WO2021114631A1 - Data processing method, apparatus, electronic device, and readable storage medium - Google Patents

Abstract

Provided are a data processing method, apparatus, electronic device, and readable storage medium, said data processing method comprising: obtaining a historical record, extracting a plurality of historical case data from the historical record, and processing the plurality of historical case data to obtain a baseline value set (101); receiving current data corresponding to a current epidemic, and determining a current curve on the basis of the current data (102); updating a preset classification model on the basis of the baseline value set to obtain a data classification model, and entering the current curve into the data classification model to obtain the current level data of the epidemic (103). The described method has the advantage of good user experience, can be applied to smart medical care scenarios, and promotes the further development of smart cities.

Description

Data processing method, device, electronic equipment and readable storage medium

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on May 26, 2020, the application number is 2020104542921, and the invention title is "data processing methods, devices, electronic equipment, and readable storage media". The entire content of the application is approved The reference is incorporated in this application.

Technical field

This application relates to the field of electronic technology, in particular to a data processing method, device, electronic equipment, and readable storage medium.

Background technique

Influenza is an acute viral infection that spreads through the respiratory tract. It can spread widely and people are generally susceptible. Globally, influenza causes approximately 3 million to 5 million severe cases and approximately 290,000 to 650,000 deaths related to respiratory diseases each year. During the flu epidemic, a large number of employees cannot go to work, productivity decreases, and a large amount of direct or indirect medical expenses are generated, which greatly increases the social and economic burden.

The inventor realizes that, currently, the method for grading influenza intensity is mainly based on the judgment of the current influenza epidemic situation by relevant personnel, to determine the intensity level of the current influenza epidemic situation. The entire grading process mainly depends on the experience of relevant personnel for judgment, which is highly subjective. The accuracy rate is not high, and the user experience is low.

Summary of the invention

The embodiments of the present application provide a data processing method, device, electronic equipment, and readable storage medium. Baseline values are calculated from historical data, current curves are calculated based on current data, and grade data are calculated based on the baseline values and current curves, so as to achieve control of the epidemic Classification helps reduce the subjectivity of epidemic classification, improve the accuracy of epidemic classification, and improve user experience.

In the first aspect, a data processing method of this application includes:

Acquiring historical records, extracting multiple historical case data from the historical records, and processing the multiple historical case data to obtain a baseline value set;

Receive current data corresponding to the current epidemic situation, and determine the current curve based on the current data;

A data classification model is obtained by updating a preset classification model based on the baseline value set, and inputting the current curve into the data classification model to obtain the classification data of the current epidemic situation.

In the second aspect, an embodiment of the present application provides a data processing device, including:

An acquiring unit, configured to acquire historical records, extract multiple historical case data from the historical records, and process the multiple historical case data to obtain a baseline value set;

The receiving unit is configured to receive current data corresponding to the current epidemic situation, and determine the current curve based on the current data;

The calculation unit is configured to update a preset classification model based on the baseline value set to obtain a data classification model, and input the current curve into the data classification model to obtain the classification data of the current epidemic situation.

In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, a communication device, and one or more programs. The processor, the memory, and the communication device are connected to each other. The communication device is used to communicate with an external device. For information exchange, the one or more programs are stored in the memory and configured to be executed by the processor, and the above programs include instructions for executing steps in any method of the first aspect of the embodiments of the present application .

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute Part or all of the steps described in the method described in one aspect.

In a fifth aspect, the embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute as implemented in this application. Examples include part or all of the steps described in the method described in the first aspect. The computer program product may be a software installation package.

It can be seen that in this embodiment of the application, the electronic device obtains historical records, extracts multiple historical case data from the historical records, and processes the multiple historical case data to obtain a baseline value set; and receives information corresponding to the current epidemic For current data, a current curve is determined based on the current data; a preset grading model is updated based on the baseline value set to obtain a data grading model, and the current curve is input to the data grading model to obtain the grading data of the current epidemic situation. Calculate the baseline value set based on historical case data, calculate the current curve based on the current data, and calculate the grade data based on the baseline value set and the current curve to achieve the classification of the epidemic, which is conducive to reducing the subjectivity of the epidemic classification, improving the accuracy of the epidemic classification, and improving users Experience degree, this application can be applied in smart medical scenarios to promote the further development of smart cities.

Description of the drawings

FIG. 1 is a schematic structural diagram of a data processing method provided by an embodiment of the present application;

2 is a schematic flowchart of another data processing method provided by an embodiment of the present application;

FIG. 3 is a schematic flowchart of another data processing method provided by an embodiment of the present application;

FIG. 4 is a schematic flowchart of another data processing method provided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

Fig. 6 is a block diagram of functional units of a data processing device provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

The terms "first", "second", "third" and "fourth" in the specification and claims of this application and the drawings are used to distinguish different objects, not to describe a specific order . In addition, the terms "including" and "having" and any variations of them are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes unlisted steps or units, or optionally also includes Other steps or units inherent to these processes, methods, products or equipment.

Reference to "embodiments" herein means that specific features, results or characteristics described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art clearly and implicitly understand that the embodiments described herein can be combined with other embodiments.

Electronic devices can include various handheld devices with wireless communication functions, vehicle-mounted devices, wearable devices (such as smart watches, smart bracelets, pedometers, etc.), computing devices or other processing devices connected to wireless modems, and various Forms of user equipment (User Equipment, UE), mobile station (Mobile Station, MS), terminal equipment (terminal device), and so on. For ease of description, the devices mentioned above are collectively referred to as electronic devices.

Data archiving is the process of moving data that is no longer frequently used to a separate storage device for long-term preservation. The data archive is composed of old data, but it is necessary and important data for future reference, and its data must be stored in compliance with the rules. The data archive has indexing and search functions so that files can be easily found.

The following describes the embodiments of the application in detail.

Please refer to FIG. 1. FIG. 1 is a schematic flowchart of a data processing method provided by an embodiment of the present application, which is applied to an electronic device. The data processing method includes:

Step 101: Obtain historical records, extract multiple historical case data from the historical records, and process the multiple historical case data to obtain a baseline value set;

Wherein, the baseline value set may include: a first baseline value, a second baseline value, and a third baseline value.

In the embodiment of the present application, the percentile calculation is performed on the multiple case data to obtain the first baseline value, the second baseline value, and the third baseline value. The first baseline value, the second baseline value, and the third baseline value are obtained. The value is used to quantify historical data, and is also used to calculate a threshold based on the first baseline value, second baseline value, and third baseline value, that is, to classify the current epidemic situation.

Step 102: Receive current data corresponding to the current epidemic situation, and determine a current curve based on the current data;

Among them, the current data includes: multiple current epidemic time points and multiple cases corresponding to multiple current epidemic time points.

Step 103: Update a preset grading model based on the baseline value set to obtain a data grading model, and input the current curve into the data grading model to obtain grade data of the current epidemic situation.

In the specific implementation process, a classification request is generated according to the baseline value set, that is, according to the first baseline value, the second baseline value, the third baseline value, and the current curve, and the classification request is sent to the preset classification server, and the preset classification server It includes a grading model, and the grading request is used to request the grading server to update based on the baseline value set to obtain a data grading model, input the current curve into the data grading model, receive the grading response returned by the grading server, and obtain the grading response from the calculated response Grade data.

In a possible example, the processing of the multiple historical case data to obtain a baseline value set includes: extracting multiple sets of historical data from the multiple historical case data; calling a preset data processing model, and The multiple sets of historical data are input into the data processing model to obtain the baseline value set, where the baseline value set includes: a first baseline value P ₁ , a second baseline value P _2, and a third baseline value P ₃ .

Optionally, the data processing model may include: a baseline calculation formula, and the data processing model calculates the multiple sets of historical data according to the baseline calculation formula to obtain the baseline value set, wherein the baseline calculation The formula includes:

Among them, x is the preset baseline parameter, P _x is the baseline value, and P _x means that among the multiple historical case data, x% of the historical case data is less than P _x and there are (100-x)% of the historical case data Greater than P _x , L is the lower limit of the historical case data group where _{P x is located, i x} is the group distance, f _x is the frequency, f _L is the cumulative frequency of each historical case data group before the historical case data group where _{P x is located, get the preset Based on the m*m historical case data and the baseline calculation formula, the first baseline value P 1} corresponding to the first baseline parameter is calculated based on the first baseline parameter, the second baseline parameter, and the third baseline parameter. The second baseline value P ₂ corresponding to the second baseline parameter, and the third baseline value P ₃ corresponding to the third baseline parameter.

In the specific implementation process, assuming that the first baseline parameter is 50, the second baseline parameter is 75, and the third baseline parameter is 95, the first baseline parameter 50 is substituted into the baseline calculation formula to obtain the first baseline value

P ₁ =L+(n*50%-f _L /f ₅₀ )i ₅₀ ,

Substitute the second baseline parameter 75 into the baseline calculation formula to get the second baseline value

P ₂ =L+(n*75％-f _L /f ₇₅ )i ₇₅ ,

Substitute the third baseline parameter 95 into the baseline calculation formula to obtain the third baseline value

P ₃ =L+(n*95%-f _L /f ₉₅ )i ₉₅ .

In a possible example, the extracting multiple sets of historical data from the multiple historical case data includes: acquiring preset data extraction rules, and performing extraction on the multiple historical case data based on the data extraction rules Operation to obtain the multiple sets of historical data; wherein, the extraction operation includes: obtaining the current year, and determining m historical years based on the current year, where m is an integer greater than 0; from the multiple historical records Extract the m historical records of the m historical years; obtain the current date, and determine m time periods according to the current date; extract the history corresponding to the m time periods for each historical record in the m historical records data;

Upload the historical data to the blockchain.

Wherein, the multiple historical case data can be stored in a distributed network such as a blockchain.

Among them, the corresponding summary information is obtained based on the historical data. Specifically, the summary information is obtained by hashing the historical data, for example, obtained by using the sha256s algorithm. Uploading summary information to the blockchain can ensure its security and fairness and transparency to users. The user equipment can download the summary information from the blockchain to verify whether the historical data has been tampered with.

The blockchain referred to in this example is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

Optionally, determine m historical years based on the current year, that is, assume that the current year is x, determine m historical years as historical years xm years to historical years x-1 years, and obtain historical years xm years to historical years x-1 years Corresponding historical records; obtain the current date, determine m time periods based on the current date, obtain the preset time interval k, suppose the current date is a, and the current time period corresponding to the current date is a ~ b, where, The time interval between date a and date b is k, and the m time periods determined based on the current date a are respectively a-14～a-8, a-7～a-1, a～b, b+1～b+ 7. b+8～b+14, obtain the time period a-14～a-8, a-7～a-1, a～b, b+1 from the historical year xm year to the historical year x-1 year M*m historical data corresponding to ~b+7, b+8～b+14.

In the specific implementation process, assuming m=5, k=7, and the current year is 2020, then the historical records of 2015-2019 are obtained. The current date is 2020.3.3, and the time period corresponding to the current date is 2.17-2.23, 2.24 3.1, 3.2-3.8, 3.9-3.15, 3.16-3.22, the historical data of these five time periods are obtained from 2015-2019, that is, 25 historical data, and the historical data is marked based on the historical year, that is, 2015 The historical case data corresponding to the five time periods 2.17-2.23, 2.24-3.1, 3.2-3.8, 3.9-3.15, 3.16-3.22 are marked, and the marked content is 2015.

In a possible example, the determining the current curve based on the current data includes: obtaining a preset curve weight β, where 0<β<1; determining k time points and each curve from the current data The number of cases at a time point, where k is an integer greater than 0; for each of the k time points, the following operations are performed: obtain the t-th time point and the number of cases M corresponding to the t-th time point _t , where 0<t≤k, use the curve weight β and the number of cases M _t as the input of the preset curve value calculation formula to obtain the current curve value EWMA _{t corresponding to the t-th time point} , The curve value calculation formula includes EWMA _t =β*M _t +(1-β)*EWMA _t-1 ; k current curve values EWMA _{t are obtained , and the current curve values EWMA t are} generated based on the k current curve values EWMA t Curve EWMA.

Optionally, determine k time points (for example, any one of the k time points may be 2020.03.03), and obtain the number of k cases corresponding to the k time points in the current data, for each time point Execute the current curve value calculation cycle to get k current curve values, and connect k current curve values to get the current curve.

In the embodiments of the present application, the exponentially weighted moving average method (EWMA) is a commonly used method to predict the number of forecasts in one or several periods in the future using a set of recent actual data. It is commonly used to detect small deviations in the process, and the function is similar to the cumulative sum control chart (CUSUM), but the setup and operation are usually easier, and it is widely used in the field of time series modeling and forecasting.

In a possible example, the updating a preset classification model based on the baseline value set to obtain a data classification model includes: based on the first baseline value P ₁ , the second baseline value P ₂ , and the first baseline value P 1 The three baseline values P ₃ and the current curve EWMA determine the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ ; the grading model is acquired, and the first threshold curve Y 1 and the _{first threshold curve Y 1 are obtained.} The second threshold curve Y ₂ and the third threshold curve Y _{3 are} used as update parameters to update the data classification model to obtain the data classification model.

Wherein, the hierarchical model is a model stored in the electronic device, or may be a model stored in a hierarchical server, which is not limited here.

In a possible example, the first threshold curve Y 1, the _{first threshold curve Y 1 is} determined based on the first baseline value P ₁ , the second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA The second threshold curve Y ₂ and the third threshold curve Y ₃ include: obtaining a preset threshold curve calculation formula; obtaining a preset calculation weight ω, where 0≦ω<1; and changing the first threshold curve Y _{1. The} second threshold value curve Y ₂ and the third threshold value curve Y ₃ and the current curve EWMA are respectively used as the input of the threshold value curve calculation formula to obtain the first threshold value curve Y ₁ and the second threshold value curve Y 1. The threshold curve Y ₂ and the third threshold curve Y ₃ , wherein the threshold curve calculation formula includes:

Y _x =ω*P _x +(1-ω)EWMA.

_{Optionally, the first threshold curve Y 1 is} determined based on the first baseline value P ₁ and the current curve EWMA, and the second threshold curve Y _{2 is} determined based on the second baseline value P ₂ and the current epidemic curve EWMA, and based on the third baseline value P ₃ , The current curve EWMA determines the third threshold curve Y ₃ ; based on the first threshold curve Y ₁ , the second threshold curve Y ₂ , the third threshold curve Y ₃ and the current curve EWMA to generate a grade curve.

In a possible example, the first threshold curve Y 1, the _{first threshold curve Y 1 is} determined based on the first baseline value P ₁ , the second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA The second threshold curve Y ₂ and the third threshold curve Y ₃ include: obtaining a preset threshold curve calculation formula; obtaining a preset calculation weight ω, where 0≦ω<1; and changing the first threshold curve Y _{1. The} second threshold value curve Y ₂ and the third threshold value curve Y ₃ and the current curve EWMA are respectively used as the input of the threshold value curve calculation formula to obtain the first threshold value curve Y ₁ and the second threshold value curve Y 1. The threshold curve Y ₂ and the third threshold curve Y ₃ , wherein the threshold curve calculation formula includes:

Y _x =ω*P _x +(1-ω)EWMA.

In the specific implementation process, the first baseline value and the current curve are used as the input of the threshold curve calculation formula to obtain the first threshold curve

Y ₁ =ω*P ₅₀ +(1-ω)EWMA _t ,

Use the second baseline value and the current curve as the input of the threshold curve calculation formula to obtain the second threshold curve

Y ₂ =ω*P ₇₅ +(1-ω)EWMA _t ,

Use the third baseline value and the current curve as the input of the threshold curve calculation formula to obtain the third threshold curve

Y ₃ =ω*P ₉₅ +(1-ω)EWMA _t .

In a possible example, the method further includes: determining a level judgment rule according to the baseline value set; and performing a judgment operation on each current curve value in the current epidemic curve according to the level judgment rule to obtain the Current level corresponding to each current curve value; generating the level data according to each current curve value and the current level corresponding to each current curve value, wherein the level data includes: a level curve.

Optionally, performing a judgment operation on each current curve value in the current curve according to the grade judgment rule may include: performing judgment on each current epidemic curve value in the current curve, and obtaining the value in the current curve The first threshold corresponding to the first threshold curve, the second threshold corresponding to the second threshold curve, and the third threshold corresponding to the third threshold curve, judging whether the current curve value is less than the first threshold, If the current curve value is less than the first threshold, it is determined that the level corresponding to the current epidemic curve value is the first level, and if the current curve value is not less than the first threshold, it is determined whether the current epidemic curve value is Less than the second threshold, if the current epidemic curve value is less than the second threshold, determine that the level corresponding to the current curve value is the second level; if the current curve value is not less than the second threshold, determine Whether the current curve value is less than the third threshold value, if the current curve value is less than the third threshold value, it is determined that the level corresponding to the current curve value is the third pole, and if the current curve value is not less than the The third threshold determines that the level corresponding to the current curve value is the fourth level.

It can be seen that in this embodiment of the application, the electronic device obtains historical records, extracts multiple historical case data from the historical records, and processes the multiple historical case data to obtain a baseline value set; and receives information corresponding to the current epidemic For current data, a current curve is determined based on the current data; a preset grading model is updated based on the baseline value set to obtain a data grading model, and the current curve is input to the data grading model to obtain the grading data of the current epidemic situation. Calculate the baseline value set based on historical case data, calculate the current curve based on the current data, and calculate the grade data based on the baseline value set and the current curve to achieve the classification of the epidemic, which is conducive to reducing the subjectivity of the epidemic classification, improving the accuracy of the epidemic classification, and improving users Experience degree, this application can be applied in smart medical scenarios to promote the further development of smart cities.

Please refer to FIG. 2. FIG. 2 is a schematic flowchart of another data processing method provided by an embodiment of the present application, which is applied to an electronic device. The data processing method includes:

Step 201: Obtain historical records, extract multiple historical case data from the historical records, and extract multiple sets of historical data from the multiple historical case data;

Step 202: Call a preset data processing model, and input the multiple sets of historical data into the data processing model to obtain the baseline value set, wherein the baseline value set includes: a first baseline value P ₁ , a second baseline value set The baseline value P ₂ and the third baseline value P ₃ ;

Step 203: Receive current data corresponding to the current epidemic situation, and determine a current curve based on the current data;

Step 204: Update a preset grading model based on the baseline value set to obtain a data grading model, and input the current curve into the data grading model to obtain grade data of the current epidemic situation.

For the specific description of the above steps 201 to 204, reference may be made to the corresponding steps of the data processing method described in FIG. 1, which will not be repeated here.

It can be seen that, in this embodiment of the application, the electronic device obtains historical records, extracts multiple historical case data from the historical records, extracts multiple sets of historical data from the multiple historical case data, and calls preset data Processing model, inputting the multiple sets of historical data into the data processing model to obtain the baseline value set, wherein the baseline value set includes: a first baseline value P ₁ , a second baseline value P ₂ and a third baseline A value of P ₃ ; receiving current data corresponding to the current epidemic situation, and determining a current curve based on the current data; updating a preset grading model based on the baseline value set to obtain a data grading model, and inputting the current curve into the data grading model, Obtain the level data of the current epidemic situation. In this way, the corresponding historical data can be obtained from historical years, and the baseline value set based on historical data can be calculated, which is conducive to improving the rationality of the baseline value calculation. The calculation of the grade curve based on historical data and current data is conducive to improving the accuracy of epidemic classification and improving User experience.

Please refer to FIG. 3. FIG. 3 is a schematic flowchart of another data processing method provided by an embodiment of the present application, which is applied to an electronic device. The data processing method includes:

Step 301: Obtain historical records, extract multiple historical case data from the historical records, and extract multiple sets of historical data from the multiple historical case data;

Step 302: Obtain preset data extraction rules, and perform an extraction operation on the multiple historical case data based on the data extraction rules to obtain the multiple sets of historical data; wherein, the extraction operation includes: obtaining the current year, based on The current year determines m historical years, where m is an integer greater than 0; extracts m historical records of the m historical years from the multiple historical records; obtains the current date, and determines according to the current date m time periods; extract historical data corresponding to each of the m historical records and the m time periods; upload the historical data to the blockchain;

Step 303: Invoke a preset data processing model, and input the multiple sets of historical data into the data processing model to obtain the baseline value set, wherein the baseline value set includes: a first baseline value P ₁ , a second baseline value set The baseline value P ₂ and the third baseline value P ₃ ;

Step 304: Calculate the first baseline value, the second baseline value, the third baseline value, and the current epidemic curve as a preset influenza warning model to obtain a grade curve corresponding to the current influenza epidemic;

Step 305: Receive current data corresponding to the current epidemic situation, and determine the current curve based on the current data;

Step 306: Update a preset grading model based on the baseline value set to obtain a data grading model, and input the current curve into the data grading model to obtain grade data of the current epidemic situation.

For the detailed description of the foregoing steps 301 to 306, reference may be made to the corresponding steps of the data processing method described in FIG. 1, which will not be repeated here.

It can be seen that in the embodiment of the present application, the electronic device obtains historical records, extracts multiple historical case data from the historical records, extracts multiple sets of historical data from the multiple historical case data; obtains preset data Extraction rules, perform an extraction operation on the multiple historical case data based on the data extraction rules to obtain the multiple sets of historical data; wherein the extraction operation includes: obtaining the current year, and determining m histories based on the current year Year, where m is an integer greater than 0; extract m historical records of the m historical years from the multiple historical records; obtain the current date, and determine m time periods according to the current date; extract the Each historical record in the m historical records corresponds to the historical data of the m time periods; uploads the historical data to the blockchain; calls a preset data processing model, and inputs the multiple sets of historical data to the office According to the data processing model, the baseline value set is obtained, wherein the baseline value set includes: a first baseline value P ₁ , a second baseline value P ₂ and a third baseline value P ₃ ; and the first baseline value, The second baseline value, the third baseline value, and the current epidemic curve are calculated as a preset influenza warning model to obtain the grade curve corresponding to the current influenza epidemic; current data corresponding to the current epidemic is received, based on the The current data determines the current curve; the preset grading model is updated based on the baseline value set to obtain a data grading model, and the current curve is input into the data grading model to obtain the grading data of the current epidemic situation. In this way, the three baseline values are calculated from historical data, the current curve is calculated from current data, and the grade data is determined based on the three baseline values and the current curve, so as to achieve the classification of influenza epidemics, which is conducive to reducing the subjectivity of influenza epidemic classification and improving influenza epidemics. Classification accuracy, improve user experience. This application can be applied to smart medical scenarios to promote the further development of smart cities.

Please refer to FIG. 4. FIG. 4 is a schematic flowchart of another stream data processing method provided by an embodiment of the present application, which is applied to an electronic device. The data processing method includes:

Step 401: Obtain historical records, extract multiple historical case data from the historical records, and process the multiple historical case data to obtain a baseline value set;

Step 402: Receive current data corresponding to the current epidemic situation, and determine a current curve based on the current data.

Step 403: Determine a level judgment rule according to the baseline value set;

Step 404: Perform a judgment operation on each current curve value in the current epidemic curve according to the grade judgment rule to obtain the current grade corresponding to each current curve value;

Step 405: Generate the grade data according to each current curve value and the current grade corresponding to each current curve value, where the grade data includes a grade curve.

For the specific description of the above steps 401 to 405, reference may be made to the corresponding steps of the data processing method described in FIG. 1, which will not be repeated here.

It can be seen that in this embodiment of the application, the electronic device obtains historical records, extracts multiple historical case data from the historical records, and processes the multiple historical case data to obtain a baseline value set; and receives information corresponding to the current epidemic Current data, determine the current curve based on the current data; determine the level judgment rule according to the baseline value set; perform a judgment operation on each current curve value in the current epidemic curve according to the level judgment rule to obtain each The current level corresponding to the current curve value; the level data is generated according to each current curve value and the current level corresponding to each current curve value, wherein the level data includes: a level curve. In this way, each current curve value level in the current curve can be divided by the first threshold, the second threshold, and the third threshold to achieve the epidemic classification, which is beneficial to reduce subjectivity and improve user experience. This application can be applied to smart medical scenarios to promote the further development of smart cities.

Please refer to FIG. 5. FIG. 5 is a schematic structural diagram of an electronic device 500 provided by an embodiment of the present application. As shown in the figure, the electronic device 500 includes an application processor 510, a memory 520, a communication interface 530, and one or more A program 521, wherein the one or more programs 521 are stored in the foregoing memory 520 and configured to be executed by the foregoing application processor 510, and the one or more programs 521 include instructions for performing the following steps:

Acquiring historical records, extracting multiple historical case data from the historical records, and processing the multiple historical case data to obtain a baseline value set;

Receive current data corresponding to the current epidemic situation, and determine the current curve based on the current data;

A data classification model is obtained by updating a preset classification model based on the baseline value set, and inputting the current curve into the data classification model to obtain the classification data of the current epidemic situation.

In a possible example, the processing for the multiple historical case data to obtain a baseline value set, the instructions in the program are specifically used to perform the following operations: extract multiple sets of historical case data from the multiple historical case data Data; call a preset data processing model, input the multiple sets of historical data into the data processing model to obtain the baseline value set, wherein the baseline value set includes: a first baseline value P ₁ , a second baseline The value P ₂ and the third baseline value P ₃ .

In a possible example, the multiple sets of historical data are extracted from the multiple historical case data, and the instructions in the program are specifically used to perform the following operations: obtain preset data extraction rules, and extract data based on the data. The rule performs an extraction operation on the multiple historical case data to obtain the multiple sets of historical data; wherein the extraction operation includes: obtaining the current year, and determining m historical years based on the current year, where m is greater than 0 Integer of; extract m historical records of the m historical years from the multiple historical records; obtain the current date, and determine m time periods according to the current date; extract each history of the m historical records Record historical data corresponding to the m time periods; upload the historical data to the blockchain.

In a possible example, the current curve is determined based on the current data, and the instructions in the program are specifically used to perform the following operations: obtain a preset curve weight β, where 0<β<1; Determine k time points and the number of cases at each time point in the current data, where k is an integer greater than 0; perform the following operations for each time point in the k time points: obtain the t-th time point and the total number of cases _{The number of cases M t} corresponding to the t-th time point, where 0<t≤k, the curve weight β and the number of cases M _{t are} used as the input of the preset curve value calculation formula to obtain the first The current curve value EWMA _t corresponding to t time points, and the curve value calculation formula includes: EWMA _t =β*M _t +(1-β)*EWMA _t-1 ; to obtain k current curve values EWMA _t , based on all The k current curve values EWMA _t generate the current curve EWMA.

In a possible example, the preset classification model is updated based on the baseline value set to obtain a data classification model, and the instructions in the program are specifically used to perform the following operations: based on the first baseline value P ₁ , The second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA determine a first threshold value curve Y ₁ , a second threshold value curve Y ₂ and a third threshold value curve Y ₃ ; acquiring the classification model, The data classification model is updated by using the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ as update parameters to obtain the data classification model.

In a possible example, the first threshold curve Y 1, the _{first threshold curve Y 1 is} determined based on the first baseline value P ₁ , the second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA The second threshold value curve Y ₂ and the third threshold value curve Y ₃ , the instructions in the program are specifically used to perform the following operations: obtain a preset threshold curve calculation formula; obtain a preset calculation weight ω, where 0≦ω <1; use the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ as the input of the threshold curve calculation formula, respectively, and the current curve EWMA to obtain the The first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ , wherein the threshold curve calculation formula includes:

Y _x =ω*P _x +(1-ω)EWMA.

In a possible example, the instructions in the program are also used to perform the following operations: determine the level judgment rule according to the baseline value set; execute each current curve value in the current epidemic curve according to the level judgment rule The judgment operation obtains the current level corresponding to each current curve value; the level data is generated according to each current curve value and the current level corresponding to each current curve value, wherein the level data includes: level curve.

The foregoing mainly introduces the solution of the embodiment of the present application from the perspective of the execution process on the method side. It can be understood that, in order to implement the above-mentioned functions, an electronic device includes hardware structures and/or software modules corresponding to each function. Those skilled in the art should easily realize that in combination with the units and algorithm steps of the examples described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software-driven hardware depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.

FIG. 6 is a block diagram of the functional unit composition of the data processing device 600 involved in an embodiment of the present application. The data processing device 600 is applied to electronic equipment. The data processing device 600 includes an acquiring unit 601, a receiving unit 602, and a calculating unit 603, wherein:

The acquiring unit 601 is configured to acquire historical records, extract multiple historical case data from the historical records, and process the multiple historical case data to obtain a baseline value set;

The receiving unit 602 is configured to receive current data corresponding to the current epidemic situation, and determine the current curve based on the current data;

The calculation unit 603 is configured to update a preset classification model based on the baseline value set to obtain a data classification model, and input the current curve into the data classification model to obtain the classification data of the current epidemic situation.

In a possible example, in terms of processing the multiple historical case data to obtain a baseline value set, the acquiring unit 601 is specifically configured to: extract multiple sets of historical data from the multiple historical case data ; Call a preset data processing model, input the multiple sets of historical data into the data processing model to obtain the baseline value set, wherein the baseline value set includes: a first baseline value P ₁ , a second baseline value P ₂ and the third baseline value P ₃ .

In a possible example, in terms of extracting multiple sets of historical data from the multiple historical case data, the acquiring unit 601 is specifically configured to: acquire preset data extraction rules based on the data extraction rules Perform an extraction operation on the multiple historical case data to obtain the multiple sets of historical data; wherein the extraction operation includes: obtaining the current year, and determining m historical years based on the current year, where m is greater than 0 Integer; extract m historical records of the m historical years from the multiple historical records; obtain the current date, and determine m time periods according to the current date; extract each historical record of the m historical records The historical data corresponding to the m time periods; upload the historical data to the blockchain.

In a possible example, in the aspect of determining the current curve based on the current data, the receiving unit 602 is specifically configured to: obtain a preset curve weight β, where 0<β<1; Determine k time points and the number of cases at each time point in the data, where k is an integer greater than 0; perform the following operations for each of the k time points: obtain the t-th time point and the _{The number of cases M t} corresponding to the t-th time point, where 0<t≤k, the curve weight β and the number of cases M _{t are} used as the input of the preset curve value calculation formula to obtain the t-th The current curve value EWMA _t corresponding to each time point, and the curve value calculation formula includes:

EWMA _t = β*M _t +(1-β)*EWMA _t-1 ;

Obtain k current curve values EWMA _t , and generate the current curve EWMA based on the k current curve values EWMA _t.

In a possible example, in terms of obtaining a data classification model by updating a preset classification model based on the baseline value set, the calculation unit 603 is specifically configured to: based on the first baseline value P ₁ , the The second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA determine the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ ; the classification model is obtained, and the The first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y _{3 are} used as update parameters to update the data classification model to obtain the data classification model.

_{In a possible example, the first threshold curve Y 1 is} determined based on the first baseline value P ₁ , the second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA In terms of the second threshold curve Y ₂ and the third threshold curve Y ₃ , the calculation unit 603 is specifically configured to: obtain a preset threshold curve calculation formula; obtain a preset calculation weight ω, where 0≦ω<1; Use the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ respectively and the current curve EWMA as the input of the threshold curve calculation formula to obtain the first threshold curve A threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ , wherein the threshold curve calculation formula includes:

Y _x =ω*P _x +(1-ω)EWMA.

In a possible example, the calculation unit 603 is further configured to: determine a level judgment rule according to the baseline value set; perform a judgment operation on each current curve value in the current epidemic curve according to the level judgment rule, Obtain the current level corresponding to each current curve value; generate the level data according to each current curve value and the current level corresponding to each current curve value, wherein the level data includes a level curve.

The embodiments of the present application also provide a computer-readable storage medium, where the computer-readable storage medium may be non-volatile or volatile. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, they are used to implement the following steps:

Acquiring historical records, extracting multiple historical case data from the historical records, and processing the multiple historical case data to obtain a baseline value set;

Receive current data corresponding to the current epidemic situation, and determine the current curve based on the current data;

A data classification model is obtained by updating a preset classification model based on the baseline value set, and inputting the current curve into the data classification model to obtain the classification data of the current epidemic situation.

In a possible example, the processing for the multiple historical case data to obtain a baseline value set, when the program instructions are executed by the processor, is used to implement the following steps: extracting from the multiple historical case data Multiple sets of historical data; call a preset data processing model, and input the multiple sets of historical data into the data processing model to obtain the baseline value set, wherein the baseline value set includes: a first baseline value P ₁ , The second baseline value P ₂ and the third baseline value P ₃ .

In a possible example, when the multiple sets of historical data are extracted from the multiple historical case data, when the program instructions are executed by the processor, they are also used to implement the following steps: obtaining preset data extraction rules, based on The data extraction rule performs an extraction operation on the multiple historical case data to obtain the multiple sets of historical data; wherein, the extraction operation includes: obtaining the current year, and determining m historical years based on the current year, wherein, m is an integer greater than 0; extract m historical records of the m historical years from the multiple historical records; obtain the current date, and determine m time periods according to the current date; extract the m historical records Each historical record in the historical data corresponding to the m time periods; upload the historical data to the blockchain.

In a possible example, when the current curve is determined based on the current data, when the program instructions are executed by the processor, the program instructions are further used to implement the following step: obtain a preset curve weight β, where 0<β<1 Determine k time points and the number of cases at each time point from the current data, where k is an integer greater than 0; perform the following operations for each time point in the k time points: obtain the t th The time point and the number of cases M _t corresponding to the t-th time point, where 0<t≤k, the curve weight β and the number of cases M _{t are} used as the input of the preset curve value calculation formula, _{Obtain the current curve value EWMA t} corresponding to the t-th time point, and the curve value calculation formula includes: EWMA _t =β*M _t +(1-β)*EWMA _t-1 ; Obtain k current curve values EWMA _t , generating the current curve EWMA based on the k current curve values EWMA _t.

In a possible example, the data classification model is obtained by updating the preset classification model based on the baseline value set, and when the program instructions are executed by the processor, they are further used to implement the following steps: based on the first baseline value P ₁ , the second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA determine the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ ; obtain all In the grading model, the data grading model is updated by using the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ as update parameters to obtain the data grading model.

In a possible example, the first threshold curve Y 1, the _{first threshold curve Y 1 is} determined based on the first baseline value P ₁ , the second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA When the program instructions are executed by the processor, the second threshold curve Y ₂ and the third threshold curve Y ₃ are also used to implement the following steps: obtain a preset threshold curve calculation formula; obtain a preset calculation weight ω, where , 0≦ω<1; use the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ respectively and the current curve EWMA as the input of the threshold curve calculation formula , Obtain the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ , wherein the threshold curve calculation formula includes:

Y _x =ω*P _x +(1-ω)EWMA.

In a possible example, when the program instructions are executed by the processor, they are also used to implement the following steps: determine a level judgment rule according to the baseline value set; The current curve value performs a judgment operation to obtain the current level corresponding to each current curve value; the level data is generated according to each current curve value and the current level corresponding to each current curve value, wherein the level Data includes: grade curve.

The embodiments of the present application also provide a computer program product. The above-mentioned computer program product includes a non-transitory computer-readable storage medium storing a computer program. The above-mentioned computer program is operable to cause a computer to execute any of the methods described in the above-mentioned method embodiments. Part or all of the steps of the method. The computer program product may be a software installation package, and the above-mentioned computer includes electronic equipment.

The embodiments of the application are described in detail above, and specific examples are used in this article to illustrate the principles and implementation of the application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the application; at the same time, for Those of ordinary skill in the art, based on the ideas of the application, will have changes in the specific implementation and the scope of application. In summary, the content of this specification should not be construed as limiting the application.

Claims (20)

1. A data processing method, which includes:

   Acquiring historical records, extracting multiple historical case data from the historical records, and processing the multiple historical case data to obtain a baseline value set;

   Receive current data corresponding to the current epidemic situation, and determine the current curve based on the current data;

   A data classification model is obtained by updating a preset classification model based on the baseline value set, and inputting the current curve into the data classification model to obtain the classification data of the current epidemic situation.
2. The method according to claim 1, wherein the processing for the multiple historical case data to obtain a baseline value set comprises:

   Extracting multiple sets of historical data from the multiple historical case data;

   Call a preset data processing model, and input the multiple sets of historical data into the data processing model to obtain the baseline value set, where the baseline value set includes: a first baseline value P ₁ , a second baseline value P ₂ and the third baseline value P ₃ .
3. The method according to claim 2, wherein said extracting multiple sets of historical data from said multiple historical case data comprises:

   Acquiring preset data extraction rules, and performing extraction operations on the multiple historical case data based on the data extraction rules to obtain the multiple sets of historical data;

   Wherein, the extraction operation includes: obtaining the current year, determining m historical years based on the current year, where m is an integer greater than 0; extracting m of the m historical years from the multiple historical records Historical records; acquiring the current date, and determining m time periods according to the current date; extracting historical data corresponding to each of the m historical records and the m time periods;

   Upload the historical data to the blockchain.
4. The method according to claim 1, wherein the determining the current curve based on the current data comprises:

   Obtain the preset curve weight β, where 0＜β＜1;

   Determine k time points and the number of cases at each time point from the current data, where k is an integer greater than 0;

   Perform the following operations for each of the k time points: obtain the t-th time point and the number of cases M _t corresponding to the t-th time point, where 0<t≤k, weight the curve The value β and the number of cases M _{t are} used as the input of the preset curve value calculation formula to obtain the current curve value EWMA _t corresponding to the t-th time point, and the curve value calculation formula includes:

   EWMA _t = β*M _t +(1-β)*EWMA _t-1 ;

   Obtain k current curve values EWMA _t , and generate the current curve EWMA based on the k current curve values EWMA _t.
5. The method according to claim 1, wherein said updating a preset classification model based on said baseline value set to obtain a data classification model comprises:

   Based on the first baseline value P ₁ , the second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA determine the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third Threshold curve Y ₃ ;

   The grading model is acquired, _{and the data grading model is updated by using the first threshold curve Y 1} , the second threshold curve Y ₂ and the third threshold curve Y ₃ as update parameters to obtain the data grading model.
6. The method according to claim 5, wherein the first threshold value is determined _{based on the first baseline value P 1} , the second baseline value P ₂ , the third baseline value P _{3 and the current curve EWMA} The curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ include:

   Obtain a preset threshold curve calculation formula;

   Obtain the preset calculation weight ω, where 0≦ω<1;

   The first threshold value curve Y ₁ , the second threshold value curve Y ₂ and the third threshold value curve Y ₃ and the current curve EWMA are used as the input of the threshold value curve calculation formula to obtain the first threshold value Curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ , wherein the threshold curve calculation formula includes:

   Y _x =ω*P _x +(1-ω)EWMA.
7. The method according to any one of claims 1-6, wherein the method further comprises:

   Determining the level judgment rule according to the baseline value set;

   Performing a judgment operation on each current curve value in the current epidemic curve according to the grade judgment rule to obtain the current grade corresponding to each current curve value;

   The grade data is generated according to each current curve value and the current grade corresponding to each current curve value, wherein the grade data includes a grade curve.
8. A data processing device, which includes:

   An acquiring unit, configured to acquire historical records, extract multiple historical case data from the historical records, and process the multiple historical case data to obtain a baseline value set;

   The receiving unit is configured to receive current data corresponding to the current epidemic situation, and determine the current curve based on the current data;

   The calculation unit is configured to update a preset classification model based on the baseline value set to obtain a data classification model, and input the current curve into the data classification model to obtain the classification data of the current epidemic situation.
9. An electronic device, wherein the electronic device includes a memory and a processor, the processor and the memory are connected to each other, wherein the memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to execute the program instructions of the memory, wherein:

   Acquiring historical records, extracting multiple historical case data from the historical records, and processing the multiple historical case data to obtain a baseline value set;

   Receive current data corresponding to the current epidemic situation, and determine the current curve based on the current data;

   A data classification model is obtained by updating a preset classification model based on the baseline value set, and inputting the current curve into the data classification model to obtain the classification data of the current epidemic situation.
10. The electronic device according to claim 9, wherein the processor is configured to:

    Extracting multiple sets of historical data from the multiple historical case data;

    Call a preset data processing model, and input the multiple sets of historical data into the data processing model to obtain the baseline value set, where the baseline value set includes: a first baseline value P ₁ , a second baseline value P ₂ and the third baseline value P ₃ .
11. The electronic device according to claim 10, wherein the processor is configured to:

    Acquiring preset data extraction rules, and performing extraction operations on the multiple historical case data based on the data extraction rules to obtain the multiple sets of historical data;

    Wherein, the extraction operation includes: obtaining the current year, determining m historical years based on the current year, where m is an integer greater than 0; extracting m of the m historical years from the multiple historical records Historical records; acquiring the current date, and determining m time periods according to the current date; extracting historical data corresponding to each of the m historical records and the m time periods;

    Upload the historical data to the blockchain.
12. The electronic device according to claim 9, wherein the processor is configured to:

    Obtain the preset curve weight β, where 0＜β＜1;

    Determine k time points and the number of cases at each time point from the current data, where k is an integer greater than 0;

    Perform the following operations for each of the k time points: obtain the t-th time point and the number of cases M _t corresponding to the t-th time point, where 0<t≤k, weight the curve The value β and the number of cases M _{t are} used as the input of the preset curve value calculation formula to obtain the current curve value EWMA _t corresponding to the t-th time point, and the curve value calculation formula includes:

    EWMA _t = β*M _t +(1-β)*EWMA _t-1 ;

    Obtain k current curve values EWMA _t , and generate the current curve EWMA based on the k current curve values EWMA _t.
13. The electronic device according to claim 9, wherein the processor is configured to:

    Based on the first baseline value P ₁ , the second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA determine the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third Threshold curve Y ₃ ;

    The grading model is acquired, _{and the data grading model is updated by using the first threshold curve Y 1} , the second threshold curve Y ₂ and the third threshold curve Y ₃ as update parameters to obtain the data grading model.
14. The electronic device according to claim 13, wherein the processor is configured to:

    Obtain a preset threshold curve calculation formula;

    Obtain the preset calculation weight ω, where 0≦ω<1;

    The first threshold value curve Y ₁ , the second threshold value curve Y ₂ and the third threshold value curve Y ₃ and the current curve EWMA are used as the input of the threshold value curve calculation formula to obtain the first threshold value Curve Y ₁ , the second threshold curve Y ₂ and the third threshold curve Y ₃ , wherein the threshold curve calculation formula includes:

    Y _x =ω*P _x +(1-ω)EWMA.
15. The electronic device according to any one of claims 9-14, wherein the processor is configured to:

    Determining the level judgment rule according to the baseline value set;

    Performing a judgment operation on each current curve value in the current epidemic curve according to the grade judgment rule to obtain the current grade corresponding to each current curve value;

    The grade data is generated according to each current curve value and the current grade corresponding to each current curve value, wherein the grade data includes a grade curve.
16. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, they are used to implement the following steps:

    Acquiring historical records, extracting multiple historical case data from the historical records, and processing the multiple historical case data to obtain a baseline value set;

    Receive current data corresponding to the current epidemic situation, and determine the current curve based on the current data;

    A data classification model is obtained by updating a preset classification model based on the baseline value set, and inputting the current curve into the data classification model to obtain the classification data of the current epidemic situation.
17. The computer-readable storage medium according to claim 16, wherein, when the program instructions are executed by the processor, they are further used to implement the following steps:

    Extracting multiple sets of historical data from the multiple historical case data;

    Call a preset data processing model, and input the multiple sets of historical data into the data processing model to obtain the baseline value set, where the baseline value set includes: a first baseline value P ₁ , a second baseline value P ₂ and the third baseline value P ₃ .
18. The computer-readable storage medium according to claim 17, wherein, when the program instructions are executed by the processor, they are further used to implement the following steps:

    Acquiring preset data extraction rules, and performing extraction operations on the multiple historical case data based on the data extraction rules to obtain the multiple sets of historical data;

    Wherein, the extraction operation includes: obtaining the current year, determining m historical years based on the current year, where m is an integer greater than 0; extracting m of the m historical years from the multiple historical records Historical records; acquiring the current date, and determining m time periods according to the current date; extracting historical data corresponding to each of the m historical records and the m time periods;

    Upload the historical data to the blockchain.
19. The computer-readable storage medium according to claim 16, wherein, when the program instructions are executed by the processor, they are further used to implement the following steps:

    Obtain the preset curve weight β, where 0＜β＜1;

    Determine k time points and the number of cases at each time point from the current data, where k is an integer greater than 0;

    Perform the following operations for each of the k time points: obtain the t-th time point and the number of cases M _t corresponding to the t-th time point, where 0<t≤k, weight the curve The value β and the number of cases M _{t are} used as the input of the preset curve value calculation formula to obtain the current curve value EWMA _t corresponding to the t-th time point, and the curve value calculation formula includes:

    EWMA _t = β*M _t +(1-β)*EWMA _t-1 ;

    Obtain k current curve values EWMA _t , and generate the current curve EWMA based on the k current curve values EWMA _t.
20. The computer-readable storage medium according to claim 16, wherein, when the program instructions are executed by the processor, they are further used to implement the following steps:

    Based on the first baseline value P ₁ , the second baseline value P ₂ , the third baseline value P ₃ and the current curve EWMA determine the first threshold curve Y ₁ , the second threshold curve Y ₂ and the third Threshold curve Y ₃ ;

    The grading model is acquired, _{and the data grading model is updated by using the first threshold curve Y 1} , the second threshold curve Y ₂ and the third threshold curve Y ₃ as update parameters to obtain the data grading model.

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