CN110363439A - A kind of credit-graded approach based on consumer demographics' portrait - Google Patents
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Abstract
The present invention discloses a kind of credit-graded approach based on consumer demographics' portrait, is related to credit evaluation technical field.It is not perfect for existing grading system, think to judge the strong defect of subjectivity, the consumption data acquired using technical solution based on common carrier, known credit scoring corresponding with acquisition consumption data, classify first to the consumption data of acquisition, then consumption classification belonging to the consumption data and consumption data acquired by word2vec model treatment, portray consumer demographics' portrait, learn the consumption data of affiliated identity set using light GBM as base learner, consumption classification belonging to consumption data, the output information of word2vec model, credit scoring model is constructed with training, finally, the consumption data of affiliated identity set is analyzed using the credit scoring model that building is completed, consumption classification belonging to consumption data, the output information of word2vec model, that is, exportable Credit scoring realizes the purpose that automatic scoring is carried out using consumption data.
Description
Technical field
The present invention relates to credit evaluation technical field, specifically a kind of credit scoring based on consumer demographics' portrait
Method.
Background technique
With the deep propulsion of the establishment of social credit system, the rapid development of social credibility standard working-out, relevant standard phase
After publication, including the acquisition of credit services standard, credit data and service standard, credit repairing standard, city credit standard, industry
Multi-level standards system including credit standard etc. is urgently put into effect, and social credibility standards system is expected to quickly propel.
Credit scoring refers to the credit history data according to client, using certain credit scoring model, obtains different etc.
The credit score of grade.According to the credit score of client, the credit grade that providers of credit can analyze consumer is consumed with this to give
The different degrees of discount of person and preferential.During credit scoring, most critical be exactly credit scoring model building.It is used to
The model for generating credit scoring is too numerous to enumerate, each model has its unique rule.
The rule of development of the credit score of a usual people is: basis point obtained, as time goes by, has bonus point event,
Occur with deduction event.Along with these events, credit score gradually goes up or declines.The basic principle of credit scoring model is
It determines the factor for influencing Default Probability, then gives weight, calculate its credit score.
Traditional credit scoring is mainly measured with the dimension of the minority such as client's consuming capacity, it is difficult to comprehensive, objective, timely
Reaction client credit.How to carry out intelligent scoring to client based on big data abundant is current problem.This patent from
Business is set out, and based on the analysis to data, more powerful features is sufficiently excavated using word2vec, and construct reasonable
Model realizes the intelligent scoring to client.
Based on this, a kind of credit-graded approach based on consumer demographics' portrait is researched and developed in design, by constructing reasonable letter
With Rating Model, common carrier is facilitated to carry out intellectual analysis to the communication service of consumer, so to the credit of consumer into
Row intelligent scoring.
Summary of the invention
The present invention for traditional credit scoring model because measured according to the dimension of the minority such as client's consuming capacity into
And be difficult to it is comprehensive, objective, timely react customers' credit the shortcomings that, provide it is a kind of based on consumer demographics portrait credit scoring
Method facilitates common carrier to score the credit of consumer by constructing reasonable credit scoring model.
A kind of credit-graded approach based on consumer demographics' portrait of the invention, solves the skill that above-mentioned technical problem uses
Art scheme is as follows:
A kind of credit-graded approach based on consumer demographics' portrait, the credit-graded approach are acquired based on common carrier
Consumption data, the consumption data of acquisition belong to same user unique identification be phone number；
The credit-graded approach the realization process includes:
According to unique identification, the consumption data for belonging to different user is stored in different set；
According to the feature of consumption data, the consumption data for being subordinated to the same set is divided into different consumption classifications；
Consumption classification belonging to the consumption data for being subordinated to the same set, consumption data is inputted in natural language processing
Word2vec model, portray consumer demographics portrait；
Known credit scoring corresponding with acquisition consumption data is obtained, known credit scoring is based on, utilizes light GBM
As base learner, study be subordinated to consumption classification belonging to the consumption data of the same set, consumption data and
The output information of word2vec model constructs credit scoring model with training；
The credit scoring model of building receives the consumption data for being subordinated to the same set, different consumption classifications are included
When the output information of consumption data and word2vec model, direct export credit is scored.
Optionally, involved consumption data includes 29 kinds of consumption information, specifically:
It is subscriber-coded；Whether user's system of real name passes through verification；Age of user；
Whether university student client；Whether blacklist client；Whether the unhealthy client of 4G；User's length of surfing the Net (moon)；
User's the last time paid the fees away from modern duration (moon)；It pays the fees user's the last time payment amount of money (member)；
The nearly 6 monthly average consumption values (member) of user；Subscriber's account this month total cost (member)；
User's this month account balance (member)；Paying the fees, user currently whether pay the fees by arrearage；
User telephone fee susceptibility；This month call relationship cycle number；The people whether often to go shopping；
Nearly three months monthly market frequency of occurrence；Whether this month strolled market；
Whether this month arrived chartered shop；Whether this month watches movie；
It is of that month that whether sight spot is gone sight-seeing；It is of that month that whether stadiums are consumed；
Of that month online shopping class is using number；Of that month logistics express delivery class is using number；
Of that month finance and money management class is using total degree；Of that month video playback class is using number；
Of that month aircraft class is using number；Of that month train class is using number；
Of that month travel information class is using number.
Optionally, according to the feature of consumption data, 29 kinds of consumption information for being subordinated to the same set are divided into four
Consume classification:
A) user go on a journey class, include: subscriber-coded, user's system of real name whether pass through verification, age of user, whether university student
Client, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, of that month aircraft class are using number, of that month train
Class is using number, of that month travel information class using number；
B) customer consumption class includes: subscriber-coded, user's system of real name whether pass through verification, age of user, whether university student
Client, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, user's the last time pay the fees and use away from modern duration, payment
Family the last time payment amount of money, the nearly 6 monthly average consumption values of user, subscriber's account this month total cost, user's this month account balance,
Pay the fees user currently whether arrearage payment, user telephone fee susceptibility, of that month call relationship cycle number；
C) user's life kind includes: subscriber-coded, user's system of real name whether pass through verification, age of user, whether university student
Client, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, the people whether often to go shopping, nearly three months it is monthly
Whether market frequency of occurrence, this month strolled market, whether this month arrived whether chartered shop, this month watch movie, is of that month
Whether sight spot is gone sight-seeing, of that month whether stadiums are consumed, of that month online shopping class makes using number, of that month logistics express delivery class application
With number, of that month finance and money management class using total degree, of that month video playback class using number, of that month aircraft class application
Access times, of that month train class are using number, of that month travel information class using number；
D) user's intersection information class includes: whether subscriber-coded, user's system of real name passes through verification, age of user, whether big
Student client, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, of that month online shopping class are using number, this month
Logistics express delivery class is using number, of that month finance and money management class using total degree, of that month video playback class using secondary
Number, of that month aircraft class are using number, of that month train class using number, of that month travel information class using number.
Optionally, before portraying consumer demographics' portrait, the Skip- of word2vec model in natural language processing is utilized
Consumption classification belonging to the consumption data for being subordinated to the same set, consumption data is projected to k dimension by Gram mode
Vector space constitute sentence, excavate different user consumption habit and contract rule characterized user positioning；
In projection process, each word is indicated with a K dimensional vector, distance of the word similar in relationship in K dimension space
It is same close, mathematic(al) representation such as:
P (s)=p (w1, w2..., wn)=Π p (wn|context)
Wherein behalf current sentence, w1Road wnFor the word for forming the sentence, Context is context p (wn|context)
To there is this w in given contextnProbability.
Optionally, the concrete operations for constructing credit scoring model include:
1) high level critical point and low value critical point are set, relatively more known credit scoring, high level critical point, low value critical point three
Person's size；
2) MSE loss function and MAE loss function are introduced；
3) known credit scoring be greater than high level critical point or be less than low value critical point when, using light GBM as
Base learner, study be subordinated to the consumption data of the same set, the consumption data that different consumption classification is included and
The output information of word2vec model introduces MSE loss function and constructs MSE credit scoring model；
4) when known credit scoring is between high level critical point and low value critical point, using light GBM as base
Learner, study are subordinated to consumption classification and word2vec model belonging to the consumption data of the same set, consumption data
Output information, introduce MAE loss function construct MSE credit scoring model；
5) MSE credit scoring model and MSE credit scoring model are constructed as credit scoring model, credit scoring mould
The output information of type analysis word2vec model show that credit scoring, the credit scoring are MSE credit scoring model and MSE
The average value of credit scoring model output scoring.
Specifically, involved high level critical point is 650 points, the low value critical point is 525 points.
Optionally, during constructing credit scoring model, it is based on known credit scoring, in MSE credit scoring model
When output scoring and the deviation value of known credit scoring with MSE credit scoring model are more than ± 5%, in light GBM base
The range and step-length that self-setting parameter is found in device are practised, is then carried out using the GridSearchCV parameter adjustment method in python
The grid search of automation obtains optimal parameter, MSE credit scoring model and MSE credit scoring model.
Specifically, involved parameter includes learning rate learning_rate, leaf number num_leaves, depth capacity
max_depth。
A kind of credit-graded approach based on consumer demographics' portrait of the invention, what is had compared with prior art is beneficial
Effect is:
It is consumption data that credit-graded approach of the invention is acquired based on common carrier, corresponding with acquisition consumption data
Known credit scoring, by word2vec model portray consumer demographics portrait, be based on known credit scoring, utilize light
GBM as the study of base learner be subordinated to consumption classification belonging to the consumption data of the same set, consumption data and
The output information of word2vec model constructs credit scoring model with training, finally, the credit scoring model completed using building
Analysis is subordinated to the output of consumption classification and word2vec model belonging to the consumption data of the same set, consumption data
Information, can export credit scoring, solve that existing grading system is not perfect, thinks to judge the strong defect of subjectivity, realize and utilize
The purpose of consumption data progress automatic scoring.
Detailed description of the invention
Attached drawing 1 is the flow diagram of present invention building credit scoring model；
Attached drawing 2 is the flow diagram that credit scoring model of the invention scores.
Specific embodiment
The technical issues of to make technical solution of the present invention, solving and technical effect are more clearly understood, below in conjunction with tool
Body embodiment carries out clear, complete description to technical solution of the present invention, it is clear that described embodiment is only this hair
Bright a part of the embodiment, instead of all the embodiments.
Embodiment one:
In conjunction with attached drawing 1,2, the present embodiment proposes a kind of credit-graded approach based on consumer demographics' portrait, which comments
The consumption data for dividing method to acquire based on common carrier, the unique identification that the consumption data of acquisition belongs to same user is mobile phone
Number.
The credit-graded approach the realization process includes:
According to unique identification, the consumption data for belonging to different user is stored in different set；
According to the feature of consumption data, the consumption data for being subordinated to the same set is divided into different consumption classifications；
Consumption classification belonging to the consumption data for being subordinated to the same set, consumption data is inputted in natural language processing
Word2vec model, portray consumer demographics portrait；
Known credit scoring corresponding with acquisition consumption data is obtained, known credit scoring is based on, utilizes light GBM
As base learner, study be subordinated to consumption classification belonging to the consumption data of the same set, consumption data and
The output information of word2vec model constructs credit scoring model with training；
The credit scoring model of building receives the consumption data for being subordinated to the same set, different consumption classifications are included
When the output information of consumption data and word2vec model, direct export credit is scored.
In the present embodiment, involved consumption data includes 29 kinds of consumption information, specifically:
It is subscriber-coded；Whether user's system of real name passes through verification；Age of user；
Whether university student client；Whether blacklist client；Whether the unhealthy client of 4G；User's length of surfing the Net (moon)；
User's the last time paid the fees away from modern duration (moon)；It pays the fees user's the last time payment amount of money (member)；
The nearly 6 monthly average consumption values (member) of user；Subscriber's account this month total cost (member)；
User's this month account balance (member)；Paying the fees, user currently whether pay the fees by arrearage；
User telephone fee susceptibility；This month call relationship cycle number；The people whether often to go shopping；
Nearly three months monthly market frequency of occurrence；Whether this month strolled market；
Whether this month arrived chartered shop；Whether this month watches movie；
It is of that month that whether sight spot is gone sight-seeing；It is of that month that whether stadiums are consumed；
Of that month online shopping class is using number；Of that month logistics express delivery class is using number；
Of that month finance and money management class is using total degree；Of that month video playback class is using number；
Of that month aircraft class is using number；Of that month train class is using number；
Of that month travel information class is using number.
In the present embodiment, according to the feature of consumption data, 29 kinds of consumption information for being subordinated to the same set are divided
For four consumption classifications:
A) user go on a journey class, include: subscriber-coded, user's system of real name whether pass through verification, age of user, whether university student
Client, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, of that month aircraft class are using number, of that month train
Class is using number, of that month travel information class using number；
B) customer consumption class includes: subscriber-coded, user's system of real name whether pass through verification, age of user, whether university student
Client, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, user's the last time pay the fees and use away from modern duration, payment
Family the last time payment amount of money, the nearly 6 monthly average consumption values of user, subscriber's account this month total cost, user's this month account balance,
Pay the fees user currently whether arrearage payment, user telephone fee susceptibility, of that month call relationship cycle number；
C) user's life kind includes: subscriber-coded, user's system of real name whether pass through verification, age of user, whether university student
Client, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, the people whether often to go shopping, nearly three months it is monthly
Whether market frequency of occurrence, this month strolled market, whether this month arrived whether chartered shop, this month watch movie, is of that month
Whether sight spot is gone sight-seeing, of that month whether stadiums are consumed, of that month online shopping class makes using number, of that month logistics express delivery class application
With number, of that month finance and money management class using total degree, of that month video playback class using number, of that month aircraft class application
Access times, of that month train class are using number, of that month travel information class using number；
D) user's intersection information class includes: whether subscriber-coded, user's system of real name passes through verification, age of user, whether big
Student client, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, of that month online shopping class are using number, this month
Logistics express delivery class is using number, of that month finance and money management class using total degree, of that month video playback class using secondary
Number, of that month aircraft class are using number, of that month train class using number, of that month travel information class using number.
In the present embodiment, before portraying consumer demographics' portrait, word2vec model in natural language processing is utilized
Skip-Gram mode is projected using consumption classification belonging to the consumption data for being subordinated to the same set, consumption data as word
The vector space tieed up to k constitutes sentence, excavates user's positioning that different user consumption habit and contract rule are characterized；
In projection process, each word is indicated with a K dimensional vector, distance of the word similar in relationship in K dimension space
It is same close, mathematic(al) representation such as:
P (s)=p (w1, w2..., wn)=Π p (wn|context)
Wherein behalf current sentence, w1Road wnFor the word for forming the sentence, Context is context p (wn|context)
To there is this w in given contextnProbability.
In the present embodiment, the concrete operations for constructing credit scoring model include:
1) high level critical point and low value critical point are set, involved high level critical point is 650 points, involved low value critical point
It is 525 points, relatively more known credit scoring, high level critical point, low value critical point three's size；
2) MSE loss function and MAE loss function are introduced；
3) known credit scoring be greater than high level critical point or be less than low value critical point when, using light GBM as
Base learner, study are subordinated to consumption classification and word2vec mould belonging to the consumption data of the same set, consumption data
The output information of type introduces MSE loss function and constructs MSE credit scoring model；
4) when known credit scoring is between high level critical point and low value critical point, using light GBM as base
Learner, study are subordinated to consumption classification and word2vec model belonging to the consumption data of the same set, consumption data
Output information, introduce MAE loss function construct MSE credit scoring model；
5) MSE credit scoring model and MSE credit scoring model are constructed as credit scoring model, credit scoring mould
The output information of type analysis word2vec model show that credit scoring, the credit scoring are MSE credit scoring model and MSE
The average value of credit scoring model output scoring.
In the present embodiment, during constructing credit scoring model, it is based on known credit scoring, is commented in MSE credit
When the output scoring of sub-model and MSE credit scoring model and the deviation value of known credit scoring are more than ± 5%:
The range and step-length that self-setting parameter is found in light GBM base learner, involved parameter include study
Rate learning_rate, leaf number num_leaves, depth capacity max_depth；
The grid search automated using the GridSearchCV parameter adjustment method in python, obtain optimal parameter,
MSE credit scoring model and MSE credit scoring model.
It is consumption data that the credit-graded approach of the present embodiment is acquired based on common carrier, opposite with acquisition consumption data
The known credit scoring answered portrays consumer demographics' portrait by word2vec model, is based on known credit scoring, utilizes
Light GBM as the study of base learner be subordinated to consumption classification belonging to the consumption data of the same set, consumption data, with
And the output information of word2vec model, credit scoring model is constructed with training, finally, the credit scoring mould completed using building
Type analysis is subordinated to the defeated of consumption classification belonging to the consumption data of the same set, consumption data and word2vec model
Information can export credit scoring out.
In summary, using a kind of credit-graded approach based on consumer demographics' portrait of the invention, existing comment is solved
Point system is not perfect, thinks to judge the strong defect of subjectivity, realizes the purpose using consumption data progress automatic scoring.
Use above specific case elaborates the principle of the present invention and embodiment, these embodiments are
It is used to help understand core of the invention technology contents.Based on above-mentioned specific embodiment of the invention, the technology of the art
Without departing from the principle of the present invention, any improvement and modification to made by the present invention should all fall into the present invention to personnel
Scope of patent protection.
Claims (8)
1. a kind of credit-graded approach based on consumer demographics' portrait, which is characterized in that the credit-graded approach is based on communication
The consumption data of operator's acquisition, the unique identification that the consumption data belongs to same user is phone number；
The credit-graded approach the realization process includes:
According to unique identification, the consumption data for belonging to different user is stored in different set；
According to the feature of consumption data, the consumption data for being subordinated to the same set is divided into different consumption classifications；
Consumption classification belonging to the consumption data for being subordinated to the same set, consumption data is inputted in natural language processing
Word2vec model portrays consumer demographics' portrait；
Obtain known credit scoring corresponding with acquisition consumption data, based on known credit scoring, using light GBM as
Base learner, study are subordinated to consumption classification and word2vec mould belonging to the consumption data of the same set, consumption data
The output information of type constructs credit scoring model with training；
The consumption that the credit scoring model of building receives the consumption data for being subordinated to the same set, different consumption classifications are included
When data and the output information of word2vec model, direct export credit is scored.
2. a kind of credit-graded approach based on consumer demographics' portrait according to claim 1, which is characterized in that described
Consumption data includes 29 kinds of consumption information, specifically:
It is subscriber-coded；Whether user's system of real name passes through verification；Age of user；
Whether university student client；Whether blacklist client；Whether the unhealthy client of 4G；User's length of surfing the Net；
User's the last time pays the fees away from modern duration；Payment user's the last time payment amount of money；
The nearly 6 monthly average consumption values of user；Subscriber's account this month total cost；
User's this month account balance；Paying the fees, user currently whether pay the fees by arrearage；
User telephone fee susceptibility；This month call relationship cycle number；The people whether often to go shopping；
Nearly three months monthly market frequency of occurrence；Whether this month strolled market；
Whether this month arrived chartered shop；Whether this month watches movie；
It is of that month that whether sight spot is gone sight-seeing；It is of that month that whether stadiums are consumed；
Of that month online shopping class is using number；Of that month logistics express delivery class is using number；
Of that month finance and money management class is using total degree；Of that month video playback class is using number；
Of that month aircraft class is using number；Of that month train class is using number；
Of that month travel information class is using number.
3. a kind of credit-graded approach based on consumer demographics' portrait according to claim 2, which is characterized in that according to
29 kinds of consumption information for being subordinated to the same set are divided into four consumption classifications by the feature of consumption data:
A) user go on a journey class, include: subscriber-coded, user's system of real name whether pass through verification, age of user, whether university unfamiliar guest
Family, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, of that month aircraft class are using number, of that month train class
Using number, of that month travel information class using number；
B) customer consumption class includes: subscriber-coded, user's system of real name whether pass through verification, age of user, whether university unfamiliar guest
Family, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, user's the last time pay the fees away from modern duration, payment user
The last payment amount of money, subscriber's account this month total cost, user's this month account balance, is paid at the nearly 6 monthly average consumption values of user
Expense family currently whether arrearage payment, user telephone fee susceptibility, of that month call relationship cycle number；
C) user's life kind includes: subscriber-coded, user's system of real name whether pass through verification, age of user, whether university unfamiliar guest
Family, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, the people whether often to go shopping, nearly three months monthly quotient
Frequency of occurrence, it is of that month whether strolled market, it is of that month whether arrived chartered shop, whether this month watches movie, this month is
The visit of no sight spot, it is of that month whether stadiums consumption, of that month online shopping class using number, of that month logistics express delivery class using
Number, of that month finance and money management class make using total degree, of that month video playback class using number, of that month aircraft class application
With number, of that month train class using number, of that month travel information class using number；
D) user's intersection information class includes: subscriber-coded, user's system of real name whether pass through verification, age of user, whether university student
Client, whether blacklist client, whether the unhealthy client of 4G, user's length of surfing the Net, of that month online shopping class are using number, of that month logistics
Express delivery class using number, of that month finance and money management class using total degree, of that month video playback class using number, when
Month aircraft class is using number, of that month train class using number, of that month travel information class using number.
4. a kind of credit-graded approach based on consumer demographics' portrait according to claim 1, which is characterized in that portray
Before consumer demographics' portrait, using the Skip-Gram mode of word2vec model in natural language processing, it will be subordinated to same
Consumption classification belonging to the consumption data of a set, consumption data constitutes sentence as the vector space that word projects to k dimension, digs
User's positioning that pick different user consumption habit and contract rule are characterized；
In projection process, each word indicates that word similar in relationship is same in the distance of K dimension space with a K dimensional vector
It is close, mathematic(al) representation such as:
P (s)=p (w1, w2..., wn)=Π p (wk|context)
Wherein behalf current sentence, w1Road wnFor the word for forming the sentence, Context is context p (wn| context) be
There is this w when given contextnProbability.
5. a kind of credit-graded approach based on consumer demographics' portrait according to claim 1, which is characterized in that building
The concrete operations of credit scoring model include:
1) high level critical point and low value critical point are set, relatively more known credit scoring, high level critical point, low value critical point three are big
It is small；
2) MSE loss function and MAE loss function are introduced；
3) when known credit scoring is greater than high level critical point or is less than low value critical point, using light GBM as base
Practise device, study is subordinated to consumption classification belonging to the consumption data of the same set, consumption data and word2vec model
Output information introduces MSE loss function and constructs MSE credit scoring model；
4) when known credit scoring is between high level critical point and low value critical point, learnt using light GBM as base
Device, study are subordinated to the defeated of consumption classification belonging to the consumption data of the same set, consumption data and word2vec model
Information out introduces MAE loss function and constructs MSE credit scoring model；
5) MSE credit scoring model and MSE credit scoring model are constructed as credit scoring model, credit scoring model point
The output information for analysing word2vec model, show that credit scoring, the credit scoring are MSE credit scoring model and MSE credit
The average value of Rating Model output scoring.
6. a kind of credit-graded approach based on consumer demographics' portrait according to claim 5, which is characterized in that described
High level critical point is 650 points, and the low value critical point is 525 points.
7. a kind of credit-graded approach based on consumer demographics' portrait stated according to claim 5, which is characterized in that constructing
During credit scoring model, it is based on known credit scoring, in the defeated of MSE credit scoring model and MSE credit scoring model
When the deviation value of scoring and known credit scoring is more than ± 5% out, self-setting parameter is found in light GBM base learner
Range and step-length, the grid search then automated using the GridSearchCV parameter adjustment method in python obtained
Optimal parameter, MSE credit scoring model and MSE credit scoring model.
8. a kind of credit-graded approach based on consumer demographics' portrait according to claim 6, which is characterized in that described
Parameter includes learning rate learning_rate, leaf number num_leaves, depth capacity max_depth.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910653033.9A CN110363439A (en) | 2019-07-19 | 2019-07-19 | A kind of credit-graded approach based on consumer demographics' portrait |
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| CN111931717A (en) * | 2020-09-22 | 2020-11-13 | 平安科技（深圳）有限公司 | Semantic and image recognition-based electrocardiogram information extraction method and device |
| CN112084242A (en) * | 2020-09-02 | 2020-12-15 | 深圳市铭数信息有限公司 | Consumption information display method, device, terminal and medium |
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