CN109064301A - A kind of preferential tax policy method for pushing and system - Google Patents
A kind of preferential tax policy method for pushing and system Download PDFInfo
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- CN109064301A CN109064301A CN201810762102.5A CN201810762102A CN109064301A CN 109064301 A CN109064301 A CN 109064301A CN 201810762102 A CN201810762102 A CN 201810762102A CN 109064301 A CN109064301 A CN 109064301A
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- H04L67/55—Push-based network services
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Abstract
The invention discloses a kind of preferential tax policy method for pushing and systems.This method comprises: obtaining the essential information of taxpayer；Data are carried out to the essential information and extract conversion, obtain essential information feature vector；Obtain the preferential tax policy that taxpayer currently enjoys；Data extraction is carried out to the preferential tax policy, obtains preferential tax policy feature vector；Calculate the similarity between the essential information feature vector and the preferential tax policy feature vector；According to the similarity and given threshold, first object taxpayer set is obtained；The second target taxpayer set is obtained according to the similarity by collaborative filtering；According to first object taxpayer set and the second target taxpayer set, final goal taxpayer is determined；Frame, which is pushed, by message pushes preferential tax policy to the final goal taxpayer.Preferential tax policy can be accurately pushed by this method or system.
Description
Technical field
The present invention relates to tax big data fusion calculation field, more particularly to a kind of preferential tax policy method for pushing and
System.
Background technique
The diversification of internet development bring taxpayer's type, does tax mode diversification at business diversification, necessarily causes to receive
The diversification of tax demand for services, there is an urgent need to the personalization of " one-to-one " precisely services for " internet+information customization " action content.
Battalion changes the successful pilot for increasing work simultaneously, and there is an urgent need to the tax authorities to carry out comprehensive analysis phase, based on " focus analysis, essence
Quasi- service " on the one hand makes full use of tax revenue big data, and operating condition of taxpayer in terms of policy is analyzed in combing, separately
On the one hand actively operation analysis result services taxpayer.How more targeted to the preferential tax policy popularization of taxpayer,
Guided bone, the enabled taxpayer for enjoying preferential tax policy enjoy the bonus of preferential tax policy to the full in time, are the tax authorities
One of difficult point precisely serviced.
Existing preferential tax policy publicity is mainly passed by school guidance of paying taxes, special topic lecture, Website policy bulletin etc.
The mode of system, traditional approach one side timeliness is poor, does not on the other hand have specific aim to taxpayer, makes much to meet the requirements
The bonus that taxpayer can not enjoy preferential policies in time.
Summary of the invention
The object of the present invention is to provide a kind of preferential tax policy method for pushing and systems, excellent for accurately push tax revenue
Benevolent administration's plan.
To achieve the above object, the present invention provides following schemes:
A kind of preferential tax policy method for pushing, which comprises
The essential information of taxpayer is obtained, the essential information includes industry type, business scope, class of eligibility, enterprise
Type, organization's type, information of paying taxes, worker's information and preferential tax policy information；
Data are carried out to the essential information and extract conversion, obtain essential information feature vector；
Obtain the preferential tax policy that taxpayer currently enjoys；
Data extraction is carried out to the preferential tax policy, obtains preferential tax policy feature vector；
Calculate the similarity between the essential information feature vector and the preferential tax policy feature vector；
According to the similarity and given threshold, first object taxpayer set is obtained；
The second target taxpayer set is obtained according to the similarity by collaborative filtering；
According to first object taxpayer set and the second target taxpayer set, determine that final goal is paid taxes
People；
Frame, which is pushed, by message pushes preferential tax policy to the final goal taxpayer.
Optionally, described that data extraction conversion is carried out to the essential information, essential information feature vector is obtained, it is specific to wrap
It includes:
The essential information is segmented, multiple first keywords are obtained；
Calculate the word frequency of multiple first keywords；The word frequency indicates the frequency that first keyword occurs；
According to the number that the essential information and first keyword occur, inverse document frequency is calculated；
According to the word frequency and the inverse document frequency, essential information feature vector is determined.
Optionally, described that data extraction is carried out to the preferential tax policy, preferential tax policy feature vector is obtained, is had
Body includes:
The preferential tax policy is segmented, multiple second keywords are obtained；
Multiple second keywords are optimized, the second keyword after being optimized；
According to the second keyword after the optimization, preferential tax policy feature vector is determined.
Optionally, the meter of the similarity between the essential information feature vector and the preferential tax policy feature vector
Calculate formula are as follows:
Wherein AiFor the n dimension of i-th of preferential tax policy
Feature vector, BiFor the n dimensional feature vector of i-th of taxpayer, θ is the angle of two feature vectors.
Optionally, described by collaborative filtering, according to the similarity, determine that the second target taxpayer gathers, tool
Body includes:
According to the similarity, initial target taxpayer is obtained；
The initial target taxpayer is screened by collaborative filtering, obtains the second target taxpayer set.
A kind of preferential tax policy supplying system, the system comprises:
First obtains module, and for obtaining the essential information of taxpayer, the essential information includes industry type, manages model
It encloses, class of eligibility, the type of business, organization's type, information of paying taxes, worker's information and preferential tax policy information；
First extraction module carries out data to the essential information and extracts conversion, obtains essential information feature vector；
Second obtains module, the preferential tax policy currently enjoyed for obtaining taxpayer；
Second extraction module obtains preferential tax policy feature for carrying out data extraction to the preferential tax policy
Vector；
Computing module, for calculating between the essential information feature vector and the preferential tax policy feature vector
Similarity；
First determining module, for determining that first object taxpayer gathers according to the similarity and given threshold；
Second determining module, for according to the similarity, determining that the second target taxpayer collects by collaborative filtering
It closes；
Third determining module, for being collected according to first object taxpayer set and the second target taxpayer
It closes, determines final goal taxpayer；
Pushing module pushes preferential tax policy to the final goal taxpayer for pushing frame by message.
Optionally, first extraction module includes:
First participle unit obtains multiple first keywords for segmenting to the essential information；
Word frequency computing unit, for calculating the word frequency of multiple first keywords；The word frequency indicates that described first closes
The frequency that keyword occurs；
Inverse document frequency computing unit, the number for being occurred according to the essential information and first keyword,
Calculate inverse document frequency；
First determination unit, for determining essential information feature vector according to the word frequency and the inverse document frequency.
Optionally, second extraction module includes
Second participle unit obtains multiple second keywords for segmenting to the preferential tax policy；
Optimize unit, for optimizing to multiple second keywords, the second keyword after being optimized；
Second determination unit, for determining preferential tax policy feature vector according to the second keyword after the optimization.
Optionally, second determining module includes:
Initial target taxpayer's acquiring unit, for obtaining initial target taxpayer according to the similarity；
Screening unit obtains the second mesh for screening by collaborative filtering to the initial target taxpayer
Mark taxpayer's set.
Compared with prior art, the present invention has following technical effect that the present invention calculates the essential information feature first
Similarity between vector and the preferential tax policy feature vector；According to the similarity and given threshold, the is obtained
One target taxpayer set；Then it obtains the second target taxpayer according to the similarity by collaborative filtering and gathers root
According to first object taxpayer set and the second target taxpayer set, final goal taxpayer is determined；It avoids passing
The nothing of system is targetedly extensively casted net mode, and target taxpayer is precisely found；Frame is pushed to the final goal by message again
Taxpayer pushes preferential tax policy, realizes the accurate service of preferential tax policy.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is the flow chart of preferential tax policy of embodiment of the present invention method for pushing；
Fig. 2 is the structural block diagram of preferential tax policy of embodiment of the present invention supplying system.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
Fig. 1 is the flow chart of preferential tax policy of embodiment of the present invention method for pushing.As shown in Figure 1, a kind of preferential tax revenue
Policy method for pushing, which is characterized in that the described method includes:
Step 101: obtaining the essential information of taxpayer, the essential information includes industry type, business scope, qualification class
Type, the type of business, organization's type, information of paying taxes, worker's information and preferential tax policy information.Wherein preferential tax revenue political affairs
Plan information is 12366 consulting preferential tax policy information, is carried out after text need to be converted speech into using speech-to-text technology
Information extraction seeks advice from situation according to taxpayer and obtains the preferential tax policy information that taxpayer is concerned about.
Step 102: data being carried out to the essential information and extract conversion, obtain essential information feature vector.To the base
This information is segmented, and multiple first keywords are obtained；Calculate the word frequency of multiple first keywords；The word frequency indicates institute
State the frequency of the first keyword appearance；According to the number that the essential information and first keyword occur, inverse text is calculated
Shelves frequency；According to the word frequency and the inverse document frequency, essential information feature vector is determined.
Step1: participle.It includes industry type, business scope, class of eligibility, enterprise that taxpayer's essential information is integrated in collection
The information such as type, organization's type, information of paying taxes, worker's information, the data letter such as 12366 consulting preferential tax policy information
All extraction information of taxpayer are realized participle, the voice of 12366 consulting preferential tax policys by Chinese word segmentation tool by breath
Text need to be first converted to and then realize participle；
Step2: word frequency TF is calculated.Word frequency TF (t, d) is the number that word t occurs in document d.Spark calculates word frequency
Term vector commonly a kind of expression in similar NLP (Natural Language Processing natural language processing) is used
Mode: One-hot Representaion numbers in order each word, and each word is exactly a very long vector, to
The length of amount is equal to the size of vocabulary, and the only digital number on corresponding position is 1, remaining position is 0.Different SparkML
The feature vector that set is converted into regular length using hashing trick by TF is calculated, primitive character is by applying Hash letter
Number is mapped in index, then calculates word frequency according to the index of mapping；
Step3: inverse document frequency IDF is calculated.It obtains total number of documents in corpus D and the total number of documents DF of word t occurs
IDF inverse document frequency numerical value is calculated by taking natural logrithm to (D+1)/(DF (t, d)+1) in (t, D)；
Step4: TF-IDF is calculated.It is obtained by word frequency multiplied by inverse document frequency.What is obtained is taxpayer's feature vector.
Step 103: obtaining the preferential tax policy that taxpayer currently enjoys.
Step 104: data extraction being carried out to the preferential tax policy, obtains preferential tax policy feature vector.To institute
It states preferential tax policy to be segmented, obtains multiple second keywords；Multiple second keywords are optimized, are obtained excellent
The second keyword after change；According to the second keyword after the optimization, preferential tax policy feature vector is determined.
Step1: preferential tax policy keyword is generated.Obtain the file for all preferential tax policys enjoyed, root
It is combined according to file content and has enjoyed taxpayer's information, the keyword of preferential tax policy is generated by participle；
Step2: using in step 102 Step2, Step3, Step4 calculate preferential tax policy feature vector, calculate with
The similarity for having enjoyed taxpayer, for carry out keyword excellent with the lower preferential tax policy of taxpayer's similarity has been enjoyed
Change；
Step3: repeating Step2 until 60% or more preferential tax policy is not with the similarity for having enjoyed taxpayer
0, it determines final keyword and forms preferential tax policy feature vector.
Step 105: calculating similar between the essential information feature vector and the preferential tax policy feature vector
Degree.
Similarity S calculates as follows:
Wherein AiFor i-th preferential tax policy
N dimensional feature vector, BiFor the n dimensional feature vector of i-th of taxpayer, θ is the angle of two feature vectors.
Since the TF-IDF feature vector calculated is positive vector, so similarity S is in [0,1] section, and similarity S
It is bigger to illustrate that similarity is higher.
Step 106: according to the similarity and given threshold, obtaining first object taxpayer set.By setting phase
It is greater than threshold value T like degree S and obtains target taxpayer；Threshold value T is different and different according to preferential tax policy, and it is excellent to be set as a certain tax revenue
The minimum value of benevolent administration's plan and the similarity for having enjoyed taxpayer.Gathered based on content using the target taxpayer that NLP analysis obtains
U1={ U1 (i), i=1,2 ..., n }, the first object taxpayer collection that wherein U1 (i) is the preferential tax policy i that NLP is obtained
It closes.
Step 107: the second target taxpayer set is obtained according to the similarity by collaborative filtering.
Step1: tax payment assessed people's co-occurrence similarity finds out taxpayer's set similar with target taxpayer.Taxpayer is same
Existing similarity is improved cosine similarity, is calculated as follows:
Wherein wuvIndicate the similarity of taxpayer u and taxpayer v, N (u) indicates the preferential tax policy that taxpayer u is enjoyed
Set, N (v) indicate the preferential tax policy set that taxpayer v is enjoyed, and M (i) is the taxpayer's collection for enjoying preferential tax policy i
It closes, the inverse in molecule has punished popular preferential tax policy in preferential tax policy that taxpayer u and taxpayer v are enjoyed jointly
Their similarity is influenced.
Similar taxpayer's set is obtained by calculating similarity, indicates and the immediate taxpayer of taxpayer gathers.
Step2: preferential tax revenue that similar taxpayer enjoys but that target taxpayer does not enjoy is found out in taxpayer's set
Policy recommends target taxpayer.Taxpayer calculates the interest-degree of preferential tax policy as follows:
Wherein p (u, i) indicates interest-degree of the taxpayer u to preferential tax policy i, rviIndicate taxpayer v to preferential tax revenue
The interest of policy i is equal to the number that taxpayer v enjoys preferential tax policy i herein.Behavior-based control is based on user using improved
Collaborative filtering obtain target taxpayer's set U2={ U2 (i), i=1,2 ..., n }, wherein U2 (i) be it is improved
The the second target taxpayer set for the preferential tax policy i that collaborative filtering based on user obtains.
Step 108: being gathered according to first object taxpayer set and the second target taxpayer, determined final
Target taxpayer.To obtain the principle that final goal taxpayer follows " accurate ", using the set union that two methods obtain come
It obtains.Enjoy taxpayer set U (i)=U1 (i) the ∪ U2 (i), final goal taxpayer U={ U (i), i of preferential tax policy i
=1,2 ..., n }.
Step 109: frame being pushed by message and pushes preferential tax policy to the final goal taxpayer.
The specific embodiment provided according to the present invention, the invention discloses following technical effects:
(1) relevance based on preferential tax policy content with the taxpayer's information enjoyed constantly trains tax using NLP
Preferential policy keyword is received, is established " preferential tax policy dictionary "；
(2) the improved collaborative filtering based on user eliminates popular preferential tax revenue in taxpayer's similarity calculation
Policy influences the similarity of taxpayer, and the result for obtaining algorithm has more realistic meaning；
(3) NLP is combined with collaborative filtering, it then follows " accurate " principle, the taxpayer's collection for taking two kinds of models to obtain
Merge collection and be used as final goal taxpayer, realize the accurate service of preferential tax policy, traditional nothing is avoided targetedly extensively to spread
Net mode precisely finds target taxpayer；
(4) Fusion Model can constantly train Optimized model according to the importing of real time data, and technically integrated message push
Frame realizes that tax end and the intercommunication of taxpayer end, residence expropriation and management are providing what administrative decision supported to tax cadre among service
Meanwhile for taxpayer push personalized service, for Tax accurate service provide it is convenient.
Fig. 2 is the structural block diagram of preferential tax policy of embodiment of the present invention supplying system.As shown in Fig. 2, a kind of tax revenue is excellent
Benevolent administration's plan supplying system includes:
First obtains module 201, and for obtaining the essential information of taxpayer, the essential information includes industry type, warp
Seek range, class of eligibility, the type of business, organization's type, information of paying taxes, worker's information and preferential tax policy information.
First extraction module 202 carries out data to the essential information and extracts conversion, obtains essential information feature vector.
First extraction module 202 includes:
First participle unit obtains multiple first keywords for segmenting to the essential information；
Word frequency computing unit, for calculating the word frequency of multiple first keywords；The word frequency indicates that described first closes
The frequency that keyword occurs；
Inverse document frequency computing unit, the number for being occurred according to the essential information and first keyword,
Calculate inverse document frequency；
First determination unit, for determining essential information feature vector according to the word frequency and the inverse document frequency.
Second obtains module 203, the preferential tax policy currently enjoyed for obtaining taxpayer.
Second extraction module 204 obtains preferential tax policy spy for carrying out data extraction to the preferential tax policy
Levy vector.
Second extraction module 204 includes:
Second participle unit obtains multiple second keywords for segmenting to the preferential tax policy；
Optimize unit, for optimizing to multiple second keywords, the second keyword after being optimized；
Second determination unit, for determining preferential tax policy feature vector according to the second keyword after the optimization.
Computing module 205, for calculate the essential information feature vector and the preferential tax policy feature vector it
Between similarity.
First determining module 206, for determining that first object taxpayer collects according to the similarity and given threshold
It closes.
Second determining module 207, for determining that the second target is paid taxes according to the similarity by collaborative filtering
People's set.
Second determining module includes:
Initial target taxpayer's acquiring unit, for obtaining initial target taxpayer according to the similarity；
Screening unit obtains the second mesh for screening by collaborative filtering to the initial target taxpayer
Mark taxpayer's set.
Third determining module 208, for according to first object taxpayer set and the second target taxpayer
Set, determines final goal taxpayer.
Pushing module 209 pushes preferential tax policy to the final goal taxpayer for pushing frame by message.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For system disclosed in embodiment
For, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is said referring to method part
It is bright.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation
Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (9)
1. a kind of preferential tax policy method for pushing, which is characterized in that the described method includes:
Obtain taxpayer essential information, the essential information include industry type, business scope, class of eligibility, the type of business,
Organization's type, information of paying taxes, worker's information and preferential tax policy information；
Data are carried out to the essential information and extract conversion, obtain essential information feature vector；
Obtain the preferential tax policy that taxpayer currently enjoys；
Data extraction is carried out to the preferential tax policy, obtains preferential tax policy feature vector；
Calculate the similarity between the essential information feature vector and the preferential tax policy feature vector；
According to the similarity and given threshold, first object taxpayer set is obtained；
The second target taxpayer set is obtained according to the similarity by collaborative filtering；
According to first object taxpayer set and the second target taxpayer set, final goal taxpayer is determined；
Frame, which is pushed, by message pushes preferential tax policy to the final goal taxpayer.
2. preferential tax policy method for pushing according to claim 1, which is characterized in that it is described to the essential information into
Row data extract conversion, obtain essential information feature vector, specifically include:
The essential information is segmented, multiple first keywords are obtained；
Calculate the word frequency of multiple first keywords；The word frequency indicates the frequency that first keyword occurs；
According to the number that the essential information and first keyword occur, inverse document frequency is calculated；
According to the word frequency and the inverse document frequency, essential information feature vector is determined.
3. preferential tax policy method for pushing according to claim 1, which is characterized in that described to the preferential tax revenue political affairs
Plan carries out data extraction, obtains preferential tax policy feature vector, specifically includes:
The preferential tax policy is segmented, multiple second keywords are obtained；
Multiple second keywords are optimized, the second keyword after being optimized；
According to the second keyword after the optimization, preferential tax policy feature vector is determined.
4. preferential tax policy method for pushing according to claim 1, which is characterized in that the essential information feature vector
The calculation formula of similarity between the preferential tax policy feature vector are as follows:
Wherein AiFor i-th of preferential tax policy n dimensional feature to
Amount, BiFor the n dimensional feature vector of i-th of taxpayer, θ is the angle of two feature vectors.
5. preferential tax policy method for pushing according to claim 1, which is characterized in that described to be calculated by collaborative filtering
Method determines that the second target taxpayer gathers, specifically includes according to the similarity:
According to the similarity, initial target taxpayer is obtained；
The initial target taxpayer is screened by collaborative filtering, obtains the second target taxpayer set.
6. a kind of preferential tax policy supplying system, which is characterized in that the system comprises:
First obtains module, for obtaining the essential information of taxpayer, the essential information include industry type, business scope,
Class of eligibility, the type of business, organization's type, information of paying taxes, worker's information and preferential tax policy information；
First extraction module carries out data to the essential information and extracts conversion, obtains essential information feature vector；
Second obtains module, the preferential tax policy currently enjoyed for obtaining taxpayer；
Second extraction module obtains preferential tax policy feature vector for carrying out data extraction to the preferential tax policy；
Computing module, it is similar between the essential information feature vector and the preferential tax policy feature vector for calculating
Degree；
First determining module, for determining that first object taxpayer gathers according to the similarity and given threshold；
Second determining module, for according to the similarity, determining that the second target taxpayer gathers by collaborative filtering；
Third determining module, for being gathered according to first object taxpayer set and the second target taxpayer, really
Determine final goal taxpayer；
Pushing module pushes preferential tax policy to the final goal taxpayer for pushing frame by message.
7. system according to claim 6, which is characterized in that first extraction module includes:
First participle unit obtains multiple first keywords for segmenting to the essential information；
Word frequency computing unit, for calculating the word frequency of multiple first keywords；The word frequency indicates first keyword
The frequency of appearance；
Inverse document frequency computing unit is calculated for the number according to the essential information and first keyword appearance
Inverse document frequency；
First determination unit, for determining essential information feature vector according to the word frequency and the inverse document frequency.
8. system according to claim 6, which is characterized in that second extraction module includes
Second participle unit obtains multiple second keywords for segmenting to the preferential tax policy；
Optimize unit, for optimizing to multiple second keywords, the second keyword after being optimized；
Second determination unit, for determining preferential tax policy feature vector according to the second keyword after the optimization.
9. system according to claim 6, which is characterized in that second determining module includes:
Initial target taxpayer's acquiring unit, for obtaining initial target taxpayer according to the similarity；
Screening unit obtains the second target and receives for being screened by collaborative filtering to the initial target taxpayer
Tax people set.
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| CN201810762102.5A CN109064301A (en) | 2018-07-12 | 2018-07-12 | A kind of preferential tax policy method for pushing and system |
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| CN201810762102.5A CN109064301A (en) | 2018-07-12 | 2018-07-12 | A kind of preferential tax policy method for pushing and system |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110825685A (en) * | 2019-11-27 | 2020-02-21 | 新华蓝海(北京)人工智能技术有限公司 | 360 intelligent tax accounting device |
| CN111639161A (en) * | 2020-05-29 | 2020-09-08 | 中国工商银行股份有限公司 | System information processing method, apparatus, computer system and medium |
| CN111815421A (en) * | 2019-04-09 | 2020-10-23 | 百度在线网络技术（北京）有限公司 | Tax policy processing method and device, terminal equipment and storage medium |
| CN112308693A (en) * | 2020-11-03 | 2021-02-02 | 桂林格林拜尔网络科技有限公司 | Finance and tax optimization system and method based on big data |
| CN112434224A (en) * | 2020-12-08 | 2021-03-02 | 神州数码信息系统有限公司 | Tax preferential policy recommendation method and system based on knowledge graph |
-
2018
- 2018-07-12 CN CN201810762102.5A patent/CN109064301A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111815421A (en) * | 2019-04-09 | 2020-10-23 | 百度在线网络技术（北京）有限公司 | Tax policy processing method and device, terminal equipment and storage medium |
| CN111815421B (en) * | 2019-04-09 | 2024-03-01 | 百度在线网络技术（北京）有限公司 | Tax policy processing method and device, terminal equipment and storage medium |
| CN110825685A (en) * | 2019-11-27 | 2020-02-21 | 新华蓝海(北京)人工智能技术有限公司 | 360 intelligent tax accounting device |
| CN111639161A (en) * | 2020-05-29 | 2020-09-08 | 中国工商银行股份有限公司 | System information processing method, apparatus, computer system and medium |
| CN112308693A (en) * | 2020-11-03 | 2021-02-02 | 桂林格林拜尔网络科技有限公司 | Finance and tax optimization system and method based on big data |
| CN112434224A (en) * | 2020-12-08 | 2021-03-02 | 神州数码信息系统有限公司 | Tax preferential policy recommendation method and system based on knowledge graph |
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