Intelligent electric bed adjusting method and system based on artificial intelligence

Abstract

The invention provides an intelligent electric bed adjusting method and system based on artificial intelligence, belongs to the technical field of artificial intelligence, and is used for prolonging the deep sleep time. The method comprises the steps that a control device collects respective physiological state information of a monitored object in M continuous time periods, M physiological state information is M, M is an integer larger than 2, and the monitored object is located on an intelligent electric bed; the control equipment processes the M pieces of physiological state information through a neural network model, and determines whether the monitored object enters a deep sleep state or not; and if the monitored object enters the deep sleep state, the control equipment adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular, to an intelligent electric bed adjustment method and system based on artificial intelligence. Background Art

[0002] Deep sleep, also known as slow-wave sleep, is one of the most important stages in the sleep cycle. During this stage, the brain waves show high-amplitude and low-frequency δ waves, and the cerebral cortex cells are in a fully rested state, which plays a crucial role in eliminating fatigue, restoring energy, immune resistance, etc. Research shows that during deep sleep, the secretion of human growth hormone increases, muscles, bones and other tissues are repaired, and metabolic wastes are effectively removed, thus promoting the recovery of physical fatigue.

[0003] Therefore, how to extend the time of deep sleep is a current research issue. Summary of the Invention

[0004] Embodiments of the present invention provide an intelligent electric bed adjustment method and system based on artificial intelligence to extend the time of deep sleep.

[0005] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, there is provided an intelligent electric bed adjustment method based on artificial intelligence, which is applied to a control device. The method includes: the control device collects the physiological state information of the monitored object in each of the consecutive M time periods, a total of M physiological state information, where M is an integer greater than 2, and the monitored object is located on the intelligent electric bed; the control device processes the M physiological state information through a neural network model to determine whether the monitored object enters the deep sleep state; if the monitored object enters the deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object.

[0006] Optionally, the control device processes the M physiological state information through a neural network model to determine whether the monitored object enters the deep sleep state, including: the control device converts the M physiological state information into a physiological state matrix; the control device processes the physiological state matrix through a neural network model to determine whether the monitored object enters the deep sleep state.

[0007] Optionally, the control device converts the M physiological state information into a physiological state matrix, including: the control device converts each physiological state information in the M physiological state information into a vector set, a total of M vector sets are obtained, and each vector set in the M vector sets contains vectors that are parameters of the corresponding row in the initial matrix; the control device performs a row association operation on the initial matrix to obtain the physiological state matrix.

[0008] Optionally, for the i-th physiological state information in the M physiological state information, i is any integer from 1 to M, the i-th physiological state information includes physiological state parameters collected respectively at K moments in the i-th time period in the M time periods, a total of K physiological state parameters, K is an integer greater than 2, and any physiological state parameter in the K physiological state parameters includes multiple physiological state values; accordingly, the control device converts each physiological state information in the M physiological state information into a vector set, and a total of M vector sets are obtained, including: the control device maps the multiple physiological state values ​​included in each physiological state parameter in the K physiological state parameters into a vector, and a total of K vectors are obtained, the K vectors are the i-th vector set in the M vector sets, when i traverses 1 to M, M vector sets are obtained, the initial matrix is ​​an M*K matrix, M is the number of rows of the initial matrix, and K is the number of columns of the initial matrix; accordingly, the control device performs a row association operation on the initial matrix to obtain a physiological state matrix, including: the control device randomly extracts J from the K vectors in the first row of the initial matrix 1 vector, and J 1 The control device randomly extracts J vectors from the K vectors in the second row of the initial matrix. 2 vector, and J 2 The control device randomly extracts J vectors from the K vectors in the Mth row of the initial matrix. M vector, and J M vectors are randomly inserted into the K vectors in the first row of the initial matrix, and thus the physiological state matrix is ​​obtained; among them, J 1 =J 2…… =J M =J, J=Floor(K / 3), Floor() means rounding down, the physiological state matrix is ​​a matrix of M*(K+J), M is the number of rows of the physiological state matrix, and K+J is the number of columns of the physiological state matrix.

[0009] Optionally, each of the K physiological state parameters includes X physiological state values, where X is an integer greater than 2. The control device maps the multiple physiological state values included in each of the K physiological state parameters to a vector, obtaining a total of K vectors, including: for any one of the K physiological state parameters: the control device maps the X physiological state values included in the physiological state parameter to X initial vectors one by one; the control device determines the vector with the largest value among the X initial vectors as the reference vector; the control device determines the relative vectors of the X - 1 initial vectors relative to the reference vector, a total of X - 1 relative vectors, where the X - 1 initial vectors are the vectors among the X initial vectors other than the reference vector; the control device synthesizes the X - 1 relative vectors into one vector, that is, obtaining one vector mapped by the physiological state parameter.

[0010] Optionally, the control device processes the physiological state matrix through a neural network model to determine whether the monitored object enters the deep sleep state, including: the control device inputs the physiological state matrix and the matrix after the physiological state matrix is conjugate transposed into the neural network model for processing, obtaining the result of whether the monitored object enters the deep sleep state output by the neural network model.

[0011] Optionally, the control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object, including: the control device determines whether the intelligent electric bed needs to be adjusted according to the sleeping posture of the monitored object; if the intelligent electric bed needs to be adjusted, the control device gradually reduces the support degree of the area covered by the monitored object in the intelligent electric bed by a preset value; the control device collects the (M + N)-th physiological state information, where the (M + N)-th physiological state information is the physiological state information of the monitored object in the (M + N)-th time period, and N is an integer whose value increases from 1; the control device determines whether to gradually increase the support degree of the area covered by the monitored object in the intelligent electric bed by a preset value according to the relationship between the (M + N)-th physiological state information and the physiological state information in the M-th time period among the M time periods.

[0012] Optionally, the control device determines whether the intelligent electric bed needs to be adjusted according to the sleeping posture of the monitored object, including: the control device obtains the area of the force-bearing area of the monitored object on the intelligent electric bed; if the area of the force-bearing area of the monitored object is less than or equal to the area threshold, the control device determines that the sleeping posture of the monitored object is a high-force-bearing sleeping posture; the control device determines that the intelligent electric bed needs to be adjusted according to the sleeping posture of the monitored object being a high-force-bearing sleeping posture.

[0013] Optionally, the control device determines whether to gradually increase the support degree of the area covered by the monitored object in the intelligent electric bed by a preset value according to the relationship between the (M + N)-th physiological state information and the physiological state information within the M-th time period among the M time periods, including: the control device converts the (M + N)-th physiological state information into the (M + N)-th physiological state sequence, and converts the physiological state information within the M-th time period into the M-th physiological state sequence, and both the (M + N)-th physiological state sequence and the M-th physiological state sequence contain K vectors; the control device determines the sequence correlation degree between the (M + N)-th physiological state sequence and the M-th physiological state sequence; if the sequence correlation degree is less than or equal to the correlation threshold, the control device determines to gradually increase the support degree of the area covered by the monitored object in the intelligent electric bed by a preset value, otherwise, no processing is performed.

[0014] In a second aspect, an intelligent electric bed adjustment system based on artificial intelligence is provided. The system includes a control device and an intelligent electric bed. The control device is configured to: the control device collects the respective physiological state information of the monitored object within M consecutive time periods, a total of M physiological state information, where M is an integer greater than 2, and the monitored object is located on the intelligent electric bed; the control device processes the M physiological state information through a neural network model to determine whether the monitored object enters a deep sleep state; if the monitored object enters a deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object.

[0015] Specifically, the system is configured to execute the functions of the method described in the first aspect above. For specific understanding, refer to the method described in the first aspect above, and details will not be elaborated here.

[0016] In a third aspect, a computer-readable storage medium is provided, including: a computer program or instruction; when the computer program or instruction runs on a computer, the computer is caused to execute the method described in the first aspect.

[0017] In a fourth aspect, a computer program product is provided, including a computer program or instruction, which when running on a computer, causes the computer to execute the method described in the first aspect.

[0018] In summary, the above method and system have the following technical effects: By collecting the respective physiological state information of the monitored object within M consecutive time periods, a total of M physiological state information, and analyzing it through a neural network model, the control device can accurately determine whether the monitored object enters a deep sleep state; if the monitored object enters a deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object, thereby prolonging the duration of the deep sleep state. Description of the Drawings

[0019] Figure 1 Schematic diagram of the architecture of an intelligent electric bed adjustment system based on artificial intelligence provided by an embodiment of the present application; Figure 2 Schematic flow chart of an intelligent electric bed adjustment method based on artificial intelligence provided by an embodiment of the present application; Figure 3 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0020] The present invention will present various aspects, embodiments or features around a system that may include multiple devices, components, modules, etc. It should be understood and clear that each system may include additional devices, components, modules, etc., and / or may not include all the devices, components, modules, etc. discussed in conjunction with the drawings. In addition, combinations of these solutions can also be used.

[0021] In the embodiments of the present invention, "indicating" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. Let the information indicated by a certain piece of information (such as the first indication information, the second indication information, or the third indication information, etc. below) be the information to be indicated. Then, in the specific implementation process, there are many ways to indicate the information to be indicated. For example, but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated, etc. It is also possible to indirectly indicate the information to be indicated by indicating other information, where there is an association relationship between the other information and the information to be indicated. It is also possible to only indicate a part of the information to be indicated, while the other parts of the information to be indicated are known or pre-agreed. For example, it is also possible to implement the indication of specific information by means of the arrangement order of each piece of information pre-agreed (such as stipulated by a protocol), so as to reduce the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by separately indicating the same information.

[0022] In addition, the specific indication manner can also be various existing indication manners, such as, but not limited to, the above indication manners and their various combinations, etc. The specific details of various indication manners can refer to the prior art and will not be elaborated herein. As can be seen from the above, for example, when it is necessary to indicate multiple pieces of information of the same type, there may be a situation where the indication manners of different pieces of information are different. In the specific implementation process, the required indication manner can be selected according to specific needs. The embodiments of the present invention do not limit the selected indication manner. In this way, the indication manners involved in the embodiments of the present invention should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0023] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately, and the sending periods and / or sending timings of these sub-information can be the same or different. The specific sending method is not limited in the embodiments of the present invention. Among them, the sending periods and / or sending timings of these sub-information can be predefined, for example, predefined according to a protocol, or can be configured by the sending device by sending configuration information to the receiving device.

[0024] "Predefined" or "preconfigured" can be implemented by pre-saving corresponding codes, tables or other ways that can be used to indicate relevant information in the device. The embodiments of the present invention do not limit its specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be separately provided, or integrated in an encoder, decoder, processor, or communication device. The one or more memories can also be partly separately provided and partly integrated in a decoder, processor, or communication device. The type of the memory can be any form of storage medium, which is not limited in the embodiments of the present invention.

[0025] The "protocol" involved in the embodiments of the present invention can refer to a protocol family in the communication field, a standard protocol with a frame structure similar to that of a protocol family, or a related protocol applied to a future communication system. The embodiments of the present invention do not make specific limitations on this.

[0026] In the embodiments of the present invention, descriptions such as "when...", "in the case of...", "if", and "when" all refer to the fact that the device will perform corresponding processing under a certain objective situation, which does not limit the time, and does not require the device to have a judgment action during implementation, nor does it mean that there are other limitations.

[0027] In the description of the embodiments of the present invention, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B may represent A or B. The "and / or" in the embodiments of the present invention is merely a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B may be singular or plural. Also, in the description of the embodiments of the present invention, unless otherwise specified, "a plurality of" means two or more than two. "At least one (item)" or its similar expression refers to any combination of these items, including any combination of a single item or plural items. For example, at least one (item) of a, b, and c, or at least one (item) of a, b, or c, may represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c may be single or multiple. Additionally, for the convenience of clearly describing the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and roles. Those skilled in the art can understand that the terms "first", "second", etc. do not limit the quantity and execution order, and the terms "first", "second", etc. do not necessarily mean different. At the same time, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific way for easy understanding.

[0028] For ease of understanding the embodiments of the present invention, first, taking the Figure 1 control system shown in Figure 1 as an example, exemplary,

[0029] as Figure 1 shown, the control system may include: a control device and a smart electric bed.

[0030] The control device can be a device in the form of a terminal, i.e., a terminal. The terminal can be a terminal with transceiver functions, or a chip or chip system that can be set in the terminal. The terminal can also be referred to as a user equipment (UE), an access terminal, a subscriber unit, a user station, a mobile station (MS), a mobile phone, a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless communication device, a user agent, or a user device. The terminal in the embodiments of the present application can be a mobile phone, a cellular phone, a smart phone, a tablet (Pad), a wireless data card, a personal digital assistant (PDA), a wireless modem, a handset, a laptop computer, a machine type communication (MTC) terminal, a computer with wireless transceiver functions, a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, an in-vehicle terminal, a roadside unit (RSU) with terminal functions, etc. The terminal in the present application can also be an in-vehicle module, an in-vehicle module, an in-vehicle component, an in-vehicle chip, or an in-vehicle unit built into a vehicle as one or more components or units. Alternatively, the terminal can also be a customer-premises equipment (CPE).

[0031] Exemplarily, Figure 2 The flowchart of the intelligent electric bed adjustment method based on artificial intelligence provided by the embodiments of the present application. This method can be applied to a control device.

[0032] As Figure 2 shown, the process of the intelligent electric bed adjustment method based on artificial intelligence is as follows: S201. The control device collects the physiological state information of the monitored object in each of the consecutive M time periods, a total of M physiological state information. M is an integer greater than 2. The monitored object (i.e., the user in sleep) is located on the intelligent electric bed, and the monitored object wears a physiological sign detection device attached to the intelligent electric bed, which can collect the physiological state information of the user. For the i-th physiological state information among the M physiological state information, where i is any integer from 1 to M, the i-th physiological state information includes the physiological state parameters collected at K moments in the i-th time period among the M time periods, a total of K physiological state parameters. K is an integer greater than 2. Any one of the K physiological state parameters includes various physiological state values, such as heart rate, respiratory rate, body temperature, blood pressure, etc. For example, each time period is 30 seconds and K = 15, which means that the physiological state parameters are collected every 2 seconds within each time period, and each physiological state information corresponding to a time period contains 15 physiological state parameters. The control device can collect the physiological state information in the form of a sliding window. For example, M = 4. At the end of the 4th time period, the control device obtains the physiological state information within the 1st to 4th time periods. Then, at the end of the 5th time period, the control device obtains the physiological state information within the 2nd to 5th time periods. Then, at the end of the 6th time period, the control device obtains the physiological state information within the 3rd to 6th time periods, and so on.

[0033] S202. The control device processes the M physiological state information through a neural network model to determine whether the monitored object enters the deep sleep state.

[0034] Among them, S202 may include the following steps: Step 1: The control device can convert the M physiological state information into a physiological state matrix.

[0035] For example, the control device can convert each of the M physiological state information into a set of vectors, obtaining a total of M sets of vectors. Each set of vectors in the M sets of vectors contains vectors that are the parameters of the corresponding row in the initial matrix. Among them, the control device can map the multiple physiological state values included in each of the K physiological state parameters into a vector, obtaining a total of K vectors. The K vectors are the i-th set of vectors in the M sets of vectors. When i traverses from 1 to M, M sets of vectors are obtained. The initial matrix is an M×K matrix, where M is the number of rows of the initial matrix and K is the number of columns of the initial matrix. Specifically, each of the K physiological state parameters includes X physiological state values, where X is an integer greater than 2. For any one of the K physiological state parameters, the control device can map the X physiological state values included in the physiological state parameter to X initial vectors one by one. In one example, assume that X = 4, including 4 physiological state values: heart rate, respiratory rate, body temperature, and blood pressure. These 4 parameters in one physiological state parameter can be mapped to 4 initial vectors one by one, such as vector 1, vector 2, vector 3, and vector 4. The control device determines the vector with the largest value among the X initial vectors as the reference vector; the control device determines the relative vectors of the X - 1 initial vectors with respect to the reference vector, obtaining a total of X - 1 relative vectors. The X - 1 initial vectors are the vectors other than the reference vector among the X initial vectors; the control device synthesizes the X - 1 relative vectors into one vector, that is, obtaining the vector mapped by the physiological state parameter. Continuing with the above example, assume that vector 1 has the largest value. The control device can determine the end point of vector 1 to the end point of vector 2 as relative vector 1, the end point of vector 1 to the end point of vector 3 as relative vector 2, and the end point of vector 1 to the end point of vector 4 as relative vector 3. Then the control device can synthesize relative vector 1, relative vector 2, and relative vector 3 into one vector, such as multiplying or adding these 3 relative vectors, so as to obtain the vector mapped by one physiological state parameter. It can be understood that since the mapping of vectors is to determine the relative vectors between parameters, the vectors obtained in this way can endow the correlation between the X physiological state values in one physiological state parameter, so as to better analyze whether it enters the deep sleep state using the model.

[0036] After that, the control device performs a row association operation on the initial matrix to obtain a physiological state matrix. For example, the control device randomly extracts J 1 vectors from the K vectors in the first row of the initial matrix and randomly inserts the J 1 vectors into the K vectors in the second row of the initial matrix; the control device randomly extracts J 2 vectors from the K vectors in the second row of the initial matrix and inserts the J 2Insert J vectors randomly into the K vectors in the 3rd row of the initial matrix; and so on. The control device randomly selects J vectors from the K vectors in the Mth row of the initial matrix and inserts the J vectors randomly into the K vectors in the 1st row of the initial matrix. Thus, a physiological state matrix is obtained. Where J = J = J = J, J = Floor(K / 3), Floor() represents rounding down. The physiological state matrix is an M*(K + J) matrix, where M is the number of rows of the physiological state matrix and K + J is the number of columns of the physiological state matrix. In this way, not only can the matrix dimension be expanded, thereby increasing the amount of data analyzed by the input model and enhancing the robustness of the model, but also since each row contains the vectors of the previous row, the coupling of parameters between different time periods can be increased, avoiding the situation where the model cannot make good use of the change trend of parameters during the processing due to parameter isolation. In this way, the robustness of the model can also be improved, so as to more accurately determine whether the monitored object enters the deep sleep state. M J M vectors and insert the J 1 = J 2…… = J M = J, J = Floor(K / 3), Floor() represents rounding down. The physiological state matrix is an M*(K + J) matrix, where M is the number of rows of the physiological state matrix and K + J is the number of columns of the physiological state matrix. In this way, not only can the matrix dimension be expanded, thereby increasing the amount of data analyzed by the input model and enhancing the robustness of the model, but also since each row contains the vectors of the previous row, the coupling of parameters between different time periods can be increased, avoiding the situation where the model cannot make good use of the change trend of parameters during the processing due to parameter isolation. In this way, the robustness of the model can also be improved, so as to more accurately determine whether the monitored object enters the deep sleep state.

[0037] Step 2: The control device can process the physiological state matrix through a neural network model to determine whether the monitored object enters the deep sleep state.

[0038] The control device can input the physiological state matrix and the matrix after the physiological state matrix is conjugate transposed into the neural network model for processing, and obtain the result of whether the monitored object enters the deep sleep state output by the neural network model, so as to further improve the accuracy of the analysis result.

[0039] S203, if the monitored object enters the deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object.

[0040] The control device determines whether the intelligent electric bed needs to be adjusted according to the sleeping posture of the monitored object. For example, the control device obtains the area of the force-bearing area of the monitored object on the intelligent electric bed. Wherein, the bed surface of the intelligent electric bed is grid-shaped, and each grid is provided with a corresponding pressure sensor. Thus, the control device can determine the area of the force-bearing area of the monitored object according to the number of grids where the pressure sensors with feedback pressure are located.

[0041] If the area of the force-bearing area of the monitored object is less than or equal to the area threshold, the control device determines that the sleeping posture of the monitored object is a high-force-bearing sleeping posture, such as a side sleeping posture, a posture close to side sleeping, or a posture of lying obliquely on the stomach, etc., where the body pressure is not dispersed as much as possible to the mattress. Of course, if the sleeping posture of the monitored object is not a high-force-bearing sleeping posture, such as lying flat, the control device may not perform control processing. Therefore, the control device can determine that the intelligent electric bed needs to be adjusted according to the high-force-bearing sleeping posture of the monitored object. Further, if the intelligent electric bed needs to be adjusted, the control device (such as controlling the electric adjustment of the intelligent electric bed) gradually reduces the support degree of the area covered by the monitored object in the intelligent electric bed by a preset value (such as reducing 10%-15% of the current support degree, that is, reducing the hardness of the mattress). Among them, the area covered by the monitored object can be drawn based on the position of the grille where the pressure sensor that feeds back pressure is located. For example, the area covering these grilles is the area covered by the monitored object. It can be understood that the granularity of the grille needs to be as fine as possible, such as being gridded at 100*180. In this way, the pressure generated by the sleeping posture of the monitored object can be better shared by the mattress, that is, the area of the force-bearing area increases, so that the monitored object is not easily caused to end the deep sleep state due to the change of the sleeping posture caused by the oppression of the limbs. After that, the control device can collect the (M + N)-th physiological state information, where the (M + N)-th physiological state information is the physiological state information of the monitored object in the (M + N)-th time period, and N is an integer that increases from 1; the control device determines whether to gradually increase the support degree of the area covered by the monitored object in the intelligent electric bed by a preset value according to the relationship between the (M + N)-th physiological state information and the physiological state information in the M-th time period among the M time periods. For example, the control device can convert the (M + N)-th physiological state information into the (M + N)-th physiological state sequence (which can be understood as a row in the above matrix, including K vectors), and convert the physiological state information in the M-th time period into the M-th physiological state sequence (which can also be understood as a row in the above matrix, including K vectors). That is, the number of vectors included in the (M + N)-th physiological state sequence and the M-th physiological state sequence is both K; the control device can determine the sequence correlation degree between the (M + N)-th physiological state sequence and the M-th physiological state sequence, specifically, it can calculate the inner product of the sequences; if the sequence correlation degree is less than or equal to the correlation threshold, the control device determines to gradually increase the support degree of the area covered by the monitored object in the intelligent electric bed by a preset value, otherwise, no processing is performed. That is to say, if the sequence correlation degree is less than or equal to the correlation threshold, it means that the physiological state of the monitored object has changed and may end the deep sleep state. Therefore, at this time, the hardness of the mattress can be restored to avoid the monitored object ending the deep sleep state due to being in a softer mattress state for a long time. In this way, the deep sleep state can also be extended as much as possible.

[0042] In summary, the control device can accurately determine whether the monitored object enters the deep sleep state by collecting the physiological state information of the monitored object in each of the consecutive M time periods, a total of M physiological state information, and analyzing it through a neural network model; if the monitored object enters the deep sleep state, the control device adjusts the intelligent electric bed to the state suitable for the deep sleep state of the monitored object, thereby extending the duration of the deep sleep state.

[0043] Figure 3 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Exemplarily, the electronic device may be a terminal device, or a chip (system) or other components or assemblies that can be set in a terminal device. As Figure 3 shown, the electronic device 400 may include a processor 401. Optionally, the electronic device 400 may further include a memory 402 and / or a transceiver 403. Among them, the processor 401 is coupled to the memory 402 and the transceiver 403, and may be connected through a communication bus, for example. In addition, the electronic device 400 may also be a chip, such as including a processor 401. At this time, the transceiver may be an input / output interface of the chip.

[0044] Next, a specific introduction will be made to each component of Figure 3 the electronic device 400: Among them, the processor 401 is the control center of the electronic device 400, and may be a single processor or a collective term for multiple processing elements. For example, the processor 401 is one or more central processing units (CPUs), or may be an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, for example: one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs).

[0045] Optionally, the processor 401 may execute various functions of the electronic device 400 by running or executing software programs stored in the memory 402 and calling data stored in the memory 402, such as executing the above Figure 2 shown method.

[0046] In a specific implementation, as an embodiment, the processor 401 may include one or more CPUs, such as Figure 3 the CPU0 and CPU1 shown in

[0047] In a specific implementation, as an example, the electronic device 400 may also include multiple processors. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer programs or instructions).

[0048] Among them, the memory 402 is used to store the software program for implementing the solution of the present invention and is controlled by the processor 401 for execution. The specific implementation method can refer to the above method embodiment and will not be elaborated here.

[0049] Optionally, the memory 402 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but not limited thereto. The memory 402 can be integrated with the processor 401 or exist independently and is coupled to the processor 401 through the interface circuit ( Figure 3 not shown in the figure), and the embodiments of the present invention do not make specific limitations on this.

[0050] The transceiver 403 is used for communication with other electronic devices. For example, if the electronic device 400 is a terminal device, the transceiver 403 can be used for communication with a network device or with another terminal device. Another example is that if the electronic device 400 is a network device, the transceiver 403 can be used for communication with a terminal device or with another network device.

[0051] Optionally, the transceiver 403 may include a receiver and a transmitter ( Figure 3 not shown separately). Among them, the receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0052] Optionally, the transceiver 403 may be integrated with the processor 401 or exist independently, and is coupled to the processor 401 through an interface circuit ( Figure 3 not shown) of the electronic device 400. The embodiments of the present invention do not make specific limitations thereto.

[0053] It can be understood that Figure 3 the structure of the electronic device 400 shown in

[0054] does not constitute a limitation to the electronic device. The actual electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0054] In addition, the technical effects of the electronic device 400 may refer to the technical effects of the method described in the above method embodiments, and will not be elaborated herein.

[0055] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0056] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0057] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0058] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0059] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0060] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0061] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0062] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0063] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0064] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0065] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0066] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0067] As described above, the above are only specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for adjusting an intelligent electric bed based on artificial intelligence, characterized in that: Applied to a control device, the method comprises: The control device collects physiological status information of each monitored object in M ​​consecutive time periods, a total of M physiological status information, M is an integer greater than 2, and the monitored object is located on the intelligent electric bed; The control device processes the M physiological state information through a neural network model to determine whether the monitored object enters a deep sleep state; If the monitored object enters a deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the monitored object's deep sleep state.

2. The method according to claim 1, characterized in that The control device processes the M physiological state information through a neural network model to determine whether the monitored object enters a deep sleep state, including: The control device converts the M physiological state information into a physiological state matrix; The control device processes the physiological state matrix through the neural network model to determine whether the monitored object enters a deep sleep state.

3. The method according to claim 2, characterized in that The control device converts the M physiological state information into a physiological state matrix, including: The control device converts each physiological state information in the M physiological state information into a vector set, and obtains M vector sets in total, and the vector contained in each vector set in the M vector sets is a parameter of a corresponding row in the initial matrix; The control device performs a row association operation on the initial matrix to obtain the physiological state matrix.

4. The method according to claim 3, characterized in that For the i-th physiological state information in the M physiological state information, i is any integer from 1 to M, the i-th physiological state information includes physiological state parameters collected at K moments in the i-th time period in the M time periods, a total of K physiological state parameters, K is an integer greater than 2, and any physiological state parameter in the K physiological state parameters includes multiple physiological state values; accordingly, the control device converts each physiological state information in the M physiological state information into a vector set, and a total of M vector sets are obtained, including: The control device maps the multiple physiological state values ​​included in each of the K physiological state parameters into a vector, and obtains K vectors in total, wherein the K vectors are the i-th vector set in the M vector sets, and when i traverses from 1 to M, the M vector sets are obtained, and the initial matrix is ​​an M*K matrix, where M is the number of rows of the initial matrix and K is the number of columns of the initial matrix; Accordingly, the control device performs a row association operation on the initial matrix to obtain the physiological state matrix, including: The control device randomly extracts J1 vectors from the K vectors in the first row of the initial matrix, and randomly inserts the J1 vectors into the K vectors in the second row of the initial matrix; The control device randomly extracts J2 vectors from the K vectors in the second row of the initial matrix, and randomly inserts the J2 vectors into the K vectors in the third row of the initial matrix; Similarly, the control device randomly extracts J from the K vectors in the Mth row of the initial matrix. M vector, and J M vectors are randomly inserted into the K vectors in the first row of the initial matrix, so that the physiological state matrix is ​​obtained; Where J1=J 2…… =J M =J, J=Floor(K / 3), Floor() means rounding down, the physiological state matrix is ​​a matrix of M*(K+J), M is the number of rows of the physiological state matrix, and K+J is the number of columns of the physiological state matrix.

5. The method according to claim 4, characterized in that The multiple physiological state values ​​included in each of the K physiological state parameters are X physiological state values, where X is an integer greater than 2, and the control device maps the multiple physiological state values ​​included in each of the K physiological state parameters into a vector, obtaining a total of K vectors, including: For any one of the K physiological state parameters: The control device maps the X physiological state values ​​included in the physiological state parameter into X initial vectors in a one-to-one correspondence; The control device determines a vector having the largest value among the X initial vectors as a reference vector; The control device determines a relative vector of each of X-1 initial vectors relative to the reference vector, a total of X-1 relative vectors, wherein the X-1 initial vectors are vectors other than the reference vector among the X initial vectors; The control device synthesizes the X-1 relative vectors into one vector, that is, obtains a vector mapped by the physiological state parameter.

6. The method according to claim 4, characterized in that The control device processes the physiological state matrix through the neural network model to determine whether the monitored object enters a deep sleep state, including: The control device inputs the physiological state matrix and the matrix obtained by conjugate transposing the physiological state matrix into the neural network model for processing, and obtains the result of whether the monitored object enters a deep sleep state output by the neural network model.

7. The method according to claim 1, characterized in that The control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object, including: The control device determines whether the intelligent electric bed needs to be adjusted according to the sleeping posture of the monitored subject; If the intelligent electric bed needs to be adjusted, the control device gradually reduces the support degree of the area covered by the monitored object in the intelligent electric bed by a preset value; The control device collects the M+Nth physiological state information, where the M+Nth physiological state information is the physiological state information of the monitored object in the M+Nth time period, and N is an integer increasing from 1; The control device determines whether to gradually increase the support degree of the area covered by the monitored object in the intelligent electric bed by the preset value based on the relationship between the M+Nth physiological state information and the physiological state information in the Mth time period of the M time periods.

8. The method according to claim 7, characterized in that The control device determines whether the intelligent electric bed needs to be adjusted according to the sleeping posture of the monitored object, including: The control device obtains the area of ​​the force-bearing region of the monitored object on the intelligent electric bed; If the area of ​​the force-bearing region of the monitored object is less than or equal to the area threshold, the control device determines that the sleeping posture of the monitored object is a high-force sleeping posture; The control device determines that the intelligent electric bed needs to be adjusted according to the monitored object's sleeping posture being a high-stress sleeping posture.

9. The method according to claim 7, characterized in that: The control device determines whether to gradually increase the support degree of the area covered by the monitored object in the intelligent electric bed by the preset value according to the relationship between the M+Nth physiological state information and the physiological state information in the Mth time period of the M time periods, including: The control device converts the M+Nth physiological state information into an M+Nth physiological state sequence, and converts the physiological state information in the Mth time period into an Mth physiological state sequence, and the number of vectors included in the M+Nth physiological state sequence and the Mth physiological state sequence are both K; The control device determines a sequence correlation between the M+Nth physiological state sequence and the Mth physiological state sequence; If the sequence correlation is less than or equal to the correlation threshold, the control device determines to gradually increase the support degree of the area covered by the monitored object in the intelligent electric bed by the preset value; otherwise, no processing is performed.

10. An intelligent electric bed adjustment system based on artificial intelligence, characterized in that: The system includes a control device and an intelligent electric bed, wherein the control device is configured as: The control device collects physiological status information of each monitored object in M ​​consecutive time periods, a total of M physiological status information, M is an integer greater than 2, and the monitored object is located on the intelligent electric bed; The control device processes the M physiological state information through a neural network model to determine whether the monitored object enters a deep sleep state; If the monitored object enters a deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the monitored object's deep sleep state.

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