CN111198953B - Case text information based case recommending method, system and readable storage medium - Google Patents
Case text information based case recommending method, system and readable storage medium Download PDFInfo
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- CN111198953B CN111198953B CN201811369302.0A CN201811369302A CN111198953B CN 111198953 B CN111198953 B CN 111198953B CN 201811369302 A CN201811369302 A CN 201811369302A CN 111198953 B CN111198953 B CN 111198953B
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Abstract
The invention discloses a method, a system and a readable storage medium for recommending a case text information based case, wherein the method for recommending the case text information based case comprises the steps of constructing a database of specific labels of case text elements, wherein the case text elements comprise a case, facts, plots, criminal subjects and criminal objects; importing a case text to be processed; analyzing a case text to be processed, and marking a corresponding specific label according to a case text element in the case text to be processed; matching a specific label of a case text to be processed with a class case in a server; and sequencing according to the matching degree and displaying at least part of corresponding case information. The case text information based case recommending method provided by the invention has the advantages that the case is more quickly searched, the use is more convenient and efficient, and the matching accuracy is higher.
Description
Technical Field
The invention relates to the technical field of legal case recommendation cases, in particular to a case text information recommendation case based method, a case text information recommendation system and a readable storage medium.
Background
Currently, when a judicial staff of a public inspection agency needs to search a case, the corresponding case, facts and plots are summarized through reading a case document, and then information such as the case is input on a case website to search the corresponding case. This approach has mainly the following drawbacks: 1. by looking up the case document to refine the correspondence, facts and facts information, this process is time consuming and may have some errors. 2. The comprehensive matching degree cannot be calculated according to a group of fact plots on the case website, so that the case which is matched with the whole current case is difficult to find. 3. Local comparative typical instructional cases cannot be recommended. Therefore, the related cases are time-consuming and labor-consuming to review, the accuracy is low, and the use is very inconvenient.
Disclosure of Invention
The invention mainly aims to provide a case text information based case recommending method, which aims to find cases more quickly, is more convenient and efficient to use and has higher matching accuracy.
In order to achieve the above object, the present invention provides a case text information based case recommendation method, which includes the following steps:
s10: constructing a database of specific labels of case text elements, wherein the case text elements comprise a case, facts, plots, crime subjects and crime objects;
s30: importing a case text to be processed;
s40: analyzing a case text to be processed, and marking a corresponding specific label according to a case text element in the case text to be processed;
s50: matching a specific label of a case text to be processed with a class case in a server;
s60: and sequencing according to the matching degree and displaying at least part of corresponding case information.
Preferably, the specific tags of the case include bribed crime tags, greedy crime tags, rape crime tags, swindle crime tags, intentional injury crime tags, illegal absorption of public deposit crime tags, illegal restraint crime tags, disguise tags, concealing crime-derived tags, crime-derived profit crime tags, aggressive crime tags, and job encroachment crime tags;
specific tags of fact include smuggling tags, vending tags, shipping tags, manufacturing drug quantity tags, personal fund fraud amount heavy tags, telecom fraud amount hard to verify as other severe episode tags, illegally absorbing public deposit amount heavy tags, illegally disabling person death tags, fraud amount heavy tags, manufacturing drug quantity heavy tags, personal fund fraud amount heavy tags, illegally disabling person re-injury tags, unit fund fraud amount heavy tags, illegally holding drug episode heavy tags, and fraud amount extra heavy tags;
specific labels for episodes include achievement criminal and explanation labels, master labels, refund labels, reimbursement labels, tank labels, aggressive reimbursement labels, acquisition understanding labels, faithful supply labels, crime resumption labels, forensic inferior labels, voluntary crime labels, offender labels, common crime labels, and crime aborting labels;
specific labels of criminal subjects include deaf and dumb labels, blind labels, mental patient labels, elderly labels, and minor labels;
specific tags for crime subjects include juvenile tags, elderly tags, and disabled tags.
Preferably, S30 comprises the steps of:
s31: dividing the text of the case to be processed into a case, facts, plots, crime subjects and crime objects according to the regular matching rules;
s32: and respectively marking the specific labels corresponding to the case text to be processed, namely the case law, the facts, the plots, the criminal main body and the criminal object according to the regular matching rules and the specific labels of the case text elements in the database.
Preferably, S40 specifically includes the following steps:
s41: the key sentences and/or words in the case text elements are respectively matched with the corresponding case essences, facts, plots, criminal subjects and criminal objects of the case text to be processed according to the regular matching rules,
s42: and marking a specific label on the whole of the to-be-processed case text or the corresponding paragraph of the to-be-processed case text according to the matching result.
Preferably, S50 specifically includes the following steps:
s51: respectively assigning weights to the case, facts, plots, criminal subjects, criminal objects and aesthetic grades;
s52: calculating the score D of the text of the case to be processed, wherein the calculation formula is as follows: d=a× (number of specific tags by case×r1+number of specific tags in fact×r2+number of specific tags in case×r3+number of specific tags in subject×r4+number of specific tags in subject×r5+r6),
s53: the score L of each class is calculated, and the calculation formula is as follows: l=a× (the number of case-by-case specific tags of the case-by hit of the case text of the case 1-X1/y1 xr1+ type, the number of specific tags of the fact hit of the case text of the case 2-X2/y2 xr2+ type, the number of specific tags of the case hit of the case text of the case 3X 3/y3 xr3+ type, the number of specific tags of the body hit of the case text of the case 4-X4/y4 xr4+ type, the number of specific tags of the object hit of the case text of the case 5X 5/y5 xr5+ type, the number of trial hits of the case 6),
s54: calculating the matching degree P, wherein the calculation formula is P=D/L multiplied by 100%,
wherein R1 is case weight, R2 is fact weight, R3 is story weight, R4 is crime subject weight, R5 is crime object weight, R6 is aesthetic weight, a is matched single tag score,
x1 is the number of specific labels in the case, which are different from the text of the case to be processed, X2 is the number of specific labels in the case, which are different from the text of the case to be processed, X3 is the number of specific labels in the case, which are different from the text of the case to be processed, X4 is the number of specific labels in the criminal body, which are different from the text of the case to be processed, X5 is the number of specific labels in the criminal object, which are different from the text of the case to be processed,
y1 is the de-duplication number of the specific label and the class label of the case text to be processed in the case, Y2 is the de-duplication number of the specific label and the class label of the case text to be processed in the fact, Y3 is the de-duplication number of the specific label and the class label of the case text to be processed in the scenario, Y4 is the de-duplication number of the specific label and the class label of the case text to be processed in the crime subject, and Y5 is the de-duplication number of the specific label and the class label of the case text to be processed in the crime subject.
Preferably, S60 specifically includes the following steps:
s61: and sequencing from high to low according to the matching degree of the class information and displaying at least partially.
Preferably, after S10, before S30, the following steps are included:
s20: analyzing the class case in the server and marking the corresponding specific label.
Preferably, S60 further comprises the following steps:
s70: when at least one specific label of the case text to be processed is selected, the corresponding case is highlighted.
The invention also provides a system based on the case text information recommending case, which comprises: a memory, a processor, and a case text information recommendation-based program stored on the memory and executable on the processor, wherein:
the method for recommending the case text information based on the case text information comprises the steps that the program based on the case text information recommended case is executed by the processor.
The invention also provides a readable storage medium, wherein the readable storage medium stores a program based on the case text information recommendation type, and the program based on the case text information recommendation type realizes the steps of the method based on the case text information recommendation type when being executed by a processor.
According to the technical scheme, a database of specific labels of case text elements is constructed, wherein the case text elements comprise a case, facts, plots, a crime main body and crime objects; importing a case text to be processed; analyzing a case text to be processed, and marking a corresponding specific label according to a case text element in the case text to be processed; matching a specific label of a case text to be processed with a class case in a server; and sequencing according to the matching degree and displaying at least part of corresponding case information. When the method and the device are used for searching the case of the text of the case to be processed, the case to be processed is only required to be imported, the imported specific label of the case to be processed is used for searching the case by automatic operation processing, and the case information with accurate matching degree is obtained, so that the case searching is faster, the use is more convenient and efficient, and the matching accuracy is higher.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an embodiment of a case text information recommendation method according to the present invention;
FIG. 2 is a schematic flow chart of another embodiment of a case text information recommendation method according to the present invention;
fig. 3 is a schematic structural diagram of a refinement flow of step S30 in fig. 1 and 2;
fig. 4 is a schematic structural diagram of a refinement flow of step S40 in fig. 1 and 2;
fig. 5 is a schematic diagram of a refinement flow structure of step S50 in fig. 1 and 2;
fig. 6 is a schematic diagram of a refinement flow structure of step S60 in fig. 1 and 2.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
In the present invention, unless specifically stated and limited otherwise, the terms "connected," "affixed," and the like are to be construed broadly, and for example, "affixed" may be a fixed connection, a removable connection, or an integral body; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
Furthermore, descriptions such as those referred to as "first," "second," and the like, are provided for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implying an order of magnitude of the indicated technical features in the present disclosure. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The invention provides a system for recommending a case based on case text information. The system based on the case text information recommendation case can be mobile devices such as mobile phones, smart phones, notebook computers, PAD (tablet computers) and the like, and fixed terminals such as desktop computers, servers and the like. The system based on the case text information recommended class comprises a memory, a processor and a program which is stored on the memory and can run on the processor and is based on the case text information recommended class.
The memory comprises at least one computer readable storage medium for storing an operating system and various application software installed on the system based on the case text information recommendation type, such as program codes of programs based on the case text information recommendation type. In addition, the memory may be used to temporarily store various types of data that have been output or are to be output.
The processor may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor is typically used to control the overall operation of the system for recommending a category based on the case text information. In this embodiment, the processor is configured to execute the program code or process data stored in the memory, for example, execute the program based on the case text information recommendation class.
Referring to fig. 1, when the program based on the case text information recommendation case is executed by the processor, the following steps are implemented:
s10: constructing a database of specific labels of case text elements, wherein the case text elements comprise a case, facts, plots, crime subjects and crime objects;
s30: importing a case text to be processed;
s40: analyzing a case text to be processed, and marking a corresponding specific label according to a case text element in the case text to be processed;
s50: matching a specific label of a case text to be processed with a class case in a server;
s60: and sequencing according to the matching degree and displaying at least part of corresponding case information.
The database can be stored on a server memory or a cloud server, case information in the database is obtained through direct reading or Internet, specific labels in the database are classified according to case text elements, and particularly specific labels for case by include a british crime label, a greedy crime label, a jail crime label, an intentional injury crime label, an illegal absorption public deposit crime label, an illegal restraint crime label, a mask label, a hidden crime obtained label, a crime obtained profit crime label, an hunting crime label and a job encroaching crime label;
specific tags of fact include smuggling tags, vending tags, shipping tags, manufacturing drug quantity tags, personal fund fraud amount heavy tags, telecom fraud amount hard to verify as other severe episode tags, illegally absorbing public deposit amount heavy tags, illegally disabling person death tags, fraud amount heavy tags, manufacturing drug quantity heavy tags, personal fund fraud amount heavy tags, illegally disabling person re-injury tags, unit fund fraud amount heavy tags, illegally holding drug episode heavy tags, and fraud amount extra heavy tags;
specific labels for episodes include achievement criminal and explanation labels, master labels, refund labels, reimbursement labels, tank labels, aggressive reimbursement labels, acquisition understanding labels, faithful supply labels, crime resumption labels, forensic inferior labels, voluntary crime labels, offender labels, common crime labels, and crime aborting labels;
specific labels of criminal subjects include deaf and dumb labels, blind labels, mental patient labels, elderly labels, and minor labels;
specific tags for crime subjects include juvenile tags, elderly tags, and disabled tags.
One embodiment is: inputting information such as a case number of a case to be processed and the like which can uniquely determine the case to be processed into a computer or a server, directly reading the case text to be processed by the computer, analyzing and processing by an operation processor, and splitting case text elements in the case text to be processed into case text elements according to a regular matching rule, wherein the method specifically comprises the following steps of:
referring to fig. 3, S31: dividing the text of the case to be processed into a case, facts, plots, crime subjects and crime objects according to the regular matching rules;
s32: and respectively marking the specific labels corresponding to the case text to be processed, namely the case law, the facts, the plots, the criminal main body and the criminal object according to the regular matching rules and the specific labels of the case text elements in the database.
Wherein the fact's regular matching rule is:
(theft total sum) [ ≡a. The following is carried out The method comprises the steps of carrying out a first treatment on the surface of the A "] ([ \s\d, ] + [ ten million superfluous ]) element;
([ \d two three five six seven ninety degrees ]) + [ kilomilli ] {0,1} g [ ], a. The! The method comprises the steps of carrying out a first treatment on the surface of the The method comprises the steps of carrying out a first treatment on the surface of the Heroin;
(robbery|robbery|fetch) [ ≡a. The following is carried out {0,30} ([ \d, ] + [ tens of millions ] excess ]) elements, (resulting in the formation of |composition| made) ([ ],; }) 0,5} bruise.
Scenario elements correspond to regular matching rules such as:
(. The%! ,. ]? Master, (;
(use |adoption|) + (violence means| stress means| anaesthesia means);
fraud {0,4} (disaster relief |rescue |flood prevention|support|lean|immigrants|medical treatment| {0,4} (money |property|material);
impersonation (army |judicial) {0,5} person is illegal (withholding|forbidden).
Criminal subjects correspond to regular matching rules such as:
deaf and dumb;
blind person;
mental patients;
elderly people who are full of seventies and five years old;
minors.
Crime objects correspond to regular matching rules such as:
(victim (crime);
(victim (crime);
(victim (crime).
Referring to fig. 4, S41 is again performed: the key sentences and/or words in the case text elements are respectively matched with the corresponding case essences, facts, plots, criminal subjects and criminal objects of the case text to be processed according to the regular matching rules,
s42: and marking a specific label on the whole of the to-be-processed case text or the corresponding paragraph of the to-be-processed case text according to the matching result.
The method specifically comprises the steps of matching related keywords, words, sentences and the like in each case text element through a regular matching rule to mark specific tags, wherein the related keywords, words and sentences can be identical or equivalent to information in the specific tags, analyzing the content of the case text to be processed according to each case text element sentence by sentence and the related keywords, words and sentences, and marking the corresponding specific tags, the whole of the case text to be processed or the corresponding paragraph of the case text to be processed with the specific tags after the matching is successful.
And then, matching the specific label of the case text to be processed with the case in the server through a regular matching rule to ensure that the matching efficiency and accuracy are higher, and specifically comprising the following steps:
referring to fig. 5, S51: respectively assigning weights to the case, facts, plots, criminal subjects, criminal objects and aesthetic grades;
s52: calculating the score D of the text of the case to be processed, wherein the calculation formula is as follows: d=a× (number of specific tags by case×r1+number of specific tags in fact×r2+number of specific tags in case×r3+number of specific tags in subject×r4+number of specific tags in subject×r5+r6),
s53: the score L of each class is calculated, and the calculation formula is as follows: l=a× (the number of case-by-case specific tags of the case-by hit of the case text of the case 1-X1/y1 xr1+ type, the number of specific tags of the fact hit of the case text of the case 2-X2/y2 xr2+ type, the number of specific tags of the case hit of the case text of the case 3X 3/y3 xr3+ type, the number of specific tags of the body hit of the case text of the case 4-X4/y4 xr4+ type, the number of specific tags of the object hit of the case text of the case 5X 5/y5 xr5+ type, the number of trial hits of the case 6),
s54: calculating the matching degree P, wherein the calculation formula is P=D/L multiplied by 100%,
wherein R1 is case weight, R2 is fact weight, R3 is story weight, R4 is crime subject weight, R5 is crime object weight, R6 is aesthetic weight, a is matched single tag score,
x1 is the number of specific labels in the case, which are different from the text of the case to be processed, X2 is the number of specific labels in the case, which are different from the text of the case to be processed, X3 is the number of specific labels in the case, which are different from the text of the case to be processed, X4 is the number of specific labels in the criminal body, which are different from the text of the case to be processed, X5 is the number of specific labels in the criminal object, which are different from the text of the case to be processed,
y1 is the de-duplication number of the specific label and the class label of the case text to be processed in the case, Y2 is the de-duplication number of the specific label and the class label of the case text to be processed in the fact, Y3 is the de-duplication number of the specific label and the class label of the case text to be processed in the scenario, Y4 is the de-duplication number of the specific label and the class label of the case text to be processed in the crime subject, and Y5 is the de-duplication number of the specific label and the class label of the case text to be processed in the crime subject.
And finally, sequencing according to the matching degree and displaying at least part of corresponding case information on a display screen of the user side. For the user to review the case, further referring to fig. 6, through S61: and sequencing from high to low according to the matching degree of the class information and displaying at least partially. The user can conveniently and quickly select the case with higher matching degree, and the use is more convenient and quick.
Referring to fig. 2, further, S60 further includes the following steps:
s70: when at least one specific label of the case text to be processed is selected, the corresponding case is highlighted.
Specifically, after step S40, the specific label of the case text to be processed may be displayed, the user may adjust and confirm the specific label, and when looking up the case, the corresponding case of the specific label or the corresponding word, sentence in the corresponding case may be highlighted, such as one or more of word, sentence highlighting, color changing, increasing, font changing, increasing the ground color, etc., so that the user may further look up the case, and the use is more efficient and convenient.
Referring to fig. 2, after S10, the following steps are included before S30:
s20: analyzing the class case in the server and marking the corresponding specific label.
The specific labels of the case text to be processed can be matched with the specific labels of the cases in the server, and the corresponding at least partial case information is ordered and displayed according to the matching degree, so that the operation processing is faster, the matching efficiency is higher, and the matching is more accurate.
Specific cases are, for example, the following:
the reported people XX, men, 24 days 6 of 1999, are in the Inlet of the View, han nationality, junior middle school cultural degree, and live in the XX town of the XX city. The criminal is arrested in the case of 21 days 7 in 2016 and 26 days 8 in 2016.
Legal agency Li XX, men, 1976, 12 and 7 days, han nationality, farmers, and XX town. Is the father of the parent XX.
Legal attorney XX, female, 1978, birth on 6-month 14, han nationality, farmers, and XX town. Is the mother of the recipient XX.
Designating the dialect forest X, XX law firm lawyer.
The case is finished by the state public security office, and the inspection and prosecution are transferred to the home for prosecution on the 10 th and 12 th of 2016 when the interviewee XX suspects to steal. After the reception of the hospital, the appraised person is informed of the right to entrust the dialer on the 10 th and 12 th of 2016, the appraised person is informed of the right to entrust the litigation agency on the 10 th and 12 th of 2016, the appraised person is asked in law, the opinion of the appraised person is heard, and all the case materials are inspected.
Examination and finding:
1. the year 2016, month 5, month 22 and day 11, the victims XX, ancient XX and "mobile phone" come near the large courage mall parking lot in the great town in the city of the Yangzhou, see that the victims leaf X ride the electric vehicle out of the large courage mall parking lot, place leaf X in a handbag below the headstock to be stolen, and the handbag is filled with the cash rmb 14783 yuan, the apple phone 1 part and the bank card. After the case is broken, the stolen property is recovered and is also remitted.
Because the price identification matters do not meet the relevant regulations, the price identification is not accepted by the Yangzhou price authentication center on one apple phone with the stolen leaf X.
2. The date of day 19 is 29 in 5 of 2016, the victims XX, ancient XX and 'mobile phone' come to front of the Jiahui supermarket in the middle-aged Daojia in the Qingzhou, the satchel carried by the victims X is scratched by using a sharp edge, a wallet is stolen, and a cash RMB 6817 is arranged in the wallet. The victim is a disabled person. After the case is broken, the stolen property is recovered and is also remitted.
The police officer captures the suspicious XX at the solving south road of the town, and the XX faithfully supplies the criminal facts of the theft after the questioning education is carried out in 2016, 7 and 21.
Evidence of the above facts is considered as follows:
the resident population information, the history of criminal, the criminal judgment and the release certificate of the book;
statement of victim leaf X, sheet X;
the provision of the interviewee XX;
price approval is not accepted;
and (5) searching the records and the photos on site.
The council considers that the XX of the reported person is disregarded by national laws, and the purpose is that the partner takes secret means to carry the murder to secondarily steal other people and property in public places, namely the mass of the RMB is 21600 yuan, the mass is larger, and the behavior of the personnel is in touch with the regulations of the second hundred sixty four of criminal law of the people's republic of China, and the criminal responsibility of the reported person should be pursued by theft. The victims XX, when they are stolen, are over sixteen years and under eighteen years, and are the crimes of minors, and are applicable to the regulations of the seventeenth clause and the third clause of the criminal law of the people's republic of China, and the penalties should be reduced or lightened. After the XX trace of the person to be reported is questioned and educated, the criminal fact is actively treated, which can be considered as the first, and is applicable to the rule of the sixty-seven law of litigation of criminal law of the people's republic of China, and the law can be lightened or lightened.
Matching the case result:
1. by means of semantic analysis technology and combining with a database of specific labels, the specific label information of the current case part is analyzed as follows:
the scheme is as follows: crime of theft
Case type: criminals
And (3) judging program: one examination
Case attribution: hainan province, delirium city, delirium, state people court
Case type: disclosure case
Criminal subject: gender: age at the time of men and work: 17 years old, ethnicity: degree of Chinese, cultural: junior middle school and occupation: farmers, address: XX town, crime name: crime of theft
Crime object: disabled person
Crime facts: the actual point label-the amount is larger, the actual point label-carrying murder theft-the actual value label-the case-involved amount is 3 thousands to 3 thousands, the actual value label-the number of times of theft is more than 3 times
Crime scenario: minors, initiatives, dirt and claims
2. According to the analyzed specific label information, the recommended partial classification result is as follows:
criminal decision book for theft of Wu-bang on Huang-bang
[ PROBLEMS ] the theft amount is large
Dirt and claims are returned; tank white
The court believes that some people are somehow and some people are somehow in yellow to occupy illegally, and steals 1692 yuan of money of other people's property value, and the amount is large, and the behavior of the money is a theft crime and should be punished. . The crime facts pointed by the public complaint authorities are clear, the evidence is true and sufficient, and the pointed crime names are established. Law may be penalized in view of the fact that the reported person somehow, somehow yellow somehow faithfully contributes to his crime and that the stolen item has been sent back to the victim. According to the fact, nature, plot and extent of harm of behavior of the scheme, the person is told to the society, according to the regulations of the second hundred sixty four, the twenty-fifth first, the sixty-seventh third, the fifty-second and the sixty-fourth of the criminal law of the people's republic of China.
Criminal decision book for continental crime
Multiple theft; the theft amount is larger
Dirt and claims are returned; tank white
The council holds that the reported person steals the property of other people with illegal occupation by taking certain national law as an aim, the covalent value of the RMB 16180 yuan is large, the behavior of the RMB is composed of theft crimes, the fact that the customs authority controls the crimes is clear, the evidence is sufficient, and the crime is established and should be supported. The principal of the subject is to take some form of drug taking and theft, with the principal of the follow-up scenario being appropriate. Since the reported land has some tank and the stolen article has been recovered, the method can be penalized from light. According to the second hundred sixty-four, fifty-second, sixty-four and sixty-seven third regulations of the criminal law of the people's republic of China.
When the method and the device are used for searching the case of the text of the case to be processed, the case to be processed is only required to be imported, the imported specific label of the case to be processed is subjected to case searching through automatic operation processing, and case information with accurate matching degree is obtained, so that case searching is faster, more convenient and efficient to use, and matching accuracy is higher.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the description of the present invention and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the invention.
Claims (9)
1. The method for recommending the case based on the case text information is characterized by comprising the following steps of:
s10: constructing a database of specific labels of case text elements, wherein the case text elements comprise a case, facts, plots, crime subjects and crime objects;
s30: importing a case text to be processed;
s40: analyzing a case text to be processed, and marking a corresponding specific label according to a case text element in the case text to be processed;
s50: matching a specific label of a case text to be processed with a class case in a server;
s60: sorting according to the matching degree and displaying at least part of corresponding case information;
s30, the method comprises the following steps of:
s31: dividing the text of the case to be processed into a case, facts, plots, crime subjects and crime objects according to the regular matching rules;
s32: respectively marking specific labels corresponding to the case text to be processed, such as case law, facts, plots, criminal subjects and criminal objects according to the regular matching rules and the specific labels of the case text elements in the database;
wherein the fact's regular matching rule is:
(theft total sum) [ ≡a. The following is carried out The method comprises the steps of carrying out a first treatment on the surface of the A "] ([ \s\d, ] + [ ten million superfluous ]) element;
([ \d two three five six seven ninety degrees ]) + [ kilomilli ] {0,1} g [ ], a. The! The method comprises the steps of carrying out a first treatment on the surface of the The method comprises the steps of carrying out a first treatment on the surface of the Heroin;
(robbery|robbery|fetch) [ ≡a. The following is carried out {0,30} ([ \d, ] + [ tens of millions ] excess ]) members, (resulting in the formation of |composition| into) ([),; } {0,5} light injury;
scenario elements correspond to regular matching rules:
(. The%! ,. ]? Master, (;
(use |adoption|) + (violence means| stress means| anaesthesia means);
fraud {0,4} (disaster relief |rescue |flood prevention|support|lean|immigrants|medical treatment| {0,4} (money |property|material);
impersonation (army |judicial) {0,5} person illegal (withholding|forbidden);
criminal subjects correspond to regular matching rules:
deaf and dumb;
blind person;
mental patients;
elderly people who are full of seventies and five years old;
minors;
crime object corresponds to regular matching rule:
(victim (crime);
(victim (crime);
(victim (crime).
2. The method for recommending a case based text information according to claim 1, wherein,
specific tags for the case include british crime tags, greedy crime tags, rape crime tags, equity crime tags, intentional injury crime tags, illegal absorption public deposit crime tags, illegal restraint crime tags, disguise tags, concealing crime obtained tags, crime obtained earning crime tags, opponent crime tags, job encroaching crime tags;
specific tags of fact include smuggling tags, vending tags, shipping tags, manufacturing drug quantity tags, personal fund fraud amount heavy tags, telecom fraud amount hard to verify as other severe episode tags, illegally absorbing public deposit amount heavy tags, illegally disabling person death tags, fraud amount heavy tags, manufacturing drug quantity heavy tags, personal fund fraud amount heavy tags, illegally disabling person re-injury tags, unit fund fraud amount heavy tags, illegally holding drug episode heavy tags, and fraud amount extra heavy tags;
specific labels for episodes include achievement criminal and explanation labels, master labels, refund labels, reimbursement labels, tank labels, aggressive reimbursement labels, acquisition understanding labels, faithful supply labels, crime resumption labels, forensic inferior labels, voluntary crime labels, offender labels, common crime labels, and crime aborting labels;
specific labels of criminal subjects include deaf and dumb labels, blind labels, mental patient labels, elderly labels, and minor labels;
specific tags for crime subjects include juvenile tags, elderly tags, and disabled tags.
3. The case-text-information-based recommendation-type method as claimed in claim 1, wherein S40 specifically comprises the steps of:
s41: the key sentences and/or words in the case text elements are respectively matched with the corresponding case essences, facts, plots, criminal subjects and criminal objects of the case text to be processed according to the regular matching rules,
s42: and marking a specific label on the whole of the to-be-processed case text or the corresponding paragraph of the to-be-processed case text according to the matching result.
4. The case-text-information-based recommendation-type method as claimed in claim 1, wherein S50 specifically comprises the steps of:
s51: respectively assigning weights to the case, facts, plots, criminal subjects, criminal objects and aesthetic grades;
s52: calculating the score D of the text of the case to be processed, wherein the calculation formula is as follows: d=a× (number of specific tags by case×r1+number of specific tags in fact×r2+number of specific tags in case×r3+number of specific tags in subject×r4+number of specific tags in subject×r5+r6),
s53: the score L of each class is calculated, and the calculation formula is as follows: l=a× (the number of case-by-case specific tags of the case-by hit of the case text of the case 1-X1/y1 xr1+ type, the number of specific tags of the fact hit of the case text of the case 2-X2/y2 xr2+ type, the number of specific tags of the case hit of the case text of the case 3X 3/y3 xr3+ type, the number of specific tags of the body hit of the case text of the case 4-X4/y4 xr4+ type, the number of specific tags of the object hit of the case text of the case 5X 5/y5 xr5+ type, the number of trial hits of the case 6),
s54: calculating the matching degree P, wherein the calculation formula is P=D/L multiplied by 100%,
wherein R1 is case weight, R2 is fact weight, R3 is story weight, R4 is crime subject weight, R5 is crime object weight, R6 is aesthetic weight, a is matched single tag score,
x1 is the number of specific labels in the case, which are different from the text of the case to be processed, X2 is the number of specific labels in the case, which are different from the text of the case to be processed, X3 is the number of specific labels in the case, which are different from the text of the case to be processed, X4 is the number of specific labels in the criminal body, which are different from the text of the case to be processed, X5 is the number of specific labels in the criminal object, which are different from the text of the case to be processed,
y1 is the de-duplication number of the specific label and the class label of the case text to be processed in the case, Y2 is the de-duplication number of the specific label and the class label of the case text to be processed in the fact, Y3 is the de-duplication number of the specific label and the class label of the case text to be processed in the scenario, Y4 is the de-duplication number of the specific label and the class label of the case text to be processed in the crime subject, and Y5 is the de-duplication number of the specific label and the class label of the case text to be processed in the crime subject.
5. The case-text-information-based recommendation-type method as claimed in claim 4, wherein S60 specifically comprises the steps of:
s61: and sequencing from high to low according to the matching degree of the class information and displaying at least partially.
6. The case-text-information-based recommendation-type method according to claim 5, wherein after S10, before S30, comprising the steps of:
s20: analyzing the class case in the server and marking the corresponding specific label.
7. The case-text-information-based recommendation-type method as recited in claim 4, further comprising the step of, after S60:
s70: when at least one specific label of the case text to be processed is selected, the corresponding case is highlighted.
8. A system for recommending a case based text information, the system comprising: a memory, a processor, and a case text information recommendation-based program stored on the memory and executable on the processor, wherein:
the program based on case text information recommendation class, when executed by the processor, implements the steps of the method based on case text information recommendation class of any of claims 1 to 7.
9. A readable storage medium, characterized in that it has stored thereon a program based on a case text information recommendation class, which when executed by a processor, implements the steps of the case text information recommendation class based method according to any of claims 1 to 7.
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| CN112100212A (en) * | 2020-09-04 | 2020-12-18 | 中国航天科工集团第二研究院 | Case scenario extraction method based on machine learning and rule matching |
| CN113486158B (en) * | 2021-09-08 | 2021-12-14 | 中国司法大数据研究院有限公司 | Case situation comparison-based case retrieval method, device, equipment and storage medium |
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