CN113256093A - Dynamic risk pool monitoring method, system, equipment and readable storage medium - Google Patents

Dynamic risk pool monitoring method, system, equipment and readable storage medium Download PDF

Info

Publication number
CN113256093A
CN113256093A CN202110528135.5A CN202110528135A CN113256093A CN 113256093 A CN113256093 A CN 113256093A CN 202110528135 A CN202110528135 A CN 202110528135A CN 113256093 A CN113256093 A CN 113256093A
Authority
CN
China
Prior art keywords
data set
risk pool
offset
observation data
dynamic risk
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110528135.5A
Other languages
Chinese (zh)
Inventor
孙静春
邓飞
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian Jiaotong University
Original Assignee
Xian Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xian Jiaotong University filed Critical Xian Jiaotong University
Priority to CN202110528135.5A priority Critical patent/CN113256093A/en
Publication of CN113256093A publication Critical patent/CN113256093A/en
Priority to CN202111285274.6A priority patent/CN113793076B/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Operations Research (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a dynamic monitoring method, a system, equipment and a readable storage medium for a risk pool, wherein the method comprises the steps of constructing a dynamic risk pool to form a dynamic risk pool index vector X; obtaining a comparison data set and an observation data set according to the index vector X of the dynamic risk pool, constructing a distance cost function between the comparison data set and the observation data set, and calculating data offset according to the cost function; after the index vector X of the dynamic risk pool changes along with the change of time, the observation data set is updated to form an updated observation data set, the data offset is calculated according to the distance cost function between the comparison data set and the updated observation data set, the average value of the data offset calculated for many times is compared with the tolerance limit value of the data set offset, the conclusion that the risk pool is controllable or uncontrollable is obtained, and the safety of the risk pool can be dynamically monitored.

Description

Dynamic risk pool monitoring method, system, equipment and readable storage medium
Technical Field
The invention belongs to the field of information security, and particularly relates to a dynamic risk pool monitoring method, system, equipment and readable storage medium.
Background
Today, intelligent terminals and networks have become an important part of modern life, so that entertainment, economy and communication aspects are not separated from computer networks. The security of the computer system is influenced by a plurality of factors, the factors are combined to form a risk pool influencing the security of the computer system, and the elements can become risk indexes in the whole risk pool. Moreover, these risk indicators may change over time and may not be static. However, in the prior art, there is no device or method for monitoring the security of the risk pool, so that the security of the risk pool is unknown, and the risk judgment on the risk pool cannot be effectively performed.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method, a system, equipment and a readable storage medium for dynamically monitoring a risk pool, which can dynamically monitor the security of the risk pool.
In order to achieve the purpose, the invention provides the following technical scheme:
a dynamic risk pool monitoring method comprises the following processes of constructing a dynamic risk pool and forming a dynamic risk pool index vector X; obtaining a comparison data set and an observation data set according to the index vector X of the dynamic risk pool, constructing a distance cost function between the comparison data set and the observation data set, and calculating data offset according to the distance cost function; and after the index vector X of the dynamic risk pool changes along with the change of time, updating the observation data set to form an updated observation data set, calculating the data offset according to a distance cost function between the comparison data set and the updated observation data set, and comparing the average value of the data offset calculated for many times with the tolerance limit value of the data set offset to obtain the conclusion that the risk pool is controllable or uncontrollable.
Preferably, the method specifically comprises the following steps:
step 1, for each original risk index xiSolving to obtain each original risk index xiThe variance of neutron elements, the variance is sequenced and summed, and the sum of the variances and the total variance sum are greater than the original risk index x with the preset valueiBringing in to form a dynamic risk pool index vector X;
step 2, determining a comparison data set H according to the index vector X of the dynamic risk pool0And observation data set H1Determining a comparison data set H0And observation data set H1Constructing a distance cost function between the comparison data set and the observation data set according to the sample mean value and the covariance matrix;
step 3, dynamic risk pool index vector X is carried out at any timeAfter the inter-variation changes, the observation data set is updated to form an updated observation data set H1For the updated observation data set H1Re-determining sample mean and covariance matrix, and comparing data set H0Keeping the function unchanged and reconstructing the function;
step 4, the target value of the time width is T, the current time value is T, and when T is less than T, the step 3 is repeated; calculating an average value ARL of data offsets when T is T, wherein the time width target value T is satisfied;
step 5, comparing the average value ARL of the data offset with the tolerance A of the data set offset0When ARL is less than or equal to A0When the offset of the observation data set is within the tolerance range, judging that the risk pool X is controllable; when ARL > A0And (4) judging that the risk pool X is uncontrollable when the offset of the observation data set exceeds the tolerance.
Further, the dynamic risk pool indicator vector X is expressed as X ═ X (X)1,x2,…,xi,…,xn) Wherein x isiThe number of the ith risk index in the risk pool index vector X is n, and the n is the number of the risk indexes contained in the risk pool X;
alignment dataset H0Is represented by H0＝(X1,X2,…,Xm)；
Observation data set H1Is represented by H1＝(Xt1,Xt2,…,Xtm),t＝1,2,…。
Further, the observation data set H1According to the time sequence, a moving window function is set for real-time updating, and the window width of the moving window function is m;
updating the observation data set H by a moving window function within the range of the target value T of the time width1And (4) recalculating the sample mean and covariance matrix of the updated observation data set.
Further, in step 3, mahalanobis distance is used as the comparison data set H0And observation data set H1The distance between them is a cost function.
Further, the formula of the ARL is:
Figure BDA0003066835620000031
in the formula, AtRepresenting the data alignment at time t H0To H1T is a time width target value.
A dynamic risk pool monitoring system comprises a risk pool module, a data acquisition module, a data updating module, a cost function module, a data offset calculation module and an output comparison module;
the risk pool module is used for constructing a dynamic risk pool to form a dynamic risk pool index vector X;
the data acquisition module is used for acquiring a comparison data set and an observation data set from the dynamic risk pool index vector X;
the cost function module is used for determining a cost function between the comparison data set and the observation data set;
the data updating module is used for updating the observation data set in real time;
the data offset calculation module is used for calculating the data offset between the comparison data set and the observation data set and calculating the average value of the data offset;
and the output comparison module is used for comparing the average value of the data offset with the tolerance degree of the data set offset and judging whether the risk pool is controllable or uncontrollable.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program performs the steps of a method for dynamic risk pool monitoring as described in any one of the preceding claims.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of a method for dynamic risk pool monitoring as defined in any of the above.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention provides a dynamic monitoring method of a risk pool, which constructs the dynamic risk pool, highlights the dynamic property of the risk pool and better accords with the characteristics of the risk pool; by extracting the comparison data set and the observation data set from the risk pool, a cost function can be conveniently constructed; the cost function is expressed by adopting the Mahalanobis distance, so that the calculation is convenient, and the aim of simplification is fulfilled; the observation data set has time sequence characteristics, is continuously updated within the maximum time width range, and improves the accuracy of judgment by calculating the data offset between the updated observation data set and the comparison data set and calculating the average value.
Drawings
Fig. 1 is a flowchart of a dynamic risk pool monitoring method according to the present invention.
Detailed Description
The present invention will now be described in further detail with reference to specific examples, which are intended to be illustrative, but not limiting, of the invention.
As shown in fig. 1, a dynamic risk pool monitoring method first constructs a dynamic risk pool. Solving the variances of the sub-elements contained in each original risk index at each time point, and then sorting and summing the variances, wherein the sum of the variances and the total sum of the variances are larger than a certain value (such as 10%), so as to bring the indexes into a risk pool index vector X, thereby forming the whole dynamic risk pool index vector (along with the change of the time sequence, the original indexes brought into the risk pool index vector change, so that the accumulation process of the whole risk also changes); secondly, extracting a comparison data set and an observation data set from the risk pool index vector X, and constructing a distance cost function of the comparison data set and the observation data set; thirdly, when the maximum time width target value is reached, obtaining the average value of the data offset; and finally, comparing the average value of the data offset with the maximum tolerance degree of the data set offset to obtain the conclusion that the risk pool is controllable or uncontrollable.
Risk pool definition: risk pool refers to a multidimensional risk accumulation process that combines factors that affect the security of a computer systemThen, the risk index x meeting the conditions is screened out by solving the variance sorting modeiRisk stacking is described by a risk pool index vector X that constitutes the security of the entire computer system, where X ═ X1,x2,…,xi,…,xn) Wherein x isiThe risk index vector is the ith risk index in the risk pool index vector X, n is the number of the risk indexes contained in X, n is not fixed and is changed along with time.
Risk index X in risk pool index vector XiThe screening principle of (2):
for each original risk index xiSolving the sum of variances of the included sub-elements, then sorting, and making the sum of variances and the index x with the ratio of the sum of variances to the total sum of variances larger than a certain value (such as 10 percent)iIncorporation, discarding the remaining indices, xiThe entire risk pool index vector X is constructed. As a function of time series, xiAnd changes are also occurring, resulting in the change of the whole risk pool index vector X.
Selecting H0As a comparison data set, H0＝(X1,X2,…,Xm) M is a data set H0The number of the obtained index vectors. Suppose H0Obey a normal distribution, H0～N(μ0Σ) where μ0As a data set H0The sample mean, Σ, is the covariance matrix of the overall risk pool samples.
Setting an observation moving window function with a window width of m (and data set H)1The number of the taken vectors is consistent), H is selected1As observation data set, H1＝(Xt1,Xt2,…,Xtm) T 1,2, …, t representing a time series, data set H1The number of indexes is m (and H)0Same) is maintained as H0The dimensions are consistent, and data comparison is facilitated. Suppose H1Obey a normal distribution, H1～N(μ1，∑1) Wherein, mu1As a data set H1Sample mean, ∑1As a data set H1A covariance matrix of the samples.
In order to carry out H more intuitively and conveniently0And H1The data comparison between the two methods is necessary to construct a cost function. In this patent, mahalanobis distance is used as a cost function for data alignment, which represents μ1To mu0I.e. the data set H0To H1Is an expression for measuring data deviation, so H0And H1The cost function of the data alignment between, as follows:
At＝(μ1-μ0)'(∑1)-1(μ1-μ0),t＝1,2,…
wherein t and H1The time series of (A) is kept consistenttRepresenting the data alignment at time t H0To H1The distance of (c). When H is present1When the data set is not shifted, At＝0。
Setting the maximum time width of the time sequence as T (when the change frequency of the risk pool is high, the value of T is small, when the change frequency of the risk pool is low, the value of T is large), and when T is high, the maximum time width of the time sequence is T<T, returning to and reselecting the updated data set H1(ii) a When T is T, the following steps are carried out:
definition of ARL: represents the average of the offset of the alignment data over the maximum time width as follows:
Figure BDA0003066835620000061
given an A0As the maximum tolerance for data set migration (when the security requirement of the risk pool is high, a)0Should be small; when the safety requirement of the risk pool is low, A0Should be large):
when ARL is less than or equal to A0When is represented by H1The offset of the data set is within the tolerance range, and the risk pool X is controllable;
when ARL > A0When is represented by H1The offset of the data set is beyond tolerance and the risk pool X is not controllable.
Examples
A dynamic risk pool monitoring method. First, for all the original indices, each index x is calculatediSorting and summing the variances of the sub-elements, and selecting an index with the ratio of the sum of the variances to the sum of the total variances larger than a certain value (such as 10%) to be included in an index vector X of the risk pool; secondly, at the moment when t is 1, an alignment data set H is extracted0And observation data set H1Constructing a cost function, and calculating the data offset distance A at the moment when t is 11(ii) a Third, the observation data set H is subjected to a moving window within the maximum time width1Update in real time, now 1<t<T, then constructing a cost function at the time T, and calculating the data offset distance A at the time Tt(ii) a Fourthly, when the maximum time width is reached, namely T is T, calculating the average value ARL of the data offset; finally, by comparing ARL and A0A conclusion is drawn that the risk pool is controllable or uncontrollable.
A dynamic risk pool monitoring system comprises a risk pool module, a data acquisition module, a data updating module, a cost function module, a data offset calculation module and an output comparison module.
And the risk pool module is used for constructing a dynamic risk pool to form a dynamic risk pool index vector X.
The data acquisition module is used for acquiring a comparison data set and an observation data set from the dynamic risk pool index vector X.
The cost function module is to determine a cost function between the alignment dataset and the observation dataset.
And the data updating module is used for updating the observation data set in real time.
The data offset calculation module is used for calculating the data offset between the comparison data set and the observation data set and calculating the average value of the data offset.
And the output comparison module is used for comparing the average value of the data offset with the tolerance degree of the data set offset and judging whether the risk pool is controllable or uncontrollable.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details of non-careless mistakes in the embodiment of the apparatus, please refer to the embodiment of the method of the present invention.
In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor of the embodiment of the invention can be used for the operation of the dynamic risk pool monitoring method.
In yet another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for dynamically monitoring a risk pool in the above-described embodiments.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (9)

1. A dynamic risk pool monitoring method is characterized by comprising the following processes of constructing a dynamic risk pool and forming a dynamic risk pool index vector X; obtaining a comparison data set and an observation data set according to the index vector X of the dynamic risk pool, constructing a distance cost function between the comparison data set and the observation data set, and calculating data offset according to the distance cost function; and after the index vector X of the dynamic risk pool changes along with the change of time, updating the observation data set to form an updated observation data set, calculating the data offset according to a distance cost function between the comparison data set and the updated observation data set, and comparing the average value of the data offset calculated for many times with the tolerance limit value of the data set offset to obtain the conclusion that the risk pool is controllable or uncontrollable.
2. The dynamic risk pool monitoring method according to claim 1, specifically comprising the steps of:
step 1, for each original risk index xiSolving to obtain each original risk index xiThe variance of neutron elements, the variance is sequenced and summed, and the sum of the variances and the total variance sum are greater than the original risk index x with the preset valueiBringing in to form a dynamic risk pool index vector X;
step 2, determining a comparison data set H according to the index vector X of the dynamic risk pool0And observation data set H1Determining a comparison data set H0And observation data set H1Constructing a distance cost function between the comparison data set and the observation data set according to the sample mean value and the covariance matrix;
and 3, after the index vector X of the dynamic risk pool changes along with the change of time, updating the observation data set to form an updated observation data set H1For the updated observation data set H1Re-determining sample mean and covariance matrix, and comparing data set H0Keeping the function unchanged and reconstructing the function;
step 4, the target value of the time width is T, the current time value is T, and when T is less than T, the step 3 is repeated; calculating an average value ARL of data offsets when T is T, wherein the time width target value T is satisfied;
step 5, comparing the average value ARL of the data offset with the tolerance A of the data set offset0When ARL is less than or equal to A0When the offset of the observation data set is within the tolerance range, judging that the risk pool X is controllable; when ARL > A0And (4) judging that the risk pool X is uncontrollable when the offset of the observation data set exceeds the tolerance.
3. The dynamic risk pool monitoring method according to claim 2, wherein the dynamic risk pool indicator vector X is expressed as X ═ X (X ═ X)1,x2,…,xi,…,xn) Wherein x isiThe number of the ith risk index in the risk pool index vector X is n, and the n is the number of the risk indexes contained in the risk pool X;
alignment dataset H0Is represented by H0＝(X1,X2,…,Xm)；
Observation data set H1Is represented by H1＝(Xt1,Xt2,…,Xtm),t＝1,2,…。
4. The dynamic risk pool monitoring method according to claim 3, wherein the observation dataset H1By setting a moving window function according to the time sequenceUpdating in real time, wherein the window width of the moving window function is m;
updating the observation data set H by a moving window function within the range of the target value T of the time width1And (4) recalculating the sample mean and covariance matrix of the updated observation data set.
5. The dynamic risk pool monitoring method as claimed in claim 2, wherein in step 3, mahalanobis distance is used as comparison data set H0And observation data set H1The distance between them is a cost function.
6. The dynamic risk pool monitoring method according to claim 2, wherein the formula of the ARL is:
Figure FDA0003066835610000021
in the formula, AtRepresenting the data alignment at time t H0To H1T is a time width target value.
7. A dynamic risk pool monitoring system is characterized by comprising a risk pool module, a data acquisition module, a data updating module, a cost function module, a data offset calculation module and an output comparison module;
the risk pool module is used for constructing a dynamic risk pool to form a dynamic risk pool index vector X;
the data acquisition module is used for acquiring a comparison data set and an observation data set from the dynamic risk pool index vector X;
the cost function module is used for determining a cost function between the comparison data set and the observation data set;
the data updating module is used for updating the observation data set in real time;
the data offset calculation module is used for calculating the data offset between the comparison data set and the observation data set and calculating the average value of the data offset;
and the output comparison module is used for comparing the average value of the data offset with the tolerance degree of the data set offset and judging whether the risk pool is controllable or uncontrollable.
8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program performs the steps of a method for dynamic risk pool monitoring according to any of claims 1-6.
9. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of a method for dynamic risk pool monitoring according to any one of claims 1 to 6.
CN202110528135.5A 2021-05-14 2021-05-14 Dynamic risk pool monitoring method, system, equipment and readable storage medium Pending CN113256093A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN202110528135.5A CN113256093A (en) 2021-05-14 2021-05-14 Dynamic risk pool monitoring method, system, equipment and readable storage medium
CN202111285274.6A CN113793076B (en) 2021-05-14 2021-11-01 Dynamic risk pool monitoring method, system, equipment and readable storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110528135.5A CN113256093A (en) 2021-05-14 2021-05-14 Dynamic risk pool monitoring method, system, equipment and readable storage medium

Publications (1)

Publication Number Publication Date
CN113256093A true CN113256093A (en) 2021-08-13

Family

ID=77181936

Family Applications (2)

Application Number Title Priority Date Filing Date
CN202110528135.5A Pending CN113256093A (en) 2021-05-14 2021-05-14 Dynamic risk pool monitoring method, system, equipment and readable storage medium
CN202111285274.6A Active CN113793076B (en) 2021-05-14 2021-11-01 Dynamic risk pool monitoring method, system, equipment and readable storage medium

Family Applications After (1)

Application Number Title Priority Date Filing Date
CN202111285274.6A Active CN113793076B (en) 2021-05-14 2021-11-01 Dynamic risk pool monitoring method, system, equipment and readable storage medium

Country Status (1)

Country Link
CN (2) CN113256093A (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114844696B (en) * 2022-04-28 2023-01-17 西安交通大学 Network intrusion dynamic monitoring method, system, equipment and readable storage medium based on risk pool minimization

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10839962B2 (en) * 2016-09-26 2020-11-17 International Business Machines Corporation System, method and computer program product for evaluation and identification of risk factor
WO2019216427A1 (en) * 2018-05-11 2019-11-14 株式会社 Ｐｒｅｆｅｒｒｅｄ Ｎｅｔｗｏｒｋｓ Risk index evaluating device, risk index evaluating method, and program
CN110191137A (en) * 2019-06-12 2019-08-30 北京理工大学 A kind of network system quantization safety evaluation method and device
CN112032034A (en) * 2020-05-21 2020-12-04 和尘自仪(嘉兴)科技有限公司 Sea water pump health state assessment method, model, device and storage medium
CN112258093A (en) * 2020-11-25 2021-01-22 京东城市(北京)数字科技有限公司 Risk level data processing method and device, storage medium and electronic equipment
CN112529303A (en) * 2020-12-15 2021-03-19 建信金融科技有限责任公司 Risk prediction method, device, equipment and storage medium based on fuzzy decision

Also Published As

Publication number Publication date
CN113793076A (en) 2021-12-14
CN113793076B (en) 2022-08-30

Similar Documents

Publication Publication Date Title
CN108959187B (en) Variable box separation method and device, terminal equipment and storage medium
CN111368887B (en) Training method of thunderstorm weather prediction model and thunderstorm weather prediction method
US10635078B2 (en) Simulation system, simulation method, and simulation program
CN111199186A (en) Image quality scoring model training method, device, equipment and storage medium
CN113793076B (en) Dynamic risk pool monitoring method, system, equipment and readable storage medium
CN112801315A (en) State diagnosis method and device for power secondary equipment and terminal
CN113743650B (en) Power load prediction method, device, equipment and storage medium
CN110348540B (en) Clustering-based method and device for screening transient power angle stability faults of power system
CN112463564B (en) Method and device for determining associated index influencing host state
CN113537614A (en) Construction method, system, equipment and medium of power grid engineering cost prediction model
CN117170980A (en) Early warning method, device, equipment and storage medium for server hardware abnormality
CN113570070B (en) Streaming data sampling and model updating method, device, system and storage medium
CN115358314A (en) Method, device and equipment for training wind power prediction model and predicting power
KR102289396B1 (en) Application of reinforcement learning for the advancement of forecasting item demand of repair parts of military equipment
CN111565118B (en) Virtualized network element fault analysis method and system based on multi-observation dimension HMM
CN113516275A (en) Power distribution network ultra-short term load prediction method and device and terminal equipment
CN113516185A (en) Model training method and device, electronic equipment and storage medium
CN112906652A (en) Face image recognition method and device, electronic equipment and storage medium
EP3748549A1 (en) Learning device and learning method
CN112423031A (en) KPI monitoring method, device and system based on IPTV
CN113065644B (en) Method, apparatus, device and medium for compressing neural network model
CN116541252B (en) Computer room fault log data processing method and device
CN116150666B (en) Energy storage system fault detection method and device and intelligent terminal
CN110323743B (en) Clustering method and device for transient power angle stability evaluation historical data
CN109766255B (en) Equipment state analysis method and device, storage medium and electronic equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20210813

WD01 Invention patent application deemed withdrawn after publication