CN116295741A - Weight monitoring method and system based on air cushion - Google Patents
Weight monitoring method and system based on air cushion Download PDFInfo
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- CN116295741A CN116295741A CN202310157671.8A CN202310157671A CN116295741A CN 116295741 A CN116295741 A CN 116295741A CN 202310157671 A CN202310157671 A CN 202310157671A CN 116295741 A CN116295741 A CN 116295741A
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- A61G7/00—Beds specially adapted for nursing; Devices for lifting patients or disabled persons
- A61G7/05—Parts, details or accessories of beds
- A61G7/0527—Weighing devices
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/44—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing persons
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G23/00—Auxiliary devices for weighing apparatus
- G01G23/01—Testing or calibrating of weighing apparatus
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G5/00—Weighing apparatus wherein the balancing is effected by fluid action
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Abstract
The invention relates to the technical field of weight monitoring and identification, and particularly discloses a weight monitoring method and a weight monitoring system based on an air cushion, wherein the method comprises the steps of obtaining size information of the air cushion, building an air cushion model, and determining the installation position of a sensor based on the air cushion model; reading and counting the acquired data of each sensor to obtain a sensing data table containing time indexes; the sensing data table comprises a sensor number item, an installation position item and a data item; acquiring air pressure parameters of an air cushion in real time, and determining a weight curve according to the air pressure parameters; and determining the user behaviors and the weight of the user in each time period according to the weight curve and the sensing data table. When the patient is in a motion state, a sensor triggering sequence during the motion of the user is determined based on the sensor data table, the user behavior is determined according to the sensor triggering sequence, the weight data in a dynamic state is corrected according to the user behavior, the fluctuation condition can be eliminated, the weight of the user is obtained, and the robustness is very high.
Description
Technical Field
The invention relates to the technical field of weight monitoring and identification, in particular to a weight monitoring method and system based on an air cushion.
Background
During the rehabilitation of a user, there is often a need to monitor the weight of the user, which is an important parameter in response to the physical state of the user, in real time.
The existing monitoring mode is that an inflatable air cushion with an air pressure sensor is adopted, the change of the air pressure value in the air cushion is monitored through the air pressure sensor, the dynamic weight of a human body is converted, the mode is equivalent to real-time weighing, certain accuracy is achieved, but the detected data are all fluctuated in real time, and the processing difficulty is extremely high.
Specifically, the weight data on the air cushion mainly comprises two changes, one is that the user is in a state of sleeping at a deep level, and the change situation of the weight data is very stable and easy to identify; the other is that the user is in a waking state, and the user can perform some activities, so that the fluctuation degree of the data collected by the sensor is greatly improved, and the analysis difficulty is great; wherein, the abnormal change state of the total weight data on the air cushion comprises but is not limited to:
the total weight of the air cushion is increased and reduced due to the increase and the decrease of the articles on the air cushion;
abnormal movement of a person on the air cushion, monitoring of the adjustment of the air cushion itself and monitoring of the change of the pressure of the air cushion by the person on the air cushion caused by the movement of the air cushion.
How to ensure the processing of the sensing data with large fluctuation range and obtain more stable and accurate weight data is the technical problem to be solved by the technical scheme of the invention.
Disclosure of Invention
The invention aims to provide a weight monitoring method and a weight monitoring system based on an air cushion, which are used for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method of weight monitoring based on an air cushion, the method comprising:
acquiring size information of an air cushion, building an air cushion model, and determining the installation position of a sensor based on the air cushion model;
reading and counting the acquired data of each sensor to obtain a sensing data table containing time indexes; the sensing data table comprises a sensor number item, an installation position item and a data item;
acquiring air pressure parameters of an air cushion in real time, and determining a weight curve according to the air pressure parameters;
and determining the user behaviors and the weight of the user in each time period according to the weight curve and the sensing data table.
As a further scheme of the invention: the step of acquiring the size information of the air cushion, building an air cushion model and determining the mounting position of the sensor based on the air cushion model comprises the following steps:
acquiring size information of an air cushion, and building an air cushion model;
inputting the air cushion model into preset simulation software, and determining a load area according to granularity input by a user;
stress load is sequentially applied to the load area, and strain amounts of all positions in the air cushion model are obtained;
and determining the installation position of the sensor according to the strain quantity.
As a further scheme of the invention: the step of determining the user behavior and the weight of the user in each time period according to the weight curve and the sensing data table comprises the following steps:
reading a weight curve, calculating a first derivative and a second derivative of the weight curve, and segmenting the weight curve according to the first derivative and the second derivative;
calculating the derivative mean value of the first derivative of each segment, and marking the weight curve of each segment as a motion segment and a static segment according to the derivative mean value and the second derivative;
when a certain section of weight curve is in a static section, the weight curve is identified, and the weight of the user is obtained;
when a certain section of weight curve is in a dynamic section, the sensing data table is identified, and the user behavior and the user weight are determined.
As a further scheme of the invention: when a certain section of weight curve is in a static section, the weight curve is identified, and the step of obtaining the weight of the user comprises the following steps:
when a certain section of weight curve is in a static section, marking an abnormal region according to the second derivative;
inquiring environment information in the time span of an abnormal region, and correcting the abnormal region in a static section according to the environment information;
sampling the corrected static segment according to a preset time step length to obtain a discrete curve;
determining monotonicity of a discrete curve according to the first derivative, and determining the weight of a user taking a time span as an index according to the monotonicity;
when monotonicity exists, the discrete value at the final moment is selected as the weight of the user, and when monotonicity does not exist, the average value of the discrete values is calculated as the weight of the user.
As a further scheme of the invention: when a certain section of weight curve is in a dynamic section, the step of identifying the sensing data table and determining the user behavior and the user weight comprises the following steps:
when a certain section of weight curve is in a dynamic section, inquiring a sensing data table, and converting the sensing data table into a binary table;
determining a start acquisition chain and a close acquisition chain of each sensor according to the binary table; the start acquisition chain and the close acquisition chain are arrays of sensor numbers; the sequencing standard of the acquisition chain is time;
identifying the starting acquisition chain and the closing acquisition chain based on a preset acquisition chain library, and determining user behaviors; the acquisition chain library comprises acquisition chain items, behavior items and additional curve items; the additional curve is used for representing the influence degree of the user behavior on the weight curve;
and correcting the dynamic segment according to the user behavior, taking the corrected dynamic segment as a static segment, and determining the weight of the user.
As a further scheme of the invention: the step of identifying the starting acquisition chain and the closing acquisition chain based on a preset acquisition chain library and determining user behaviors comprises the following steps:
traversing a preset starting acquisition chain library based on the starting acquisition chain, and calculating a first spearman correlation coefficient;
traversing a preset closed acquisition chain library based on the closed acquisition chain, and calculating a second spearman correlation coefficient;
selecting a first prediction behavior according to the first spearman correlation coefficient, and selecting a second prediction behavior according to the second spearman correlation coefficient;
and determining user behaviors according to the first predicted behaviors and the second predicted behaviors.
The calculation formula of the spearman correlation coefficient is as follows:
wherein P is a Szelman correlation coefficient, C i Numbering the ith element in the acquisition chain library;
the number average value in the acquisition chain library is used; d (D) i Numbering for the i-th element in the acquisition chain is started or closed; />
The average value of numbers in the acquisition chain is started or closed; n is the minimum of the number of array elements of the two arrays.
The technical scheme of the invention also provides a body weight monitoring system based on the air cushion, which comprises:
the position determining module is used for acquiring the size information of the air cushion, constructing an air cushion model and determining the mounting position of the sensor based on the air cushion model;
the data table establishing module is used for reading and counting the acquired data of each sensor to obtain a sensing data table containing time indexes; the sensing data table comprises a sensor number item, an installation position item and a data item;
the weight curve acquisition module is used for acquiring air pressure parameters of the air cushion in real time and determining a weight curve according to the air pressure parameters;
and the behavior weight determining module is used for determining the user behavior and the weight of the user in each time period according to the weight curve and the sensing data sheet.
As a further scheme of the invention: the location determination module includes:
the modeling unit is used for acquiring the size information of the air cushion and building an air cushion model;
the load area determining unit is used for inputting the air cushion model into preset simulation software and determining a load area according to granularity input by a user;
the strain quantity acquisition unit is used for sequentially applying stress load at the load area and acquiring strain quantities at various positions in the air cushion model;
and the position selection unit is used for determining the installation position of the sensor according to the strain quantity.
As a further scheme of the invention: the behavioral weight determination module includes:
the curve segmentation unit is used for reading a weight curve, calculating a first derivative and a second derivative of the weight curve, and segmenting the weight curve according to the first derivative and the second derivative;
the curve marking unit is used for calculating the average value of the first derivative of each section and marking the weight curve of each section as a moving section and a static section according to the average value of the derivative and the second derivative;
the static identification unit is used for identifying the weight curve when a certain section of weight curve is in a static section to obtain the weight of the user;
and the dynamic identification unit is used for identifying the sensing data table when a certain section of weight curve is in a dynamic section and determining the user behavior and the user weight.
Compared with the prior art, the invention has the beneficial effects that: the invention adds a sensor on the air cushion based on the air cushion model, and numbers the sensor; the method comprises the steps of acquiring gravity data through an air pressure sensor, and simultaneously, counting the data acquired by the sensor to obtain a sensor data table; when the user is in a motion state, determining a sensor triggering sequence when the user moves based on the sensor data table, determining user behaviors according to the sensor triggering sequence, and correcting the weight data in a dynamic state according to the user behaviors to remove fluctuation conditions and obtain the weight of the user.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
FIG. 1 is a block flow diagram of a method of weight monitoring based on an air cushion.
Fig. 2 is a first sub-flowchart of the air cushion based weight monitoring method.
Fig. 3 is a second sub-flowchart of the air cushion based weight monitoring method.
Fig. 4 is a block diagram of the composition of the air cushion based weight monitoring system.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
Fig. 1 is a flow chart of a weight monitoring method based on an air cushion, and in an embodiment of the invention, the method includes:
step S100: acquiring size information of an air cushion, building an air cushion model, and determining the installation position of a sensor based on the air cushion model;
the hardware of the technical scheme of the invention must comprise an air cushion with preset size, a person to be detected (generally a patient) lies on the air cushion, the air cushion is pressurized, the change of the air pressure value in the air cushion is monitored through an air pressure sensor, the dynamic weight of the human body is converted (the human body urinates through a catheter, the weight is gradually reduced, and the weight is gradually increased after the human body infuses or eats); the method comprises the steps that an execution main body builds an air cushion model according to size information of an air cushion, and then the installation position of a sensor is determined in the air cushion model; the sensors can be pressure sensors or pressure sensors, and the types of the sensors at all the installation positions are the same.
Step S200: reading and counting the acquired data of each sensor to obtain a sensing data table containing time indexes; the sensing data table comprises a sensor number item, an installation position item and a data item;
reading acquisition data of a sensor, and establishing a sensing data table according to the acquisition data; the sensing data table is used for indicating which sensors acquire data at different time points; these data may reflect what the user is doing.
Step S300: acquiring air pressure parameters of an air cushion in real time, and determining a weight curve according to the air pressure parameters;
the mapping relation exists between the air pressure parameter of the air cushion and the weight, and the mapping relation is calibrated in advance by staff; the weight of the person to be detected changes in real time with time or with the movement of the person to be detected, and thus, the weight of the person to be detected is represented in the form of a curve.
Step S400: determining user behaviors and weights of the user behaviors in each time period according to the weight curve and the sensing data table;
the weight curve reflects the whole weight change of the user, the sensing data table reflects the pressure applied by the user at each position, the user behavior can be predicted by the weight curve and the sensing data, and the acquired weight can be processed by the user behavior, so that more accurate weight can be obtained.
Fig. 2 is a first sub-flowchart of an air cushion-based weight monitoring method, wherein the steps of obtaining size information of an air cushion, constructing an air cushion model, and determining an installation position of a sensor based on the air cushion model include:
step S101: acquiring size information of an air cushion, and building an air cushion model;
the size information of the air cushion is determined in the design process, and the process of building an air cushion model by the size information is not difficult.
Step S102: inputting the air cushion model into preset simulation software, and determining a load area according to granularity input by a user;
the simulation software may be finite element analysis software, such as ANSYS software; the granularity is the density of the loading area, and is generally expressed by a grid; the load area is a small area determined based on the grid nodes.
Step S103: stress load is sequentially applied to the load area, and strain amounts of all positions in the air cushion model are obtained;
and applying stress loads at the determined load areas, wherein the stress loads are of preset quantities, and the stress loads at each load area have an influence on the whole air cushion model for the air cushion, and the influence is reflected by the strain quantities.
Step S104: determining the mounting position of the sensor according to the strain quantity;
selecting a position with obvious strain quantity, and installing a sensor; the strain amount obviously refers to stress loads of a plurality of load areas, and the strain amount reaches a preset strain threshold value.
FIG. 3 is a second sub-flowchart of the air cushion based weight monitoring method, wherein the step of determining the user behavior and the weight thereof in each time period according to the weight curve and the sensing data table comprises the following steps:
step S401: reading a weight curve, calculating a first derivative and a second derivative of the weight curve, and segmenting the weight curve according to the first derivative and the second derivative;
the first derivative represents whether the weight is changed, and the second derivative represents whether the change is severe, for example, the first derivative is a certain value, but the second derivative is zero, which indicates that the current weight is changed, but the change is steady; the body weight curve can be segmented according to the first derivative and the second derivative, and the segmentation is based on whether the curve changes and whether the change is severe.
Step S402: calculating the derivative mean value of the first derivative of each segment, and marking the weight curve of each segment as a motion segment and a static segment according to the derivative mean value and the second derivative;
calculating a derivative mean value of the first derivative, wherein the derivative mean value reflects the speed of weight change in the current curve segment, and the static segment is marked when the derivative mean value is smaller and the dynamic segment is marked when the derivative mean value is larger; in this process, there is a special case that the weight rises by a value and then falls by a value, and the process is repeated, but the final derivative mean value is close to zero due to positive or negative, and the curve section is marked as a static section according to the logic, which is obviously wrong, because when the weight is changed drastically, it is indicated that the person to be detected continuously turns over the air cushion and is actually a moving section, so that in the judging logic, a judging process based on the second derivative needs to be added.
Step S403: when a certain section of weight curve is in a static section, the weight curve is identified, and the weight of the user is obtained;
when a certain section of weight curve is in a static section, the person to be detected may be in a sleeping state, and at this time, the weight curve is identified, so that the weight of the user can be obtained.
Step S404: when a certain section of weight curve is in a dynamic section, identifying a sensing data table, and determining user behaviors and user weight;
when a certain section of weight curve is in a dynamic section, the sensing data table is required to be identified, the user behavior is determined by the sensing data table, and then the dynamic section with severe fluctuation condition is analyzed through the user behavior to obtain the weight of the user.
As a preferred embodiment of the present invention, when a certain section of the weight curve is in a static section, the step of identifying the weight curve to obtain the weight of the user includes:
when a certain section of weight curve is in a static section, marking an abnormal region according to the second derivative;
inquiring environment information in the time span of an abnormal region, and correcting the abnormal region in a static section according to the environment information;
sampling the corrected static segment according to a preset time step length to obtain a discrete curve;
and determining monotonicity of the discrete curve according to the first derivative, and determining the weight of the user taking the time span as an index according to the monotonicity.
The above description describes the identification process of the static segment, and as the curve of the static segment is relatively gentle, the curve is digitized (discretized and converted into a jump edge curve), so that the weight at each moment can be obtained; in the process, abnormal fluctuation caused by external factors needs to be removed according to a second-order curve; in addition, regarding the weight of the user, the weight data required by the demander is mostly weight data for a period of time, and therefore, the time span is input by the staff, and then the weight of the user in the time span is calculated according to a preset rule; the rule is as follows: when monotonicity exists, the discrete value at the final moment is selected as the weight of the user, and when monotonicity does not exist, the average value of the discrete values is calculated as the weight of the user.
As a preferred embodiment of the present invention, when a certain section of weight curve is in a dynamic section, the step of identifying the sensing data table and determining the user behavior and the user weight includes:
when a certain section of weight curve is in a dynamic section, inquiring a sensing data table, and converting the sensing data table into a binary table;
the sensing data table reflects data acquired by each sensor at each moment; when the data reach the preset threshold value, the corresponding data are marked as 1, and when the data do not reach the preset threshold value, the sensing data table can be converted into a binary table based on the data.
Determining a start acquisition chain and a close acquisition chain of each sensor according to the binary table; the start acquisition chain and the close acquisition chain are arrays of sensor numbers; the sequencing standard of the acquisition chain is time;
starting means that the jump is changed from 0 to 1, and closing means that the jump is changed from 1 to zero; determining the starting sequence and the closing sequence of each sensor according to the binary table; the start-up sequence and the shut-down sequence are each represented by an array, the elements in the array being the numbers of the sensors.
Identifying the starting acquisition chain and the closing acquisition chain based on a preset acquisition chain library, and determining user behaviors; the acquisition chain library comprises acquisition chain items, behavior items and additional curve items; the additional curve is used for representing the influence degree of the user behavior on the weight curve;
the start acquisition chain and the close acquisition chain represent sensors to which actions of the person to be detected are triggered, the sequence of the triggering corresponds to the actions of the person to be detected, for example, the actions of the person to be detected when getting up are almost repeated, the sequence of the sensors triggered is similar, the triggering sequences of the sensors of various actions are counted in advance, and an acquisition chain library can be obtained; in practical application, the most similar user behavior can be queried by performing traversal matching in the acquisition chain library.
Correcting the dynamic segment according to the user behavior, taking the corrected dynamic segment as a static segment, and determining the weight of the user;
when the user behavior is recorded, the weight change degree (fluctuation curve) is synchronously inquired, and in practical application, the weight change degree (fluctuation curve) is removed from the dynamic section, so that the dynamic section can be converted into the static section, and the weight of the user can be determined by means of the static section analysis flow.
As a preferred embodiment of the present invention, the step of identifying the start acquisition chain and the close acquisition chain based on a preset acquisition chain library, and determining the user behavior includes:
traversing a preset starting acquisition chain library based on the starting acquisition chain, and calculating a first spearman correlation coefficient;
traversing a preset closed acquisition chain library based on the closed acquisition chain, and calculating a second spearman correlation coefficient;
selecting a first prediction behavior according to the first spearman correlation coefficient, and selecting a second prediction behavior according to the second spearman correlation coefficient;
and determining user behaviors according to the first predicted behaviors and the second predicted behaviors.
In one example of the technical scheme of the invention, the acquisition chain library comprises a start acquisition chain library and a close acquisition chain library, which are respectively used for analyzing the start acquisition chain and the close acquisition chain; the analysis mode is to calculate the correlation coefficient between two arrays, and because the elements in the two arrays are numbered, the calculation is performed by adopting the Szelman correlation coefficient, and the efficiency is higher.
On the basis, the starting acquisition chain and the closing acquisition chain can obtain a prediction behavior, which is respectively called a first prediction behavior and a second prediction behavior, wherein the first prediction behavior and the second prediction behavior are the same in large probability, but are different in few cases, at the moment, further judgment is needed, and specific judgment rules are preset by a designer according to the situation, so that the method is not limited.
The calculation formula of the spearman correlation coefficient is as follows:
wherein P is a Szelman correlation coefficient, C i Numbering the ith element in the acquisition chain library;
to collect in chain librariesCollecting the number average value in a chain; d (D) i Numbering for the i-th element in the acquisition chain is started or closed; />
The average value of numbers in the acquisition chain is started or closed; n is the minimum of the number of array elements of the two arrays.
In the above, it should be noted that the second equality number followed by the formula is an empirical formula for fast calculation of the correlation coefficient, in other words, the second equality number may be replaced with approximately equality number.
Example 2
Fig. 4 is a block diagram of the composition of an air cushion based weight monitoring system, in which the system 10 includes:
the position determining module 11 is used for acquiring the size information of the air cushion, constructing an air cushion model and determining the mounting position of the sensor based on the air cushion model;
the data table establishing module 12 is used for reading and counting the acquired data of each sensor to obtain a sensing data table containing time indexes; the sensing data table comprises a sensor number item, an installation position item and a data item;
the weight curve acquisition module 13 is used for acquiring air pressure parameters of the air cushion in real time and determining a weight curve according to the air pressure parameters;
the behavior weight determining module 14 is configured to determine the user behavior and the weight of the user in each time period according to the weight curve and the sensing data table.
The location determination module 11 comprises:
the modeling unit is used for acquiring the size information of the air cushion and building an air cushion model;
the load area determining unit is used for inputting the air cushion model into preset simulation software and determining a load area according to granularity input by a user;
the strain quantity acquisition unit is used for sequentially applying stress load at the load area and acquiring strain quantities at various positions in the air cushion model;
and the position selection unit is used for determining the installation position of the sensor according to the strain quantity.
The behavioral weight determination module 14 includes:
the curve segmentation unit is used for reading a weight curve, calculating a first derivative and a second derivative of the weight curve, and segmenting the weight curve according to the first derivative and the second derivative;
the curve marking unit is used for calculating the average value of the first derivative of each section and marking the weight curve of each section as a moving section and a static section according to the average value of the derivative and the second derivative;
the static identification unit is used for identifying the weight curve when a certain section of weight curve is in a static section to obtain the weight of the user;
and the dynamic identification unit is used for identifying the sensing data table when a certain section of weight curve is in a dynamic section and determining the user behavior and the user weight.
The functions that can be achieved by the air cushion based weight monitoring method are all completed by computer equipment, the computer equipment comprises one or more processors and one or more memories, at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to achieve the functions of the air cushion based weight monitoring method.
The processor takes out instructions from the memory one by one, analyzes the instructions, then completes corresponding operation according to the instruction requirement, generates a series of control commands, enables all parts of the computer to automatically, continuously and cooperatively act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
For example, a computer program may be split into one or more modules, one or more modules stored in memory and executed by a processor to perform the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device.
It will be appreciated by those skilled in the art that the foregoing description of the service device is merely an example and is not meant to be limiting, and may include more or fewer components than the foregoing description, or may combine certain components, or different components, such as may include input-output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device described above, and which connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used for storing computer programs and/or modules, and the processor may implement various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as an information acquisition template display function, a product information release function, etc.), and the like; the storage data area may store data created according to the use of the berth status display system (e.g., product information acquisition templates corresponding to different product types, product information required to be released by different product providers, etc.), and so on. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may implement all or part of the modules/units in the system of the above-described embodiments, or may be implemented by instructing the relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the functions of the respective system embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A method of weight monitoring based on an air cushion, the method comprising:
acquiring size information of an air cushion, building an air cushion model, and determining the installation position of a sensor based on the air cushion model;
reading and counting the acquired data of each sensor to obtain a sensing data table containing time indexes; the sensing data table comprises a sensor number item, an installation position item and a data item;
acquiring air pressure parameters of an air cushion in real time, and determining a weight curve according to the air pressure parameters;
and determining the user behaviors and the weight of the user in each time period according to the weight curve and the sensing data table.
2. The air cushion-based weight monitoring method according to claim 1, wherein the steps of acquiring size information of the air cushion, constructing an air cushion model, and determining the installation position of the sensor based on the air cushion model include:
acquiring size information of an air cushion, and building an air cushion model;
inputting the air cushion model into preset simulation software, and determining a load area according to granularity input by a user;
stress load is sequentially applied to the load area, and strain amounts of all positions in the air cushion model are obtained;
and determining the installation position of the sensor according to the strain quantity.
3. The cushion-based weight monitoring method according to claim 1, wherein the step of determining the user behavior and the weight thereof for each time period from the weight curve and the sensing data table comprises:
reading a weight curve, calculating a first derivative and a second derivative of the weight curve, and segmenting the weight curve according to the first derivative and the second derivative;
calculating the derivative mean value of the first derivative of each segment, and marking the weight curve of each segment as a motion segment and a static segment according to the derivative mean value and the second derivative;
when a certain section of weight curve is in a static section, the weight curve is identified, and the weight of the user is obtained;
when a certain section of weight curve is in a dynamic section, the sensing data table is identified, and the user behavior and the user weight are determined.
4. The method of claim 3, wherein the step of identifying the weight curve when the certain weight curve is in the static segment, and obtaining the weight of the user comprises:
when a certain section of weight curve is in a static section, marking an abnormal region according to the second derivative;
inquiring environment information in the time span of an abnormal region, and correcting the abnormal region in a static section according to the environment information;
sampling the corrected static segment according to a preset time step length to obtain a discrete curve;
determining monotonicity of a discrete curve according to the first derivative, and determining the weight of a user taking a time span as an index according to the monotonicity;
when monotonicity exists, the discrete value at the final moment is selected as the weight of the user, and when monotonicity does not exist, the average value of the discrete values is calculated as the weight of the user.
5. The method of claim 3, wherein the step of identifying the sensing data table when a certain segment of the weight curve is in a dynamic segment, and determining the user behavior and the user weight comprises:
when a certain section of weight curve is in a dynamic section, inquiring a sensing data table, and converting the sensing data table into a binary table;
determining a start acquisition chain and a close acquisition chain of each sensor according to the binary table; the start acquisition chain and the close acquisition chain are arrays of sensor numbers; the sequencing standard of the acquisition chain is time;
identifying the starting acquisition chain and the closing acquisition chain based on a preset acquisition chain library, and determining user behaviors; the acquisition chain library comprises acquisition chain items, behavior items and additional curve items; the additional curve is used for representing the influence degree of the user behavior on the weight curve;
and correcting the dynamic segment according to the user behavior, taking the corrected dynamic segment as a static segment, and determining the weight of the user.
6. The cushion-based weight monitoring method of claim 5, wherein the step of identifying the start acquisition chain and the shut down acquisition chain based on a preset acquisition chain library, determining user behavior comprises:
traversing a preset starting acquisition chain library based on the starting acquisition chain, and calculating a first spearman correlation coefficient;
traversing a preset closed acquisition chain library based on the closed acquisition chain, and calculating a second spearman correlation coefficient;
selecting a first prediction behavior according to the first spearman correlation coefficient, and selecting a second prediction behavior according to the second spearman correlation coefficient;
and determining user behaviors according to the first predicted behaviors and the second predicted behaviors.
7. The cushion-based weight monitoring method of claim 6, wherein the calculation formula of the spearman correlation coefficient is:
wherein P is a Szelman correlation coefficient, C i Numbering the ith element in the acquisition chain library;
the number average value in the acquisition chain library is used; d (D) i Numbering for the i-th element in the acquisition chain is started or closed; />
The average value of numbers in the acquisition chain is started or closed; n is the minimum of the number of array elements of the two arrays.
8. An air cushion based weight monitoring system, the system comprising:
the position determining module is used for acquiring the size information of the air cushion, constructing an air cushion model and determining the mounting position of the sensor based on the air cushion model;
the data table establishing module is used for reading and counting the acquired data of each sensor to obtain a sensing data table containing time indexes; the sensing data table comprises a sensor number item, an installation position item and a data item;
the weight curve acquisition module is used for acquiring air pressure parameters of the air cushion in real time and determining a weight curve according to the air pressure parameters;
and the behavior weight determining module is used for determining the user behavior and the weight of the user in each time period according to the weight curve and the sensing data sheet.
9. The cushion-based weight monitoring system of claim 8, wherein the position determination module comprises:
the modeling unit is used for acquiring the size information of the air cushion and building an air cushion model;
the load area determining unit is used for inputting the air cushion model into preset simulation software and determining a load area according to granularity input by a user;
the strain quantity acquisition unit is used for sequentially applying stress load at the load area and acquiring strain quantities at various positions in the air cushion model;
and the position selection unit is used for determining the installation position of the sensor according to the strain quantity.
10. The cushion-based weight monitoring system of claim 8, wherein the behavioral weight determination module comprises:
the curve segmentation unit is used for reading a weight curve, calculating a first derivative and a second derivative of the weight curve, and segmenting the weight curve according to the first derivative and the second derivative;
the curve marking unit is used for calculating the average value of the first derivative of each section and marking the weight curve of each section as a moving section and a static section according to the average value of the derivative and the second derivative;
the static identification unit is used for identifying the weight curve when a certain section of weight curve is in a static section to obtain the weight of the user;
and the dynamic identification unit is used for identifying the sensing data table when a certain section of weight curve is in a dynamic section and determining the user behavior and the user weight.
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