CN117035406A - Intelligent control method, device and equipment for judging flow - Google Patents
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Abstract
The application provides an intelligent control method, device and equipment for an judgment flow, wherein the method comprises the following steps: constructing a risk database of the trial node; the risk database is used for storing risk information corresponding to each judging node; extracting characteristic information from the files of the target trial cases; and generating risk prompt information corresponding to each judging node of the target judging case according to the risk database and the characteristic information. The method of the application improves the supervision and control efficiency and the real-time performance of the trial cases.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to an intelligent control method, device and equipment for an judgment flow.
Background
In legal procedure, case judgment is a very critical stage, and the result directly relates to the rights and interests of case parties and the maintenance of social fairness sense, so that the stability and development of society are effectively promoted, and the method has important significance.
In the related art, the normalization and risk in each judgment node of the judgment cases are checked in a manual auditing mode, and due to the large number of the judgment cases and the different judgment nodes of each case, the judgment cases cannot be effectively supervised and managed in advance, in the middle and after.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the application provides an intelligent management and control method, device and equipment for an judgment flow.
Specifically, the embodiment of the application provides the following technical scheme:
in a first aspect, an embodiment of the present application provides an intelligent control method for an trial flow, including:
constructing a risk database of the trial node; the risk database is used for storing risk information corresponding to each judging node;
extracting characteristic information from the files of the target trial cases;
and generating risk prompt information corresponding to each judgment node of the target judgment case according to the risk database and the characteristic information.
Further, the extracting feature information from the file of the target trial case includes:
determining at least one keyword in the file of the target trial case according to the keyword information of each word in the file of the target trial case; the criticality information is used for evaluating the importance degree of each word in the file of the target trial case in case trial risk identification;
and taking the keywords in the file of the target trial case as characteristic information in the file of the target trial case.
Further, the criticality information of each term in the volume of the target trial case is determined using the following formula:
wherein Score (W) i ) Representation word W i Is a key degree information of the (a); vd (Vd) i Representation word W i Semantic contribution values of (2); vdw the semantic contribution value weights; tw is the weight of the statistical characteristic value; loc ij Representation word W i Whether it has occurred at position j; low (low) j The weight of the position j in the statistical feature is represented, wherein the value of j is 1, 2 and 3, and the represented position types are respectively a title, a section head and a section tail; len (len) i Representation word W i Is a word length of (a); lenw represents word length weight in the statistical feature; pos i Representation word W i Part of speech value of (2); posw represents the lexical weight in the statistical feature; fr represents the word W i Frequency of occurrence and word W in the volume of the target trial case i The ratio of occurrence frequencies in the files of the historical trial cases; frw the word W i Frequency of occurrence and word W in the volume of the target trial case i The weight corresponding to the ratio of occurrence frequency in the files of the historical trial cases.
Further, after determining at least one keyword in the file of the target trial case, the method further includes:
changing the label information of the keywords, and determining the difference of the importance degree of each keyword under different labels;
and cleaning the keywords according to the difference of the importance degrees of the keywords under different labels to obtain the cleaned keywords.
Further, the generating risk prompt information corresponding to each trial node of the target trial case according to the risk database and the feature information includes:
and matching the characteristic information in the file of the target trial case with the risk information in the risk database to generate risk prompt information corresponding to each trial node of the target trial case.
Further, before constructing the risk database of the trial node, the method further comprises:
reconstructing an judging node; the judging nodes comprise 7 judging nodes before complaints, case setting, case dividing, before court trial, after court trial, judge and after judge.
In a second aspect, an embodiment of the present application further provides an intelligent control device for an judgment process, including:
the construction module is used for constructing a risk database of the judging node; the risk database is used for storing risk information corresponding to each judging node;
the extraction module is used for extracting characteristic information from the files of the target trial cases;
and the management and control module is used for generating risk prompt information corresponding to each judgment node of the target judgment case according to the risk database and the characteristic information.
In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the intelligent control method for the trial flow according to the first aspect when executing the program.
In a fourth aspect, an embodiment of the present application further provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements the intelligent control method for an inspection flow according to the first aspect.
In a fifth aspect, an embodiment of the present application further provides a computer program product, including a computer program, where the computer program is executed by a processor to implement the intelligent control method for an inspection flow according to the first aspect.
According to the intelligent management and control method, the intelligent management and control device and the intelligent management and control equipment for the trial flow, provided by the embodiment of the application, the risk information which possibly influences the fairness and the legality of the trial in each trial node is stored in the risk database, so that comprehensive and accurate management of various risks possibly existing in each node of the case trial is realized; and then, the characteristic information extracted from the files of the trial cases is matched, correlated and analyzed with each risk information in the risk database, namely, only the characteristic information in the files is required to be extracted and analyzed, and all the information in the files are not required to be checked one by one, so that whether the target trial cases have risk information affecting the fairness and the legality of the trial in the process of the trial can be effectively and accurately determined, and the supervision and control efficiency and the instantaneity of the trial cases are effectively improved.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of an intelligent control method for an judgment flow provided by an embodiment of the application;
fig. 2 is a schematic structural diagram of an intelligent control device for an judgment flow provided by an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order to facilitate a clearer understanding of the technical solutions of the embodiments of the present application, some technical contents related to the embodiments of the present application will be first described.
The standardized construction of the trial flow is enhanced, one of the purposes is to integrate through an information technology, promote the realization of intelligent assistance such as mark remaining, reminding, early warning, freezing of illegal matters and the like of each node from case establishment to case filing, and practically solve the problem of supervision and management in advance and in the event of case handling. In particular to trial practice, the procedural value of litigation is embodied in the functional aggregation of multiple process nodes, each of which may affect the final case handling effect. In the intelligent age, the information technology provides conditions for realizing the extensive-to-fine transition of trial supervision and management, and the supervision mode of capturing nodes is being formed by depending on the whole course, stage and stage of science and technology. Therefore, the intelligent system monitors and analyzes the condition of the case flow nodes in real time, timely reminds hidden danger and accurately positions the problems, facilitates court supervision and case handling personnel to the greatest extent, promotes the judgment efficiency, realizes judicial fairness, and is a specific target for assisting in managing and controlling the judgment flow nodes by the intelligent system.
The method provided by the embodiment of the application can be applied to the intelligent management and control scene of the trial flow, and the supervision and control efficiency and the instantaneity of the trial cases are improved.
In the related art, the normalization and risk in each judgment node of the judgment cases are checked in a manual auditing mode, and due to the large number of the judgment cases and the different judgment nodes of each case, the judgment cases cannot be effectively supervised and managed in advance, in the middle and after.
According to the intelligent management and control method for the judging flow, disclosed by the embodiment of the application, the risk information which possibly influences the fairness and the legality of judgment in each judging node is stored in the risk database, so that comprehensive and accurate carding of various types of risks possibly existing in each node of case judgment is realized; and then, the characteristic information extracted from the files of the trial cases is matched, correlated and analyzed with each risk information in the risk database, namely, only the characteristic information in the files is required to be extracted and analyzed, and all the information in the files are not required to be checked one by one, so that whether the target trial cases have risk information affecting the fairness and the legality of the trial in the process of the trial can be effectively and accurately determined, and the supervision and control efficiency and the instantaneity of the trial cases are effectively improved.
The following describes the technical scheme of the present application in detail with reference to fig. 1 to 3. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Fig. 1 is a flow chart of an embodiment of an intelligent control method for an judgment flow provided by an embodiment of the present application. As shown in fig. 1, the method provided in this embodiment includes:
step 101, constructing a risk database of the trial node; the risk database is used for storing risk information corresponding to each judging node;
specifically, in the related art, the normalization and the risk in each judgment node of the judgment cases are checked in a manual auditing mode, and due to the huge number of the judgment cases and the different judgment nodes of each case, the judgment cases cannot be effectively supervised and managed in advance, in the middle and after.
In order to solve the above problems, in the embodiment of the present application, a risk database of an trial node is first constructed; the risk database stores risk information corresponding to each judging node; optionally, the risk information corresponding to each judging node includes necessary materials in each judging node, time limit of each judging node and other information possibly influencing fairness and legitimacy of judgment, so that comprehensive and accurate carding of various risks possibly existing in each node of case judgment is realized.
Step 102, extracting characteristic information from the files of the target trial cases;
specifically, since the number of trial cases is huge and the trial nodes of each case are different, if all contents in the files of all the trial cases are checked one by one, a great amount of manpower and material resources are consumed, so that the efficiency and accuracy of the trial case monitoring are poor. According to the embodiment of the application, the characteristic information is extracted from the file of the target trial case, so that the supervision and the management of the trial case are realized based on the characteristic information in the file, and the efficiency and the instantaneity of the trial case are effectively improved. Optionally, the target trial case may be a case to be trial, a case being trial or a case being trial completed, so as to implement supervision and management of the whole process of the trial case in advance, in the event and after the event. Alternatively, the extracted feature information may be feature information of multiple dimensions, such as the approval period information recorded in the case, the material information in the approval, the influence range and influence degree of the case, and the property of the case, which is not particularly limited in the embodiment of the present application.
And step 103, generating risk prompt information corresponding to each judgment node of the target judgment case according to the risk database and the characteristic information.
Specifically, after feature information is extracted from the file of the target trial case, risk prompt information corresponding to each trial node of the target trial case can be generated according to the risk database and the feature information; optionally, the extracted characteristic information can be matched, associated and analyzed with each risk information in the risk database, so that only the characteristic information in the file is required to be extracted and analyzed, and all the information in the file is not required to be checked one by one, so that whether the target trial case has risk information affecting the fairness and the legality of the trial in the trial process can be accurately determined, and further, the case trial personnel can pertinently improve according to the risk prompt information, thereby greatly improving the efficiency of managing and controlling the trial case supervision; optionally, the method can analyze the files of all the trial cases in a period, and output the risk prompt information corresponding to each trial node, so that the risk information distribution situation in the trial process is mined, the generation reasons of the high-frequency risk information are analyzed and purposefully modified, the risk and the number of violations in the case trial process are reduced, and the fairness and the legality of the case trial are improved.
According to the method, the risk database is constructed, and the risk information which possibly influences the fairness and the legality of the judgment in each judgment node is stored in the risk database, so that comprehensive and accurate carding of various risks possibly existing in each node of the case part judgment is realized; and then, the characteristic information extracted from the files of the trial cases is matched, correlated and analyzed with each risk information in the risk database, namely, only the characteristic information in the files is required to be extracted and analyzed, and all the information in the files are not required to be checked one by one, so that whether the target trial cases have risk information affecting the fairness and the legality of the trial in the process of the trial can be effectively and accurately determined, and the supervision and control efficiency and the instantaneity of the trial cases are effectively improved.
In one embodiment, extracting feature information from a volume of a target trial case includes:
determining at least one keyword in the file of the target trial case according to the keyword information of each word in the file of the target trial case; the key degree information is used for evaluating the importance degree of each word in the file of the target trial case in case trial risk identification;
and taking the keywords in the file of the target trial case as characteristic information in the file of the target trial case.
Specifically, in the embodiment of the application, the feature information extracted from the file of the trial case is matched, correlated and analyzed with each risk information in the risk database, so that whether the target trial case has risk information affecting the fairness and the legality of the trial or not is determined, and therefore, the selection of the feature information in the trial case has important significance for the risk identification and the judgment in each trial node.
In order to extract characteristic information from the files of the trial cases more reasonably and accurately, and further accurately and efficiently identify risk information in the case trial process based on the characteristic information, in the embodiment of the application, the key degree information of each word in the files of the trial cases is determined by evaluating the importance degree of each word in the files of the trial cases in the case trial risk identification, and further, the key degree information of each word is used as the characteristic information in the files of the target trial cases; optionally, the keywords with the keyword degree information larger than the preset threshold value can be used as the feature information in the volume of the target trial case, so that the feature information in the volume of the target trial case is reasonably and accurately extracted, and further the extracted feature information in the volume and the risk information in the risk database are associated, analyzed and matched, and whether the risk information affecting the fairness and the legality of the trial case exists in the trial process can be effectively and accurately determined, and therefore the efficiency and the accuracy of the supervision and control of the trial case can be effectively improved.
According to the method, the key degree information of each word in the file of the trial case is determined through evaluating the importance degree of each word in the estimated file of the trial case in the case trial risk recognition, and further, the key degree information of each word is used for determining which words are used as the characteristic information in the file of the target trial case, so that the characteristic information in the trial case is accurately and reasonably extracted; and then, the characteristic information in the extracted file and the risk information in the risk database are correlated, analyzed and matched, so that whether the target trial case has risk information affecting the fairness and the legality of the trial in the process of trial can be effectively and accurately determined, and the efficiency and the accuracy of the supervision and control of the trial case can be effectively improved.
In one embodiment, the criticality information for each term in the volume of the target trial case is determined using the following formula:
wherein Score (W) i ) Representation word W i Is a key degree information of the (a); vd (Vd) i Representation word W i Semantic contribution values of (2); vdw the semantic contribution value weights; tw is the weight of the statistical characteristic value; loc ij Representation word W i Whether it has occurred at position j; low (low) j The weight of the position j in the statistical feature is represented, wherein the value of j is 1, 2 and 3, and the represented position types are respectively a title, a section head and a section tail; len (len) i Representation word W i Is a word length of (a); lenw represents the statistical featureWord length weights; pos i Representation word W i Part of speech value of (2); posw represents the lexical weight in the statistical feature; fr represents the word W i Frequency of occurrence and word W in the volume of the target trial case i The ratio of occurrence frequencies in the files of the historical trial cases; frw the word W i Frequency of occurrence and word W in the volume of the target trial case i The weight corresponding to the ratio of occurrence frequency in the files of the historical trial cases.
Specifically, the importance degree of each word in the file of the trial case in the case trial risk recognition is evaluated, so that the criticality information of each word in the file of the trial case is accurately determined, and further, based on the criticality information of each word, the feature information of which words are taken as the feature information in the file of the target trial case can be accurately determined, so that the accurate and reasonable extraction of the feature information in the trial case is accurately and efficiently realized; alternatively, the criticality information for each term in the volume of the target trial case may be determined based on the following formula:
wherein Score (W) i ) Representation word W i Is a key degree information of the (a); vd (Vd) i Representation word W i Semantic contribution values of (2); vdw the semantic contribution value weights; tw is the weight of the statistical characteristic value; loc ij Representation word W i Whether it has occurred at position j; low (low) j The weight of the position j in the statistical feature is represented, wherein the value of j is 1, 2 and 3, and the represented position types are respectively a title, a section head and a section tail; optionally, different semantic contribution values, semantic contribution value weights and statistical feature value weights can be set for words in different positions respectively; alternatively, len i Representation word W i Is a word length of (a); lenw represents word length weights in the statistical features, that is, different word length weights can be set for words with different lengths; optionally, the longer the term length, the greater the term length weight corresponding thereto; pos i Representation word W i Part of speech value of (2); posw represents the lexical weight in the statistical feature; optionally, the part-of-speech values and part-of-speech weights of nouns and verbs are larger, and the part-of-speech values and part-of-speech weights of prepositions and conjunctions are smaller; fr represents the word W i Frequency of occurrence and word W in the volume of the target trial case i The ratio of occurrence frequencies in the files of the historical trial cases; frw the word W i Frequency of occurrence and word W in the volume of the target trial case i The weight corresponding to the ratio of occurrence frequency in the files of the historical trial cases; alternatively, the word W i The higher the frequency of occurrence of the word W in the file of the target trial case i The less frequently it occurs in a volume of historical trial cases, i.e., word W i Frequency of occurrence and word W in the volume of the target trial case i Under the condition that the ratio of occurrence frequency in the files of the historical trial cases is larger, the word W i The larger the score value corresponding to the key degree information of the key degree information; correspondingly, the word W i The smaller the frequency of occurrence in the file of the target trial case, the word W i The greater the frequency of occurrence in the volume of the history trial cases, namely the word W i Frequency of occurrence and word W in the volume of the target trial case i Under the condition that the smaller the ratio of occurrence frequency in the files of the historical trial cases is, the word W i The smaller the score value corresponding to the key degree information of the file is, the accurate evaluation of the importance degree of each word in the file in the case trial risk recognition is realized.
According to the method, the importance degree of each word in the case judgment risk identification is accurately, comprehensively and effectively determined based on the information of multiple dimension consideration and multiple dimensions, and the accurate evaluation of the importance degree of each word in the case judgment risk identification in the case judgment is realized, so that the accuracy and the effectiveness of supervision and control of each node of the case judgment are improved.
In one embodiment, after determining at least one keyword in the set of target trial cases, the method further includes:
changing the label information of the keywords, and determining the difference of the importance degrees of the keywords under different labels;
and cleaning the keywords according to the difference of the importance degrees of the keywords under different labels to obtain the cleaned keywords.
Specifically, the embodiment of the application realizes that the key degree information of each term in the document can be accurately determined after the importance degree of each term in the document in the case trial risk recognition is accurately, comprehensively and effectively determined by comprehensively considering the position of each term in the document, the semantic contribution value of each term, the semantic contribution value weight, the term length of the term and other dimensional information, and further can effectively and accurately determine which terms are used as the key terms and the characteristic information in the document of the target trial case based on the key degree information of each term.
Optionally, in order to further improve accuracy and precision of keywords in a file of an trial case, in the embodiment of the application, after determining that a word with the keyword degree information greater than a preset threshold value in the file of the trial case is a keyword, label information of the keyword is replaced, differences of importance degrees of all keywords under different labels are determined, and then the keyword is cleaned according to the differences of the importance degrees of all keywords under different labels, so that the cleaned keyword is obtained; optionally, keywords with importance degree differences of each keyword under different labels being larger than a threshold value can be used as washed keywords, and the washed keywords are further used as feature information extracted from the files of the trial cases, so that more accurate, reasonable and accurate extraction of the feature information in the files of the trial cases is realized, and the accuracy of supervision and control of the trial cases is improved. Optionally, each keyword and label (keyword information Score) in the file of the trial case can be trained through a preset model to obtain the importance index of each keyword; optionally, the preset model may be a tree model or other models, which is not limited in the embodiment of the present application; further, the labels (key information Score) of the keywords are adjusted and disturbed, and the importance indexes of the keywords are trained and recorded again; finally, the importance indexes of the keywords before and after the label adjustment and disorder are compared, and if the difference of the importance indexes of the keywords under different labels is smaller, the keywords are removed; if the difference of the importance indexes of the keywords under different labels is large, the keywords are reserved and used as the characteristic information in the files of the trial cases, so that the characteristic information in the files of the trial cases is further screened, and the accuracy of the characteristic information extracted from the files of the target trial cases is improved. The method comprises the steps of determining the keyword information of each word in a file by comprehensively considering the position of each word in the file, the semantic contribution value of each word, the semantic contribution value weight, the word length of each word and the like, determining the word with the keyword information larger than a preset threshold as the keyword, and then cleaning the keyword according to the difference of importance indexes of each keyword under different labels to obtain the cleaned keyword, so that on the basis of improving the efficiency of supervision and control of the trial cases based on the characteristic information in the trial cases, further on the basis of more accurate characteristic information, the supervision and control of the trial cases can be more accurately carried out, and double improvement of the supervision and control efficiency and accuracy of each node of the trial cases is realized.
According to the method, through comprehensively considering the position of each word in the file of the trial case, the semantic contribution value weight, the word length and other dimensional information of each word, the keyword degree information of each word in the file is determined, after the word with the keyword degree information larger than the preset threshold value is determined as the keyword, the keyword is cleaned according to the difference of the importance indexes of each keyword under different labels, and the cleaned keyword is obtained, so that on the basis of improving the efficiency of the supervision and control of the trial case based on the feature information in the trial case, the supervision and control of the trial case can be more accurately carried out further based on the more accurate feature information, and the double improvement of the supervision and control efficiency and accuracy of each node of the trial case is realized.
Before constructing the risk database of the trial node, the method further comprises the following steps:
reconstructing an judging node; the judging nodes comprise 7 judging nodes before complaints, case setting, case dividing, before court trial, after court trial, judging and after judging.
Specifically, under normal conditions, the trial flow comprises 5 main stages before prosecution, case establishment, trial, judgment and post-judgment, and in the embodiment of the application, a case division link is added after case establishment, and the trial stages with longer time limit are divided into two stages before trial and after trial, namely, the trial case is divided into 7 trial nodes, namely, 7 node stages before prosecution, case division, before trial, after trial, judgment and post-judgment, so that the more comprehensive and refined supervision and control of the trial flow are realized, the node division in the trial flow is clearer, the supervision and control of the trial flow are facilitated, and the rationality, convenience, comprehensiveness and fineness of supervision and control of the trial flow are improved.
In an embodiment, according to the risk database and the feature information, generating risk prompt information corresponding to each trial node of the target trial case includes:
and matching the characteristic information in the file of the target trial case with the risk information in the risk database to generate risk prompt information corresponding to each trial node of the target trial case.
Specifically, after the feature information in the volume of the trial case is determined, the embodiment of the application can correlate, match and analyze the feature information in the volume of the trial case with the risk information corresponding to each trial node stored in the risk database, so as to determine whether each feature information has a correlation with the risk information corresponding to each trial node, and further generate the risk prompt information corresponding to each trial node of the target trial case according to the correlation of the feature information and the risk information, thereby realizing efficient and accurate comprehensive supervision and management of the risks of each node in the trial case.
The embodiment of the application discloses an intelligent control method for an judgment flow, which comprises the following specific flows:
step one: and constructing an judgment flow node, conforming to BPR (Business Process Reengineering) theory, namely business flow reconstruction theory, determining that the judgment flow tends to be reasonable, and refining the stages which are easy to divide, have larger influence and have more nodes. Under normal conditions, the trial flow comprises 5 main stages of before-complaint, case establishment, trial, judge and post-judgment, but a case division link is also arranged after case establishment, the trial stage has the longest time limit and more nodes, and the trial flow can be practically divided into two parts of before-court trial and after-court trial (including re-court trial). Therefore, in order to facilitate node determination and sectional control, the trial flow can be divided into 7 stages before complaint, case establishment, case division, before trial in a court, after trial in a court, judge and after judge.
Step two: and constructing a risk database of the trial flow node.
For example, risk data includes, but is not limited to, requisite materials and time-limited risk:
(1) Each judging node is matched with necessary file materials, and when the nodes lack the necessary materials, the system responds to prompt risks and risk grades in time. If the necessary materials of the nodes are not matched in the case-setting nodes, the system can automatically prompt that the judging process has risks and risk grades, and prompt case-setting staff to timely supplement the materials; if a civil examination case is applied, the pre-complaint stage comprises three necessary materials of a pre-complaint security application form, a pre-complaint executing application form and a pre-complaint reconciliation form, and the three materials are in an OR relationship, namely the three materials are risk data;
(2) And constructing a risk database according to the attribute of the node. For key judging nodes, fixed limits are set by legal civilization, and the limits are used as risk data; if a civil one-examination case is applied, at the judge node, the configuration is given an aging attribute for 6 months, namely 6 months is risk data.
Step three: and acquiring data in the case. For target volume data, calculating word criticality, wherein the calculation function is as follows:
wherein Score (W) i ) Representation word W i Is a key degree information of the (a); vd (Vd) i Representation word W i Semantic contribution values of (2); vdw the semantic contribution value weights; tw is the weight of the statistical characteristic value; loc ij Representation word W i Whether it has occurred at position j; low (low) j The weight of the position j in the statistical feature is represented, wherein the value of j is 1, 2 and 3, and the represented position types are respectively a title, a section head and a section tail; len (len) i Representation word W i Is a word length of (a); lenw represents word length weight in the statistical feature; pos i Representation word W i Part of speech value of (2); posw represents the lexical weight in the statistical feature; fr represents the word W i Frequency of occurrence and word W in the volume of the target trial case i The ratio of occurrence frequencies in the files of the historical trial cases; frw the word W i Frequency of occurrence and word W in the volume of the target trial case i The weight corresponding to the ratio of occurrence frequency in the files of the historical trial cases; the higher the final value, the higher the criticality of the term, i.e., the more characteristic.
Step four: changing the label information of the keywords, and determining the difference of the importance degrees of the keywords under different labels; according to the difference of the importance degrees of the keywords under different labels, cleaning the keywords to obtain cleaned keywords; thereby improving the accuracy of extracting the key words and the characteristic information.
Step five: analyzing the judging nodes where the risk data are located and providing a solution. And (3) comparing the characteristic information obtained in the step four with a risk database for constructing the nodes of the judging flow, marking the judging nodes corresponding to the data and prompting a user if the nodes have risks, and the nodes need to be solved in time. If the court trial is finished, the system automatically reminds the conference court members to complete the case comment in a specific period. The prompting content is mainly as follows: A. the specific requirements of handling matters are met; B. the remaining time period of the transaction should be handled. C. The necessary operations required to eliminate the risk.
The following describes the intelligent control device for the trial flow provided by the application, and the intelligent control device for the trial flow described below and the intelligent control method for the trial flow described above can be correspondingly referred to each other.
Fig. 2 is a schematic structural diagram of the intelligent control device for the judgment flow provided by the application. The intelligent control device for the judging process provided by the embodiment comprises:
a construction module 210, configured to construct a risk database of the trial node; the risk database is used for storing risk information corresponding to each judging node;
an extracting module 220, configured to extract feature information from a file of the target trial case;
the management and control module 230 is configured to generate risk prompt information corresponding to each trial node of the target trial case according to the risk database and the feature information.
Optionally, the extracting module 220 is specifically configured to: determining at least one keyword in the file of the target trial case according to the keyword information of each word in the file of the target trial case; the key degree information is used for evaluating the importance degree of each word in the file of the target trial case;
and taking the keywords in the file of the target trial case as characteristic information in the file of the target trial case.
Optionally, the extracting module 220 is specifically configured to: determining the criticality information of each word in the file of the target trial case by using the following formula:
wherein Score (W) i ) Representation word W i Is a key degree information of the (a); vd (Vd) i Representation word W i Semantic contribution values of (2); vdw semantic contribution valuesWeighting; tw is the weight of the statistical characteristic value; loc ij Representation word W i Whether it has occurred at position j; low (low) j The weight of the position j in the statistical feature is represented, wherein the value of j is 1, 2 and 3, and the represented position types are respectively a title, a section head and a section tail; len (len) i Representation word W i Is a word length of (a); lenw represents word length weight in the statistical feature; pos i Representation word W i Part of speech value of (2); posw represents the lexical weight in the statistical feature; fr represents the word W i Frequency of occurrence and word W in the volume of the target trial case i The ratio of occurrence frequencies in the files of the historical trial cases; frw the word W i Frequency of occurrence and word W in the volume of the target trial case i The weight corresponding to the ratio of occurrence frequency in the files of the historical trial cases.
Optionally, the extracting module 220 is specifically configured to: changing the label information of the keywords, and determining the difference of the importance degrees of the keywords under different labels;
and cleaning the keywords according to the difference of the importance degrees of the keywords under different labels to obtain the cleaned keywords.
Optionally, the management and control module 230 is specifically configured to: and matching the characteristic information in the file of the target trial case with the risk information in the risk database to generate risk prompt information corresponding to each trial node of the target trial case.
Optionally, the construction module 210 is specifically configured to: reconstructing an judging node; the judging nodes comprise 7 judging nodes before complaints, case setting, case dividing, before court trial, after court trial, judging and after judging.
The device of the embodiment of the present application is configured to perform the method of any of the foregoing method embodiments, and its implementation principle and technical effects are similar, and are not described in detail herein.
Fig. 3 illustrates a physical schematic diagram of an electronic device, which may include: processor 310, communication interface (Communications Interface) 320, memory 330 and communication bus 340, wherein processor 310, communication interface 320, memory 330 accomplish communication with each other through communication bus 340. The processor 310 may invoke logic instructions in the memory 330 to perform an trial flow intelligent management method comprising: constructing a risk database of the trial node; the risk database is used for storing risk information corresponding to each judging node; extracting characteristic information from the files of the target trial cases; and generating risk prompt information corresponding to each judging node of the target judging case according to the risk database and the characteristic information.
Further, the logic instructions in the memory 330 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present application also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the intelligent control method of the judging flow provided by the above methods, the method comprising: constructing a risk database of the trial node; the risk database is used for storing risk information corresponding to each judging node; extracting characteristic information from the files of the target trial cases; and generating risk prompt information corresponding to each judging node of the target judging case according to the risk database and the characteristic information.
In still another aspect, the present application further provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, is implemented to perform the above-provided trial flow intelligent management method, the method comprising: constructing a risk database of the trial node; the risk database is used for storing risk information corresponding to each judging node; extracting characteristic information from the files of the target trial cases; and generating risk prompt information corresponding to each judging node of the target judging case according to the risk database and the characteristic information.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present application without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. The intelligent control method for the trial flow is characterized by comprising the following steps:
constructing a risk database of the trial node; the risk database is used for storing risk information corresponding to each judging node;
extracting characteristic information from the files of the target trial cases;
and generating risk prompt information corresponding to each judgment node of the target judgment case according to the risk database and the characteristic information.
2. The intelligent control method for an examination process according to claim 1, wherein the extracting feature information from the set of target examination cases comprises:
determining at least one keyword in the file of the target trial case according to the keyword information of each word in the file of the target trial case; the criticality information is used for evaluating the importance degree of each word in the file of the target trial case in case trial risk identification;
and taking the keywords in the file of the target trial case as characteristic information in the file of the target trial case.
3. The intelligent control method for the trial flow according to claim 2, wherein,
determining the criticality information of each word in the file of the target trial case by using the following formula:
wherein Score (W) i ) Representation word W i Is a key degree information of the (a); vd (Vd) i Representation word W i Semantic contribution values of (2); vdw semantic contribution valuesWeighting; tw is the weight of the statistical characteristic value; loc ij Representation word W i Whether it has occurred at position j; low (low) j The weight of the position j in the statistical feature is represented, wherein the value of j is 1, 2 and 3, and the represented position types are respectively a title, a section head and a section tail; len (len) i Representation word W i Is a word length of (a); lenw represents word length weight in the statistical feature; pow i Representation word W i Part of speech value of (2); posw represents the lexical weight in the statistical feature; fr represents the word W i Frequency of occurrence and word W in the volume of the target trial case i The ratio of occurrence frequencies in the files of the historical trial cases; frw the word W i Frequency of occurrence and word W in the volume of the target trial case i The weight corresponding to the ratio of occurrence frequency in the files of the historical trial cases.
4. The intelligent control method for an trial flow according to claim 3, further comprising, after determining at least one keyword in a set of volumes of target trial cases:
changing the label information of the keywords, and determining the difference of the importance degree of each keyword under different labels;
and cleaning the keywords according to the difference of the importance degrees of the keywords under different labels to obtain the cleaned keywords.
5. The intelligent control method for the trial flow according to claim 4, wherein the generating risk prompt information corresponding to each trial node of the target trial case according to the risk database and the feature information includes:
and matching the characteristic information in the file of the target trial case with the risk information in the risk database to generate risk prompt information corresponding to each trial node of the target trial case.
6. The intelligent control method for an trial flow according to claim 5, further comprising, before constructing the risk database for the trial node:
reconstructing an judging node; the judging nodes comprise 7 judging nodes before complaints, case setting, case dividing, before court trial, after court trial, judge and after judge.
7. An intelligent control device for an judgment flow is characterized by comprising:
the construction module is used for constructing a risk database of the judging node; the risk database is used for storing risk information corresponding to each judging node;
the extraction module is used for extracting characteristic information from the files of the target trial cases;
and the management and control module is used for generating risk prompt information corresponding to each judgment node of the target judgment case according to the risk database and the characteristic information.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the intelligent control method of the trial flow of any one of claims 1 to 6 when the program is executed by the processor.
9. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the intelligent control method of the trial flow of any one of claims 1 to 6.
10. A computer program product having stored thereon executable instructions which, when executed by a processor, cause the processor to implement the trial flow intelligent management method of any one of claims 1 to 6.
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