CN111160801A - Electronic evidence risk judgment method - Google Patents
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Abstract
The invention relates to the field of electronic evidence data analysis, in particular to an electronic evidence risk judgment method, which comprises the following steps: s1, importing electronic evidence data; s2, calculating the individual correlation degree of each electronic evidence and the case; s3, calculating the overall correlation degree of the electronic evidence and the case according to the weight of each electronic evidence; and S4, calculating evidence risk according to the overall correlation. After the individual relevance of each electronic evidence and the case is calculated, the overall relevance of the whole electronic evidence and the case is calculated according to the weight of each electronic evidence. The overall relevance reflects the proving force of the electronic evidence on the case as a whole, so the risk of the electronic evidence can be predicted by the overall relevance. Therefore, the risk of a plurality of risk factors can be quantified, so that an approximate risk range and expectation are formed, and the litigation risk can be objectively known, analyzed and judged conveniently.
Description
Technical Field
The invention relates to the field of electronic evidence data analysis, in particular to an electronic evidence risk judgment method.
Background
The electronic evidence is generated based on electronic technology and exists in various electronic equipment carriers such as a magnetic disk, an optical disk, a memory card, a mobile phone and the like in a digital form, the content of the electronic evidence can be separated from the carriers and can be copied to other carriers for multiple times, the electronic evidence mainly comprises ① word processing files, wherein the files formed through a word processing system comprise words, punctuations, tables, various symbols or other coded texts, ② graphic processing files, graphic data which are designed or manufactured by assistance of a special software system of a computer, and ③ images, sounds and image files which are generally edited comprehensively through scanning recognition, video capture, audio input and the like.
With the increasing legal awareness of people, more and more disputes are being resolved by litigation. Meanwhile, electronic products are also widely used for investigation and evidence collection. When legal services are needed, lawyers can only be consulted in the past, and most of the time is to inquire relevant information on the Internet. But for some groups of users, the users cannot be skilled in surfing the internet, and legal consultation information is obtained through the intelligent robot.
Timely application of legal weapons may inherently protect the benefits, but the outcome of a victory complaint depends on evidence. Therefore, official officials are often at risk for litigation. Moreover, various industries have different types of risks. In view of the above, the document CN107844914A discloses a risk management and control system based on group management and an implementation method thereof, where the system includes a risk collection unit, a risk management server, a risk assessment server, and a risk processing unit; the risk management server comprises a risk classification module, a risk identification module, a keyword extraction module and a risk storage module; the risk assessment server comprises a risk pre-storage module, a risk assessment module and a risk management module. According to the invention, the risk items are classified in the risk management server through a keyword extraction and comparison method, and the risk probability value of the classified risk items is calculated in the risk assessment server through the keywords, so that the accurate quantitative calculation of the risk items is realized, a larger error caused by subjective judgment of the risk value is avoided, the risk management and control efficiency can be improved, the risk can be automatically managed and controlled in real time, and further, the occurrence of major risk items is avoided.
However, the risk of litigation is of special character, and its risk is mainly derived from evidence. In other words, litigation risk results primarily from the lack or lack of proof strength in the collected evidence, which is rooted in the correlation of the evidence with the case fact. Therefore, to assess the risk of evidence, one must start with its proof power. There is no method for determining evidence risk by assessing the proof strength of evidence.
Disclosure of Invention
The invention provides an electronic evidence risk judgment method, which determines the proof power and judges the risk of an evidence by evaluating the correlation between the evidence and a case.
The invention provides a basic scheme of an electronic evidence risk judgment method, which comprises the following steps: s1, importing electronic evidence data; s2, calculating the individual correlation degree of each electronic evidence and the case; s3, calculating the overall correlation degree of the electronic evidence and the case according to the weight of each electronic evidence; and S4, calculating evidence risk according to the overall correlation.
The working principle of the invention is as follows: and after the individual correlation degree of each electronic evidence and the case is calculated, the overall correlation degree of the whole electronic evidence and the case is calculated according to the weight of each electronic evidence. The overall relevance reflects the proving force of the electronic evidence on the case as a whole, so the risk of the electronic evidence can be predicted by the overall relevance. If the overall correlation degree is high, the proof force is strong, and the victory probability is high; if the overall correlation degree is small, the force is proved to be weak, and the probability of victory complaints is small. The invention has the advantages that: the risk of multiple risk factors may be quantified, thereby forming a general risk range and expectation that facilitates objectively understanding, analyzing, and judging evidence risk.
The invention evaluates the integral relevance of the electronic evidence and the case according to the individual relevance and the weight of each electronic evidence and the case, and further determines the proving power. Therefore, the evidence risk can be objectively and accurately evaluated according to the overall relevance of the electronic evidence and the case.
Further, step S2 calculates the individual correlation degree of each electronic evidence with the case, including the following steps;
s21: acquiring a text vector A corresponding to the ith electronic evidence textiAnd its case type;
s22: extracting corresponding electronic evidence standard text from a database according to case types, and acquiring a text vector B of the electronic evidence standard texti；
S23: from the text vector AiAnd text vector BiCalculating individual correlations αi，αi＝cos<Ai，Bi>；
S24: and repeating the steps S21 to S23, and calculating the individual relevance of all the electronic evidences and the case.
Relevancy refers to the percentage of the two things that are related to each other. Thus, the relative importance of each e-proof to the case may be known from its individual relevance to the case, thereby facilitating the targeted collection of evidence.
Further, the step S3 calculates the overall relevance between the electronic evidence and the case according to the weight of each electronic evidence, including the following steps:
s31: the ith electronic evidence is weighted to be Wi；
S32 according to individual correlation αiAnd its corresponding weight WiCalculating the overall correlation β ═ Σ αi×Wi。
The weight is the importance of a factor relative to something, which is different from the specific gravity, and represents not only the percentage of a factor but also the relative importance of the factor. The law enforcement officer's identification of the case fact is based on an evidence chain consisting of single evidences. Therefore, both the proof force of a single electronic proof and the weight of the influence of each electronic proof on the case proof force must be considered.
Further, step S4 is to calculate the evidence risk p according to the overall correlation β,
P＝1-β×100％
most people are risk aversion type, fear of suffering loss. The overall relevance is converted into the risk, and an effective risk prompting effect can be achieved.
Further, step S5 is included, evaluating the evidence risk level; the method comprises the following specific steps:
s51: presetting a risk grade interval;
s52: determining a risk grade interval of the evidence risk p according to the evidence risk p;
s53: a risk level is determined.
According to the risk grade, the method is beneficial to preliminarily judging the influence or loss possibly brought by the risk.
Further, step S6 is included to estimate the loss L caused by litigation risk. By estimating the loss, the method can effectively prompt the action to reduce the loss possibly brought by the risk of litigation.
Further, the method includes a step S7 of calculating an expected loss e (L), and e (L) ═ L × p. The mathematical expectation is the result of random state averaging and can provide psychological expectation to people.
Further, the method also comprises a step S8 of carrying out early warning according to a preset threshold value of the expected loss E (L). And the early warning is prompted according to the preset expected loss value, and the effect is good.
Further, step S9 is included, wherein the risk of evidence p, the loss L and the expected loss e (L) are pushed to the staff. The staff can know the situation or take measures conveniently in time.
Further, when the electronic evidence data input in step S1 is a sound recording or a video, the sound recording or the video is first converted into a text. Due to the use of a large number of electronic products, many people store evidences in the form of audio recording or video recording, and the evidences are visual and strong in persuasion.
Drawings
Fig. 1 is a flowchart of an embodiment of an electronic evidence risk judgment method according to the present invention.
Detailed Description
The following is further detailed by the specific embodiments:
example 1
An embodiment of the electronic evidence risk judgment method of the invention is substantially as shown in the attached figure 1, and comprises the following steps: importing electronic evidence data, calculating the individual correlation degree of each electronic evidence and the case, calculating the overall correlation degree of the whole electronic evidence and the case according to the weight of each electronic evidence, and calculating the evidence risk according to the overall correlation degree.
First the user imports the e-proof data. If the electronic evidence belongs to a word processing document, the electronic evidence is a document formed by a word processing system. Consisting of words, punctuation, tables, various symbols or other encoded text, such as the common doc or txt format text. For this reason, to calculate the individual correlation between an electronic evidence and a case, it is only necessary to calculate the similarity between the electronic evidence text and the electronic evidence text adopted for case fact identification in the instructive case judgment issued by the highest civil court (in this embodiment, the case where the instructive case judgment issued by the highest civil court is tampered is not considered).
The highest people court usually releases guiding cases regularly, and the judgment book of the court mainly comprises two parts of fact identification and law evaluation. By studying the judgment books of the instructive cases, the evidence types and the proof strength of each evidence can be determined according to which the judge carries out the fact confirmation. Evidence adopted for case fact identification in the guiding case judgment book released by the highest people's court can be regarded as evidence standard.
Therefore, if a case is consistent with the category of the guiding case and the coincidence degree of the evidence is high, the evidence of the case is strong in proving power, low in risk and high in probability of winning, and therefore, at least the following information can be obtained from the judgment book of the guiding case, namely the category of the ① evidence, the persuasion of the ② evidence to the case and the weight of the ③ evidence in the fact confirmation part.
Thus, a database can be established containing standard electronic evidence texts for instructional cases, each standard electronic evidence text including a title and a body. The title states the type, the body states the type of evidence adopted and the weight of each evidence. Taking a trade contract as an example: the title of the electronic evidence standard text is ' trade contract dispute ', and the body part shows the adopted evidence types and the proportion of each evidence, such as ' contracting documents: 0.5 "," bill of lading: 0.1 "," transfer check: 0.2 "," talk recording: 0.2".
Assuming that there are N electronic proofs, the individual relevance of the ith electronic proof to the case is calculated αiThe method comprises the following specific steps: step one, acquiring a text vector A corresponding to the ith electronic evidence textiAnd its case type. Step two, extracting a corresponding electronic evidence standard text from a database according to case types, and acquiring a text vector B of the electronic evidence standard texti. Step three, according to the text vector AiAnd text vector BiCalculating individual correlations αi，αi＝cos<Ai，Bi>. And step four, repeating the step one to the step three, and calculating the individual correlation degree of all the electronic evidences and the cases.
After the calculation of the individual correlation degree of each electronic evidence and the case is finished, the integral correlation degree of the whole electronic evidence and the case is calculated β according to the weight of each electronic evidence, and the concrete step of calculating the integral correlation degree of the whole electronic evidence and the case β is that the first step is to give weight to the ith electronic evidence and is WiStep two, α according to the single degree of correlationiAnd its corresponding weight WiCalculating the overall correlation β ═ Σ αi×Wi。
Finally, the evidence risk p is calculated according to the overall correlation β, and the calculation formula is as follows:
P＝1-β×100％
taking a trade contract as an example: the title of the electronic evidence standard text in the database is 'trade contract dispute', and the body part writes the adopted evidence types and the proportion of each evidence, such as a contracting document: w10.5, bill of lading: w20.1, transfer check: w30.2, talk recording: w4If the individual degree of correlation α of the contracting instrument is calculated as 0.21Individual degree of correlation α for bill of lading 0.720.6 individual correlation of transfer checks α30.4, individual correlation of talk recordings α40.8, then the overall correlation β ∑ α of the e-proof with the case can be calculatedi×WiThus, the evidence risk P, P1- β × 100%: 1-0.65 × 100%: 35%, that is, the risk of the e-proof data is 35%, and the official has a 35% probability of aborting it.
Example 2
The difference from the embodiment 1 is only that:
assessing a level of evidence risk; the method comprises the following specific steps: step one, presetting a risk grade interval. If P is more than 0 and less than or equal to 10, the risk is general; p is more than 10 and less than or equal to 20, is grade II and represents a mild risk; p is more than 20 and less than or equal to 30, and is grade III, which represents moderate risk; p is more than 30 and less than or equal to 40, is grade IV and represents serious risk. And step two, determining the risk grade interval of the evidence risk p according to the evidence risk p. Because P is 35 percent, the interval of 30 < P is less than or equal to 40. And step three, determining the risk level. As can be seen, the evidence risk rating is grade IV, representing a serious risk. Then, the loss L due to litigation risk is estimated. Losses that may be caused by a complaint, such as interest, default money, etc., may be obtained by accounting, such as L100 ten thousand dollars. The expected loss e (L), e (L) ═ L × p ═ 100 × 0.35 ═ 35 ten thousand yuan, is then calculated. And after the calculation, carrying out early warning according to a preset expected loss E (L) threshold value, wherein the preset expected loss E (L) threshold value is 30 ten thousand yuan. Because the expected loss exceeds 30 ten thousand yuan, early warning is carried out, and the risk of litigation is high. Finally, the evidence risk p is 0.35, the loss L is 100 ten thousand yuan and the expected loss E (L) is 35 ten thousand yuan are pushed to the staff.
Example 3
The difference from embodiment 2 is only that the electronic proof data is a graphic processing file or a video, audio, or image file. Graphic processing files are graphic data designed or manufactured by the aid of a special software system of a computer; the video, audio and image files are generally edited by scanning, video capture, audio input and the like. Therefore, it is necessary to convert them into word processing documents first, i.e., documents in which the electronic proof is formed by a word processing system. Consisting of words, punctuation, tables, various symbols or other encoded text, such as the common doc or txt format text.
Example 4
The only difference from example 3 is that: the steps are operated on the intelligent robot, and in the process of interaction with a user, in order to ensure user experience, the system also comprises personification. The personification method specifically comprises the following steps: s01, collecting facial expression data of the user in the process of interacting with the user; s02, analyzing emotion information of the user, and judging whether the user needs comfort or not; s03, performing the operations of comfort and non-comfort; s04, after the first expression unit executes the comfort and non-comfort operation, collecting the next facial expression data of the user; s05, analyzing emotion information of the user, and judging whether the user really needs comfort or pretends to need comfort; s06, collecting facial expression data of the user after the user is assumed to be comforted; s07, the execution system has known that the user pretends to require comfort for normal operation.
In this embodiment, the personification adopts Face Reader, which is expression recognition software developed by Noldus. The software can analyze 6 basic facial expressions (happy, surprised, disgust, angry, fear, sadness), as well as blankness and slight libel; the gaze direction, head direction and character characteristics of the person may also be calculated.
When the user inputs legal problems, the Face Reader software synchronously collects the facial expressions of the user. The user is comforted when negative expressions of the user's face are captured. For example, when the fact that the face of the user leaks sadness and pain expressions is collected, the system judges that the emotion of the user is low and needs to be comforted, and encouraged words are played through voice.
And after the voice playing is finished, acquiring the facial expression of the user, and analyzing whether the target object really receives consolation or falsely receives consolation according to the facial expression. If the user's expression is "sad" before the comfort and "calm" after the comfort, then the system determines that the user is really receiving comfort; if the user's expression is "sad" before the comfort and "happy" after the comfort, the system determines that the user is pretending to accept the comfort. If the user is false to accept comfort, the utterance "please operate normally" is played by voice. And when the voice playing of 'please operate normally' is finished, the facial expression of the user is collected. If the user's expression is "calm" or "sad", playing the inspired words by voice; if the expression of the user is "happy," the word "please operate normally" is still played.
At the same time, the system also analyzes the personality traits of the user, and the inspired words are selected specifically according to the personality traits of the object. If the system analyzes that the user is in an optimistic character, then a fact-oriented speech is played, and if the fact-oriented speech is 'believing law, the problem can be successfully solved'; if the system analyzes that the user is in pessimistic character, then the emotion-oriented words are played, such as "better tomorrow". Therefore, the method can play a good role in mood placating for the user.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. An electronic evidence risk judgment method is characterized by comprising the following steps: the method comprises the following steps: s1, importing electronic evidence data; s2, calculating the individual correlation degree of each electronic evidence and the case; s3, calculating the overall correlation degree of the electronic evidence and the case according to the weight of each electronic evidence; and S4, calculating evidence risk according to the overall correlation.
2. The electronic evidence risk judgment method according to claim 1, wherein: step S2, calculating the individual correlation degree of each electronic evidence and the case, comprising the following steps;
s21: acquiring a text vector A corresponding to the ith electronic evidence textiAnd its case type;
s22: extracting corresponding electronic evidence standard text from a database according to case types, and acquiring a text vector B of the electronic evidence standard texti；
S23: from the text vector AiAnd text vector BiCalculating individual correlations αi，αi＝cos<Ai,Bi>；
S24: and repeating the steps S21 to S23, and calculating the individual relevance of all the electronic evidences and the case.
3. The electronic evidence risk judgment method according to claim 2, wherein: step S3 is a step of calculating the overall relevance of the electronic evidence and the case according to the weight of each electronic evidence, and includes the following steps:
s31: the ith electronic evidence is weighted to be Wi；
S32 according to individual correlation αiAnd its corresponding weight WiCalculating the overall correlation β ═ Σ αi×Wi。
4. The method for judging the risk of electronic evidence according to claim 3, wherein the formula for calculating the risk of evidence p according to the overall correlation β in step S4 is as follows,
P＝1-β×100％
5. the electronic evidence risk judgment method according to claim 4, wherein: further comprising a step S5 of assessing the level of evidence risk; the method comprises the following specific steps:
s51: presetting a risk grade interval;
s52: determining a risk grade interval of the evidence risk p according to the evidence risk p;
s53: a risk level is determined.
6. The electronic evidence risk judgment method according to claim 5, wherein: further comprising a step S6 of estimating the loss L caused by the risk of litigation.
7. The electronic evidence risk judgment method according to claim 6, wherein: further includes step S7, calculating an expected loss e (L), e (L) ═ L × p.
8. The electronic evidence risk judgment method according to claim 7, wherein: and step S8, carrying out early warning according to a preset expected loss E (L) threshold value.
9. The electronic evidence risk judgment method according to claim 8, wherein: step S9 is also included, pushing the evidence risk p, the loss L, and the expected loss e (L) to the staff.
10. The electronic evidence risk judgment method according to claim 1, wherein: when the electronic evidence data input in step S1 is a sound recording or video, the sound recording or video is first converted into a text.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110377632A (en) * | 2019-06-17 | 2019-10-25 | 平安科技（深圳）有限公司 | Lawsuit prediction of result method, apparatus, computer equipment and storage medium |
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| CN110490439A (en) * | 2019-08-05 | 2019-11-22 | 北京市律典通科技有限公司 | Litigation risk appraisal procedure, device, electronic equipment and computer can storage mediums |
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| CN110377632A (en) * | 2019-06-17 | 2019-10-25 | 平安科技（深圳）有限公司 | Lawsuit prediction of result method, apparatus, computer equipment and storage medium |
| CN110428149A (en) * | 2019-07-18 | 2019-11-08 | 中经柏诚科技(北京)有限责任公司 | A kind of judicial risk evaluation model construction method based on blur method |
| CN110490439A (en) * | 2019-08-05 | 2019-11-22 | 北京市律典通科技有限公司 | Litigation risk appraisal procedure, device, electronic equipment and computer can storage mediums |
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