Work and rest habit determination method, system and device based on cloud big data management

Abstract

The invention belongs to the technical field of auxiliary medical equipment, and relates to a work and rest habit determination method, system and device based on cloud big data management, and the method comprises the steps: obtaining the pressure information of a baby acting on a plurality of detection units, enabling the pressure information to be in one-to-one correspondence with the position information of the plurality of detection units, and forming a feature matrix; the method comprises the following steps: converting a pressure space cloud picture into a three-dimensional space coordinate system to form a pressure space cloud picture, repeating the steps for multiple times in a preset fixed time period to obtain a plurality of pressure space cloud pictures, selecting one of the plurality of pressure space cloud pictures as a reference, and simultaneously performing fitting error on the rest of the plurality of pressure space cloud pictures and the reference to obtain a pressure space cloud picture. When the fitting error is larger than a preset threshold value, it is judged that the baby is in a waking state, otherwise, it is judged that the baby is in a sleeping state, a plurality of preset fixed time periods are set in one day, the steps are repeated for multiple times to judge the state of the baby, and the work and rest habits of the baby are determined. The work and rest habits of the baby are determined through big data so as to assist medical treatment.

Description

Technical Field

[0001] The present invention belongs to the technical field of auxiliary medical equipment, and specifically relates to a method, system and device for determining work and rest habits based on cloud big data management. Background Art

[0002] Every day, a large number of babies are born in pediatric departments of hospitals around the world, and the medical status of each baby at birth is different. Some babies are defined as healthy babies. These babies only need daily attention to their body temperature and diet, and routine care and monitoring. Some babies are defined as sub-healthy babies. These babies need medical staff to regularly monitor their substandard or critical health indicators, and provide first-level care and routine monitoring. Some babies are defined as unhealthy babies. These babies need medical staff to conduct high-frequency monitoring of their substandard health indicators, and provide special care and frequent status monitoring. Therefore, how to provide targeted and differentiated health care and monitoring based on the baby's health status is very important for babies.

[0003] Currently, hospitals commonly use an incubator number to create a file for each infant. Medical staff then register their weight, temperature, feeding habits, skin color, and bowel movement frequency, and provide feedback to the corresponding doctor. Doctors then use the monitoring data collected by the medical staff during rounds to prescribe and adjust medications, tests, and care. Under this system, most observation, care, monitoring, and record-keeping tasks require medical staff to perform during specific infant sleep patterns. For example, feeding observations are performed during awake hours, while temperature monitoring, whether performed through the axillary, cervical, or rectal methods, is most easily performed during sleep. Consequently, medical staff must frequently observe infants in incubators to determine whether they are awake or asleep, and then apply appropriate monitoring or care measures based on their sleep patterns. This, coupled with the large number of infants, can significantly increase the workload for medical staff. Summary of the Invention

[0004] In view of this, the present invention provides a method, system and device for determining work and rest habits based on cloud-based big data management. The method determines the work and rest habits of infants through big data, thereby assisting medical treatment and solving the problem that medical staff need to frequently observe back and forth to determine the condition of the infant before they can apply monitoring or nursing items corresponding to the work and rest status, which leads to a doubling of workload.

[0005] The technical solution of the present invention is:

[0006] The method for determining work and rest habits based on cloud-based big data management includes the following steps:

[0007] Obtaining pressure information exerted by the infant on a plurality of detection units, the plurality of detection units being distributed under the infant;

[0008] The position information and pressure information of multiple detection units are mapped one by one to form a feature matrix, which is then converted into a three-dimensional spatial coordinate system to form a pressure space cloud map. The above steps are repeated multiple times within a preset fixed time period to obtain multiple pressure space cloud maps;

[0009] One of the multiple pressure space cloud maps is selected as a benchmark, and the remaining multiple pressure space cloud maps are fitted with the benchmark at the same time. When the fitting error is greater than the preset threshold, the baby is judged to be awake, otherwise, it is judged to be asleep. Multiple preset fixed time periods are set in a day, and the above steps are repeated multiple times to judge the baby's state, based on which the baby's daily routine is determined.

[0010] Preferably, the position information and pressure information of multiple detection units are mapped one by one using the following formula to form a feature matrix:

[0011]

[0012] Where n represents the nth detection unit, 1≤n≤N, N is the total number of detection units, m represents the mth acquisition of the pressure information of the infant on multiple detection units, m is a natural number, X mn Y represents the horizontal coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn F represents the vertical coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn A represents the pressure exerted by the infant on the nth detection unit obtained for the mth time, m It represents the feature matrix formed when the pressure information of the infant acting on multiple detection units is obtained for the mth time.

[0013] Preferably, converting the characteristic matrix into a three-dimensional space coordinate system to form a pressure space cloud map includes the following steps:

[0014] Each column in the feature matrix is separated into a three-dimensional feature column vector;

[0015] Use the following formula to flatten the three-dimensional feature column vector into a three-dimensional row vector,

[0016]

[0017] Where n represents the nth detection unit, 1≤n≤N, N is the total number of detection units, m represents the mth acquisition of the pressure information of the infant on the detection unit, m is a natural number, Xmn Y represents the horizontal coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn F represents the vertical coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn A represents the pressure exerted by the infant on the nth detection unit obtained for the mth time, mn represents the three-dimensional feature column vector corresponding to the position and pressure formed when the pressure information of the infant on the nth detection unit is obtained for the mth time;

[0018] The above three-dimensional row vector A mn T The three elements in the three-dimensional space coordinate system correspond one-to-one to the three axes, realizing the three-dimensional row vector A mn T Corresponding to the position of the spatial point in the three-dimensional space coordinate system;

[0019] The positions of multiple spatial points corresponding to multiple three-dimensional feature column vectors formed when the pressure information of the infant exerted on multiple detection units obtained for the mth time are fitted with a surface to form an mth pressure space cloud map located in the three-dimensional space coordinate system.

[0020] Preferably, at least three pressure space cloud maps are obtained within a preset fixed time period.

[0021] Preferably, the method for determining the fitting error comprises the following steps:

[0022] The coordinates of the points representing the two pressure space cloud maps remain unchanged, and the coordinates representing the pressure information are calculated by difference operation, so as to obtain an error amount. The coordinates of the point and the error amount are in a one-to-one correspondence relationship, and an error amount space cloud map in a three-dimensional space coordinate system is generated;

[0023] Using two envelope planes to form an envelope surface for the error space cloud map, the two envelope planes are parallel to each other and pass through the upper and lower limit position points of the error space cloud map respectively;

[0024] Determining a median surface using the two envelope planes;

[0025] Determine a first limit error surface and a second limit error surface with the median surface as a reference surface, wherein the distances between the first limit error surface and the second limit error surface and the median surface are preset thresholds, and the first limit error surface and the second limit error surface are parallel and located above and below the median surface, respectively;

[0026] Determine whether the two envelope planes are located inside or outside the space formed by the first limit error surface and the second limit error surface. When the two envelope planes are located outside the space formed by the first limit error surface and the second limit error surface, determine that the baby is awake.

[0027] The infant sleep and rest habit determination system based on cloud-based big data management, based on the above method, includes:

[0028] an acquisition unit, configured to acquire pressure information exerted by the infant on a plurality of detection units, the plurality of detection units being distributed under the infant;

[0029] A conversion unit is used to associate the position information and pressure information of multiple detection units one by one to form a feature matrix, and convert it into a three-dimensional spatial coordinate system to form a pressure space cloud map. Within a preset fixed time period, the above steps are repeated multiple times to obtain multiple pressure space cloud maps;

[0030] The determination unit is used to select one of the multiple pressure space cloud maps as a benchmark, and simultaneously perform fitting errors on the remaining multiple pressure space cloud maps and the benchmark. When the fitting error is greater than a preset threshold, the infant is determined to be awake; otherwise, the infant is determined to be asleep. Multiple preset fixed time periods are set in a day, and the above steps are repeated multiple times to determine the infant's state, based on which the infant's daily routine is determined.

[0031] An insulated box includes: a storage device, a processor, and a computer program stored in the storage device and executable on the processor. The processor executes the computer program to implement the method for determining work and rest habits based on cloud-based big data management as described above.

[0032] A computer-readable storage medium stores a computer program thereon, which is executed by a processor to implement the method for determining work and rest habits based on cloud big data management as described above.

[0033] Compared with the existing technology, the method, system and device for determining work and rest habits based on cloud big data management provided by the present invention obtain the pressure information of the baby on multiple detection units, correspond the position information and pressure information of the multiple detection units one by one, form a feature matrix, and convert it into a three-dimensional space coordinate system to form a pressure space cloud map. Within a preset fixed time period, the above steps are repeated multiple times to obtain multiple pressure space cloud maps, and one of the multiple pressure space cloud maps is selected as a benchmark. The remaining multiple pressure space cloud maps are simultaneously fitted with the benchmark. When the fitting error is greater than the preset threshold, the baby's state is determined to be awake, otherwise, it is determined to be asleep. Multiple preset fixed time periods are set in a day, and the above steps are repeated multiple times to determine the baby's state. Based on this, the baby's work and rest habits are determined, which can assist medical treatment and solve the problem that medical staff need to frequently observe back and forth to determine the baby's state before they can apply monitoring or nursing items under the corresponding state to it, which leads to doubling of workload. It is highly practical and worthy of promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is the main flow chart of the present invention.

[0035] Figure 2 The sub-process of the present invention Figure 1 .

[0036] Figure 3 The sub-process of the present invention Figure 2 .

[0037] Figure 4 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0038] After birth, it's crucial to provide targeted, differentiated health care and monitoring tailored to the infant's health status. However, the current common hospital practice for unhealthy infants involves creating a file for each infant using their incubator number. Medical staff then register their weight, temperature, feeding habits, skin color, and bowel movement frequency, providing feedback to the corresponding doctor. Doctors then use this monitoring data during rounds to prescribe and adjust medications, tests, and care. This practice requires most observation, care, monitoring, and recording to be performed by medical staff during specific infant states. For example, feeding observations are performed during awake hours, while temperature monitoring, whether performed through the axillary, cervical, or rectal methods, is most easily performed while the infant is asleep. Consequently, medical staff must frequently observe the infant in the incubator to determine whether it is awake or asleep, and then implement appropriate monitoring and care measures tailored to the infant's sleep and rest status. This significantly increases the workload for medical staff, given the large number of infants present.

[0039] In order to solve the technical problem that medical staff need to frequently observe back and forth to determine the condition of the baby before they can apply monitoring or nursing items under the corresponding condition, which leads to a doubling of workload, the technical solution of this application is specially designed.

[0040] In order to enable those skilled in the art to better understand and implement the technical solution of the present invention, the present invention is further described below with reference to specific embodiments and drawings.

[0041] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the insulation box or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. The terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0042] The present invention provides a method, system, and device for determining work and rest habits based on cloud big data management. The present invention is described below with reference to the structural schematic diagram of the accompanying drawings.

[0043] Example 1

[0044] like Figure 1 As shown, the method for determining work and rest habits based on cloud big data management provided by the present invention includes the following steps:

[0045] The pressure information exerted by the baby on the multiple detection units is obtained, and the multiple detection units are distributed under the baby's body.

[0046] The position information and pressure information of multiple detection units are matched one by one to form a feature matrix, which is then converted into a three-dimensional space coordinate system to form a pressure space cloud map. Within a preset fixed time period, the above steps are repeated multiple times to obtain multiple pressure space cloud maps.

[0047] One of the multiple pressure space cloud maps is selected as a benchmark, and the remaining multiple pressure space cloud maps are fitted with the benchmark at the same time. When the fitting error is greater than the preset threshold, the baby is judged to be awake, otherwise, it is judged to be asleep. Multiple preset fixed time periods are set in a day, and the above steps are repeated multiple times to judge the baby's state, based on which the baby's daily routine is determined.

[0048] The above method can also be implemented by relying on an electronic device. The structure of the electronic device specifically includes a storage device, a processor, and a computer program stored on the storage device and runnable on the processor. The processor executes the computer program to implement the above-mentioned method for determining work and rest habits based on cloud big data management.

[0049] A communication interface is provided between the memory and the processor to realize signal connection between the two and to transfer data. The communication interface can be a serial interface or a parallel interface.

[0050] The processor may be a central processing unit (CPU), or a specific integrated circuit (ASIC), or may be one or more integrated circuits configured to implement the embodiments of the present application.

[0051] The above method can also be implemented by relying on a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the above-mentioned method for determining work and rest habits based on cloud big data management.

[0052] The baby's daily routine determination system based on cloud big data management can also be implemented based on the above method. Its structure is as follows: Figure 4 As shown, including:

[0053] The acquisition unit 1 is used to acquire pressure information exerted by the baby on a plurality of detection units, where the plurality of detection units are distributed under the baby's body.

[0054] The conversion unit 2 is used to correspond the position information and pressure information of multiple detection units one by one to form a feature matrix, and convert it into a three-dimensional space coordinate system to form a pressure space cloud map. Within a preset fixed time period, the above steps are repeated multiple times to obtain multiple pressure space cloud maps.

[0055] The judgment unit 3 is used to select one of the multiple pressure space cloud maps as a benchmark, and perform fitting errors on the remaining multiple pressure space cloud maps and the benchmark. When the fitting error is greater than a preset threshold, the baby is judged to be awake; otherwise, the baby is judged to be asleep. Multiple preset fixed time periods are set in a day, and the above steps are repeated multiple times to judge the baby's state, based on which the baby's daily routine is determined.

[0056] The above-mentioned baby daily routine determination system based on cloud-based big data management is applied to an incubator. Specifically, the structure of the incubator includes a shell and a cover. A pad is provided on the inner bottom surface of the shell. The size of the pad is consistent with the size of the inner bottom surface of the shell, and the pad can be fixed on the inner bottom surface of the shell.

[0057] The pad body includes a first functional layer, a second functional layer and a third functional layer arranged in sequence from top to bottom. The first functional layer, the second functional layer and the third functional layer are connected by a zipper to form a whole, which is convenient for laying on the bottom surface of the shell and convenient for taking out the whole.

[0058] As a further refinement of this embodiment, the first functional layer and the third functional layer have the same structure, and together they constitute a support structure to improve the comfort of the baby.

[0059] Specifically, the first functional layer and the third functional layer include a waterproof bag and an inner core arranged inside the waterproof bag. As an implementation method, the waterproof bag adopts waterproof material PU, and the inner core adopts multi-layer non-woven fabric. During use, a detachable protective bag can be put on the multi-layer non-woven fabric.

[0060] The second functional layer in the middle includes a support plate. For the infant's safety, this plate is preferably made of the relatively high-end and safe material polyphenylsulfone (PPSU). The size of the support plate matches that of the first and third functional layers. It is divided into multiple evenly sized panels, each equipped with a detection unit that measures the pressure exerted by the infant on each panel. Multiple detection units are wirelessly connected to the medical data cloud platform, enabling simultaneous sharing of the infant's status.

[0061] In practical applications, the pressure information of the baby on multiple detection units can be continuously monitored in multiple continuous time periods. The continuous monitoring is performed by sampling once at a specific time interval, so that the force F exerted by the baby on the bottom surface of the shell in different postures can be obtained. n The collection of forces F n By carrying out corresponding processing and analysis, it is possible to determine in what state the baby is awake and in what state the baby is asleep. Based on this, a database of the baby's daily habits is constructed to assist medical treatment.

[0062] Specifically, the detection unit can be implemented using a micro pressure sensor, and the model of the pressure sensor can be the commercially available F1Y-20N (Grade A).

[0063] Among them, the force F n The corresponding processing and analysis process can be achieved by the following methods:

[0064] You can first find a detection unit on the support plate, define the position of the detection unit on it in a two-dimensional coordinate system, preset it to zero position, mark it as 1 (0,0), and then determine the position of other detection units relative to the first point in turn, and use n (X n ,Y n ), the detection information on each pressure sensor forms a one-to-one correspondence with each mark, and the relationship is expressed as a feature matrix, which is marked with the following expression:

[0065]

[0066] Where n represents the nth detection unit, 1≤n≤N, N is the total number of detection units, m represents the mth acquisition of the pressure information of the infant on multiple detection units, m is a natural number, X mn Y represents the horizontal coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn F represents the vertical coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn A represents the pressure exerted by the infant on the nth detection unit obtained for the mth time, m It represents the feature matrix formed when the pressure information of the infant acting on multiple detection units is obtained for the mth time.

[0067] In order to facilitate the subsequent data processing, Figure 2 As shown, each column in the feature matrix can be separated into a three-dimensional feature column vector, and the three-dimensional feature column vector can be flattened into a three-dimensional row vector using the following formula:

[0068]

[0069] Where n represents the nth detection unit, 1≤n≤N, N is the total number of detection units, m represents the mth acquisition of the pressure information of the infant on the detection unit, m is a natural number, X mn Y represents the horizontal coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn F represents the vertical coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn A represents the pressure exerted by the infant on the nth detection unit obtained for the mth time, mn It represents the three-dimensional feature column vector corresponding to the position and pressure formed when the pressure information of the infant on the n-th detection unit is obtained for the m-th time.

[0070] In order to facilitate the corresponding marking and form a three-dimensional intuitive display, the three-dimensional row vector can be converted into a three-dimensional space and displayed using a cloud map. When the pressure space cloud map is displayed, the coordinate of the first direction in the three-dimensional space coordinate system is represented by X. mn Mark, which represents the coordinate of the first direction of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, and the coordinate of the second direction in the three-dimensional space coordinate system is represented by Y mn The mark represents the coordinate of the second direction of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time. The coordinate of the third direction in the three-dimensional space coordinate system is F mn A mark indicates the size of the pressure exerted by the infant on the nth detection unit obtained for the mth time. The corresponding position and detection information of the detection unit on each detection unit at a detection time can now correspond to the position of a spatial point in the three-dimensional space coordinate system. At the same detection time, the corresponding positions and detection information of the detection units on multiple detection units can now correspond to the positions of multiple spatial points in the three-dimensional space coordinate system.

[0071] The positions of multiple spatial points in the three-dimensional space coordinate system at the same detection time are fitted with a surface. That is, the positions of multiple spatial points corresponding to the multiple three-dimensional feature column vectors formed when the pressure information of the infant acting on multiple detection units obtained for the mth time are fitted with a surface. The fitting constitutes a curved surface in the three-dimensional space coordinate system, which is the pressure space cloud map.

[0072] During actual monitoring, the following methods can be used to determine the baby's condition:

[0073] Preset sampling interval, obtain once every sampling interval N The detection information on the pressure sensors is obtained by corresponding the detection information with the position mark of each pressure sensor one by one to obtain N row vectors. The N row vectors correspond to the N spatial position coordinates one by one to generate a pressure space cloud map in a three-dimensional space coordinate system.

[0074] In order to prevent misjudgment, fitting error analysis can be performed simultaneously on multiple pressure space cloud maps generated within a fixed time period. When the error is greater than the preset threshold, it can be determined that the baby is awake; otherwise, it can be determined that the baby is asleep.

[0075] for example:

[0076] At the first monitoring moment t1 Get real-time N The detection information on the pressure sensors and the marks of each pressure sensor have N one-to-one correspondences, and the N mark points will generate corresponding N first row vectors, and the N first row vectors will generate a first pressure space cloud map in the three-dimensional space coordinate system.

[0077] At the second monitoring moment t2 Get real-time N The detection information on each pressure sensor is obtained by comparing it with the mark of each pressure sensor. N A one-to-one relationship, N The corresponding marking point will be generated N The second row vectors and N second row vectors generate a second pressure space cloud map in a three-dimensional space coordinate system.

[0078] At the third monitoring moment t3 Get real-time N The detection information on the pressure sensors and the marks of each pressure sensor have N one-to-one correspondences, and the N mark points have corresponding N third-row vectors. The N third-row vectors generate a third pressure space cloud map in the three-dimensional space coordinate system.

[0079] And so on, in the M Monitoring time tm Get real-time N The detection information on each pressure sensor has N one-to-one correspondences with the mark of each pressure sensor, and the N mark points have corresponding N The Mth row vector, N The Mth row vector generates the Mth pressure space cloud map in the three-dimensional space coordinate system.

[0080] To improve the accuracy of the measurement, at least three pressure space cloud maps are generated within a fixed time period. Taking the three pressure space cloud maps within a fixed time period as an example, the first pressure space cloud map within the fixed time period is used as the reference surface for error analysis. The second and third pressure space cloud maps following the first pressure space cloud map are fitted to the first pressure space cloud map to obtain two fitting errors. Since the infant will have dynamic movements when awake, which is reflected in the pressure information as a dynamic pressure change process, the two fitting errors will not be zero but will vary. When two adjacent fitting errors are both greater than a preset threshold, it can be determined that the infant has continuous dynamic movements, and therefore the infant is awake. In actual use, the fixed monitoring period can be preset to 2 minutes to 5 minutes. Within this fixed period, multiple monitoring event points are set to generate multiple pressure space cloud maps. The first pressure space cloud map within the fixed monitoring period is used as the reference, and the fitting errors of the subsequent pressure space cloud maps relative to the first pressure space cloud map are calculated. When the multiple fitting errors are all greater than the preset threshold, it can be determined that the infant has continuous dynamic movements, and therefore the infant is awake. Because the length of an infant's sleep is uncertain, and the intervals between them are uncertain, in order to ensure the accuracy of the detection, a number of fixed time periods can be preset, and each fixed time period can be separated by a fixed time interval, for example, the interval can be selected to be 3 to 5 minutes. This interval is repeated multiple times to monitor the infant's sleep and rest status over a 24-hour period, and a database of the infant's sleep and rest habits is constructed based on this monitoring data.

[0081] In actual use, the fitting error can be determined point by point, such as Figure 3 As shown in the figure, since we only need to determine whether the pressure acting on a specific point changes, the coordinates representing the position points of multiple spatial points in the three-dimensional space coordinate system can be kept unchanged, and the coordinates representing the pressure information can be calculated by difference calculation, so as to obtain an error amount. The coordinates of the position point and the error amount form a one-to-one correspondence, and the error amount space cloud map in the three-dimensional space coordinate system can be generated. The representation of the error amount space cloud map is consistent with the representation method of the previous pressure space cloud map, where the coordinate of the first direction in the three-dimensional space coordinate system is represented by X. n Mark, the coordinate of the second direction in the three-dimensional space coordinate system is Y n Mark, the coordinate of the third direction in the three-dimensional space coordinate system is ΔF n Mark, where ΔF n =F n1 -F n2 , F n1 is the pressure detection information on the first pressure space cloud map, F n2is the pressure detection information on the second pressure space cloud map, ΔF n It represents the error amount. The error amount can correspond to the position of a spatial point in the three-dimensional space coordinate system. The positions and error amounts corresponding to multiple detection units can correspond to the positions of multiple spatial points in the three-dimensional space coordinate system. The positions of multiple spatial points in the three-dimensional space coordinate system are fitted with a surface to form a curved surface in the three-dimensional space coordinate system. The curved surface is the error amount space cloud map.

[0082] Two envelope planes are used to form an envelope surface for the error space cloud map. The two envelope planes are parallel to each other and pass through the upper and lower limit position points of the error space cloud map respectively.

[0083] The median plane is determined by using the two envelope planes. The median plane is parallel to the two envelope planes and has an equal distance from the two envelope planes.

[0084] The first limit error surface and the second limit error surface are determined with the median surface as the reference surface. The distances between the first limit error surface and the second limit error surface and the median surface are respectively preset thresholds, and the first limit error surface and the second limit error surface are parallel and located on the upper and lower sides of the median surface, respectively.

[0085] Determine whether the two envelope planes are located inside or outside the space formed by the first limit error surface and the second limit error surface. When the two envelope planes are located outside the space formed by the first limit error surface and the second limit error surface, it can be determined that the infant has continuous dynamic movements, and therefore the infant is determined to be awake.

[0086] The method, system and device for determining work and rest habits based on cloud-based big data management provided by the present invention are equipped with multiple detection units to monitor the pressure information of the baby acting on each detection unit, and continuously monitor the pressure information of the baby acting on the multiple detection units in multiple continuous time periods to obtain the set of forces exerted by the baby on the bottom surface of the shell in different postures, and perform corresponding processing and analysis on the forces, so as to determine in which state the baby is awake and in which state the baby is asleep. Based on this, the work and rest habits of the baby can be known by performing monitoring at specific time intervals multiple times every day, and a database of the baby's work and rest habits can be constructed based on this, which can assist in the implementation of medical means, and solves the problem that medical staff need to frequently observe back and forth to determine the baby's state before applying monitoring or nursing items under the corresponding state, which leads to doubling of workload. The method is highly practical and worthy of promotion.

[0087] The above disclosure is only a preferred specific embodiment of the present invention. However, the embodiments of the present invention are not limited thereto. Any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present invention.

Claims

1. A method for determining work and rest habits based on cloud-based big data management, characterized in that: The following steps are involved: Obtaining pressure information exerted by the infant on a plurality of detection units, the plurality of detection units being distributed under the infant; The position information and pressure information of multiple detection units are mapped one by one to form a feature matrix, which is then converted into a three-dimensional spatial coordinate system to form a pressure space cloud map. The above steps are repeated multiple times within a preset fixed time period to obtain multiple pressure space cloud maps; One of the multiple pressure space cloud maps is selected as a benchmark, and the remaining multiple pressure space cloud maps are fitted with the benchmark at the same time. When the fitting error is greater than the preset threshold, the baby is judged to be awake, otherwise, it is judged to be asleep. Multiple preset fixed time periods are set in a day, and the above steps are repeated multiple times to judge the baby's state, based on which the baby's daily routine is determined.

2. The method for determining work and rest habits based on cloud big data management according to claim 1, characterized in that: The position information and pressure information of multiple detection units are mapped one by one using the following formula to form a feature matrix: Where n represents the nth detection unit, 1≤n≤N, N is the total number of detection units, m represents the mth acquisition of the pressure information of the infant on multiple detection units, m is a natural number, X mn Y represents the horizontal coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn F represents the vertical coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn A represents the pressure exerted by the infant on the nth detection unit obtained for the mth time, m It represents the feature matrix formed when the pressure information of the infant acting on multiple detection units is obtained for the mth time.

3. The method for determining work and rest habits based on cloud big data management according to claim 2, characterized in that: The characteristic matrix is converted into a three-dimensional space coordinate system to form a pressure space cloud map, which includes the following steps: Each column in the feature matrix is separated into a three-dimensional feature column vector; Use the following formula to flatten the three-dimensional feature column vector into a three-dimensional row vector, Where n represents the nth detection unit, 1≤n≤N, N is the total number of detection units, m represents the mth acquisition of the pressure information of the infant on the detection unit, m is a natural number, X mn Y represents the horizontal coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn F represents the vertical coordinate of the nth detection unit in the two-dimensional coordinate system when the pressure information of the baby on the nth detection unit is obtained for the mth time, mn A represents the pressure exerted by the infant on the nth detection unit obtained for the mth time, mn represents the three-dimensional feature column vector corresponding to the position and pressure formed when the pressure information of the infant on the nth detection unit is obtained for the mth time; The above three-dimensional row vector A mn T The three elements in the three-dimensional space coordinate system correspond one-to-one to the three axes, realizing the three-dimensional row vector A mn T Corresponding to the position of the spatial point in the three-dimensional space coordinate system; The positions of multiple spatial points corresponding to multiple three-dimensional feature column vectors formed when the pressure information of the infant exerted on multiple detection units obtained for the mth time are fitted with a surface to form an mth pressure space cloud map located in the three-dimensional space coordinate system.

4. The method for determining work and rest habits based on cloud big data management according to claim 1, characterized in that: At least three pressure space cloud maps are obtained within a preset fixed time period.

5. The method for determining work and rest habits based on cloud big data management according to claim 3, characterized in that: The method for determining the fitting error includes the following steps: The coordinates of the points representing the two pressure space cloud maps remain unchanged, and the coordinates representing the pressure information are calculated by difference operation, so as to obtain an error amount. The coordinates of the point and the error amount are in a one-to-one correspondence relationship, and an error amount space cloud map in a three-dimensional space coordinate system is generated; Using two envelope planes to form an envelope surface for the error space cloud map, the two envelope planes are parallel to each other and pass through the upper and lower limit position points of the error space cloud map respectively; Determining a median surface using the two envelope planes; Determine a first limit error surface and a second limit error surface with the median surface as a reference surface, wherein the distances between the first limit error surface and the second limit error surface and the median surface are preset thresholds, and the first limit error surface and the second limit error surface are parallel and located above and below the median surface, respectively; Determine whether the two envelope planes are located inside or outside the space formed by the first limit error surface and the second limit error surface. When the two envelope planes are located outside the space formed by the first limit error surface and the second limit error surface, determine that the baby is awake.

6. A system for determining infant sleep and rest habits based on cloud-based big data management, based on the method of claim 5, characterized in that: include: An acquisition unit (1) is used to acquire pressure information exerted by the baby on a plurality of detection units, the plurality of detection units being distributed under the baby; The conversion unit (2) is used to form a characteristic matrix by matching the position information and pressure information of the plurality of detection units one by one, and convert the characteristic matrix into a three-dimensional space coordinate system to form a pressure space cloud map. The above steps are repeated multiple times within a preset fixed time period to obtain multiple pressure space cloud maps. A determination unit (3) is used to select one of the multiple pressure space cloud maps as a reference, and simultaneously perform fitting errors on the remaining multiple pressure space cloud maps and the reference. When the fitting error is greater than a preset threshold, it is determined that the baby is in an awake state; otherwise, it is determined that the baby is in a sleeping state. Set multiple preset fixed time periods in a day, repeat the above steps multiple times to judge the baby's condition, and determine the baby's daily routine based on this.

7. Insulation box, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for determining work and rest habits based on cloud big data management as claimed in claim 1.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the method for determining work and rest habits based on cloud big data management as claimed in claim 1.

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