CN112168188A

CN112168188A - Processing method and device for pressure detection data

Info

Publication number: CN112168188A
Application number: CN202011074735.0A
Authority: CN
Inventors: 欧博; 赵国朕
Original assignee: Beijing Zhongke Xinyan Technology Co ltd
Current assignee: Beijing Zhongke Xinyan Technology Co ltd
Priority date: 2020-10-09
Filing date: 2020-10-09
Publication date: 2021-01-05
Anticipated expiration: 2040-10-09
Also published as: CN112168188B

Abstract

The invention provides a processing method and a processing device for pressure detection data, which relate to the technical field of psychological pressure detection and are characterized in that a first preset time period is obtained; according to a first preset time period, first pressure data of a first user at a first moment and second pressure data of a second moment are obtained until Nth pressure data of the Nth moment are obtained; optimizing the second pressure data according to the first neural network model to obtain a second output result; repeating the steps until an Nth output result is obtained; obtaining a first lower pressure limit value and a first upper pressure limit value; obtaining a first pressure warning value; sequentially judging whether the output result exceeds the first pressure warning value or not; if the time exceeds the preset time, acquiring a first warning duration; judging whether the first warning duration meets a first preset condition or not; if the judgment result does not meet the requirement, the first warning information is sent to the first user, the stability and readability of the pressure value are improved, and the technical effect of accurately judging the psychological state of the user can be achieved.

Description

Processing method and device for pressure detection data

Technical Field

The invention relates to the technical field of psychological stress detection, in particular to a method and a device for processing stress detection data.

Background

In the context of intense competition and social evolution, people are increasingly facing the threat of various sources of stress. If the fruits are in a pressure environment for a long time, the fruits not only can affect the psychological health, but also can threaten the life of the serious people. Therefore, the pressure faced by people needs to be detected and evaluated, and then scientific and comprehensive health management is performed, so that the purposes of improving the quality of life and prolonging the service life are achieved.

However, the applicant of the present invention finds that the prior art has at least the following technical problems:

in the prior art, the pressure value can be derived through the value of a sensor such as a PPG or a GSR, but the value appears at 1 second or a very small time interval, the stability is very poor, the difference between two continuous values is very large, the real pressure level cannot be reflected, and the interpretation of the data by a user is very difficult.

Disclosure of Invention

The embodiment of the invention provides a processing method and a processing device for pressure detection data, which solve the technical problems that the stability of the detected pressure value is poor, the real pressure level of a user is difficult to reflect, the data reading by the user is not convenient, the psychological state of the user is accurately judged and processed, the stability and the readability of the pressure value are improved, the psychological state of the user can be accurately judged, and the processing can be timely carried out when problems occur.

In view of the above problems, the present application has been made to provide a processing method and apparatus for pressure detection data.

In a first aspect, the present invention provides a processing method for pressure detection data, wherein the method includes: step 1: obtaining a first preset time period; step 2: according to a first preset time period, first pressure data of a first user at a first moment and second pressure data of a second moment are obtained until Nth pressure data of the Nth moment are obtained, wherein the first pressure data, the second pressure data and the Nth pressure data are acquired in a PPG or GSR mode; and step 3: according to the first pressure data and the first neural network model, optimizing the second pressure data to obtain a second output result; and 4, step 4: repeating the step 3 until an Nth output result is obtained; and 5: obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result; step 6: obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value; and 7: sequentially judging whether the first output result and the second output result exceed the first pressure warning value or not until the Nth output result; and 8: if the time exceeds the preset time, acquiring a first warning duration; and step 9: judging whether the first warning duration meets a first preset condition or not according to a first preset time period; step 10: and if not, sending first warning information to the first user.

In a second aspect, the present invention provides a processing apparatus for pressure detection data, the apparatus comprising:

a first obtaining unit, configured to obtain a first preset time period;

the second obtaining unit is used for obtaining first pressure data of a first user at a first moment and second pressure data of a second moment according to a first preset time period until Nth pressure data of the Nth moment are obtained, wherein the first pressure data, the second pressure data and the Nth pressure data are acquired in a PPG or GSR mode;

the third obtaining unit is used for obtaining a second output result after optimizing the second pressure data according to the first pressure data and the first neural network model;

a fourth obtaining unit, configured to repeat step 3 above until an nth output result is obtained;

a fifth obtaining unit, configured to obtain a first lower pressure limit value and a first upper pressure limit value according to the first output result, the second output result, and up to an nth output result;

a sixth obtaining unit, configured to obtain a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value;

the first judgment unit is used for sequentially judging the first output result and the second output result until the Nth output result exceeds the first pressure warning value or not;

a seventh obtaining unit, configured to obtain the first warning duration if the first warning duration exceeds the first warning duration;

the second judging unit is used for judging whether the first warning duration meets a first preset condition or not according to a first preset time period;

and the first sending unit is used for sending first warning information to the first user if the first warning information does not meet the first requirement.

In a third aspect, the present invention provides a processing apparatus for pressure detection data, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of the preceding first aspects when executing the program.

One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:

the embodiment of the invention provides a processing method and a device for pressure detection data, wherein the searching method comprises the following steps: step 1: obtaining a first preset time period; step 2: according to a first preset time period, first pressure data of a first user at a first moment and second pressure data of a second moment are obtained until Nth pressure data of the Nth moment are obtained, wherein the first pressure data, the second pressure data and the Nth pressure data are acquired in a PPG or GSR mode; and step 3: according to the first pressure data and the first neural network model, optimizing the second pressure data to obtain a second output result; and 4, step 4: repeating the step 3 until an Nth output result is obtained; and 5: obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result; step 6: obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value; and 7: sequentially judging whether the first output result and the second output result exceed the first pressure warning value or not until the Nth output result; and 8: if the time exceeds the preset time, acquiring a first warning duration; and step 9: judging whether the first warning duration meets a first preset condition or not according to a first preset time period; step 10: if the pressure value does not meet the preset pressure value, first warning information is sent to the first user, so that the technical problems that the stability of the detected pressure value is poor, the real pressure level of the user is difficult to reflect, the data reading of the user is inconvenient, the psychological state of the user is accurately judged and processed are solved, the stability and readability of the pressure value are improved, the psychological state of the user can be accurately judged, and the technical effect that the processing can be timely carried out when the problem occurs is achieved.

The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.

Drawings

FIG. 1 is a flow chart illustrating a method for processing pressure detection data according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of a first training model for pressure detection data according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart illustrating a method for processing stress detection data to obtain an alert level of a first user according to an embodiment of the present invention;

fig. 4 is a schematic flow chart of a method for processing pressure detection data to obtain first image information of the first user according to an embodiment of the present invention;

fig. 5 is a schematic flow chart illustrating an effect of correcting the first warning information in the method for processing pressure detection data according to the embodiment of the present invention;

FIG. 6 is a schematic flow chart illustrating the effect of the processing method for pressure detection data to provide health guidance to a user in time according to an embodiment of the present invention;

FIG. 7 is a schematic flow chart illustrating a process for obtaining a first warning duration for pressure detection data according to an embodiment of the present invention;

FIG. 8 is a schematic structural diagram of a processing device for pressure detection data according to an embodiment of the present invention;

fig. 9 is a schematic structural diagram of another exemplary electronic device in an embodiment of the present invention.

Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a first judging unit 17, a seventh obtaining unit 18, a second judging unit 19, a first transmitting unit 20, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.

Detailed Description

The embodiment of the invention provides a processing method and a processing device for pressure detection data, which are used for solving the technical problems that the stability of a detected pressure value is poor, the real pressure level of a user is difficult to reflect, the data reading by the user is not convenient, the psychological state of the user is accurately judged and processed, the stability and the readability of the pressure value are improved, the psychological state of the user can be accurately judged, and the processing can be timely carried out when problems occur. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

Summary of the application

In the context of intense competition and social evolution, people are increasingly facing the threat of various sources of stress. If the fruits are in a pressure environment for a long time, the fruits not only can affect the psychological health, but also can threaten the life of the serious people. Therefore, the pressure faced by people needs to be detected and evaluated, and then scientific and comprehensive health management is performed, so that the purposes of improving the quality of life and prolonging the service life are achieved. In the prior art, the pressure value can be derived through the value of a sensor such as a PPG or a GSR, but the value appears at 1 second or a very small time interval, the stability is very poor, the difference between two continuous values is very large, the real pressure level cannot be reflected, and the interpretation of the data by a user is very difficult.

In order to solve the technical problems, the technical scheme provided by the invention has the following general idea:

the embodiment of the application provides a processing method for pressure detection data, wherein the method comprises the following steps: step 1: obtaining a first preset time period; step 2: according to a first preset time period, first pressure data of a first user at a first moment and second pressure data of a second moment are obtained until Nth pressure data of the Nth moment are obtained, wherein the first pressure data, the second pressure data and the Nth pressure data are acquired in a PPG or GSR mode; and step 3: according to the first pressure data and the first neural network model, optimizing the second pressure data to obtain a second output result; and 4, step 4: repeating the step 3 until an Nth output result is obtained; and 5: obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result; step 6: obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value; and 7: sequentially judging whether the first output result and the second output result exceed the first pressure warning value or not until the Nth output result; and 8: if the time exceeds the preset time, acquiring a first warning duration; and step 9: judging whether the first warning duration meets a first preset condition or not according to a first preset time period; step 10: and if not, sending first warning information to the first user.

The embodiment of the application provides a processing method for pressure detection data, which is applied to a central pressure data platform of intelligent electronic equipment, wherein the pressure data platform is associated with pressure data of mobile phone software of a user, such as pressure monitoring APP (application). The various pressure data obtained in the embodiment of the invention are obtained by automatically matching, correlating and processing the pressure data from the pressure database in the pressure monitoring APP through the computer communication technology. Furthermore, various pressure data can be efficiently and automatically matched, associated and processed through a computer technology, so that the technical problem to be solved by the invention is solved, and the technical effect of the invention is realized.

After the fundamental principle of the present application is introduced, the technical solutions of the present invention are described in detail with reference to the accompanying drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.

Example one

Fig. 1 is a flowchart illustrating a processing method for pressure detection data according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a processing method for pressure detection data, where the method includes:

step 1: a first preset time period is obtained.

Step 2: according to a first preset time period, first pressure data of a first user at a first moment and second pressure data of a second moment are obtained until Nth pressure data of the Nth moment are obtained, wherein the first pressure data, the second pressure data and the Nth pressure data are acquired in a PPG or GSR mode.

Specifically, the first preset time period is a time of a task of monitoring the pressure of the user, for example, the pressure change of the user in one day needs to be collected, the first preset time period is one day, and if the pressure change of the user in one week needs to be collected, the first preset time period is one week. Further, according to a first preset time period and a preset acquisition time interval, pressure data of the first user at each moment are acquired. In the present embodiment, the raw pressure data at each time is acquired by a sensor such as PPG (photo pulse graph) or GSR (galvanic skin response) which is a physiological signal of galvanic skin response. In other words, first pressure data of the first user at a first time and second pressure data of the second user at a second time are obtained until nth pressure data of the nth time are obtained, and a pressure value is derived after data are acquired by a sensor such as a PPG or a GSR.

And step 3: and according to the first pressure data and the first neural network model, optimizing the second pressure data to obtain a second output result.

Further, in order to obtain a second output result, as shown in fig. 2, step 3 in this embodiment of the present application further includes:

step 301: inputting the first pressure data and the second pressure data into a first training model, wherein the first training model is obtained by training a plurality of sets of training data, and each set of training data in the plurality of sets comprises: the first pressure data, the second pressure data, and identification information to identify a second output of the second pressure data;

step 302: obtaining output information of the first training model, wherein the output information includes a second output of the second pressure data.

Specifically, the first training model is a neural network model in a machine learning model, and the machine learning model can continuously learn through a large amount of pressure data, further continuously correct the model, and finally obtain satisfactory experience to process other pressure data. The machine model is obtained by training a plurality of groups of training pressure data, and the process of training the neural network model by the training pressure data is essentially a process of supervised learning. The first training model in the embodiment of the present application is obtained by performing machine learning training on a plurality of sets of training pressure data, where each set of training pressure data in the plurality of sets includes: the first pressure data, the second pressure data, and identification information for identifying a second output result of the second pressure data.

Wherein the identification information of the second output result of the second pressure data is taken as the supervised pressure data. And inputting the first pressure data and the second pressure data into each group of training pressure data, and performing supervised learning on the first pressure data and the second pressure data to determine that the output information of the first training model reaches a convergence state. Comparing second output result information of second pressure data with the output result of the first training model, and when the second output result information of the second pressure data is consistent with the output result of the first training model, finishing the supervised learning of the group of pressure data and carrying out the supervised learning of the next group of pressure data; when the first training model is inconsistent with the second training model, the first training model performs self-correction until the output result of the first training model is consistent with the second output result information of the identified second pressure data, the group of supervised learning is completed, and the next group of pressure data is supervised learning; and (4) through supervised learning of a large amount of pressure data, enabling the output result of the machine learning model to reach a convergence state, and finishing the supervised learning. Through the process of supervising and learning the first training model, the second output result information of the second pressure data output by the first training model is more accurate, the effect of accurately monitoring the pressure of the user in real time, optimizing the pressure data and improving the stability of the data is achieved.

Further, a second output result at the second time is output result information after optimization is performed according to the first pressure data and the second pressure data, and when the first time is a starting point of a first preset time period, the first output result is set as the first pressure data, that is, the first output result is the raw data of the first time acquired by the PPG/GSR sensor. The optimization method is to perform data stabilization by means of an exponential algorithm, for example, if the output value of the first second raw data (PPG/GSR) is n1, and the second raw data is n2, the result r2 output by the second is n1 × 80% + n2 × 20% after the calculation after optimization, and the data stabilization is performed by means of such an exponential method, so that the output data result is more stable and is convenient to read.

And 4, step 4: and (4) repeating the step (3) until an Nth output result is obtained.

And 5: and obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result.

Step 6: and obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value.

Specifically, after the pressure data at each moment is optimized, N pieces of output result information can be obtained, and then the first output result, the second output result, and the nth output result are compared to obtain the first lower pressure limit value and the first upper pressure limit value, that is, the highest point and the lowest point of the first user pressure value in the N output results can be further determined. Then, a pressure warning value may be calculated according to the first lower pressure limit value and the first upper pressure limit value, for example, a 75% high point may be used as a high risk line according to a highest point and a lowest point of the pressure value, and further, the first pressure warning value may also be set according to a specific situation, which is not limited in this embodiment. Therefore, the purpose that the pressure data can be read by a user conveniently and the detection result is more accurate is achieved.

Further, in order to ensure the safety of the pressure alarm data storage, a first verification code is generated according to a first pressure alarm value, wherein the first verification code and the first pressure alarm value are in one-to-one correspondence; and generating a second verification code … according to the second pressure warning value and the first verification code, and so on, using the first pressure warning value and the first verification code as a first storage unit, using the second pressure warning value and the second verification code as a second storage unit …, and so on, and obtaining M storage units. The verification code information is used as main body identification information, and the identification information of the main body is used for distinguishing from other main bodies. When the training data needs to be called, after each next node receives the data stored by the previous node, the data is verified through a common identification mechanism and then stored, and each storage unit is connected in series through a Hash technology, so that the training data is not easy to lose and damage, the safety and the accuracy of the pressure warning data are improved through a data information processing technology based on a block chain, the accuracy of calling the pressure warning value by a verification code is ensured, and the accuracy of obtaining the pressure warning value is ensured.

And 7: and sequentially judging whether the first output result and the second output result exceed the first pressure warning value or not until the Nth output result.

And 8: and if so, acquiring a first warning duration.

Further, in order to obtain the first warning duration, as shown in fig. 7, step 8 in this embodiment of the present application further includes:

step 801: recording all output results exceeding the first pressure warning value in the first output result, the second output result and the Nth output result;

step 802: and accumulating the moments corresponding to all the output results exceeding the first pressure alarm value to obtain a first alarm duration.

Specifically, after a task is finished, that is, after pressure values in a first preset time period are acquired, all pressure values (after optimization) in the task interval are calculated, the lowest point and the highest point of data are confirmed, then 75% of the points are found, and then according to a first pressure warning value, whether a first output result and a second output result exceed the first pressure warning value or not is sequentially judged until an nth output result exceeds the first pressure warning value, if an output result exceeding the first pressure warning value exists, time corresponding to all output results exceeding the first pressure warning value is accumulated, and a first warning duration is obtained, wherein the first warning duration is time when a pressure output result exceeds the warning value in the first preset time period by a user. And then, accumulating the time corresponding to the output result exceeding the first pressure alarm value to obtain a first alarm duration. For example, after obtaining all optimized pressure values, a pressure curve graph can be drawn according to the data, and then a line with a height of 75% is found in the curve graph as a high-risk line, the data above the high-risk line represents that the pressure value is high, and then the first warning duration can be obtained after accumulating the moments corresponding to all the pressure values above the high-risk line.

And step 9: judging whether the first warning duration meets a first preset condition or not according to a first preset time period;

step 10: and if not, sending first warning information to the first user.

Specifically, after the first warning duration is obtained, whether the first warning duration meets a first preset condition or not can be judged according to a first preset time period, wherein the first preset condition is a threshold range of a ratio of the preset first warning duration to the first preset time period. When the first warning duration does not meet the first preset condition, that is, the ratio of the first warning duration to the first preset time period exceeds the preset ratio range, which indicates that the pressure value of the first user exceeding the warning line exceeds a certain ratio, the first warning information needs to be further sent to the first user, so as to achieve the purpose of high-risk warning of the user and prevent the user from influencing physical and mental health due to long-term high pressure.

Further, in order to obtain the alert level of the first user, as shown in fig. 3, step 10 of the embodiment of the present application further includes:

step 101: obtaining first proportional relation information according to the first warning duration and the first preset time period;

step 102: obtaining first image information of the first user;

step 103: inputting the first proportional relationship information and the first image information into a second training model, wherein the second training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups of training data comprises: said first scale relationship information, said first portrait information and identification information for identifying a level of vigilance of a user;

step 104: obtaining output information of the second training model, wherein the output information comprises warning level information of the first user;

step 105: and obtaining first instruction information according to the alert level information, wherein the first instruction information is used for sending the first alert information to the first user after obtaining the first alert information from a preset alert information list.

Specifically, as mentioned above, the second training model is also a neural network model in a machine learning model, and the machine learning model can continuously learn through a large amount of pressure data, and further continuously modify the model, and finally obtain satisfactory experience to process other pressure data. The machine model is obtained by training a plurality of groups of training pressure data, and the process of training the neural network model by the training pressure data is essentially a process of supervised learning. The second training model in the embodiment of the present application is obtained by performing machine learning training on a plurality of sets of training pressure data, where each set of training pressure data in the plurality of sets includes: first scale relationship information, first portrait information, and identification information for identifying a level of vigilance of the user.

Wherein the identification information of the level of vigilance of the user is used as the supervision stress data. And inputting the first proportional relation information and the first image information into each group of training pressure data, and determining that the output information of the second training model reaches a convergence state. Comparing the warning grade information of the user with the output result of the second training model, and when the warning grade information of the user is consistent with the output result of the second training model, finishing the supervised learning of the group of pressure data and carrying out the supervised learning of the next group of pressure data; when the output result is inconsistent with the warning level information of the identified user, the second training model carries out self-correction until the output result is consistent with the warning level information of the identified user, the group of supervised learning is finished, and the next group of pressure data supervised learning is carried out; and (4) through supervised learning of a large amount of pressure data, enabling the output result of the machine learning model to reach a convergence state, and finishing the supervised learning. Through the process of supervising and learning the second training model, the warning grade information of the user output by the second training model is more accurate, the effects of accurately monitoring the pressure of the user in real time and timely discovering and processing abnormal values are achieved.

Further, the first proportional relationship information is a ratio between the first warning duration and a first preset time period, the first image information is personal tag information of the first user, after the warning level information of the first user is obtained, first instruction information can be generated according to the warning level information, then the first warning information corresponding to the warning level is obtained from a preset warning information list, and then the first warning information is sent to the first user. The preset warning information list is a list of a preset corresponding relationship between warning levels and warning information, that is, the warning information sent is different at different warning levels. For example, when the alert level is high, it indicates that the high-pressure data of the user accounts for a relatively large proportion, the user needs to be reminded in time, and corresponding measures can be taken after the user responds, and if the user does not respond within a preset time, the alert reminding needs to be continuously performed at a certain interval. Therefore, the effects of guaranteeing the personal health of the user and timely reminding when a problem is found are further achieved.

Further, in order to obtain the first image information of the first user, as shown in fig. 4, step 102 in this embodiment of the present application further includes:

step 1021: obtaining basic attribute information of the first user;

step 1022: obtaining personal form information of the first user;

step 1023: obtaining health condition information of the first user;

step 1024: and obtaining first image information of the first user according to the basic attribute information, the health condition information and the personal form information.

Specifically, the basic attribute information is basic information personally related to the first user, including but not limited to age, occupation, work, gender, and the like of the first user; the personal form information is the body form information of the first user, such as the height condition, the fat-thin condition and the like of the user; the health condition information is the personal physical health condition of the first user, such as health, sub-health, presence of a certain disease, etc. After the basic attribute information, the health condition information and the personal form information of the first user are acquired, the three types of information can be combined to form first image information, for example, the image information of the first user is as follows: a male computer programmer aged 30 years, healthy, and lean in size. After the basic attribute information, the health condition information and the personal form information of the first user are collected, the related information can be further analyzed and processed, so that the model can be more conveniently learned, the accuracy of the model for learning portrait information is further improved, the data processing speed is increased, and the effect of the accuracy of data is realized.

Further, in order to achieve the effect of correcting the first warning information, as shown in fig. 5, step 10 in this embodiment of the present application further includes:

step 106: obtaining historical pressure data of the first user in a second preset time period;

step 107: obtaining historical behavior data of the first user in the second preset time period;

step 108: obtaining a first influence coefficient of the first user according to the historical pressure data and the historical behavior data;

step 109: obtaining second instruction information according to the first influence coefficient, wherein the second instruction information is used for obtaining second warning information after the first warning information is adjusted;

step 110: and sending the second warning information to the first user.

Specifically, historical pressure data of the first user in a second preset time period is obtained, where the second preset time period may be selected according to actual needs, and this embodiment is not particularly limited. For example, the second preset time period may be one day, two days, one week, etc. Furthermore, historical behavior data of the first user in a second preset time period can be correspondingly collected, that is, whether the user participates in some more exciting activities or whether a change occurs in the home or not and whether the user individual suffers from unexpected conditions such as loss of interest and loss of love in an examination or not can be correspondingly collected in the second preset time period. Furthermore, according to the historical pressure data and the historical behavior data, a first influence coefficient for the pressure value of the user can be obtained, then, second instruction information is generated according to the first influence coefficient, the first warning information is adjusted according to the first influence coefficient and the second instruction information, the adjusted second warning information is obtained, and then, the second warning information is sent to the first user, so that the effects of accurately monitoring, adjusting and early warning the pressure of the user in real time and improving the accuracy of the psychological pressure data of the user are achieved.

Further, in order to realize real-time monitoring of the user pressure data, avoid causing harm and influence to the physical and mental health of the user, and provide an effect of health guidance for the user in time, as shown in fig. 6, step 5 in the embodiment of the present application further includes:

step 501: drawing a pressure curve graph of the first user according to the first output result, the second output result and the Nth output result;

step 502: obtaining a pressure standard curve graph;

step 503: obtaining pressure deviation information of the first user according to the pressure curve graph and the pressure standard curve graph;

step 504: judging whether the pressure deviation information of the first user meets a second preset condition or not;

step 505: if not, acquiring the first hospital information, wherein the first hospital information is a hospital within a preset distance from the first user;

step 506: obtaining first doctor information according to the first hospital information, wherein the first doctor information has a first degree of association with the first user;

step 507: and sending the first hospital information and the first doctor information to the first user or a first contact of the first user.

Specifically, after obtaining all optimized output results within a first preset time period, a pressure curve graph of the first user can be drawn according to all the output results, then a pressure standard curve graph corresponding to the pressure curve graph is obtained according to the portrait information of the first user, and then the pressure curve graph is compared with the pressure standard curve graph to obtain pressure deviation information of the first user, and whether the pressure deviation information meets a second preset condition is judged, that is, whether the pressure deviation exceeds a preset deviation range is judged, and if the pressure deviation information exceeds the preset deviation range, the pressure deviation information does not meet the second preset condition is judged. Further, it is indicated that the pressure value of the user is very high, and medical treatment needs to be performed so as to obtain the best diagnosis and treatment result. Therefore, it is further needed to obtain first hospital information, where the first hospital information is a hospital within a preset distance from the first user, and then the first doctor information can be obtained, and the first doctor information and the first user have a first degree of association, that is, there is a corresponding association between the psychological disorder treated by the first doctor and the first user, and finally, the first hospital information, the first doctor information, and the route information can all be sent to the first user, or a first contact of the first user, so as to further achieve real-time monitoring of user pressure data, avoid injury and influence on physical and mental health of the user, and provide a health guidance effect for the user in time.

Example two

Based on the same inventive concept as the processing method for pressure detection data in the foregoing embodiment, the present invention further provides a processing method apparatus for pressure detection data, as shown in fig. 8, the apparatus comprising:

a first obtaining unit 11, wherein the first obtaining unit 11 is configured to obtain a first preset time period;

a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain, according to a first preset time period, first pressure data of a first user at a first time and second pressure data of a second time until obtaining nth pressure data of the nth time, where the first pressure data, the second pressure data, and the nth pressure data are all acquired in a PPG or GSR manner;

a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain a second output result after optimizing the second pressure data according to the first pressure data and according to the first neural network model;

a fourth obtaining unit 14, wherein the fourth obtaining unit 14 is configured to repeat the step 3 until an nth output result is obtained;

a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to obtain a first lower pressure limit value and a first upper pressure limit value according to the first output result, the second output result, and up to an nth output result;

a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to obtain a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value;

a first judging unit 17, where the first judging unit 17 is configured to sequentially judge whether the first output result and the second output result exceed the first pressure warning value until the nth output result;

a seventh obtaining unit 18, where the seventh obtaining unit 18 is configured to obtain the first warning duration if the first warning duration exceeds the first warning duration;

a second judging unit 19, where the second judging unit 19 is configured to judge whether the first warning duration meets a first preset condition according to a first preset time period;

a first sending unit 20, where the first sending unit 20 is configured to send first warning information to the first user if the first warning information is not satisfied.

Further, obtaining a second output result after optimizing the second pressure data according to the first output result and the first neural network model includes:

a first training unit, configured to input the first pressure data and the second pressure data into a first training model, where the first training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the first pressure data, the second pressure data, and identification information to identify a second output of the second pressure data;

an eighth obtaining unit, configured to obtain output information of the first training model, where the output information includes a second output result of the second pressure data.

Further, the apparatus further comprises:

a ninth obtaining unit, configured to obtain first proportional relationship information according to the first warning duration and the first preset time period;

a tenth obtaining unit configured to obtain first image information of the first user;

a second training unit, configured to input the first proportional relationship information and the first pictorial information into a second training model, where the second training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: said first scale relationship information, said first portrait information and identification information for identifying a level of vigilance of a user;

an eleventh obtaining unit, configured to obtain output information of the second training model, where the output information includes warning level information of the first user;

a twelfth obtaining unit, configured to obtain first instruction information according to the alert level information, where the first instruction information is used to send first alert information to the first user after obtaining the first alert information from a preset alert information list.

Further, obtaining the first image information of the first user includes:

a thirteenth obtaining unit configured to obtain basic attribute information of the first user;

a fourteenth obtaining unit configured to obtain personal form information of the first user;

a fifteenth obtaining unit, configured to obtain health condition information of the first user;

a sixteenth obtaining unit, configured to obtain the first image information of the first user according to the basic attribute information, the health condition information, and the personal form information.

Further, the apparatus further comprises:

a seventeenth obtaining unit, configured to obtain historical pressure data of the first user in a second preset time period;

an eighteenth obtaining unit, configured to obtain historical behavior data of the first user in the second preset time period;

a nineteenth obtaining unit, configured to obtain a first influence coefficient of the first user according to the historical pressure data and the historical behavior data;

a twentieth obtaining unit, configured to obtain second instruction information according to the first influence coefficient, where the second instruction information is used to obtain second warning information after adjusting the first warning information;

a second sending unit, configured to send the second warning information to the first user.

Further, the apparatus further comprises:

the first execution unit is used for drawing a pressure curve graph of the first user according to the first output result, the second output result and the Nth output result;

a twenty-first obtaining unit for obtaining a pressure standard curve graph;

a twenty-second obtaining unit, configured to obtain pressure deviation information of the first user according to the pressure graph and the pressure standard graph;

a third judging unit, configured to judge whether the pressure deviation information of the first user meets a second preset condition;

a twenty-third obtaining unit, configured to obtain the first hospital information if the first hospital information is not satisfied, where the first hospital information is a hospital within a preset distance from the first user;

a twenty-fourth obtaining unit, configured to obtain first doctor information according to the first hospital information, where the first doctor information has a first degree of association with the first user;

and the third sending unit is used for sending the first hospital information and the first doctor information to the first user or the first contact of the first user.

Further, the obtaining the first warning duration includes:

the second execution unit is used for recording all output results exceeding the first pressure warning value in the first output result, the second output result and the Nth output result;

and the twenty-fifth obtaining unit is used for accumulating the moments corresponding to all the output results exceeding the first pressure warning value to obtain a first warning duration.

Various changes and specific examples of a processing method for pressure detection data in the first embodiment of fig. 1 are also applicable to a processing apparatus for pressure detection data in the present embodiment, and a method for implementing a processing apparatus for pressure detection data in the present embodiment is clearly known to those skilled in the art from the foregoing detailed description of a processing method for pressure detection data, so for the brevity of the description, detailed description is omitted here.

EXAMPLE III

Based on the same inventive concept as one of the processing methods for pressure detection data in the foregoing embodiments, the present invention further provides an exemplary electronic device, as shown in fig. 9, including a memory 304, a processor 302, and a computer program stored on the memory 304 and executable on the processor 302, wherein the processor 302 implements the steps of any one of the processing methods for pressure detection data described above when executing the program.

Where in fig. 9 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 may be used to store pressure data used by the processor 302 in performing operations.

the embodiment of the invention provides a processing method and a device for pressure detection data, wherein the method comprises the following steps: step 1: obtaining a first preset time period; step 2: according to a first preset time period, first pressure data of a first user at a first moment and second pressure data of a second moment are obtained until Nth pressure data of the Nth moment are obtained, wherein the first pressure data, the second pressure data and the Nth pressure data are acquired in a PPG or GSR mode; and step 3: according to the first pressure data and the first neural network model, optimizing the second pressure data to obtain a second output result; and 4, step 4: repeating the step 3 until an Nth output result is obtained; and 5: obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result; step 6: obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value; and 7: sequentially judging whether the first output result and the second output result exceed the first pressure warning value or not until the Nth output result; and 8: if the time exceeds the preset time, acquiring a first warning duration; and step 9: judging whether the first warning duration meets a first preset condition or not according to a first preset time period; step 10: if the pressure value does not meet the preset pressure value, sending first warning information to the first user, solving the technical problems that the stability of the detected pressure value is poor, the real pressure level of the user is difficult to embody, the data reading by the user is inconvenient, and the psychological state of the user is accurately judged and processed, achieving the technical effects of improving the stability and readability of the pressure value, accurately judging the psychological state of the user and timely processing when problems occur.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable pressure data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable pressure data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable pressure data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable pressure data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims

1. A processing method for pressure detection data, wherein the method comprises:

step 1: obtaining a first preset time period;

step 2: according to a first preset time period, first pressure data of a first user at a first moment and second pressure data of a second moment are obtained until Nth pressure data of the Nth moment are obtained, wherein the first pressure data, the second pressure data and the Nth pressure data are acquired in a PPG or GSR mode;

and step 3: according to the first pressure data and the first neural network model, optimizing the second pressure data to obtain a second output result;

and 4, step 4: repeating the step 3 until an Nth output result is obtained;

and 5: obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result;

step 6: obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value;

and 7: sequentially judging whether the first output result and the second output result exceed the first pressure warning value or not until the Nth output result;

and 8: if the time exceeds the preset time, acquiring a first warning duration;

step 10: and if not, sending first warning information to the first user.

2. The method of claim 1, wherein obtaining a second output after optimizing the second pressure data according to the first output and based on the first neural network model comprises:

inputting the first pressure data and the second pressure data into a first training model, wherein the first training model is obtained by training a plurality of sets of training data, and each set of training data in the plurality of sets comprises: the first pressure data, the second pressure data, and identification information to identify a second output of the second pressure data;

obtaining output information of the first training model, wherein the output information includes a second output of the second pressure data.

3. The method of claim 1, wherein if not, transmitting first alert information to the first user, the method further comprising:

obtaining first proportional relation information according to the first warning duration and the first preset time period;

obtaining first image information of the first user;

inputting the first proportional relationship information and the first image information into a second training model, wherein the second training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups of training data comprises: said first scale relationship information, said first portrait information and identification information for identifying a level of vigilance of a user;

obtaining output information of the second training model, wherein the output information comprises warning level information of the first user;

and obtaining first instruction information according to the alert level information, wherein the first instruction information is used for sending the first alert information to the first user after obtaining the first alert information from a preset alert information list.

4. The method of claim 3, wherein the obtaining first pictorial information of the first user comprises:

obtaining basic attribute information of the first user;

obtaining personal form information of the first user;

obtaining health condition information of the first user;

and obtaining first image information of the first user according to the basic attribute information, the health condition information and the personal form information.

5. The method of claim 3, wherein prior to said transmitting said first alert information to said first user, said method further comprises:

obtaining historical pressure data of the first user in a second preset time period;

obtaining historical behavior data of the first user in the second preset time period;

obtaining a first influence coefficient of the first user according to the historical pressure data and the historical behavior data;

obtaining second instruction information according to the first influence coefficient, wherein the second instruction information is used for obtaining second warning information after the first warning information is adjusted;

and sending the second warning information to the first user.

6. The method of claim 1, wherein the method further comprises:

drawing a pressure curve graph of the first user according to the first output result, the second output result and the Nth output result;

obtaining a pressure standard curve graph;

obtaining pressure deviation information of the first user according to the pressure curve graph and the pressure standard curve graph;

judging whether the pressure deviation information of the first user meets a second preset condition or not;

if not, acquiring the first hospital information, wherein the first hospital information is a hospital within a preset distance from the first user;

obtaining first doctor information according to the first hospital information, wherein the first doctor information has a first degree of association with the first user;

and sending the first hospital information and the first doctor information to the first user or a first contact of the first user.

7. The method of claim 1, wherein said obtaining a first alert duration comprises:

recording all output results exceeding the first pressure warning value in the first output result, the second output result and the Nth output result;

and accumulating the moments corresponding to all the output results exceeding the first pressure alarm value to obtain a first alarm duration.

8. A processing apparatus for pressure measurement data, the apparatus comprising:

a first obtaining unit, configured to obtain a first preset time period;

9. A processing device for pressure detection data, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-7 are implemented when the processor executes the program.