CN111461932A - Administrative punishment discretion rationality assessment method and device based on big data - Google Patents
Administrative punishment discretion rationality assessment method and device based on big data Download PDFInfo
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Abstract
The invention relates to a method and a device for assessing the reasonableness of administrative punishment discretion based on big data, which comprises the steps of obtaining basic data; performing characteristic extraction on the basic data, determining case law of the case according to the extracted characteristic information, determining a penalty amount range according to the case law, judging whether the penalty amount of the case is in the penalty amount range, constructing a penalty amount rationality evaluation model after judging that the penalty amount of the case is in the penalty amount range, and judging the rationality of the penalty amount of the case according to the penalty amount rationality evaluation model; the invention establishes the rationality evaluation method and the rationality evaluation device for the discretion right, can assist law enforcement personnel to accurately exercise the administrative punishment discretion right and scientifically enforce law enforcement and provide a scientific and efficient solution for standardizing the application of the administrative punishment discretion right and solving the problem of abusing the discretion right in the administrative law enforcement.
Description
Technical Field
The invention belongs to the technical field of big data, and particularly relates to a method and a device for evaluating the reasonability of administrative punishment discretion based on big data.
Background
In the face of the unlimited possibility of social development and the complexity of the administrative affairs, the rationality assessment of the amount of the administrative penalties is increasingly important, which is a very important loop in the course of the administrative penalties. At present, in laws and regulations related to administrative punishment in China, the problem that the judgment range of the punishment amount of the same illegal action is large exists, the punishment amount cannot be quantitatively explained, and the problem of abusing the discretionary right in the administrative law enforcement process easily occurs.
In the administrative punishment process, due to the influence of certain factors, some regions and units have the problems of 'punishment is too light, punishment is excessive, and penalty is different on the same occasion', and in the administrative law enforcement process, law enforcement personnel cannot accurately exercise the administrative punishment discretion based on individual subjective awareness and experience, so that relatively serious adverse effects are easily generated. Therefore, the above problems need to be solved by developing corresponding technologies, and the evaluation of the reasonableness of the penalty amount of the administrative penalty discretion becomes a problem which needs to be solved urgently at present.
Disclosure of Invention
In view of the above, the present invention aims to overcome the defects of the prior art, and provides a method and an apparatus for assessing the rationality of the administrative punishment discretion based on big data, so as to solve the problem that the law enforcement officers cannot accurately exercise the administrative punishment discretion based on subjective consciousness and experience of the law enforcement officers in the administrative law enforcement process of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a big data-based administrative penalty discretionary weight rationality evaluation method comprises the following steps:
acquiring basic data;
performing characteristic extraction on the basic data, determining case law of the case according to the extracted characteristic information, determining a penalty amount range according to the case law, and judging whether the penalty amount of the case is within the penalty amount range;
and after the punishment amount of the case is judged to be within the punishment amount range, a punishment amount rationality evaluation model is constructed, and the rationality of the punishment amount of the case is judged according to the punishment amount rationality evaluation model.
Further, the acquiring the basic data includes:
acquiring initial data in a database;
and cleaning the initial data to obtain basic data.
Further, the performing feature extraction on the basic data and determining the case relationship of the case according to the extracted feature information includes:
extracting basic features of basic data by adopting an information extraction technology;
and determining the case routing of the case according to the extracted basic characteristics.
Further, the determining a penalty amount range according to the case basis and judging whether the penalty amount of the case is within the penalty amount range includes:
determining the range of the punishment amount specified by the law and regulation according to the case;
and judging whether the penalty amount of the case is within the range of the penalty amount specified by the law and regulation.
Further, the method for determining the penalty amount range specified by the law and regulation according to the case comprises the following steps:
determining a punishment basis according to the basic characteristics of the case;
and determining a penalty amount range regulated by laws and regulations according to the penalty.
Further, the judging the reasonability of the punishment amount of the case by the punishment amount reasonability evaluation model comprises the following steps:
carrying out model training on the class case;
judging whether free adjustment of the penalty amount exists or not according to the penalty basis of the case; if yes, obtaining similar cases through a case similarity judgment model according to case contents, and judging whether the penalty amount is reasonable or not according to the similarity degree of the similar cases; otherwise, calculating a distribution function of the administrative punishment amount, and judging whether the punishment amount of the case meets the distribution.
Further, the model training of the class includes:
acquiring class case data, acquiring non-class case data in the same proportion, formatting the class case data and the non-class case data, and constructing a formatted data set;
the formatted data set comprises a training data set and a testing data set, and the training data set is divided into N parts;
performing word segmentation processing on the training data, performing word vector mapping by using word2vec, inputting the word vector into a Bi L STM model for training, and outputting a result;
obtaining an optimal model through N times of cross validation, and storing the optimal model;
and loading the optimal model for testing, inputting the test data in the test data set into the optimal model, sequentially performing word segmentation, word vector mapping and class case similarity calculation, and evaluating the performance of the model.
The Bi L STM model further comprises a word vector embedding layer, a bidirectional L STM layer, an attention machine making layer and a classifier, wherein the word vector embedding layer is used for converting training data into word vectors, the bidirectional L STM layer takes the word vectors as input and converts the word vectors to output feature vectors with fixed lengths, the attention machine making layer takes the feature vectors output by the bidirectional L STM layer as input and adds different weights according to the importance degree of words in a text to obtain more accurate text representation, and the classification layer normalizes the attention weight by softmax, weights and sums the weights and then carries out cosine similarity calculation with the parameters.
The embodiment of the application provides an administrative punishment discretion rationality evaluation device based on big data, includes:
the acquisition module is used for acquiring basic data;
the extraction module is used for performing characteristic extraction on the basic data, determining case law of the case according to the extracted characteristic information, determining the punishment amount range according to the case law, and judging whether the punishment amount of the case is within the punishment amount range;
and the judging module is used for constructing a punishment amount rationality evaluation model and judging the rationality of the punishment amount of the case according to the punishment amount rationality evaluation model after judging that the punishment amount of the case is within the punishment amount range.
Further, the judging module includes:
the training unit is used for carrying out model training on the class plan;
the calculation module is used for judging whether free adjustment of the penalty amount exists or not according to the penalty basis of the case; if yes, obtaining similar cases through a case similarity judgment model according to case contents, and judging whether the penalty amount is reasonable or not according to the similarity degree of the similar cases; otherwise, calculating a distribution function of the administrative punishment amount, and judging whether the punishment amount of the case meets the distribution.
By adopting the technical scheme, the invention can achieve the following beneficial effects:
the method for assessing the rationality of the administrative punishment discretionary right based on the big data is formulated, a rationality assessment function for the amount of the discretionary right punishment is established, and law enforcement personnel can be assisted to accurately exercise the administrative punishment discretionary right and scientifically enforce law enforcement scientific decisions; the method provides a scientific and efficient solution for standardizing the application of the administrative punishment discretionary right and preventing and solving the problem of abusing the discretionary right in the administrative law enforcement.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the steps of a big data-based method for assessing the reasonableness of an administrative penalty discretionary right according to the present invention;
FIG. 2 is a flow chart of a big data-based method for assessing the rationality of the administrative penalty discretion right according to the present invention;
FIG. 3 is a schematic structural diagram of an administrative penalty discretion rationality assessment device based on big data according to the present invention;
FIG. 4 is a schematic structural diagram of a computer device of a hardware operating environment related to a big data-based administrative penalty discretionary weight rationality assessment method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
The following describes a concrete administrative penalty discretionary rationality evaluation method and device based on big data provided in the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, the method for assessing the reasonableness of the administrative penalty discretion based on big data provided in the embodiment of the present application includes:
s101, acquiring basic data;
initial data are stored in the database, the initial data in the database are firstly obtained, and the initial data are preprocessed, namely, the initial data are cleaned, so that basic data are obtained.
S102, extracting characteristics of basic data, determining case law of cases according to extracted characteristic information, determining a penalty amount range according to the case law, and judging whether the penalty amount of cases is within the penalty amount range;
and (4) extracting the characteristics of the basic data, and determining the case routing of the case through the characteristics. After the case is determined to be successful, the penalty amount range is determined according to the relevant laws and regulations, whether the penalty amount of the case is in the penalty amount range or not is judged, if the case is not in the penalty amount range, the case is directly judged to be unreasonable, the whole process is ended, and if the case is in accordance with the penalty amount range, the next step S103 is carried out.
S103, after the punishment amount of the case is judged to be within the punishment amount range, a punishment amount rationality evaluation model is constructed, and the punishment amount rationality of the case is judged according to the punishment amount rationality evaluation model.
And outputting a result of the rationality of the case punishment amount through the punishment amount rationality evaluation model.
The working principle of the administrative penalty free-form cutting right rationality evaluation method based on big data is as follows: acquiring basic data; performing characteristic extraction on the basic data, determining case law of the case according to the extracted characteristic information, determining a penalty amount range according to the case law, and judging whether the penalty amount of the case is within the penalty amount range; and after the punishment amount of the case is judged to be within the punishment amount range, a punishment amount rationality evaluation model is constructed, and the rationality of the punishment amount of the case is judged according to the punishment amount rationality evaluation model.
In some embodiments, referring to fig. 2, obtaining the underlying data comprises:
acquiring initial data in a database;
and cleaning the initial data to obtain basic data.
In some embodiments, the extracting features of the basic data and determining the pattern of the case according to the extracted feature information includes:
extracting basic features of basic data by adopting an information extraction technology;
and determining the case routing of the case according to the extracted basic characteristics.
Preferably, the determining the penalty amount range according to the case basis and judging whether the penalty amount of the case is within the penalty amount range includes:
determining the range of the punishment amount specified by the law and regulation according to the case;
and judging whether the penalty amount of the case is within the range of the penalty amount specified by the law and regulation.
Specifically, the case routing of the case is found by extracting the basic features of the basic data, the penalty amount range is determined according to the case routing, and whether the basic data related to the case is in the range is judged; specifically, it is preliminarily determined that the penalty amount of the case is within the range of the penalty amount prescribed by the law and regulation. And preliminarily determining that the penalty amount of the case is not within the range of the penalty amount specified by the law and regulation. If the sum is not within the range of the penalty amount, the judgment is unreasonable, and if the sum is within the range of the penalty amount, the next step is carried out.
Specifically, when the rationality of the punishment amount of the case is judged, firstly, related laws and regulations are searched according to the case, the punishment amount range specified in the laws and regulations is found, whether the punishment amount of the case is within the specified punishment amount range is judged, if the punishment amount of the case is not within the specified punishment amount range, the case is directly judged to be unreasonable, and if the case is within the specified punishment amount range, the case is continuously judged in the next step, namely, whether the punishment amount is reasonable is judged according to the rationality evaluation model.
In some embodiments, determining the range of penalty amounts mandated by legal regulations on a case-by-case basis includes:
determining a punishment basis according to the basic characteristics of the case;
and determining the penalty amount range according to the penalty foundation.
Specifically, extracting the basic characteristics of the case, determining a penalty basis, and judging whether the penalty amount is reasonable or not according to the amount range related to the penalty basis, wherein the penalty basis is related laws and regulations.
In some embodiments, constructing a penalty amount rationality assessment model and determining the rationality of the penalty amount for the case according to the penalty amount rationality assessment model comprises:
carrying out model training on the class case;
judging whether free adjustment of the penalty amount exists or not according to the penalty basis of the case; if yes, obtaining similar cases through a case similarity judgment model according to case contents, and judging whether the penalty amount is reasonable or not according to the similarity degree of the similar cases; otherwise, calculating a distribution function of the administrative punishment amount, and judging whether the punishment amount of the case meets the distribution.
Specifically, model training is performed on the class, including:
(1) acquiring the class case data, acquiring the non-class case data in the same proportion, formatting the class case data and the non-class case data, and constructing a formatted data set; the same ratio is 1:1, and the application obtains the class data according to the information crawled on the internet and obtains the class data according to the ratio of 1:1, obtaining non-pattern data, and then formatting the data to construct a formatted data set.
(2) The formatted data set comprises a training data set and a testing data set, and the training data set is divided into N parts; specifically, the formatted data is randomly divided into a training data set and a testing data set, and the proportion of the training data set to the testing data set is 8: 2, dividing the training data set into N parts after the training data set is disturbed so as to facilitate subsequent N times of cross validation for evaluating the performance of the model;
(3) the method comprises the steps of performing word segmentation processing on training data, performing word vector mapping by using word2vec, inputting the word vector mapping into a Bi L STM model for training, and outputting a result, wherein the Bi L STM model comprises a word vector embedding layer, a bidirectional L STM layer, an attention machine layer and a classifier, the word vector embedding layer is used for converting the training data into word vectors, the bidirectional L STM layer takes the word vectors as input and converts the word vectors into feature vectors with fixed lengths, the attention machine layer takes the feature vectors output by the bidirectional L STM layer as input and adds different weights according to the importance degree of words in a text to obtain more accurate text representation, the classification layer normalizes the importance weight by using softmax, and performs weighted summation and calculation with reference cosine similarity.
(4) Obtaining an optimal model through N times of cross validation, and storing the optimal model;
(5) and loading the optimal model for testing, inputting the test data in the test data set into the optimal model, sequentially performing word segmentation, word vector mapping and class case similarity calculation, and evaluating the performance of the model.
As shown in fig. 3, an embodiment of the present application provides an administrative penalty discretion rationality evaluation device based on big data, including:
an obtaining module 301, configured to obtain basic data;
the extraction module 302 is used for performing feature extraction on the basic data, determining case law of the case according to the extracted feature information, determining a penalty amount range according to the case law, and judging whether the penalty amount of the case is within the penalty amount range;
and the judging module 303 is used for constructing a punishment amount rationality evaluation model and judging the rationality of the punishment amount of the case according to the punishment amount rationality evaluation model after judging that the punishment amount of the case is within the punishment amount range.
Preferably, the judging module includes:
the training unit is used for carrying out model training on the class plan;
the calculation module is used for judging whether free adjustment of the penalty amount exists or not according to the penalty basis of the case; if yes, obtaining similar cases through a case similarity judgment model according to case contents, and judging whether the penalty amount is reasonable or not according to the similarity degree of the similar cases; otherwise, calculating a distribution function of the administrative punishment amount, and judging whether the punishment amount of the case meets the distribution.
The working principle of the administrative punishment discretion rationality assessment device based on big data is that the acquisition module 301 is used for acquiring basic data; the extraction module 302 is used for performing feature extraction on the basic data, determining case law of the case according to the extracted feature information, determining a penalty amount range according to the case law, and judging whether the penalty amount of the case is within the penalty amount range; the judging module 303 is configured to establish a penalty amount rationality evaluation model and judge the rationality of the penalty amount of the case according to the penalty amount rationality evaluation model after judging that the penalty amount of the case is within the penalty amount range.
As shown in fig. 4, the present application provides a computer device comprising: the memory, which may include volatile memory on a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The computer device stores an operating system, and the memory is an example of a computer-readable medium. The computer program, when executed by a processor, causes the processor to perform a big data based administrative penalty discretion rationality assessment method, the structure shown in fig. 4 is a block diagram of only a part of the structure relating to the solution of the present application, and does not constitute a limitation of the computer device to which the solution of the present application is applied, a specific computer device may comprise more or less components than shown in the figure, or combine certain components, or have a different arrangement of components.
In one embodiment, the big data-based administrative penalty discretionary rationality assessment method provided herein may be implemented in the form of a computer program that is executable on a computer device such as that shown in fig. 4.
In some embodiments, the computer program, when executed by the processor, causes the processor to perform the steps of: acquiring basic data; performing feature extraction on the basic data, and determining case routing of the case according to the extracted feature information; and judging whether the case basis conforms to the basic data or not, and judging the rationality of the punishment amount of the case after the case basis conforms to the basic data.
The present application also provides a computer storage medium, examples of which include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassette storage or other magnetic storage devices, or any other non-transmission medium, that can be used to store information that can be accessed by a computing device.
In some embodiments, the present invention further provides a computer-readable storage medium storing a computer program, which when executed by a processor, causes the processor to perform the steps of: acquiring basic data; performing feature extraction on the basic data, and determining case routing of the case according to the extracted feature information; and judging whether the case basis conforms to the basic data or not, and judging the rationality of the punishment amount of the case after the case basis conforms to the basic data.
In conclusion, the invention provides a method for evaluating the reasonability of the administrative punishment discretionary right based on big data, which comprises the steps of obtaining basic data; performing feature extraction on the basic data, and determining case routing of cases according to the extracted feature information; and judging whether the case basis conforms to the basic data or not, and judging the rationality of the punishment amount of the case after the case basis conforms to the basic data. The invention establishes the rationality evaluation method and the rationality evaluation device for the discretion right, can assist law enforcement personnel to accurately exercise the administrative punishment discretion right and scientifically enforce law enforcement, and provides a scientific and efficient solution for standardizing the application of the administrative punishment discretion right and solving the problem of abusing the discretion right in the administrative law enforcement.
It is to be understood that the embodiments of the method provided above correspond to the embodiments of the apparatus described above, and the corresponding specific contents may be referred to each other, which is not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A big data-based method for evaluating the reasonableness of administrative penalty discretionary right comprises the following steps:
acquiring basic data;
performing characteristic extraction on the basic data, determining case law of the case according to the extracted characteristic information, determining a penalty amount range according to the case law, and judging whether the penalty amount of the case is within the penalty amount range;
and after the punishment amount of the case is judged to be within the punishment amount range, a punishment amount rationality evaluation model is constructed, and the rationality of the punishment amount of the case is judged according to the punishment amount rationality evaluation model.
2. The method of claim 1, wherein the obtaining the base data comprises:
acquiring initial data in a database;
and cleaning the initial data to obtain basic data.
3. The method according to claim 1, wherein said extracting features from said basic data and determining case routing of a case based on the extracted feature information comprises:
extracting basic features of basic data by adopting an information extraction technology;
and determining the case routing of the case according to the extracted basic characteristics.
4. The method of claim 1, wherein determining whether the penalty amount for a case is within the penalty amount range by determining the penalty amount range according to the case comprises:
determining the range of the punishment amount specified by the law and regulation according to the case;
and judging whether the penalty amount of the case is within the range of the penalty amount specified by the law and regulation.
5. The method of claim 4, wherein determining the range of penalty amounts mandated by law and regulation on a case-by-case basis comprises:
determining a punishment basis according to the basic characteristics of the case;
and determining a penalty amount range regulated by laws and regulations according to the penalty.
6. The method according to claim 1, wherein the penalty amount rationality assessment model determines the rationality of the penalty amount for the case, comprising:
carrying out model training on the class case;
judging whether free adjustment of the penalty amount exists or not according to the penalty basis of the case; if yes, obtaining similar cases through a case similarity judgment model according to case contents, and judging whether the penalty amount is reasonable or not according to the similarity degree of the similar cases; otherwise, calculating a distribution function of the administrative punishment amount, and judging whether the punishment amount of the case meets the distribution.
7. The method of claim 6, wherein the model training of the class comprises:
acquiring class case data, acquiring non-class case data in the same proportion, formatting the class case data and the non-class case data, and constructing a formatted data set;
the formatted data set comprises a training data set and a testing data set, and the training data set is divided into N parts;
performing word segmentation processing on the training data, performing word vector mapping by using word2vec, inputting the word vector into a Bi L STM model for training, and outputting a result;
obtaining an optimal model through N times of cross validation, and storing the optimal model;
and loading the optimal model for testing, inputting the test data in the test data set into the optimal model, sequentially performing word segmentation, word vector mapping and class case similarity calculation, and evaluating the performance of the model.
8. The method of claim 7, wherein the Bi L STM model comprises a word vector embedding layer, a bidirectional L STM layer, an attention machine layer and a classifier, the word vector embedding layer is used for converting training data into word vectors, the bidirectional L STM layer converts the word vectors into fixed-length feature vectors by taking the word vectors as input, the attention machine layer obtains more accurate text representation by taking the feature vectors output by the bidirectional L STM layer as input and adding different weights according to the importance degree of words in the text, and the classification layer normalizes the orientation weights by softmax, weights and sums the weights and then performs cosine-with-reference similarity calculation.
9. An administrative penalty discretion rationality evaluation device based on big data, comprising:
the acquisition module is used for acquiring basic data;
the extraction module is used for performing characteristic extraction on the basic data, determining case law of the case according to the extracted characteristic information, determining the punishment amount range according to the case law, and judging whether the punishment amount of the case is within the punishment amount range;
and the judging module is used for constructing a punishment amount rationality evaluation model and judging the rationality of the punishment amount of the case according to the punishment amount rationality evaluation model after judging that the punishment amount of the case is within the punishment amount range.
10. The evaluation device of claim 9, wherein the determining module comprises:
the training unit is used for carrying out model training on the class plan;
the calculation module is used for judging whether free adjustment of the penalty amount exists or not according to the penalty basis of the case; if yes, obtaining similar cases through a case similarity judgment model according to case contents, and judging whether the penalty amount is reasonable or not according to the similarity degree of the similar cases; otherwise, calculating a distribution function of the administrative punishment amount, and judging whether the punishment amount of the case meets the distribution.
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| CN202010273409.6A CN111461932B (en) | 2020-04-09 | Administrative punishment free-cutting right rationality evaluation method and device based on big data |
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| CN202010273409.6A CN111461932B (en) | 2020-04-09 | Administrative punishment free-cutting right rationality evaluation method and device based on big data |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113888006A (en) * | 2021-10-20 | 2022-01-04 | 支付宝(杭州)信息技术有限公司 | Behavior risk assessment method and device |
| CN113918706A (en) * | 2021-10-15 | 2022-01-11 | 山东大学 | Information extraction method for administrative punishment decision book |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100274833A1 (en) * | 2009-04-24 | 2010-10-28 | Kabushiki Kaisha Toshiba | Monitoring device and a server |
| US7961956B1 (en) * | 2009-09-03 | 2011-06-14 | Thomas Cecil Minter | Adaptive fisher's linear discriminant |
| CN107818138A (en) * | 2017-09-28 | 2018-03-20 | 银江股份有限公司 | A kind of case legal regulation recommends method and system |
| CN107918921A (en) * | 2017-11-21 | 2018-04-17 | 南京擎盾信息科技有限公司 | Criminal case court verdict measure and system |
| CN109582950A (en) * | 2018-09-25 | 2019-04-05 | 南京大学 | A kind of judgement document argues appraisal procedure |
| WO2019069865A1 (en) * | 2017-10-03 | 2019-04-11 | 日本電気株式会社 | Parameter estimation system, parameter estimation method, and parameter estimation program recording medium |
| CN110335181A (en) * | 2019-07-05 | 2019-10-15 | 北京梦天门科技股份有限公司 | A kind of Health Inspection discretion algorithm based on artificial intelligence |
| CN110457479A (en) * | 2019-08-12 | 2019-11-15 | 贵州大学 | A kind of judgement document's analysis method based on criminal offence chain |
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100274833A1 (en) * | 2009-04-24 | 2010-10-28 | Kabushiki Kaisha Toshiba | Monitoring device and a server |
| US7961956B1 (en) * | 2009-09-03 | 2011-06-14 | Thomas Cecil Minter | Adaptive fisher's linear discriminant |
| CN107818138A (en) * | 2017-09-28 | 2018-03-20 | 银江股份有限公司 | A kind of case legal regulation recommends method and system |
| WO2019069865A1 (en) * | 2017-10-03 | 2019-04-11 | 日本電気株式会社 | Parameter estimation system, parameter estimation method, and parameter estimation program recording medium |
| CN107918921A (en) * | 2017-11-21 | 2018-04-17 | 南京擎盾信息科技有限公司 | Criminal case court verdict measure and system |
| CN109582950A (en) * | 2018-09-25 | 2019-04-05 | 南京大学 | A kind of judgement document argues appraisal procedure |
| CN110335181A (en) * | 2019-07-05 | 2019-10-15 | 北京梦天门科技股份有限公司 | A kind of Health Inspection discretion algorithm based on artificial intelligence |
| CN110457479A (en) * | 2019-08-12 | 2019-11-15 | 贵州大学 | A kind of judgement document's analysis method based on criminal offence chain |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113918706A (en) * | 2021-10-15 | 2022-01-11 | 山东大学 | Information extraction method for administrative punishment decision book |
| CN113888006A (en) * | 2021-10-20 | 2022-01-04 | 支付宝(杭州)信息技术有限公司 | Behavior risk assessment method and device |
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