CN110704469B - Updating method and updating device of early warning level and readable storage medium - Google Patents
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Abstract
The application provides an updating method, an updating device and a readable storage medium of an early warning grade, wherein the updating method comprises the following steps: acquiring at least one correlation characteristic of a person to be calibrated under historical correlation information and a historical early warning grade of the person to be calibrated from an information database, and acquiring at least one correlation characteristic of the person to be calibrated under real-time correlation information within a preset time period from an information data stream; determining a first element incidence matrix, a second element incidence matrix and a personnel feature matrix of the personnel to be calibrated based on at least one incidence feature under the historical incidence information and at least one incidence feature under the real-time incidence information, and determining an updated early warning level of the personnel to be calibrated through a personnel early warning model; and replacing the historical early warning level of the personnel to be calibrated by the updated early warning level. Therefore, the early warning grade of the personnel to be calibrated can be effectively and accurately calibrated and updated, and the method is simple and convenient and has high accuracy.
Description
Technical Field
The present application relates to the technical field of personal early warning, and in particular, to an updating method and an updating apparatus for an early warning level, and a readable storage medium.
Background
At present of information rapid development, relevant information of important observers can be stored in a network, police officers or relevant personnel can track and early warn the relevant personnel for a long time through the stored information, and timely early warning is carried out on the important observers when the early warning level of the important observers reaches a certain level.
At present, police officers or related personnel generate early warning information of people needing important observation according to related information stored in a database in advance, and early warning is carried out on the people needing important observation. However, the information in the database is recorded before, the information of the key observers changes along with the lapse of time, the danger degree of the key observers may increase along with the change of the information, and if the early warning level of the key observers is determined according to the previous information all the time, the determined early warning level of the key observers is not accurate enough.
Disclosure of Invention
In view of this, an object of the present application is to provide an updating method, an updating apparatus, and a readable storage medium for an early warning level, which can effectively and accurately obtain a current early warning level of a current person to be calibrated by combining a correlation characteristic under historical correlation information of the person to be calibrated and a correlation characteristic under real-time correlation information within a preset time period, so that the early warning level of the person to be calibrated can be accurately calibrated and updated, and the updating method, the updating apparatus, and the readable storage medium are simple and convenient and have high accuracy.
The embodiment of the application provides an updating method of an early warning grade, which comprises the following steps:
acquiring at least one associated feature of a person to be calibrated under historical associated information and a historical early warning grade of the person to be calibrated from an information database, and acquiring at least one associated feature of the person to be calibrated under real-time associated information within a preset time period from an information data stream;
determining a first element incidence matrix, a second element incidence matrix and a personnel feature matrix of the personnel to be calibrated based on at least one incidence feature under the historical incidence information and at least one incidence feature under the real-time incidence information;
inputting the first element incidence matrix, the second element incidence matrix and the personnel feature matrix into a personnel early warning model, and determining the updated early warning level of the personnel to be calibrated;
and replacing the historical early warning level of the personnel to be calibrated by using the updated early warning level.
Further, the association feature includes at least one of an identity feature, an associated person feature, an associated event feature, an associated organization feature, an associated location feature, and an associated item feature.
Further, a first element incidence matrix of the person to be calibrated is determined based on the at least one incidence characteristic under the historical incidence information and the at least one incidence characteristic under the real-time incidence information through the following steps:
determining a first association index between two association features of different feature dimensions under the historical association information from the at least one association feature under the historical association information, and determining a first association index between two association features of different feature dimensions under the real-time association information from the at least one association feature under the real-time association information;
and determining a first element incidence matrix of the person to be calibrated based on a plurality of first incidence indexes under historical incidence information and a plurality of first incidence indexes under real-time incidence information.
Further, a second element incidence matrix of the person to be calibrated is determined based on the at least one incidence characteristic under the historical incidence information and the at least one incidence characteristic under the real-time incidence information through the following steps:
determining a second correlation index between two correlation characteristics of the same characteristic dimension under the historical correlation information from the at least one correlation characteristic under the historical correlation information, and determining a second correlation index between two correlation characteristics of the same characteristic dimension under the real-time correlation information from the at least one correlation characteristic under the real-time correlation information;
and determining a second element incidence matrix of the person to be calibrated based on a plurality of second incidence indexes under historical incidence information and a plurality of second incidence indexes under real-time incidence information.
Further, first association data between two association features of different feature dimensions is determined by the following steps:
obtaining an evaluation index and a track evaluation index between two associated features of different feature dimensions;
calculating an evaluation correlation index between two correlation characteristics of different characteristic dimensions based on the evaluation index, and calculating a track correlation index between two correlation characteristics of different characteristic dimensions based on the track evaluation index;
based on a plurality of the evaluation correlation indexes and a plurality of the trajectory correlation indexes, a first correlation index between two correlation features of different feature dimensions is determined.
Further, the evaluation index includes at least one of confidence, freshness, homologous frequency, and heterologous frequency, and the trajectory evaluation index includes at least one of same trajectory similarity, trajectory freshness, trajectory homologous frequency, and trajectory heterologous frequency.
The embodiment of the present application further provides an updating apparatus for an early warning level, the updating apparatus includes:
the characteristic acquisition module is used for acquiring at least one associated characteristic of a person to be calibrated under historical associated information and a historical early warning grade of the person to be calibrated from an information database, and acquiring at least one associated characteristic of the person to be calibrated under real-time associated information within a preset time period from an information data stream;
the matrix determination module is used for determining a first element incidence matrix, a second element incidence matrix and a personnel feature matrix of the personnel to be calibrated based on at least one incidence feature under the historical incidence information and at least one incidence feature under the real-time incidence information;
the early warning grade determining module is used for inputting the first element incidence matrix, the second element incidence matrix and the personnel feature matrix into a personnel early warning model and determining the updated early warning grade of the personnel to be calibrated;
and the replacing module is used for replacing the historical early warning level of the personnel to be calibrated by using the updated early warning level.
Further, the associated features include at least one of identity features, associated people features, associated events features, associated organization features, associated places features, and associated items features.
Further, when the matrix determination module is configured to determine the first element association matrix, the second element association matrix, and the person feature matrix of the person to be calibrated based on the at least one associated feature in the historical associated information and the at least one associated feature in the real-time associated information, the matrix determination module determines the first element association matrix of the person to be calibrated based on the at least one associated feature in the historical associated information and the at least one associated feature in the real-time associated information by:
determining a first association index between two association features of different feature dimensions under the historical association information from the at least one association feature under the historical association information, and determining a first association index between two association features of different feature dimensions under the real-time association information from the at least one association feature under the real-time association information;
and determining a first element incidence matrix of the person to be calibrated based on a plurality of first incidence indexes under historical incidence information and a plurality of first incidence indexes under real-time incidence information.
Further, when the matrix determination module is configured to determine the first element association matrix, the second element association matrix, and the person feature matrix of the person to be calibrated based on the at least one associated feature in the historical associated information and the at least one associated feature in the real-time associated information, the matrix determination module determines the second element association matrix of the person to be calibrated based on the at least one associated feature in the historical associated information and the at least one associated feature in the real-time associated information by:
determining a second correlation index between two correlation characteristics of the same characteristic dimension under the historical correlation information from the at least one correlation characteristic under the historical correlation information, and determining a second correlation index between two correlation characteristics of the same characteristic dimension under the real-time correlation information from the at least one correlation characteristic under the real-time correlation information;
and determining a second element incidence matrix of the person to be calibrated based on a plurality of second incidence indexes under historical incidence information and a plurality of second incidence indexes under real-time incidence information.
Further, the matrix determination module is configured to determine, when determining a first correlation index between two correlation features of different feature dimensions under the historical correlation information from the at least one correlation feature under the historical correlation information, and determining a first correlation index between two correlation features of different feature dimensions under the real-time correlation information from the at least one correlation feature under the real-time correlation information, first correlation data between the two correlation features of different feature dimensions by:
obtaining an evaluation index and a track evaluation index between two associated features with different feature dimensions;
calculating an evaluation correlation index between two correlation characteristics of different characteristic dimensions based on the evaluation index, and calculating a track correlation index between two correlation characteristics of different characteristic dimensions based on the track evaluation index;
based on a plurality of the evaluation correlation indexes and a plurality of the trajectory correlation indexes, a first correlation index between two correlation features of different feature dimensions is determined.
Further, the evaluation index includes at least one of confidence, freshness, homologous frequency, and heterologous frequency, and the trajectory evaluation index includes at least one of same trajectory similarity, trajectory freshness, trajectory homologous frequency, and trajectory heterologous frequency.
An embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine readable instructions when executed by the processor performing the steps of the warning level updating method as described above.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for updating an early warning level as described above are performed.
According to the updating method, the updating device and the readable storage medium of the early warning grade, at least one correlation characteristic of a person to be calibrated under historical correlation information and the historical early warning grade of the person to be calibrated are obtained from an information database, and at least one correlation characteristic of the person to be calibrated under real-time correlation information in a preset time period is obtained from an information data stream; determining a first element incidence matrix, a second element incidence matrix and a personnel feature matrix of the personnel to be calibrated based on at least one incidence feature under the historical incidence information and at least one incidence feature under the real-time incidence information; inputting the first element incidence matrix, the second element incidence matrix and the personnel feature matrix into a personnel early warning model, and determining the updated early warning level of the personnel to be calibrated; and replacing the historical early warning level of the personnel to be calibrated by using the updated early warning level.
Therefore, the method and the device combine at least one correlation characteristic of the to-be-calibrated person under the historical correlation information with at least one correlation characteristic under the real-time correlation information, determine the updated early warning level of the to-be-calibrated person through the person early warning model, and replace the historical early warning level of the to-be-calibrated person with the determined updated early warning level. Therefore, the current early warning grade of the current personnel to be calibrated can be effectively and accurately obtained, so that the early warning grade of the personnel to be calibrated can be accurately calibrated and updated, and the method is simple and convenient and has high accuracy.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart of an updating method for an early warning level according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a first element association matrix determining method in an updating method of an early warning level according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a second element association matrix determining method in an updating method of an early warning level according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an apparatus for updating an early warning level according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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 a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. Every other embodiment that one skilled in the art can obtain without inventive effort based on the embodiments of the present application falls within the scope of protection of the present application.
First, an application scenario to which the present application is applicable will be described. The application can be applied to the technical field of personnel early warning. The method comprises the steps of obtaining at least one associated feature of a person to be calibrated under historical associated information from an information database, obtaining at least one associated feature of the person to be calibrated under real-time associated information within a preset time period from an information data stream, determining an updated early warning grade of the person to be calibrated through a person early warning model, and replacing the historical early warning grade of the person to be calibrated with the updated early warning grade, so that the updating of the personnel early warning grade is realized.
According to research, police officers or related personnel generate early warning information of people needing important observation according to related information stored in a database in advance, and early warning is carried out on people needing important observation. However, the information in the database is recorded before, the information of the key observers changes along with the lapse of time, the danger degree of the information may increase along with the change of the information, and if the early warning level of the key observers is determined according to the previous information all the time, the early warning level of the key observers is not accurate enough.
Based on this, the embodiment of the application provides an updating method of an early warning level, by combining at least one associated feature of a to-be-calibrated person under historical associated information with at least one associated feature under real-time associated information, an updated early warning level of the to-be-calibrated person is determined through a person early warning model, and the determined updated early warning level is used for replacing the historical early warning level of the to-be-calibrated person. The current early warning grade of the current personnel to be calibrated can be effectively and accurately obtained, so that the early warning grade of the personnel to be calibrated can be accurately calibrated and updated, and the method is simple and convenient and has high accuracy.
Referring to fig. 1, fig. 1 is a flowchart illustrating an updating method of an early warning level according to an embodiment of the present disclosure. As shown in fig. 1, the method for updating an early warning level provided in the embodiment of the present application includes:
s101, obtaining at least one correlation characteristic of a person to be calibrated under historical correlation information and a historical early warning grade of the person to be calibrated from an information database, and obtaining at least one correlation characteristic of the person to be calibrated under real-time correlation information within a preset time period from an information data stream.
In the step, when the person to be calibrated performs early warning, at least one associated feature of the person to be calibrated under the historical associated information and a historical early warning grade determined by the person to be calibrated according to the at least one associated feature under the historical associated information are obtained from an information database, and at least one associated feature of the person to be calibrated under the real-time associated information within a preset time period is obtained from an information data stream.
The historical associated information refers to historical information recorded by the person to be calibrated, that is, some information of recent activities of the person to be calibrated cannot be timely recorded in the historical associated information, for example, "the person to be calibrated has just bought a train ticket", so that in order to accurately predict the early warning level of the person to be calibrated, at least one associated feature of the real-time associated information in the latest preset time period of the person to be calibrated is acquired from an information data stream.
The associated features include at least one of identity features, associated people features, associated event features, associated organization features, associated location features, and associated item features.
The identity features may include: key human characteristics, natural characteristics, identity characteristics, space-time behavior characteristics, case-involved characteristics, geographic characteristics and the like.
The associated person features may include: the same kind of company, the same kind of residence, the same family and the same place of residence, etc.
The associated event features may include: the method comprises the following steps of case composition means, case types, case event influence degrees, event space-time characteristics and the like.
The associated organizational characteristics may include: member characteristics, organization case-related characteristics, organization region characteristics, organization article characteristics, and the like.
The associated location features may include: key area features, person density features, case related features, etc.
The associated item features may include: key article characteristics, article belonging characteristics, article spatiotemporal behavior characteristics and the like.
S102, determining a first element incidence matrix, a second element incidence matrix and a personnel feature matrix of the personnel to be calibrated based on at least one incidence feature under the historical incidence information and at least one incidence feature under the real-time incidence information.
In the step, a first element incidence matrix, a second element incidence matrix and a personnel feature matrix of the personnel to be calibrated, which can be input into a personnel early warning model, are determined based on at least one incidence feature under the historical incidence information acquired from an information database and at least one incidence feature under the real-time incidence information acquired from an information data stream.
S103, inputting the first element incidence matrix, the second element incidence matrix and the personnel feature matrix into a personnel early warning model, and determining the updated early warning level of the personnel to be calibrated.
In this step, the first element incidence matrix, the second element incidence matrix and the personnel feature matrix determined in step S102 are input into a personnel early warning model, and an updated early warning level of the personnel to be calibrated is determined through the personnel early warning model.
Wherein, the personnel early warning model can be any machine learning model, such as: neural networks, deep neural networks, conditional random fields, and markov models, among others.
And S104, replacing the historical early warning level of the personnel to be calibrated by using the updated early warning level.
In the step, the determined updated early warning information of the personnel to be calibrated is used for replacing the historical early warning information of the personnel to be calibrated, so that the early warning grade of the personnel to be calibrated can be updated again on the basis of the early warning at the next time.
According to the updating method of the early warning level, at least one correlation characteristic of a person to be calibrated under historical correlation information and the historical early warning level of the person to be calibrated are obtained from an information database, and at least one correlation characteristic of the person to be calibrated under real-time correlation information within a preset time period is obtained from an information data stream; determining a first element incidence matrix, a second element incidence matrix and a personnel feature matrix of the personnel to be calibrated based on at least one incidence feature under the historical incidence information and at least one incidence feature under the real-time incidence information; inputting the first element incidence matrix, the second element incidence matrix and the personnel feature matrix into a personnel early warning model, and determining the updated early warning level of the personnel to be calibrated; and replacing the historical early warning level of the personnel to be calibrated by using the updated early warning level.
Therefore, the method and the device combine at least one correlation characteristic of the to-be-calibrated person under the historical correlation information with at least one correlation characteristic under the real-time correlation information, determine the updated early warning level of the to-be-calibrated person through the person early warning model, and replace the historical early warning level of the to-be-calibrated person with the determined updated early warning level. The current early warning grade of the current personnel to be calibrated can be effectively and accurately obtained, so that the early warning grade of the personnel to be calibrated can be accurately calibrated and updated, and the method is simple and convenient and has high accuracy.
Referring to fig. 2, fig. 2 is a flowchart of a first element association matrix determining method in an updating method of an early warning level according to an embodiment of the present disclosure. As shown in fig. 2, a method for updating an early warning level and a method for determining a first element association matrix provided in the embodiment of the present application include:
s201, determining a first association index between two association features of different feature dimensions under the historical association information from at least one association feature under the historical association information, and determining a first association index between two association features of different feature dimensions under the real-time association information from at least one association feature under the real-time association information.
In the step, associated features of two different feature dimensions are determined at will from at least one associated feature under the historical associated information (for example, one associated feature belongs to an associated event feature, and the other associated feature belongs to an associated person feature), and a first association index existing between the two associated features under the historical associated information is determined; meanwhile, from at least one correlation characteristic under the real-time correlation information, correlation characteristics of two different characteristic dimensions are randomly determined (for example, one correlation characteristic belongs to a correlation event characteristic, and the other correlation characteristic belongs to a correlation person characteristic), and a first correlation index existing between the two correlation characteristics under the real-time correlation information is determined.
Specifically, under historical associated information or real-time associated information, one associated feature is arbitrarily selected from one feature dimension as an associated feature to be calculated, all associated features of other feature dimensions (i.e., associated features except for the feature dimension to which the associated feature to be calculated belongs in all associated features) are traversed, and a first association index between the associated feature and all associated features of other feature dimensions is calculated.
Illustratively, the related personnel features under historical related information or real-time related information comprise 'feature A and feature B', the related event features comprise 'feature C and feature D', the related item features comprise 'feature E and feature F', one related feature is arbitrarily selected to be 'feature A', and first association indexes between the feature A and the feature B, between the feature A and the feature C, between the feature D and the feature E and between the feature A and the feature F are respectively calculated.
S202, determining a first element incidence matrix of the person to be calibrated based on a plurality of first incidence indexes under historical incidence information and a plurality of first incidence indexes under real-time incidence information.
In the step, all the first relevance indexes are arranged according to a plurality of calculated first relevance indexes under historical relevance information and a plurality of calculated first relevance indexes under real-time relevance information, and are combined to form a first element relevance matrix of the person to be calibrated.
Further, first association data between two association features of different feature dimensions are determined by the following steps: obtaining an evaluation index and a track evaluation index between two associated features with different feature dimensions; calculating an evaluation correlation index between two correlation characteristics of different characteristic dimensions based on the evaluation index, and calculating a track correlation index between two correlation characteristics of different characteristic dimensions based on the track evaluation index; based on a plurality of the evaluation correlation indexes and a plurality of the trajectory correlation indexes, a first correlation index between two correlation features of different feature dimensions is determined.
In the step, under historical associated information or real-time associated information, a plurality of evaluation indexes between associated features of two different feature dimensions are obtained, evaluation associated indexes between two associated features of the two different feature dimensions are calculated, meanwhile, under the historical associated information or the real-time associated information, a plurality of track evaluation indexes between the associated features of the two different feature dimensions are obtained, track associated indexes between the two associated features of the two different feature dimensions are calculated, and a first associated index between the two associated features of the two different dimensions is determined through superposition calculation based on the plurality of evaluation associated indexes and the plurality of track associated indexes.
Wherein the evaluation index includes at least one of confidence, freshness, homologous frequency and heterologous frequency, and the trajectory evaluation index includes at least one of same trajectory similarity, trajectory freshness, trajectory homologous frequency and trajectory heterologous frequency.
According to the updating method of the early warning level, a first association index between two association features of different feature dimensions under the historical association information is determined from at least one association feature under the historical association information, and a first association index between two association features of different feature dimensions under the real-time association information is determined from at least one association feature under the real-time association information; and determining a first element incidence matrix of the person to be calibrated based on a plurality of first incidence indexes under historical incidence information and a plurality of first incidence indexes under real-time incidence information.
Therefore, the first element incidence matrix of the to-be-calibrated person is determined based on the plurality of first incidence indexes under the historical incidence information and the plurality of first incidence indexes under the real-time incidence information, so that the historical incidence information and the real-time incidence information are comprehensively considered while the incidence characteristics of different dimensions are associated, and the prediction of the early warning grade of the to-be-calibrated person is more accurate.
Referring to fig. 3, fig. 3 is a flowchart of a second element association matrix determining method in an updating method of an early warning level according to an embodiment of the present disclosure. As shown in fig. 3, in the updating method of the warning level provided in the embodiment of the present application, the method for determining the second element association matrix includes:
s301, determining a second correlation index between two correlation characteristics with the same characteristic dimension under the historical correlation information from at least one correlation characteristic under the historical correlation information, and determining a second correlation index between two correlation characteristics with the same characteristic dimension under the real-time correlation information from at least one correlation characteristic under the real-time correlation information.
In the step, a relevant feature (for example, a relevant feature belonging to the relevant event feature) is arbitrarily determined in a feature dimension from at least one relevant feature under the historical relevant information, and a second relevance index between the relevant feature and all relevant features except the relevant feature in the same feature dimension under the historical relevant information is determined; meanwhile, a related feature (for example, a related feature belonging to the related event feature in the feature dimension) is arbitrarily determined from at least one related feature under the real-time related information, and a second association index between the related feature and all related features except the related feature in the same feature dimension under the real-time related information is determined.
Specifically, under historical association information or real-time association information, one association feature is arbitrarily selected from one feature dimension to serve as an association feature to be calculated, all association features in the same feature dimension (i.e., all association features except the association feature to be calculated in the feature dimension to which the association feature to be calculated belongs) are traversed, and a second association index between the association feature and all association features except the association feature in the same feature dimension is calculated.
Corresponding to the above embodiment, the related person features under the history related information or the real-time related information include "feature a and feature B", the related event features include "feature C and feature D", the related item features include "feature E, feature F and feature G", one related feature "feature a" is arbitrarily selected, a second correlation index between feature a and feature B in the same feature dimension, a second correlation index between feature C and feature D, and a second correlation index between feature E and feature F and feature G are calculated.
S302, determining a second element incidence matrix of the person to be calibrated based on a plurality of second incidence indexes under historical incidence information and a plurality of second incidence indexes under real-time incidence information.
In the step, according to a plurality of calculated second correlation indexes under historical correlation information and a plurality of calculated second correlation indexes under real-time correlation information, all the second correlation indexes are arranged and combined to form a second element correlation matrix of the personnel to be calibrated.
Further, the updating method of the early warning level determines a personnel feature matrix of the personnel to be calibrated based on at least one correlation feature under the historical correlation information and at least one correlation feature under the real-time correlation information.
In the step, under the historical associated information and the real-time associated information, at least one associated feature is divided according to feature dimensions, a plurality of associated features included in each feature dimension are determined, namely, at least one associated feature is divided according to the feature dimensions, and a personnel feature matrix of the personnel to be calibrated is formed on the basis of the historical associated information and the real-time associated information of the personnel to be calibrated.
According to the updating method of the early warning level, a second correlation index between two correlation characteristics with the same characteristic dimension under the historical correlation information is determined from at least one correlation characteristic under the historical correlation information, and a second correlation index between two correlation characteristics with the same characteristic dimension under the real-time correlation information is determined from at least one correlation characteristic under the real-time correlation information; and determining a second element incidence matrix of the person to be calibrated based on a plurality of second incidence indexes under historical incidence information and a plurality of second incidence indexes under real-time incidence information.
Therefore, the second element incidence matrix of the personnel to be calibrated is determined based on the plurality of second incidence indexes under the historical incidence information and the plurality of second incidence indexes under the real-time incidence information, and therefore, the historical incidence information and the real-time incidence information are comprehensively considered while the incidence characteristics of the same dimensionality are correlated, and the early warning grade of the personnel to be calibrated is more accurately predicted.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an updating apparatus for an early warning level according to an embodiment of the present disclosure. As shown in fig. 4, the warning level updating apparatus 400 includes:
the characteristic obtaining module 410 is configured to obtain at least one relevant characteristic of a person to be calibrated under historical relevant information and a historical early warning level of the person to be calibrated from an information database, and obtain at least one relevant characteristic of the person to be calibrated under real-time relevant information within a preset time period from an information data stream;
a matrix determining module 420, configured to determine a first element incidence matrix, a second element incidence matrix, and a personnel feature matrix of the personnel to be calibrated based on at least one incidence feature in the historical incidence information and at least one incidence feature in the real-time incidence information;
the early warning level determining module 430 is configured to input the first element association matrix, the second element association matrix, and the personnel feature matrix into a personnel early warning model, and determine an updated early warning level of the personnel to be calibrated;
and a replacing module 440, configured to replace the historical warning level of the person to be calibrated with the updated warning level.
Further, the association feature includes at least one of an identity feature, an associated person feature, an associated event feature, an associated organization feature, an associated location feature, and an associated item feature.
Further, when the matrix determination module 420 is configured to determine the first element association matrix, the second element association matrix, and the person feature matrix of the person to be calibrated based on the at least one associated feature in the historical associated information and the at least one associated feature in the real-time associated information, the first element association matrix of the person to be calibrated is determined based on the at least one associated feature in the historical associated information and the at least one associated feature in the real-time associated information by:
determining a first association index between two association features of different feature dimensions under the historical association information from the at least one association feature under the historical association information, and determining a first association index between two association features of different feature dimensions under the real-time association information from the at least one association feature under the real-time association information;
and determining a first element incidence matrix of the person to be calibrated based on a plurality of first incidence indexes under historical incidence information and a plurality of first incidence indexes under real-time incidence information.
Further, when the matrix determination module 420 is configured to determine the first element association matrix, the second element association matrix, and the person feature matrix of the person to be calibrated based on the at least one associated feature in the historical associated information and the at least one associated feature in the real-time associated information, the matrix determination module determines the second element association matrix of the person to be calibrated based on the at least one associated feature in the historical associated information and the at least one associated feature in the real-time associated information by:
determining a second correlation index between two correlation characteristics with the same characteristic dimension under the historical correlation information from at least one correlation characteristic under the historical correlation information, and determining a second correlation index between two correlation characteristics with the same characteristic dimension under the real-time correlation information from at least one correlation characteristic under the real-time correlation information;
and determining a second element incidence matrix of the person to be calibrated based on a plurality of second incidence indexes under historical incidence information and a plurality of second incidence indexes under real-time incidence information.
Further, the matrix determination module 420 is configured to determine, when determining a first correlation index between two correlation features of different feature dimensions under the historical correlation information from the at least one correlation feature under the historical correlation information and determining a first correlation index between two correlation features of different feature dimensions under the real-time correlation information from the at least one correlation feature under the real-time correlation information, first correlation data between two correlation features of different feature dimensions by:
obtaining an evaluation index and a track evaluation index between two associated features of different feature dimensions;
calculating an evaluation correlation index between two correlation characteristics of different characteristic dimensions based on the evaluation index, and calculating a track correlation index between two correlation characteristics of different characteristic dimensions based on the track evaluation index;
based on a plurality of the evaluation correlation indexes and a plurality of the trajectory correlation indexes, a first correlation index between two correlation features of different feature dimensions is determined.
Further, the evaluation index includes at least one of confidence, freshness, homologous frequency, and heterologous frequency, and the trajectory evaluation index includes at least one of same trajectory similarity, trajectory freshness, trajectory homologous frequency, and trajectory heterologous frequency.
The updating device for the early warning level, provided by the embodiment of the application, acquires at least one correlation characteristic of a to-be-calibrated person under historical correlation information and the historical early warning level of the to-be-calibrated person from an information database, and acquires at least one correlation characteristic of the to-be-calibrated person under real-time correlation information within a preset time period from an information data stream; determining a first element incidence matrix, a second element incidence matrix and a personnel feature matrix of the personnel to be calibrated based on at least one incidence feature under the historical incidence information and at least one incidence feature under the real-time incidence information; inputting the first element incidence matrix, the second element incidence matrix and the personnel feature matrix into a personnel early warning model, and determining the updated early warning level of the personnel to be calibrated; and replacing the historical early warning level of the personnel to be calibrated by using the updated early warning level.
Therefore, the method and the device combine at least one correlation characteristic of the personnel to be calibrated under historical correlation information with at least one correlation characteristic of the personnel to be calibrated under real-time correlation information, determine the updated early warning level of the personnel to be calibrated through the personnel early warning model, and replace the historical early warning level of the personnel to be calibrated with the determined updated early warning level. The current early warning grade of the current personnel to be calibrated can be effectively and accurately obtained, so that the early warning grade of the personnel to be calibrated can be accurately calibrated and updated, and the method is simple and convenient and has high accuracy.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 5, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.
The memory 520 stores machine-readable instructions executable by the processor 510, when the electronic device 500 runs, the processor 510 communicates with the memory 520 through the bus 530, and when the machine-readable instructions are executed by the processor 510, the steps of the method for updating the warning level in the embodiment of the method shown in fig. 1 may be executed.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the step of the method for updating an early warning level in the embodiment of the method shown in fig. 1 may be executed.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units into only one type of logical function may be implemented in other ways, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical 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 functions, if implemented in software functional units and sold or used as a stand-alone product, may be stored in a non-transitory computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including 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 removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit the technical solutions, and the scope of the present application is not limited to the above-mentioned embodiments, although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. An updating method of an early warning level is characterized in that the updating method comprises the following steps:
acquiring at least one association characteristic of a person to be calibrated under historical association information and a historical early warning level of the person to be calibrated from an information database, and acquiring at least one association characteristic of the person to be calibrated under real-time association information within a preset time period from an information data stream; wherein the associated features comprise at least one of identity features, associated personnel features, associated event features, associated organization features, associated location features, and associated item features;
determining a first element incidence matrix, a second element incidence matrix and a personnel feature matrix of the personnel to be calibrated based on at least one incidence feature under the historical incidence information and at least one incidence feature under the real-time incidence information; the historical associated information refers to historical information recorded by the personnel to be calibrated;
inputting the first element incidence matrix, the second element incidence matrix and the personnel feature matrix into a personnel early warning model, and determining the updated early warning level of the personnel to be calibrated; the personnel early warning model can be any machine learning model;
replacing the historical early warning level of the personnel to be calibrated by the updated early warning level;
determining the personnel feature matrix by:
under the historical associated information and the real-time associated information, dividing at least one associated feature according to feature dimensions, and determining a plurality of associated features included in each feature dimension;
and forming a personnel feature matrix of the personnel to be calibrated based on the historical associated information and the real-time associated information of the personnel to be calibrated.
2. The updating method according to claim 1, wherein the first element incidence matrix of the person to be calibrated is determined based on the at least one incidence characteristic under the historical incidence information and the at least one incidence characteristic under the real-time incidence information by the following steps:
determining a first association index between two association features with different feature dimensions under the historical association information from at least one association feature under the historical association information, and determining a first association index between two association features with different feature dimensions under the real-time association information from at least one association feature under the real-time association information;
and arranging all the first correlation indexes based on the plurality of first correlation indexes under the historical correlation information and the plurality of first correlation indexes under the real-time correlation information, and combining to form a first element correlation matrix of the personnel to be calibrated.
3. The updating method according to claim 1, wherein the second element incidence matrix of the person to be calibrated is determined based on the at least one incidence characteristic under the historical incidence information and the at least one incidence characteristic under the real-time incidence information by the following steps:
determining a second correlation index between two correlation characteristics with the same characteristic dimension under the historical correlation information from at least one correlation characteristic under the historical correlation information, and determining a second correlation index between two correlation characteristics with the same characteristic dimension under the real-time correlation information from at least one correlation characteristic under the real-time correlation information;
and arranging all the second correlation indexes based on the plurality of second correlation indexes under the historical correlation information and the plurality of second correlation indexes under the real-time correlation information, and combining to form a second element correlation matrix of the personnel to be calibrated.
4. The updating method according to claim 2, characterized in that a first correlation index between two correlation features of different feature dimensions is determined by the following steps:
obtaining an evaluation index and a track evaluation index between two associated features with different feature dimensions;
calculating an evaluation association index between two associated features of different feature dimensions based on the evaluation index, and calculating a trajectory association index between two associated features of different feature dimensions based on the trajectory evaluation index;
and determining a first association index between two associated features under different feature dimensions by superposition calculation based on the plurality of evaluation association indexes and the plurality of track association indexes.
5. The updating method of claim 4, wherein the evaluation index comprises at least one of confidence, freshness, homologous frequency, and heterologous frequency, and the trajectory evaluation index comprises at least one of same trajectory similarity, trajectory freshness, trajectory homologous frequency, and trajectory heterologous frequency.
6. An updating apparatus for an early warning level, the updating apparatus comprising:
the characteristic acquisition module is used for acquiring at least one associated characteristic of a person to be calibrated under historical associated information and a historical early warning grade of the person to be calibrated from an information database, and acquiring at least one associated characteristic of the person to be calibrated under real-time associated information within a preset time period from an information data stream; wherein the associated features comprise at least one of identity features, associated personnel features, associated event features, associated organization features, associated location features, and associated item features;
the matrix determination module is used for determining a first element incidence matrix, a second element incidence matrix and a personnel feature matrix of the personnel to be calibrated based on at least one incidence feature under the historical incidence information and at least one incidence feature under the real-time incidence information; the historical associated information refers to historical information recorded by the personnel to be calibrated;
the early warning level determining module is used for inputting the first element incidence matrix, the second element incidence matrix and the personnel feature matrix into a personnel early warning model and determining the updated early warning level of the personnel to be calibrated; the personnel early warning model can be any machine learning model;
the replacing module is used for replacing the historical early warning level of the personnel to be calibrated by using the updated early warning level;
determining the personnel feature matrix by:
under the historical associated information and the real-time associated information, dividing at least one associated feature according to feature dimensions, and determining a plurality of associated features included in each feature dimension;
and forming a personnel feature matrix of the personnel to be calibrated based on the historical associated information and the real-time associated information of the personnel to be calibrated.
7. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the warning level updating method of any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the method for updating a warning level according to any one of claims 1 to 5.
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