CN109409645A - The method and storage medium that electronic device, lawyer recommend - Google Patents
The method and storage medium that electronic device, lawyer recommend Download PDFInfo
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Abstract
The method and storage medium recommended the present invention relates to a kind of electronic device, lawyer, this method comprises: acquiring lawyer's data from multiple data sources, lawyer's data include lawyer's relevant information, rule institute's relevant information, law court's data and case data；Based on pre-set matching factor, lawyer's data of acquisition are matched, the data that will match to are as characterization factor；Corresponding weighting coefficient is set based on the characterization factor, lawyer's Rating Model is constructed based on the characterization factor and weighting coefficient；The demand data for receiving user's input, matching obtains the corresponding lawyer's data of the demand data from lawyer's data of acquisition, and the lawyer's data and lawyer's Rating Model obtained based on matching are scored and shown to lawyer.The present invention can integrate lawyer, comprehensively, objective appraisal, help user quickly to make reasonable selection.
Description
Technical field
The present invention relates to methods and storage that technical field of data processing more particularly to a kind of electronic device, lawyer are recommended
Medium.
Background technique
Currently, user can find more and more required products or service, such as lawyer's service by internet platform
Deng.But lawyer's information in existing internet platform typically only shows its service content, it is comprehensive there is no being carried out to lawyer
It closes, comprehensive, objective appraisal, user can not quickly make reasonable selection according to these lawyer's information.
Summary of the invention
The method and storage medium recommended the purpose of the present invention is to provide a kind of electronic device, lawyer, it is intended to lawyer
Carry out comprehensive, comprehensive, objective appraisal.
To achieve the above object, the present invention provides a kind of electronic device, the electronic device include memory and with it is described
The processor of memory connection, is stored with the processing system that can be run on the processor, the processing in the memory
System realizes following steps when being executed by the processor:
Acquisition step, acquires lawyer's data from multiple data sources, and lawyer's data include lawyer's relevant information, rule institute
Relevant information, law court's data and case data；
Matching step is based on pre-set matching factor, matches to lawyer's data of acquisition, the number that will match to
According to as characterization factor；
Corresponding weighting coefficient is arranged based on the characterization factor in construction step, based on the characterization factor and weighting system
Number building lawyer's Rating Model；
Score step, receives the demand data of user's input, and matching obtains the demand data from lawyer's data of acquisition
Corresponding lawyer's data, the lawyer's data and lawyer's Rating Model obtained based on matching are scored and are shown to lawyer.
Preferably, the characterization factor is divided into Main Factors and confactor, the Main Factors packet according to significance level
The case for including attorney's procuration handles the time by type, caseload, the rate of winning a lawsuit/detraction rate, case, and the confactor includes lawyer
Essential information and rule institute's essential information, by attorney's procuration case by type and caseload determine attorney's procuration case it is similar
Case by counting, be arranged the similar case of the case of attorney's procuration by counting, similar case by the rate of winning a lawsuit or detraction rate, similar case by it is corresponding when
Between factor three the corresponding weighting coefficient of product be a1, the victory of total case number of packages of attorney's procuration, total case number of packages of agency is set
Tell that the corresponding weighting coefficient of the product of both rate or detraction rate is a2, the corresponding weighting coefficient of setting lawyer's essential information is a3,
The corresponding weighting coefficient of setting rule institute's essential information is a4, lawyer's Rating Model of building are as follows:
(similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by the corresponding time for lawyer's scoring=a1*In
The factor)+a2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+a3*In (lawyer's essential information)+a4*In (rule institute
Essential information), wherein the time factor is the inverse that similar case is handled average time by case, lawyer's essential information
And rule institute's essential information obtains corresponding numerical value after scheduled quantization step.
Preferably, the characterization factor is divided into Main Factors and confactor, the Main Factors packet according to significance level
The case of attorney's procuration is included by type, caseload, the rate of winning a lawsuit/detraction rate, case processing time and the rate that runs succeeded, the auxiliary
The factor includes lawyer's essential information and rule institute's essential information, determines attorney's procuration by type and caseload by the case of attorney's procuration
Case similar case by counting, be arranged the similar case of the case of attorney's procuration by counting, similar case is by the rate of winning a lawsuit or detraction rate, similar
Case is b1 by the corresponding weighting coefficient of product of corresponding time factor and the rate that runs succeeded, and total case of attorney's procuration is arranged
Number of packages, agency total case number of packages win a lawsuit both rate or detraction rate the corresponding weighting coefficient of product be b2, setting lawyer it is basic
The corresponding weighting coefficient of information is b3, and the corresponding weighting coefficient of setting rule institute's essential information is b4, lawyer's Rating Model of building
Are as follows:
(similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by the corresponding time for lawyer's scoring=b1*In
Factor * runs succeeded rate)+b2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+b3*In (lawyer's essential information)
+ b4*In (rule institute's essential information), wherein the time factor is the inverse that similar case is handled average time by case, the rule
Teacher's essential information and rule institute's essential information obtain corresponding numerical value after scheduled quantization step.
Preferably, the scheduled quantization step includes that lawyer's essential information and rule institute's essential information are divided into quantization
Information and non-quantitative information extract the numerical value in quantitative information, obtain non-quantitative information using preset quantization mapping table
The numerical value of quantitative information in lawyer's essential information is added with the numerical value of non-quantitative information to obtain lawyer and believe substantially by corresponding numerical value
Corresponding numerical value is ceased, the numerical value for restraining quantitative information in institute's essential information is added to obtain rule institute substantially with the numerical value of non-quantitative information
The corresponding numerical value of information.
To achieve the above object, the present invention also provides the methods that one kind, lawyer are recommended, which is characterized in that the lawyer pushes away
The method recommended includes:
S1, acquires lawyer's data from multiple data sources, and lawyer's data include lawyer's relevant information, the related letter of rule institute
Breath, law court's data and case data；
S2 is based on pre-set matching factor, matches to lawyer's data of acquisition, the data conduct that will match to
Characterization factor；
S3 is arranged corresponding weighting coefficient based on the characterization factor, is constructed based on the characterization factor and weighting coefficient
Lawyer's Rating Model；
S4 receives the demand data of user's input, and it is corresponding to obtain the demand data for matching from lawyer's data of acquisition
Lawyer's data, the lawyer's data and lawyer's Rating Model obtained based on matching are scored and are shown to lawyer.
Preferably, the characterization factor is divided into Main Factors and confactor, the Main Factors packet according to significance level
The case for including attorney's procuration handles the time by type, caseload, the rate of winning a lawsuit/detraction rate, case, and the confactor includes lawyer
Essential information and rule institute's essential information, by attorney's procuration case by type and caseload determine attorney's procuration case it is similar
Case by counting, be arranged the similar case of the case of attorney's procuration by counting, similar case by the rate of winning a lawsuit or detraction rate, similar case by it is corresponding when
Between factor three the corresponding weighting coefficient of product be a1, the victory of total case number of packages of attorney's procuration, total case number of packages of agency is set
Tell that the corresponding weighting coefficient of the product of both rate or detraction rate is a2, the corresponding weighting coefficient of setting lawyer's essential information is a3,
The corresponding weighting coefficient of setting rule institute's essential information is a4, lawyer's Rating Model of building are as follows:
(similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by the corresponding time for lawyer's scoring=a1*In
The factor)+a2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+a3*In (lawyer's essential information)+a4*In (rule institute
Essential information), wherein the time factor is the inverse that similar case is handled average time by case, lawyer's essential information
And rule institute's essential information obtains corresponding numerical value after scheduled quantization step.
Preferably, the characterization factor is divided into Main Factors and confactor, the Main Factors packet according to significance level
The case of attorney's procuration is included by type, caseload, the rate of winning a lawsuit/detraction rate, case processing time and the rate that runs succeeded, the auxiliary
The factor includes lawyer's essential information and rule institute's essential information, determines attorney's procuration by type and caseload by the case of attorney's procuration
Case similar case by counting, be arranged the similar case of the case of attorney's procuration by counting, similar case is by the rate of winning a lawsuit or detraction rate, similar
Case is b1 by the corresponding weighting coefficient of product of corresponding time factor and the rate that runs succeeded, and total case of attorney's procuration is arranged
Number of packages, agency total case number of packages win a lawsuit both rate or detraction rate the corresponding weighting coefficient of product be b2, setting lawyer it is basic
The corresponding weighting coefficient of information is b3, and the corresponding weighting coefficient of setting rule institute's essential information is b4, lawyer's Rating Model of building
Are as follows:
(similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by the corresponding time for lawyer's scoring=b1*In
Factor * runs succeeded rate)+b2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+b3*In (lawyer's essential information)
+ b4*In (rule institute's essential information), wherein the time factor is the inverse that similar case is handled average time by case, the rule
Teacher's essential information and rule institute's essential information obtain corresponding numerical value after scheduled quantization step.
Preferably, the scheduled quantization step includes that lawyer's essential information and rule institute's essential information are divided into quantization
Information and non-quantitative information extract the numerical value in quantitative information, obtain non-quantitative information using preset quantization mapping table
The numerical value of quantitative information in lawyer's essential information is added with the numerical value of non-quantitative information to obtain lawyer and believe substantially by corresponding numerical value
Corresponding numerical value is ceased, the numerical value for restraining quantitative information in institute's essential information is added to obtain rule institute substantially with the numerical value of non-quantitative information
The corresponding numerical value of information.
Preferably, whether the step S2, specifically includes: analyzing in lawyer's data of acquisition has unstructured data and half
Structural data, if so, then converting structural data for unstructured data and semi-structured data, and to the lawyer of acquisition
All structural datas in data carry out text resolution, pre-set matching factor are based on, to the structure after text resolution
Change data to be matched, the data that will match to are as characterization factor.
The present invention also provides a kind of computer readable storage medium, processing is stored on the computer readable storage medium
The step of system, the processing system realizes the method that above-mentioned lawyer recommends when being executed by processor.
The beneficial effects of the present invention are: the present invention collects lawyer's data from multiple data source various dimensions, to lawyer's number
According to being matched, characterization factor is obtained, the corresponding weighting coefficient of setting characterization factor is constructed according to characterization factor and weighting coefficient
Lawyer's Rating Model matches corresponding lawyer's data in user query lawyer according to the demand of user, then using above-mentioned
Lawyer's Rating Model lawyer involved in these lawyer's data is scored and is shown, lawyer can be integrated, is complete
Face, objective appraisal help user quickly to make reasonable selection.
Detailed description of the invention
Fig. 1 is the schematic diagram of the hardware structure of one embodiment of electronic device of the present invention；
Fig. 2 is the flow diagram for one embodiment of method that lawyer of the present invention recommends.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.Based on the embodiments of the present invention, those of ordinary skill in the art are not before making creative work
Every other embodiment obtained is put, shall fall within the protection scope of the present invention.
It should be noted that the description for being related to " first ", " second " etc. in the present invention is used for description purposes only, and cannot
It is interpreted as its relative importance of indication or suggestion or implicitly indicates the quantity of indicated technical characteristic.Define as a result, " the
One ", the feature of " second " can explicitly or implicitly include at least one of the features.In addition, the skill between each embodiment
Art scheme can be combined with each other, but must be based on can be realized by those of ordinary skill in the art, when technical solution
Will be understood that the combination of this technical solution is not present in conjunction with there is conflicting or cannot achieve when, also not the present invention claims
Protection scope within.
As shown in fig.1, being the structural schematic diagram of one embodiment of electronic device of the present invention.Electronic device 1 is that one kind can
According to the instruction for being previously set or storing, the automatic equipment for carrying out numerical value calculating and/or information processing.The electronic device 1
It can be computer, be also possible to single network server, the server group of multiple network servers composition or based on cloud
The cloud being made of a large amount of hosts or network server calculated, wherein cloud computing is one kind of distributed computing, loose by a group
One super virtual computer of the computer set composition of coupling.
In the present embodiment, electronic device 1 may include, but be not limited only to, and can be in communication with each other connection by system bus
Memory 11, processor 12, network interface 13, memory 11 are stored with the processing system that can be run on the processor 12.It needs
, it is noted that Fig. 1 illustrates only the electronic device 1 with component 11-13, it should be understood that being not required for implementing all
The component shown, the implementation that can be substituted is more or less component.
Wherein, memory 11 includes the readable storage medium storing program for executing of memory and at least one type.Inside save as the fortune of electronic device 1
Row provides caching；Readable storage medium storing program for executing can be for if flash memory, hard disk, multimedia card, card-type memory are (for example, SD or DX memory
Deng), random access storage device (RAM), static random-access memory (SRAM), read-only memory (ROM), electric erasable can compile
Journey read-only memory (EEPROM), programmable read only memory (PROM), magnetic storage, disk, CD etc. it is non-volatile
Storage medium.In some embodiments, readable storage medium storing program for executing can be the internal storage unit of electronic device 1, such as the electronics
The hard disk of device 1；In further embodiments, the external storage which is also possible to electronic device 1 is set
Plug-in type hard disk that is standby, such as being equipped on electronic device 1, intelligent memory card (Smart Media Card, SMC), secure digital
(Secure Digital, SD) card, flash card (Flash Card) etc..In the present embodiment, the readable storage medium storing program for executing of memory 11
Commonly used in storing the operating system and types of applications software that are installed on electronic device 1, such as the place in one embodiment of the invention
The program code etc. of reason system.It has exported or will export each in addition, memory 11 can be also used for temporarily storing
Class data.
The processor 12 can be in some embodiments central processing unit (Central Processing Unit,
CPU), controller, microcontroller, microprocessor or other data processing chips.The processor 12 is commonly used in the control electricity
The overall operation of sub-device 1, such as execute control relevant to other equipment progress data interaction or communication and processing etc..This
In embodiment, the processor 12 is used to run the program code stored in the memory 11 or processing data, such as transports
Row processing system etc..
The network interface 13 may include radio network interface or wired network interface, which is commonly used in
Communication connection is established between the electronic device 1 and other electronic equipments.
The processing system is stored in memory 11, is stored in including at least one computer-readable in memory 11
Instruction, at least one computer-readable instruction can be executed by processor device 12, the method to realize each embodiment of the application；With
And the function that at least one computer-readable instruction is realized according to its each section is different, can be divided into different logic moulds
Block.
In one embodiment, following steps are realized when above-mentioned processing system is executed by the processor 12:
Acquisition step, acquires lawyer's data from multiple data sources, and lawyer's data include lawyer's relevant information, rule institute
Relevant information, law court's data and case data；
Lawyer's data can be acquired from multiple data sources or by multiple channel, most comprehensively, widely be restrained with obtaining
Teacher's data, such as: lawyer's relevant information, rule institute's relevant information are acquired in the database of subsidiary company inner accumulation；It is connect by default
Mouth reads acquisition lawyer's relevant information, rule institute's relevant information from other systems or database, and passes through crawler technology from rule
The websites such as institute, employer's organization, law court and government department, which are swashed, follows the example of institute's data and case data, etc..
Wherein, lawyer's relevant information includes information relevant to case and the essential information unrelated with case, with case phase
The information of pass may include attorney docket quantity, the rate of winning a lawsuit/detraction rate, the rate that runs succeeded etc., the essential information unrelated with case
It may include main operation field, lawyer's practicing requirements card type, legal profession ranking, the operation time limit, rewards and punishments record, society times
Duty and judicial domain influence power, loyalty information and the past legal profession research achievement etc..Where rule institute's relevant information may include
Area, ranking, rule institute's scale, operation field, attorney docket type and quantity, the rate of winning a lawsuit/detraction rate, run succeeded rate etc.；
Law court's data include location, law court's level etc.；Case data include case by type, the lawsuit amount of money, the judgement amount of money, Cai Panwen
Book, trial program etc..
Matching step is based on pre-set matching factor, matches to lawyer's data of acquisition, the number that will match to
According to as characterization factor；
Wherein, matching factor can be related to the various pieces in lawyer's data, for example, can be related to plaintiff, defendant, area,
Case can also relate to the rate of winning a lawsuit/detraction rate, executed by type, lawyer's operation time limit, case-involving law court's level and trial program etc.
Success rate acts on behalf of effect scoring, caseload, the entire period of actual operation, good lawyer's comprehensive score etc..Matching factor can be preset,
Which information of lawyer needed to pay close attention to, matching factor can be set by corresponding data, to obtain corresponding characterization factor
Corresponding lawyer's Rating Model is constructed, for example, if the case type and caseload of the processing of concern lawyer, by case by class
Type and caseload are set as matching factor.Matching factor based on setting can match the data of acquisition, if matching
Success then extracts the data of successful match in lawyer's data as characterization factor, for example, characterization factor is the lawsuit amount of money, judge
The amount of money, plaintiff, defendant, entrusted agent, judge and Reference Number etc..
Preferably due to which lawyer's data include various types of data, therefore lawyer's data of acquisition are analyzed first
In whether have unstructured data and semi-structured data, for example whether there is office documents, text, picture, XML, HTML, all kinds of
Report etc., if can directly carry out text resolution without if, if there is if unstructured data and semi-structured data turned
Structural data is turned to, text resolution then is carried out to all structural datas in lawyer's data of acquisition, wherein text solution
Analysis includes participle division, part-of-speech tagging and syntactic analysis etc., is matched using matching factor to the data after text resolution, example
Such as, the data after text resolution are matched with the pattern string based on matching factor, or calculates matching factor and predetermined word
The similarity of the participle of property, the two that similarity reaches certain threshold value is determined as successful match, etc., by the data of successful match
It extracts as characterization factor.
The present embodiment converts structural data for lawyer's data, using participle by the structure of analysis lawyer's data
The digging technologies such as division, part-of-speech tagging and syntactic analysis carry out text resolution to structural data, and final realize will match factor
It is comprehensively and accurately matched with the data after text resolution, to extract corresponding characterization factor.
Corresponding weighting coefficient is arranged based on the characterization factor in construction step, based on the characterization factor and weighting system
Number building lawyer's Rating Model；
In the present embodiment, characterization factor is incorporated into according to significance level as Main Factors and confactor.Main Factors are
Factor relevant to case, confactor for and the unrelated factor of case (for example, factor corresponding with rule institute's relevant information, with
The corresponding factor of essential information in lawyer's relevant information).
In a preferred embodiment, Main Factors may include attorney's procuration case by type, caseload, rate of winning a lawsuit/
Detraction rate, case processing time etc., in another embodiment, Main Factors may include the case of attorney's procuration by type, case
Quantity, the rate of winning a lawsuit/detraction rate, case handle time and the rate that runs succeeded etc..Confactor may include the main operation of lawyer
Field, lawyer's practicing requirements card type, legal profession ranking, the operation time limit, rewards and punishments record, society's tenure and judicial domain influence
Power, loyalty information and the past legal profession research achievement etc., and including rule institute location, ranking, rule institute's scale, operation neck
Domain, attorney docket type and quantity, the rate of winning a lawsuit/detraction rate, the rate that runs succeeded etc..It is corresponding in setting Main Factors and confactor
Weighting coefficient when, which can be specifically arranged according to different business scene.Main Factors and confactor
Quantity is more, then the Rating Model constructed is more objective, can comprehensively, it is comprehensive, objectively score lawyer.
In a preferred embodiment, with Main Factors include attorney's procuration case case by type, caseload, similar
Case includes lawyer's essential information and rule institute's essential information by handling average time, confactor by the rate of winning a lawsuit/detraction rate, similar case
For, be arranged the similar case of the case of attorney's procuration by counting, similar case by the rate of winning a lawsuit or detraction rate, similar case by the corresponding time
The corresponding weighting coefficient of the product of factor three is a1, total case number of packages of attorney's procuration is arranged, total case number of packages of agency is won a lawsuit
The corresponding weighting coefficient of product of both rate or detraction rate is a2, and the corresponding weighting coefficient of setting lawyer's essential information is a3, if
Setting the corresponding weighting coefficient of rule institute's essential information is a4, lawyer's Rating Model of building are as follows:
(similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by the corresponding time for lawyer's scoring=a1*In
The factor)+a2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+a3*In (lawyer's essential information)+a4*In (rule institute
Essential information).
In another preferred embodiment, with Main Factors include attorney's procuration case case by type, caseload, same
Class case includes lawyer's essential information by processing average time and the rate that runs succeeded, confactor by the rate of winning a lawsuit/detraction rate, similar case
And for rule institute's essential information, be arranged the similar case of the case of attorney's procuration by counting, similar case is by the rate of winning a lawsuit or detraction rate, similar
Case is b1 by the corresponding weighting coefficient of product of corresponding time factor and the rate that runs succeeded, and total case of attorney's procuration is arranged
Number of packages, agency total case number of packages win a lawsuit both rate or detraction rate the corresponding weighting coefficient of product be b2, setting lawyer it is basic
The corresponding weighting coefficient of information is b3, and the corresponding weighting coefficient of setting rule institute's essential information is b4, lawyer's Rating Model of building
Are as follows:
(similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by the corresponding time for lawyer's scoring=b1*In
Factor * runs succeeded rate)+b2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+b3*In (lawyer's essential information)
+ b4*In (rule institute's essential information).
In two embodiments of lawyer's Rating Model of above-mentioned building, case by type include many kinds, such as marriage
Dispute, contract dispute, IP dispute, labour dispute etc..The similar case of attorney's procuration can be according to attorney's procuration by counting
Case determined by type, caseload；Time factor refers to similar case by the inverse of processing average time；Lawyer believes substantially
Breath includes main operation field, lawyer's practicing requirements card type, legal profession ranking, the operation time limit, rewards and punishments record, society's tenure
And judicial domain influence power, loyalty information and the past legal profession research achievement etc.；Restraining institute's essential information may include location
Area, ranking, rule institute's scale, operation field, attorney docket type and quantity, the rate of winning a lawsuit/detraction rate, run succeeded rate etc., rule
Teacher's essential information and rule institute's essential information obtain corresponding numerical value after scheduled quantization step.In one embodiment, add
Weight coefficient a1 and a2, which can be disposed as 1, weighting coefficient a3 and a4, can be disposed as 0.5；Weighting coefficient b1 and b2 can be equal
0.5 can be disposed as by being set as 1, weighting coefficient b3 and b4.
In two embodiments of lawyer's Rating Model of above-mentioned building, lawyer's essential information and rule are believed substantially
Breath, it is preferable that scheduled quantization step includes: that lawyer's essential information and rule institute's essential information are divided into quantitative information and do not measured
Change information, the lawyer's essential information that can quantify or rule institute's essential information are directly embodied with the numerical value of quantization (such as rule institute's generation
The caseload of reason, rule institute the rate of winning a lawsuit/detraction rate, restrain the rate that runs succeeded etc.), not energetic lawyer's essential information or
The quantization that institute's essential information carries out information by preset quantization mapping table is restrained, by quantitative information in lawyer's essential information
Numerical value and the numerical value of non-quantitative information are added to obtain the corresponding numerical value of lawyer's essential information, will quantitative information in rule institute's essential information
Numerical value and the numerical value of non-quantitative information be added to obtain the corresponding numerical value of rule institute's essential information.In the quantization mapping table,
Including each single item information and the corresponding quantized values of association in lawyer's essential information or rule institute's essential information.
For lawyer's essential information, illustrated with an example: in the operation field of lawyer, the case of client by group with
The operation field of lawyer is identical, can correspond to numerical value 5, only the case of client is by major class correspondence identical as the operation field of lawyer
Numerical value 1, by major class and the operation field difference of lawyer, then numerical value is 0 to the case of client.Such as the case of client is that knowledge produces by type
It weighs, in the operation field of lawyer's first: major class is civil law, and group is intellectual property, then this numerical value of lawyer's first is 5；Lawyer's second
Operation field in: major class is civil law, and group is house prosperity transaction, then this numerical value of lawyer's second be 1；The operation field of lawyer third
In: major class is criminal law, then this numerical value of lawyer third is 0.By this numerical value numerical value corresponding with the other information of the lawyer into
Row is added or weighting summation, the corresponding numerical value of available lawyer's essential information.
Similarly, it restrains in institute's essential information, such as area, then corresponds to numerical value 5 in the same city with customer demand, save
Other interior cities correspond to numerical value 3, other domestic provinces correspond to numerical value 1, and other countries correspond to numerical value 0 etc..By this numerical value and it is somebody's turn to do
Rule institute other information carry out addition or weighting summation, the corresponding numerical value of available rule institute essential information.
Score step, receives the demand data of user's input, and matching obtains the demand data from lawyer's data of acquisition
Corresponding lawyer's data, the lawyer's data and lawyer's Rating Model obtained based on matching are scored and are shown to lawyer.
In the present embodiment, according to the demand of user from lawyer's data of acquisition matching obtain the user demand it is corresponding
Lawyer's data, such as the demand of user is case-involving type (plaintiff or defendant), case-involving case by type (marriage, contract or labour
Deng) etc., matching according to the demand of user obtains corresponding lawyer's data, then using above-mentioned lawyer's Rating Model to these rules
Lawyer in teacher's data carries out comprehensive score, is finally shown the comprehensive score of lawyer, such as according to scoring from high to low
Sequence be ranked up displaying, to help user to make reasonable selection.
Compared with prior art, the present invention collects lawyer's data from multiple data source various dimensions, to lawyer's data into
Row matching, obtains characterization factor, and the corresponding weighting coefficient of setting characterization factor constructs lawyer according to characterization factor and weighting coefficient
Rating Model matches corresponding lawyer's data in user query lawyer according to the demand of user, then utilizes above-mentioned rule
Teacher's Rating Model scores and shows to lawyer involved in these lawyer's data, can integrate to lawyer, is comprehensive, visitor
The evaluation of sight helps user quickly to make reasonable selection.
As shown in Fig. 2, Fig. 2 is the flow diagram for one embodiment of method that lawyer of the present invention recommends, what which recommended
Method the following steps are included:
Step S1 acquires lawyer's data from multiple data sources, and lawyer's data include lawyer's relevant information, Lv Suoxiang
Close information, law court's data and case data；
Lawyer's data can be acquired from multiple data sources or by multiple channel, most comprehensively, widely be restrained with obtaining
Teacher's data, such as: lawyer's relevant information, rule institute's relevant information are acquired in the database of subsidiary company inner accumulation；It is connect by default
Mouth reads acquisition lawyer's relevant information, rule institute's relevant information from other systems or database, and passes through crawler technology from rule
The websites such as institute, employer's organization, law court and government department, which are swashed, follows the example of institute's data and case data, etc..
Wherein, lawyer's relevant information includes information relevant to case and the essential information unrelated with case, with case phase
The information of pass may include attorney docket quantity, the rate of winning a lawsuit/detraction rate, the rate that runs succeeded etc., the essential information unrelated with case
It may include main operation field, lawyer's practicing requirements card type, legal profession ranking, the operation time limit, rewards and punishments record, society times
Duty and judicial domain influence power, loyalty information and the past legal profession research achievement etc..Where rule institute's relevant information may include
Area, ranking, rule institute's scale, operation field, attorney docket type and quantity, the rate of winning a lawsuit/detraction rate, run succeeded rate etc.；
Law court's data include location, law court's level etc.；Case data include case by type, the lawsuit amount of money, the judgement amount of money, Cai Panwen
Book, trial program etc..
Step S2 is based on pre-set matching factor, matches to lawyer's data of acquisition, the data that will match to
As characterization factor；
Wherein, matching factor can be related to the various pieces in lawyer's data, for example, can be related to plaintiff, defendant, area,
Case can also relate to the rate of winning a lawsuit/detraction rate, executed by type, lawyer's operation time limit, case-involving law court's level and trial program etc.
Success rate acts on behalf of effect scoring, caseload, the entire period of actual operation, good lawyer's comprehensive score etc..Matching factor can be preset,
Which information of lawyer needed to pay close attention to, matching factor can be set by corresponding data, to obtain corresponding characterization factor
Corresponding lawyer's Rating Model is constructed, for example, if the case type and caseload of the processing of concern lawyer, by case by class
Type and caseload are set as matching factor.Matching factor based on setting can match the data of acquisition, if matching
Success then extracts the data of successful match in lawyer's data as characterization factor, for example, characterization factor is the lawsuit amount of money, judge
The amount of money, plaintiff, defendant, entrusted agent, judge and Reference Number etc..
Preferably due to which lawyer's data include various types of data, therefore lawyer's data of acquisition are analyzed first
In whether have unstructured data and semi-structured data, for example whether there is office documents, text, picture, XML, HTML, all kinds of
Report etc., if can directly carry out text resolution without if, if there is if unstructured data and semi-structured data turned
Structural data is turned to, text resolution then is carried out to all structural datas in lawyer's data of acquisition, wherein text solution
Analysis includes participle division, part-of-speech tagging and syntactic analysis etc., is matched using matching factor to the data after text resolution, example
Such as, the data after text resolution are matched with the pattern string based on matching factor, or calculates matching factor and predetermined word
The similarity of the participle of property, the two that similarity reaches certain threshold value is determined as successful match, etc., by the data of successful match
It extracts as characterization factor.
The present embodiment converts structural data for lawyer's data, using participle by the structure of analysis lawyer's data
The digging technologies such as division, part-of-speech tagging and syntactic analysis carry out text resolution to structural data, and final realize will match factor
It is comprehensively and accurately matched with the data after text resolution, to extract corresponding characterization factor.
Step S3 is arranged corresponding weighting coefficient based on the characterization factor, is based on the characterization factor and weighting coefficient
Construct lawyer's Rating Model；
In the present embodiment, characterization factor is incorporated into according to significance level as Main Factors and confactor.Main Factors are
Factor relevant to case, confactor for and the unrelated factor of case (for example, factor corresponding with rule institute's relevant information, with
The corresponding factor of essential information in lawyer's relevant information).
In a preferred embodiment, Main Factors may include attorney's procuration case by type, caseload, rate of winning a lawsuit/
Detraction rate, case processing time etc., in another embodiment, Main Factors may include the case of attorney's procuration by type, case
Quantity, the rate of winning a lawsuit/detraction rate, case handle time and the rate that runs succeeded etc..Confactor may include the main operation of lawyer
Field, lawyer's practicing requirements card type, legal profession ranking, the operation time limit, rewards and punishments record, society's tenure and judicial domain influence
Power, loyalty information and the past legal profession research achievement etc., and including rule institute location, ranking, rule institute's scale, operation neck
Domain, attorney docket type and quantity, the rate of winning a lawsuit/detraction rate, the rate that runs succeeded etc..It is corresponding in setting Main Factors and confactor
Weighting coefficient when, which can be specifically arranged according to different business scene.Main Factors and confactor
Quantity is more, then the Rating Model constructed is more objective, can comprehensively, it is comprehensive, objectively score lawyer.
In a preferred embodiment, with Main Factors include attorney's procuration case case by type, caseload, similar
Case includes lawyer's essential information and rule institute's essential information by handling average time, confactor by the rate of winning a lawsuit/detraction rate, similar case
For, be arranged the similar case of the case of attorney's procuration by counting, similar case by the rate of winning a lawsuit or detraction rate, similar case by the corresponding time
The corresponding weighting coefficient of the product of factor three is a1, total case number of packages of attorney's procuration is arranged, total case number of packages of agency is won a lawsuit
The corresponding weighting coefficient of product of both rate or detraction rate is a2, and the corresponding weighting coefficient of setting lawyer's essential information is a3, if
Setting the corresponding weighting coefficient of rule institute's essential information is a4, lawyer's Rating Model of building are as follows:
(similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by the corresponding time for lawyer's scoring=a1*In
The factor)+a2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+a3*In (lawyer's essential information)+a4*In (rule institute
Essential information).
In another preferred embodiment, with Main Factors include attorney's procuration case case by type, caseload, same
Class case includes lawyer's essential information by processing average time and the rate that runs succeeded, confactor by the rate of winning a lawsuit/detraction rate, similar case
And for rule institute's essential information, be arranged the similar case of the case of attorney's procuration by counting, similar case is by the rate of winning a lawsuit or detraction rate, similar
Case is b1 by the corresponding weighting coefficient of product of corresponding time factor and the rate that runs succeeded, and total case of attorney's procuration is arranged
Number of packages, agency total case number of packages win a lawsuit both rate or detraction rate the corresponding weighting coefficient of product be b2, setting lawyer it is basic
The corresponding weighting coefficient of information is b3, and the corresponding weighting coefficient of setting rule institute's essential information is b4, lawyer's Rating Model of building
Are as follows:
(similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by the corresponding time for lawyer's scoring=b1*In
Factor * runs succeeded rate)+b2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+b3*In (lawyer's essential information)
+ b4*In (rule institute's essential information).
In two embodiments of lawyer's Rating Model of above-mentioned building, case by type include many kinds, such as marriage
Dispute, contract dispute, IP dispute, labour dispute etc..The similar case of attorney's procuration can be according to attorney's procuration by counting
Case determined by type, caseload；Time factor refers to similar case by the inverse of processing average time；Lawyer believes substantially
Breath includes main operation field, lawyer's practicing requirements card type, legal profession ranking, the operation time limit, rewards and punishments record, society's tenure
And judicial domain influence power, loyalty information and the past legal profession research achievement etc.；Restraining institute's essential information may include location
Area, ranking, rule institute's scale, operation field, attorney docket type and quantity, the rate of winning a lawsuit/detraction rate, run succeeded rate etc., rule
Teacher's essential information and rule institute's essential information obtain corresponding numerical value after scheduled quantization step.In one embodiment, add
Weight coefficient a1 and a2, which can be disposed as 1, weighting coefficient a3 and a4, can be disposed as 0.5；Weighting coefficient b1 and b2 can be equal
0.5 can be disposed as by being set as 1, weighting coefficient b3 and b4.
In two embodiments of lawyer's Rating Model of above-mentioned building, lawyer's essential information and rule are believed substantially
Breath, it is preferable that scheduled quantization step includes: that lawyer's essential information and rule institute's essential information are divided into quantitative information and do not measured
Change information, the lawyer's essential information that can quantify or rule institute's essential information are directly embodied with the numerical value of quantization (such as rule institute's generation
The caseload of reason, rule institute the rate of winning a lawsuit/detraction rate, restrain the rate that runs succeeded etc.), not energetic lawyer's essential information or
The quantization that institute's essential information carries out information by preset quantization mapping table is restrained, by quantitative information in lawyer's essential information
Numerical value and the numerical value of non-quantitative information are added to obtain the corresponding numerical value of lawyer's essential information, will quantitative information in rule institute's essential information
Numerical value and the numerical value of non-quantitative information be added to obtain the corresponding numerical value of rule institute's essential information.In the quantization mapping table,
Including each single item information and the corresponding quantized values of association in lawyer's essential information or rule institute's essential information.
For lawyer's essential information, illustrated with an example: in the operation field of lawyer, the case of client by group with
The operation field of lawyer is identical, can correspond to numerical value 5, only the case of client is by major class correspondence identical as the operation field of lawyer
Numerical value 1, by major class and the operation field difference of lawyer, then numerical value is 0 to the case of client.Such as the case of client is that knowledge produces by type
It weighs, in the operation field of lawyer's first: major class is civil law, and group is intellectual property, then this numerical value of lawyer's first is 5；Lawyer's second
Operation field in: major class is civil law, and group is house prosperity transaction, then this numerical value of lawyer's second be 1；The operation field of lawyer third
In: major class is criminal law, then this numerical value of lawyer third is 0.By this numerical value numerical value corresponding with the other information of the lawyer into
Row is added or weighting summation, the corresponding numerical value of available lawyer's essential information.
Similarly, it restrains in institute's essential information, such as area, then corresponds to numerical value 5 in the same city with customer demand, save
Other interior cities correspond to numerical value 3, other domestic provinces correspond to numerical value 1, and other countries correspond to numerical value 0 etc..By this numerical value and it is somebody's turn to do
Rule institute other information carry out addition or weighting summation, the corresponding numerical value of available rule institute essential information.
Step S4 receives the demand data of user's input, and matching obtains the demand data pair from lawyer's data of acquisition
The lawyer's data answered, the lawyer's data and Rating Model obtained based on matching are scored and are shown to lawyer.
In the present embodiment, according to the demand of user from lawyer's data of acquisition matching obtain the user demand it is corresponding
Lawyer's data, such as the demand of user is case-involving type (plaintiff or defendant), case-involving case by type (marriage, contract or labour
Deng) etc., matching according to the demand of user obtains corresponding lawyer's data, then using above-mentioned lawyer's Rating Model to these rules
Lawyer in teacher's data carries out comprehensive score, is finally shown the comprehensive score of lawyer, such as according to scoring from high to low
Sequence be ranked up displaying, to help user to make reasonable selection.
The present invention also provides a kind of computer readable storage medium, processing is stored on the computer readable storage medium
The step of system, the processing system realizes above-mentioned method when being executed by processor.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in a storage medium
In (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal device (can be mobile phone, computer, clothes
Business device, air conditioner or the network equipment etc.) execute method described in each embodiment of the present invention.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of electronic device, which is characterized in that the electronic device includes memory and the processing that connect with the memory
Device is stored with the processing system that can be run on the processor in the memory, and the processing system is by the processor
Following steps are realized when execution:
Acquisition step, acquires lawyer's data from multiple data sources, and lawyer's data include lawyer's relevant information, rule institute correlation
Information, law court's data and case data；
Matching step is based on pre-set matching factor, matches to lawyer's data of acquisition, and the data that will match to are made
It is characterized the factor；
Construction step is arranged corresponding weighting coefficient based on the characterization factor, is based on the characterization factor and weighting coefficient structure
Build lawyer's Rating Model；
Score step, receives the demand data of user's input, and it is corresponding to obtain the demand data for matching from lawyer's data of acquisition
Lawyer's data, lawyer is scored and is shown based on the obtained lawyer's data of matching and lawyer's Rating Model.
2. electronic device according to claim 1, which is characterized in that the characterization factor is divided into mainly according to significance level
The factor and confactor, the Main Factors include the case of attorney's procuration by type, caseload, the rate of winning a lawsuit/detraction rate, case
The time is handled, the confactor includes lawyer's essential information and rule institute's essential information, by the case of attorney's procuration by type and case
Number of packages amount determines the similar case of the case of attorney's procuration by counting, be arranged the similar case of the case of attorney's procuration by counting, similar case by
Rate of winning a lawsuit or detraction rate, similar case are a1 by the corresponding weighting coefficient of product of corresponding time factor three, and lawyer's generation is arranged
Total case number of packages of reason, the corresponding weighting coefficient of product of win a lawsuit both rate or detraction rate of total case number of packages of agency are a2, setting
The corresponding weighting coefficient of lawyer's essential information is a3, and the corresponding weighting coefficient of setting rule institute's essential information is a4, the lawyer of building
Rating Model are as follows:
Lawyer's scoring=a1*In (similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by corresponding time factor)
(rule institute is basic by+a2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+a3*In (lawyer's essential information)+a4*In
Information), wherein the time factor is the inverse that similar case is handled average time by case, lawyer's essential information and rule
Institute's essential information obtains corresponding numerical value after scheduled quantization step.
3. electronic device according to claim 1, which is characterized in that the characterization factor is divided into mainly according to significance level
The factor and confactor, the Main Factors include the case of attorney's procuration by type, caseload, the rate of winning a lawsuit/detraction rate, case
Time and the rate that runs succeeded are handled, the confactor includes lawyer's essential information and rule institute's essential information, by attorney's procuration's
Case by type and caseload determine attorney's procuration case similar case by counting, be arranged the similar case of the case of attorney's procuration by
Several, similar case is corresponding by the product of corresponding time factor and the rate that runs succeeded by the rate of winning a lawsuit or detraction rate, similar case
Weighting coefficient is b1, and the product of win a lawsuit both rate or detraction rate of total case number of packages of attorney's procuration, total case number of packages of agency is arranged
Corresponding weighting coefficient is b2, and the corresponding weighting coefficient of setting lawyer's essential information is b3, and setting rule institute's essential information is corresponding
Weighting coefficient is b4, lawyer's Rating Model of building are as follows:
(similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by corresponding time factor * for lawyer's scoring=b1*In
Run succeeded rate)+b2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+b3*In (lawyer's essential information)+b4*
In (rule institute's essential information), wherein the time factor is the inverse that similar case is handled average time by case, lawyer's base
This information and rule institute's essential information obtain corresponding numerical value after scheduled quantization step.
4. electronic device according to claim 2 or 3, which is characterized in that the scheduled quantization step includes, by lawyer
Essential information and rule institute's essential information are divided into quantitative information and non-quantitative information, extract the numerical value in quantitative information, using pre-
If quantization mapping table obtain the corresponding numerical value of non-quantitative information, by the numerical value of quantitative information in lawyer's essential information with not
The numerical value of quantitative information is added to obtain the corresponding numerical value of lawyer's essential information, will restrain institute's essential information in quantitative information numerical value with
The numerical value of non-quantitative information is added to obtain the corresponding numerical value of rule institute's essential information.
5. a kind of method that lawyer recommends, which is characterized in that the method that the lawyer recommends includes:
S1, from multiple data sources acquire lawyer's data, lawyer's data include lawyer's relevant information, rule institute's relevant information,
Law court's data and case data；
S2 is based on pre-set matching factor, matches to lawyer's data of acquisition, the data that will match to are as feature
The factor；
S3, is arranged corresponding weighting coefficient based on the characterization factor, constructs lawyer based on the characterization factor and weighting coefficient
Rating Model；
S4 receives the demand data of user's input, and matching obtains the corresponding lawyer of the demand data from lawyer's data of acquisition
Data, the lawyer's data and lawyer's Rating Model obtained based on matching are scored and are shown to lawyer.
6. the method that lawyer according to claim 5 recommends, which is characterized in that the characterization factor is according to significance level point
For Main Factors and confactor, the Main Factors include the case of attorney's procuration by type, caseload, the rate of winning a lawsuit/detraction
Rate, case handle the time, and the confactor includes lawyer's essential information and rule institute's essential information, by the case of attorney's procuration by class
Type and caseload determine the similar case of the case of attorney's procuration by counting, be arranged the similar case of the case of attorney's procuration by counting, together
Class case is a1, setting by the corresponding weighting coefficient of product of corresponding time factor three by the rate of winning a lawsuit or detraction rate, similar case
Total case number of packages of attorney's procuration, the corresponding weighting coefficient of product of win a lawsuit both rate or detraction rate of total case number of packages of agency are
A2, the corresponding weighting coefficient of setting lawyer's essential information are a3, and the corresponding weighting coefficient of setting rule institute's essential information is a4, building
Lawyer's Rating Model are as follows:
Lawyer's scoring=a1*In (similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by corresponding time factor)
(rule institute is basic by+a2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+a3*In (lawyer's essential information)+a4*In
Information), wherein the time factor is the inverse that similar case is handled average time by case, lawyer's essential information and rule
Institute's essential information obtains corresponding numerical value after scheduled quantization step.
7. the method that lawyer according to claim 5 recommends, which is characterized in that the characterization factor is according to significance level point
For Main Factors and confactor, the Main Factors include the case of attorney's procuration by type, caseload, the rate of winning a lawsuit/detraction
Rate, case processing time and the rate that runs succeeded, the confactor includes lawyer's essential information and rule institute's essential information, by lawyer
The case of agency by type and caseload determine attorney's procuration case similar case by counting, the same of the case of attorney's procuration is set
Class case by counting, similar case by the rate of winning a lawsuit or detraction rate, similar case by corresponding time factor and the product for the rate that runs succeeded
Corresponding weighting coefficient is b1, and win a lawsuit both the rate or detraction rate of total case number of packages of attorney's procuration, total case number of packages of agency is arranged
The corresponding weighting coefficient of product be b2, the corresponding weighting coefficient of setting lawyer's essential information is b3, setting rule institute's essential information
Corresponding weighting coefficient is b4, lawyer's Rating Model of building are as follows:
(similar case is by the similar case of several * by the rate of winning a lawsuit or the similar case of detraction rate * by corresponding time factor * for lawyer's scoring=b1*In
Run succeeded rate)+b2*In (win a lawsuit rate or the detraction rate of total total case number of packages of case number of packages *)+b3*In (lawyer's essential information)+b4*
In (rule institute's essential information), wherein the time factor is the inverse that similar case is handled average time by case, lawyer's base
This information and rule institute's essential information obtain corresponding numerical value after scheduled quantization step.
8. the method that lawyer according to claim 6 or 7 recommends, which is characterized in that the scheduled quantization step includes,
Lawyer's essential information and rule institute's essential information are divided into quantitative information and non-quantitative information, extract the numerical value in quantitative information,
The corresponding numerical value of non-quantitative information is obtained using preset quantization mapping table, by the number of quantitative information in lawyer's essential information
Value and the numerical value of non-quantitative information are added to obtain the corresponding numerical value of lawyer's essential information, will restrain quantitative information in institute's essential information
Numerical value and the numerical value of non-quantitative information are added to obtain the corresponding numerical value of rule institute's essential information.
9. the method recommended according to lawyer described in claim 5,6 or 7, which is characterized in that the step S2 is specifically included:
Whether analyze in lawyer's data of acquisition has unstructured data and semi-structured data, if so, then by unstructured data and
Semi-structured data is converted into structural data, and carries out text solution to all structural datas in lawyer's data of acquisition
Analysis is based on pre-set matching factor, matches to the structural data after text resolution, the data conduct that will match to
Characterization factor.
10. a kind of computer readable storage medium, which is characterized in that be stored with processing system on the computer readable storage medium
System, the method that the lawyer as described in any one of claim 5 to 9 recommends is realized when the processing system is executed by processor
Step.
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| CN110457357B (en) * | 2019-07-05 | 2023-02-14 | 中国平安财产保险股份有限公司 | Lawyer recommendation method based on neural network and related equipment |
| CN110502694A (en) * | 2019-07-23 | 2019-11-26 | 平安科技（深圳）有限公司 | Lawyer's recommended method and relevant device based on big data analysis |
| WO2021012793A1 (en) * | 2019-07-23 | 2021-01-28 | 平安科技（深圳）有限公司 | Lawyer recommendation method based on big data analysis, and related device |
| CN113626700A (en) * | 2021-08-09 | 2021-11-09 | 深圳市鼎法科技有限公司 | Lawyer recommendation method, system and equipment |
| CN113626700B (en) * | 2021-08-09 | 2023-08-22 | 深圳市鼎法科技有限公司 | Lawyer recommendation method, system and equipment |
| CN114358983A (en) * | 2021-12-27 | 2022-04-15 | 北京金堤科技有限公司 | Lawyer recommendation method and device and readable storage medium |
| CN115759692A (en) * | 2022-12-01 | 2023-03-07 | 徐玉峰 | Moderator allocation method, system, medium and equipment based on two-dimensional code |
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