Control and cleaning method and device applied to nursing bed and electronic equipment

Abstract

The embodiment of the invention discloses a control and cleaning method and device applied to a nursing bed and electronic equipment. A specific embodiment of the method comprises the following steps: determining body posture information corresponding to a target object; a real-time temperature signal and a real-time humidity signal are collected through a temperature and humidity sensor included in the nursing bed; determining temperature and humidity control information according to the real-time temperature signal and the real-time humidity signal; according to the temperature and humidity control information, controlling a temperature and humidity adjusting device included in the nursing bed to perform temperature and humidity adjustment; in response to the acquired active control signal, performing signal analysis on the active control signal; performing nursing bed control on the nursing bed; and in response to the arrival of the preset time point, a body cleaning prompt for the target object is initiated to the remote monitoring terminal, and the nursing bed further comprises an auxiliary cleaning device for the patient. According to the embodiment, the problems that medical staff cannot monitor the posture of the patient effectively and timely due to the fact that the doctor-patient proportion is unbalanced, and the body temperature of the patient is unbalanced due to long-term bed lying are effectively solved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the medical field and the computer field, and specifically to a control and cleaning method, device, and electronic device applied to a nursing bed. Background Art

[0002] Nursing beds are special beds for patients who need to stay in bed for a long time or need medical care. However, due to the imbalance between the doctor-patient ratio, it is difficult for medical staff to monitor the patient's posture effectively and in a timely manner. In addition, patients who stay in bed for a long time are prone to problems such as temperature imbalance.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0004] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0005] Some embodiments of the present disclosure propose control and cleaning methods, devices, and electronic devices for nursing beds to solve the technical problems mentioned in the above background technology section.

[0006] In a first aspect, some embodiments of the present disclosure provide a control and cleaning method for a nursing bed, the method comprising: in response to a target object being located in an area to be detected, collecting a real-time image group sequence, wherein the area to be detected is the area where the nursing bed is located, the target object wears a wireless radio frequency bracelet bound to the identity of the nursing bed, and the real-time image group comprises: a real-time infrared image and a real-time non-infrared image; according to the real-time image group sequence, determining body posture information corresponding to the target object, wherein the body posture information comprises: a torso key point information set and a posture type; in response to the body posture information indicating that the posture of the target object is normal and the posture type is a lying posture type, using the temperature control unit included in the nursing bed A humidity sensor collects real-time temperature signals and real-time humidity signals; determines temperature and humidity control information based on the real-time temperature signals and the real-time humidity signals, wherein the temperature and humidity control information includes: control mode, control area and control gear; controls the temperature and humidity adjustment device included in the nursing bed to adjust the temperature and humidity according to the temperature and humidity control information; in response to obtaining an active control signal, performs signal analysis on the active control signal to generate nursing bed control information; controls the nursing bed according to the nursing bed control information; in response to reaching a predetermined time point, initiates a body cleaning reminder for the target object to a remote monitoring terminal, wherein the nursing bed also includes an auxiliary cleaning device for the patient.

[0007] In a second aspect, some embodiments of the present disclosure provide a control and cleaning device for a nursing bed, the device comprising: a first acquisition unit, configured to acquire a real-time image group sequence in response to a target object being located in an area to be detected, wherein the area to be detected is the area where the nursing bed is located, the target object wears a wireless radio frequency bracelet bound to the identity of the nursing bed, and the real-time image group includes: a real-time infrared image and a real-time non-infrared image; a first determination unit, configured to determine body posture information corresponding to the target object according to the real-time image group sequence, wherein the body posture information includes: a torso key point information set and a posture type; a second acquisition unit, configured to acquire real-time temperature and humidity through the temperature and humidity sensors included in the nursing bed in response to the body posture information characterizing that the posture of the target object is normal and the posture type is a lying posture. signal and real-time humidity signal; a second determination unit is configured to determine temperature and humidity control information according to the real-time temperature signal and the real-time humidity signal, wherein the temperature and humidity control information includes: control mode, control area and control gear; a first control unit is configured to control the temperature and humidity adjustment device included in the nursing bed to adjust the temperature and humidity according to the temperature and humidity control information; a signal analysis unit is configured to perform signal analysis on the active control signal in response to obtaining the active control signal to generate nursing bed control information; a second control unit is configured to control the nursing bed according to the nursing bed control information; a body cleaning reminder unit is configured to initiate a body cleaning reminder for the target object to the remote monitoring terminal in response to reaching a predetermined time point, wherein the nursing bed also includes an auxiliary cleaning device for the patient.

[0008] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0009] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0010] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the control and cleaning method applied to the nursing bed of some embodiments of the present disclosure, effective and timely monitoring of the patient's posture is achieved, and the problem of body temperature imbalance caused by the patient's long-term bed rest is effectively solved. Specifically, the present disclosure first responds to the target object being located in the area to be detected, and collects a real-time image group sequence, wherein the above-mentioned area to be detected is the area where the nursing bed is located, and the above-mentioned target object wears a wireless radio frequency bracelet bound to the identity of the above-mentioned nursing bed, and the real-time image group includes: real-time infrared image and real-time non-infrared image. In practice, when the target object (patient) is located in the area to be detected, the real-time image group sequence is collected again, so as to avoid image collection when the target object is not located in the area to be detected, and reduce the number of image collection and subsequent image processing. Secondly, according to the above-mentioned real-time image group sequence, the body posture information corresponding to the above-mentioned target object is determined, wherein the above-mentioned body posture information includes: a set of trunk key point information and a posture type. In practice, since the target object may be covered (such as bedding) when lying on the nursing bed, the body posture is recognized by combining infrared images and non-infrared images. In addition, in response to the above-mentioned body posture information indicating that the posture of the above-mentioned target object is normal, and the above-mentioned posture type is a lying type, the real-time temperature signal and the real-time humidity signal are collected through the temperature and humidity sensor included in the above-mentioned nursing bed. In addition, according to the above-mentioned real-time temperature signal and the above-mentioned real-time humidity signal, the temperature and humidity control information is determined, wherein the above-mentioned temperature and humidity control information includes: control mode, control area and control gear. Then, according to the above-mentioned temperature and humidity control information, the temperature and humidity adjustment device included in the above-mentioned nursing bed is controlled to adjust the temperature and humidity. By combining the temperature and humidity sensor, whether the target object is in a lying state on the nursing bed, whether the body temperature imbalance phenomenon occurs, and the corresponding temperature and humidity adjustment is performed. Further, in response to obtaining the active control signal, the above-mentioned active control signal is signal parsed to generate the nursing bed control information. Then, according to the above-mentioned nursing bed control information, the nursing bed is controlled. Finally, in response to reaching the predetermined time point, a body cleaning reminder for the above-mentioned target object is initiated to the remote monitoring terminal, wherein the nursing bed also includes an auxiliary cleaning device for the patient. This method effectively solves the problem of medical staff being unable to effectively and timely monitor the patient's posture due to the imbalance in the doctor-patient ratio. In addition, it also effectively solves the problem of body temperature imbalance caused by long-term bed rest. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0012] Figure 1 is a flow chart of some embodiments of the control and cleaning method applied to a nursing bed according to the present disclosure; Figure 2 It is a schematic diagram of the position relationship between the camera and the nursing bed; Figure 3 It is a schematic diagram of body postures of different posture types; Figure 4 It is a schematic diagram of the local model structure of the dual-path image feature extraction model; Figure 5 It is a schematic diagram of the process of determining the basic detection area; Figure 6 is a schematic diagram of the model structure of the body posture information prediction model; Figure 7 It is a schematic diagram of the control effect of the nursing bed in heating mode and ventilation mode; Figure 8 is a schematic diagram of the structure of some embodiments of the control and cleaning device applied to a nursing bed according to the present disclosure; Fig. 9 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0013] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0014] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0015] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0016] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0017] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0018] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0019] refer to Figure 1 , shows a process 100 of some embodiments of the control and cleaning method applied to a nursing bed according to the present disclosure. The control and cleaning method applied to a nursing bed comprises the following steps: Step 101 : In response to a target object being located in a region to be detected, a real-time image group sequence is acquired.

[0020] In some embodiments, the execution subject (e.g., a computing device) of the control and cleaning method applied to the nursing bed can collect a real-time image group sequence in response to the target object being located in the area to be detected. In practice, the execution subject can control the camera to collect the real-time image group sequence. The area to be detected is the area where the nursing bed is located. In practice, the camera can use a wide-angle camera with a fixed shooting angle. Under the premise of a fixed shooting angle, the relative position of the nursing bed and the camera is fixed, so the area to be detected can be pre-set in the picture taken by the camera. The target object wears a wireless radio frequency bracelet bound to the identity of the above-mentioned nursing bed. The target object can be a patient using the nursing bed. The wireless radio frequency bracelet can be worn on the hand of the target object in the form of wear. Specifically, the wireless radio frequency bracelet can include 2 radio frequency transmitters. The real-time images in the real-time image group sequence are images of the above-mentioned area to be detected. The real-time image group includes: a real-time infrared image and a real-time non-infrared image. In practice, the camera can include: a conventional wide-angle camera and an infrared camera to realize the acquisition of non-infrared images and infrared images. In addition, the camera can also include a radio frequency receiver for receiving a radio frequency signal emitted by the radio frequency transmitter included in the wireless radio frequency bracelet.

[0021] It should be noted that in practice, since there are a large number of nursing beds inside the hospital, there is a need for monitoring of multiple nursing beds. In order to cope with the corresponding large computing power requirements, the above-mentioned computing device can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0022] As an example, see the schematic diagram of the position relationship between the camera and the nursing bed shown in Figure 2, where: Figure 2The nursing ward shown may include: nursing bed A, nursing bed B, nursing bed C and nursing bed D. Among them, the camera may adopt a wide-angle camera so that the picture taken by the camera can include nursing bed A, nursing bed B, nursing bed C and nursing bed D, thereby effectively reducing the layout cost of the camera. Specifically, since the positional relationship between the nursing bed and the camera is relatively fixed, each nursing bed is pre-set with a detection area in the picture taken by the camera. In addition, considering that there may be a problem of "multiple people and multiple beds" in the nursing ward, it is impossible to effectively fix the corresponding relationship between the nursing bed and the patient. Using conventional image recognition methods, a large number of patient facial images need to be recorded, and the maintenance cost is extremely high. Considering that patients need to wear identity bracelets when they are admitted to the hospital, by using a wireless radio frequency bracelet and combining the radio frequency receiver included in the camera, the binding of the patient and the nursing bed identity and the detection of whether the target object is located in the detection area can be realized. Specifically, since the wireless radio frequency bracelet includes two radio frequency transmitters, the positioning of the target object can be achieved according to the time difference between the radio frequency signals of the two radio frequency transmitters received by the radio frequency receiver (at least 3). On this basis, since the camera position is fixed and the camera is pre-calibrated, the three-dimensional coordinates of the identified target object in the geodetic coordinate system can be mapped to the image coordinate system through conversion between the geodetic coordinate system and the image coordinate system, so as to determine whether the target object is located in the area to be detected.

[0023] Step 102: determining body posture information corresponding to the target object according to the real-time image group sequence.

[0024] In some embodiments, the execution subject can determine the body posture information corresponding to the target object according to the real-time image group sequence. The body posture information includes: a trunk key point information set and a posture type. The posture type represents the body posture of the target object. The trunk key point information in the trunk key point information set includes: key point coordinates and trunk type. The trunk type represents the type of the trunk corresponding to the trunk key point information. In practice, the posture types include: supine posture type, left side lying posture type, right side lying posture type, left curled side lying posture type, and right curled side lying posture type. In practice, the YOLOv8 model can be used to determine the body posture information corresponding to the target object according to the real-time image group sequence.

[0025] As an example, see Figure 3 The schematic diagram of body postures of different posture types is shown, wherein body postures of a supine posture type, a left side lying posture type, a right side lying posture type, a left curled up side lying posture type, and a right curled up side lying posture type are shown.

[0026] In some optional implementations of some embodiments, the execution subject determines the body posture information corresponding to the target object according to the real-time image group sequence, including: In the first step, for each real-time image group in the real-time image group sequence, the following first processing step is performed: In the first sub-step, image features are extracted from the real-time infrared image and the real-time non-infrared image included in the real-time image group through a dual-path image feature extraction model to generate an image feature map group.

[0027] Among them, the image feature map group includes: an infrared image feature map and a non-infrared image feature map. The feature map size of the infrared image feature map and the above-mentioned non-infrared image feature map is consistent. Specifically, the dual-path image feature extraction model includes: an image feature extraction model A and an image feature extraction model B. The model structures of the image feature extraction model A and the image feature extraction model B are consistent and symmetrical. The input of the image feature extraction model A is a real-time infrared image, and the output is an infrared image feature map. The input of the image feature extraction model B is a real-time non-infrared image, and the output is a non-infrared image feature map.

[0028] As an example, see Figure 4 The schematic diagram of the local model structure of the dual-path image feature extraction model shown in the figure shows the model structure of the image feature extraction model A. Specifically, the image feature extraction model A includes: image feature extraction model A1 and image feature extraction model A2. The image feature extraction model A1 adopts a conventional convolutional neural network structure. Specifically, the image feature extraction model A1 includes: convolution layer A1, convolution layer A2, convolution layer A3, convolution layer A4 and convolution layer A5. The input of convolution layer A1 is a real-time infrared image. The connection relationship between the convolution layer and the fully connected layer included in the image feature extraction model A1 is as shown in FIG. Figure 4 As shown. Convolutional layer A2, convolutional layer A3, convolutional layer A4 and convolutional layer A5 are all residual convolutional layers, and all use ReLU activation function. Image feature extraction model A2 adopts Unet neural network structure. Specifically, image feature extraction model A2 adopts conventional convolutional neural network structure including: convolutional layer A6, convolutional layer A7, convolutional layer A8, convolutional layer A9, convolutional layer A10, convolutional layer A11, convolutional layer A12, convolutional layer A13, convolutional layer A14, convolutional layer A15, convolutional layer A16 and fully connected layer A17. The connection relationship between the convolutional layer and the fully connected layer included in the image feature extraction model A2 is shown as follows: Figure 4As shown. The fully connected layer A17 is used to superimpose the features output by the convolutional layer A5 and the convolutional layer A16. In addition, the model structure of the image feature extraction model B is consistent with the model structure of the image feature extraction model A. The image feature extraction model B includes: an image feature extraction model B1 and an image feature extraction model B2. Specifically, the above-mentioned two-way image feature extraction model is an inventive point of the present disclosure, and the image feature extraction model A1 and the image feature extraction model B1 correspond to the parameter sharing between the convolutional layers. The image feature extraction model A2 and the image feature extraction model B2 correspond to the parameter sharing between the convolutional layers. Taking the image feature extraction model A as an example, the fine image features in the image are captured by setting a lightweight image feature extraction model A1, and the spatial context information in the image is captured by setting a lightweight image feature extraction model A2. In addition, considering that when the target object is in a covered state (such as when covered by bedding), only the real-time non-infrared image is recognized, and the recognition result is poor, therefore, the real-time infrared image is recognized in combination with the image feature extraction model B, and the parameters between the corresponding models are shared, thereby reducing the number of parameters of the model.

[0029] The second sub-step is to construct a basic detection area according to the area to be detected.

[0030] The area corner points of the above-mentioned area to be detected are located on the area boundary of the above-mentioned basic detection area.

[0031] In practice, see Figure 2 The schematic diagram of the position relationship between the camera and the nursing bed shown in the figure shows that the camera needs to shoot at a certain tilt angle to collect images containing the nursing bed. Therefore, the nursing bed in the detection area exists in the real-time infrared image and the real-time non-infrared image at a certain tilt angle. For details, please refer to Figure 5 The schematic diagram of the process of determining the basic detection area is shown, wherein the nursing bed in the area to be detected 501 in the real-time non-infrared image 502 is tilted at a certain angle. On this basis, the four image boundaries of the real-time non-infrared image 502 are horizontally shrunk inward by parallel movement until they contact the corner points of the area to be detected 501. At this time, the area enclosed by the image boundaries is used as the basic detection area 503.

[0032] The third sub-step is to construct a set of candidate detection areas according to the above basic detection areas and the preset window magnification ratio.

[0033] Among them, the above-mentioned basic detection area is a regional subset of the candidate detection area in the above-mentioned candidate detection area set. In practice, the window magnification ratio refers to the magnification ratio of the basic detection area along the diagonal. For example, the window magnification ratio can be 1%. In practice, for target detection, it is usually necessary to construct a large number of detection frames and perform target detection on each detection frame. However, the present disclosure takes into account the need to perform body posture detection of the target object in combination with the area to be detected, and the target object is already located in the area to be detected. Therefore, a small number of candidate detection area sets can be constructed based on the basic detection area constructed on the area to be detected, thereby reducing the number of detection frames and improving the detection speed.

[0034] The fourth sub-step is to determine, for each candidate detection area in the candidate detection area set, the area confidence of the candidate detection area according to the candidate detection area, the infrared image feature map and the non-infrared image feature map included in the image feature map group.

[0035] Among them, the above-mentioned regional confidence represents the confidence of whether the candidate detection area contains the target object. Specifically, the above-mentioned execution subject can determine the regional confidence of the candidate detection area according to the local features of the candidate detection area in the infrared image feature map and the non-infrared image feature map through a binary classifier. Specifically, the binary classifier is used to determine whether the candidate detection area contains the target object. In the model training stage, the binary classifier and the two-way image feature extraction model can be trained together. Specifically, the binary classifier and the two-way image feature extraction model are trained as a whole using training samples marked with the location of the target object and a supervised training method.

[0036] The fifth sub-step is to set the feature values ​​outside the target detection area in the infrared image feature map included in the above-mentioned image feature map group to 0, and to set the feature values ​​outside the target detection area in the non-infrared image feature map included in the above-mentioned image feature map group to 0, so as to obtain an updated image feature map group.

[0037] Among them, the above-mentioned updated image feature map group includes: an updated infrared image feature map and an updated non-infrared image feature map. The target detection area is the candidate detection area corresponding to the maximum regional confidence in the above-mentioned candidate detection area set. The candidate detection area corresponding to the maximum regional confidence should be a candidate detection area that includes the complete target object and the area to be detected. In practice, since it is only necessary to perform body posture detection on the target object within the target detection area, in order to reduce the subsequent feature processing amount, the feature values ​​outside the target detection area in the non-infrared image feature map and the infrared image feature map are set to 0, thereby reducing the subsequent feature processing amount.

[0038] In the second step, for each updated image feature map group in the obtained updated image feature map group sequence, the updated infrared image feature map and the updated non-infrared image feature map included in the updated image feature map group are superimposed to obtain a superimposed image feature map.

[0039] In practice, since the feature map sizes of the updated infrared image feature map and the updated non-infrared image feature map are consistent, the feature maps can be directly superimposed to obtain the superimposed image feature map.

[0040] The third step is to perform low-level image feature extraction on the obtained superimposed image feature map sequence through a low-level image feature extractor to obtain a low-level image feature map sequence.

[0041] In practice, see Figure 6 The schematic diagram of the model structure of the body posture information prediction model shown in FIG. 1 , wherein the low-level image feature extractor includes: a bottleneck layer, a residual block A, a residual block B, a residual block C, a residual block D, and a residual block E. Specifically, the features output by the residual block A and the residual block B are fused in a channel fusion manner, and the fused features are respectively used as inputs of the residual block C, the residual block D, and the residual block E. For example, if the feature size of the residual block A output is a×b×c1, and the feature size of the residual block B output is a×b×c2, then the feature size of the fused features obtained by the residual block A and the residual block B in a channel fusion manner is a×b×(c1+c2).

[0042] The fourth step is to perform high-level image feature extraction on the above low-level image feature map sequence through a high-level image feature extractor to obtain a high-level image feature map sequence.

[0043] In practice, see Figure 6 Schematic diagram of the model structure of the body posture information prediction model shown, wherein the advanced image feature extractor includes: a multi-head attention mechanism module, a multi-layer perceptron and a 2D convolutional layer.

[0044] The fifth step is to generate the above-mentioned body posture information based on the above-mentioned high-level image feature map sequence and the body posture information predictor.

[0045] Among them, the above-mentioned two-way image feature extraction model, the above-mentioned low-level image feature extractor, the above-mentioned high-level image feature extractor and the above-mentioned body posture information predictor are included in the body posture information prediction model, and the above-mentioned body posture information predictor includes: a key point predictor, a key point type classifier and a posture type classifier.

[0046] In practice, see Figure 6The model structure diagram of the key point predictor body posture information prediction model shown in FIG. 1 , wherein the input of the 2D convolution layer is input to the key point predictor, the key point type classifier, and the posture type classifier in parallel. Specifically, the key point predictor can use a heat map regression predictor. The key point type classifier can be implemented using multiple classifiers to classify the body organ type at the key point, and the posture type classifier can be implemented using multiple classifiers to classify the posture type of the target object.

[0047] Optionally, the above method further includes: In response to the body posture information indicating that the posture of the target object is abnormal, a reminder device included in the nursing bed initiates a posture adjustment reminder for the target object, or initiates a posture abnormality reminder for the target object to a remote monitoring terminal.

[0048] In some embodiments, the above-mentioned execution subject can respond to the body posture information characterizing that the posture of the target object is abnormal, initiate a posture adjustment reminder for the target object through the reminder device included in the nursing bed, or initiate a posture abnormality reminder for the target object to the remote monitoring terminal. In practice, in order to avoid the backlog of patient wounds and thus affect recovery, a body posture type that is not recommended for the target object can be set. When the posture type included in the body posture information is consistent with the body posture type that is not recommended, the posture of the target object can be considered abnormal. The reminder device can be a vibration reminder device set at the head of the nursing bed. In practice, since the nursing ward contains at least one nursing bed, in order to avoid the sound emitted by the reminder device affecting other patients, a vibration reminder can be used to remind the target object to adjust its posture. The remote monitoring terminal can be a monitoring terminal set at a nurse station for monitoring multiple nursing wards. By sending a posture abnormality reminder to the remote monitoring terminal, a nurse or doctor is actively reminded to check the current status of the target object.

[0049] Step 103, in response to the body posture information indicating that the posture of the target object is normal and the posture type is a lying posture type, a real-time temperature signal and a real-time humidity signal are collected through a temperature and humidity sensor included in the nursing bed.

[0050] In some embodiments, the above-mentioned execution subject can respond to the body posture information to characterize that the posture of the target object is normal, and the posture type is a lying type, and collect real-time temperature signals and real-time humidity signals through the temperature and humidity sensors included in the nursing bed. In practice, the nursing bed includes: a temperature and humidity signal collection area. Among them, the above-mentioned temperature and humidity signal collection area corresponds to the waist and hip position of the target object in the standard lying position, and the above-mentioned temperature and humidity sensors are arranged in an array in the above-mentioned temperature and humidity signal collection area. Therefore, at each moment, the matrix temperature and humidity signal values ​​can be collected, and the temperature and humidity signal values ​​at multiple moments constitute the real-time temperature signal and real-time humidity signal. Specifically, the temperature and humidity sensor is arranged under the cushion layer of the nursing bed to prevent the target object from having a foreign body sensation when lying down.

[0051] Step 104, determining temperature and humidity control information according to the real-time temperature signal and the real-time humidity signal.

[0052] In some embodiments, the above-mentioned execution subject can determine the temperature and humidity control information based on the real-time temperature signal and the real-time humidity signal. Among them, the temperature and humidity control information includes: control mode, control area and control gear. In practice, the control mode includes: ventilation mode and heating mode. The control gear can characterize the control strength under different control modes. For example, taking the heating mode as an example, the control gear may include: heating gear, constant temperature gear, and sleep gear.

[0053] In some optional implementations of some embodiments, the execution subject determines the temperature and humidity control information according to the real-time temperature signal and the real-time humidity signal, which may include the following steps: The first step is to extract signal features from the real-time temperature signal and the real-time humidity signal to generate a temperature feature graph sequence and a humidity feature graph sequence.

[0054] In practice, since the temperature and humidity sensors are arranged in an array, the humidity value and temperature value at a single moment of the real-time temperature signal and the real-time humidity signal are represented in the form of a matrix. Therefore, the RNN (Recurrent Neural Network) model can be used to extract signal features of the real-time temperature signal and the real-time humidity signal to generate a temperature feature map sequence and a humidity feature map sequence.

[0055] The second step is to intercept the updated infrared image feature map included in the updated image feature map group sequence and the local infrared image feature map at the corresponding position of the temperature and humidity signal acquisition area for each updated image feature map group sequence in the updated image feature map group sequence.

[0056] In practice, since the temperature and humidity signal acquisition area is located in the area to be detected, and considering that the infrared image can characterize the temperature changes in the area to be detected, it can be used as one of the bases for temperature control. In order to avoid the waste of computing resources caused by repeated feature extraction, the updated infrared image feature map included in the updated image feature map group can be directly intercepted, and the local infrared image feature map at the corresponding position of the above-mentioned temperature and humidity signal acquisition area can be directly intercepted.

[0057] The third step is to generate the above-mentioned temperature and humidity control information based on the obtained local infrared image feature map sequence, temperature feature map sequence and humidity feature map sequence and the pre-trained temperature and humidity control information prediction model.

[0058] Among them, the above-mentioned temperature and humidity control information prediction model includes: a deep feature extraction model, a control mode classifier, a control area predictor and a control gear classifier. In practice, the deep feature extraction model can be three convolutional neural network models set in parallel. Specifically, the three convolutional neural network models are connected to a feature fusion layer for feature fusion of the outputs of the three convolutional neural network models. The control area predictor can be implemented by a coordinate regressor to predict the regional coordinates of the control area. Both the control mode classifier and the control gear classifier can be implemented using multiple classifiers.

[0059] Step 105: Control the temperature and humidity adjustment device included in the nursing bed to adjust the temperature and humidity according to the temperature and humidity control information.

[0060] In some embodiments, the above-mentioned execution subject can control the temperature and humidity adjustment device included in the nursing bed to adjust the temperature and humidity according to the temperature and humidity control information.

[0061] Optionally, the temperature and humidity regulating device includes: a ventilation device and a heating device. The ventilation device is arranged on the back of the nursing bed. The ventilation device is composed of an array-type cooling fan. The cooling fan included in the ventilation device is independently controlled. The heating device is arranged on the front of the nursing bed. The heating device is composed of a matrix-type heating cloth. The heating cloth included in the heating device is independently controlled. In order to avoid direct contact between the heating cloth and the skin, thereby causing problems such as low-temperature burns. A thermal conductive lining cloth woven from polyester yarn is arranged on the upper layer of the heating cloth. The heating cloth is woven with flame-retardant materials and embedded with a heating wire, which is electrically heated by a heating circuit. Since the cushion layers such as the nursing bed and the bedding on the bed are ventilation structures, the cooling fan included in the ventilation device can adopt an air suction method, that is, the wind direction flows from the upper side of the nursing bed to the lower side of the nursing bed, so as to achieve a heat dissipation effect on the target object on the nursing bed. The array design can be independently controlled to achieve a more precise temperature control and ventilation effect.

[0062] In some optional implementations of some embodiments, the execution subject controls the temperature and humidity adjustment device included in the nursing bed to adjust the temperature and humidity according to the temperature and humidity control information, including: The first step, in response to the control mode included in the above-mentioned temperature and humidity control information being a temperature control mode, activates the cooling fan in the above-mentioned ventilation device corresponding to the control area included in the above-mentioned temperature and humidity control information, and controls the fan speed of the cooling fan in the above-mentioned ventilation device corresponding to the control area included in the above-mentioned temperature and humidity control information according to the control gear included in the above-mentioned temperature and humidity control information.

[0063] In practice, since the camera has been calibrated, that is, the coordinates between the geodetic coordinate system and the image coordinate system can be directly switched, when the control area is known, the fan identification of the cooling fan located in the control area can be obtained by coordinate mapping. Combined with the fact that the cooling fan can be independently controlled, only the cooling fan located in the control area can be activated for heat dissipation.

[0064] In the second step, in response to the control mode included in the above-mentioned temperature and humidity control information being a humidity control mode, the heating cloth in the above-mentioned heating device corresponding to the control area included in the above-mentioned temperature and humidity control information is activated, and the heating temperature of the heating cloth in the above-mentioned heating device corresponding to the control area included in the above-mentioned temperature and humidity control information is controlled according to the control gear included in the above-mentioned temperature and humidity control information.

[0065] In practice, since the camera has been calibrated, that is, the coordinates between the geodetic coordinate system and the image coordinate system can be directly switched, when the control area is known, the heating cloth identification of the heating cloth located in the control area can be obtained by coordinate mapping. Combined with the fact that the heating cloth can be independently controlled, only the heating cloth located in the control area can be activated for heating.

[0066] Step 106: In response to obtaining the active control signal, perform signal analysis on the active control signal to generate nursing bed control information.

[0067] In some embodiments, the execution subject may perform signal analysis on the active control signal in response to obtaining the active control signal to generate the nursing bed control information. The active control signal is a control signal initiated by the signal source, and the signal source includes: a remote monitoring terminal and a nursing bed controller. In practice, steps 101 to 106 provide passive nursing bed control, especially for patients without autonomous consciousness or autonomous activity ability, which can be effectively monitored. In addition, considering the monitoring control initiated remotely by nurses and doctors for patients with autonomous consciousness or autonomous activity ability, the active control method of step 108 can also be adopted to realize the control of the nursing bed. Specifically, the active control signal can be initiated by the remote monitoring terminal through a wired or wireless connection, or by a wired nursing bed controller on the nursing bed side. After receiving the active control signal, the active control signal can be signal analyzed to obtain a control instruction as the nursing bed control information. In practice, the nursing bed control information includes: control mode, control gear and control area.

[0068] In some optional implementations of some embodiments, the execution subject performs signal parsing on the active control signal in response to acquiring the active control signal to generate nursing bed control information, including: The first step is to perform content recognition on the voice signal in response to the active control signal being a voice signal to generate the nursing bed control information.

[0069] In practice, for patients who have autonomous consciousness but limited mobility, the nursing bed controller can obtain active control signals by voice input. At this time, it is necessary to perform voice recognition on the active control signal to obtain the nursing bed control information. Specifically, a recurrent neural network model, a long short-term memory network model, etc. can be used to perform content recognition on the above voice signal to generate the above nursing bed control information.

[0070] The second step is to respond to the above-mentioned active control signal non-voice signal, perform command analysis on the above-mentioned active control signal to obtain the above-mentioned nursing bed control information.

[0071] In practice, when the active control signal is a non-voice signal, the nursing bed control information can be obtained by means of function mapping. For example, when the user presses the heating mode button and the gear selection button included in the nursing bed controller, the corresponding active control signal can be used to control the heating cloth to heat according to the gear.

[0072] Step 107: Control the nursing bed according to the nursing bed control information.

[0073] In some embodiments, the above-mentioned execution subject can control the nursing bed according to the nursing bed control information. In practice, since the nursing bed control information includes the control mode, control gear and control area, the cooling fan or heating cloth corresponding to the control area can be activated according to the control mode, and the cooling or heating can be performed according to the control gear.

[0074] As an example, see Figure 7 The control effect diagram of the nursing bed in the heating mode and ventilation mode shown in the figure can be controlled by the nursing bed controller 701. Specifically, the nursing bed controller may include a control mode knob, a control gear knob and a control area selection screen. The control area selection screen may be touch-controlled to select the control area. In the heating mode, the heating device 702 may be controlled to heat the heating cloth in the corresponding control area. In the heat dissipation mode, the heat dissipation device 703 may be controlled to dissipate heat by the heat dissipation fan in the corresponding control area.

[0075] Step 108: In response to reaching a predetermined time point, a body cleaning reminder for the target object is initiated to the remote monitoring terminal.

[0076] Among them, the nursing bed also includes an auxiliary cleaning device for the patient. In practice, the auxiliary cleaning device can be a buttocks cleaning device arranged under the nursing bed. Specifically, the buttocks cleaning device can be a washing basin. The washing basin is arranged under the nursing bed in a pull-out manner. The patient's buttocks can be cleaned regularly by setting a washing basin. In addition, for patients who have no autonomous consciousness or no autonomous movement ability, there may be a problem that excrement cannot be excreted autonomously. At the same time, taking into account the uneven ratio of doctors to patients, doctors or nurses are reminded to clean the target patients' bodies through regular reminders to avoid the problem of the target objects excreting on the nursing bed.

[0077] In some optional implementations of some embodiments, the above method further includes: The first step is to respond to the remote monitoring terminal initiating a remote monitoring instruction for the nursing bed, for each real-time image group in the real-time image group sequence, perform Gaussian blur on the image content of the real-time image group outside the corresponding target detection area to obtain a blurred real-time image group.

[0078] In practice, the remote monitoring terminal can also be a mobile terminal of the family member corresponding to the target object. The remote monitoring terminal is used to realize the remote monitoring of the family member. On this basis, considering the privacy of other patients, when transmitting the video, it is necessary to perform Gaussian blur on the image content of the above real-time image group outside the corresponding target detection area.

[0079] In the second step, the blurred real-time image group sequence is end-to-end encrypted to obtain an encrypted video.

[0080] In practice, end-to-end encryption is used to ensure the security of transmitted video transmission.

[0081] The third step is to send the encrypted video to the remote monitoring terminal.

[0082] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the control and cleaning method applied to the nursing bed of some embodiments of the present disclosure, effective and timely monitoring of the patient's posture is achieved, and the problem of body temperature imbalance caused by the patient's long-term bed rest is effectively solved. Specifically, the present disclosure first responds to the target object being located in the area to be detected, and collects a real-time image group sequence, wherein the above-mentioned area to be detected is the area where the nursing bed is located, and the above-mentioned target object wears a wireless radio frequency bracelet bound to the identity of the above-mentioned nursing bed, and the real-time image group includes: real-time infrared image and real-time non-infrared image. In practice, when the target object (patient) is located in the area to be detected, the real-time image group sequence is collected again, so as to avoid image collection when the target object is not located in the area to be detected, and reduce the number of image collection and subsequent image processing. Secondly, according to the above-mentioned real-time image group sequence, the body posture information corresponding to the above-mentioned target object is determined, wherein the above-mentioned body posture information includes: a set of trunk key point information and a posture type. In practice, since the target object may be covered (such as bedding) when lying on the nursing bed, the body posture is recognized by combining infrared images and non-infrared images. In addition, in response to the above-mentioned body posture information indicating that the posture of the above-mentioned target object is normal, and the above-mentioned posture type is a lying type, the real-time temperature signal and the real-time humidity signal are collected through the temperature and humidity sensor included in the above-mentioned nursing bed. In addition, according to the above-mentioned real-time temperature signal and the above-mentioned real-time humidity signal, the temperature and humidity control information is determined, wherein the above-mentioned temperature and humidity control information includes: control mode, control area and control gear. Then, according to the above-mentioned temperature and humidity control information, the temperature and humidity adjustment device included in the above-mentioned nursing bed is controlled to adjust the temperature and humidity. By combining the temperature and humidity sensor, whether the target object is in a lying state on the nursing bed, whether the body temperature imbalance phenomenon occurs, and the corresponding temperature and humidity adjustment is performed. Further, in response to obtaining the active control signal, the above-mentioned active control signal is signal parsed to generate the nursing bed control information. Then, according to the above-mentioned nursing bed control information, the nursing bed is controlled. Finally, in response to reaching the predetermined time point, a body cleaning reminder for the above-mentioned target object is initiated to the remote monitoring terminal, wherein the nursing bed also includes an auxiliary cleaning device for the patient. This method effectively solves the problem of medical staff being unable to effectively and timely monitor the patient's posture due to the imbalance in the doctor-patient ratio. In addition, it also effectively solves the problem of body temperature imbalance caused by long-term bed rest. Further references Figure 8As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a control and cleaning device for a nursing bed. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the control and cleaning device applied to the nursing bed can be specifically applied to various electronic devices.

[0083] like Figure 8 As shown, in some embodiments, the control and cleaning device 800 applied to the nursing bed includes: a first acquisition unit 801, a first determination unit 802, a second acquisition unit 803, a second determination unit 804, a first control unit 805, a signal analysis unit 806, a second control unit 807 and a body cleaning reminder unit 808. Among them, the first acquisition unit 801 is configured to acquire a real-time image group sequence in response to the target object being located in the area to be detected, wherein the above-mentioned area to be detected is the area where the nursing bed is located, and the above-mentioned target object wears a wireless radio frequency bracelet bound to the identity of the above-mentioned nursing bed, and the real-time image group includes: a real-time infrared image and a real-time non-infrared image; the first determination unit 802 is configured to determine the body posture information corresponding to the above-mentioned target object according to the above-mentioned real-time image group sequence, wherein the above-mentioned body posture information includes: a torso key point information set and a posture type; the second acquisition unit 803 is configured to acquire a real-time temperature signal and a real-time humidity signal through the temperature and humidity sensor included in the above-mentioned nursing bed in response to the above-mentioned body posture information indicating that the posture of the above-mentioned target object is normal and the above-mentioned posture type is a lying type; the second determination unit 804 is Configured to determine temperature and humidity control information according to the above-mentioned real-time temperature signal and the above-mentioned real-time humidity signal, wherein the above-mentioned temperature and humidity control information includes: control mode, control area and control gear; a first control unit 805 is configured to control the temperature and humidity adjustment device included in the above-mentioned nursing bed to adjust the temperature and humidity according to the above-mentioned temperature and humidity control information; a signal analysis unit 806 is configured to perform signal analysis on the above-mentioned active control signal in response to obtaining the active control signal to generate nursing bed control information; a second control unit 807 is configured to control the above-mentioned nursing bed according to the above-mentioned nursing bed control information; a body cleaning reminder unit 808 is configured to initiate a body cleaning reminder for the above-mentioned target object to the remote monitoring terminal in response to reaching a predetermined time point, wherein the nursing bed also includes an auxiliary cleaning device for the patient.

[0084] It is understood that the units described in the control and cleaning device 800 for the nursing bed are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the control and cleaning device 800 for the nursing bed and the units contained therein, and will not be described in detail here. Reference below Fig. 9 , which shows a structural schematic diagram of an electronic device (eg, a computing device) 900 suitable for implementing some embodiments of the present disclosure. Fig. 9 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0085] like Fig. 9 As shown, the electronic device 900 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory 902 or a program loaded from a storage device 908 into a random access memory 903. Various programs and data required for the operation of the electronic device 900 are also stored in the random access memory 903. The processing device 901, the read-only memory 902, and the random access memory 903 are connected to each other via a bus 904. An input / output interface 905 is also connected to the bus 904.

[0086] Typically, the following devices may be connected to the I / O interface 905: an input device 906 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 907 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 908 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 909. The communication device 909 may allow the electronic device 900 to communicate with other devices wirelessly or by wire to exchange data. Although Fig. 9 The electronic device 900 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Fig. 9 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0087] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 909, or installed from the storage device 908, or installed from the read-only memory 902. When the computer program is executed by the processing device 901, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0088] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: 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 some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0089] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0090] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: in response to the target object being located in the area to be detected, collects a real-time image group sequence, wherein the above-mentioned area to be detected is the area where the nursing bed is located, and the above-mentioned target object wears a wireless radio frequency bracelet bound to the identity of the above-mentioned nursing bed, and the real-time image group includes: real-time infrared image and real-time non-infrared image; according to the above-mentioned real-time image group sequence, determines the body posture information corresponding to the above-mentioned target object, wherein the above-mentioned body posture information includes: a torso key point information set and a posture type; in response to the above-mentioned body posture information indicating that the posture of the above-mentioned target object is normal, and the above-mentioned posture type is a lying type, through the above-mentioned nursing bed The bed includes a temperature and humidity sensor, which collects real-time temperature signals and real-time humidity signals; determines temperature and humidity control information based on the above-mentioned real-time temperature signals and the above-mentioned real-time humidity signals, wherein the above-mentioned temperature and humidity control information includes: control mode, control area and control gear; controls the temperature and humidity adjustment device included in the above-mentioned nursing bed to adjust the temperature and humidity according to the above-mentioned temperature and humidity control information; in response to obtaining an active control signal, performs signal analysis on the above-mentioned active control signal to generate nursing bed control information; controls the above-mentioned nursing bed according to the above-mentioned nursing bed control information; in response to reaching a predetermined time point, initiates a body cleaning reminder for the above-mentioned target object to a remote monitoring terminal, wherein the nursing bed also includes an auxiliary cleaning device for the patient.

[0091] Computer program code for performing the operations of some embodiments of the present disclosure 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 separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of 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., via the Internet using an Internet service provider).

[0092] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0093] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor including a first acquisition unit, a first determination unit, a reminder or prompt unit, a second acquisition unit, a second determination unit, a first control unit, a signal parsing unit, and a second control unit. The names of these units do not, in some cases, constitute limitations on the units themselves, for example, the second control unit may also be described as "a unit for controlling the above-mentioned nursing bed according to the above-mentioned nursing bed control information".

[0094] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0095] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A control and cleaning method for a nursing bed, comprising: In response to the target object being located in the area to be detected, a real-time image group sequence is collected, wherein the area to be detected is the area where the nursing bed is located, the target object wears a wireless radio frequency bracelet bound to the identity of the nursing bed, and the real-time image group includes: a real-time infrared image and a real-time non-infrared image; Determine the body posture information corresponding to the target object according to the real-time image group sequence, wherein the body posture information includes: a torso key point information set and a posture type; In response to the body posture information indicating that the posture of the target object is normal and the posture type is a lying posture type, a real-time temperature signal and a real-time humidity signal are collected through a temperature and humidity sensor included in the nursing bed; Determine temperature and humidity control information according to the real-time temperature signal and the real-time humidity signal, wherein the temperature and humidity control information includes: control mode, control area and control gear; According to the temperature and humidity control information, controlling the temperature and humidity adjustment device included in the nursing bed to adjust the temperature and humidity; In response to acquiring the active control signal, performing signal analysis on the active control signal to generate nursing bed control information; Controlling the nursing bed according to the nursing bed control information; In response to reaching a predetermined time point, a body cleaning reminder for the target object is initiated to the remote monitoring terminal, wherein the nursing bed also includes an auxiliary cleaning device for the patient.

2. The method according to claim 1, wherein: The method further comprises: In response to the body posture information indicating that the target object has an abnormal posture, a reminder for posture adjustment of the target object is initiated through a reminder device included in the nursing bed, or a reminder for abnormal posture of the target object is initiated to a remote monitoring terminal.

3. The method according to claim 1, wherein: In response to acquiring the active control signal, performing signal parsing on the active control signal to generate nursing bed control information includes: In response to the active control signal being a voice signal, performing content recognition on the voice signal to generate the nursing bed control information; In response to the active control signal being a non-voice signal, the active control signal is subjected to instruction parsing to obtain the nursing bed control information.

4. The method according to claim 3, wherein: The temperature and humidity regulating device comprises: a ventilation device and a heating device, wherein the ventilation device is arranged at the back of the nursing bed, the ventilation device is composed of an array of cooling fans, and the cooling fans included in the ventilation device are independently controlled, and the heating device is arranged at the front of the nursing bed, the heating device is composed of a matrix of heating cloths, and the heating cloths included in the heating device are independently controlled; and The step of controlling the temperature and humidity adjustment device included in the nursing bed to adjust the temperature and humidity according to the temperature and humidity control information comprises: In response to the control mode included in the temperature and humidity control information being the temperature control mode, activating the cooling fan in the ventilation device corresponding to the control area included in the temperature and humidity control information, and controlling the fan speed of the cooling fan in the ventilation device corresponding to the control area included in the temperature and humidity control information according to the control gear included in the temperature and humidity control information; In response to the control mode included in the temperature and humidity control information being a humidity control mode, the heating cloth in the heating device corresponding to the control area included in the temperature and humidity control information is activated, and the heating temperature of the heating cloth in the heating device corresponding to the control area included in the temperature and humidity control information is controlled according to the control gear included in the temperature and humidity control information.

5. The method according to claim 4, wherein: Determining the body posture information corresponding to the target object according to the real-time image group sequence includes: For each real-time image group in the real-time image group sequence, the following first processing step is performed: By using a dual-path image feature extraction model, image features are extracted from the real-time infrared image and the real-time non-infrared image included in the real-time image group to generate an image feature map group, wherein the image feature map group includes: an infrared image feature map and a non-infrared image feature map, wherein the infrared image feature map and the non-infrared image feature map have the same feature map size; Constructing a basic detection area according to the area to be detected, wherein the area corner point of the area to be detected is located on the area boundary of the basic detection area; According to the basic detection area and a preset window magnification ratio, a set of candidate detection areas is constructed, wherein the basic detection area is a region subset of the candidate detection areas in the set of candidate detection areas; For each candidate detection area in the candidate detection area set, determining a region confidence of the candidate detection area according to the candidate detection area, the infrared image feature map and the non-infrared image feature map included in the image feature map group, wherein the region confidence represents the confidence of whether the candidate detection area contains the target object; The feature values ​​outside the target detection area in the infrared image feature map included in the image feature map group are set to 0, and the feature values ​​outside the target detection area in the non-infrared image feature map included in the image feature map group are set to 0, to obtain an updated image feature map group, wherein the updated image feature map group includes: an updated infrared image feature map and an updated non-infrared image feature map, and the target detection area is a candidate detection area in the candidate detection area set that corresponds to the maximum area confidence.

6. The method according to claim 5, wherein: The step of determining the body posture information corresponding to the target object according to the real-time image group sequence further includes: For each updated image feature map group in the obtained updated image feature map group sequence, superimposing the updated infrared image feature map and the updated non-infrared image feature map included in the updated image feature map group to obtain a superimposed image feature map; Performing low-level image feature extraction on the obtained superimposed image feature map sequence through a low-level image feature extractor to obtain a low-level image feature map sequence; By using a high-level image feature extractor, extracting high-level image features from the low-level image feature map sequence to obtain a high-level image feature map sequence; The body posture information is generated according to the high-level image feature map sequence and the body posture information predictor, wherein the two-way image feature extraction model, the low-level image feature extractor, the high-level image feature extractor and the body posture information predictor are included in a body posture information prediction model, and the body posture information predictor includes: a key point predictor, a key point type classifier and a posture type classifier.

7. The method according to claim 6, wherein: The nursing bed comprises: a temperature and humidity signal collection area, wherein the temperature and humidity signal collection area corresponds to the waist and hip position of the target object in a standard lying position, and the temperature and humidity sensors are arranged in an array in the temperature and humidity signal collection area; and The step of determining temperature and humidity control information according to the real-time temperature signal and the real-time humidity signal comprises: Performing signal feature extraction on the real-time temperature signal and the real-time humidity signal to generate a temperature feature graph sequence and a humidity feature graph sequence; For each updated image feature map group sequence in the updated image feature map group sequence, intercepting the updated infrared image feature map included in the updated image feature map group and the local infrared image feature map at the position corresponding to the temperature and humidity signal acquisition area; The temperature and humidity control information is generated according to the obtained local infrared image feature map sequence, temperature feature map sequence and humidity feature map sequence and a pre-trained temperature and humidity control information prediction model, wherein the temperature and humidity control information prediction model includes: a deep feature extraction model, a control mode classifier, a control area predictor and a control gear classifier.

8. The method according to claim 7, wherein: The method further comprises: In response to the remote monitoring terminal initiating a remote monitoring instruction for the nursing bed, for each real-time image group in the real-time image group sequence, Gaussian blurring the image content of the real-time image group outside the corresponding target detection area to obtain a blurred real-time image group; Perform end-to-end encryption on the blurred real-time image group sequence to obtain an encrypted video; The encrypted video is sent to the remote monitoring terminal.

9. A control and cleaning device for a nursing bed, comprising: A first acquisition unit is configured to acquire a sequence of real-time image groups in response to a target object being located in a to-be-detected area, wherein the to-be-detected area is an area where a nursing bed is located, the target object wears a wireless radio frequency bracelet bound to the identity of the nursing bed, and the real-time image group includes: a real-time infrared image and a real-time non-infrared image; A first determining unit is configured to determine body posture information corresponding to the target object according to the real-time image group sequence, wherein the body posture information includes: a torso key point information set and a posture type; A second collecting unit is configured to collect a real-time temperature signal and a real-time humidity signal through a temperature and humidity sensor included in the nursing bed in response to the body posture information indicating that the posture of the target object is normal and the posture type is a lying posture type; A second determining unit is configured to determine temperature and humidity control information according to the real-time temperature signal and the real-time humidity signal, wherein the temperature and humidity control information includes: a control mode, a control area, and a control gear; A first control unit is configured to control the temperature and humidity adjustment device included in the nursing bed to adjust the temperature and humidity according to the temperature and humidity control information; a signal analysis unit, configured to perform signal analysis on the active control signal in response to acquiring the active control signal, so as to generate nursing bed control information; a second control unit, configured to perform nursing bed control on the nursing bed according to the nursing bed control information; The body cleaning reminder unit is configured to initiate a body cleaning reminder for the target object to the remote monitoring terminal in response to reaching a predetermined time point, wherein the nursing bed also includes an auxiliary cleaning device for the patient.

10. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

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