CN109492821A - A kind of stability maintenance method for early warning and system, electronic equipment - Google Patents
A kind of stability maintenance method for early warning and system, electronic equipment Download PDFInfo
- Publication number
- CN109492821A CN109492821A CN201811381093.1A CN201811381093A CN109492821A CN 109492821 A CN109492821 A CN 109492821A CN 201811381093 A CN201811381093 A CN 201811381093A CN 109492821 A CN109492821 A CN 109492821A
- Authority
- CN
- China
- Prior art keywords
- steady
- personnel
- relating
- information data
- data
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012423 maintenance Methods 0.000 title claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 16
- 238000012549 training Methods 0.000 claims abstract description 16
- 230000015654 memory Effects 0.000 claims description 34
- 230000006399 behavior Effects 0.000 claims description 25
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000005516 engineering process Methods 0.000 abstract description 4
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 238000003860 storage Methods 0.000 description 15
- 238000004590 computer program Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 5
- 241001465754 Metazoa Species 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F18/2148—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the process organisation or structure, e.g. boosting cascade
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Business, Economics & Management (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of stability maintenance method for early warning and systems, electronic equipment, and the essential information data of steady personnel are related to including acquiring；Based on the essential information data for relating to steady personnel, the characteristic for relating to steady personnel is extracted；According to the essential information data and characteristic for relating to steady personnel, data correlation collision is carried out, obtains the characteristic parameter for relating to steady personnel；According to the characteristic parameter for relating to steady personnel, the characteristic parameter is handled to obtain feature vector, described eigenvector input is used to predict to relate to the training pattern that steady personnel participate in relating to steady behavior, predict after training pattern processing described in relate to the prediction probability that steady personnel's participation relates to steady behavior.Present invention combination big data and artificial intelligence technology, can predict to relate to steady personnel's different condition, prediction result more science, accurate.
Description
Technical field
The present invention relates to Police Information technical field, a kind of stability maintenance method for early warning and system, electronic equipment are particularly related to.
Background technique
In recent years, as social reform deepens continuously, social transformation process is gradually accelerated, inside all kinds of social contradications and the people
Contradiction is constantly superimposed, interweaves, and especially into information age and big data era, the channel for expressing Interest demands is more more
Member, expression way is more various, and derivative result is more changeable.It is all kinds of to relate to steady personnel based on " personal right-safeguarding " psychology and a variety of hearts
Reason factor dominates the lower various improper letters and calls forms of generation, affects social harmony to a certain extent and stablizes.
By the steady control work of society of many years, various regions have all accumulated substantially and have formd a set of stability and high efficiency for government bodies
Major event handling device and prediction scheme, but still in blind area in terms of precognition in advance.And established at present relate to steady personnel control
System does not make full use of informationization technology, and the synthesis of the discrete scores and weight that mostly use data greatly carrys out calculation risk index,
With more subjective factor, reliability is relatively low.
Summary of the invention
In view of this, can be predicted it is an object of the invention to propose a kind of stability maintenance method for early warning and system, electronic equipment
Steady personnel's different condition is related to, prediction result is more scientific, accurate.
Based on above-mentioned purpose, the present invention provides a kind of stability maintenance method for early warning, comprising:
Acquisition relates to the essential information data of steady personnel；
Based on the essential information data for relating to steady personnel, the characteristic for relating to steady personnel is extracted；
According to the essential information data and characteristic for relating to steady personnel, data correlation collision is carried out, obtains described relate to
The characteristic parameter of steady personnel；
According to the characteristic parameter for relating to steady personnel, the characteristic parameter is handled to obtain feature vector, it will be described
Feature vector input is used to predict to relate to the training pattern that steady personnel participate in relating to steady behavior, obtains after training pattern processing pre-
The prediction probability that steady personnel participate in relating to steady behavior is related to described in survey.
Optionally, the method also includes:
According to the prediction probability, determine that the prediction probability is greater than the preset emphasis for relating to steady threshold value and relates to steady personnel's name
It is single.
Optionally, the method also includes: relate to the essential information data of steady personnel according to the emphasis, acquisition with it is described heavy
Point relates to steady personnel and relates to steady personal information and the emphasis relates to steady personnel and the non-connection for relating to steady personnel with the non-of connection relationship
It is the frequency, determines that the emphasis relates to steady personnel and the non-intimate value for relating to steady personnel, is drawn according to the intimate value described heavy
Point relates to the relation map of steady personnel.
Optionally, the essential information data include identity information data, traffic information data, location information data, go through
History relates to steady data, family information data, and the characteristic parameter includes the identity information data, historical traffic information data, close
Phase location information data, the history relate to steady data.
Optionally, to the identity information data for relating to steady personnel, traffic information data, location information data, go through
History relates to steady data, family information data, the characteristic and merges, takes intersection, removal repeated data according to specified conditions
Item processing, obtains the characteristic parameter.
The embodiment of the present invention also provides a kind of stability maintenance early warning system, comprising:
Data acquisition module, for acquiring the essential information data for relating to steady personnel；
Characteristic extracting module described relates to steady personnel's for extracting based on the essential information data for relating to steady personnel
Characteristic；
Analysis and processing module carries out data pass for relating to the essential information data and characteristic of steady personnel according to
Connection collision obtains the characteristic parameter for relating to steady personnel, is handled to obtain feature vector to the characteristic parameter, by the spy
Sign vector input is used to predict to relate to the training pattern that steady personnel participate in relating to steady behavior, is predicted after training pattern processing
The prediction probability for relating to steady personnel and participating in relating to steady behavior.
Optionally, the system also includes:
List determining module, for determining that the prediction probability relates to steady threshold value greater than preset according to the prediction probability
Emphasis relate to steady staff list.
Optionally, the system also includes:
Data acquisition module contacts the non-of relationship and relates to steady personal information for acquiring to relate to steady personnel with the emphasis and have,
And the emphasis relate to steady personnel and it is described it is non-relate to steady personnel contact the frequency；
Atlas analysis module, for relating to the essential information data of steady personnel according to the emphasis, described non-relating to steady personnel letter
Breath, the emphasis relate to steady personnel and it is described it is non-relate to steady personnel contact the frequency, determine that the emphasis relates to steady personnel and non-relates to described
The intimate value of steady personnel draws the relation map that the emphasis relates to steady personnel according to the intimate value.
Optionally, the essential information data include identity information data, traffic information data, location information data, go through
History relates to steady data, family information data, and the characteristic parameter includes the identity information data, historical traffic information data, close
Phase location information data, the history relate to steady data.
The embodiment of the present invention also provides a kind of electronic equipment, including memory, processor and storage are on a memory and can
The computer program run on a processor, the processor realize the method when executing described program.
From the above it can be seen that stability maintenance method for early warning provided by the invention and system, electronic equipment, are related to by acquisition
The every terms of information of steady personnel carries out feature extraction to the every terms of information for relating to steady personnel, obtains that relate to steady behavior with participation related
Characteristic is handled according to characteristic using training pattern, is obtained relating to steady personnel and is possible to participate in relating to the pre- of steady behavior
Probability value is surveyed, according to the probability value of prediction, determines that the emphasis for being possible to participate in relating to steady behavior relates to steady staff list.The present invention utilizes
The technologies such as big data analysis, artificial intelligence carry out the prediction for relating to steady personnel's unusual fluctuation, prediction knot in conjunction with stability maintenance application demand scene
Fruit is scientific, accurate, can accomplish that control in advance and early warning, reduction relate to steady events incidence, maintain social stability；Applied to existing
Stability maintenance work in, the workload of stability maintenance case analysis artificial treatment can be reduced, stability maintenance fistulae problem is avoided, greatly reduce police
The working strength of business personnel and working time improve stability maintenance working efficiency.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is the method flow schematic diagram of the embodiment of the present invention；
Fig. 2 is the system composed structure schematic diagram of the embodiment of the present invention；
Fig. 3 is the relation map schematic diagram of the embodiment of the present invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with specific embodiment, and reference
Attached drawing, the present invention is described in more detail.
It should be noted that all statements for using " first " and " second " are for differentiation two in the embodiment of the present invention
The non-equal entity of a same names or non-equal parameter, it is seen that " first " " second " only for the convenience of statement, does not answer
It is interpreted as the restriction to the embodiment of the present invention, subsequent embodiment no longer illustrates this one by one.
Fig. 1 is the method flow schematic diagram of the embodiment of the present invention.As shown, stability maintenance early warning provided in an embodiment of the present invention
Method, comprising:
S10: acquisition relates to the essential information data of steady personnel；
In the embodiment of the present invention, it can be acquired from the public security data source such as public security system database and data form and relate to steady people
The essential information data of member.Essential information data include identity information data, traffic information data, location information data, history
Relate to steady data, family information data etc..As shown in table 1, identity information data includes name, passport NO., gender, cultural journey
Data item and the picture datas such as degree, income situation, symbolic animal of the birth year, home address.Wherein, to simplify data mode, reducing memory space,
Partial data item can be encoded, for example, male is encoded to 1, women is encoded to 0, for symbolic animal of the birth year for gender data item
Data item is separately encoded according to 12 symbolic animal of the birth year sequence as 01-12.
1 identity information data example of table
As shown in Table 2-4, traffic information data includes civil aviaton's data, rds data, passenger traffic data.Wherein, civil aviaton's data
Including data item such as name, passport NO., the departure time, departure location, landing time, landing places；Rds data includes surname
Name, passport NO., the data item such as time, starting point, time getting off, terminal of driving；Passenger traffic data include name, passport NO., open
The data item such as vehicle time, starting point, time getting off, terminal.
2 civil aviaton's data instance of table
3 rds data example of table
4 passenger traffic data instance of table
As illustrated in tables 5-7, location information data includes fence data, Internet bar's Internet data, lodging data.Wherein,
Fence data include time, ownership place, operator, international mobile subscriber identity (IMSI), longitude, latitude, website etc.
Data item；Internet bar's Internet data includes name, passport NO., on-line time, downtime, Internet bar's code, Internet bar address, terminal
The data item such as number；Lodging data include name, passport NO., the number such as move in time, check-out time, hotel's code, room number
According to item.
5 fence data instance of table
6 Internet bar's Internet data example of table
7 lodging data instance of table
It includes that visit number, history complain to the higher authorities about an injustice and request fair settlement place, acquisition relates to the data item such as steady clue time in history that history, which relates to steady data,.Family
Front yard information data includes the data item such as same family personnel name, kinsfolk's information.
S11: storage relates to the essential information data of steady personnel；
S12: based on the essential information data for relating to steady personnel, the characteristic for relating to steady personnel is extracted；
According to the essential information data for relating to steady personnel, the characteristic for relating to steady personnel is therefrom extracted.As shown in table 8, special
Sign data include person appealing for help's name, with family personnel's name, person appealing for help's age bracket, income situation, kinsfolk's information, acquisition
Relate to the data item such as steady clue time.
8 characteristic example of table
In the embodiment of the present invention, is screened from non-structured essential information data using regular expression and extract structure
The characteristic of change.
S13: according to the essential information data and characteristic for relating to steady personnel, data correlation collision is carried out, obtains relating to steady people
The characteristic parameter of member；
In conjunction with steady business needs are related to, according to the essential information data and characteristic for relating to steady personnel, carries out data correlation and touch
It hits, obtains the characteristic parameter for relating to steady personnel.Characteristic parameter includes identity information data, historical traffic information data, recent location
Information data, history relate to steady data etc..As shown in table 9, in a specific embodiment, according to the essential information data for relating to steady personnel
With characteristic, the data handling procedures such as union, intersection, removal repeated data are carried out to data, i.e., to the identity for relating to steady personnel
Information data, traffic information data, location information data, history relate to steady data, family information data, characteristic and are closed
And the processing such as intersection, removal repeated data item are taken according to specified conditions, obtain the characteristic parameter for relating to steady personnel.
9 characteristic parameter example of table
S14: according to the characteristic parameter for relating to steady personnel, characteristic parameter is handled to obtain feature vector, by feature vector
Input training pattern is handled, and is obtained prediction and is related to the prediction probability that steady personnel participate in relating to steady behavior；
In the embodiment of the present invention, by machine learning algorithm training prediction model, generation can be according to the spy for relating to steady personnel
Sign vector forecasting, which relates to steady personnel, may participate in relating to the probability value of steady behavior.The characteristic parameter for relating to steady personnel is handled, is generated
Feature vector suitable for prediction model processing.In the embodiment of the present invention, using Xgboost classifier, joined according to the feature of input
Number prediction relates to steady personnel within a period of time may participate in relating to the prediction probability of steady behavior, and prediction result is as shown in table 10,
In, predicted value 1, probability 0.812865 indicates that the probability complained to the higher authorities about an injustice and request fair settlement is 0.812865；Predicted value is 0, and probability is
0.613274, indicate that the probability that do not complain to the higher authorities about an injustice and request fair settlement is 0.613274.
10 prediction result example of table
S15: relating to steady personnel and may participate in relating to the prediction probability of steady behavior according to what is obtained, determine prediction probability be greater than relate to it is steady
The emphasis of threshold value relates to steady staff list.
It relates to steady personnel according to what is obtained and may participate in relating to the prediction probability of steady behavior, include all according to descending arrangement output
The list of steady personnel is related to, setting relates to steady threshold value, determines that prediction probability is greater than the emphasis for relating to steady threshold value in the list of Cong Shewen personnel
Relate to steady staff list.
S16: relating to the essential information data of steady personnel according to emphasis, obtains and relates to steady personnel with contacting the non-of relationship with emphasis
Relate to steady personal information and emphasis relate to steady personnel and it is non-relate to steady personnel contact the frequency, determine that emphasis relates to steady personnel and non-relates to steady people
The intimate value of member draws the relation map that emphasis relates to steady personnel.
The essential information data that steady personnel are related to according to emphasis are come from the public security data such as public security system database and data form
It is obtained in source and relates to steady personnel with emphasis there is the non-of the relationship that contacts to relate to steady personal information and emphasis relates to steady personnel and non-relates to steady personnel
The connection frequency, determine that emphasis relates to steady personnel and the non-intimate value for relating to steady personnel, drawn according to intimate value and relate to steady people including emphasis
Member and the non-relation map (as shown in Figure 3) for relating to steady personnel with it with the relationship that contacts.As shown in table 11, non-to relate to steady personnel's letter
Breath include it is non-relate to steady personnel's name, it includes common number by train, altogether that emphasis, which relates to steady personnel and the non-frequency that contacts for relating to steady personnel,
With data item such as lodging number, common online numbers, emphasis is calculated according to the connection frequency and relates to steady personnel and non-relates to steady personnel
Intimate value.
| It is non-to relate to steady personnel's name | Common number by train | Common lodging number | Common online number | Intimate value |
| Lu * * | 5 | 3 | 0 | 8 |
| King * * | 6 | 7 | 1 | 14 |
11 emphasis of table relate to steady personnel and it is non-relate to steady personnel contact the frequency and intimate value example
Fig. 2 is the system composed structure schematic diagram of the embodiment of the present invention.As shown, stability maintenance provided in an embodiment of the present invention
Early warning system, comprising:
Data acquisition module, for acquiring the essential information data for relating to steady personnel；
In the embodiment of the present invention, it can be acquired from the public security data source such as public security system database and data form and relate to steady people
The essential information data of member.Essential information data include identity information data, traffic information data, location information data, history
Relate to steady data, family information data etc..
Data memory module, for storing the essential information data for relating to steady personnel；
For mass data, data storage server storage can be used to relate to the various numbers such as essential information data of steady personnel
According to.
Characteristic extracting module, for extracting the characteristic for relating to steady personnel based on the essential information data for relating to steady personnel；
Optionally, characteristic may include person appealing for help's name, with family personnel's name, person appealing for help's age bracket, income
Situation, kinsfolk's information, acquisition relate to the data item such as steady clue time.
Analysis and processing module, for carrying out data correlation and touching according to the essential information data and characteristic for relating to steady personnel
Hit, obtain the characteristic parameter for relating to steady personnel, according to the characteristic parameter for relating to steady personnel, to characteristic parameter handled to obtain feature to
Amount handles feature vector input training pattern, obtains prediction and relates to the prediction probability that steady personnel participate in relating to steady behavior；
In the embodiment of the present invention, by machine learning algorithm training prediction model, generation can be according to the spy for relating to steady personnel
Sign vector forecasting, which relates to steady personnel, may participate in relating to the probability value of steady behavior.The characteristic parameter for relating to steady personnel is handled, is generated
Feature vector suitable for prediction model processing.In the embodiment of the present invention, using Xgboost classifier, joined according to the feature of input
Number prediction relates to steady personnel within a period of time may participate in relating to the probability of steady behavior.
List determining module determines pre- for relating to steady personnel according to what is obtained and may participate in relating to the prediction probability of steady behavior
It surveys probability and relates to steady staff list greater than the emphasis for relating to steady threshold value.
It relates to steady personnel according to what analysis and processing module was handled and may participate in relating to the prediction probability of steady behavior, according to descending
Arrangement output includes all lists for relating to steady personnel, and setting relates to steady threshold value, determines that prediction probability is big in the list of Cong Shewen personnel
Steady staff list is related in the emphasis for relating to steady threshold value.
In the embodiment of the present invention, the stability maintenance early warning system further include:
Data acquisition module contacts the non-of relationship and relates to steady personal information for acquiring to relate to steady personnel with emphasis and have, and again
Point relate to steady personnel and it is non-relate to steady personnel contact the frequency；
Atlas analysis module, for related to according to emphasis steady personnel essential information data, it is non-relate to steady personal information, emphasis relates to
Steady personnel and it is non-relate to steady personnel contact the frequency, determine that emphasis relates to steady personnel and the non-intimate value for relating to steady personnel, according to being intimately worth
Draw the relation map that emphasis relates to steady personnel.
Stability maintenance method for early warning of the invention and system relate to steady people by obtaining from public security system database, data table items
The every terms of information of member carries out feature extraction to the every terms of information for relating to steady personnel, obtains and relate to the related feature of steady behavior with participation
Data are handled according to characteristic using training pattern, and it is general to obtain relating to the prediction that steady personnel are possible to participate in relating to steady behavior
Rate value determines that the emphasis for being possible to participate in relating to steady behavior relates to steady staff list according to the probability value of prediction；Further, it can adopt
Collection obtains and relates to steady personnel and be related the non-information for relating to steady personnel of relationship, and analysis, which determines, non-relates to steady personnel and emphasis relates to steady personnel
Intimate value, draw and non-relate to steady personnel and emphasis relates to the relation map of steady personnel.
Based on above-mentioned purpose, the embodiment of the present invention also proposed one of a kind of device for executing the stability maintenance method for early warning
Embodiment.Described device includes:
One or more processors and memory.
The device for executing the stability maintenance method for early warning can also include: input unit and output device.
Processor, memory, input unit and output device can be connected by bus or other modes.
Memory as a kind of non-volatile computer readable storage medium storing program for executing, can be used for storing non-volatile software program,
Non-volatile computer executable program and module, as the corresponding program of stability maintenance method for early warning in the embodiment of the present invention refers to
Order/module (for example, analysis and processing module shown in Fig. 2).Processor is by running non-volatile software stored in memory
Program, instruction and module, thereby executing the various function application and data processing of server, i.e. the realization above method is implemented
The stability maintenance method for early warning of example.
Memory may include storing program area and storage data area, wherein storing program area can storage program area, extremely
Application program required for a few function；Storage data area can be stored to be made according to the device for executing the stability maintenance method for early warning
With the data etc. created.In addition, memory may include high-speed random access memory, it can also include non-volatile memories
Device, for example, at least a disk memory, flush memory device or other non-volatile solid state memory parts.In some embodiments
In, optional memory includes the memory remotely located relative to processor, these remote memories can pass through network connection
To member user's behavior monitoring device.The example of above-mentioned network includes but is not limited to internet, intranet, local area network, shifting
Dynamic communication network and combinations thereof.
Input unit can receive the number or character information of input, and generate and the device of execution stability maintenance method for early warning
User setting and the related key signals input of function control.Output device may include that display screen etc. shows equipment.
One or more of module storages in the memory, are executed when by one or more of processors
When, execute the stability maintenance method for early warning in above-mentioned any means embodiment.The reality of the device for executing the stability maintenance method for early warning
Example is applied, technical effect is same or similar with aforementioned any means embodiment.
The embodiment of the invention also provides a kind of non-transient computer storage medium, the computer storage medium is stored with
The place of the operation of the list items in above-mentioned any means embodiment can be performed in computer executable instructions, the computer executable instructions
Reason method.The embodiment of the non-transient computer storage medium, technical effect it is identical as aforementioned any means embodiment or
Person is similar.
Finally, it should be noted that those of ordinary skill in the art will appreciate that realizing the whole in above-described embodiment method
Or part process, it is that related hardware can be instructed to complete by computer program, the program can be stored in a calculating
In machine read/write memory medium, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, described
Storage medium can be magnetic disk, CD, read-only memory (Read-Only Memory, ROM) or random access memory
(Random Access Memory, RAM) etc..The embodiment of the computer program, technical effect and aforementioned any means
Embodiment is same or similar.
In addition, typically, device described in the disclosure, equipment etc. can be various electric terminal equipments, such as mobile phone, individual
Digital assistants (PDA), tablet computer (PAD), smart television etc. are also possible to large-scale terminal device, such as server, therefore this
Disclosed protection scope should not limit as certain certain types of device, equipment.Client described in the disclosure can be with electricity
The combining form of sub- hardware, computer software or both is applied in any one of the above electric terminal equipment.
In addition, being also implemented as the computer program executed by CPU, the computer program according to disclosed method
It may be stored in a computer readable storage medium.When the computer program is executed by CPU, executes and limited in disclosed method
Fixed above-mentioned function.
In addition, above method step and system unit also can use controller and for storing so that controller is real
The computer readable storage medium of the computer program of existing above-mentioned steps or Elementary Function is realized.
In addition, it should be appreciated that computer readable storage medium (for example, memory) as described herein can be it is volatile
Property memory or nonvolatile memory, or may include both volatile memory and nonvolatile memory.As example
And not restrictive, nonvolatile memory may include read-only memory (ROM), programming ROM (PROM), electrically programmable to son
ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory.Volatile memory may include arbitrary access
Memory (RAM), the RAM can serve as external cache.As an example and not restrictive, RAM can be with more
Kind form obtains, such as synchronous random access memory (DRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate SDRAM
(DDR SDRAM), enhancing SDRAM (ESDRAM), synchronization link DRAM (SLDRAM) and directly RambusRAM (DRRAM).Institute
The storage equipment of disclosed aspect is intended to the memory of including but not limited to these and other suitable type.
The device of above-described embodiment for realizing method corresponding in previous embodiment there is corresponding method to implement
The beneficial effect of example, details are not described herein.
It should be understood by those ordinary skilled in the art that: the discussion of any of the above embodiment is exemplary only, not
It is intended to imply that the scope of the present disclosure (including claim) is limited to these examples；Under thinking of the invention, above embodiments
Or can also be combined between the technical characteristic in different embodiments, step can be realized with random order, and be existed such as
Many other variations of the upper different aspect of the invention, for simplicity, they are not provided in details.
In addition, to simplify explanation and discussing, and in order not to obscure the invention, it can in provided attached drawing
It is connect with showing or can not show with the well known power ground of integrated circuit (IC) chip and other components.Furthermore, it is possible to
Device is shown in block diagram form, to avoid obscuring the invention, and this has also contemplated following facts, i.e., about this
The details of the embodiment of a little block diagram arrangements be height depend on will implementing platform of the invention (that is, these details should
It is completely within the scope of the understanding of those skilled in the art).Elaborating that detail (for example, circuit) is of the invention to describe
In the case where exemplary embodiment, it will be apparent to those skilled in the art that can be in these no details
In the case where or implement the present invention in the case that these details change.Therefore, these descriptions should be considered as explanation
Property rather than it is restrictive.
Although having been incorporated with specific embodiments of the present invention, invention has been described, according to retouching for front
It states, many replacements of these embodiments, modifications and variations will be apparent for those of ordinary skills.Example
Such as, discussed embodiment can be used in other memory architectures (for example, dynamic ram (DRAM)).
The embodiment of the present invention be intended to cover fall into all such replacements within the broad range of appended claims,
Modifications and variations.Therefore, all within the spirits and principles of the present invention, any omission, modification, equivalent replacement, the improvement made
Deng should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of stability maintenance method for early warning characterized by comprising
Acquisition relates to the essential information data of steady personnel；
Based on the essential information data for relating to steady personnel, the characteristic for relating to steady personnel is extracted；
According to the essential information data and characteristic for relating to steady personnel, data correlation collision is carried out, obtains described relating to steady people
The characteristic parameter of member；
According to the characteristic parameter for relating to steady personnel, the characteristic parameter is handled to obtain feature vector, by the feature
Vector input is used to predict to relate to the training pattern that steady personnel participate in relating to steady behavior, and prediction institute is obtained after training pattern processing
It states and relates to the prediction probability that steady personnel participate in relating to steady behavior.
2. the method according to claim 1, wherein further include:
According to the prediction probability, determine that the prediction probability is greater than the preset emphasis for relating to steady threshold value and relates to steady staff list.
3. according to the method described in claim 2, it is characterized by further comprising: relating to the basic letter of steady personnel according to the emphasis
Data are ceased, acquisition relates to steady personnel with the emphasis, and there is the non-of the relationship that contacts to relate to steady personal information and the emphasis relates to steady personnel
With it is described it is non-relate to steady personnel contact the frequency, determine that the emphasis relates to steady personnel and the non-intimate value for relating to steady personnel, according to
The intimate value draws the relation map that the emphasis relates to steady personnel.
4. the method according to claim 1, wherein the essential information data include identity information data, hand over
Logical information data, location information data, history relate to steady data, family information data, and the characteristic parameter includes the identity letter
Breath data, historical traffic information data, recent location information data, the history relate to steady data.
5. according to the method described in claim 4, it is characterized in that, to the identity information data for relating to steady personnel, friendship
Logical information data, location information data, history relate to steady data, family information data, the characteristic and merge, according to spy
Fixed condition takes intersection, removal repeated data item processing, obtains the characteristic parameter.
6. a kind of stability maintenance early warning system characterized by comprising
Data acquisition module, for acquiring the essential information data for relating to steady personnel；
Characteristic extracting module, for based on the essential information data for relating to steady personnel, extracting the feature for relating to steady personnel
Data；
Analysis and processing module carries out data correlation and touches for relating to the essential information data and characteristic of steady personnel according to
Hit, obtain the characteristic parameter for relating to steady personnel, the characteristic parameter is handled to obtain feature vector, by the feature to
Amount input is used to predict to relate to the training pattern that steady personnel participate in relating to steady behavior, obtains described in prediction after training pattern processing
Relate to the prediction probability that steady personnel participate in relating to steady behavior.
7. system according to claim 6, which is characterized in that further include:
List determining module, for determining that the prediction probability is greater than the preset weight for relating to steady threshold value according to the prediction probability
Point relates to steady staff list.
8. system according to claim 7, which is characterized in that further include:
Data acquisition module contacts the non-of relationship and relates to steady personal information and institute for acquiring to relate to steady personnel with the emphasis and have
State emphasis relate to steady personnel and it is described it is non-relate to steady personnel contact the frequency；
Atlas analysis module, for relating to the essential information data of steady personnel according to the emphasis, described non-relating to steady personal information, institute
State emphasis relate to steady personnel and it is described it is non-relate to steady personnel contact the frequency, determine that the emphasis relates to steady personnel and described non-relates to steady personnel
Intimate value, the relation map that the emphasis relates to steady personnel is drawn according to the intimate value.
9. system according to claim 6, which is characterized in that the essential information data include identity information data, hand over
Logical information data, location information data, history relate to steady data, family information data, and the characteristic parameter includes the identity letter
Breath data, historical traffic information data, recent location information data, the history relate to steady data.
10. a kind of electronic equipment including memory, processor and stores the calculating that can be run on a memory and on a processor
Machine program, which is characterized in that the processor realizes the side as described in claim 1 to 5 any one when executing described program
Method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811381093.1A CN109492821A (en) | 2018-11-20 | 2018-11-20 | A kind of stability maintenance method for early warning and system, electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811381093.1A CN109492821A (en) | 2018-11-20 | 2018-11-20 | A kind of stability maintenance method for early warning and system, electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109492821A true CN109492821A (en) | 2019-03-19 |
Family
ID=65696319
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811381093.1A Pending CN109492821A (en) | 2018-11-20 | 2018-11-20 | A kind of stability maintenance method for early warning and system, electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109492821A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110322049A (en) * | 2019-06-03 | 2019-10-11 | 浙江图灵软件技术有限公司 | A kind of public security big data method for early warning |
| CN110688469A (en) * | 2019-09-27 | 2020-01-14 | 厦门市美亚柏科信息股份有限公司 | Method and device for automatically analyzing similar behavior characteristics |
| CN112132357A (en) * | 2020-09-29 | 2020-12-25 | 佳都新太科技股份有限公司 | Behavior prediction method, behavior prediction device, behavior prediction equipment and storage medium based on big data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678428A (en) * | 2016-01-28 | 2016-06-15 | 温州职业技术学院 | Criminal suspicion probability prediction method and system |
| CN106101359A (en) * | 2016-06-06 | 2016-11-09 | 北京奇虎科技有限公司 | The display packing of address list information and device |
| CN106096623A (en) * | 2016-05-25 | 2016-11-09 | 中山大学 | A kind of crime identifies and Forecasting Methodology |
| CN106204292A (en) * | 2016-06-28 | 2016-12-07 | 合肥讯飞数码科技有限公司 | The methods of exhibiting of a kind of topological relation and device |
| CN108596386A (en) * | 2018-04-20 | 2018-09-28 | 上海市司法局 | A kind of prediction convict repeats the method and system of crime probability |
-
2018
- 2018-11-20 CN CN201811381093.1A patent/CN109492821A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678428A (en) * | 2016-01-28 | 2016-06-15 | 温州职业技术学院 | Criminal suspicion probability prediction method and system |
| CN106096623A (en) * | 2016-05-25 | 2016-11-09 | 中山大学 | A kind of crime identifies and Forecasting Methodology |
| CN106101359A (en) * | 2016-06-06 | 2016-11-09 | 北京奇虎科技有限公司 | The display packing of address list information and device |
| CN106204292A (en) * | 2016-06-28 | 2016-12-07 | 合肥讯飞数码科技有限公司 | The methods of exhibiting of a kind of topological relation and device |
| CN108596386A (en) * | 2018-04-20 | 2018-09-28 | 上海市司法局 | A kind of prediction convict repeats the method and system of crime probability |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110322049A (en) * | 2019-06-03 | 2019-10-11 | 浙江图灵软件技术有限公司 | A kind of public security big data method for early warning |
| CN110688469A (en) * | 2019-09-27 | 2020-01-14 | 厦门市美亚柏科信息股份有限公司 | Method and device for automatically analyzing similar behavior characteristics |
| CN112132357A (en) * | 2020-09-29 | 2020-12-25 | 佳都新太科技股份有限公司 | Behavior prediction method, behavior prediction device, behavior prediction equipment and storage medium based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102635987B1 (en) | Method, apparatus, device and storage medium for training an image semantic segmentation network | |
| CN112329659B (en) | Weak supervision semantic segmentation method based on vehicle image and related equipment thereof | |
| CN109493199A (en) | Products Show method, apparatus, computer equipment and storage medium | |
| CN109492821A (en) | A kind of stability maintenance method for early warning and system, electronic equipment | |
| CN109345130A (en) | Method, apparatus, computer equipment and the storage medium of Market Site Selection | |
| CN103635905A (en) | Device for providing information, method for providing information, program for providing information, and computer-readable recording medium storing program for same | |
| CN107609563A (en) | Picture semantic describes method and device | |
| CN105468671A (en) | Method for realizing personnel relationship modeling | |
| US10607160B2 (en) | Unique accommodation search improvement founded in listing booking conversion | |
| CN109598430A (en) | Distribution range generation method, distribution range generation device, electronic equipment and storage medium | |
| CN108073645A (en) | A kind of job-hunter of recruitment platform recommends page display method and device | |
| CN112651342A (en) | Face recognition method and device, electronic equipment and storage medium | |
| CN112837142A (en) | Financial risk model training method and device | |
| CN116011322A (en) | Urban information display method, device, equipment and medium based on digital twinning | |
| CN117114514A (en) | Talent information analysis management method, system and device based on big data | |
| Rangrej et al. | Consistency driven sequential transformers attention model for partially observable scenes | |
| CN110807463A (en) | Image segmentation method and device, computer equipment and storage medium | |
| CN114090898A (en) | Information recommendation method and device, terminal equipment and medium | |
| CN114780735B (en) | Policy matching method, system and readable storage medium based on data analysis | |
| Nguyen et al. | Fusion schemes for image-to-video person re-identification | |
| CN110942056A (en) | Clothing key point positioning method and device, electronic equipment and medium | |
| CN114862173A (en) | Shop data processing method and device, computer equipment and storage medium | |
| Ristea et al. | Opinion mining from Twitter and spatial crime distribution for hockey events in Vancouver | |
| CN114880296B (en) | Storage method and system suitable for big data of park | |
| Gacharich | Deep learning for crime prediction |
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 | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 1002-1, 10th floor, No.56, Beisihuan West Road, Haidian District, Beijing 100080 Applicant after: Ronglian Technology Group Co., Ltd Address before: 100080, Beijing, Haidian District, No. 56 West Fourth Ring Road, glorious Times Building, 10, 1002-1 Applicant before: UNITED ELECTRONICS Co.,Ltd. |
|
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190319 |