Method, device, medium and equipment for analyzing null values of human body pressure data

Abstract

The present application provides a method, device, medium and equipment for analyzing null values in human body pressure data, which obtains initial human body pressure data; wherein the initial human body pressure data includes multiple data columns; identifies and marks null value data columns in zero data columns; wherein the null value data columns indicate that the sensor at the corresponding position has not collected human body pressure data; inputs non-zero data columns, unmarked zero data columns and null value data columns into a neural network model to obtain analysis values of the null value data columns; based on the analysis values of the non-zero data columns, unmarked zero data columns and null value data columns, obtains target human body pressure data; the present application automatically identifies and marks null value data columns in human body pressure data, and uses a neural network model to automatically calculate the analysis values of the null value data columns to supplement the missing values of the null value data columns, thereby improving the accuracy and reliability of human body pressure testing.

Description

Technical Field

[0001] The present application relates to the technical field of pressure data experiments on a human body acting on a seat, and in particular to a method, device, medium and equipment for analyzing null values of human body pressure data. Background Art

[0002] Physiological data analysis is a crucial basis for testing the comprehensive performance of automotive seats. Pressure sensors are widely used to monitor pressure distribution in both sitting and reclining positions. These sensors capture the natural shape and pressure distribution of the human body and its contact surfaces, providing crucial information about comfort, posture, and health. However, due to pressure sensor material or environmental factors, some sensors may experience missing data (i.e., all zeros) in a column during use, resulting in apparent null columns in the pressure matrix. These all-zero columns can appear randomly, interfering with subsequent data analysis and leading to erroneous health assessments or posture recognition.

[0003] Existing fault detection methods often rely on empirical rules and lack intelligent processing. These methods struggle to distinguish between all-zero columns caused by sensor failure and null columns caused by naturally stress-free areas (such as wrinkled clothing). Furthermore, the null value columns with missing data can only be estimated based on empirical evidence, making it difficult to guarantee test accuracy. Therefore, a method is needed to accurately and objectively supplement missing data. Summary of the Invention

[0004] In order to solve the above technical problems, the present application is proposed. The embodiments of the present application provide a method, device, medium and equipment for analyzing null values of human body pressure data.

[0005] According to one aspect of the present application, a method for analyzing null values in human body pressure data is provided, comprising: obtaining initial human body pressure data; wherein the initial human body pressure data comprises a plurality of data columns, and the initial human body pressure data comprises a non-zero data column and a zero data column, and the zero data column indicates that the human body pressure data collected by all corresponding collection points are zero; identifying and marking null value data columns in the zero data column; wherein the null value data column indicates that the sensor at the corresponding position has not collected human body pressure data; inputting the non-zero data column, the unmarked zero data column, and the null value data column into a neural network model to obtain analysis values of the null value data column; and obtaining target human body pressure data based on the analysis values of the non-zero data column, the unmarked zero data column, and the null value data column.

[0006] In one embodiment, the identifying and marking of the null value data column in the zero data column includes: for each of the data columns, calculating the data sum of all its elements; if the data sum of the current data column is equal to zero, calculating the preceding data sum of the preceding data column of the current data column and the following data sum of the following data column; if the preceding data sum and the following data sum are greater than zero, determining that the current data column is the null value data column and marking the current data column.

[0007] In one embodiment, the inputting the non-zero data column, the unlabeled zero data column and the null value data column into a neural network model to obtain the analysis value of the null value data column includes: inputting the non-zero data column, the unlabeled zero data column and the null value data column into a neural network model in the original arrangement order to obtain the analysis value of the null value data column.

[0008] In one embodiment, the inputting the non-zero data column, the unlabeled zero data column and the null value data column into the neural network model in the original arrangement order to obtain the analysis value of the null value data column comprises: j data values, and analyze to obtain the first j analysis values; among them, j is a positive integer that is less than or equal to the number of rows in the column with null values.

[0009] In one embodiment, the step of inputting the non-zero data column, the unlabeled zero data column, and the null value data column into a neural network model in the original arrangement order to obtain the analysis value of the null value data column further includes: correcting multiple analysis values of the null value data column based on the relative relationship between each data value in the non-zero data column and the unlabeled zero data column.

[0010] In one embodiment, the initial human body pressure data includes multiple frames of pressure data; wherein, the analysis method of the null value of the human body pressure data also includes: for the null value data column in the current frame pressure data, obtaining multiple frames of pressure data adjacent to the current frame; if there is a non-zero data column in the data column corresponding to the null value data column of the current frame pressure data in the multiple frames of pressure data adjacent to the current frame, then verifying the analysis value of the null value data column of the current frame pressure data based on the non-zero data column.

[0011] In one embodiment, the verification of the analysis value of the null-value data column of the current frame pressure data based on the non-zero data column includes: calculating the estimated value of the null-value data column of the current frame pressure data based on the pressure data where the non-zero data column is located and the current frame pressure data; calculating the variance between the estimated value and the analysis value of the null-value data column of the current frame pressure data; if the variance is less than a preset value, determining that the analysis value of the null-value data column of the current frame pressure data is verified to be qualified.

[0012] According to another aspect of the present application, a device for analyzing null values of human body pressure data is provided, comprising: a data acquisition module for acquiring initial human body pressure data; wherein the initial human body pressure data includes multiple data columns, and the initial human body pressure data includes a non-zero data column and a zero data column, and the zero data column indicates that the human body pressure data collected by all corresponding collection points are zero; a null value identification module for identifying and marking null value data columns in the zero data column; wherein the null value data column indicates that the sensor at the corresponding position has not collected human body pressure data; a null value analysis module for inputting the non-zero data column, the unmarked zero data column and the null value data column into a neural network model to obtain analysis values of the null value data column; and a result determination module for obtaining target human body pressure data based on the analysis values of the non-zero data column, the unmarked zero data column and the null value data column.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute any of the above methods.

[0014] According to another aspect of the present application, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor for executing any of the above methods.

[0015] The present application provides a method, device, medium and equipment for analyzing null values of human body pressure data, which obtains initial human body pressure data; wherein the initial human body pressure data includes multiple data columns, and the initial human body pressure data includes non-zero data columns and zero data columns, and the zero data column indicates that the human body pressure data collected by all corresponding collection points are zero; identifies and marks the null value data columns in the zero data column; wherein the null value data column indicates that the sensor at the corresponding position has not collected human body pressure data; inputs the non-zero data column, the unmarked zero data column and the null value data column into a neural network model to obtain the analysis value of the null value data column; based on the analysis values of the non-zero data column, the unmarked zero data column and the null value data column, obtains the target human body pressure data; the present application automatically identifies and marks the null value data column in the human body pressure data, and uses the neural network model to automatically calculate the analysis value of the null value data column to supplement the missing value of the null value data column, thereby improving the accuracy and reliability of human body pressure testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 It is a flowchart of a method for analyzing null values of human body pressure data provided by an exemplary embodiment of the present application.

[0018] Figure 2 It is a logic diagram of analyzing the value calculation of a null value data column provided by an exemplary embodiment of the present application.

[0019] Figure 3 It is a structural diagram of a device for analyzing null values of human body pressure data provided by an exemplary embodiment of the present application.

[0020] Figure 4 It is a structural diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0021] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0022] Figure 1 FIG. 1 is a flow chart of a method for analyzing null values of human body pressure data provided by an exemplary embodiment of the present application. Figure 1As shown, the method for analyzing null values of human body pressure data includes the following steps:

[0023] Step 110: Acquire initial human body pressure data.

[0024] The initial human body pressure data includes multiple data columns, and the initial human body pressure data includes non-zero data columns and zero data columns. The zero data columns indicate that the human body pressure data collected at all corresponding collection points are zero. The present application arranges multiple pressure sensors on the seat to collect the pressure distribution of the human body in a sitting or lying position in real time. The pressure sensors are distributed in a matrix on the seat, and the collected pressure data is also matrix data, that is, it contains multiple rows and columns of data values. The pressure data corresponding to the points where the pressure values are collected is greater than zero, and the pressure data corresponding to the points where the pressure values are not collected is zero.

[0025] Step 120: Identify and mark the null value data columns in the zero data columns.

[0026] A null value data column indicates that the sensor at the corresponding position did not collect human pressure data. This application identifies null value data columns in the zero data column to distinguish data columns in the zero data column due to abnormal pressure sensor collection, that is, to obtain null value data columns with missing data, thereby excluding zero data columns with actual zero data, thereby improving the accuracy of data testing.

[0027] Step 130: Input the non-zero data column, the unlabeled zero data column and the null value data column into the neural network model to obtain the analysis value of the null value data column.

[0028] This application obtains a neural network model through training. Based on the correlation between each data column, non-zero data columns, unlabeled zero data columns and null value data columns are input into the neural network model, and the analysis value of the null value data column is obtained by self-analysis. The missing data of the null value data column is obtained by accurately analyzing the correlation between each data column, which can reduce the uncertainty of human estimation and improve the accuracy of data testing.

[0029] Step 140: Obtain target human body pressure data based on the analysis values of the non-zero data column, the unlabeled zero data column, and the null value data column.

[0030] After analyzing and obtaining the analysis value of the null value data column, this application combines the analysis values of the non-zero data column, the unmarked zero data column and the null value data column to obtain the target human body pressure data, thereby realizing the test of the pressure data of the human body on the seat.

[0031] The present application provides a method for analyzing null values in human body pressure data, which obtains initial human body pressure data; wherein the initial human body pressure data includes multiple data columns, and the initial human body pressure data includes non-zero data columns and zero data columns, and the zero data columns indicate that the human body pressure data collected by all corresponding collection points are zero; identifies and marks null value data columns in the zero data columns; wherein the null value data columns indicate that the sensor at the corresponding position has not collected human body pressure data; inputs the non-zero data columns, unmarked zero data columns and null value data columns into a neural network model to obtain analysis values of the null value data columns; based on the analysis values of the non-zero data columns, unmarked zero data columns and null value data columns, obtains target human body pressure data; the present application automatically identifies and marks null value data columns in human body pressure data, and uses a neural network model to automatically calculate the analysis values of the null value data columns to supplement the missing values of the null value data columns, thereby improving the accuracy and reliability of human body pressure testing.

[0032] In one embodiment, a specific implementation of the above step 120 may be: for each data column, calculating the data sum of all its elements; if the data sum of the current data column is equal to zero, calculating the preceding data sum of the preceding data column of the current data column and the following data sum of the following data column; if the preceding data sum and the following data sum are greater than zero, determining that the current data column is a null value data column and marking the current data column.

[0033] This application calculates the data sum of all elements in each data column and determines whether the data sum is equal to zero. If it is equal to zero, it indicates that the data column is a zero data column. If it is not equal to zero, it indicates that the data column is not a zero data column. It should be understood that this application can set a smaller threshold and record data sums less than the threshold as equal to zero to improve the generalization ability of data. After determining that a zero data column is obtained, the data sum of the preceding data column and the subsequent data column of the zero data column is calculated. If the preceding data sum of the preceding data column and the subsequent data sum of the subsequent data column are both greater than zero (i.e., non-zero data columns), it means that both sides of the zero data column (current data column) are non-zero data columns. Based on the continuity of human body pressure, the current data column is judged to be a null value data column. Furthermore, if there is a non-zero data column on one side of the current data column and a zero data column on the other side, the zero data column on the other side is further judged. If there is a non-zero data column on the side of the zero data column on the other side away from the current data column, it is determined that the current data column and the zero data column on the other side are both null value data columns. If there is no non-zero data column on the side of the zero data column on the other side away from the current data column, it is determined that the data of the current data column and the zero data column on the other side are both zero (that is, not a null value data column).

[0034] In one embodiment, the specific implementation of the above step 130 can be: inputting the non-zero data column, the unlabeled zero data column and the null value data column into the neural network model according to the original arrangement order to obtain the analysis value of the null value data column.

[0035] This application uses a large number of normal pressure value matrices (including multiple continuous pressure data columns, and all pressure data columns are not null value data columns) as training samples to train the neural network model to learn the pressure change patterns and characteristics between each pressure data column. After the training is completed, the non-zero data columns, unlabeled zero data columns and null value data columns are input into the neural network model in the original arrangement order to automatically analyze and interpolate the null value data columns to obtain the analysis value of the null value data columns. Specifically, Figure 2 As shown, this application converts the human body pressure data [x1,x2,x3,x4,x i ,x5,x6], where x1, x2, x3, x4, x5, and x6 are accurate data columns (including non-zero data columns and zero data columns with non-null values), and x i For null value data columns, the neural network model identifies the correlation between each accurate data column and uses interpolation method to estimate the analysis value x of the null value data column. fi Replace x i , the neural network model outputs the result [x f1 ,x f2 ,x f3 ,x f4 ,x fi ,x f5 ,x f6 ], where x f1 、x f2 、x f3 、x f4 、x f5 、x f6 Corresponding to x1, x2, x3, x4, x5, and x6 respectively.

[0036] In one embodiment, the specific implementation of step 130 may be: based on the non-zero data column and the unmarked zero data column j data values, analyze and get the first column of null value data j analysis values; among them, j A positive integer that is less than or equal to the number of rows in the column containing null data.

[0037] The present application can split a data column into multiple independent data values, and use the corresponding data values (data values on the same row) to analyze and obtain the analysis value corresponding to the null value data column. Specifically, the present application can analyze the null value data column based on the first j The data value interpolation estimates the null value data columnj An analysis value is obtained by utilizing row-wise correlation to obtain each data value of the null value data column to improve the accuracy of data analysis.

[0038] In one embodiment, the specific implementation of step 130 may be: based on the relative relationship between each data value in the non-zero data column and the unmarked zero data column, correcting multiple analysis values of the null value data column.

[0039] After analyzing the data values on the same row to obtain the analysis value corresponding to the null-valued data column, the present application further corrects multiple analysis values of the null-valued data column based on the relative relationship between the data values in the non-zero data column and the unmarked zero data column to improve the accuracy of the analysis value of the null-valued data column. For example, after obtaining the analysis value, based on the relative size relationship between the data values in the non-zero data column, it is verified whether the analysis values of the null-valued data column meet the size relationship, and the analysis value is corrected based on the verification result to ensure its data accuracy.

[0040] In one embodiment, the initial human body pressure data includes multiple frames of pressure data; wherein, the above-mentioned analysis method of null values of human body pressure data may also include: for the null value data column in the current frame pressure data, obtaining multiple frames of pressure data adjacent to the current frame; if there is a non-zero data column in the data column corresponding to the null value data column of the current frame pressure data in the multiple frames of pressure data adjacent to the current frame, then verifying the analysis value of the null value data column of the current frame pressure data based on the non-zero data column.

[0041] The present application obtains multiple frames of pressure data and utilizes the correlation between adjacent multiple frames of pressure data to verify the empty value data column of the current frame data. Specifically, the empty value data column in the current frame data is verified using the data column in the adjacent frame data (only non-zero data columns are used) to improve the accuracy of the analysis value of the empty value data column in the current frame data.

[0042] In one embodiment, the specific implementation method of the above-mentioned verification of the analysis value of the empty value data column of the current frame pressure data can be: based on the pressure data where the non-zero data column is located and the current frame pressure data, the estimated value of the empty value data column of the current frame pressure data is calculated; the variance between the estimated value and the analysis value of the empty value data column of the current frame pressure data is calculated; if the variance is less than the preset value, it is determined that the analysis value of the empty value data column of the current frame pressure data is verified to be qualified.

[0043] This application calculates the estimated value of the null-value data column of the current frame pressure data through the pressure data of the frame where the non-zero data column corresponding to the null-value data column of the current frame in the adjacent frames is located and the pressure data of the current frame, and calculates the variance between the estimated value and the analyzed value of the null-value data column of the current frame pressure data (that is, the square root of the sum of the squares of the differences of all corresponding elements). If the variance is small, it means that the analyzed value of the null-value data column of the current frame pressure data has been verified to be qualified, that is, the analyzed value of the null-value data column in the current frame pressure data obtained by analysis is relatively accurate.

[0044] Figure 3 Schematic diagram of the structure of the analysis device of the null value of human body pressure data provided by an exemplary embodiment of the present application. Figure 3 As shown, the analysis device 30 for null values of human body pressure data includes: a data acquisition module 31, used to obtain initial human body pressure data; wherein the initial human body pressure data includes multiple data columns, and the initial human body pressure data includes non-zero data columns and zero data columns, and the zero data column indicates that the human body pressure data collected by all corresponding collection points are zero; a null value identification module 32, used to identify and mark null value data columns in the zero data column; wherein the null value data column indicates that the sensor at the corresponding position has not collected human body pressure data; a null value analysis module 33, used to input the non-zero data column, the unmarked zero data column and the null value data column into the neural network model to obtain the analysis value of the null value data column; a result determination module 34, used to obtain the target human body pressure data based on the analysis values of the non-zero data column, the unmarked zero data column and the null value data column.

[0045] The present application provides an analysis device for null values of human body pressure data, which obtains initial human body pressure data through a data acquisition module 31; wherein the initial human body pressure data includes multiple data columns, and the initial human body pressure data includes non-zero data columns and zero data columns, and the zero data column indicates that the human body pressure data collected by all corresponding collection points are zero; the null value identification module 32 identifies and marks the null value data column in the zero data column; wherein the null value data column indicates that the sensor at the corresponding position has not collected human body pressure data; the null value analysis module 33 inputs the non-zero data column, the unmarked zero data column and the null value data column into a neural network model to obtain the analysis value of the null value data column; the result determination module 34 obtains the target human body pressure data based on the analysis values of the non-zero data column, the unmarked zero data column and the null value data column; the present application automatically identifies and marks the null value data column in the human body pressure data, and uses the neural network model to automatically calculate the analysis value of the null value data column to supplement the missing value of the null value data column, thereby improving the accuracy and reliability of the human body pressure test.

[0046] In one embodiment, the above-mentioned null value identification module 32 can be further configured as: for each data column, calculating the data sum of all its elements; if the data sum of the current data column is equal to zero, then calculating the preceding data sum of the preceding data column of the current data column and the succeeding data sum of the succeeding data column; if the preceding data sum and the succeeding data sum are greater than zero, then determining that the current data column is a null value data column and marking the current data column.

[0047] In one embodiment, the null value analysis module 33 may be further configured to input the non-zero data column, the unlabeled zero data column and the null value data column into the neural network model in the original arrangement order to obtain the analysis value of the null value data column.

[0048] In one embodiment, the null value analysis module 33 can be further configured to: j data values, analyze and get the first column of null value data j analysis values; among them, j A positive integer that is less than or equal to the number of rows in the column containing null data.

[0049] In one embodiment, the null value analysis module 33 may be further configured to correct multiple analysis values of the null value data column based on the relative relationship between each data value in the non-zero data column and the unmarked zero data column.

[0050] In one embodiment, the initial human body pressure data includes multiple frames of pressure data; wherein, the above-mentioned human body pressure data null value analysis device 30 can be further configured to: for the null value data column in the current frame pressure data, obtain multiple frames of pressure data adjacent to the current frame; if there is a non-zero data column in the data column corresponding to the null value data column of the current frame pressure data in the multiple frames of pressure data adjacent to the current frame, then verify the analysis value of the null value data column of the current frame pressure data based on the non-zero data column.

[0051] In one embodiment, the above-mentioned analysis device 30 for null values of human body pressure data can be further configured as follows: based on the pressure data where the non-zero data column is located and the current frame pressure data, calculating the estimated value of the null value data column of the current frame pressure data; calculating the variance between the estimated value and the analyzed value of the null value data column of the current frame pressure data; if the variance is less than a preset value, determining that the analyzed value of the null value data column of the current frame pressure data is verified to be qualified.

[0052] Below, reference Figure 4 The electronic device according to the embodiment of the present application is described. The electronic device may be either or both of the first device and the second device, or a standalone device independent of them, and the standalone device may communicate with the first device and the second device to receive collected input signals from them.

[0053] Figure 4 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0054] like Figure 4 As shown, the electronic device 10 includes one or more processors 11 and a memory 12 .

[0055] The processor 11 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0056] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods of the various embodiments of the present application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0057] In one example, the electronic device 10 may further include an input device 13 and an output device 14 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0058] When the electronic device is a stand-alone device, the input device 13 may be a communication network connector, configured to receive collected input signals from the first device and the second device.

[0059] In addition, the input device 13 may also include, for example, a keyboard, a mouse, and the like.

[0060] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0061] Of course, to simplify, Figure 4 Only some of the components related to the present application in the electronic device 10 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device 10 may further include any other appropriate components according to specific application scenarios.

[0062] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0063] The computer program product may be written in any combination of one or more programming languages to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages such as Java, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0064] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0065] The computer-readable storage medium may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0066] The basic principles of this application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to being implemented using the above specific details.

[0067] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0068] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0069] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0070] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for analyzing null values of human body pressure data, characterized in that: include: Acquire initial human body pressure data; wherein the initial human body pressure data includes multiple data columns, and the initial human body pressure data includes non-zero data columns and zero data columns, and the zero data column indicates that the human body pressure data collected at all corresponding collection points are zero; Identifying and marking a null value data column in the zero data column; wherein the null value data column indicates that the sensor at the corresponding position has not collected human body pressure data; Inputting the non-zero data column, the unlabeled zero data column and the null value data column into a neural network model to obtain an analysis value of the null value data column; Obtaining target human body pressure data based on analysis values of the non-zero data column, the unmarked zero data column, and the null value data column; The identifying and marking of the null value data column in the zero data column comprises: For each of the data columns, calculate the data sum of all elements thereof; If the data sum of the current data column is equal to zero, then calculating the sum of the preceding data column of the current data column and the sum of the following data column of the following data column; If the sum of the preceding data and the sum of the succeeding data is greater than zero, determining that the current data column is the null value data column and marking the current data column; If one side of the current data column is a non-zero data column and the other side is a zero data column, the zero data column on the other side is further judged. If there is a non-zero data column on the side of the zero data column on the other side away from the current data column, it is determined that the current data column and the zero data column on the other side are both null value data columns; if there is no non-zero data column on the side of the zero data column on the other side away from the current data column, it is determined that the data of the current data column and the zero data column on the other side are both zero.

2. The method for analyzing null values of human body pressure data according to claim 1, characterized in that: Inputting the non-zero data column, the unlabeled zero data column, and the null value data column into a neural network model to obtain an analysis value of the null value data column includes: The non-zero data column, the unlabeled zero data column and the null value data column are input into the neural network model according to the original arrangement order to obtain the analysis value of the null value data column.

3. The method for analyzing null values of human body pressure data according to claim 2, characterized in that: The step of inputting the non-zero data column, the unlabeled zero data column, and the null value data column into the neural network model in the original arrangement order to obtain the analysis value of the null value data column includes: Based on the non-zero data column and the unmarked zero data column j data values, and analyze to obtain the first j analysis values; among them, j is a positive integer that is less than or equal to the number of rows in the column with null values.

4. The method for analyzing null values of human body pressure data according to claim 3, characterized in that: The step of inputting the non-zero data column, the unlabeled zero data column, and the null value data column into the neural network model in the original arrangement order to obtain the analysis value of the null value data column further comprises: Based on the relative relationship between the non-zero data column and the data values in the unmarked zero data column, the multiple analysis values of the null value data column are corrected.

5. The method for analyzing null values of human body pressure data according to claim 1, characterized in that: The initial human body pressure data includes multiple frames of pressure data; wherein the method for analyzing null values of the human body pressure data further includes: For a null value data column in the current frame pressure data, obtaining multiple frames of pressure data adjacent to the current frame; If there is a non-zero data column in the data columns corresponding to the null value data column of the current frame pressure data in the multiple frames of pressure data adjacent to the current frame, the analysis value of the null value data column of the current frame pressure data is verified based on the non-zero data column.

6. The method for analyzing null values of human body pressure data according to claim 5, characterized in that: The analyzing value of the null value data column of the current frame pressure data is verified based on the non-zero data column, including: Calculating an estimated value of a null-valued data column of the current frame pressure data based on the pressure data where the non-zero data column is located and the current frame pressure data; Calculating the variance between the estimated value and the analyzed value of the null value data column of the current frame pressure data; If the variance is smaller than a preset value, it is determined that the analysis value of the null value data column of the current frame pressure data is qualified.

7. A device for analyzing null values of human body pressure data, characterized in that: include: a data acquisition module, configured to acquire initial human body pressure data; wherein the initial human body pressure data includes a plurality of data columns, and the initial human body pressure data includes a non-zero data column and a zero data column, wherein the zero data column indicates that the human body pressure data collected at all corresponding collection points are zero; a null value identification module, configured to identify and mark a null value data column in the zero data column; wherein the null value data column indicates that the sensor at the corresponding position has not collected human body pressure data; a null value analysis module, configured to input the non-zero data column, the unlabeled zero data column, and the null value data column into a neural network model to obtain an analysis value of the null value data column; A result determination module, configured to obtain target human body pressure data based on analysis values of the non-zero data column, the unmarked zero data column, and the null value data column; The null value identification module is further configured to: For each of the data columns, calculate the data sum of all elements thereof; If the data sum of the current data column is equal to zero, then calculating the sum of the preceding data column of the current data column and the sum of the following data column of the following data column; If the sum of the preceding data and the sum of the succeeding data is greater than zero, determining that the current data column is the null value data column and marking the current data column; If one side of the current data column is a non-zero data column and the other side is a zero data column, the zero data column on the other side is further judged. If there is a non-zero data column on the side of the zero data column on the other side away from the current data column, it is determined that the current data column and the zero data column on the other side are both null value data columns; if there is no non-zero data column on the side of the zero data column on the other side away from the current data column, it is determined that the data of the current data column and the zero data column on the other side are both zero.

8. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the method according to any one of claims 1 to 6.

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