CN109002538A - Legal advice cloud platform and method based on database - Google Patents
Legal advice cloud platform and method based on database Download PDFInfo
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Abstract
The present patent application discloses a kind of legal advice cloud platform based on database, is related to legal advice field, the cloud platform including collection terminal and with collection terminal communication connection；The collection terminal, for acquiring asking questions and will ask questions and sending cloud platform to for consultant's proposition；The cloud platform, the database including being used to store all kinds of legal provisions, and the feedback module being connect with database communication；The feedback module asks questions matched legal provision as consulting answer feedback to consultant with this for asking questions to extract from database.Present invention also provides a kind of law inquiry methods based on database.Legal advice Self-Service is able to carry out by the application.
Description
Technical field
The present invention relates to legal advice service fields, and in particular to a kind of legal advice cloud platform based on database.
Background technique
Law is that civilized society is kept order, and constrains public means and method.Law and everyone is closely bound up.So
And because law is with higher professional, the amateur talent is difficult to grasp and flexibly use.Therefore, rule of seeking help is generally required
The consulting of Shi Jinhang account and relative legal problems.
However, consultant expenses is relatively expensive, and the counseling services of lawyer can not because the number of employees of lawyer is relatively fewer
Enough meet proprietary consulting requirement.It is able to carry out to solve the problems, such as that the legal advice of ordinary populace is badly in need of providing one kind now
The legal advice cloud platform and law inquiry method of Self-Service.
Summary of the invention
The invention is intended to provide a kind of to provide the legal advice cloud platform based on database of self-service law counseling services.
To achieve the above objectives, following scheme is provided:
Scheme one: the legal advice cloud platform based on database, the cloud including collection terminal and with collection terminal communication connection
Platform；
The collection terminal, for acquiring asking questions and will ask questions and sending cloud platform to for consultant's proposition；
The cloud platform including the database for storing all kinds of legal provisions, and is connect anti-with database communication
Present module；
The feedback module asks questions matched legal provision conduct with this for asking questions to extract from database
Answer feedback is seeked advice to consultant.
Explanation of nouns:
Legal provision: referring to intercepting by law expert, can directly use the complete words and phrases of case result description.
The effect and advantage of this programme are:
Asking questions for consultant's proposition is acquired by collection terminal, is found from database by feedback module and the consulting
The legal provision that problem matches, to consultant, makes consultant with the help of no lawyer as consulting answer feedback, can
Self-Service is carried out using platform.
This programme can accomplish the unified pipe to storage content by all kinds of legal provisions of database purchase in cloud platform
Reason updates.By the setting of collection terminal, asking questions for ordinary populace can be acquired, form input and be supplied to feedback mould
Block, so make feedback module for each ask questions offer targetedly seek advice from answer, make everyone that can access a pair
One legal advice service.
Scheme two: further, the database includes legal provision memory module, be used to classification storage legal provision and
For characterizing the label of each legal provision.
Legal provision classification storage facilitates feedback module that can more rapidly match and asks questions and seek advice from answer, more
Add and efficiently provides counseling services for consultant.With each legal provision of tag characterization, label is made to form the index of legal provision,
Facilitate legal provision corresponding with the matching asked questions.
Scheme three: further, the database includes relationship storage module, is made for storing the label of each legal provision
Each legal provision forms level association.
Level association refers to the framework relationship according to legal provision level distribution from top to bottom itself, by legal provision
The subordinate relation and communication with one another of itself, the association being substituted between the corresponding label of each legal provision are convenient directly to pass through
Label is corresponding with the matching asked questions to ask questions corresponding legal provision to be quickly found out.
Scheme four: further, the feedback module includes elements recognition module；For defining element word, and according to element
Word is from asking questions middle extraction keyword.
By the element word of definition, it is transformed into the keyword one by one for meeting element word by asking questions, makes keyword can
To compare with label, by legal provision and carry out Rapid matching is asked questions, forms consulting answer.
Scheme five: further, each legal provision corresponds to multiple labels, and label includes being used to indicate that the legal provision is preferential
The grade word and conjunctive word for indicating the legal provision Yu other legal provision correlation degrees of grade；The relationship stores mould
Block according to each legal provision priority and correlation degree opening relationships net；The feedback module according to the grade of priority from
The corresponding legal provision of high to Low push label.
By relationship storage module set up each legal provision and other legal provisions relationship and each law item
The priority of text makes during matching push, and legal provision push can be carried out according to priority level, and is matching certain
Other relative legal provisions can be quickly found out after one legal provision by network of personal connections.
Scheme six: further, the feedback module further includes case processing module, and the case processing module includes being used to
The consulting model that legal advice problem is handled；The consulting model is according to decision-tree model to the keyword asked questions
It is handled to obtain output result；The output result is used to be matched to obtain the method for feedback with the label of legal provision
Restrain provision.
By case processing module, making to ask questions can carry out being calculated according to consulting model output as a result, and leading to
It crosses and exports result and can find matching legal provision as consulting answer feedback to consultant.Make all ask questions
It can accurately be answered by model.
The present invention also provides a kind of law inquiry method based on database for being able to carry out legal advice Self-Service,
Include the following steps:
Step 1 is asked questions by collection terminal to consultant's acquisition.
Step 2, collection terminal will ask questions and be sent to cloud platform；
Step 3, the feedback module in cloud platform are input in consulting model from middle extraction keyword is asked questions；
Output result is calculated according to pre-set decision Tree algorithms in step 4, consulting model；
Step 5, feedback module will export result and matched to obtain consulting answer with the legal provision in database；
Step 6, cloud platform will seek advice from answer feedback to consultant.
The advantages of this method and effect are:
After this method is by acquisition the asking questions of consultant, pass through the consulting model and law being set in advance in platform
Provision obtains consulting answer feedback to consultant, and what consultant needed to do is only that description clearly asks questions.It can make
Consultant passes through the self-service legal advice answer for obtaining profession of this method.Be conducive to promote this method in ordinary populace, with
Promotion of legal knowledge dynamics is improved, masses is helped to find quality-high and inexpensive and one-to-one legal advice solution.
Detailed description of the invention
Fig. 1 is that the present invention is based on the logic diagrams of the legal advice cloud platform of database.
Fig. 2 is that the present invention is based on the flow charts of the law inquiry method of database.
Specific embodiment
Below by the further details of explanation of specific embodiment:
Embodiment is substantially as shown in Fig. 1: the legal advice cloud platform based on database, including collection terminal and with acquisition
Hold the cloud platform of communication connection.
The collection terminal acquires for acquiring asking questions and will ask questions and sending cloud platform to for consultant's proposition
End can be using any equipment with the audio inputs such as sound pick-up or touch screen, keyboard or text entry capacity, can be with
It is mobile phone, computer, robot or other electronic equipments.
The cloud platform including the database for storing all kinds of legal provisions, and is connect anti-with database communication
Present module.
Wherein, database includes that legal provision memory module, semantics recognition module, subscriber information storing module and relationship are deposited
Module is stored up, feedback module includes problem acquisition memory module, elements recognition module and case processing module.
Legal provision memory module corresponds to table for storing legal provision, be used to carry out according to law classification and chapters and sections by
Layer classification storage and all kinds of legal provisions of real-time update；Legal provision corresponds in table, including a plurality of legal provision and correspondence are often
The set of tags of a legal provision, set of tags include all multiple labels for being used to indicate this legal provision feature, are specifically included
For indicating the summary word of the legal provision content, for indicating that the legal provision is subordinate to the classification of some chapters and sections of certain code
Word, for indicate the legal provision in the database the grade word of access privileges sequence, for indicating the legal provision and its
The conjunctive word of his legal provision correlation and other words that can be used to indicate the legal provision feature, for example be used to describe
The word of age level, the gender of region, related party that the legal provision often occurs etc..Here legal provision, refers to
Be effective law, regulation, in code according to the law original text sentence divided that completely looks like.Summarizing word is by law
Expert extracts in the sentence, indicates each emphasis word of its causation of law and conditional relationship.In each legal provision
Summarizing is specifically causality or conditional relationship between word, is determined by the law meaning of each legal provision itself.
Relationship storage module, for storing the corresponding priority of grade word and each method of each classification legal provision
Restrain provision relevance grades corresponding with the conjunctive word of other legal provision；For storing in same portion's law, regulation or code,
Chapters and sections relationship, subordinate relation between each legal provision are created as the relational graph of a similar tree.For storing not
In same law, regulation or code, each legal provision and an other law, regulation, between the legal provision in code
Relationship forms the relational graph of a similar reticular structure.
Problem acquires memory module, for storing asking questions for consultant's proposition；The acquisition mode asked questions has more
Kind, it can be direct text input, be also possible to after being inputted by voice input or image after progress semantics recognition extraction again
It is transformed into text input.The problems in the present embodiment acquisition memory module can connect the touch for carrying out text information input
Screen, or connection are able to carry out the sound pick-up of voice pickup, will directly be asked questions by touch screen and be write as text input transmission
Into problem acquisition memory module, it will be asked questions by sound pick-up and existing Iflytek speech recognition is input to by voice
In device, voice input is changed into after text input by Iflytek speech recognition device to be sent in problem acquisition memory module.
Elements recognition module, definition are used to characterize the element word of keyword, and according to element word from asking questions middle extraction
The keyword asked questions is characterized out；And record the number of keyword appearance.Element refers to law expert according to law article system
Fixed is used to judge various conditions required for a case, and element word is exactly the word for describing these elements, and keyword is just
Be it is each ask questions for each element word, showed in currently asking questions embodied case, meet element
The particular content of word.
The semantics recognition module of similar semantic net is provided between elements recognition module and problem acquisition memory module.Language
Adopted net is to the network settings by inputting information extraction semanteme in brief, and the semantic net of broad sense is to carry out semantic
The tool of identification.In general, ordinary populace, when proposing to ask questions, all very colloquial style, many words are not special
Industry inaccuracy, by semantics recognition module, can by it is colloquial ask questions to be converted into redescribed by legal profession term
Law vocabulary composition sentence.
It wherein, include vocabulary memory module for storing each legal profession vocabulary in semantics recognition module, semanteme is known
Problem is acquired in memory module and has been converted into asking questions and the law in vocabulary memory module for text information by other module
Specialized vocabulary compares, and will ask questions the keyword for being converted to and being indicated one by one with law specialized vocabulary.It is consulted to improve
The precision of law conversion is inscribed in inquiry, and synonym dictionary and homonym dictionary are provided in vocabulary memory module.
By synonym dictionary, the word in asking questions can be carried out with the spoken vocabulary in synonym dictionary one by one
Comparison, finds same spoken vocabulary, and then find the corresponding law vocabulary of the spoken language vocabulary, is replaced with the law vocabulary
The word successively carries out, and colloquial ask questions is converted to law vocabulary.There is provided for the subsequent matching for seeking advice from answer can
Energy.The comparison of synonym dictionary is handled mainly for asking questions for textual.
By homonym dictionary, can by saved with audio-frequency information ask questions in word one by one with homonym dictionary
In spoken vocabulary compare, find the spoken vocabulary of same pronunciation, and then find the corresponding law of spoken language vocabulary
Vocabulary, replaces the word with the law vocabulary, successively carries out, and colloquial ask questions is converted to law vocabulary.
In synonym dictionary and homonym dictionary, a law vocabulary corresponds to multiple spoken vocabulary.Generally, a word
There are many kinds of the expression ways of language spoken language, and multiple spoken vocabulary are corresponding with a law vocabulary, can be different by form of presentation
Word be changed into unified, rigorous law expression.
Equally there is the attribute word label for indicating preferred selection order on each law vocabulary, makes semantics recognition module right
When than the word in asking questions with spoken vocabulary, the high law vocabulary pair of those attribute word priority can be preferentially pushed
The spoken vocabulary answered.The time of semantics recognition can not only be saved in this way, additionally it is possible to which help solves those one spoken vocabulary pair
The case where answering multiple law vocabulary.Because Chinese of extensive knowledge and profound scholarship, look like more, along with the differentiation of various spoken languages, make one
Word may contain there are many meaning or the word of pronunciation of the same race agree can there are many meanings, therefore in synonym dictionary and unisonance
In word dictionary, the case where corresponding to multiple law vocabulary there are a spoken vocabulary, at this time, the attribute word of law vocabulary is just played
Identify the effect of judgement.The higher law vocabulary of attribute word priority-labeled, it is easier be selected as replacement ask questions middle correspondence
The law of word is expressed.And when extracting keyword below, law vocabulary its attribute word in the same sentence it is preferential
The grade the high also easier to be extracted as keyword.The number that the priority of attribute word and each vocabulary are selected is positively correlated, some
Vocabulary is selected more, and attribute priority is higher.In the present embodiment, semantics recognition module can be directly with semantic net
Form exists.
Subscriber information storing module, for storing and updating user information；The user information mainly includes consultant's information
One of them is only recorded if consultant and party are a people with party's information.By collecting record user's letter
Breath, can record related personnel's information of a certain specific case as much as possible, so occurred by the case region, case kind
Class distinguishes different crowds, for relevant department's legislation, amends the law and the adjustment of relevant policies and decision provide data and support.Its
In, party's information includes at least essential informations, these essential informations and the methods such as age, gender, birthplace, residence, nationality
Other words in rule provision memory module in the label of each legal provision mutually echo, and enable using party's information in law
Provision selection is upper more accurate.
Case processing module, is stored with consulting model, the consulting model include multiple legal advice position paper templates with
And judgment models corresponding with each legal advice position paper template；Each judgment models are all made of decision-tree model, each sentence
Disconnected model includes multiple input factors and output factor.Input factor is made of multiple element words.Element in input factor
Word is for asking questions.Output factor is made of multiple pre-set introducers, the introducer in output factor
It is for legal provision.Different judgment models, input factor are different with the output set content of factor.
Firstly, consulting model is by one by one by according to the element word of all judgment models, the method from semantics recognition module
Language is specialized asks questions extraction keyword for rule, when all element words of a judgment models can be asked questions from this
In extract keyword, then select the judgment models as currently used model carry out model calculating.If there is multiple judgements simultaneously
Model is all met the requirements, then is calculated separately simultaneously using multiple judgment models, obtain multiple consulting answers.Such case,
The case where being suitble to the same case to be related to different laws.
Judgment models, according to the decision tree that the judgment models are formed, are selected defeated when calculating from all keywords
Enter the corresponding keyword of factor to be input in decision-tree model, according to each node of decision tree and corresponding secondary input because
Element, successively selection corresponds to the keyword of each node from remaining keyword, is finally exported according to the structure of decision tree
Each label of legal provision each in the introducer for the factor that exports, with legal provision memory module is compared, is looked for by factor
Out with the matched label of introducer, the corresponding legal provision of the label is extracted from legal provision memory module, is answered as consulting
Case output.In order to make consultant more intuitively obtain specialty legal advisory opinion, by the legal provision of extraction and collect
Input factor and all secondary input factors seek union together, all information are all filled up to the correspondence of legal advice consulting book
On position, complete legal advice consulting book is formed.
In order to guarantee the accurate of legal advice consulting book, when specifically forming legal advice consulting book, adopted from problem
Collection memory module and the same timing node of subscriber information storing module extraction, the same case of same consultant, and according to element
Extraction module and case processing module extract this from legal provision module and ask questions corresponding legal provision, integrate shape
At legal advice consultant service.
Problem, which acquires memory module, can pass through elements recognition module and relationship to obtain complete case information input
Memory module forms multiple problems relevant to consultant's presentation content, is formed with consultant and interact communication, until all and official communication
Inquiry person describes after associated all problems have all asked, by all description informations of consultant, formation is completely asked questions.
Specifically, when problem acquisition memory module receive the initial description content of consultant after, semantics recognition module from
According to all elements recognition keywords in elements recognition module, and by these keywords directly with it is each in legal provision module
The label of a legal provision compares, and corresponding legal provision is searched out in legal provision module, and store according to relationship
Other legal provisions of the legal provision correlation degree from high to low are found in module, because for each in legal provision module
Legal provision is previously provided with corresponding booting problem, these booting problems are successively fed back to consultant, guidance consultant into
Row is answered, and problem acquisition memory module is enable to obtain more fully case information, until the description content of consultant will not
Cause new relative laws provision again, and the related booting problem of institute all inquires and finish that problem acquires memory module at this time
All description contents being collected into are exactly asking questions for the case.
When constructing legal advice consulting Database, firstly, all legal provisions are intercepted by law expert, are torn open
Classification storage carries out remarks mark into law striped memory module, and for each legal provision sentence completely explained after point
Note forms the set of tags that is made of multiple labels, in these labels, including at least showing which law the legal provision belongs to
The classificating word of which chapters and sections, indicates each label priority of the legal provision at the summary word for indicating legal provision content summary
Grade word, indicate the conjunctive word of the legal provision Yu other legal provision correlation degrees, and for indicating the legal provision
Often there are other words of other attributes such as region in frequency of occurrence.
Then, all labels of all legal provisions are closed according to the subordinate relation of legal provision, chapters and sections relationship, association
System etc. carries out label contextual definition, and all labels is made to be capable of forming a network of personal connections from top to bottom.
Third, the initial value by problem acquisition memory module is zero, when user terminal starts to acquire asking questions for consultant
Afterwards, it records and stores and asks questions, extract the keyword in asking questions, progress will be asked questions keyword same number more
Classification storage and update.
4th, in elements recognition module, store original factor word and these corresponding priority of element word and association
Degree obtains the most element word of frequency of occurrence after user terminal starts to acquire and ask questions automatically from all ask questions
Its priority is improved, when making carry out keyword extraction next time, the advantage distillation corresponding keyword of element word.
5th, zero is set by the original storage of subscriber information storing module, after user terminal is started to work, according to element
The element word for belonging to user information in extraction module carries out user information collection and update.
6th, in case processing module, be stored in advance multiple judgment models and with the one-to-one law of judgment models
Position paper template, with same timing node, based on what same consultant proposed asks questions, according in elements recognition module
Element word extracts the keyword in asking questions, and according to the judgment models of selection, it is defeated will to meet its keyword for inputting factor
Enter into judgment models, by the judgement of decision tree, obtains output factor.According to the introducer in output factor, in law item
Label identical with these introducers is found in literary memory module, extracts the corresponding legal provision of these labels and consultant's consulting
Ask questions together, fill in as content into legal opinion template, formed legal opinion.
As shown in Fig. 2, specifically comprising the following steps: when carrying out legal advice using database
S1 is asked questions by collection terminal to consultant's acquisition.
S2, by ask questions by extraction of semantics be converted to stated with law nomenclature ask questions after, will ask questions
It is sent to cloud platform.
S3, from asking questions middle extraction keyword.
Keyword is input in consulting model and calculates according to decision Tree algorithms by S4, obtains output result.
S5 matches output result with the label of legal provision, and the introducer for finding output result is identical as label
Legal provision.
S6 extracts legal provision in legal opinion template together with all input factors, forms legal advice
Position paper.
S7 feeds back to consulting using legal advice position paper as consulting answer feedback to collected user terminal is mounted with
Person.
For example, there is a consultant that can adopt to a description as described in divorce, consultant is taught before collection terminal
With oral account or perhaps pen is write to carry out event description, and while description, the description information that collection terminal will acquire is sent to
These description informations are divided into word one by one by problem acquisition module, semantics recognition module, and by these word synonyms
Spoken vocabulary of the dictionary in other words in homonym dictionary compares, and finds same or unisonance spoken vocabulary, and will
The corresponding law vocabulary of the spoken language vocabulary replaces corresponding word, if a spoken vocabulary corresponds to multiple law vocabulary, selects
Select that law vocabulary of attribute word highest priority.All description informations that consultant provides to collection terminal are all gradually stored
Into problem acquisition memory module, until acquisition terminal is terminated to the acquisition of the case description information, the same case
All case description informations of the same consultant in range at the same time constitute one and ask questions.It is same in asking questions
One time range and same consultant and same case, these information are all that collection terminal is direct using the prior art in acquisition
Obtain, for example, using direct access inquiry consultant user information or be directly that collection terminal generates when communicating
The existing means such as self-clocking, unique access username, IP address on physical layer are defined.
In elements recognition module, be previously stored with some classification words for defining requirement, be such as related to law name,
Bridegroom's or husband's side name, wife's side name, mistake description etc..When asking questions when continuous collecting is not over, elements recognition mould
Block will carry out keyword extraction to the legal issue after the translation of semantics recognition module, and legal issue at this time is because of consulting
Problem is not acquired and is finished, and legal issue is also finished without conversion, is all the state carried out simultaneously, is conducive to quickly carry out in this way
Information identification, the case description information for also facilitating control collection terminal to collect sufficiently complete completely ask questions to constitute.?
Consultant describes a case about divorce in this example, then according to element word law name to should belong to such word
It, quickly can will be in " marriage law " in " marriage " two words and set in case description information that consultant provides in set
Two word successful match of marriage, so that two words of marriage is come out as the corresponding keyword extraction of element word, with such
It pushes away, can also extract mistake and be described as overstepping the limit, during collection terminal acquisition, by having the keyword being extracted
Compared with the label in legal provision memory module, find corresponding legal provision, then found by relationship module and this
The asked questions of the corresponding storage of each legal provision being found are sent to by the associated other legal provision of a little legal provisions
Consultant answer, wherein asked questions refer to it is pre-stored be used to receiving asking questions and mutually coping with for consultant
After words, it is used to put question to consultant, so that consultant provides the default problem of more relevant informations.Make consultant in description case
When incomplete, consultant is inquired in a manner of problem by collection terminal and display end.It is closed from relationship module
When joining legal provision and problem selection, pushed from big to small according to the grade of the degree of association.Until the method duplicated
It restrains provision and when asked questions, illustrates that all relative laws provisions and correspondence problem have been asked, at this time collection terminal and aobvious
Show end stop with regard to this case and consultant continue interact communication, formed completely to seek advice from problem acquisition memory module and be asked
Topic, and then complete legal issue is obtained by the identification of semantics recognition module.
Keyword is extracted in the continuation of elements recognition module from legal issue, until the legal issue is according to case processing module
In until the corresponding element word extraction of all input factors finishes keyword.Because consulting model includes multiple judgment models, and
The element word that the input factor of each judgment models includes is different, if general one asks questions independent case, then its quilt
The keyword extracted can only fully meet the input factor an of judgment models, this judgment models being satisfied is i.e. at this time
Asking questions for this needs judgment models to be used.It such as, is divorce case in this example, then its crucial base for being extracted
The relevant word of corresponding to marriage law judgment models on this, for example, overstep the limit, be small three, family is sudden and violent, get married, divorce etc., and very
It will appear murder less, set fire, steal etc. in other types law and judging the word that will appear.
After selected judgment models, which is to pre-set the input of input factor and secondary input factor is defeated
Enter, judges the decision-tree model constructed according to decision Tree algorithms finally by continuous selection.For example, marriage law in this example
In judgment models, the element word of the decision tree of the first order is that both sides are the servicemen or both sides are not the servicemen, should
It is soldier that the corresponding descriptor of element word, which has, be all soldier, be not soldier etc., by all keys for comparing the legal issue
Word is selected, and the corresponding element word of the input factor is that soldier enters if having selected not to be soldier after comparison
The operation of next stage, the bridegroom's or husband's side whether there is bigamy, which is bigamy, twice wedding etc., then at case
Reason module continuation looks for whether the keyword same or similar with these descriptors in all keywords, and then under entrance
The calculating of level-one.In addition to the factor that inputs in the first order is we term it other than input factor, our other all habitual addresses
For secondary input factor.The judgment models of each method type of law are the bodies constructed in strict accordance with the judgement thinking of its relevant law
The decision-tree model of existing statutory rules.After last calculate, obtained result is pre-stored certain introducers.This example
In ask questions after calculating obtained introducer be " the 32nd article of marriage law ", case processing module is according to this guidance
Word is compared with each label in legal provision memory module, finds matching legal provision, by marriage law the 32nd
The legal provision content of item feeds back to consultant as a part of consulting answer.Because being directed to each judgement mould in consulting model
Type is stored with legal opinion template, and each composition legal opinion is provided on the automatic filling column in legal opinion template
Element word, according to the prior art, the corresponding key words content of element word that will be extracted in judgment models deterministic process automatically
And the result generated after each decision node in judgment models is filled on corresponding automatic filling column, forms one completely
Divorce legal opinion.
It is true that the generation of legal opinion and the reasonable judgement of case all rely on asking questions for consultant's offer
Reliably, it however, if being difficult to identify whether consultant's those set forth is true not by proof, and during deciding a case, requires
Juristic fact is proved with evidence, and in these evidences, invoice is the most frequently used evidence for being also easiest to obtain.On collection terminal
It is provided with camera and ultraviolet light, camera opens ultraviolet light when shooting invoice, by existing image comparison technology, will clap
The invoice photo to be formed is taken the photograph to compare with pre-stored fluorescence photo, for identifying that invoice photo whether there is fluorescence, if
There are fluorescence then to prove that the invoice is true.So the corresponding juristic fact of the invoice is also identified as really, before this
The legal opinion formed under mentioning has probative effect advantageously.
Meanwhile further proving that the juristic fact of consultant's offer is true and reliable, pass through the pickup being arranged on collection terminal
Device complete documentation consultant it is all ask questions and it is corresponding be described, enable consultant afterwards and can not deny from
The juristic fact that oneself once described forms the constraint of a kind of couple of consultant, and consultant is enable to demonstrate,prove it certainly by the behavior of itself
The content of description is true.The audio that collection terminal enrolls sound pick-up saves as former state, and collection terminal is by system in interacting with consultant
All information provided are converted into the synchronous preservation of text information.It in this way can be by the interacting Question-Answer between system and consultant
The complete dialogue of record, understands the true intention of consultant's expression by talking with when helping to inquire afterwards.Meanwhile by right
Lossless, the preservation as former state of consultant's audio, avoid the fraud operation for being possible to occur below, make the audio of preservation as former state more
With probative effect.
What has been described above is only an embodiment of the present invention, and the common sense such as well known specific structure and characteristic are not made herein in scheme
Excessive description, technical field that the present invention belongs to is all before one skilled in the art know the applying date or priority date
Ordinary technical knowledge can know the prior art all in the field, and have using routine experiment hand before the date
The ability of section, one skilled in the art can improve and be implemented in conjunction with self-ability under the enlightenment that the application provides
This programme, some typical known features or known method should not become one skilled in the art and implement the application
Obstacle.It should be pointed out that for those skilled in the art, without departing from the structure of the invention, can also make
Several modifications and improvements out, these also should be considered as protection scope of the present invention, these all will not influence the effect that the present invention is implemented
Fruit and patent practicability.The scope of protection required by this application should be based on the content of the claims, the tool in specification
The records such as body embodiment can be used for explaining the content of claim.
Claims (7)
1. the legal advice cloud platform based on database, it is characterised in that: communicated to connect including collection terminal and with collection terminal
Cloud platform；
The collection terminal, for acquiring asking questions and will ask questions and sending cloud platform to for consultant's proposition；
The cloud platform, the database including being used to store all kinds of legal provisions, and the feedback mould being connect with database communication
Block；
The feedback module asks questions matched legal provision as consulting with this for asking questions to extract from database
Answer feedback is to consultant.
2. the legal advice cloud platform according to claim 1 based on database, it is characterised in that: the database includes
Legal provision memory module, for classification storage legal provision and for characterizing the label of each legal provision.
3. the legal advice cloud platform according to claim 2 based on database, it is characterised in that: the database includes
Relationship storage module makes each legal provision form level association for storing the label of each legal provision.
4. the legal advice cloud platform according to claim 1 based on database, it is characterised in that: the feedback module packet
Include elements recognition module；For defining element word, and according to element word from asking questions middle extraction keyword.
5. the legal advice cloud platform according to claim 3 based on database, it is characterised in that: each legal provision pair
Answer multiple labels, label includes grade word for indicating the legal provision priority and for indicating the legal provision and its
The conjunctive word of his legal provision correlation degree；The relationship storage module according to each legal provision priority and correlation degree
Opening relationships net；The feedback module pushes the corresponding legal provision of label according to the grade of priority from high to low.
6. the legal advice cloud platform according to claim 5 based on database, it is characterised in that: the feedback module is also
Including case processing module, the case processing module includes the consulting model for being handled legal advice problem；Institute
Consulting model is stated the keyword asked questions is handled according to decision-tree model to obtain output result；The output result is used
To be matched to obtain the legal provision for feedback with the label of legal provision.
7. the law inquiry method based on database, characterized by the following steps:
Step 1 is asked questions by collection terminal to consultant's acquisition.
Step 2, collection terminal will ask questions and be sent to cloud platform；
Step 3, the feedback module in cloud platform are input in consulting model from middle extraction keyword is asked questions；
Output result is calculated according to pre-set decision Tree algorithms in step 4, consulting model；
Step 5, feedback module will export result and matched to obtain consulting answer with the legal provision in database；
Step 6, cloud platform will seek advice from answer feedback to consultant.
Priority Applications (1)
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| CN201810806019.3A CN109002538A (en) | 2018-07-20 | 2018-07-20 | Legal advice cloud platform and method based on database |
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| CN201810806019.3A CN109002538A (en) | 2018-07-20 | 2018-07-20 | Legal advice cloud platform and method based on database |
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| CN201810806019.3A Pending CN109002538A (en) | 2018-07-20 | 2018-07-20 | Legal advice cloud platform and method based on database |
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| CN109726278A (en) * | 2018-12-29 | 2019-05-07 | 重庆木舌科技有限公司 | A method of legal risk prevention and control information is pushed to college student |
| CN109726275A (en) * | 2018-12-29 | 2019-05-07 | 重庆工商大学融智学院 | Legal knowledge intelligent inquiry method applied to legal advice service |
| CN109740927A (en) * | 2018-12-29 | 2019-05-10 | 重庆工商大学融智学院 | The assessment of institution of higher learning's legal risk and crime prevention system |
| CN111324719A (en) * | 2020-03-04 | 2020-06-23 | 重庆百事得大牛机器人有限公司 | Fuzzy recognition system for legal consultation |
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| CN111368032A (en) * | 2020-02-29 | 2020-07-03 | 重庆百事得大牛机器人有限公司 | Daily language identification method for legal consultation |
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| CN109670118A (en) * | 2018-12-29 | 2019-04-23 | 重庆木舌科技有限公司 | College student legal risk prevention and control information transmission system |
| CN109726278A (en) * | 2018-12-29 | 2019-05-07 | 重庆木舌科技有限公司 | A method of legal risk prevention and control information is pushed to college student |
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| CN111324206A (en) * | 2020-02-28 | 2020-06-23 | 重庆百事得大牛机器人有限公司 | Gesture interaction-based confirmation information identification system and method |
| CN111324206B (en) * | 2020-02-28 | 2023-07-18 | 重庆百事得大牛机器人有限公司 | System and method for identifying confirmation information based on gesture interaction |
| CN111368032A (en) * | 2020-02-29 | 2020-07-03 | 重庆百事得大牛机器人有限公司 | Daily language identification method for legal consultation |
| CN111324719A (en) * | 2020-03-04 | 2020-06-23 | 重庆百事得大牛机器人有限公司 | Fuzzy recognition system for legal consultation |
| CN111324719B (en) * | 2020-03-04 | 2023-05-05 | 重庆百事得大牛机器人有限公司 | Fuzzy recognition system for legal consultation |
| CN111400453A (en) * | 2020-03-16 | 2020-07-10 | 上海百事通信息技术股份有限公司 | Intelligent interaction system and method based on legal consultation |
| CN111400453B (en) * | 2020-03-16 | 2024-03-08 | 上海百事通公共法律服务有限公司 | Intelligent interaction system and method based on legal consultation |
| CN111755091A (en) * | 2020-06-28 | 2020-10-09 | 北京嘉和海森健康科技有限公司 | Electronic medical record processing method and device |
| CN111755091B (en) * | 2020-06-28 | 2024-02-27 | 北京嘉和海森健康科技有限公司 | Electronic medical record processing method and device |
| CN112150322A (en) * | 2020-09-30 | 2020-12-29 | 信阳农林学院 | Intelligent interactive system for legal consultation |
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