CN111091269A - Criminal risk assessment method based on multi-dimensional risk index - Google Patents
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Abstract
The invention discloses a risk assessment method for prisoners based on a multi-dimensional risk index, which relates to the technical field of big data in computer information processing technology and comprises the following steps: s1, extracting risk characteristics based on prison information system data, S2, evaluating absolute risk indexes, S3, evaluating relative risk indexes, S4, evaluating mutation risk indexes, and S5, evaluating comprehensive risk indexes. According to the risk assessment method for the prisoner based on the multidimensional risk index, the multi-dimensional monitoring data of the prisoner provided by the existing information system can be fully utilized, and the risks of behavior characteristics and behavior modes are quantitatively expressed; the method comprehensively considers three evaluation dimensions of absolute risk of the prisoner in an evaluation system, relative risk compared with other prisoners and mutation risk expressed by the prisoner per se, reduces the possibility of early warning misjudgment to the maximum extent, has stronger self-adaptive capacity and meets the actual requirement.
Description
Technical Field
The invention relates to the technical field of big data in computer information processing technology, in particular to a criminal risk assessment method based on a multi-dimensional risk index.
Background
With the gradual promotion of the informatization and intelligentization construction process of the prisons, the prisons are provided with a series of service management systems including prison administrative management, penalty execution, life sanitation, labor improvement, education improvement, family calls, remote interviews, psychological assessment and the like; meanwhile, a comprehensive safety management platform comprising access control management, video monitoring, virtual inspection, sound and light alarm, a digital power grid, external vehicle personnel access management, emergency command scheduling, system operation and maintenance and the like is established. The data provides a large amount of basic characteristic data for the situation and trend judgment of the intelligent prison data analysis and research system on prison situations, police situations and prison situations.
At present, in the management standard of prisoner, only the qualitative method of putting forward the classification of prisoner class such as key prisoner, the obstinate crime and so on, do not fully utilize the data resource that relevant system produced in the actual operation to carry out effectual prisoner risk analysis and aassessment yet, current method still has certain flawed and weak point when carrying out the analysis.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a risk assessment method for prisoners based on a multi-dimensional risk index, which solves the problems that in the prisoner management standard, supervision grade classification methods such as key crimes and obstinate crimes are only put forward qualitatively, and in the actual operation, data resources generated by related systems are not fully utilized to carry out effective risk analysis and assessment on the prisoners, and the existing method has certain flakiness and defects in the process of analyzing and early warning.
(II) technical scheme
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows: a risk assessment method for prisoners based on multi-dimensional risk index is characterized in that: the risk assessment method comprises the following steps:
s1, extracting risk characteristics based on prison information system data;
s2, evaluating an absolute risk index;
s3, evaluating relative risk indexes;
s4, mutation risk index evaluation;
and S5, evaluating the comprehensive risk index.
Preferably, the step S1 specifically includes: the method is characterized in that main supervision safety dynamic characteristic samples of prisoners are obtained, characteristic item data come from all related information systems of prisons, characteristic items can be dynamically configured according to risk assessment requirements and basic data providing capacity of the prison information systems, and prisoners are provided with M risk factors and are provided with X factorsiHas the following characteristics of Ni
Wherein i is more than or equal to 1 and less than or equal to M, and j is more than or equal to 1 and less than or equal to NiThe value of each feature item is extracted by a feature extraction function
Mapping to discrete risk assessment variables Xi，j，(0≤Xi，j≤5)。
Preferably, the step S2 specifically includes: setting criminal personnel risk assessment variable Xi，jMax (x) is the maximum value ofi，j) The relative weight of the feature in the risk category is wi，j，(0≤wi，jLess than or equal to 1), the weight value is obtained by the pre-training of the neural network model or is given by an expert system, and then the risk factor X of the prisoner at the moment tiRisk value of (A)i(t) the calculation formula is:
risk factor X for criminal personneliThe relative weight among the M factors is wi， (0≤wiLess than or equal to 1), the absolute risk index A (t) of the prisoner at the moment t is calculated by the formula:
risk factor X for criminal personneliThe relative weight among the M factors is wi， (0≤wiLess than or equal to 1), the relative risk index B of the prisoner at the moment tkThe formula for calculation of (t) is:
risk factor X for criminal personneliThe relative weight among the M factors is wi， (0≤wiLess than or equal to 1), the prisoner is at tLRisk mutation index at time C (t)L) The calculation formula of (2) is as follows:
Y(t)＝αA(t)+βB(t)/5+γC(t)/5，
α, gamma (α + β + gamma is 1) is used as a blending parameter of the three risk assessment indexes, parameter values can be determined by adopting a multiple linear regression method based on training samples, and can also be dynamically configured by a user, the three parameters can respectively adjust the influence of corresponding assessment dimensions in the comprehensive risk index, the absolute risk influence of individual criminals can be controlled by the adjusting parameter α, the relative risk influence of the criminals in a group can be controlled by the adjusting parameter β, and the mutation risk influence of the criminals can be controlled by the adjusting parameter gamma.
(III) advantageous effects
The invention has the beneficial effects that:
according to the risk assessment method for prisoners based on the multidimensional risk index, various service information systems of prisons can collect and extract multidimensional information of prisoners to form basic characteristic data, the data analysis, study and judgment system can adopt machine learning related intelligent algorithms to convert multidimensional characteristics into judgment results for risk assessment of prisoners and perform effective early warning, and in a data analysis link, the risk assessment method can improve the effectiveness of the risk assessment of the prisoners and is mainly embodied in that: 1. the method has the advantages that the multi-dimensional monitoring data of the prisoners provided by the existing information system can be fully utilized, the risk of the behavior characteristics and the behavior mode is quantitatively expressed, 2, the method comprehensively considers three evaluation dimensions of the absolute risk of the prisoners in an evaluation system, the relative risk compared with other prisoners and the mutation risk expressed by the prisoners per se, the possibility of early warning misjudgment is reduced to the maximum extent, the method has stronger self-adaptive capacity, and the method accords with the actual requirement.
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The invention is explained in further detail below with reference to the drawing.
Fig. 1 is a flow chart of risk index assessment of a prisoner according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and 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 invention.
The invention provides a technical scheme that: a risk assessment method for prisoners based on multi-dimensional risk indexes can more effectively assess the risk trend of prisoners based on multi-dimensional data provided by various informatization systems and assist supervision and safety management work of prisons, and a risk index assessment flow is shown in figure 1. The risk assessment method comprises the following steps:
s1, risk feature extraction based on prison information system data:
as shown in Table 1, characteristic item data come from all relevant information systems of prisons, characteristic items can be dynamically configured according to risk assessment requirements and basic data providing capacity of the prison information systems, and prisoners are provided with M risk factors and are provided with factor XiHas the following characteristics of Ni
Wherein i is more than or equal to 1 and less than or equal to M, and j is more than or equal to 1 and less than or equal to NiThe value of each feature item is extracted by a feature extraction function
Mapping to discrete risk assessment variables Xi，j，(0≤Xi，j≤5)。
Table 1 supervision safety dynamic characteristic table for prisoners
S2, absolute risk index assessment:
setting criminal personnel risk assessment variable Xi，jMax (x) is the maximum value ofi，j) The relative weight of the feature in the risk category is wi，j，(0≤w′i，jLess than or equal to 1), the weight values are obtained by pre-training of a neural network model or by expert systemIf the system is given, the risk factor X of the prisoner at the time tiRisk value of (A)i(t) the calculation formula is:
according to Ai(t). times.100 preliminary fractionation:
TABLE 2 Risk factor Absolute index Classification
| Serial number | Grade | Ai(t). times.100 |
| 1 | High risk | ＞50 |
| 2 | Higher risk | 40-50 |
| 3 | General risks | 30-40 |
| 4 | Low risk | ＜30 |
Risk factor X for criminal personneliThe relative weight among the M factors is wi， (0≤wiLess than or equal to 1), the absolute risk index A (t) of the prisoner at the moment t is calculated by the formula:
preliminary fractionation (absolute index) by a (t) x 100:
TABLE 3 Absolute Risk index rating
| Serial number | Grade | A (t) x 100 |
| 1 | High risk | ＞50 |
| 2 | Higher risk | 40-50 |
| 3 | General risks | 30-40 |
| 4 | Low risk | ＜30 |
S3, relative risk index assessment:
the risk factor X of a certain prisoner k at the moment t can be obtained by an absolute risk index evaluation methodiRisk value of (A)k，i(t), the total number of the persons serving sentences in a certain management range (such as a prison area) is K, and the risk factors X of all persons serving sentences at the time tiHas an average risk value of
Deviation of Δ Ak，i(t)＝Ak，i(t)-<Ai(t)>Standard deviation of
The relative deviation of risk zk，i(t) the calculation formula is:
according to zk，i(t) value the relative risk rating (radar chart) of the offender on factor i is assessed and discretized to map to Bk，i(t)：
TABLE 4 Risk factor relative index rating
Risk factor X for criminal personneliThe relative weight among the M factors is wi， (0≤wiLess than or equal to 1), the relative risk index B of the prisoner at the moment tkThe formula for calculation of (t) is:
according to Bk(t) value assessing relative risk rating (relative index) of the person serving a criminal:
TABLE 5 relative Risk indices Classification
| Serial number | Grade | Bk(t) |
| 1 | High risk | ＞3 |
| 2 | Higher risk | 2-3 |
| 3 | General risks | 1-2 |
| 4 | Low risk | 0-1 |
S4, mutation risk index assessment:
the absolute risk assessment method can obtain the time sequence t of a criminal in a certain historical period (sliding window) by a certain prisoner1，t2，…，tLRisk factor X of timeiRisk value of (A)i(t1)，Ai(t2)，…，Ai(tL) The risk factor X of the person taking the criminal at that time periodiHas an average risk value of
Deviation at time L is Δ Ai(tL)＝Ai(tL)-(Ai(tL)>Standard deviation of
The relative deviation of risk zi(tL) The calculation formula is as follows:
according to zi(tL) Value evaluation of mutation risk level of prisoner on factor i, and discretization mapping to Ci(tL)：
TABLE 6 Risk factor mutation index grading
| Serial number | Grade | zi(tL) Range of values | Ci(tL) |
| 1 | High risk | ＞3 | 5 |
| 2 | Higher risk | 1-3 | 3 |
| 3 | General risks | 0-1 | 1 |
| 4 | Low risk | ＜0 | 0 |
Risk factor X for criminal personneliThe relative weight among the M factors is wi， (0≤wiLess than or equal to 1), the prisoner is at tLRisk mutation index at time C (t)L) The calculation formula of (2) is as follows:
according to C (t)L) Value assessment of mutation risk rating (mutation index) of the person serving the criminal:
TABLE 7 mutation Risk index grading
S5, comprehensive risk index assessment:
the comprehensive risk index evaluation can be carried out by combining the absolute index, the relative index and the mutation index of the risk evaluation of prisoners. Let the absolute risk assessment index of a certain criminal at time t be A (t), the relative risk assessment index be B (t), and the mutation risk assessment index be C (t), then the comprehensive risk assessment index of the criminal is:
Y(t)＝αA(t)+βB(t)/5+γC(t)/5，
α, gamma (α + β + gamma is 1) is used as a blending parameter of the three risk assessment indexes, parameter values can be determined by adopting a multiple linear regression method based on training samples, and can also be dynamically configured by a user, the three parameters can respectively adjust the influence of corresponding assessment dimensions in the comprehensive risk index, the absolute risk influence of individual criminals can be controlled by the adjusting parameter α, the relative risk influence of the criminals in a group can be controlled by the adjusting parameter β, and the mutation risk influence of the criminals can be controlled by the adjusting parameter gamma.
Grading according to Y (t) x 100 (comprehensive Risk index):
TABLE 8 comprehensive Risk indices Classification
The risk assessment method for the prisoners with the multidimensional risk index can provide the risk index of the prisoners for prison management, and is applied to a risk assessment system for the prisoners in prison data analysis and research.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A risk assessment method for prisoners based on multi-dimensional risk index is characterized in that: the risk assessment method comprises the following steps:
s1, extracting risk characteristics based on prison information system data;
s2, evaluating an absolute risk index;
s3, evaluating relative risk indexes;
s4, mutation risk index evaluation;
and S5, evaluating the comprehensive risk index.
2. The risk assessment method for prisoners based on multi-dimensional risk index as claimed in claim 1, characterized by: the step S1 specifically includes: the method is characterized in that main supervision safety dynamic characteristic samples of prisoners are obtained, characteristic item data come from all related information systems of prisons, characteristic items can be dynamically configured according to risk assessment requirements and basic data providing capacity of the prison information systems, and prisoners are provided with M risk factors and are provided with X factorsiUnder NiA characteristic
Wherein i is more than or equal to 1 and less than or equal to M, and j is more than or equal to 1 and less than or equal to NiThe value of each feature item is extracted by a feature extraction function
Mapping to discrete risk assessment variables Xi，j，(0≤Xi，j≤5)。
3. The risk assessment method for prisoners based on multi-dimensional risk index as claimed in claim 1, characterized by: the step S2 specifically includes: setting criminal personnel risk assessment variable Xi，jMax (x) is the maximum value ofi，j) The relative weight of the feature in the risk category is wi，j，(0≤wi，jLess than or equal to 1), the weight value is obtained by the pre-training of the neural network model or is given by an expert system, and then the risk factor X of the prisoner at the moment tiRisk value of (A)i(t) the calculation formula is:
risk factor X for criminal personneliThe relative weight among the M factors is wi，(0≤wiLess than or equal to 1), the absolute risk index A (t) of the prisoner at the moment t is calculated by the formula:
risk factor X for criminal personneliThe relative weight among the M factors is wi，(0≤wiLess than or equal to 1), the relative risk index B of the prisoner at the moment tkThe formula for calculation of (t) is:
risk factor X for criminal personneliThe relative weight among the M factors is wi，(0≤wiLess than or equal to 1), the prisoner is at tLRisk mutation index at time C (t)L) The calculation formula of (2) is as follows:
Y(t)＝αA(t)+βB(t)/5+YC(t)/5，
α, gamma (α + β + gamma is 1) is used as a blending parameter of the three risk assessment indexes, parameter values can be determined by adopting a multiple linear regression method based on training samples, and can also be dynamically configured by a user, the three parameters can respectively adjust the influence of corresponding assessment dimensions in the comprehensive risk index, the absolute risk influence of individual criminals can be controlled by the adjusting parameter α, the relative risk influence of the criminals in a group can be controlled by the adjusting parameter β, and the mutation risk influence of the criminals can be controlled by the adjusting parameter gamma.
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