CN110765760B - Legal case distribution method and device, storage medium and server - Google Patents
Legal case distribution method and device, storage medium and server Download PDFInfo
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- CN110765760B CN110765760B CN201910828776.5A CN201910828776A CN110765760B CN 110765760 B CN110765760 B CN 110765760B CN 201910828776 A CN201910828776 A CN 201910828776A CN 110765760 B CN110765760 B CN 110765760B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
- G06Q10/063112—Skill-based matching of a person or a group to a task
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/18—Legal services; Handling legal documents
Abstract
The invention relates to the technical field of computers, and provides a legal case distribution method, a legal case distribution device, a storage medium and a server. The method comprises the following steps: receiving a case allocation request sent by a terminal, wherein the case allocation request comprises a legal text of a legal case; extracting first keywords contained in the legal text according to a first legal keyword library; acquiring user portrait of each judge from a judge portrait database, and respectively matching the first keyword with feature tags of the user portrait; determining a target judge matched with the legal text, wherein the proportion of the feature tags successfully matched with the first keyword in the feature tags of the user portrait of the target judge is greater than a preset threshold value; and feeding back the personal information of the target judge to the terminal, and sending the legal text of the legal case to the terminal corresponding to the personal information of the target judge so as to prompt the target judge to process the legal case.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a legal case distribution method, a legal case distribution device, a storage medium and a server.
Background
At present, when distributing legal cases to be distributed to officers for processing, a situation distribution person generally refers to the data texts of each officer on a situation distribution system, then determines the characteristics of each officer and the type of the legal case which is skilled in processing according to the referred data texts, and finally manually distributes the legal cases to be distributed to the appropriate officers. However, the manual scheme matching method has low scheme matching efficiency and accuracy.
Disclosure of Invention
In view of this, embodiments of the present invention provide a legal case allocation method, an apparatus, a storage medium, and a server, which can improve the allocation efficiency and accuracy of the legal case allocation.
In a first aspect of the embodiments of the present invention, a legal case allocation method is provided, including:
receiving a case allocation request sent by a terminal, wherein the case allocation request comprises a legal text of a legal case;
extracting first keywords contained in the legal text according to a first legal keyword library which is constructed in advance;
acquiring user portrait of each judge from a pre-constructed judge picture database, and respectively matching the first keyword with the feature tags of each user portrait, wherein the user portrait of any judge in the judge picture database is obtained after performing word segmentation model processing and clustering processing corresponding to the type of the legal document on the related legal document;
determining a target judge matched with the legal text, wherein the proportion of feature tags successfully matched with the first keyword in feature tags of a user portrait of the target judge is larger than a preset threshold value;
and feeding back the personal information of the target judge to the terminal, and sending the legal text of the legal case to the terminal corresponding to the personal information of the target judge so as to prompt the target judge to process the legal case.
In a second aspect of an embodiment of the present invention, there is provided a legal case distribution apparatus, including:
the system comprises a case allocation request receiving module, a case allocation request receiving module and a case allocation request transmitting module, wherein the case allocation request receiving module is used for receiving a case allocation request sent by a terminal, and the case allocation request comprises legal texts of legal cases;
the first keyword extraction module is used for extracting first keywords contained in the legal text according to a first legal keyword library which is constructed in advance;
the user portrait matching module is used for acquiring user portraits of each judge from a pre-constructed judge portrait database and matching the first key words with feature labels of the user portraits respectively, wherein the user portrait of any judge in the judge portrait database is obtained after word segmentation model processing and clustering processing corresponding to the type of the legal document are executed on the related legal document;
the target judge determining module is used for determining a target judge matched with the legal text, and the proportion of the feature tags successfully matched with the first key word in the feature tags of the user portrait of the target judge is greater than a preset threshold value;
and the information sending module is used for feeding back the personal information of the target judge to the terminal and sending the legal text of the legal case to the terminal corresponding to the personal information of the target judge so as to prompt the target judge to process the legal case.
In a third aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor, carry out the steps of the legal case distribution method as set forth in the first aspect of embodiments of the present invention.
In a fourth aspect of embodiments of the present invention, there is provided a server comprising a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, the processor implementing the steps of the method for legal case distribution as set forth in the first aspect of embodiments of the present invention when executing the computer readable instructions.
The legal case distribution method provided by the embodiment of the invention comprises the following steps: receiving a case allocation request sent by a terminal, wherein the case allocation request comprises legal texts of legal cases; extracting first keywords contained in the legal text according to a first legal keyword library which is constructed in advance; acquiring user figures of all judges from a preset judge picture database, and respectively matching the first keywords with the feature tags of all the user figures; determining a target judge matched with the legal text, wherein the proportion of the feature tags successfully matched with the first keyword in the feature tags of the user portrait of the target judge is greater than a preset threshold value; and feeding back the personal information of the target judge to the terminal, and sending the legal text of the legal case to the terminal corresponding to the personal information of the target judge so as to prompt the target judge to process the legal case. According to the process, corresponding keywords are extracted from the legal text of the legal case, the extracted keywords are matched with the feature tags of the user figures of all judges respectively, corresponding target judges are determined according to matching results, and the legal case is distributed to the target judges to be processed, so that automatic distribution of the legal case is achieved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flow chart of one embodiment of a legal case assignment method provided by an embodiment of the present invention;
FIG. 2 is a block diagram of one embodiment of a legal case distribution device provided by an embodiment of the present invention;
fig. 3 is a schematic diagram of a server according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a legal case distribution method, a legal case distribution device, a storage medium and a server, which can improve case distribution efficiency and accuracy of legal case distribution.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, an embodiment of a legal case distribution method according to an embodiment of the present invention includes:
101. receiving a case allocation request sent by a terminal, wherein the case allocation request comprises legal texts of legal cases;
when legal cases are distributed, the server receives a case distribution request sent by the terminal, wherein the case distribution request comprises legal texts of the legal cases to be distributed. Specifically, the case allocator may operate on the terminal to upload the legal texts (such as case brief introduction) related to the legal cases to be allocated, and then send a case allocation request to the server.
102. Extracting first keywords contained in the legal text according to a first legal keyword library which is constructed in advance;
after the server obtains the legal text, the server extracts a first keyword contained in the legal text according to a first legal keyword library which is constructed in advance. The keyword library records various types of legal keywords such as criminal, civil, administrative, marital, robbery, intentional injury, and the like. The server detects whether the legal text has the keywords in the first legal keyword library or not, and if yes, the keywords are extracted to serve as the first keywords.
103. Acquiring user figures of all judges from a preset judge picture database, and respectively matching the first keywords with the feature tags of all the user figures;
then, the server acquires the user portrait of each judge from a pre-constructed judge portrait database, and matches the first keyword with the feature tag of each user portrait respectively, so as to determine the user portrait which is most matched with the first keyword. The user portrait of any judge in the judge portrait database is obtained after word segmentation model processing and clustering processing corresponding to the type of the legal documents are performed on the related legal documents, and the feature label of the judge can be determined by performing word segmentation and clustering processing on the legal documents related to the judge, so that the user portrait is constructed.
Specifically, the user representation of any judge can be obtained through the following steps:
(1) Obtaining a legal instrument associated with said any judge;
(2) Determining a target word segmentation model from a plurality of pre-constructed word segmentation models according to the legal document, and segmenting the content of the legal document to obtain a target word group set, wherein each word segmentation model corresponds to the type of one legal document;
(3) Performing clustering processing on the target word group set to obtain a plurality of clustering categories of the target word group set;
(4) And constructing the user portrait of any judge according to the plurality of cluster categories.
For step (1), the obtained legal documents may be electronic versions of various legal documents related to the judge, such as a case judgment document and a case scenario document of each case judged by the judge.
For the step (2), a plurality of different word segmentation models can be constructed in advance, and then a target word segmentation model is reasonably selected according to actual needs to segment the content of the legal document. Each word segmentation model corresponds to the type of one legal document, each word segmentation model can be generated by utilizing samples of different types of legal documents, and the samples can be obtained by performing artificial phrase division on the original legal documents by law workers. A large number of keywords in the legal documents of the type are obtained by summarizing phrases, cleaning and counting word frequency of a certain number of samples, and a word library is formed by combining the keywords to construct a corresponding word segmentation model. The structure of the legal document can be generally divided into three major parts of fact identification, legal reason and judgment main texts according to contents, and different word segmentation models can be respectively adopted for word segmentation aiming at different parts. In addition, if the number of the legal documents is multiple, for multiple legal documents, corresponding word segmentation models can be respectively selected to segment the content of each legal document to obtain multiple sub-word group sets, and then the multiple word group sets are combined to obtain the target word group set.
Specifically, determining a target word segmentation model from a plurality of pre-constructed word segmentation models according to the legal document may include:
(2.1) detecting a preset keyword from the contents of the legal document;
(2.2) determining the type of the legal document according to the detected second keyword;
(2.3) selecting a word segmentation model corresponding to the type of the legal document from the plurality of word segmentation models as the target word segmentation model.
With respect to step (2.1), after obtaining the legal document, preset keywords are detected from the content of the legal document, and the legal document may be specifically subjected to full-text search to determine whether the content of the legal document contains certain preset keywords, which may be used to determine the type of the legal document, such as criminal, civil, administrative, first review, second review, judgment, adjudication, mediation and other legal keywords.
In addition, in actual operation, the legal keywords recorded in the second legal keyword library constructed in advance may be detected from the content of the legal document, the word frequency of each detected legal keyword is counted respectively, and the keyword with the highest word frequency among the detected legal keywords is determined as the detected second keyword.
For some legal documents, many different keywords may be included, and too many keywords may bring inconvenience to the type determination of the legal documents, so that the detected keywords may be screened in a word frequency statistics manner. Specifically, a legal keyword library may be constructed in advance, and the keyword library records various types of legal keywords, such as criminals, civil affairs, administration, and the like. And then detecting keywords in the legal keyword library from the content of the legal documents, respectively counting the word frequency of each detected legal keyword, and determining the keyword with the highest word frequency in the detected legal keywords as a detected second keyword. For example, if criminal, civil and administrative keywords are detected, wherein the word frequency of the civil keywords is the highest, the civil is determined as the second keyword.
For step (2.2), the type of the legal instrument is determined from the detected second keyword. For example, if the detected second keyword is a keyword corresponding to a "criminal" type legal document, the type of the legal document is determined to be "criminal"; and if the detected second keyword is the keyword corresponding to the 'civil' type legal document, determining that the type of the legal document is 'civil', and if the detected second keyword is the keyword corresponding to the 'one-trial' type legal document, determining that the type of the legal document is 'one-trial'.
Specifically, if the number of the detected second keywords is one, determining the type of the legal document according to the detected second keywords; if the number of the detected second keywords is more than two, dividing the detected second keywords into more than one keyword combination, and determining the type of the legal document according to the keyword combinations, wherein each keyword combination comprises more than two second keywords.
If the number of the detected second keywords is only one, determining the type of the legal document directly according to the second keywords; if the number of the detected second keywords is more than two, dividing the second keywords into more than one keyword combination, and then determining the type of the legal document according to the divided keyword combinations, wherein each keyword combination comprises more than two second keywords. For example, if the second keyword is "civil affairs", the type of the legal document is directly determined to be "civil affairs" type; the divided key words are combined into 'civil affairs and first examination', and then the type of the legal document can be determined to be 'civil affairs of first examination'.
Further, if the number of the keyword combinations is one, the type of the legal document can be determined according to the keyword combinations; if the number of the keyword combinations is more than two, the text distance of each second keyword contained in each keyword combination in the legal document can be respectively counted, and the type of the legal document is determined according to the keyword combination with the minimum text distance.
If the number of the keyword combinations obtained by dividing is one, the type of the legal document is directly determined according to the keyword combinations, if the number of the keyword combinations obtained by dividing is more than two, the text distance of each second keyword contained in each keyword combination in the legal document, namely the number of characters separated by the two keywords in the content of the legal document, can be respectively counted, and finally the type of the legal document is determined according to the keyword combination with the minimum text distance. By means of the setting, application scenes with complex semantics and legal document types which cannot be judged through a single keyword can be dealt with.
For example, in the content of the legal document, "cancel first item, fourth item, fifth item, sixth item of the national judgment No. 10216 of second-middle-grade national institute of Beijing (2017) Beijing City and first item of the national judgment No. Jing 0101 national institute of Ministry 7939 of Beijing Toyota national institute of Ministry (2016); the second item of the national judgment of the second middle-grade people court of Beijing City (2017) Jing 02 Min final 10216 and the second item of the national judgment of Beijing City Dongchong people court (2016) Jing 0101 Min preliminary 7939 are maintained, and the target keywords such as 'cancel', 'first item of the national judgment', fourth item, fifth item, sixth item ',' maintain 'and' second item of the national judgment 'are selected and combined into a keyword combination' cancel 8230 ', a second item of the national judgment', and a keyword combination 'maintain 8230and a second item of the national judgment'. Then the text distance of the key words in the key word combination is larger than the text distance of ' maintain ' 8230, second term of civil judgment ' is selected to determine the type of the legal document through statistics.
Additionally, the type of legal document can also be determined by identifying the legal reason in the legal document, i.e., the statute to which the decision is made. That is, when a full-text search is performed on a legal document, the title number in the document can be firstly identified, the title name in the title number can be taken out, and then the legal document can be determined to be criminal, civil affair or other types according to the title name.
And (2.3) selecting a word segmentation model corresponding to the type of the legal document from the word segmentation models as the target word segmentation model, and then segmenting the content of the legal document by adopting the target word segmentation model to obtain a target word group set. The composition of terms, document structures and paragraphs of different legal document types has great difference, so that different word segmentation models are generated in advance according to different document types, and proper word segmentation models are selected for word segmentation processing during application, thereby being beneficial to improving the rationality of word segmentation and obtaining more accurate word segmentation results.
And (4) for the step (3), after the target word group set is obtained, clustering processing is carried out on the target word group set to obtain a plurality of clustering categories of the target word group set. In actual operation, various clustering algorithms in the prior art can be adopted for clustering. The target phrase set comprises a plurality of phrases, a plurality of known clustering categories can be constructed in advance, and clustering processing is carried out on the phrases of the target word set so as to determine the clustering categories of the phrases. For example, the target phrase set includes a plurality of keyword phrases in the civil law clause, and these phrases can be clustered to obtain a cluster category "civil"; the target phrase set contains a plurality of phrases in the criminal law clause, and the phrases can be clustered to obtain a cluster category 'criminal'.
And (5) constructing the user portrait of the judge according to the plurality of cluster categories in the step (4). That is, a series of feature label combinations are constructed by these cluster categories as the user representation of the judge. For example, if the obtained plurality of cluster categories are "civil affairs", "criminals", and "10-year experience", feature labels "senior civil officer" and "senior criminal officer" can be constructed as the user figure of the officer.
104. Determining a target judge matched with the legal text, wherein the proportion of the feature tags successfully matched with the first keyword in the feature tags of the user portrait of the target judge is greater than a preset threshold value;
and after the first keywords are respectively matched with the feature tags of the user portrait, determining a target judge matched with the legal text, wherein the proportion of the feature tags successfully matched with the first keywords in the feature tags of the user portrait of the target judge is greater than a preset threshold value.
Specifically, when matching is performed, the first keyword and all feature tags of one user portrait are matched, if the first keyword and the feature tags are the same or similar words, the matching is determined to be successful, and when the proportion of the feature tags, which are successfully matched with the first keyword, in the feature tags of one user portrait is greater than a certain preset threshold (for example, 80%), it may be determined that the user portrait and the first keyword are successfully matched, and the judge corresponding to the user portrait may be determined to be the target judge matched with the legal text.
105. And feeding back the personal information of the target judge to the terminal, and sending the legal text of the legal case to the terminal corresponding to the personal information of the target judge so as to prompt the target judge to process the legal case.
Finally, the server can feed back the relevant personal information of the target judge to the terminal, so that the partner can know to which judge the legal case is assigned. In addition, the server also sends the legal text of the legal case to a terminal corresponding to the personal information of the target judge so as to prompt the target judge to process the legal case, thereby completing a case matching process of the legal case.
The legal case distribution method provided by the embodiment of the invention comprises the following steps: receiving a case allocation request sent by a terminal, wherein the case allocation request comprises a legal text of a legal case; extracting first keywords contained in the legal text according to a first legal keyword library which is constructed in advance; acquiring user images of all judges from a pre-constructed judge image database, and respectively matching the first keyword with feature tags of all the user images; determining a target judge matched with the legal text, wherein the proportion of the feature tags successfully matched with the first keyword in the feature tags of the user portrait of the target judge is greater than a preset threshold value; and feeding back the personal information of the target judge to the terminal, and sending the legal text of the legal case to the terminal corresponding to the personal information of the target judge so as to prompt the target judge to process the legal case. According to the process, corresponding keywords are extracted from the legal text of the legal case, the extracted keywords are matched with the feature tags of the user portrait of each judge, the corresponding target judge is determined according to the matching result, and the legal case is distributed to the target judge to be processed, so that the automatic distribution of the legal case is achieved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
A legal case distribution method is mainly described above, and a legal case distribution apparatus will be described below.
Referring to fig. 2, an embodiment of a legal case distribution device according to an embodiment of the present invention includes:
a case allocation request receiving module 201, configured to receive a case allocation request sent by a terminal, where the case allocation request includes a legal text of a legal case;
the first keyword extraction module 202 is configured to extract a first keyword included in the legal text according to a first legal keyword library established in advance;
the user portrait matching module 203 is used for acquiring user portraits of each judge from a pre-constructed judge portrait database, and matching the first keyword with the feature tags of the user portraits respectively, wherein the user portrait of any judge in the judge portrait database is obtained by performing word segmentation model processing and clustering processing corresponding to the type of the legal document on the related legal document;
a target judge determining module 204, configured to determine a target judge matched with the legal text, where a ratio of feature tags successfully matched with the first keyword in feature tags of a user portrait of the target judge is greater than a preset threshold;
the information sending module 205 is configured to feed back the personal information of the target judge to the terminal, and send the legal text of the legal case to the terminal corresponding to the personal information of the target judge, so as to prompt the target judge to process the legal case.
Further, the legal case distribution apparatus may further include:
the legal document acquisition module is used for acquiring the legal document related to any judge;
the legal document word segmentation module is used for determining a target word segmentation model from a plurality of pre-constructed word segmentation models according to the legal document, performing word segmentation on the content of the legal document and obtaining a target word group set, wherein each word segmentation model corresponds to the type of one legal document;
the phrase clustering module is used for performing clustering processing on the target phrase set to obtain a plurality of clustering categories of the target phrase set;
and the user portrait construction module is used for constructing the user portrait of any judge according to the plurality of cluster categories.
Further, the legal document segmentation module may include:
a keyword detection unit for detecting a preset keyword from the content of the legal document;
a legal document determining unit for determining the type of the legal document according to the detected second keyword;
and the word segmentation model selection unit is used for selecting a word segmentation model corresponding to the type of the legal document from the plurality of word segmentation models as the target word segmentation model.
Further, the preset keyword is a legal keyword, and the keyword detection unit may include:
a legal keyword detection subunit, configured to detect a legal keyword recorded in a second legal keyword library that is constructed in advance from the content of the legal document;
the word frequency counting subunit is used for respectively counting the word frequency of each detected legal keyword;
and the second keyword determining subunit is used for determining the keyword with the highest word frequency in the detected legal keywords as the detected second keyword.
Further, the legal document determination unit may include:
the first document type determining subunit is used for determining the type of the legal document according to the detected second keyword if the number of the detected second keyword is one;
and a second document type determining subunit, configured to, if the number of the detected second keywords is two or more, divide the detected second keywords into one or more keyword combinations, and determine the type of the legal document according to the keyword combinations, where each keyword combination includes two or more second keywords.
Still further, the second document type determination subunit may include:
a first document type determining grandchild unit, configured to determine, if the number of the keyword combinations is one, a type of the legal document according to the keyword combinations;
and the second document type determining grandchild unit is used for respectively counting the text distance of each second keyword contained in each keyword combination in the legal document if the number of the keyword combinations is more than two, and determining the type of the legal document according to the keyword combination with the minimum text distance.
Embodiments of the present invention also provide a computer readable storage medium having computer readable instructions stored thereon, which when executed by a processor, implement the steps of any one of the legal case distribution methods as represented in FIG. 1.
Embodiments of the present invention also provide a server, which includes a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, wherein the processor executes the computer readable instructions to implement any of the steps of the legal case allocation method as shown in fig. 1.
Fig. 3 is a schematic diagram of a server according to an embodiment of the present invention. As shown in fig. 3, the server 3 of this embodiment includes: a processor 30, a memory 31, and computer readable instructions 32 stored in the memory 31 and executable on the processor 30. The processor 30, when executing the computer readable instructions 32, implements the steps in the above-described embodiments of the method for evaluating an application promotion effect, such as the steps 101 to 105 shown in fig. 1. Alternatively, the processor 30, when executing the computer readable instructions 32, implements the functions of the modules/units in the above device embodiments, such as the functions of the modules 201 to 205 shown in fig. 2.
Illustratively, the computer-readable instructions 32 may be partitioned into one or more modules/units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules/units may be a series of computer-readable instruction segments capable of performing specific functions, which are used to describe the execution process of the computer-readable instructions 32 in the server 3.
The server 3 may be a computing device such as a smart phone, a notebook, a palm computer, and a cloud server. The server 3 may include, but is not limited to, a processor 30, a memory 31. Those skilled in the art will appreciate that fig. 3 is merely an example of a server 3 and is not meant to be limiting with respect to server 3, and may include more or less components than those shown, or some components in combination, or different components, e.g., server 3 may also include input output devices, network access devices, buses, etc.
The Processor 30 may be a CentraL Processing Unit (CPU), other general purpose Processor, a DigitaL SignaL Processor (DSP), an AppLication Specific Integrated Circuit (ASIC), an off-the-shelf ProgrammabLe Gate Array (FPGA) or other ProgrammabLe logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 31 may be an internal storage unit of the server 3, such as a hard disk or a memory of the server 3. The memory 31 may also be an external storage device of the server 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure DigitaL (SD) Card, a FLash memory Card (FLash Card), or the like, provided on the server 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the server 3. The memory 31 is used to store the computer readable instructions and other programs and data required by the server. The memory 31 may also be used to temporarily store data that has been output or is to be output.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-OnLy Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A legal case distribution method, comprising:
receiving a case allocation request sent by a terminal, wherein the case allocation request comprises a legal text of a legal case;
extracting first keywords contained in the legal text according to a first legal keyword library which is constructed in advance;
acquiring user portrait of each judge from a pre-constructed judge picture database, and respectively matching the first keyword with the feature tags of each user portrait, wherein the user portrait of any judge in the judge picture database is obtained after performing word segmentation model processing and clustering processing corresponding to the type of the legal document on the related legal document;
determining a target judge matched with the legal text, wherein the proportion of the feature tags successfully matched with the first keyword in the feature tags of the user portrait of the target judge is greater than a preset threshold value;
feeding back the personal information of the target judge to the terminal, and sending the legal text of the legal case to the terminal corresponding to the personal information of the target judge so as to prompt the target judge to process the legal case;
wherein the user representation of any judge is obtained by the steps of:
obtaining a legal instrument associated with said any judge;
determining a target word segmentation model from a plurality of pre-constructed word segmentation models according to the legal document, and segmenting the content of the legal document to obtain a target word group set, wherein each word segmentation model corresponds to the type of the legal document;
performing clustering processing on the target word group set to obtain a plurality of clustering categories of the target word group set;
constructing a user portrait of the arbitrary judge according to the plurality of cluster categories;
the process of determining the type of the legal instrument includes:
identifying a title number of the legal instrument;
extracting the French name in the book name number;
and determining the type of the legal document according to the legal name.
2. The method of claim 1, wherein the determining a target segmentation model from a plurality of pre-constructed segmentation models according to the legal documents comprises:
detecting preset keywords from the content of the legal documents;
determining the type of the legal document according to the detected second key words;
and selecting a word segmentation model corresponding to the type of the legal document from the plurality of word segmentation models as the target word segmentation model.
3. The legal case distribution method of claim 2, wherein the preset keywords are legal keywords, and the detecting preset keywords from the content of the legal documents comprises:
detecting legal keywords recorded in a second legal keyword library which is constructed in advance from the content of the legal document;
respectively counting the word frequency of each detected legal keyword;
and determining the keyword with the highest word frequency in the detected legal keywords as the detected second keyword.
4. The legal case assignment method of claim 2, wherein said determining the type of legal document based on the detected second keyword comprises:
if the number of the detected second keywords is one, determining the type of the legal document according to the detected second keywords;
if the number of the detected second keywords is more than two, dividing the detected second keywords into more than one keyword combination, and determining the type of the legal document according to the keyword combinations, wherein each keyword combination comprises more than two second keywords.
5. The legal case assignment method of claim 4, wherein said determining the type of legal document from the keyword combination comprises:
if the number of the keyword combinations is one, determining the type of the legal document according to the keyword combinations;
and if the number of the keyword combinations is more than two, respectively counting the text distance of each second keyword contained in each keyword combination in the legal document, and determining the type of the legal document according to the keyword combination with the minimum text distance.
6. A legal case distribution apparatus, comprising:
the system comprises a case allocation request receiving module, a case allocation request receiving module and a case allocation request transmitting module, wherein the case allocation request receiving module is used for receiving a case allocation request sent by a terminal, and the case allocation request comprises legal texts of legal cases;
the first keyword extraction module is used for extracting first keywords contained in the legal text according to a first legal keyword library which is constructed in advance;
the user portrait matching module is used for acquiring user portraits of each judge from a pre-constructed judge portrait database and matching the first keyword with the feature tags of the user portraits respectively, wherein the user portrait of any judge in the judge portrait database is obtained by performing word segmentation model processing and clustering processing corresponding to the type of the legal document on the related legal document;
the target judge determining module is used for determining a target judge matched with the legal text, and the proportion of the feature tags successfully matched with the first key word in the feature tags of the user portrait of the target judge is greater than a preset threshold value;
the information sending module is used for feeding back the personal information of the target judge to the terminal and sending the legal text of the legal case to the terminal corresponding to the personal information of the target judge so as to prompt the target judge to process the legal case;
the legal document acquisition module is used for acquiring the legal document related to any judge;
the legal document word segmentation module is used for determining a target word segmentation model from a plurality of pre-constructed word segmentation models according to the legal document, segmenting the content of the legal document to obtain a target word group set, wherein each word segmentation model corresponds to the type of one legal document;
the phrase clustering module is used for performing clustering processing on the target phrase set to obtain a plurality of clustering categories of the target phrase set;
the user portrait construction module is used for constructing a user portrait of any judge according to the plurality of cluster categories;
the book name and number identification module is used for identifying the book name and number of the legal document;
the system comprises a title name extraction module, a name extraction module and a name translation module, wherein the title name extraction module is used for extracting a title name in a book name number;
and the document type determining module is used for determining the type of the legal document according to the legal name.
7. A computer readable storage medium storing computer readable instructions, wherein the computer readable instructions, when executed by a processor, implement the steps of the legal case distribution method of any one of claims 1 to 5.
8. A server comprising a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, wherein the processor when executing the computer readable instructions performs the steps of:
receiving a case allocation request sent by a terminal, wherein the case allocation request comprises legal texts of legal cases;
extracting first keywords contained in the legal text according to a first legal keyword library which is constructed in advance;
acquiring user portrait of each judge from a pre-constructed judge picture database, and respectively matching the first keyword with the feature tags of each user portrait, wherein the user portrait of any judge in the judge picture database is obtained after performing word segmentation model processing and clustering processing corresponding to the type of the legal document on the related legal document;
determining a target judge matched with the legal text, wherein the proportion of the feature tags successfully matched with the first keyword in the feature tags of the user portrait of the target judge is greater than a preset threshold value;
feeding back the personal information of the target judge to the terminal, and sending the legal text of the legal case to the terminal corresponding to the personal information of the target judge so as to prompt the target judge to process the legal case;
wherein the user representation of any judge is obtained by the steps of:
obtaining a legal instrument associated with said any judge;
determining a target word segmentation model from a plurality of pre-constructed word segmentation models according to the legal document, and segmenting the content of the legal document to obtain a target word group set, wherein each word segmentation model corresponds to the type of the legal document;
performing clustering processing on the target word group set to obtain a plurality of clustering categories of the target word group set;
constructing a user portrait of the arbitrary judge according to the plurality of cluster categories;
the process of determining the type of legal instrument includes:
identifying a title number of the legal instrument;
extracting the French name in the book name number;
and determining the type of the legal document according to the legal name.
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| CN112150023A (en) * | 2020-09-29 | 2020-12-29 | 壹链盟生态科技有限公司 | Task allocation method, device and storage medium |
| CN112862289B (en) * | 2021-01-29 | 2022-11-25 | 上海妙一生物科技有限公司 | Information matching method and device for clinical research practitioner |
| CN114461760A (en) * | 2022-02-10 | 2022-05-10 | 北京华宇元典信息服务有限公司 | Method and device for matching case fact with law bar |
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