descriptions.

[0030] The determining module 230 is configured to determine, based on the at least one second signal, whether a living body exists in the target room; in response to the living body existing in the target room, determine, based on the at least one second signal, a biological species of the living body; and determine, based on the biological species, a management scheme for the target room.

[0031] In some embodiments, to determine, based on the at least one second signal, whether a living body exists in the target room, the determining module 230 is configured to determine, based on a signal change rate of the at least one second signal, whether a living body exists in the target room. More details about determining whether a living body exists can be found in Figure 3 and related descriptions.

[0032] In some embodiments, to determine, in response to the living body existing in the target room, based on the at least one second signal, the biological species of the living body, the determining module 230 is configured to determine the biological species by processing the at least one second signal through a machine learning model. More details about determining the biological species based on the machine learning model can be found in Figure 4A , Figure 4B , Figure 5 , Figure 6 , Figure 7 and related descriptions.

[0033] It should be noted that the above description of the living body monitoring management system 200 and its modules is for the convenience of description, and cannot limit the scope of the embodiments. It can be understood that, for those skilled in the art, after understanding the principle of the system, any combination of the modules or connection of the modules to other modules can be made without departing from the principle. In some embodiments, Figure 2 The acquisition module 210, the extraction module 220 and the determining module 230 disclosed in the specification can be different modules in a system, or one module can implement the functions of two or more modules. For example, the modules can share a storage module, or each module can have its own storage module. Such variations are within the scope of the specification.

[0034] Figure 3 is an exemplary flowchart of a living body monitoring management method according to some embodiments of the specification. The process 300 can be performed by the living body monitoring management system 100 or the living body monitoring management system 200. For example, the process 200 can be stored in the form of instructions in a storage medium (for example, the storage device 160), and the processing device 140 and / or Figure 2The modules in the memory 130 can execute the instructions, and the processing device 140 and / or the modules can be configured to perform the process 300 when executing the instructions. The operations of the process 300 shown below are for illustration purposes only. In some embodiments, the process 300 can be completed with one or more additional operations not described, and / or without one or more of the operations discussed. Additionally, Figure 3 The order of the operations of the process 300 shown in FIG. 3 and described below is not limiting.

[0035] At S310, at least one first signal transmitted by at least one transmission source is obtained based on at least one terminal device in the target room. In some embodiments, S310 can be performed by the obtaining module 210.

[0036] The target room is a space in which the user needs to perform the biological body monitoring. For example, the target room can be a residential room, a hotel room, a shop, etc.

[0037] The first signal can refer to a signal transmitted by a transmission source in the target room. For example, the first signal can be a Wi-Fi signal transmitted by a router in the target room. Each first signal can include a plurality of subcarriers. For example, the first signal A can be represented as A = [A1, A2, A3, …, An], where n is the number of subcarriers, and each A i (Ai∈[1, n]) represents the amplitude and phase of a subcarrier.

[0038] In some embodiments, the first signal can be a signal with path loss. Due to different propagation paths and distances between the transmission source and the terminal device (transmission source and terminal device), the path loss of the signal is different, and accordingly, the first signal received by different terminal devices is different.

[0039] In some embodiments, the first signal can be received by at least one terminal device in the target room. For example, the first signal can be received by a desktop computer in the target room. For another example, the first signal can be received by a smart sound in the target room. Each terminal device in the target room can receive a first signal. In some embodiments, the obtaining module 210 can communicate with each of the at least one terminal device in the target room to obtain the first signal received by each terminal device. For example, the obtaining module 210 can obtain the first signal received by the terminal device 130 via the network 150.

[0040] At S320, at least one second signal is extracted from the at least one first signal. In some embodiments, S320 can be performed by the extracting module 220.

[0041] The second signal can refer to a signal obtained after the first signal undergoes multipath fading during propagation. For example, the second signal can be a partial signal of the first signal that is attenuated due to encountering an obstacle (e.g., the signal undergoes diffraction, reflection, scattering).

[0042] In some embodiments, the second signal can include a channel state information (CSI) signal, which is used to describe the combined effects of reflection, diffraction, scattering, and other signal fading effects in the signal channel. A complete CSI signal can be described by three dimensions: time, carrier frequency, and space, which correspond to the signal changes experienced by the channel with respect to different time, carrier frequency, and spatial distribution, respectively. That is, a complete CSI signal can be represented by a three-dimensional signal matrix. For a terminal device at a fixed position, the CSI signal can be regarded as a two-dimensional matrix, which corresponds to the changes experienced by the wireless channel with respect to different time and carrier frequency. Signal fading (e.g., including path loss and multipath fading) can be reflected by the amplitude, phase, and the like of the signal.

[0043] In some embodiments, the extraction module 220 can obtain the second signal in various ways.

[0044] For example, the extraction module 220 can extract at least one second signal from at least one first signal based on a channel impulse response (CIR). An exemplary channel impulse response can be represented by the following equation (1): (1) wherein, Ck represents the channel state information (CSI) of the kth subcarrier, Ak represents the amplitude of the kth subcarrier, represents the phase of the kth subcarrier, and j is a coefficient.

[0045] For another example, the extraction module 220 can extract at least one second signal from at least one first signal based on a relationship between a transmitting end (i.e., a transmitting source) and a receiving end (i.e., a terminal device). An exemplary relationship between the transmitting end (i.e., the transmitting source) and the receiving end (i.e., the terminal device) can be represented by the following equation (2): (2) wherein, is a signal vector of the terminal device, is a signal vector of the transmitting source, represents a signal matrix of the CSI signal, The Gaussian white noise is added to the signal vector of the terminal device, the signal vector of the transmission source, and the Gaussian white noise in the above formula (2) to extract the second signal from the first signal. The signal vector can be constructed based on the amplitude characteristics and phase characteristics of the first signal.

[0046] In some embodiments, the extraction module 220 can extract one second signal from the first signal received by each terminal device to obtain at least one second signal.

[0047] In some embodiments, the extraction module 220 can also perform low-pass filtering on the extracted second signal to reduce environmental noise and improve the clarity and stability of the second signal.

[0048] S330, determining whether there is a living body in the target room based on the at least one second signal. In some embodiments, S330 can be performed by the determination module 230.

[0049] In some embodiments, the determination module 230 can determine whether there is a living body in the target room based on the at least one second signal through a preset rule. In some embodiments, an exemplary preset rule can be: performing dimensionality reduction and noise reduction processing on the second signal using a principal component analysis (PCA) method to obtain feature data of the second signal; then, matching the feature data of the second signal with a reference vector library to determine a reference vector with the highest matching degree with the feature data of the second signal, and determining the reference result corresponding to the reference vector (i.e., whether there is a living body) as the final result of whether there is a living body in the target room. The reference vector library includes reference vectors corresponding to feature data of multiple signals and reference results corresponding to the reference vectors (i.e., results of whether there is a living body). The reference vector library can be pre-set based on historical data.

[0050] In some embodiments, the determination module 230 can determine whether there is a living body in the target room based on a signal change rate of the at least one second signal.

[0051] The signal change rate of the second signal can be used to reflect the fluctuation level of the second signal in a unit of time. For example, the signal change rate of the second signal can be 10%. For another example, the signal change rate of the second signal includes (ΔC1, ΔC2, ΔC3), where ΔC1 is the signal change rate of the second signal C1, ΔC2 is the signal change rate of the second signal C2, and ΔC3 is the signal change rate of the second signal C3.

[0052] In some embodiments, the determination module 230 can determine the signal change rate of the second signal in multiple ways. For example, the signal change rate of the second signal can be determined by the following formula (3): (3) Where k is the rate of change of the second signal. The first time it occurs (first time). This refers to any time following the first time (the second time). This represents the signal difference between a first time point and a second time point. This signal difference can include the difference between the amplitude of the second signal at the first time point and the amplitude of the second signal at the second time point, or the difference between the phase characteristics of the second signal at the first time point and the phase characteristics of the second signal at the second time point. The time interval is the time interval between the first time point and the second time point. The time interval between the first time point and the second time point can be a system default value or can be manually set.

[0053] In some embodiments, the rate of change of the second signal can be inversely proportional to the matrix similarity between the signal matrix of the second signal at the first time (hereinafter referred to as the first signal matrix) and the signal matrix of the second signal at the second time (hereinafter referred to as the second signal matrix). For example, the higher the matrix similarity, the smaller the rate of change of the second signal. The matrix similarity can be determined in various ways. For example, it can be determined using a similarity calculation model. Exemplarily, the first signal matrix and the second signal matrix can be processed separately using a similarity calculation model to determine their matrix similarity.

[0054] In some embodiments, the determining module 230 can determine whether a living organism exists in a target room based on the sum of the signal change rates of at least one second signal. For example, the determining module 230 can determine the signal change rate of each of the at least one second signal, determine the sum of the signal change rates of the at least one second signal, and determine whether a living organism exists in the target room based on the relationship between the sum of the signal change rates and a change rate threshold. For example, if the sum of the change rates exceeds the change rate threshold of 20%, it can be determined that a living organism exists in the target room. In some embodiments, the determining module 230 can determine the sum of the signal change rates of at least one second signal by setting weights for the signal change rates of different second signals and performing a weighted summation of the signal change rates of each of the at least one second signal based on the weights. The weights corresponding to the signal change rates of different second signals can be manually set according to the degree of environmental interference affecting the corresponding terminal device. For example, a terminal device near a window may be more susceptible to wind and environmental interference, so the weight of the rate of change of the second signal extracted from the first signal received by that terminal device can be lower (e.g., set to 0.3); a terminal device not near a window is less susceptible to environmental interference, so the weight of the rate of change of the second signal extracted from the first signal received by that terminal device can be higher (e.g., set to 0.7).

[0055] In one or more embodiments of this specification, the presence of a living organism in the target room is determined by summing the signal change rates, which can eliminate the influence of environmental factors on the individual signal change rates. By assigning weights to terminal devices in different locations based on the degree of environmental interference at the location of the terminal device, the results of organism identification can be made more accurate.

[0056] In some embodiments, the determining module 230 may also determine whether a living organism exists in the target room based on the signal difference between the second signal and the standard signal.

[0057] A standard signal can refer to a second signal extracted from a first signal received by a terminal device in an empty room (i.e., a room that does not contain any living organisms). Each target room can correspond to one standard signal. For example, for target room A, the second signal can be extracted from the first signal received by terminal device A in target room A when there are no living organisms in target room A, thus obtaining the standard signal corresponding to target room A.

[0058] In some embodiments, signal differences may include phase difference and amplitude difference. For example, the phase difference may be 2° and the amplitude difference may be 20A.

[0059] In some embodiments, the determining module 230 can determine whether a living organism exists in the target room based on the relationship between the signal difference between at least one second signal and a standard signal and a difference threshold. When the signal difference is greater than the difference threshold, the determining module 230 can determine that a living organism exists in the target room. The difference threshold can be a system default value, an empirical value, a manually preset value, or any combination thereof, and can be set according to actual needs; this specification does not impose any restrictions on it.

[0060] In one or more embodiments of this specification, the presence of a living organism in a target room is determined by the signal difference between a second signal and a standard signal. This avoids the problem of not being able to identify a living organism when it is stationary in the room, and can further improve the accuracy and sensitivity of organism monitoring.

[0061] S340, in response to the presence of an organism in the target room, the biological species of the organism is determined based on at least one second signal. In some embodiments, step S340 may be performed by the determining module 230.

[0062] In some embodiments, the species of the organism may include, but are not limited to, humans, mice, cats, dogs, birds, snakes, etc.

[0063] In some embodiments, the determining module 230 can determine the species of the current organism by the historical species corresponding to the signal change rate of the historical second signal and / or the historical species corresponding to the signal difference between the historical second signal and the standard signal. For example, assuming that the species corresponding to a signal change rate of 25%-30% in the historical second signal is a mouse, then when the signal change rate of the current second signal is 26%, the species of the current organism can be determined to be a mouse. As another example, if the species corresponding to a phase difference between the historical second signal and the standard signal is 2°-5°, then based on a phase difference of 4° between the current second signal and the standard signal, the species of the current organism can be determined to be a human.

[0064] In some embodiments, in response to the presence of an organism in the target room, the determining module 230 can process at least one second signal using a machine learning model to determine the species of the organism. More information on determining species based on machine learning models can be found below.

[0065] S350, based on the biological species, determines the management plan for the target room. In some embodiments, S350 can be performed by the determining module 230.

[0066] The management plan for the target room refers to the actions taken to manage the target room. For example, proactively calling the room occupants to inquire if they require any services.

[0067] In some embodiments, the determining module 230 can determine the management plan for the target room based on the species of the creature in various ways. For example, when the target room is a hotel room, if the determining module 230 can identify the species of the creature as human, it will inquire whether room cleaning or dining services are required; when the target room is a shop, if the determining module 230 identifies the species of the creature as a mouse, it will implement rodent control measures for the shop; if the determining module 230 identifies the species of the creature as a cat, it will not take any action.

[0068] The descriptions of management schemes in the embodiments of this specification are for illustrative purposes only and are not intended to limit the scope of this specification. Management schemes for target rooms can also be determined in other ways. For example, the above-described organism monitoring and management system may further include an image recognition module, which identifies organisms to further determine whether a management scheme should be implemented in the target room.

[0069] In some embodiments of this specification, the presence and species of organisms in a target room are determined based on signals received by terminal devices in the target room. A management plan for the target room can be determined based on the species, achieving more efficient, accurate, and low-cost organism monitoring and management. At the same time, a corresponding management plan can be determined based on the species to provide targeted processing services for the corresponding target room.

[0070] Figure 4A This is an exemplary schematic diagram of a first prediction model shown according to some embodiments of this specification.

[0071] In some embodiments, the determining module 230 may process the signal change rate of at least one second signal, the first location distribution of at least one terminal device, and the second location distribution of at least one emission source based on a first prediction model to determine the biological species. Further explanation regarding the signal change rate of the second signal can be found in [link to relevant documentation]. Figure 2 And its related descriptions.

[0072] The first position distribution is the position distribution of at least one terminal device relative to the target room. For example, the first position distribution can be represented as [R1(x1, y1, z1), R2(x2, y2, z2), R3(x3, y3, z3)]. Where (x1, y1, z1) are the position coordinates of terminal device R1, (x2, y2, z2) are the position coordinates of terminal device R2, and (x3, y3, z3) are the position coordinates of terminal device R3.

[0073] The second position distribution is the position distribution of at least one transmitting source relative to the target room. For example, the second position distribution can be represented as [T1(x1', y1', z1'), T2(x2', y2', z2'), T3(x3', y3', z3')]. Where (x1', y1', z1') are the position coordinates of transmitting source T1, (x2', y2', z2') are the position coordinates of transmitting source T2, and (x3', y3', z3') are the position coordinates of transmitting source T3.

[0074] The first location distribution of at least one terminal device and the second location distribution of at least one transmitter can be obtained in various ways. For example, the methods for obtaining the location distribution may include, but are not limited to, proximity detection, centroid positioning, and polygon positioning. As an example, the center of the room can be determined as the coordinate origin using centroid positioning based on multiple second signals, and the location distribution of the terminal device and the transmitter relative to the room can be determined using proximity detection based on the coordinate origin and multiple second signals.

[0075] The primary predictive model can be a machine learning model used to determine the species of organism. For example, the type of primary predictive model can be a recurrent neural network (RNN) model, a deep neural network (DNN) model, a convolutional neural network (CNN) model, etc.

[0076] In some embodiments, such as Figure 4A As shown, the inputs to the first prediction model 420 may include the signal change rate 410-1 of at least one second signal, the first location distribution 410-2 of at least one terminal device, and the second location distribution 410-3 of at least one transmitter. The output may be the biological species 430 of the organism. In some embodiments, the output of the first prediction model may also be a letter code for the biological species; for example, output P represents the biological species as human, and output M represents the biological species as mouse. In some embodiments, the output of the first prediction model may also include the biological species. For example, the output of the first prediction model may be ([M,2], [P,3]), indicating that two mice and three people were detected in the target room.

[0077] In some embodiments, such as Figure 4A As shown, the input to the first prediction model 420 also includes location features 410-4.

[0078] Location features can reflect the positional relationships between various transmitters, terminal devices, and items within a room. For example, location features could be "table near the window" or "terminal device A near the table." Location features can be obtained through pre-recorded manual observation.

[0079] In some embodiments, the first prediction model 420 can be obtained by training a large number of first training samples 440-1 with first labels. An exemplary training process includes: inputting the first training samples 440-1 with first labels into an initial first prediction model 440-2; constructing a loss function using the first labels and the results of the initial first prediction model 440-2; and iteratively updating the parameters of the initial realism evaluation model based on the loss function using gradient descent or other methods. Model training is complete when preset conditions are met, resulting in a trained realism evaluation model. These preset conditions may include loss function convergence, the number of iterations reaching a threshold, etc.

[0080] In some embodiments, the first training sample may be the signal change rate of the second sample signal, the first sample location distribution of multiple sample terminal devices, the second sample location distribution of multiple sample emission sources, and sample location features. The multiple sample terminal devices and multiple sample emission sources are located in the same sample room. The first label may include the actual biological species in the sample room corresponding to the signal change rate of the second sample signal, the first sample location distribution of the multiple sample terminal devices, the second sample location distribution of the multiple sample emission sources, and the sample location features.

[0081] In some embodiments, a first training sample and a first label can be obtained by simulating the movement of a sample organism using a simulation model in different sample rooms. The sample room includes multiple sample terminal devices and multiple sample emission sources. The first position distribution of the multiple sample terminal devices and the second position distribution of the multiple sample emission sources can be pre-determined. For example, a scaled-down simulation model of a mouse can be created based on its outline, height, volume, etc., and remotely controlled to move the model in different sample rooms. The movement can include random movement or movement along various paths. When the scaled-down simulation model moves in a sample room, a second signal is acquired and its rate of change is determined. The acquired rate of change of the second signal, the first position distribution of the multiple sample terminal devices, and the second position distribution of the multiple sample emission sources are used as the first training sample, and the organism species "mouse" is used as the first label for the first training sample.

[0082] In some embodiments, the first training sample and the first label can also be obtained through simulation experiments with real people. For example, a certain number of people can move around in different sample rooms. When a certain number of people move around in a certain sample room, a second signal is acquired and the signal change rate of the second signal is determined. The acquired signal change rate of the second signal, the first position distribution of multiple sample terminal devices, and the second position distribution of multiple sample emission sources are used as the first training sample, and the biological type is human as the first label of the first training sample.

[0083] In some embodiments, when the sample room does not contain any living organisms, a second signal is acquired and its rate of change is determined. The acquired rate of change of the second signal, the first location distribution of multiple sample terminal devices, and the second location distribution of multiple sample emission sources are used as a first training sample, and the biological species value of 0 is used as the first label of the first training sample. Accordingly, the first prediction model can be used to determine whether a living organism exists in the target room. For example, when the model output is 0, it can be determined that no living organism exists in the target room; when the model output is M, it can be determined that a living organism exists in the target room, and the biological species is a mouse.

[0084] This specification describes one or more embodiments that use a machine learning model to process the signal change rate of a second signal detected in a room, as well as the location distribution of terminal devices and transmitters within the room. This facilitates the rapid and accurate identification of biological species. Furthermore, by acquiring training samples for the machine learning model through different simulation methods, different approaches can be used for different biological species, resulting in a richer variety of training samples and providing flexible selection for obtaining them. Acquiring a large number of training samples leads to more accurate model training results.

[0085] Figure 4B This is an exemplary schematic diagram of a second prediction model shown according to some embodiments of this specification.

[0086] In some embodiments, the determining module 230 can process at least one second signal, at least one standard signal, a first location distribution of at least one terminal device, and a second location distribution of at least one emission source based on a second prediction model to determine the biological species. Further explanation of the second signal and the standard signal can be found in [link to relevant documentation]. Figure 2 And its related description. More information about the first and second positional distributions can be found in [link to relevant documentation]. Figure 4A And its related descriptions.

[0087] The second predictive model can be a machine learning model used to determine the species of organism. For example, the type of the second predictive model can be an RNN model, a DNN model, a CNN model, etc.

[0088] In some embodiments, such as Figure 4B As shown, the inputs to the second prediction model 460 can be at least one second signal 450-1, at least one standard signal 450-2, at least one first location distribution of a terminal device 450-3, and at least one second location distribution of a transmitter 450-4. The output can be the biological species 470 of an organism. Each second signal can correspond to one standard signal. Similar to the first prediction model, the output of the second prediction model can also be a letter code for the biological species; for example, output P represents the biological species as human, and output M represents the biological species as mouse.

[0089] In some embodiments, such as Figure 4B As shown, the input to the second prediction model 460 also includes location features 450-5. More information about location features can be found in [link to documentation]. Figure 4A And its related descriptions.

[0090] In some embodiments, the second prediction model 460 can be obtained by training a large number of second training samples 480-1 with second labels. An exemplary training process includes: inputting the second training samples 480-1 with second labels into an initial second prediction model 480-2; constructing a loss function using the second labels and the results of the initial second prediction model 480-2; and iteratively updating the parameters of the initial realism evaluation model based on the loss function using gradient descent or other methods. Model training is complete when preset conditions are met, resulting in a trained realism evaluation model. These preset conditions may include loss function convergence, the number of iterations reaching a threshold, etc.

[0091] In some embodiments, the second training sample may include a second sample signal, a standard sample signal corresponding to the second sample signal, a first sample location distribution of multiple sample terminal devices, a second sample location distribution of multiple sample emission sources, and sample location features. The second label may include the actual biological species in the sample room corresponding to the second sample signal, the standard sample signal, the first sample location distribution of multiple sample terminal devices, the second sample location distribution of multiple sample emission sources, and the sample location features.

[0092] In some embodiments, a second training sample and a second label can be obtained by simulating the movement of a biological sample in different sample rooms using a simulation model. The method for obtaining the second training sample and the second label is similar to the method for obtaining the first training sample and the first label; further details can be found in [link to documentation]. Figure 4A The details and related descriptions will not be repeated here.

[0093] One or more embodiments of this specification use machine learning models to process the second signal detected in the room, the standard signal corresponding to the second signal, the location distribution of the terminal device and the transmission source in the room, and the location characteristics, which is beneficial for quickly and accurately determining the species of organisms.

[0094] Figure 5 This is an exemplary schematic diagram of a third prediction model shown according to some embodiments of this specification.

[0095] In different biological monitoring application scenarios, the terminal devices in different target rooms may sometimes be one or multiple. In some embodiments, in response to a single terminal device, the determining module 230 may determine a differential sequence of at least one second signal received by the single terminal device; and process the differential sequence based on a third prediction model to determine the species of organism.

[0096] The differential sequence of the second signal is a sequence composed of the signal differences between the second signal at multiple first and second time points. For an explanation of the first and second time points, please refer to [link to relevant documentation]. Figure 2 For example, the differential sequence of the second signal can be represented as (∆φ)T2-T1 ,∆φ T3-T2 ...). Where T2 and T1 represent the second time and T1 the first time, and T3 represent the second time and T2 the first time, ∆φ T2-T1 ∆φ represents the signal difference of the second signal at time T1 and time T2. T3-T2 This represents the signal difference between the second signal at time T2 and time T3, and so on. In some embodiments, the differential sequence can be a multidimensional sequence; for example, one dimension can be used to represent the amplitude difference of the signal, and another dimension can represent the phase difference of the signal, etc.

[0097] The third prediction model can be another machine learning model used to determine the species of organism. For example, the type of the third prediction model can be a CNN model, an RNN model, a DNN model, etc.

[0098] In some embodiments, such as Figure 5 As shown, the input to the third prediction model 520 includes a differential sequence 510, and the output includes a biological species 530. In some embodiments, such as Figure 5 As shown, the inputs to the third prediction model 520 may also include the room type 540 and room layout 550 of the target room.

[0099] In some embodiments, the room type of the target room may include, but is not limited to, hotel rooms, shops, warehouses, restaurants, etc. In some embodiments, the room layout of the target room may include room characteristics such as room size, ceiling height, space occupancy, size, placement, and materials of items within the room. The room type and layout of the target room can be determined by human input. For example, the target room A can be manually input as a hotel room, with a small apartment layout and a floor area of ​​15 square meters. 2 The room is 3.5m high and includes a table by the window, a bed, a wardrobe, a TV, etc.

[0100] In some embodiments, the third prediction model may include multiple processing layers. For example, such as Figure 5 As shown, the third prediction model 520 may include a first embedding layer 521 and a first prediction layer 523.

[0101] The first embedding layer can be a machine learning model used to process the differential sequence of the second signal to determine the first feature vector. For example, the first embedding layer can be a CNN model. Figure 5As shown, the input to the first embedding layer 521 may include the differential sequence 510 of the second signal, and the output may be the first feature vector 522. For example, the differential sequence of the second signal may be ([2, 2], [3, 2], [3, 2], [3, 2]), indicating that the signal difference between the second signal at time T1 and time T2 (the time interval between T1 and T2 is 2 mins) is 2°, the signal difference between the second signal at time T2 and time T3 (the time interval between T2 and T3 is 2 mins) is 3°, the signal difference between the second signal at time T3 and time T4 (the time interval between T3 and T4 is 2 mins) is 3°, and the signal difference between the second signal at time T4 and time T5 (the time interval between T3 and T5 is 2 mins) is 3°.

[0102] The first eigenvector can be used to numerically describe a set of features. This set of features can be measurable properties or characteristics of the second signal. For example, the first eigenvector can be represented as I = (3, 2, 1), indicating that the amplitude of the second signal changes by 3 dB and the phase changes by 2° within 1 second during the time interval between the first and second times. In some embodiments, the first eigenvector can be represented as a row vector or a column vector. The first eigenvector can correspond to an N-dimensional coordinate system. The N-dimensional coordinate system can be associated with N signal features of the second signal.

[0103] The first prediction layer can be a machine learning model used to process the first feature vector of the second signal to determine the species. For example, the first prediction layer can be a neural network (NN) model. Figure 5 As shown, the input to the first prediction layer 523 may include a first feature vector 522 output from the first embedding layer 521, and the output may include the biological species 530 of the organism. In some embodiments, the input to the first prediction layer 523 may also include the room type 540 and the room layout 550 of the target room.

[0104] In some embodiments, the output of the first embedding layer can be used as the input of the first prediction layer, and the first embedding layer and the first prediction layer can be trained together.

[0105] In some embodiments, the third training sample for joint training may include a differential sequence of the second sample signal, the room type of the sample room, and the layout of the sample room. The third label may include the actual biological species in the sample room corresponding to the differential sequence of the second sample signal, the room type of the target sample room, and the room layout. In some embodiments, the third training sample and the third label can be obtained by simulating the movement of sample organisms in different sample rooms using a simulation model. The method for obtaining the third training sample and the third label is similar to the method for obtaining the first training sample and the first label; for further details, please refer to [link to documentation]. Figure 4A The details and related descriptions will not be repeated here.

[0106] An exemplary training process includes: inputting the differential sequence of the sample second signal into an initial first embedding layer to obtain a first feature vector output by the initial first embedding layer; using the first feature vector as training sample data, along with the room type and layout of the sample room, into a first prediction model to obtain the biological species output by the first prediction model. A loss function is constructed based on the third label and the biological species output by the first prediction model, and the parameters of the initial first embedding layer and the initial first prediction layer are updated synchronously until the intermediate first embedding layer and the intermediate first prediction layer meet preset conditions, resulting in a trained first embedding layer and a first prediction layer. The preset conditions may be that the loss function is less than a threshold, convergence, or the training period reaches a threshold.

[0107] In some embodiments described herein, when a single terminal device receives a signal, the differential sequence of the second signal, the room type of the target room, and the room layout are processed by the first embedding layer of the third prediction model to determine the biological species. This fully considers the influence of the target room type and the information of items within the space on the prediction of the biological species, making the predicted biological species more accurate. Obtaining the parameters of the third prediction model through the above training method helps solve the problem of difficulty in obtaining labels when training the first embedding layer alone in some cases, and also allows the first embedding layer to better reflect the features of the second signal.

[0108] Figure 6 This is an exemplary schematic diagram of a fourth prediction model shown according to some embodiments of this specification.

[0109] In some embodiments, when there are multiple terminal devices, the determining module 230 can determine the differential sequence of at least one second signal received by the multiple terminal devices respectively, obtaining multiple differential sequences; and process the multiple differential sequences based on the fourth prediction model to determine the biological species. More information about differential sequences can be found in [link to documentation]. Figure 5 And its related descriptions.

[0110] The fourth prediction model can be another machine learning model used to determine the species of organism. For example, the type of the fourth prediction model can be an RNN model, a DNN model, a CNN model, etc.

[0111] In some embodiments, such as Figure 6 As shown, the input to the fourth prediction model 620 includes multiple differential sequences 610, and the output includes biological species 630. In some embodiments, such as Figure 6 As shown, the inputs to the fourth prediction model 620 may also include the room type 640, room layout 650, and the relative positional relationship 660 of at least one terminal device of the target room. For more information on the room type and room layout of the target room, please refer to [link to relevant documentation]. Figure 5 And its related descriptions.

[0112] In some embodiments, the relative positional relationship of at least one terminal device may include the distance between at least one terminal device and the transmitter, the height of at least one terminal device above the ground, and the positional relationship of at least one terminal device from an obstruction. In some embodiments, the relative positional relationship of at least one terminal device can be determined by a first positional distribution of the terminal devices and a second positional distribution of the transmitter. For example, the first positional distribution of terminal devices A, B, and C can be represented as [A(2,8,10), B(1,4,5), C(3,5,8)], and the second positional distribution of transmitter D can be represented as (3,5,8). Therefore, the distance between terminal device A and transmitter D can be calculated based on the first positional distribution of at least one terminal device and the second positional distribution of the transmitter. wait.

[0113] In some embodiments, the fourth prediction model may include multiple processing layers. For example, such as Figure 6 As shown, the fourth prediction model 620 may include a second embedding layer 621 and a second prediction layer 623.

[0114] The second embedding layer can be a machine learning model used to process multiple differential sequences of multiple second signals to determine a second feature vector. For example, the second embedding layer can be a CNN model. Figure 6 As shown, the input to the second embedding layer 621 may include multiple differential sequences 610, and the output may be a second feature vector 622.

[0115] The second eigenvector is similar to the first eigenvector; for more details, please refer to [link to relevant documentation]. Figure 5 And its related descriptions.

[0116] The second prediction layer can be a machine learning model used to process the second feature vector to determine the species of organism. For example, the second prediction layer can be one of an neural network (NN) or a genetic neural network (GNN) model. Figure 6 As shown, the input to the second prediction layer 623 may include the second feature vector 622 output by the second embedding layer 621, and the output may include the biological species 630 of the organism. In some embodiments, the input to the second prediction layer 623 may also include the room type 640 of the target room, the room layout 650 of the target room, and the relative positional relationship 660 of at least one terminal device.

[0117] In some embodiments, the output of the second embedding layer can be used as the input of the second prediction layer, and the second embedding layer and the second prediction layer can be trained together.

[0118] In some embodiments, the fourth training sample for joint training may include differential sequences of multiple sample second signals, room type and layout of the sample target room, and relative positional relationships of sample terminal devices. The fourth label of the fourth training sample may include the actual biological species in the sample room corresponding to the differential sequences of multiple sample second signals, room type and layout of the sample target room, and relative positional relationships of sample terminal devices. In some embodiments, the fourth training sample and the fourth label can be obtained by simulating the movement of sample organisms in different sample rooms using a simulation model. The method for obtaining the fourth training sample and the fourth label is similar to the method for obtaining the first training sample and the first label; for further explanation, please refer to [link to documentation]. Figure 4A The details and related descriptions will not be repeated here.

[0119] An exemplary training process includes: inputting the differential sequences of multiple sample second signals into an initial second embedding layer to obtain a second feature vector output by the initial second embedding layer; using the second feature vector as training sample data, along with the room type, room layout, and relative positional relationship of the sample terminal devices, into a second prediction model to obtain the biological species output by the second prediction model. A loss function is constructed based on the fourth label and the biological species output by the second prediction model, and the parameters of the initial second embedding layer and the initial second prediction layer are updated synchronously until the intermediate second embedding layer and the intermediate second prediction layer meet preset conditions, resulting in a trained second embedding layer and second prediction layer. The preset conditions can be that the loss function is less than a threshold, convergence, or the training period reaches a threshold.

[0120] In some embodiments described herein, when multiple terminal devices receive signals, the second embedding layer based on the fourth prediction model processes the differential sequences of multiple second signals, the room type and layout of the target room, and the relative positional relationship of at least one terminal device. This allows for the determination of biological species based on the signals received by multiple terminal devices, resulting in more accurate predictions. Furthermore, obtaining the parameters of the fourth prediction model through the aforementioned training method can, in some cases, help address the difficulty of obtaining labels when training the second embedding layer alone, and also enable the second embedding layer to better reflect the features of multiple second signals.

[0121] Figure 7 These are exemplary schematic diagrams of signal spectra and a fifth prediction model shown according to some embodiments of this specification.

[0122] In some embodiments, the determining module 230 may construct a signal spectrum based on relevant information of the target room, at least one terminal device, and at least one emission source; and process at least one second signal and the constructed signal spectrum based on a fifth prediction model to determine the biological species.

[0123] A signal graph is a map used to reflect the signal transmission relationships between various terminal devices and the transmitting source in a target room. A graph is a data structure composed of nodes and edges; edges connect nodes, and nodes and edges can have attributes.

[0124] In some embodiments, nodes in the signal spectrum may correspond to terminal devices or transmitters within a target room. Node attributes may reflect relevant characteristics of the corresponding terminal device or transmitter. In some embodiments, nodes in the signal spectrum may include two types, such as... Figure 7 As shown, the nodes of the signal spectrum 710 may include signal transmitting nodes 720 and signal receiving nodes 730. Signal transmitting nodes 720 may include nodes A, B, and C. Signal receiving nodes 730 may include nodes A', B', and C'.

[0125] In some embodiments, the node attributes of a signal transmitting node may include the transmitting power, transmitting frequency, and transmitting location (e.g., represented by three-dimensional coordinates). The node attributes of a signal transmitting node can be represented by a three-dimensional vector. For example, the node attributes of a signal transmitting node can be (a, b, c), where a represents the transmitting power, b represents the transmitting frequency, and c represents the transmitting location.

[0126] In some embodiments, the node attributes of the signal receiving node may include the second signal received by the terminal device and the receiving position of the terminal device (e.g., it may be represented in three-dimensional coordinates).

[0127] The edges in a signal graph represent the signal transmission relationship between signal transmitting nodes and signal receiving nodes. In other words, edges exist between signal transmitting nodes and signal receiving nodes, such as... Figure 7 As shown, node C in signal transmitting node 720 and node C' in signal receiving node 730 have an edge 740, and node B in signal transmitting node 720 and node B' in signal receiving node 730 have an edge 740, etc. In some embodiments, the attributes of the edge may include the three-dimensional coordinates, material and volume of each object in the room located on the edge, as well as the length of the edge (i.e., the distance from the corresponding signal transmitting node to the signal receiving node).

[0128] The fifth prediction model can be another machine learning model used to determine the species of organism. For example, the fifth prediction model could be a GNN model, etc. It can also be other graph models, such as a graph convolutional neural network (GCNN) model, or a graph neural network model with added processing layers or modified processing methods.

[0129] In some embodiments, such as Figure 7As shown, the input to the fifth prediction model 750 includes a signal spectrum 710, and the output is the biological species 790 of the organism. In some embodiments, such as Figure 7 As shown, the input to the fifth prediction model 750 may also include the room type 760 of the target room, the room layout 770 of the target room, and the second signal 780. Here, the edge outputs in the GNN correspond to the biological species of the organisms in the target room.

[0130] The fifth prediction model can be trained using the same or different processing devices based on training data. The training data includes a fifth training sample and a fifth label. For example, the fifth training sample can be a sample signal map, sample room layout, sample room type, and sample second signal of multiple target rooms determined based on historical data. The nodes and their attributes, edges and their attributes of the sample signal map are similar to those described above. The label can be the actual biological species in the target rooms corresponding to the sample signal map, sample room layout, sample room type, and historical sample second signal. The fifth label can be obtained based on manual annotation. In some embodiments, the fifth training sample and the fifth label can be obtained by simulating the movement of sample organisms in different sample rooms using a simulation model. The method of obtaining the fifth training sample and the fifth label is similar to the method of obtaining the first training sample and the first label; further explanation can be found in [link to documentation]. Figure 4A The details and related descriptions will not be repeated here.

[0131] In some embodiments, the fifth prediction model may include multiple processing layers. For example, such as Figure 7 As shown, the fifth prediction model 750 may include a room feature layer 751, a distribution feature layer 753, and a species determination layer 755. The room feature layer 751 is used to process the room type 760 and room layout 770 of the target room to determine room features 752; the distribution feature layer 753 is used to process the signal spectrum 710 and the room features 752 output by the room feature layer 751 to determine location features 754; the species determination layer 755 is used to process at least one second signal 780 and the location features 754 output by the distribution feature layer 753 to determine biological species 790.

[0132] The room feature layer can be a machine learning model used to determine the characteristics of the target room. For example, the room feature layer could be a CNN model, etc. Figure 7 As shown, the input to the room feature layer 751 may include room type 760 and room layout 770, and the output may include room feature 752.

[0133] Room characteristics are information related to a room. For example, room characteristics may include the room's floor area, shape, and interior space occupancy. Room characteristics can be represented using feature vectors. For instance, A can represent a hotel room, B a warehouse room, etc., with specific numerical values ​​representing the room's floor area, 1 representing a cube shape, 2 representing a cuboid shape, etc., and percentages representing interior space occupancy. An example room characteristic (A, 20, 2, 20%) indicates a hotel room with a floor area of ​​2015 m². 2 It is rectangular in shape and occupies 20% of the interior space.

[0134] The distribution feature layer can be a machine learning model used to determine the distribution characteristics of the target room. For example, the distribution feature layer could be a GNN model, etc. Figure 7 As shown, the input of the distribution feature layer 753 may include the room feature 752 and the signal spectrum 710 output by the room feature layer 751, and the output may include the distribution feature 754.

[0135] Distribution characteristics can reflect the positional relationships between various transmission sources, terminal devices, and items within a room. For example, a distribution characteristic could be "table near the window." Distribution characteristics can be represented using feature vectors. For instance, 'a' can represent "table near the window," and 'b' can represent "television against the wall." An exemplary distribution characteristic of ([a, 0.1], [b, 0.2]) indicates that the table is 10cm from the window and the television is 20cm from the wall.

[0136] The species determination layer can be a machine learning model used to determine the species of an organism. For example, the species determination layer can be a DNN model. For example, the input to the species determination layer 755 can include a second signal 780 and distribution features 754 output from the distribution feature layer 753, and the output can include the species 790.

[0137] In some embodiments, the output of the room feature layer can be used as the input of the distribution feature layer, and the output of the distribution feature layer can be used as the input of the category prediction layer. The room feature layer, the distribution feature layer, and the category prediction layer can be jointly trained.

[0138] In some embodiments, the fifth prediction model can be obtained by jointly training multiple fifth training samples with fifth labels. The joint training process is similar to that of the fourth prediction model and will not be described in detail here.

[0139] In some embodiments of this specification, biological species are determined by combining signal spectrum with machine learning models. This allows for in-depth extraction of room features and the distribution characteristics of various items in the room. By combining calculations of multiple information from the target room, the reliability of the final biological species obtained is higher.

[0140] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0141] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0142] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0143] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0144] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0145] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0146] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for monitoring organisms, characterized in that, include: Based on at least one terminal device in the target room, at least one first signal transmitted by at least one transmission source is obtained; At least one second signal is extracted from the at least one first signal, wherein the second signal includes a channel state information signal obtained after the first signal has undergone multipath fading during propagation; Based on the at least one second signal, determine whether there is a living organism in the target room; In response to the presence of the organism in the target room, The biological species of the organism is determined by processing the at least one second signal using a machine learning model; Based on the biological species, a management plan for the target room is determined.

2. The method according to claim 1, characterized in that, Determining whether a living organism exists in the target room based on the at least one second signal includes: Based on the signal change rate of the at least one second signal, it is determined whether the organism exists in the target room.

3. The method according to claim 2, characterized in that, In response to the presence of the organism in the target room, the process of processing the at least one second signal using a machine learning model to determine the biological species of the organism includes: The biological species are determined by processing the signal change rate of the at least one second signal, the first location distribution of the at least one terminal device, and the second location distribution of the at least one emission source based on the first prediction model.

4. The method according to claim 3, characterized in that, The training samples for the machine learning model are obtained by simulating the movement of biological samples in different sample rooms using a simulation model.

5. The method according to claim 3, wherein processing the at least one second signal using a machine learning model to determine the species of organism comprises: A signal spectrum is constructed based on relevant information about the target room, the at least one terminal device, and the at least one transmission source; The biological species are determined by processing the at least one second signal and the signal spectrum based on the fifth prediction model, wherein the fifth prediction model is a machine learning model.

6. A biological monitoring system, characterized in that, include: The acquisition module is used to acquire at least one first signal emitted by at least one transmission source based on at least one terminal device in the target room; Extraction module, configured to extract at least one second signal from the at least one first signal, the second signal including channel state information signal obtained after the first signal has undergone multipath fading during propagation; Determine the module, used for Based on the at least one second signal, determine whether there is a living organism in the target room; In response to the presence of the organism in the target room, The biological species of the organism is determined by processing the at least one second signal using a machine learning model; Based on the biological species, a management plan for the target room is determined.

7. The system according to claim 6, characterized in that, To determine whether a living organism exists in the target room based on the at least one second signal, the determining module is used to: Based on the signal change rate of the at least one second signal, it is determined whether the organism exists in the target room.

8. The system according to claim 7, characterized in that, In response to the presence of the organism in the target room, the at least one second signal is processed using a machine learning model to determine the biological species of the organism, the determining module being configured to: The biological species are determined by processing the signal change rate of the at least one second signal, the first location distribution of the at least one terminal device, and the second location distribution of the at least one emission source based on the first prediction model.

9. The system according to claim 8, characterized in that, To determine the species of organism by processing the at least one second signal using a machine learning model, the determining module is configured to: A signal spectrum is constructed based on relevant information about the target room, the at least one terminal device, and the at least one transmission source; The biological species are determined by processing the at least one second signal and the signal spectrum based on the fifth prediction model, wherein the fifth prediction model is a machine learning model.

10. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the method as described in any one of claims 1 to 5.

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