CN110837562A

CN110837562A - Case processing method, device and system

Info

Publication number: CN110837562A
Application number: CN201810941495.6A
Authority: CN
Inventors: 周鑫; 张雅婷; 李泉志; 孙常龙; 刘晓钟; 司罗
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2018-08-17
Filing date: 2018-08-17
Publication date: 2020-02-25
Anticipated expiration: 2038-08-17
Also published as: CN110837562B

Abstract

The application discloses a case processing method, device and system. Wherein, the method comprises the following steps: determining the credibility of a plurality of nodes in a legal knowledge base based on case information of a case to be judged, wherein the legal knowledge base at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; determining risk points according to the credibility of the nodes and the legal knowledge graph, wherein the risk points are the nodes of which the credibility is lower than the preset credibility in the legal knowledge graph and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result. The method and the device solve the technical problem that the accuracy of the judgment result of the legal case is low due to inaccurate risk point identification in the related technology.

Description

Case processing method, device and system

Technical Field

The application relates to the field of laws, in particular to a case processing method, device and system.

Background

With the development of the internet, a plurality of devices realize intellectualization, and bring a plurality of convenience for the life and work of people. The intelligent system such as an intelligent judicial system or an internet court converts information in a paper or picture form into text messages by an OCR (Optical character recognition) technology, performs information structuring on a plurality of extracted text messages, and enables users (e.g., legal workers) to complete judgment of trade disputes and intellectual property cases through the internet, thereby reducing the workload of the legal workers and improving the working efficiency of the legal workers.

However, the existing intelligent systems such as intelligent judicial systems or internet courts and the like cannot accurately evaluate risk points in legal cases. Because the risk points have a great influence on the judgment result of the legal case, if the risk points in the legal case cannot be accurately evaluated, the final judgment result of the legal case may be inaccurate.

Aiming at the problem of accuracy of judgment results of legal cases caused by inaccurate risk point identification in the related technology, no effective solution is provided at present.

Disclosure of Invention

The embodiment of the invention provides a case processing method, device and system, which at least solve the technical problem of low accuracy of judgment results of legal cases caused by inaccurate risk point identification in the related technology.

According to an aspect of the embodiments of the present invention, there is provided a case processing method, including: determining the credibility of a plurality of nodes in a legal knowledge base based on case information of a case to be judged, wherein the legal knowledge base at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; determining risk points according to the credibility of the nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than the preset credibility in case information and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result.

According to another aspect of the embodiments of the present invention, there is also provided a case processing method, including: displaying case information of cases to be judged; displaying the credibility of the case information in a plurality of nodes in a legal knowledge graph, wherein the legal knowledge graph at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; displaying risk points determined based on the credibility of the nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than the preset credibility in case information and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and outputting an adjustment result of the judgment result based on the identification result of the risk point.

According to another aspect of the embodiments of the present invention, there is also provided a case processing apparatus, including: the first determination module is used for determining the credibility of a plurality of nodes in the legal knowledge base based on the case information of the case to be refereed, wherein the legal knowledge base at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; the second determining module is used for determining a risk point according to the credibility of the plurality of nodes and the legal knowledge graph, wherein the risk point is a node of which the credibility is lower than the preset credibility in the case information and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and the adjusting module is used for adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result.

According to another aspect of the embodiments of the present invention, there is also provided a case processing apparatus, including: the first display module is used for displaying the case information of the case to be judged; the second display module is used for displaying the credibility of the case information in a plurality of nodes in the legal knowledge base, wherein the legal knowledge base at least comprises the following components: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; the third display module is used for displaying risk points determined based on the credibility of the nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than the preset credibility in case information and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and the output module is used for outputting the adjustment result of the judgment result based on the identification result of the risk point.

According to another aspect of the embodiments of the present invention, there is also provided a case processing system, including: the input device is used for acquiring case information of cases to be judged; the processor is used for determining the credibility of a plurality of nodes in the legal knowledge graph based on the case information, determining a risk point according to the credibility of the plurality of nodes and the legal knowledge graph, and then adjusting the judgment result of the to-be-judged case based on the determination result of the risk point to obtain a target judgment result, wherein the legal knowledge graph at least comprises: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: the risk points are nodes with credibility lower than the preset credibility and influence degree on judgment results larger than the preset influence degree in case information; and the display is used for displaying the target judgment result.

According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein when the program runs, a device on which the storage medium is located is controlled to perform the following steps: determining the credibility of a plurality of nodes in a legal knowledge base based on case information of a case to be judged, wherein the legal knowledge base at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; determining risk points according to the credibility of the nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than the preset credibility in case information and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result.

According to another aspect of the embodiments of the present invention, there is also provided a computing device, including a processor, configured to execute a program, where the program executes to perform the following steps: determining the credibility of a plurality of nodes in a legal knowledge base based on case information of a case to be judged, wherein the legal knowledge base at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; determining risk points according to the credibility of the nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than the preset credibility in case information and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result.

In the embodiment of the invention, a legal knowledge graph-based processing mode is adopted, after the case information of the case to be judged is obtained, the judging system determines the credibility of a plurality of nodes in the legal knowledge graph based on the case information of the case to be judged, determines a risk point according to the credibility of the plurality of nodes and the legal knowledge graph, and then adjusts the judging result based on the identification result of the risk point to obtain a target judging result. Wherein, legal knowledge map includes at least: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: the risk points are nodes, wherein the credibility in the case information is lower than the preset credibility, and the influence degree of the judging result of the case to be judged is greater than the preset influence degree.

In the process, because the risk points are related to the credibility of the case information nodes in the legal knowledge graph, the credibility of each node is analyzed and processed, the risk points in the case information can be automatically determined, manual participation is not needed in the whole process, and the identification efficiency of the risk points is improved. In addition, in order to improve the accuracy of case judgment results, after the risk points of case information are obtained, the judging system also receives the affirmation results of legal workers aiming at the risk points to correct, and then adjusts the judgment results of cases, so that the accuracy of the judgment results is improved.

Therefore, the accurate risk points can be obtained through the scheme, the accuracy of the judgment results of the cases to be judged is ensured, and the technical problem that the accuracy of the judgment results of the legal cases is low due to inaccurate risk point identification in the related technology is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a hardware configuration block diagram of a computer terminal (or mobile device) for implementing a case referee method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of an alternative trial system according to an embodiment of the present application;

FIG. 3 is a flow chart of a case processing method according to an embodiment of the present application;

FIG. 4 is a schematic illustration of an alternative legal knowledge graph in accordance with embodiments of the present application;

FIG. 5 is a schematic illustration of a display interface of an alternative trial system according to an embodiment of the present application;

FIG. 6 is a schematic illustration of an alternative legal knowledge graph in accordance with embodiments of the present application;

FIG. 7 is a flow chart of a case processing method according to an embodiment of the present application;

FIG. 8 is a schematic structural diagram of a case handling apparatus according to an embodiment of the present application;

FIG. 9 is a schematic structural diagram of a case handling apparatus according to an embodiment of the present application; and

fig. 10 is a block diagram of a computing device according to an embodiment of the present application.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

There is also provided, in accordance with an embodiment of the present application, an embodiment of a method for processing a case, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.

The method provided by the first embodiment of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Fig. 1 shows a hardware configuration block diagram of a computer terminal (or mobile device) for implementing a case processing method. As shown in fig. 1, the computer terminal 10 (or mobile device 10) may include one or more (shown as 102a, 102b, … …, 102 n) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors 102 and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuit may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computer terminal 10 (or mobile device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).

The memory 104 can be used for storing software programs and modules of application software, such as program instructions/data storage devices corresponding to the case processing method in the embodiment of the present application, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the case processing method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).

It should be noted here that in some alternative embodiments, the computer device (or mobile device) shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in the computer device (or mobile device) described above.

Under the operating environment, the structure schematic diagram of the judging system shown in fig. 2 is constructed, and as can be seen from fig. 2, the judging system mainly comprises six modules, namely an information extraction module, a knowledge graph construction module, a judging reasoning module, a risk identification module, a feedback module and a result generation module. The system comprises an information extraction module, a judgment reasoning module, a knowledge graph construction module and a judgment reasoning module, wherein the information extraction module is used for providing data sources for the knowledge graph construction module and the judgment reasoning module; the knowledge map construction module is used for constructing a legal knowledge map; the judging reasoning module is used for generating a judging result of the case to be judged according to the feedback information of the user on the judging result; the risk identification module is used for identifying risk points in case information; the feedback module is used for receiving feedback information of the user aiming at the identification result of the risk point; the result generating module is used for generating a judging result of the case to be judged and/or generating a judging document according to the feedback information fed back by the feedback module and the reasoning result of the judging reasoning module.

It should be noted that, as shown in fig. 2, the knowledge-graph building module can be used as a support for other modules to provide element basis for the element extraction module for extracting objective factual elements, and at the same time, the knowledge-graph building module also describes the relationship among factual elements to realize logical reasoning for automatic judgment. The feedback module can be used for correcting the identification result of the risk point and the judgment result of the case to expand the legal knowledge base.

In addition, it should be noted that the present application provides a case processing method as shown in fig. 3, which may be executed in a trial system, where the trial system may be an application platform installed on a physical device (e.g., a computer) or an application platform on a cloud server. Fig. 3 is a flowchart of a case processing method according to an embodiment of the present application, and as can be seen from fig. 3, the case processing method may include:

step S302, determining credibility of a plurality of nodes in a legal knowledge graph based on case information of a case to be judged, wherein the legal knowledge graph at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points, and logic gates.

Alternatively, as shown in fig. 4, an optional legal knowledge graph is shown, in fig. 4, the "number of litigation in a court", "number of litigation in an internet court" and "whether or not to foretell an abuse" are elements, whether or not to foretell an abuse "," whether or not to argue an abuse "," whether or not to litigation in a court 3 or more "," whether or not to litigation in an internet court 3 or more "are discriminations, or" is a logical gate.

It should be noted that the composition of the legal knowledge base mainly includes:

(1) an entity. In the present application, the entities of the legal knowledge base mainly include objective fact elements and legal elements. An alternative legal knowledge base is shown in fig. 4, in which the objective fact elements are "number of litigation standing in court" and "number of litigation standing in internet court" in fig. 4, and the legal element is "whether to stand for abuse".

(2) And (4) relationship. In the present application, the relationship of the legal knowledge graph may refer to an attribute relationship or a logical relationship. For example, in FIG. 4, the relationship between the various legal elements is an "OR" relationship.

(3) A triplet. The following three are mainly included in the application: "objective fact element-attribute relationship-objective fact element" (e.g., "original report-attribute relationship-identification number"), "objective fact element-logical relationship-legal element" (e.g., "commodity description-logical relationship-whether or not there is a medical effect advertised"), "legal element-logical relationship-legal element" (e.g., "whether or not original price is fictional-logical relationship-whether or not price is discounted wrong").

In addition, it should be noted that the credibility of each node in the legal knowledge graph is used to characterize the possibility that the event corresponding to the node is true, for example, in fig. 4, the credibility of "whether the node has caused self-confirmed abuse" may be the probability of "causing self-confirmed abuse". Alternatively, the trustworthiness of each node may be determined based on a trustworthiness computing model. The credibility calculation model includes, but is not limited to, a symbolic logic-based C-F model (Certainty Factor), a statistical-based probability model, and the like.

And step S304, determining risk points according to the credibility of the plurality of nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than the preset credibility in the case information and the influence degree of the judging result of the case to be judged is greater than the preset influence degree.

After the credibility of each node is obtained, the trial and judgment system calculates the risk degree of each node, the node with the highest risk degree is used as a risk point, and the content in the case information corresponding to the risk point is used as a risk element. Wherein, the event or object that affects the judgment result can be used as the risk point.

Alternatively, the risk points may be determined by a legal knowledge graph, for example, in the legal knowledge graph shown in fig. 4, "whether or not there are more than 3 court actions", "whether or not there are more than 3 internet court actions", and "whether or not there is a prosecution of the complaint" may be the risk points. For another example, after determining the original complaint, whether the original complaint is a consumer may also be taken as a risk point to determine whether the original complaint is a consumer or a counterfeiter, wherein if the original complaint is a consumer, the referee result may be a refund payment; if the original is a dummy, the referee result can be compensated by three times, so that the risk point 'whether the original is a consumer' has an influence on the referee result.

Alternatively, the risk point may also be determined by combining the node and the logic gate, and the example is still illustrated in fig. 4. As can be seen from fig. 4, in the case where the determination result of any one of the two nodes "whether or not the node is in the court litigation 3 or more" and "whether or not the node is in the internet court litigation 3 or more" is yes, "the determination results obtained by the logical gate" or "whether or not the node is in the internet court litigation 3 or more" and "whether or not the node is in the internet court litigation 3 or more" are both the same, and only in the case where the determination results of the two nodes "whether or not the node is in the court litigation 3 or more" and "whether or not the node is in the internet court litigation 3 or more" are all "no," the determination results obtained by the logical gate "or" are changed. Therefore, the judging result is also influenced by the mode of combining the node and the logic gate, and the combination of the node and the logic gate can also be used as a wind direction point.

And S306, adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result.

Optionally, taking the display interface of the judging system shown in fig. 5 as an example for explanation, after the judging system determines the risk point, the judging system displays a confirmation result on the display interface, for example, in fig. 5, the judging system identifies the original transaction order, the money drawing screenshot, and the like, and the transaction record screenshot in the reported evidence as the risk point, and the user confirms the risk point through the right-side selection control, for example, in fig. 5, the user selects the "yes" control, which indicates that the user agrees to the risk point determined by the judging system.

It should be noted that, after the risk points are obtained, the judge system adjusts the judge result of the case to be judged according to the determined risk points, for example, the judge result reported with the triple compensation is adjusted to the judge result reported with the original returned goods payment.

Based on the schemes defined in the above steps S302 to S306, it can be known that, after the case information of the case to be judged is obtained by adopting the processing mode based on the legal knowledge graph, the judging system determines the credibility of a plurality of nodes in the legal knowledge graph based on the case information of the case to be judged, determines a risk point according to the credibility of the plurality of nodes and the legal knowledge graph, and then adjusts the judging result based on the determination result of the risk point to obtain the target judging result. Wherein, legal knowledge map includes at least: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: the risk points are nodes, wherein the credibility in the case information is lower than the preset credibility, and the influence degree of the judging result of the case to be judged is greater than the preset influence degree.

It is easy to notice that because the risk points are related to the credibility of the case information nodes in the legal knowledge graph, the credibility of each node is analyzed and processed, the risk points in the case information can be automatically determined, manual participation is not needed in the whole process, and the identification efficiency of the risk points is improved. In addition, in order to improve the accuracy of case judgment results, after the risk points of case information are obtained, the judging system also receives the affirmation results of legal workers aiming at the risk points to correct, and then adjusts the judgment results of cases, so that the accuracy of the judgment results is improved.

It should be noted that the legal knowledge graph needs to be constructed before calculating the credibility of each node. Optionally, in the present application, the law construction module may construct the law knowledge graph based on any one of the following manners.

The first method is as follows: and constructing a legal knowledge graph based on legal data of the case to be judged. Specifically, the information receiving module acquires legal data of a case to be judged, determines case information of the case to be judged according to the legal data of the case to be judged, and then the knowledge map construction module determines a legal knowledge map corresponding to the case to be judged according to the case information. Wherein, the legal data includes at least one of the following: prosecution, answering, evidence material.

Optionally, the information extraction module extracts original reported information from the prosecution book, extracts whether to refund or not and whether to reserve the reimbursement right or not from the evidence transaction log, and extracts discount price, actual price and the like from the evidence commodity information. The original reported information, whether refund, whether to reserve compensation right, discount price, actual price and other information extracted from legal data information are case information.

The second method comprises the following steps: and constructing a legal knowledge graph based on a data mining technology. Specifically, the information extraction module acquires a user portrait of a target object based on a data mining technology, determines case information of a case to be judged according to the user portrait, and then the knowledge map construction module determines a legal knowledge map corresponding to the case to be judged according to the case information. For example, the information extraction module mines a number of complaints of the user history from a plurality of shopping terminals (e.g., e-commerce shopping platforms).

In addition, it should be noted that the manner of constructing the legal knowledge information includes, but is not limited to, the above two manners. In addition, the method of extracting case information in the present application is not limited to the above two methods. Optionally, the information extraction module may further extract case information in different manners based on different types of evidence materials. For patterned evidence materials such as screenshots and penalty tickets of transaction logs, character information can be extracted through an OCR technology, and case information is extracted through a regular expression after the character information is extracted; for other irregular evidence materials, such as loans, food package screenshots and the like, the problem of inaccurate extraction may exist only through the automatic extraction of the trial and judgment system, so that the evidence information needs to be extracted manually or case information needs to be extracted by using an active Learning (active Learning) mode.

Optionally, after obtaining the case information of the case to be refereed, the referee reasoning module may generate the referee result based on the case information of the case to be referee. The specific steps may include:

step S30, acquiring a legal knowledge map;

step S32, determining the corresponding activation area of the case to be refereed in the legal knowledge base based on the appeal content in the case information;

and step S34, in the activation area, case information is processed by using an uncertainty reasoning technology to obtain a judgment result.

It should be noted that, in step S32, the activation region in the legal knowledge graph includes a plurality of valid nodes, where the valid nodes correspond to valid case information of the case to be refereed, for example, the original provides evidence 1, and the evidence 1 can be adopted, then the node of the evidence 1 in the legal knowledge graph is a valid node, such as the black node in fig. 6; if the story provides evidence 2, but evidence 2 is not adopted, then the nodes of evidence 2 in the legal knowledge graph are invalid nodes, such as white nodes in FIG. 6. It should be noted that fig. 6 is a schematic diagram of an alternative legal knowledge base, and the inference result obtained from the legal knowledge base shown in fig. 6 is the return payment or triple compensation.

Alternatively, the way of processing case information to obtain referee results using uncertainty reasoning techniques may include, but is not limited to, the following two ways.

Wherein, the first mode is to obtain the referee result based on the directionality of the incidence relation of the legal knowledge base, and the related steps may include:

step S40, traversing the case information in the legal knowledge graph based on the incidence relation and the direction information among a plurality of nodes in the legal knowledge graph, and acquiring nodes for pointing to referee results corresponding to the case information;

in step S42, the referee result pointed by the acquired node is used as the referee result.

Specifically, since the association (e.g., logical relationship) between a plurality of nodes in the legal knowledge base has directionality, the referee inference module can traverse the legal knowledge base along the logical relationship direction according to the logical inference rule in the legal knowledge base until the node pointing to the referee result, and take the referee result of the node as the final referee result, for example, the node S in fig. 6 is the node pointing to the referee result, and the referee result of the node S is "triple-paid" as the final referee result.

In the second way, the referee result is obtained based on atlas vectorization, and the correlation step can comprise:

step S50, vectorizing each node in the legal knowledge graph to obtain the legal knowledge graph based on vectorization expression;

step S52, on the basis of the legal knowledge map expressed by vectorization, random traversal is carried out on the legal knowledge map on the basis of case information, and the probability value corresponding to each traversed node is determined;

and step S54, when the probability value of the traversed node is greater than the preset probability, continuing traversing the next node until the node for representing the judgment result is obtained.

Optionally, the logical inference module of the referee inference module may traverse the logical graph along the logical relationship direction according to a logical inference rule in the legal knowledge graph, calculate a probability value corresponding to each node, and compare the probability value of the node with a preset probability value, for example, if the probability value corresponding to the node 1 is 80%, and the preset probability value is 90%, and the probability value of the node 1 is smaller than the preset probability value, the referee inference module does not adopt the content corresponding to the node 1 to judge the referee case, and does not traverse the next node of the node 1. If the probability value corresponding to the node 2 is 95% and the preset probability value is 90%, and the probability value of the node 2 is greater than the preset probability value, the judging reasoning module judges the case to be judged by adopting the content corresponding to the node 2 and continuously traverses the next node of the node 2.

It should be noted that, due to irregularity of evidence information and ambiguity of semantics in natural language, extraction and understanding of objective fact elements may be biased, so that errors occur in mapped legal elements, and the final case judgment result is affected. The feedback module can correct the judgment result of the case to be judged so as to ensure the accuracy of the judgment result.

In an alternative scheme, determining the credibility of a plurality of nodes in the legal knowledge base based on the case information of the case to be refereed may include the following steps:

step S3020, extracting evidence information of the case to be judged from the case information;

and step S3022, determining the credibility of each node according to the evidence information.

Optionally, the risk identification module may model the trustworthiness first. When the evidence information is insufficient or the result of the evidence information extraction is inaccurate, the credibility of the nodes deduced from the information is low. Therefore, during reasoning according to the legal knowledge graph, the risk identification module calculates the reliability for each node, and the way of calculating the reliability is not limited here, for example, a symbolic logic C-F model (Certainty Factor), a statistical-based probability model, and the like.

After obtaining the credibility of each node, the risk identification module determines the risk points according to the credibility of the plurality of nodes and the legal knowledge graph, and the method can comprise the following steps:

step S3040, determining a risk degree corresponding to the credibility of each node based on a risk model determined by a legal knowledge graph, wherein the risk degree is used for representing the conditional probability of the credibility of a judgment result to each node;

step S3042, rank the risk degrees corresponding to each node, and set the node with the highest risk degree as a risk point.

After the credibility of each node is determined, the risk identification module inputs the credibility of each node into a risk model determined by a legal knowledge graph, and calculates a conditional probability P (node | case) of the judgment result on the credibility by the risk model, wherein the node represents the credibility of the node, and the case represents the probability that the judgment result of the case to be judged is true. And after the risk degree corresponding to each node is obtained, selecting the node with the maximum risk degree as a risk point, wherein the content corresponding to the risk point is the risk point.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

Through the above description of the embodiments, those skilled in the art can clearly understand that the processing method of the case according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.

Example 2

According to an embodiment of the present application, there is also provided an embodiment of a case processing method, where fig. 7 shows a flowchart of the case processing method, and as shown in fig. 7, the method includes:

step S702, displaying the case information of the case to be judged.

It should be noted that the case information of the case to be decided may include different information in different case types, for example, in the case of the transaction type, the case information of the case to be decided may be, but is not limited to, transaction order information, transaction snapshot, and transaction log. Additionally, the type of case information may include, but is not limited to, text, pictures, voice, video, and the like.

Specifically, the user can input the case information of the case to be judged into the judging system through the input device of the judging system, and the judging system displays the case information on the display interface after obtaining the case information. On the display interface, the form of the case information can be in the form of text, pictures, videos and the like. When the user clicks the case information through the display interface of the trial system, the user can check the detailed content of the case information.

Step S704, displaying the credibility of the case information in a plurality of nodes in a legal knowledge base, wherein the legal knowledge base at least comprises: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points, and logic gates.

It should be noted that the credibility of each node in the legal knowledge graph is used to characterize the possibility that the event corresponding to the node is true, for example, in fig. 4, the credibility of "whether the node has caused self-confident complaint and complaint" may be the probability of "having caused self-confident complaint and complaint". Alternatively, the trustworthiness of each node may be determined based on a trustworthiness computing model. The credibility calculation model includes, but is not limited to, a symbolic logic-based C-F model (Certainty Factor), a statistical-based probability model, and the like.

In addition, it should be noted that the credibility of each node in the legal knowledge graph can be displayed in the display interface of the judging system in the form of a numerical value (e.g. percentage), a chart and the like.

Step S706, displaying risk points determined based on the credibility of the plurality of nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than the preset credibility in the case information and the influence degree of the judging result of the case to be judged is greater than the preset influence degree.

After the credibility of each node is obtained, the trial and judgment system calculates the risk degree of each node, and takes the node with the highest risk degree as a risk point, and the content in the case information corresponding to the risk point is a risk element. Wherein, the event or object that affects the judgment result can be used as the risk point.

In step S708, the adjustment result of the judgment result based on the determination result of the risk point is output.

Based on the schemes defined in the above steps S702 to S708, it can be known that, after the case information of the case to be judged is obtained by using the processing method based on the legal knowledge graph, the judging system determines the credibility of the plurality of nodes in the legal knowledge graph based on the case information of the case to be judged, determines the risk points according to the credibility of the plurality of nodes and the legal knowledge graph, and then adjusts the judging result based on the determination result of the risk points to obtain the target judging result. Wherein, legal knowledge map includes at least: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: the risk points are nodes, wherein the credibility in the case information is lower than the preset credibility, and the influence degree of the judging result of the case to be judged is greater than the preset influence degree.

Example 3

According to an embodiment of the present application, there is also provided a case processing apparatus for implementing the case processing method described above, as shown in fig. 8, the apparatus 80 includes: a first determination module 801, a second determination module 803, and an adjustment module 805.

The first determining module 801 is configured to determine, based on case information of a case to be refereed, reliability of a plurality of nodes in a legal knowledge base, where the legal knowledge base at least includes: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; a second determining module 803, configured to determine a risk point according to the credibility of the multiple nodes and the legal knowledge graph, where the risk point is a node whose credibility is lower than a preset credibility in the case information and whose influence degree on the referee result of the case to be refereed is greater than a preset influence degree; the adjusting module 805 is configured to adjust the referee result based on the identification result of the risk point to obtain a target referee result.

Here, it should be noted that the first determining module 801, the second determining module 803, and the adjusting module 805 described above correspond to steps S302 to S306 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of the apparatus may be run in the computer terminal 10 provided in the first embodiment.

In an optional aspect, the case processing apparatus further comprises: the device comprises a first obtaining module, a third determining module and a fourth determining module. The first obtaining module is used for obtaining legal data of a case to be judged, wherein the legal data comprises at least one of the following: prosecution, answering, evidentiary material; the third determining module is used for determining case information of the case to be judged according to legal data of the case to be judged; and the fourth determining module is used for determining the legal knowledge map corresponding to the case to be judged according to the case information.

In an optional aspect, the case processing apparatus further comprises: the device comprises a second obtaining module, a fifth determining module and a sixth determining module. The second acquisition module is used for acquiring a user portrait of the target object based on a data mining technology; the fifth determining module is used for determining the case information of the case to be judged according to the user portrait; and the sixth determining module is used for determining the legal knowledge map corresponding to the case to be judged according to the case information.

In an optional aspect, the case processing apparatus further comprises: the device comprises a third acquisition module, a seventh determination module and a first processing module. The third acquisition module is used for acquiring the legal knowledge graph; the seventh determining module is used for determining the corresponding activation region of the case to be refereed in the legal knowledge base based on the appeal content in the case information; and the first processing module is used for processing the case information by using an uncertainty reasoning technology in the activation region to obtain a judgment result.

Here, it should be noted that the third acquiring module, the seventh determining module and the first processing module correspond to steps S30 to S34 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of the apparatus may be run in the computer terminal 10 provided in the first embodiment.

In an alternative, the first processing module includes: a fourth acquisition module and a second processing module. The fourth acquisition module is used for traversing the case information in the legal knowledge graph based on the incidence relation and the direction information among the nodes in the legal knowledge graph and acquiring nodes for pointing to referee results corresponding to the case information; and the second processing module is used for taking the obtained referee result pointed by the node as a referee result.

Here, it should be noted that the fourth acquiring module and the second processing module correspond to steps S40 to S42 in embodiment 1, and the two modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of the apparatus may be run in the computer terminal 10 provided in the first embodiment.

In an alternative, the first processing module includes: the device comprises a third processing module, an eighth determining module and a fourth processing module. The third processing module is used for vectorizing each node in the legal knowledge graph to obtain the legal knowledge graph based on vectorization expression; the eighth determining module is used for performing random traversal on the legal knowledge graph based on case information on the legal knowledge graph based on vectorization expression, and determining a probability value corresponding to each traversed node; and the fourth processing module is used for continuously traversing the next node under the condition that the probability value of the traversed node is greater than the preset probability until the node for representing the judgment result is obtained.

Here, it should be noted that the third processing module, the eighth determining module and the fourth processing module correspond to steps S50 to S54 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of the apparatus may be run in the computer terminal 10 provided in the first embodiment.

In an alternative, the first determining module includes: an extraction module and a ninth determination module. The extracting module is used for extracting the evidence information of the case to be judged from the case information; and the ninth determining module is used for determining the credibility of each node according to the evidence information.

Here, it should be noted that the above extraction module and the ninth determination module correspond to steps S3020 to S3022 in embodiment 1, and the two modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure of the above embodiment. It should be noted that the modules described above as part of the apparatus may be run in the computer terminal 10 provided in the first embodiment.

In an alternative, the second determining module includes: a tenth determination module and a ranking module. The tenth determining module is used for determining a risk degree corresponding to the reliability of each node based on a risk model determined by the legal knowledge graph, wherein the risk degree is used for representing the conditional probability of the reliability of the judgment result to each node; and the sequencing module is used for sequencing the risk degree corresponding to each node and setting the node with the maximum risk degree as a risk point.

Here, it should be noted that the tenth determining module and the sorting module correspond to steps S3040 to S3042 in embodiment 1, and the two modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of the apparatus may be run in the computer terminal 10 provided in the first embodiment.

Example 4

According to an embodiment of the present application, there is also provided a case processing apparatus for implementing the case processing method described above, as shown in fig. 9, the apparatus 90 includes: a first display module 901, a second display module 903, a third display module 905, and an output module 907.

The first display module 901 is configured to display case information of a case to be refereed; a second display module 903, configured to display credibility of the case information in a plurality of nodes in a legal knowledge graph, where the legal knowledge graph at least includes: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; a third display module 905, configured to display risk points determined based on the credibility of the multiple nodes and the legal knowledge graph, where a risk point is a node in case information whose credibility is lower than a preset credibility and whose influence degree on the judgment result of the case to be judged is greater than a preset influence degree; an output module 907, configured to output an adjustment result of the judgment result based on the identification result of the risk point.

Here, it should be noted that the first display module 901, the second display module 903, the third display module 905, and the output module 907 correspond to steps S702 to S708 in embodiment 2, and the four modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in the second embodiment.

Example 5

According to an embodiment of the present application, there is also provided a case processing system for implementing the case processing method, the system being capable of executing the case processing method provided in embodiments 1 and 2, the system including: an input device, a processor, and a display.

The input device is used for acquiring case information of cases to be judged; the processor is used for determining the credibility of a plurality of nodes in the legal knowledge graph based on the case information, determining a risk point according to the credibility of the plurality of nodes and the legal knowledge graph, and then adjusting the judgment result of the to-be-judged case based on the determination result of the risk point to obtain a target judgment result, wherein the legal knowledge graph at least comprises: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: the risk points are nodes with credibility lower than the preset credibility and influence degree on judgment results larger than the preset influence degree in case information; and the display is used for displaying the target judgment result.

According to the above, after the case information of the case to be judged is obtained by adopting the processing mode based on the legal knowledge graph, the judging system determines the credibility of a plurality of nodes in the legal knowledge graph based on the case information of the case to be judged, determines the risk points according to the credibility of the plurality of nodes and the legal knowledge graph, and then adjusts the judging results based on the identification results of the risk points to obtain the target judging results. Wherein, legal knowledge map includes at least: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: the risk points are nodes, wherein the credibility in the case information is lower than the preset credibility, and the influence degree of the judging result of the case to be judged is greater than the preset influence degree.

It is easy to notice that because the risk points are related to the credibility of the case information nodes in the legal knowledge graph, the credibility of each node is analyzed and processed, the risk points in the case information can be automatically determined, manual participation is not needed in the whole process, and the identification efficiency of the risk points is improved. In addition, in order to improve the accuracy of case judgment results, after the risk points of the case information are obtained, the judging system also receives the identification results of the risk points to correct, and then adjusts the judgment results of the cases, so that the accuracy of the judgment results is improved.

In an alternative scheme, before determining the credibility of a plurality of nodes in the legal knowledge base based on the case information of the case to be refereed, the processor may obtain the evidence information of the case to be refereed in any one of the following manners. Specifically, in the first mode, the processor obtains legal data of the case to be judged, then determines case information of the case to be judged according to the legal data of the case to be judged, and determines a legal knowledge base corresponding to the case to be judged according to the case information, wherein the legal data includes at least one of the following: prosecution, answering, evidentiary material; in the second mode, the processor acquires the user portrait of the target object based on the data mining technology, then determines the case information of the case to be judged according to the user portrait, and determines the legal knowledge map corresponding to the case to be judged according to the case information.

In an optional scheme, before the referee result of the case to be refereed is adjusted according to the risk point to obtain the target referee result, the processor also obtains a legal knowledge graph, and determines the corresponding activation region of the case to be referee in the legal knowledge graph based on the appeal content in the case information. And then, in the activation region, case information is processed by using an uncertainty reasoning technology to obtain a referee result.

Specifically, the processor may determine the referee result by any one of the following methods:

the first method is as follows: the processor traverses the case information in the legal knowledge graph based on the incidence relation and the direction information among the nodes in the legal knowledge graph, acquires the nodes for pointing to the referee results corresponding to the case information, and then takes the referee results pointed by the acquired nodes as the referee results.

The second method comprises the following steps: the processor conducts vectorization processing on each node in the legal knowledge graph to obtain the legal knowledge graph based on vectorization expression, random traversal is conducted on the legal knowledge graph based on case information on the legal knowledge graph based on vectorization expression, and a probability value corresponding to each traversed node is determined. And under the condition that the probability value of the traversed node is greater than the preset probability, continuously traversing the next node until the node for representing the judgment result is obtained.

In an optional scheme, the processor extracts evidence information of the case to be judged from the case information and determines the credibility of each node according to the evidence information. And then the processor determines the risk degree corresponding to the credibility of each node based on a risk model determined by the legal knowledge graph, ranks the risk degrees corresponding to each node, and sets the node with the maximum risk degree as a risk point, wherein the risk degree is used for representing the conditional probability of the credibility of the judgment result to each node.

In an optional scheme, the display displays case information of cases to be refereed and credibility of the case information in a plurality of nodes in a legal knowledge graph, wherein the legal knowledge graph at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points, and logic gates. Further, the display displays the risk points determined based on the reliability of each node, and outputs the adjustment result of the judgment result based on the determination result of the risk points. The risk points are nodes, wherein the credibility in the case information is lower than the preset credibility, and the influence degree of the judgment result of the case to be judged is greater than the preset influence degree.

Example 6

Embodiments of the present application may provide a computing device, which may be any one of computer terminal devices in a computer terminal group. Optionally, in this embodiment, the computing device may also be replaced with a terminal device such as a mobile terminal.

Optionally, in this embodiment, the computing device may be located in at least one network device of a plurality of network devices of a computer network.

In this embodiment, the above-mentioned computing device may execute the program code of the following steps in the case processing method: determining the credibility of a plurality of nodes in a legal knowledge base based on case information of a case to be judged, wherein the legal knowledge base at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; determining risk points according to the credibility of the nodes and the legal knowledge graph, wherein the risk points are the nodes of which the credibility is lower than the preset credibility in the legal knowledge graph and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result.

Optionally, fig. 10 is a block diagram of a computing device according to an embodiment of the present application. As shown in fig. 10, the computing device 100 may include: one or more processors 1002 (only one of which is shown), a memory 1004, and a transmitting device 1006.

The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the case processing method and apparatus in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the case processing method described above. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory located remotely from the processor, which may be connected to the computing device 100 over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: determining the credibility of a plurality of nodes in a legal knowledge base based on case information of a case to be judged, wherein the legal knowledge base at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; determining risk points according to the credibility of the nodes and the legal knowledge graph, wherein the risk points are the nodes of which the credibility is lower than the preset credibility in the legal knowledge graph and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result.

Optionally, the processor may further execute the program code of the following steps: obtaining legal data of a case to be judged, wherein the legal data comprises at least one of the following: prosecution, answering, evidentiary material; determining case information of a case to be judged according to legal data of the case to be judged; and determining a legal knowledge map corresponding to the case to be judged according to the case information.

Optionally, the processor may further execute the program code of the following steps: acquiring a user portrait of a target object based on a data mining technology; determining case information of a case to be judged according to the user portrait; and determining a legal knowledge map corresponding to the case to be judged according to the case information.

Optionally, the processor may further execute the program code of the following steps: acquiring a legal knowledge map; determining an activation region corresponding to the case to be refereed in the legal knowledge base based on the appeal content in the case information; and in the activation region, case information is processed by using an uncertainty reasoning technology to obtain a judgment result.

Optionally, the processor may further execute the program code of the following steps: traversing case information in the legal knowledge graph based on the incidence relation and the direction information among a plurality of nodes in the legal knowledge graph to obtain nodes for pointing to judging results corresponding to the case information; and taking the obtained referee result pointed by the node as a referee result.

Optionally, the processor may further execute the program code of the following steps: vectorizing each node in the legal knowledge graph to obtain a legal knowledge graph based on vectorization expression; on the basis of a legal knowledge graph expressed by vectorization, random traversal is performed on the legal knowledge graph on the basis of case information, and a probability value corresponding to each traversed node is determined; and under the condition that the probability value of the traversed node is greater than the preset probability, continuously traversing the next node until the node for representing the judgment result is obtained.

Optionally, the processor may further execute the program code of the following steps: extracting evidence information of a case to be judged from the case information; and determining the credibility of each node according to the evidence information.

Optionally, the processor may further execute the program code of the following steps: determining a risk degree corresponding to the credibility of each node based on a risk model determined by a legal knowledge graph, wherein the risk degree is used for representing the conditional probability of the credibility of the judgment result to each node; and sequencing the risk degrees corresponding to each node, and setting the node with the maximum risk degree as a risk point.

Optionally, the processor may further execute the program code of the following steps: displaying case information of cases to be judged; displaying the credibility of the case information in a plurality of nodes in a legal knowledge graph, wherein the legal knowledge graph at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; displaying risk points determined based on the credibility of the nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than the preset credibility in case information and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and outputting an adjustment result of the judgment result based on the identification result of the risk point.

It can be understood by those skilled in the art that the structure shown in fig. 10 is only an illustration, and the computer terminal may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 10 is a diagram illustrating a structure of the electronic device. For example, computing device 100 may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 10, or have a different configuration than shown in FIG. 10.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

Example 7

Embodiments of the present application also provide a storage medium. Alternatively, in this embodiment, the storage medium may be configured to store program codes executed by the case processing method.

Optionally, in this embodiment, the storage medium may be located in any one of computer terminals in a computer terminal group in a computer network, or in any one of mobile terminals in a mobile terminal group.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: determining the credibility of a plurality of nodes in a legal knowledge base based on case information of a case to be judged, wherein the legal knowledge base at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; determining risk points according to the credibility of the nodes and the legal knowledge graph, wherein the risk points are the nodes of which the credibility is lower than the preset credibility in the legal knowledge graph and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: obtaining legal data of a case to be judged, wherein the legal data comprises at least one of the following: prosecution, answering, evidentiary material; determining case information of a case to be judged according to legal data of the case to be judged; and determining a legal knowledge map corresponding to the case to be judged according to the case information.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: acquiring a user portrait of a target object based on a data mining technology; determining case information of a case to be judged according to the user portrait; and determining a legal knowledge map corresponding to the case to be judged according to the case information.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: acquiring a legal knowledge map; determining an activation region corresponding to the case to be refereed in the legal knowledge base based on the appeal content in the case information; and in the activation region, case information is processed by using an uncertainty reasoning technology to obtain a judgment result.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: traversing case information in the legal knowledge graph based on the incidence relation and the direction information among a plurality of nodes in the legal knowledge graph to obtain nodes for pointing to judging results corresponding to the case information; and taking the obtained referee result pointed by the node as a referee result.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: vectorizing each node in the legal knowledge graph to obtain a legal knowledge graph based on vectorization expression; on the basis of a legal knowledge graph expressed by vectorization, random traversal is performed on the legal knowledge graph on the basis of case information, and a probability value corresponding to each traversed node is determined; and under the condition that the probability value of the traversed node is greater than the preset probability, continuously traversing the next node until the node for representing the judgment result is obtained.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: extracting evidence information of a case to be judged from the case information; and determining the credibility of each node according to the evidence information.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: determining a risk degree corresponding to the credibility of each node based on a risk model determined by a legal knowledge graph, wherein the risk degree is used for representing the conditional probability of the credibility of the judgment result to each node; and sequencing the risk degrees corresponding to each node, and setting the node with the maximum risk degree as a risk point.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: displaying case information of cases to be judged; displaying the credibility of the case information in a plurality of nodes in a legal knowledge graph, wherein the legal knowledge graph at least comprises the following steps: the method comprises the following steps of a plurality of nodes, incidence relation among the nodes and direction information, wherein each node is used for characterizing one of the following: elements, decision points and logic gates; displaying risk points determined based on the credibility of the nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than the preset credibility in case information and the influence degree of the judging result of the case to be judged is greater than the preset influence degree; and outputting an adjustment result of the judgment result based on the identification result of the risk point.

The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. A case handling method, comprising:

determining the credibility of a plurality of nodes in a legal knowledge graph based on case information of a case to be judged, wherein the legal knowledge graph at least comprises the following steps: the plurality of nodes, the incidence relation among the plurality of nodes and the direction information, each node is used for characterizing one of the following: elements, decision points and logic gates;

determining risk points according to the credibility of the plurality of nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than a preset credibility in the case information and the influence degree on the judgment result of the case to be judged is greater than a preset influence degree;

and adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result.

2. The method of claim 1, wherein prior to determining the trustworthiness of the plurality of nodes in the legal knowledge base based on case information of the case to be refereed, the method further comprises:

acquiring legal data of the case to be judged, wherein the legal data comprises at least one of the following: prosecution, answering, evidentiary material;

determining case information of the case to be judged according to legal data of the case to be judged;

and determining a legal knowledge graph corresponding to the case to be judged according to the case information.

3. The method of claim 1, wherein prior to determining the trustworthiness of the plurality of nodes in the legal knowledge base based on case information of the case to be refereed, the method further comprises:

acquiring a user portrait of a target object based on a data mining technology;

determining case information of the case to be judged according to the user portrait;

4. The method according to claim 1, wherein before adjusting the referee result of the case to be referee according to the risk point to obtain a target referee result, the method further comprises:

acquiring the legal knowledge graph;

determining an activation region corresponding to the case to be refereed in the legal knowledge base based on the appeal content in the case information;

and in the activation region, using an uncertainty reasoning technology to process the case information to obtain the judgment result.

5. The method according to claim 4, wherein in the activation area, the case information is processed using an uncertainty inference technique to obtain the referee result, comprising:

traversing the case information in the legal knowledge graph based on the incidence relation and the direction information among a plurality of nodes in the legal knowledge graph to obtain nodes for pointing to referee results corresponding to the case information;

and taking the obtained referee result pointed by the node as the referee result.

6. The method according to claim 4, wherein in the activation area, the case information is processed using an uncertainty inference technique to obtain the referee result, comprising:

vectorizing each node in the legal knowledge graph to obtain a legal knowledge graph based on vectorization expression;

on the basis of the legal knowledge graph expressed by vectorization, randomly traversing on the legal knowledge graph on the basis of the case information, and determining a probability value corresponding to each traversed node;

and under the condition that the probability value of the traversed node is greater than the preset probability, continuously traversing the next node until the node for representing the judgment result is obtained.

7. The method of claim 1, wherein determining the trustworthiness of a plurality of nodes in the legal knowledge base based on case information of a case to be refereed comprises:

extracting evidence information of the case to be judged from the case information;

and determining the credibility of each node according to the evidence information.

8. The method of claim 7, wherein determining risk points based on the trustworthiness of the plurality of nodes and the legal knowledge graph comprises:

determining a risk degree corresponding to the credibility of each node based on a risk model determined by the legal knowledge graph, wherein the risk degree is used for representing a conditional probability of the credibility of the judgment result to each node;

and sequencing the risk degree corresponding to each node, and setting the node with the maximum risk degree as the risk point.

9. A case handling method, comprising:

displaying case information of cases to be judged;

displaying the credibility of the case information in a plurality of nodes in a legal knowledge graph, wherein the legal knowledge graph at least comprises the following steps: the plurality of nodes, the incidence relation among the plurality of nodes and the direction information, each node is used for characterizing one of the following: elements, decision points and logic gates;

displaying risk points determined based on the credibility of the nodes and the legal knowledge graph, wherein the risk points are nodes of which the credibility is lower than a preset credibility in the case information and the influence degree on the judgment result of the case to be judged is greater than a preset influence degree;

and outputting an adjustment result of the judgment result based on the identification result of the risk point.

10. A case handling apparatus, comprising:

the first determination module is used for determining the credibility of a plurality of nodes in a legal knowledge base based on case information of a case to be refereed, wherein the legal knowledge base at least comprises the following steps: the plurality of nodes, the incidence relation among the plurality of nodes and the direction information, each node is used for characterizing one of the following: elements, decision points and logic gates;

a second determining module, configured to determine a risk point according to the reliability of each node, where the risk point is a node in the case information, where the reliability is lower than a preset reliability, and an influence degree on a referee result of the case to be refereed is greater than a preset influence degree;

and the adjusting module is used for adjusting the judgment result based on the identification result of the risk point to obtain a target judgment result.

11. A case handling apparatus, comprising:

the first display module is used for displaying the case information of the case to be judged;

a second display module, configured to display credibility of the case information in a plurality of nodes in a legal knowledge graph, where the legal knowledge graph at least includes: the plurality of nodes, the incidence relation among the plurality of nodes and the direction information, each node is used for characterizing one of the following: elements, decision points and logic gates;

a third display module, configured to display a risk point determined based on the reliability of each node, where the risk point is a node in the case information, where the reliability is lower than a preset reliability, and an influence degree on a referee result of the case to be refereed is greater than a preset influence degree;

and the output module is used for outputting the adjustment result of the judgment result based on the identification result of the risk point.

12. A case handling system, comprising:

the input device is used for acquiring case information of cases to be judged;

a processor, configured to determine credibility of multiple nodes in a legal knowledge graph based on the case information, determine a risk point according to the credibility of each node, and then adjust a referee result of the case to be refereed based on a determination result of the risk point to obtain a target referee result, where the legal knowledge graph at least includes: the plurality of nodes, the incidence relation among the plurality of nodes and the direction information, wherein each node is used for characterizing one of the following: the risk points are nodes, the credibility of which is lower than the preset credibility and the influence degree of which on the judgment result is greater than the preset influence degree, in the case information;

and the display is used for displaying the target judgment result.

13. A storage medium, characterized in that the storage medium includes a stored program, wherein when the program runs, a device on which the storage medium is located is controlled to execute the following steps:

determining a risk point according to the credibility of each node, wherein the risk point is a node of which the credibility is lower than a preset credibility in the case information and the influence degree on the judgment result of the case to be judged is greater than a preset influence degree;

14. A computing device comprising a processor, wherein the processor is configured to execute a program, wherein the program when executed performs the steps of: