Fall detection method and device, water heater and storage medium

Abstract

The invention discloses a fall detection method and device, a water heater and a storage medium, and the method comprises the steps: periodically transmitting a detection signal to each human body observation point of a target user, and collecting an echo signal of the detection signal transmitted each time; calculating the frequency offset of each human body observation point at different moments based on the frequencies of each detection signal and the corresponding echo signal to obtain each frequency offset sequence; based on the frequency offset sequences and the initial distances between the frequency offset sequences and the human body observation points, actual distances between the frequency offset sequences and the human body observation points at different moments are calculated, and distance sequences are obtained; calculating echo characteristics of each human body observation point at different moments according to each distance sequence and the frequency of transmitting the detection signal each time; performing spatial domain integration on the echo features of the human body observation points at the same moment to obtain echo features of the target user at different moments; and generating a time-frequency characteristic pattern based on the time-frequency characteristic pattern, and performing tumble detection on the target user based on the time-frequency characteristic pattern to obtain a tumble detection result.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of smart home technology, and in particular to a fall detection method, device, water heater, and storage medium. Background Art

[0002] With the improvement of living standards, water heaters have become a common household appliance. However, bathrooms are damp and slippery, significantly increasing the risk of falls for users. This has led to an increasingly urgent need for fall detection in bathing scenarios.

[0003] Currently, existing fall detection methods mostly rely on wearable devices, but such methods have obvious limitations in bathroom scenarios: first, users usually do not wear any devices when bathing, resulting in the inability to detect; second, most wearable devices are not waterproof enough, and are prone to signal distortion or hardware failure in humid environments, which directly affects the accuracy and reliability of detection.

[0004] Therefore, it is urgent to propose a new method to solve the above problems. Summary of the Invention

[0005] The present invention provides a fall detection method, device, water heater and storage medium to improve the accuracy and reliability of detection.

[0006] In a first aspect, an embodiment of the present invention provides a fall detection method, the method comprising:

[0007] Periodically transmitting a detection signal to each human body observation point of the target user, and collecting an echo signal of each human body observation point for each transmitted detection signal;

[0008] Based on the frequency of each detection signal and the frequency of the corresponding echo signal, calculating the frequency offsets generated by the movement of each human body observation point at different times, and obtaining a frequency offset sequence of each human body observation point;

[0009] Based on the frequency offset sequence of each human body observation point and the initial distance between each human body observation point and each human body observation point, calculating the actual distance between each human body observation point and each human body observation point at different times to obtain a distance sequence of each human body observation point;

[0010] Calculating the echo characteristics of each human observation point at different times based on the distance sequence of each human observation point and the frequency of each detection signal emission; performing spatial domain integration on the echo characteristics of each human observation point at the same time to obtain the echo characteristics of the target user at different times;

[0011] A time-frequency feature graph is generated based on the echo features of the target user at different times, and fall detection is performed on the target user based on the time-frequency feature graph to obtain a fall detection result.

[0012] The technical solution of the embodiment of the present invention first periodically transmits a detection signal to each human observation point of the target user, and collects the echo signal of each human observation point for each detection signal transmitted, providing a data basis for the subsequent calculation of the frequency offset sequence of each human observation point. Then, based on the frequency of each detection signal and the frequency of the corresponding echo signal, the frequency offset generated by the movement of each human observation point at different times is calculated to obtain the frequency offset sequence of each human observation point, which provides a data basis for subsequently obtaining the distance sequence of each human observation point. Subsequently, based on the frequency offset sequence of each human observation point and the initial distance between each human observation point and each human observation point, the actual distance between each human observation point and each human observation point at different times is calculated to obtain the distance sequence of each human observation point, which presents the changes in the spatial relationship between the target human body and the surrounding environment, and provides a data basis for subsequently obtaining the echo characteristics of the target user at different times. Next, the echo signatures of each observation point at different times are calculated based on the distance sequence of each observation point and the frequency of each detection signal transmission. The echo signatures of each observation point at the same time are spatially integrated to obtain the echo signatures of the target user at different times. This integrates the information of the scattered observation points, reduces the potential errors and limitations of individual observation points, and facilitates more accurate analysis of the target user's status, improving the accuracy and reliability of fall detection. Finally, a time-frequency feature map is generated based on the echo signatures of the target user at different times. Fall detection is performed on the target user based on this time-frequency feature map, resulting in a fall detection result. By converting the echo signatures into a time-frequency feature map, the changes in the target user's status over time and frequency are visually presented, simplifying complex information and making it easier to capture key information. This visualization and simplification significantly improves the accuracy and efficiency of subsequent analysis, thereby enhancing the reliability and accuracy of fall detection results. This allows accurate judgment of whether the target user has fallen, providing a strong basis for timely initiation of rescue measures and ensuring the safety of the target user. Therefore, the technical solution of the present invention solves two major problems existing in the prior art: one is that detection cannot be performed due to not wearing the detection equipment; the other is the low accuracy and reliability when relying on wearable equipment for detection.

[0013] In a second aspect, an embodiment of the present invention further provides a fall detection device, the device comprising:

[0014] an acquisition module, configured to periodically transmit a detection signal to each human body observation point of the target user, and collect an echo signal of each human body observation point for each transmitted detection signal;

[0015] an offset calculation module, configured to calculate the frequency offsets generated by the movement of each human body observation point at different times based on the frequency of each detection signal and the frequency of the corresponding echo signal, and obtain a frequency offset sequence of each human body observation point;

[0016] a distance calculation module, configured to calculate the actual distances between the human body observation points at different times based on the frequency offset sequence of the human body observation points and the initial distances between the human body observation points and the human body observation points, thereby obtaining a distance sequence of the human body observation points;

[0017] a feature calculation module, configured to calculate the echo features of each human observation point at different times based on the distance sequence of each human observation point and the frequency of each detection signal emission; and perform spatial domain integration on the echo features of each human observation point at the same time to obtain the echo features of the target user at different times;

[0018] The detection module is used to generate a time-frequency feature map based on the echo characteristics of the target user at different times, perform fall detection on the target user based on the time-frequency feature map, and obtain a fall detection result.

[0019] In a third aspect, an embodiment of the present invention further provides a water heater, comprising:

[0020] at least one processor; and a memory communicatively coupled to the at least one processor;

[0021] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can implement any fall detection method described in the first aspect.

[0022] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, implement any of the fall detection methods described in the first aspect.

[0023] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the fall detection device or separately from the processor of the fall detection device, and this application does not limit this.

[0024] The description of the second, third and fourth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third and fourth aspects can refer to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0025] In this application, the name of the fall detection device does not limit the device or functional modules themselves. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they are within the scope of the claims of this application and their equivalents.

[0026] These and other aspects of the present application will become more readily apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A flow chart of a fall detection method provided by an embodiment of the present invention;

[0029] Figure 2a A flowchart of another fall detection method provided by an embodiment of the present invention;

[0030] Figure 2b This is an example diagram of time-frequency features in a fall scenario provided by an embodiment of the present invention;

[0031] Figure 3 A schematic structural diagram of a fall detection device provided by an embodiment of the present invention;

[0032] Figure 4 A schematic structural diagram of a water heater provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0034] The term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0035] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0036] Furthermore, the terms "including," "having," and any variations thereof, as used in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not limited to the listed steps or units but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or apparatus.

[0037] It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although flow charts describe various operations (or steps) as sequential processes, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. When its operation is completed, the process can be terminated, but can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, the features in the embodiments of the present invention and the embodiments can be combined with each other without conflict.

[0038] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being more preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0039] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0040] Figure 1 This is a flow chart of a fall detection method provided by an embodiment of the present invention. This embodiment can be used to detect whether a user falls during bathing. The method can be executed by a fall detection device, which can be implemented in hardware or software. The device can be integrated into a water heater. Figure 1 As shown, the specific steps include:

[0041] Step 110: Periodically transmit a detection signal to each human body observation point of the target user, and collect an echo signal of each human body observation point for each transmitted detection signal.

[0042] Specifically, the target user refers to the specific individual for whom fall detection is required. The body observation point refers to a specific location on the target user's body selected for transmitting detection signals and collecting echo signals. The detection signal is the signal periodically transmitted by the detection equipment (such as radar) installed on the water heater to each body observation point of the target user. The echo signal is the signal returned by each body observation point after reflecting each transmitted detection signal.

[0043] In the specific implementation, first, a human body sensor (such as an infrared sensor) installed on the water heater is used to determine whether there is anyone within the preset detection range (such as the detection range of the detection equipment installed on the water heater). If there is someone, the detected human body is identified as the target user. Subsequently, with the location of the target user as the center, within an area with a preset radius (such as 3 meters) around it (i.e., the human activity area), a detection signal is transmitted through the detection equipment (such as a radar) carried by the water heater, and the reflected signal of the detection signal is collected in real time. After that, the human body observation point is marked according to the source position of the reflected signal. After the observation point marking is completed, the detection signal is periodically transmitted to each human body observation point of the target user, and the echo signal after each transmission is synchronously collected.

[0044] In this embodiment, the above steps provide a data basis for subsequently obtaining a frequency offset sequence of each human body observation point.

[0045] Step 120 : Based on the frequency of each detection signal and the frequency of the corresponding echo signal, the frequency offsets of each human body observation point generated by movement at different times are calculated to obtain a frequency offset sequence of each human body observation point.

[0046] Specifically, the frequency offset is the frequency difference between the detection signal and the echo signal, reflecting the movement of the human observation point at different times. A frequency offset sequence is a chronological sequence of the frequency offsets generated by the movement of the human observation point at different times. This sequence records the frequency changes of the human observation point over a period of time.

[0047] In the specific implementation, for the current human body observation point, the absolute value of the difference between the frequency of the detection signal emitted to the current human body observation point at the corresponding moment and the frequency of the corresponding echo signal is calculated to obtain the frequency offset caused by the movement of the current human body observation point at the corresponding moment. Then, the frequency offsets of the current human body observation point at each moment are arranged in chronological order to obtain the frequency offset sequence of the current human body observation point.

[0048] In this embodiment, through the above steps, the movement of the human body observation points over time is obtained, which provides a data basis for subsequently obtaining the distance sequence of each human body observation point.

[0049] Step 130 : Based on the frequency offset sequence of each human body observation point and the initial distance between each human body observation point and each human body observation point, the actual distance between each human body observation point and each human body observation point at different times is calculated to obtain a distance sequence of each human body observation point.

[0050] Specifically, the initial distance refers to the distance between the water heater detection device and the human observation point at the start of the test. The actual distance refers to the actual distance between the detection device and the human observation point at different times. The distance sequence is a chronological sequence of the actual distances between the detection device and the human observation point at different times. This sequence records the position changes of the human observation point over time.

[0051] In a specific implementation, for the current human observation point, the ratio of the speed and frequency of the detection signal transmitted to the current human observation point at the corresponding moment is first calculated to obtain the wavelength of the detection signal transmitted to the current human observation point at the corresponding moment. Next, based on the wavelength of the detection signal transmitted to the current human observation point at the corresponding moment and the frequency offset sequence of the current human observation point, the speed factor of the current human observation point at the corresponding moment is calculated. The speed factor of the current human observation point at the corresponding moment is then time-integrated to obtain the travel distance of the current human observation point at the corresponding moment. The travel distance of the current human observation point at the corresponding moment and the initial distance between the current human observation point and the current human observation point are then calculated to obtain the actual distance from the current human observation point at the corresponding moment. Finally, the actual distances from the current human observation point at the corresponding moment are arranged in chronological order to obtain a distance sequence for the current human observation point.

[0052] In this embodiment, the distance sequence of each human observation point obtained through the above steps presents the change in the spatial relationship between the target human body and the surrounding environment, providing a data basis for subsequently obtaining the echo characteristics of the target user at different times.

[0053] Step 140: Calculate the echo characteristics of each human observation point at different times based on the distance sequence of each human observation point and the frequency of each detection signal emission; perform spatial domain integration on the echo characteristics of each human observation point at the same time to obtain the echo characteristics of the target user at different times.

[0054] Specifically, the echo signature of a human observation point at different times is a parameter calculated based on the distance sequence of the observation point and the frequency of each detection signal transmission. It reflects the echo signal characteristics of the human observation point at different time points and reflects the impact of the human observation point's spatial position and relative motion with the water heater detection equipment on the echo. The echo signature of the target user at different times is the result of spatially integrating the echo signatures of each human observation point at the same time.

[0055] In the specific implementation, for the current human observation point, the round-trip time of the signal at the corresponding moment is calculated based on the speed of the detection signal transmitted to the current human observation point and the distance sequence of the current human observation point. A phase operation is performed on the round-trip time of the signal at the current human observation point at the corresponding moment and the frequency of the detection signal transmitted to the current human observation point at the corresponding moment to obtain the echo phase of the current human observation point at the corresponding moment. The product of the echo phase of the current human observation point at the corresponding moment and the transmission coefficient is calculated to obtain the echo characteristics of the current human observation point at the corresponding moment. Then, the echo characteristics of each human observation point at the same moment are spatially integrated based on the human activity area to obtain the echo characteristics of the target user at different times.

[0056] In this embodiment, through the above steps, the information of scattered human body observation points can be integrated to obtain the overall echo characteristics of the target user at different times, which reflects the current status of the target user as a whole, reduces the errors and limitations that may exist in a single observation point, and thus helps to more accurately analyze the target user status, thereby improving the accuracy and reliability of fall detection.

[0057] Step 150: Generate a time-frequency feature graph based on the echo features of the target user at different times, perform fall detection on the target user based on the time-frequency feature graph, and obtain a fall detection result.

[0058] Specifically, a time-frequency feature graph is a graph generated based on the target user's echo characteristics at different times. It displays the target user's characteristic changes in both time and frequency. A fall detection result is a conclusion drawn by analyzing and judging the time-frequency feature graph as to whether the target user has fallen. For example, a fall detection result can be either "fallen" or "not fallen."

[0059] In a specific implementation, after obtaining the echo characteristics of the target user at different times, the echo characteristics of the target user at different times can be processed based on a time-frequency feature conversion algorithm (such as short-time Fourier transform, discrete Fourier transform, etc.) to obtain a time-frequency feature graph, and then the target user is subjected to fall detection based on the time-frequency feature graph to obtain a fall detection result. Specifically, the obtained time-frequency feature graph can be input into a pre-trained fall detection model to obtain a fall detection result. Among them, the pre-trained fall detection model refers to a model obtained by training a deep learning model based on each historical time-frequency feature graph and the corresponding fall detection results.

[0060] In this embodiment, the above steps enable a visual presentation of how the target user's status changes over time and frequency, simplifying complex information and making key information (such as fall characteristics) easier to capture. This visualization and simplification significantly improves the accuracy and efficiency of subsequent analysis, thereby enhancing the reliability and accuracy of fall detection results. This allows for an accurate determination of whether the target user has fallen, providing a strong basis for timely initiation of rescue measures and ensuring the safety of the target user.

[0061] The fall detection method provided by the embodiment of the present invention first periodically transmits a detection signal to each human observation point of the target user, and collects the echo signal of each human observation point for each detection signal transmitted, providing a data basis for the subsequent calculation of the frequency offset sequence of each human observation point. Then, based on the frequency of each detection signal and the frequency of the corresponding echo signal, the frequency offset generated by the movement of each human observation point at different times is calculated to obtain the frequency offset sequence of each human observation point, which provides a data basis for obtaining the distance sequence of each human observation point. Subsequently, based on the frequency offset sequence of each human observation point and the initial distance between each human observation point and each human observation point, the actual distance between each human observation point and each human observation point at different times is calculated to obtain the distance sequence of each human observation point, which provides a data basis for obtaining the echo characteristics of the target user at different times. Next, the echo signatures of each observation point at different times are calculated based on the distance sequence of each observation point and the frequency of each detection signal transmission. The echo signatures of each observation point at the same time are spatially integrated to obtain the echo signatures of the target user at different times. This integrates the information of the scattered observation points, reduces the potential errors and limitations of individual observation points, and facilitates more accurate analysis of the target user's status, improving the accuracy and reliability of fall detection. Finally, a time-frequency feature map is generated based on the echo signatures of the target user at different times. Fall detection is performed on the target user based on this time-frequency feature map, resulting in a fall detection result. By converting the echo signatures into a time-frequency feature map, the changes in the target user's status over time and frequency are visually presented, simplifying complex information and making it easier to capture key information. This visualization and simplification significantly improves the accuracy and efficiency of subsequent analysis, thereby enhancing the reliability and accuracy of fall detection results. This allows accurate judgment of whether the target user has fallen, providing a strong basis for timely initiation of rescue measures and ensuring the safety of the target user. Therefore, the technical solution of the present invention solves two major problems existing in the prior art: one is that detection cannot be performed due to not wearing the detection equipment; the other is the low accuracy and reliability when relying on wearable equipment for detection.

[0062] Figure 2aThis is a flow chart of another fall detection method provided by an embodiment of the present invention. This embodiment is a specific implementation based on the above embodiment. In this embodiment, the method may further include:

[0063] Step 211: Periodically transmit a detection signal to each human body observation point of the target user, and collect an echo signal of each human body observation point for each transmitted detection signal.

[0064] Furthermore, before periodically transmitting detection signals to each human body observation point of the target user and collecting echo signals of each human body observation point for each detection signal transmitted, it also includes: determining whether there is a person within the preset detection range; if there is a person, triggering the execution of periodically transmitting detection signals to each human body observation point of the target user, and collecting echo signals of each human body observation point for each detection signal transmitted.

[0065] Specifically, the preset detection range refers to a detection range predetermined according to actual conditions or requirements. For example, the preset detection range may be a detection range of a water heater detection device.

[0066] In a specific implementation, a human body sensor (such as an infrared sensor) installed on the water heater can be used to determine whether there is a person within the preset detection range. If the human body sensor detects the presence of a human body, it is determined that there is a person within the preset detection range. At this time, the detected object is identified as the target user. Subsequently, with the target user's location as the center, a detection signal is emitted through the detection equipment carried by the water heater within an area with a preset radius (such as 3 meters) around it (i.e., the human activity area), and the reflected signal of the signal is collected in real time. Then, the human body observation point is marked according to the source position of the reflected signal. After the observation point marking is completed, the detection signal is periodically emitted to each human body observation point, and the echo signal after each emission is synchronously collected. If the human body sensor does not detect a human body, it is determined that there is no one within the preset detection range. At this time, the detection continues until a person appears within the preset detection range.

[0067] In this embodiment, the above steps enable precise targeting of monitoring targets, avoiding invalid detection in unoccupied areas and effectively reducing the risk of misjudgment. Furthermore, upon detecting a person's entry, signal transmission and echo acquisition are immediately triggered, significantly improving detection timeliness. Furthermore, during periods of inactivity, signal transmission and echo acquisition are suspended, shortening the operating hours of the detection equipment and reducing energy consumption, thereby achieving resource conservation and optimization.

[0068] Step 212: Based on the frequency of each detection signal and the frequency of the corresponding echo signal, the frequency offsets generated by the movement of each human body observation point at different times are calculated to obtain a frequency offset sequence of each human body observation point.

[0069] Furthermore, based on the frequency of each detection signal and the frequency of the corresponding echo signal, the frequency offset generated by the movement of each human body observation point at different times is calculated, including: for the current human body observation point, based on the difference between the frequency of the detection signal transmitted to the current human body observation point at the corresponding time and the frequency of the corresponding echo signal, the frequency offset generated by the movement of the current human body observation point at the corresponding time is determined.

[0070] In the specific implementation, for the current human body observation point, the difference between the frequency of the detection signal emitted to the current human body observation point at the corresponding moment and the frequency of the corresponding echo signal can be calculated first to obtain the frequency difference of the current human body observation point at the corresponding moment, and then the absolute value of the frequency difference of the current human body observation point at the corresponding moment can be taken to obtain the frequency offset caused by the movement of the current human body observation point at the corresponding moment.

[0071] In this embodiment, the accuracy of the determined frequency offset is improved through the above steps.

[0072] Step 213: Based on the frequency offset sequence of each human body observation point and the initial distance between each human body observation point and each human body observation point, the actual distance between each human body observation point and each human body observation point at different times is calculated to obtain a distance sequence of each human body observation point.

[0073] Furthermore, based on the frequency offset sequence of each human body observation point and the initial distance between each human body observation point, the actual distance between each human body observation point at different times is calculated, including: for the current human body observation point, calculating the ratio of the speed and frequency of the detection signal emitted to the current human body observation point at the corresponding time, and obtaining the wavelength of the detection signal emitted to the current human body observation point at the corresponding time; calculating the speed factor of the current human body observation point at the corresponding time based on the wavelength of the detection signal emitted to the current human body observation point at the corresponding time and the frequency offset sequence of the current human body observation point; performing time integration on the speed factor of the current human body observation point at the corresponding time, and obtaining the moving distance of the current human body observation point at the corresponding time; calculating the sum of the moving distance of the current human body observation point at the corresponding time and the initial distance between the current human body observation point and the current human body observation point, and obtaining the actual distance from the current human body observation point at the corresponding time.

[0074] In the specific implementation, for the current human body observation point, the ratio of the speed and frequency of the detection signal transmitted to the current human body observation point at the corresponding time is first calculated to obtain the wavelength of the detection signal transmitted to the current human body observation point at the corresponding time. Then, based on the wavelength of the detection signal transmitted to the current human body observation point at the corresponding time and the frequency offset sequence of the current human body observation point, the speed factor of the current human body observation point at the corresponding time is calculated. The specific calculation formula is as follows: i (t j )=[f i (t j )×λi (t j )] / 2, where a i (t j ) represents the velocity factor of the i-th detection point at time j, f i (t j ) represents the frequency offset of the i-th detection point at time j, λ i (t j ) represents the wavelength of the i-th detection point at time j.

[0075] Next, the velocity factor of the current human observation point at the corresponding moment is integrated over time to obtain the moving distance of the current human observation point at the corresponding moment. The specific calculation formula is as follows: Among them, R i (t j ) represents the moving distance of the i-th detection point at time j, and t0 represents the detection start time.

[0076] Finally, the sum of the moving distance of the current human body observation point at the corresponding moment and the initial distance between the current human body observation point and the current human body observation point is calculated to obtain the actual distance from the current human body observation point at the corresponding moment. The specific calculation formula is as follows: S i (t j )=R i (t j )+r i (t0), where S i (t j ) represents the actual distance of the i-th detection point at time j, r i (t0) represents the initial distance of the i-th detection point.

[0077] It should be noted that the initial distance from the current human body observation point can be determined according to the time when the detection signal is transmitted at the beginning of the detection, the time when the corresponding echo signal is received, and the transmission speed of the detection signal.

[0078] In this embodiment, through the above steps, the accuracy of the determined distance sequence of each human body observation point is improved.

[0079] Step 214: Calculate the echo characteristics of each human observation point at different times based on the distance sequence of each human observation point and the frequency of each detection signal emission; perform spatial domain integration on the echo characteristics of each human observation point at the same time to obtain the echo characteristics of the target user at different times.

[0080] Furthermore, based on the distance sequence of each human body observation point and the frequency of each detection signal transmitted, the echo characteristics of each human body observation point at different moments are calculated, including: for the current human body observation point, based on the speed of the detection signal transmitted to the current human body observation point at the corresponding moment and the distance sequence of the current human body observation point, the signal round-trip time of the current human body observation point at the corresponding moment is calculated; a phase operation is performed on the signal round-trip time of the current human body observation point at the corresponding moment and the frequency of the detection signal transmitted to the current human body observation point at the corresponding moment to obtain the echo phase of the current human body observation point at the corresponding moment; and the product of the echo phase of the current human body observation point at the corresponding moment and the transmission coefficient is calculated to obtain the echo characteristics of the current human body observation point at the corresponding moment.

[0081] Specifically, the round-trip time (RTT) refers to the total time it takes for a detection signal to travel from the detection device to the observation point on the body and then reflect back to the detection device. The echo phase is the result of a phase calculation between the round-trip time and the transmission frequency, representing the phase shift of the echo signal relative to the transmitted signal. The emission coefficient (Effect) is a parameter set in advance based on actual conditions or requirements, and its value ranges from 0 to 1.

[0082] In the specific implementation, for the current human body observation point, the round-trip time of the signal at the current human body observation point at the corresponding moment is calculated based on the speed of the detection signal transmitted to the current human body observation point at the corresponding moment and the distance sequence of the current human body observation point. The specific calculation formula is as follows: i (t j )=2S i (t j ) / v i (t j ), where T i (t j ) represents the round trip time of the signal at the i-th detection point at time j, v i (t j ) represents the emission speed of the detection signal of the i-th detection point at time j (that is, the speed of emitting the detection signal).

[0083] Next, the phase calculation is performed on the round-trip time of the signal at the current human body observation point at the corresponding time and the frequency of the detection signal transmitted to the current human body observation point at the corresponding time to obtain the echo phase of the current human body observation point at the corresponding time. The specific calculation formula is as follows: i (t j )=exp[J×2π×v i (t j )×(t j -T i (t j ))], where M i (t j) represents the echo phase of the i-th detection point at time j, J represents an imaginary number, and exp represents an exponential function.

[0084] Finally, the product of the echo phase and the emission coefficient of the current human observation point at the corresponding time is calculated to obtain the echo characteristics of the current human observation point at the corresponding time. The specific calculation formula is as follows: i (t j )=ρ×M i (t j ), where x i (t j ) represents the echo characteristics of the i-th detection point at time j, and ρ represents the emission coefficient.

[0085] In this embodiment, through the above steps, the accuracy of the echo characteristics of each human body observation point at different times is improved.

[0086] Step 215: Generate a time-frequency feature graph based on the echo features of the target user at different times, perform fall detection on the target user based on the time-frequency feature graph, and obtain a fall detection result.

[0087] For example, Figure 2b As shown in the figure, six time-frequency feature maps of different fall scenarios are shown, labeled a, b, c, d, e, and f. The horizontal axis of each sub-graph is time (unit: seconds) and the vertical axis is frequency (unit: Hertz). It can be clearly seen from the figure that the frequency will change significantly when a fall occurs. Specifically, in sub-graphs (a) and (b), there are obvious and large negative peaks. This indicates that in the corresponding fall scenario, the body made a backward fall movement. In sub-graphs (c) and (d), there are obvious positive frequency peaks, which means that in the corresponding fall scenario, the body showed a forward fall movement. Sub-graphs (e) and (f) show multiple positive and negative frequency fluctuations with large amplitudes, reflecting that in the corresponding fall scenario, the body movement posture changes are complex and include a variety of action combinations (such as collisions, body twisting and swinging, etc.). Therefore, in practical applications, fall detection can be performed in the following steps: First, collect historical time-frequency feature maps for various fall scenarios; then, use a neural network (such as a convolutional neural network) to extract features from these historical time-frequency feature maps to obtain fall features; then, when the current time-frequency feature map is obtained, use the same neural network to extract features from it; finally, match the extracted current time-frequency features with the previously obtained fall features. If the match is successful, the fall detection result is determined to be a fall; if the match fails, the fall detection result is determined to be no fall.

[0088] Step 216: Determine whether the fall detection result is a fall.

[0089] If yes, execute step 217; if no, execute step 211.

[0090] In a specific implementation, after obtaining the fall detection result, it can be determined whether the fall detection result is a fall. If it is a fall, it means that the target user has accidentally fallen and is at risk of injury. At this time, a voice prompt message can be played and the power can be turned off. If it is not a fall, it means that the target user is in a normal activity state or has only performed non-fall-related actions. At this time, detection signals continue to be periodically transmitted to each human observation point of the target user, and the echo signal of each human observation point for each transmitted detection signal is collected. The user status is continuously monitored to ensure that subsequent possible falls can be discovered in a timely manner, thereby ensuring the user's safety.

[0091] Step 217: Play a voice prompt message and perform a power-off operation.

[0092] Specifically, the voice prompt information refers to the voice content set in advance according to the actual situation or needs. The power-off operation refers to the act of automatically cutting off the power supply of related electrical equipment (such as a water heater heating element) or circuit after detecting that the target user has fallen.

[0093] In the specific implementation, after determining that the fall detection result is a fall, a voice prompt message can be played and a power-off operation can be performed, thereby avoiding secondary injuries caused by equipment operation (such as water heater leakage, overheating of water temperature, etc.) when the user falls. At the same time, voice prompts are used to attract the attention of surrounding people so that timely rescue can be provided.

[0094] Step 218: Determine whether a response message is received within a preset time period.

[0095] If yes, execute step 211 ; if no, execute step 219 .

[0096] Specifically, the response information refers to the feedback information given by the target user or surrounding people after hearing the voice prompt information through some means (such as button pressing, voice response, etc.). The preset duration is a time length set in advance according to actual conditions or needs, for example: the preset duration is 1 minute.

[0097] In the specific implementation, after playing the voice prompt information and performing the power-off operation, the timing starts, and it is determined whether a response message is received within the preset time. If received, it means that the target user is conscious and has the ability to respond independently, or there are other people around who respond in time. At this time, the detection signal continues to be periodically transmitted to each human observation point of the target user, and the echo signal of each human observation point for each detection signal transmitted is collected, and the user status is continuously monitored to ensure that subsequent possible falls can be discovered in time, thereby ensuring the safety of the user. If not received, it means that the target user may be unconscious and unable to respond due to injuries from falling, or there is a lack of assistance from others on the scene. At this time, a distress message is sent to the terminal corresponding to the emergency contact and an alarm operation is triggered to maximize the intervention of external rescue forces and reduce the risks caused by delayed treatment.

[0098] Step 219: Send a distress message to the terminal corresponding to the emergency contact and trigger an alarm operation.

[0099] Specifically, an emergency contact is a person who needs to be contacted in the event of an emergency, such as a fall, and is pre-set based on actual needs. A distress message is a message sent to the terminal corresponding to the emergency contact to request help or rescue if no response is received within a preset time period, pre-set based on actual needs.

[0100] In the specific implementation, if no response information is received within the preset time, a distress message (such as user location, fall time, etc.) is immediately sent to the terminal corresponding to the emergency contact (such as a mobile phone), and an alarm operation (such as a high-decibel sound and light alarm, SMS alarm, etc.) is triggered, so as to maximize the intervention of external rescue forces and reduce the risks caused by delayed treatment.

[0101] The fall detection method provided by the embodiment of the present invention first periodically transmits a detection signal to each human observation point of the target user, and collects the echo signal of each human observation point for each detection signal transmitted, providing a data basis for the subsequent calculation of the frequency offset sequence of each human observation point. Then, based on the frequency of each detection signal and the frequency of the corresponding echo signal, the frequency offset generated by the movement of each human observation point at different times is calculated to obtain the frequency offset sequence of each human observation point, which provides a data basis for obtaining the distance sequence of each human observation point. Subsequently, based on the frequency offset sequence of each human observation point and the initial distance between each human observation point and each human observation point, the actual distance between each human observation point and each human observation point at different times is calculated to obtain the distance sequence of each human observation point, which provides a data basis for obtaining the echo characteristics of the target user at different times. Next, based on the distance sequence of each human observation point and the frequency of each detection signal transmission, the echo characteristics of each human observation point at different times are calculated. The echo characteristics of each human observation point at the same time are spatially integrated to obtain the echo characteristics of the target user at different times. This integrates the information of the scattered human observation points, reduces the errors and limitations that may exist in individual observation points, and facilitates more precise analysis of the target user's status, improving the accuracy and reliability of fall detection. Next, a time-frequency feature map is generated based on the target user's echo characteristics at different times. Fall detection is performed on the target user based on this time-frequency feature map, and the fall detection result is obtained. By converting the echo characteristics into a time-frequency feature map, the changes in the target user's status over time and frequency are visually presented, and complex information is simplified, making it easier to capture key information. This visualization and simplification process significantly improves the accuracy and efficiency of subsequent analysis, thereby enhancing the reliability and accuracy of the fall detection results. The fall detection result is then determined to be a fall. If the user is not in a fall state, the system continues to periodically transmit detection signals to each of the target user's body observation points and collects the echo signals from each of the detection signals transmitted from each of the body observation points. This system continuously monitors the user's status to ensure timely detection of any subsequent falls, thereby ensuring the user's safety. If the user is in a fall state, a voice prompt is played and the power is turned off to prevent secondary damage caused by equipment operation (such as water heater leakage, overheating, etc.) while the user is in a fall state. At the same time, the voice prompt attracts the attention of nearby personnel to facilitate timely rescue. A determination is then made as to whether a response message is received within a preset time period. If a response message is received, the system continues to periodically transmit detection signals to each of the target user's body observation points and collects the echo signals from each of the detection signals transmitted from each of the body observation points. This system continuously monitors the user's status to ensure timely detection of any subsequent falls, thereby ensuring the user's safety. If a response message is not received, a distress message is sent to the terminal corresponding to the emergency contact and an alarm is triggered to maximize the involvement of external rescue forces and minimize the risks associated with delayed treatment.

[0102] Figure 3 This is a schematic diagram of the structure of a fall detection device provided in an embodiment of the present invention. The device and the fall detection methods of the above embodiments belong to the same inventive concept. For details not fully described in the embodiments of the fall detection device, please refer to the embodiments of the above fall detection method.

[0103] like Figure 3 As shown, the device includes:

[0104] An acquisition module 310 is configured to periodically transmit a detection signal to each human body observation point of the target user, and collect an echo signal of each human body observation point for each transmitted detection signal;

[0105] An offset calculation module 320 is configured to calculate frequency offsets generated by the movement of each human body observation point at different times based on the frequency of each detection signal and the frequency of the corresponding echo signal, thereby obtaining a frequency offset sequence of each human body observation point.

[0106] A distance calculation module 330 is configured to calculate the actual distances between the human body observation points at different times based on the frequency offset sequence of the human body observation points and the initial distances between the human body observation points and the human body observation points, thereby obtaining a distance sequence of the human body observation points.

[0107] The feature calculation module 340 is configured to calculate the echo features of each human observation point at different times based on the distance sequence of each human observation point and the frequency of each detection signal emission; perform spatial domain integration on the echo features of each human observation point at the same time to obtain the echo features of the target user at different times;

[0108] The detection module 350 is configured to generate a time-frequency feature graph based on the echo features of the target user at different times, and perform fall detection on the target user based on the time-frequency feature graph to obtain a fall detection result.

[0109] Based on the above embodiment, the distance calculation module 330 calculates the actual distance between the human body observation points at different times based on the frequency offset sequence of the human body observation points and the initial distance between the human body observation points, including:

[0110] For the current human body observation point, calculate the ratio of the speed and frequency of the detection signal emitted to the current human body observation point at the corresponding moment to obtain the wavelength of the detection signal emitted to the current human body observation point at the corresponding moment; calculate the speed factor of the current human body observation point at the corresponding moment based on the wavelength of the detection signal emitted to the current human body observation point at the corresponding moment and the frequency offset sequence of the current human body observation point; perform time integration on the speed factor of the current human body observation point at the corresponding moment to obtain the moving distance of the current human body observation point at the corresponding moment; calculate the sum of the moving distance of the current human body observation point at the corresponding moment and the initial distance between the current human body observation point and the current human body observation point to obtain the actual distance from the current human body observation point at the corresponding moment.

[0111] Based on the above embodiment, the feature calculation module 340 calculates the echo features of each human body observation point at different times according to the distance sequence of each human body observation point and the frequency of each emission of the detection signal, including:

[0112] For the current human body observation point, the signal round-trip time of the current human body observation point at the corresponding moment is calculated based on the speed of the detection signal transmitted to the current human body observation point at the corresponding moment and the distance sequence of the current human body observation point; a phase operation is performed on the signal round-trip time of the current human body observation point at the corresponding moment and the frequency of the detection signal transmitted to the current human body observation point at the corresponding moment to obtain the echo phase of the current human body observation point at the corresponding moment; the product of the echo phase of the current human body observation point at the corresponding moment and the emission coefficient is calculated to obtain the echo characteristics of the current human body observation point at the corresponding moment.

[0113] Based on the above embodiment, the offset calculation module 320 calculates the frequency offsets generated by the movement of each human body observation point at different times based on the frequency of each detection signal and the frequency of the corresponding echo signal, including:

[0114] For the current human body observation point, the frequency offset generated by the movement of the current human body observation point at the corresponding moment is determined according to the difference between the frequency of the detection signal transmitted to the current human body observation point at the corresponding moment and the frequency of the corresponding echo signal.

[0115] Based on the above embodiment, the device further includes:

[0116] The judgment module is used to determine whether there is a person within a preset detection range before periodically transmitting a detection signal to each human body observation point of the target user and collecting the echo signal of each human body observation point for the detection signal transmitted each time; if there is a person, triggering the execution of periodically transmitting a detection signal to each human body observation point of the target user and collecting the echo signal of each human body observation point for the detection signal transmitted each time.

[0117] Based on the above embodiment, the device further includes:

[0118] The power-off module is used to play a voice prompt message and perform a power-off operation after obtaining a fall detection result and if the fall detection result is a fall.

[0119] Based on the above embodiment, the device further includes:

[0120] The alarm module is used to send a distress message to the terminal corresponding to the emergency contact and trigger an alarm operation if no response message is received within a preset time after playing the voice prompt message and performing the power-off operation.

[0121] The fall detection device provided in the embodiment of the present invention can execute the fall detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0122] It is worth noting that in the embodiment of the above-mentioned fall detection device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0123] Figure 4 A schematic structural diagram of a water heater provided in an embodiment of the present invention. Figure 4 A block diagram of an exemplary water heater 4 suitable for use in implementing embodiments of the present invention is shown. Figure 4 The water heater 4 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0124] like Figure 4 As shown, the water heater 4 is implemented as a general purpose computing electronic device. Components of the water heater 4 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 connecting various system components (including the system memory 28 and the processing unit 16).

[0125] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0126] The water heater 4 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the water heater 4, including volatile and non-volatile media, removable and non-removable media.

[0127] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The water heater 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be configured to read and write to non-removable, non-volatile magnetic media ( Figure 4 Not shown, often called a "hard drive"). Although Figure 4 Not shown, a magnetic disk drive for reading and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0128] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0129] The water heater 4 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the water heater 4, and / or any device that enables the water heater 4 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the water heater 4 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. Figure 4 As shown, the network adapter 20 communicates with other modules of the water heater 4 via the bus 18. Figure 4Not shown, other hardware and / or software modules may be used in conjunction with the water heater 4, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0130] The processing unit 16 executes various functional applications and page displays by running programs stored in the system memory 28, such as implementing the fall detection method provided in an embodiment of the present invention, which includes:

[0131] Periodically transmitting a detection signal to each human body observation point of the target user, and collecting an echo signal of each human body observation point for each transmitted detection signal;

[0132] Based on the frequency of each detection signal and the frequency of the corresponding echo signal, calculating the frequency offsets generated by the movement of each human body observation point at different times, and obtaining a frequency offset sequence of each human body observation point;

[0133] Based on the frequency offset sequence of each human body observation point and the initial distance between each human body observation point and each human body observation point, calculating the actual distance between each human body observation point and each human body observation point at different times to obtain a distance sequence of each human body observation point;

[0134] Calculating the echo characteristics of each human observation point at different times based on the distance sequence of each human observation point and the frequency of each detection signal emission; performing spatial domain integration on the echo characteristics of each human observation point at the same time to obtain the echo characteristics of the target user at different times;

[0135] A time-frequency feature graph is generated based on the echo features of the target user at different times, and fall detection is performed on the target user based on the time-frequency feature graph to obtain a fall detection result.

[0136] Of course, those skilled in the art will appreciate that the processor may also implement the technical solution of the fall detection method provided in any embodiment of the present invention.

[0137] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the fall detection method provided in an embodiment of the present invention is implemented, for example, including:

[0138] Periodically transmitting a detection signal to each human body observation point of the target user, and collecting an echo signal of each human body observation point for each transmitted detection signal;

[0139] Based on the frequency of each detection signal and the frequency of the corresponding echo signal, calculating the frequency offsets generated by the movement of each human body observation point at different times, and obtaining a frequency offset sequence of each human body observation point;

[0140] Based on the frequency offset sequence of each human body observation point and the initial distance between each human body observation point and each human body observation point, calculating the actual distance between each human body observation point and each human body observation point at different times to obtain a distance sequence of each human body observation point;

[0141] Calculating the echo characteristics of each human observation point at different times based on the distance sequence of each human observation point and the frequency of each detection signal emission; performing spatial domain integration on the echo characteristics of each human observation point at the same time to obtain the echo characteristics of the target user at different times;

[0142] A time-frequency feature graph is generated based on the echo features of the target user at different times, and fall detection is performed on the target user based on the time-frequency feature graph to obtain a fall detection result.

[0143] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0144] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0145] Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0146] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0147] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computer device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

[0148] In addition, the acquisition, storage, use, and processing of data in the technical solution of the present invention comply with relevant provisions of laws and regulations.

[0149] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments, and substitutions are possible for those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A fall detection method, characterized in that: The method comprises: Periodically transmitting a detection signal to each human body observation point of the target user, and collecting an echo signal of each human body observation point for each transmitted detection signal; Based on the frequency of each detection signal and the frequency of the corresponding echo signal, calculating the frequency offsets generated by the movement of each human body observation point at different times, and obtaining a frequency offset sequence of each human body observation point; Based on the frequency offset sequence of each human body observation point and the initial distance between each human body observation point and each human body observation point, calculating the actual distance between each human body observation point and each human body observation point at different times to obtain a distance sequence of each human body observation point; Calculating the echo characteristics of each human observation point at different times based on the distance sequence of each human observation point and the frequency of each detection signal emission; performing spatial domain integration on the echo characteristics of each human observation point at the same time to obtain the echo characteristics of the target user at different times; A time-frequency feature graph is generated based on the echo features of the target user at different times, and fall detection is performed on the target user based on the time-frequency feature graph to obtain a fall detection result.

2. The fall detection method according to claim 1, wherein: Calculating actual distances between the human body observation points at different times based on the frequency offset sequence of the human body observation points and the initial distances between the human body observation points and the human body observation points includes: For a current human body observation point, calculating a ratio of a speed and a frequency of a detection signal transmitted to the current human body observation point at a corresponding moment, to obtain a wavelength of the detection signal transmitted to the current human body observation point at the corresponding moment; Calculating a velocity factor of the current human body observation point at the corresponding moment based on the wavelength of the detection signal emitted to the current human body observation point at the corresponding moment and the frequency offset sequence of the current human body observation point; Performing time integration on the velocity factor of the current human body observation point at the corresponding moment to obtain the movement distance of the current human body observation point at the corresponding moment; The sum of the moving distance of the current human body observation point at the corresponding moment and the initial distance between the current human body observation point and the current human body observation point is calculated to obtain the actual distance from the current human body observation point at the corresponding moment.

3. The fall detection method according to claim 1, wherein: Calculating the echo characteristics of each human body observation point at different times according to the distance sequence of each human body observation point and the frequency of each emission of the detection signal, including: For a current human body observation point, calculating a round trip time of a signal at the current human body observation point at the corresponding moment based on the speed of the detection signal transmitted to the current human body observation point at the corresponding moment and the distance sequence of the current human body observation point; Performing a phase operation on the round-trip time of the signal of the current human body observation point at the corresponding moment and the frequency of the detection signal transmitted to the current human body observation point at the corresponding moment to obtain the echo phase of the current human body observation point at the corresponding moment; The product of the echo phase and the emission coefficient of the current human body observation point at the corresponding moment is calculated to obtain the echo characteristics of the current human body observation point at the corresponding moment.

4. The fall detection method according to claim 1, wherein: Based on the frequency of each detection signal and the frequency of the corresponding echo signal, the frequency offset generated by the movement of each human body observation point at different times is calculated, including: For the current human body observation point, the frequency offset generated by the movement of the current human body observation point at the corresponding moment is determined according to the difference between the frequency of the detection signal transmitted to the current human body observation point at the corresponding moment and the frequency of the corresponding echo signal.

5. The fall detection method according to claim 1, wherein: Before periodically transmitting a detection signal to each human body observation point of the target user and collecting an echo signal of each human body observation point for each transmitted detection signal, the method further includes: Determine whether there is anyone within the preset detection range; If there is a person, the detection signal is triggered to be periodically transmitted to each human body observation point of the target user, and the echo signal of each human body observation point for each transmission of the detection signal is collected.

6. The fall detection method according to claim 1, wherein: After obtaining the fall detection result, it also includes: When the fall detection result is a fall, a voice prompt message is played and a power-off operation is performed.

7. The fall detection method according to claim 6, characterized in that: After playing the voice prompt message and performing the power-off operation, it also includes: If no response is received within the preset time, a distress message is sent to the terminal corresponding to the emergency contact and an alarm operation is triggered.

8. A fall detection device, characterized in that: The device comprises: an acquisition module, configured to periodically transmit a detection signal to each human body observation point of the target user, and collect an echo signal of each human body observation point for each transmitted detection signal; an offset calculation module, configured to calculate the frequency offsets generated by the movement of each human body observation point at different times based on the frequency of each detection signal and the frequency of the corresponding echo signal, and obtain a frequency offset sequence of each human body observation point; a distance calculation module, configured to calculate the actual distances between the human body observation points at different times based on the frequency offset sequence of the human body observation points and the initial distances between the human body observation points and the human body observation points, thereby obtaining a distance sequence of the human body observation points; a feature calculation module, configured to calculate the echo features of each human observation point at different times based on the distance sequence of each human observation point and the frequency of each detection signal emission; and perform spatial domain integration on the echo features of each human observation point at the same time to obtain the echo features of the target user at different times; The detection module is used to generate a time-frequency feature map based on the echo characteristics of the target user at different times, perform fall detection on the target user based on the time-frequency feature map, and obtain a fall detection result.

9. A water heater, characterized in that: The water heater comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the fall detection method according to any one of claims 1 to 7.

10. A storage medium containing computer-executable instructions, characterized in that: The computer executable instructions are used to perform the fall detection method according to any one of claims 1 to 7 when executed by a computer processor.

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