CN111643056A

CN111643056A - Data processing method and system

Info

Publication number: CN111643056A
Application number: CN202010531353.XA
Authority: CN
Inventors: 孔静静
Original assignee: Kangjian Information Technology Shenzhen Co Ltd
Current assignee: Kangjian Information Technology Shenzhen Co Ltd
Priority date: 2020-06-11
Filing date: 2020-06-11
Publication date: 2020-09-11
Anticipated expiration: 2040-06-11
Also published as: CN111643056B

Abstract

The invention provides a data processing method, which comprises the steps of obtaining user data, historical physiological data and historical physiological representation data of a target user; calculating target similarity between user data of a target user and historical physiological representation data; generating a target acquisition rule of each historical physiological data based on the target similarity; feeding back the target acquisition rule to the client terminal, and receiving the physiological data to be processed sent by the client terminal based on the target acquisition rule; analyzing the physiological data to be processed based on a preset mapping rule to generate target physiological representation data, and sending the target physiological representation data to a client terminal for displaying. The time frequency for acquiring the physiological data of different users is adjusted reasonably in a targeted manner, so that the acquired physiological data are processed, the acquired data are more reasonable, and a more reliable and effective prediction result can be obtained; and the calculation pressure of the computer equipment can be reduced under the condition of ensuring the reliability of the prediction result.

Description

Data processing method and system

Technical Field

The embodiment of the invention relates to the field of big data, in particular to a data processing method and system.

Background

With the rapid development of computer technology and communication technology, more and more fields are introduced with computer technology and communication technology, for example, the computer technology and communication technology are applied in a scheme of predicting a pulse condition based on physiological data.

In the existing scheme of predicting the pulse condition state by using a computer technology and a communication technology aiming at physiological data, a terminal often transmits the physiological data collected in a fixed frequency to a back end, and the back end processes the physiological data to obtain prediction conclusion data. However, the inventors found that, due to the large individual difference of the collection objects, the data collected by the fixed frequency and the fixed mode is not reasonable enough, and thus the prediction conclusion data of each user cannot be accurately obtained for different users.

Disclosure of Invention

In view of this, embodiments of the present invention provide a data processing method, a data processing system, a computer device, and a computer readable storage medium, which are used to solve the problem that data acquired through a fixed frequency and a fixed mode is not reasonable enough due to a large individual difference of an acquired object, and thus prediction conclusion data of each user cannot be accurately obtained for different users.

The embodiment of the invention solves the technical problems through the following technical scheme:

a method of data processing, comprising:

acquiring user data, historical physiological data and historical physiological representation data of a target user;

calculating target similarity between the user data of the target user and historical physiological representation data;

generating a target acquisition rule for each of the historical physiological data based on the target similarity;

feeding the target acquisition rule back to a client terminal, and receiving to-be-processed physiological data sent by the client terminal based on the target acquisition rule;

analyzing the physiological data to be processed based on a preset mapping rule to generate target physiological representation data, and sending the target physiological representation data to the client terminal for displaying.

Further, the calculating the target similarity between the user data of the target user and the historical physiological characterization data further comprises:

respectively extracting keywords of the user data and the historical physiological representation data to generate a first keyword set corresponding to the user data and a second keyword set corresponding to the historical physiological representation data;

calculating the similarity between each first keyword in the first keyword set and each second keyword in the second keyword set to generate a weight matrix;

generating the maximum weight matching sum of the user data and the historical physiological representation data based on the weight matrix;

and calculating the average value of the maximum weight matching sum to obtain the target similarity between the user data and the historical physiological representation data.

Further, the generating the target acquisition rule for each historical physiological data based on the target similarity comprises:

traversing the user data of the target user;

when a first state identifier is detected, acquiring a first acquisition rule set according to the first state identifier;

based on the target similarity, matching a corresponding target acquisition rule for each historical physiological data in the first set of acquisition rules.

Further, when the first state identifier is not detected, the generating the target acquisition rule of each historical physiological data based on the target similarity further comprises:

traversing historical physiological characterization data of the target user;

when a second state identifier is detected, acquiring a second acquisition rule set according to the second state identifier;

matching a respective target acquisition rule for each historical physiological data in the second set of acquisition rules based on the target similarity.

Further, when the first state identifier and the second state identifier are not detected, the generating the target acquisition rule of each historical physiological data based on the target similarity further includes:

and when the target similarity is larger than a preset threshold value, matching a default acquisition rule for each historical physiological data based on the target similarity, wherein the time frequency of acquiring the physiological data in the default acquisition rule is a default value.

Further, the feeding back the target collection rule to the client terminal further includes:

generating a modification instruction according to a target acquisition rule, wherein the modification instruction carries the target acquisition rule;

sending the modification instruction to the client terminal, wherein the modification instruction is used for instructing the client terminal to update the time frequency of acquiring the physiological data based on the target acquisition rule;

and receiving a modification feedback instruction sent by the client terminal, and recording the time frequency of acquiring each physiological data of the target user based on the modification feedback instruction.

Further, before analyzing the physiological data to be processed based on the preset mapping rule, the method includes:

monitoring the data volume of the acquired physiological data to be processed;

and when the data volume is larger than a preset calculation threshold value, generating a data processing instruction.

In order to achieve the above object, an embodiment of the present invention further provides a data processing system, including:

the first acquisition module is used for acquiring user data, historical physiological data and historical physiological representation data of a target user;

the calculation module is used for calculating the target similarity between the user data of the target user and the historical physiological representation data;

the generating module is used for generating a target acquisition rule of each historical physiological data based on the target similarity;

the second acquisition module is used for feeding the target acquisition rule back to the client terminal and receiving the physiological data to be processed sent by the client terminal based on the target acquisition rule;

and the analysis module is used for analyzing the physiological data to be processed based on a preset mapping rule, generating target physiological representation data, and sending the target physiological representation data to the client terminal for displaying.

In order to achieve the above object, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the data processing method as described above when executing the computer program.

In order to achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, the computer program being executable by at least one processor to cause the at least one processor to execute the steps of the data processing method as described above.

According to the data processing method, the data processing system, the computer equipment and the computer readable storage medium, the target similarity of the user data of the target user and the historical physiological representation data is calculated, and the corresponding target acquisition rule is matched according to the target similarity, so that the time frequency of acquiring the physiological data is adjusted by the client terminal according to the target acquisition rule, and the time frequency of acquiring the physiological data of different users is adjusted in a targeted and reasonable manner, so that the acquired physiological data is processed, the acquired data is more reasonable and accurate, the interaction between the terminal and the rear end of the computer is enhanced, and a more reliable and effective prediction result can be obtained; and the calculation pressure of the computer equipment can be reduced under the condition of ensuring the reliability of the prediction result.

The invention is described in detail below with reference to the drawings and specific examples, but the invention is not limited thereto.

Drawings

FIG. 1 is a schematic diagram of an environmental application of the present invention;

FIG. 2 is a flowchart illustrating steps of a data processing method according to a first embodiment of the present invention;

FIG. 3 is a flowchart illustrating steps of calculating a target similarity in a data processing method according to a first embodiment of the present invention;

FIG. 4 is a flowchart illustrating steps of matching target collection rules in a data processing method according to a first embodiment of the present invention;

FIG. 5 is a flowchart illustrating steps of matching target collection rules in a data processing method according to a first embodiment of the present invention;

FIG. 6 is a flowchart illustrating steps of generating modification instructions in a data processing method according to a first embodiment of the present invention;

FIG. 7 is a flowchart illustrating steps of monitoring data amount in a data processing method according to a first embodiment of the present invention;

FIG. 8 is a block diagram of a second embodiment of a data processing system;

fig. 9 is a schematic hardware structure diagram of a computer device according to a third embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Technical solutions between various embodiments may be combined with each other, but must be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.

It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.

FIG. 1 shows an environmental application diagram according to an embodiment of the invention. As shown in fig. 1, in an exemplary embodiment, the client terminal 1 may be a smart wearable device such as a smart watch. The client terminal 1 may be associated with one or more users, and data for a single user may be transmitted to the computer device 2 via one or more client terminals 1. The computer device 2 may be connected to the client terminal 1 via one or more networks 3. The computer device 2 may be used to execute any aspect of the computer described herein, for example to implement the data processing methods described herein.

The network 3 may include wireless links such as cellular links, satellite links, Wi-Fi links, etc.

Example one

Referring to fig. 2, a flowchart illustrating steps of a data processing method according to an embodiment of the invention is shown. It is to be understood that the flow charts in the embodiments of the present method are not intended to limit the order in which the steps are performed. The following description is given by taking a computer device as an execution subject, specifically as follows:

as shown in fig. 2, the data processing method may include steps S100 to S500, in which:

step S100, user data, historical physiological data and historical physiological representation data of a target user are obtained.

In an exemplary embodiment, the user data of the target user includes, but is not limited to, user name, age, gender, occupation, medical records (corresponding medical records of the target user can be obtained by connecting to an external medical platform), medical conditions, lifestyle habits (diet, work and rest), and the like. Historical physiological data includes, but is not limited to: data such as heart rate, blood pressure, blood oxygen, body temperature, body movement, sleep time of a target user are continuously acquired in real time through sensors of intelligent wearable devices such as an intelligent watch. Historical physiological characterization data includes, but is not limited to, pulse profile data for different organ regions (heart, liver, kidney, etc.), state prediction data for different organ regions (health, sub-health, early warning, etc.).

Specifically, the wearable equipment of intelligence has all-weather, the advantage of monitoring of high frequency. Heart rate sensors, blood pressure sensors, blood oxygen sensors, motion sensors, etc. may be integrated into the wearable device. In one embodiment of the present invention, the heart rate sensor utilizes light transmittance measurement (photoplethysmography) to measure the heart rate. The blood pressure sensor can detect the blood pressure related signals of the user, and in one embodiment of the invention, the blood pressure sensor is a photoelectric sensor, and the blood pressure value is obtained by utilizing the collected pulse waves and analyzing the pulse waves. The blood oxygen sensor can detect the blood oxygen related signals of a user, in one embodiment of the invention, the blood oxygen sensor is a reflection type photoelectric sensor, and the blood oxygen value is obtained by adopting an LED (light emitting diode) to emit light at one side and using a CMOS (complementary metal oxide semiconductor) receiving end (complementary metal oxide semiconductor receiving end) to receive reflected light at the other side and changing the reflected light. The motion state sensor may detect a motion related signal of the user, and in one embodiment of the present invention, the motion state sensor may be an acceleration sensor, such as a linear accelerometer (gravity sensor) G sensor.

In the embodiment of the invention, after the wearable device acquires the physiological parameters of the user, the acquired physiological parameters can be uploaded to the server in real time or at regular time through the communication module in the wearable device. The communication module can be a 2G module, a 4G module and the like.

And step S200, calculating the target similarity between the user data of the target user and the historical physiological representation data.

In an exemplary embodiment, the target similarity between data may be obtained by bipartite graph matching. Referring to fig. 3, step S200 may further include:

step S210, extracting keywords of the user data and the historical physiological characterization data respectively to generate a first keyword set corresponding to the user data and a second keyword set corresponding to the historical physiological characterization data. Step S211, calculating the similarity between each first keyword in the first keyword set and each second keyword in the second keyword set, and generating a weight matrix.

In an embodiment, the first keyword and the second keyword are regarded as vertices of the bipartite graph, and may also be regarded as nodes of the bipartite graph, and the similarity between the first keyword and the second keyword is regarded as a weight coefficient of a bipartite graph edge.

Illustratively, the similarity between a first keyword X1 in the first keyword set and second keywords Y1, Y2, Y3,. and Yn in the second keyword set is W11, W12, W13,. and W1 n; the similarity between the first keyword X2 in the first keyword set and the second keyword Y1, Y2, Y3,. and Yn in the second keyword set is W21, W22, W23,. and W2n, and so on, and the similarity between the first keyword X3, X4,. and Xm in the first keyword set and the second keyword Y1, Y2, Y3,. and Yn in the second keyword set can be calculated, and the obtained similarities are combined to form a weight matrix of the vertex edge.

And step S212, generating the maximum weight matching sum of the user data and the historical physiological representation data based on the weight matrix.

Step S213, calculating the mean of the maximum weight matching sums to obtain the target similarity between the user data and the historical physiological characterization data.

And step S300, generating a target acquisition rule of each historical physiological data based on the target similarity.

In an exemplary embodiment, referring to fig. 4, the generating the target acquisition rule of each historical physiological data based on the target similarity includes:

step S310, traversing the user data of the target user.

Step S311, when the first state identifier is detected, obtaining a first collection rule set according to the first state identifier.

Specifically, the first state identifier is a medical record identifier, and the first state identifier is used for indicating that the user has medical history or is in a diseased state.

Step S312, based on the target similarity, matching a corresponding target acquisition rule for each historical physiological data in the first acquisition rule set.

Specifically, the first collection rule set includes a plurality of first collection rules. The first acquisition rule in the first acquisition rule set aims at a target user with a medical history or in a diseased state, and the higher the target similarity is, the higher the time frequency of acquiring the physiological data in the first acquisition rule set is.

In an exemplary embodiment, when the first state identifier is not detected, the generating the target acquisition rule of each historical physiological data based on the target similarity, referring to fig. 5, may further include:

and step S320, traversing the historical physiological representation data of the target user.

Step S321, when the second state identifier is detected, obtaining a second collection rule set according to the second state identifier.

Specifically, the second state identifier is used for indicating that the physical state of the user is in a sub-health state or an early warning state.

Step S322, matching a corresponding target acquisition rule for each historical physiological data in the second acquisition rule set based on the target similarity.

Specifically, the second collection rule set includes a plurality of second collection rules. The second acquisition rule in the second acquisition rule set is directed at target users who are not sick, and the lower the target similarity is, the higher the time frequency of acquiring physiological data in the second acquisition rule set is.

In an exemplary embodiment, when the first state identifier and the second state identifier are not detected, the generating the target acquisition rule of each historical physiological data based on the target similarity further includes: and comparing the target similarity with a preset threshold, and matching each historical physiological data with a default acquisition rule based on the target similarity when the target similarity is greater than the preset threshold, wherein the time frequency of acquiring the physiological data in the default acquisition rule is a default value.

And S400, feeding the target acquisition rule back to a client terminal, and receiving the to-be-processed physiological data sent by the client terminal based on the target acquisition rule.

In an exemplary embodiment, the physiological data to be processed includes, but is not limited to, heart rate, blood pressure, blood oxygen, body temperature, body movement, sleep time, and the like.

In an exemplary embodiment, referring to fig. 6, step S400 may further include:

step S410, generating a modification instruction according to the target acquisition rule, wherein the modification instruction carries the target acquisition rule.

Step S411, sending the modification instruction to the client terminal, where the modification instruction is used to instruct the client terminal to update the time frequency for acquiring the physiological data based on the target acquisition rule.

Step S412, receiving a modification feedback instruction sent by the client terminal, and recording the time frequency of acquiring each physiological data of the target user based on the modification feedback instruction.

Step S500, analyzing the physiological data to be processed based on a preset mapping rule, generating target physiological representation data, and sending the target physiological representation data to the client terminal for displaying.

In an exemplary embodiment, referring to fig. 7, before analyzing the physiological data to be processed based on the preset mapping rule, the method includes:

step S501, monitoring the data volume of the acquired to-be-processed physiological data.

Step S502, when the data volume is larger than a preset calculation threshold value, a data processing instruction is generated.

In an exemplary embodiment, a preset mapping rule is obtained based on the data processing instruction, the physiological data to be processed is analyzed based on the preset mapping rule, target physiological characterization data is generated, and the target physiological characterization data is sent to the client terminal and displayed.

Specifically, the target physiological characteristic data includes, but is not limited to, pulse condition traditional Chinese medicine evaluation results (pulse condition data of different organ regions, such as heart pulse, liver pulse, kidney pulse, and the like, state prediction data of different organ regions, such as health, sub-health, early warning, and the like), and advice data (recipe recommendation data, and the like).

In an exemplary embodiment, taking pulse conditions such as heart pulse, liver pulse, kidney pulse and the like as examples, the acquired physiological data to be processed is synchronized to a background big data center of the computer device for processing. The pulse conditions such as the heart pulse, the liver pulse and the kidney pulse are stored with standard preset interval data corresponding to various physiological data in a preset database, wherein the standard preset interval data is obtained by training a pulse condition risk prediction model established by an application program through big data, a traditional Chinese medicine midnight-noon ebb-flow theory and professional doctor evaluation conclusion data.

Furthermore, the physiological data to be processed is processed through a pulse condition risk prediction model to obtain characteristic values of all the regions, the characteristic values of all the regions are compared with standard preset interval data corresponding to all the regions for analysis, and a pulse condition evaluation result is output.

In an exemplary embodiment, the computer device performs screening, cleaning and analysis on all acquired physiological data such as heart rate, blood pressure, blood oxygen, body temperature, body movement and the like at intervals, adjusts the acquisition frequency, efficiency, interval and time period of a sensor of the monitoring device by combining with an acceleration algorithm module, updates the pulse condition risk prediction model, reduces the generation of invalid data and improves the processing efficiency of valid data.

In an exemplary embodiment, the heart vessel Chinese medical evaluation results may include weak vessels, slow vessels, chordal vessels, tense vessels, and forceful vessels; the Chinese medicinal evaluation result of the liver pulse can comprise weak pulse, slow pulse, wiry pulse, tense pulse and forceful pulse; the kidney pulse evaluation results in traditional Chinese medicine can comprise weak pulse, slow pulse, wiry pulse, tense pulse and forceful pulse.

In an exemplary embodiment, taking the heart pulse as an example, the standard values of the standard preset intervals corresponding to the heart pulse are A, A1, a2 and A3, and the heart pulse characteristic value corresponding to the heart pulse data is a.

And when the detected heart pulse characteristic value a is less than or equal to A, outputting the heart pulse evaluation result as weak heart pulse and sub-health state. When the detected heart pulse characteristic value a is larger than A and is smaller than or equal to A1, the heart pulse evaluation result is output as a slow heart pulse and normal state. And when the detected heart pulse characteristic value a is larger than A1 and smaller than or equal to A2, outputting the heart pulse evaluation result as a heart pulse chord and sub-health state. And when the detected heart pulse characteristic value a is larger than A2 and is smaller than or equal to A3, outputting the heart pulse evaluation result as a state of tense heart pulse and sub-health. And when the detected heart pulse characteristic value a is larger than A3, outputting the heart pulse evaluation result as the heart pulse is true, and warning the state.

In an exemplary embodiment, taking the liver pulse as an example, the standard values of the standard preset intervals corresponding to the liver pulse are B, B1, B2 and B3, and the liver pulse characteristic value corresponding to the liver pulse is B.

And when the detected liver pulse characteristic value B is less than or equal to B3, outputting a liver pulse evaluation result as a liver pulse excess, and early warning the state. When the detected liver pulse characteristic value B is greater than B3 and less than or equal to B2, the liver pulse evaluation result is output as a liver pulse tension and sub-health state. When the detected liver pulse characteristic value B is greater than B2 and less than or equal to B1, the liver pulse evaluation result is output as a liver pulse chord and sub-health state. And when the detected liver pulse characteristic value B is greater than B1 and less than or equal to B, outputting a liver pulse evaluation result as a slow and normal liver pulse state. And when the detected liver pulse characteristic value B is greater than B, outputting the liver pulse evaluation result as weak liver pulse and sub-health state.

In an exemplary embodiment, taking the renal arteries as an example, the standard values of the standard preset intervals corresponding to the renal arteries are C, C1, C2 and C3, and the characteristic value of the renal arteries corresponding to the renal arteries is C.

And when the detected renal artery characteristic value C is less than or equal to C, outputting the renal artery evaluation result as that the renal artery is weak and in a sub-health state. When the detected renal pulse characteristic value C is larger than C and smaller than or equal to C1, the renal pulse evaluation result is output as a slow renal pulse and normal state. When the detected renal pulse characteristic value C is larger than C1 and smaller than or equal to C2, the renal pulse evaluation result is output as a renal pulse chord and sub-health state. When the detected renal artery characteristic value C is larger than C2 and smaller than or equal to C3, the renal artery evaluation result is output as the state of renal artery tightness and sub-health. And when the detected renal artery characteristic value C is larger than C3, outputting the renal artery evaluation result as the renal artery excess, and early warning the state.

The data processing method further comprises: monitoring the network state of the client terminal; and when the network state of the client terminal is monitored to be the disconnected state, generating prompt data, and sending the prompt data to the client terminal for displaying. The prompt data is used for indicating the user to check the network state of the client terminal so as to carry out corresponding adjustment.

If the computer device monitors that the client terminal is in an unconnected state (network disconnection and other conditions), data collected by the client terminal can be temporarily stored in a data storage module of the client terminal. Prompt data generated by the computer device is sent to the client terminal to actively inform the user that the connection has been disconnected. Specifically, the prompt data includes a vibration mode, a sound mode and the like for displaying. After the devices of the client terminal are connected, the data storage module of the client terminal transmits data to the computer device in a sectional mode according to the size of the temporary storage data volume so as to avoid influencing the transmission of subsequent data.

The data processing method further comprises: monitoring electric quantity data of a client terminal; when detecting that the electric quantity data of the client terminal is smaller than a preset electric quantity threshold value, generating an electric quantity early warning signal, and generating an automatic frequency modulation instruction based on the electric quantity early warning signal; adjusting the time frequency of the collected physiological data based on the automatic frequency modulation instruction; and sending the electric quantity early warning signal to the client terminal. The computer equipment can actively intervene in the time frequency of the acquired physiological data according to the current electric quantity condition of the watch, and adjust the time frequency of the acquired physiological data in real time.

According to the embodiment of the application, the target similarity of the user data of the target user and the historical physiological representation data is calculated, and the corresponding target acquisition rule is matched according to the target similarity, so that the time frequency of acquiring the physiological data is adjusted by the client terminal according to the target acquisition rule, the time frequency of acquiring the physiological data of different users is adjusted reasonably in a targeted manner, the acquired physiological data is processed, the acquired data is more reasonable, the interaction between the terminal and the rear end of a computer is enhanced, and a more reliable and effective prediction result can be obtained; and the calculation pressure of the computer equipment can be reduced under the condition of ensuring the reliability of the prediction result.

Example two

With continued reference to FIG. 8, a program module diagram of a data processing system in accordance with the present invention is shown. In the present embodiment, the data processing system 20 may include or be divided into one or more program modules, and the one or more program modules are stored in a storage medium and executed by one or more processors to implement the present invention and implement the above-described data processing method. Program modules in accordance with embodiments of the present invention may be referred to as a series of computer program instruction segments that perform particular functions, and may be more suitable than programs themselves for describing the execution of data processing system 20 on a storage medium. The following description will specifically describe the functions of the program modules of the present embodiment:

the first obtaining module 600 is configured to obtain user data, historical physiological data, and historical physiological characterization data of a target user.

A calculating module 610, configured to calculate a target similarity between the user data of the target user and the historical physiological characterization data.

Further, the calculation module 610 is further configured to: respectively extracting keywords of the user data and the historical physiological representation data to generate a first keyword set corresponding to the user data and a second keyword set corresponding to the historical physiological representation data; calculating the similarity between each first keyword in the first keyword set and each second keyword in the second keyword set to generate a weight matrix; generating the maximum weight matching sum of the user data and the historical physiological representation data based on the weight matrix; and calculating the average value of the maximum weight matching sum to obtain the target similarity between the user data and the historical physiological representation data.

A generating module 620, configured to generate a target acquisition rule for each of the historical physiological data based on the target similarity.

Further, the generating module 620 is further configured to: traversing the user data of the target user; when a first state identifier is detected, acquiring a first acquisition rule set according to the first state identifier; based on the target similarity, a corresponding target acquisition rule is matched for each historical physiological data in a first acquisition rule set.

In an exemplary embodiment, when the first state identifier is not detected, the generating module 620 is further configured to: traversing historical physiological characterization data of the target user; when a second state identifier is detected, acquiring a second acquisition rule set according to the second state identifier; matching a respective target acquisition rule for each historical physiological data in a second set of acquisition rules based on the target similarity.

In an exemplary embodiment, when the first state identifier and the second state identifier are not detected, the generating module 620 is further configured to: and when the target similarity is larger than a preset threshold value, matching a default acquisition rule for each historical physiological data based on the target similarity, wherein the time frequency of acquiring the physiological data in the default acquisition rule is a default value.

The second obtaining module 630 is configured to feed back the target collection rule to the client terminal, and receive to-be-processed physiological data sent by the client terminal based on the target collection rule.

Further, the second obtaining module 630 is further configured to: generating a modification instruction according to a target acquisition rule, wherein the modification instruction carries the target acquisition rule; sending the modification instruction to the client terminal, wherein the modification instruction is used for instructing the client terminal to update the time frequency of acquisition of the physiological data based on the target acquisition rule; and receiving a modification feedback instruction sent by the client terminal, and recording the time frequency of acquiring each physiological data of the target user based on the modification feedback instruction.

The analysis module 640 is configured to analyze the to-be-processed physiological data based on a preset mapping rule, generate target physiological characterization data, and send the target physiological characterization data to the client terminal for display.

EXAMPLE III

Fig. 9 is a schematic diagram of a hardware architecture of a computer device according to a third embodiment of the present invention. In the present embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a preset or stored instruction. The computer device 2 may be a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers), and the like. As shown in FIG. 9, the computer device 2 includes, but is not limited to, at least a memory 21, a processor 22, a network interface 23, and a data processing system 20, which may be communicatively coupled to each other via a system bus. Wherein:

in this embodiment, the memory 21 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the computer device 2. Of course, the memory 21 may also comprise both internal and external memory units of the computer device 2. In this embodiment, the memory 21 is generally used for storing an operating system installed in the computer device 2 and various types of application software, such as the program codes of the data processing system 20 of the above-mentioned embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.

Processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 22 is typically used to control the overall operation of the computer device 2. In this embodiment, the processor 22 is configured to execute the program codes stored in the memory 21 or process data, for example, execute the data processing system 20, so as to implement the data processing method of the above-described embodiment.

The network interface 23 may comprise a wireless network interface or a wired network interface, and the network interface 23 is generally used for establishing communication connection between the computer device 2 and other electronic apparatuses. For example, the network interface 23 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.

It is noted that fig. 9 only shows the computer device 2 with components 20-23, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.

In this embodiment, the data processing system 20 stored in the memory 21 can be further divided into one or more program modules, and the one or more program modules are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 22) to complete the present invention.

For example, fig. 8 shows a schematic diagram of program modules for implementing a second embodiment of the data processing system 20, in which the data processing system 20 may be divided into a first obtaining module 600, a calculating module 610, a generating module 620, a second obtaining module 630 and an analyzing module 640. Herein, the program modules referred to herein are a series of computer program instruction segments that can perform specific functions, and are more suitable than programs for describing the execution process of the data processing system 20 in the computer device 2. The specific functions of the program modules 600-640 have been described in detail in the second embodiment, and are not described herein again.

Example four

The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer readable storage medium of the present embodiment is used for storing the data processing system 20, and when being executed by a processor, the computer readable storage medium implements the data processing method of the above embodiment.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.

The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims

1. A data processing method, comprising:

2. The data processing method of claim 1, wherein the calculating a target similarity of the user data of the target user to historical physiological characterization data further comprises:

3. The data processing method of claim 2, wherein generating the target acquisition rule for each historical physiological data based on the target similarity comprises:

traversing the user data of the target user;

4. The data processing method of claim 3, wherein when the first state identifier is not detected, the generating the target acquisition rule for each historical physiological data based on the target similarity further comprises:

traversing historical physiological characterization data of the target user;

5. The data processing method of claim 4, wherein when the first state identifier and the second state identifier are not detected, the generating the target acquisition rule for each historical physiological data based on the target similarity further comprises:

6. The data processing method of claim 1, wherein the feeding back the target collection rule to the client terminal further comprises:

7. The data processing method according to claim 1, wherein before analyzing the physiological data to be processed based on the preset mapping rule, the method comprises:

monitoring the data volume of the acquired physiological data to be processed;

8. A data processing system, comprising:

9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the data processing method according to any of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which is executable by at least one processor for causing the at least one processor to carry out the steps of the data processing method according to any one of claims 1 to 7.