CN106446941A - Unconventional emergency dynamic priority method based on model matching - Google Patents
Unconventional emergency dynamic priority method based on model matching Download PDFInfo
- Publication number
- CN106446941A CN106446941A CN201610826499.0A CN201610826499A CN106446941A CN 106446941 A CN106446941 A CN 106446941A CN 201610826499 A CN201610826499 A CN 201610826499A CN 106446941 A CN106446941 A CN 106446941A
- Authority
- CN
- China
- Prior art keywords
- key message
- data
- weight
- priority
- key information
- 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.)
- Granted
Links
Classifications
-
- 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
- G06F18/211—Selection of the most significant subset of features
- G06F18/2115—Selection of the most significant subset of features by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
Abstract
The invention discloses an unconventional emergency dynamic priority method based on model matching, and the method comprises the steps: carrying out the matching of real-time processing data flows through a set which comprises various key information rules of key information model identification key information, and filtering the key information meeting the rules through matching; assigning different values to different key information because different types of key information are different in importance in the system, wherein the size of the assigned values represents the importance of the key information in the system; calculating the frequencies of the key information, and the number of occurrence times of the key information in a time unit, i.e., the occurrence frequency of each type of key information when the key information rules are in a key information time window; calculating the priority of the key information, the arithmetic mean value of the frequencies of the key information and the arithmetic mean value of weight products, wherein the frequencies and weights of the key information decide the importance of the key information in the system.
Description
Technical field
The present invention relates to a kind of unconventional accident dynamic priority method based on Model Matching, belong to data processing skill
Art field.
Background technology
Natural Science Fund In The Light committee of China started in 2008 and implements " unconventional sudden incidents report research " weight
Since big project, numerous scholars is studied to unconventional accident with scientific worker.
Unconventional accident identification and the important step, scholar and the technical work that are the discovery that unconventional accident research
Person passes through continuous research, obtains many progress in terms of unconventional accident identification with discovery.
By the effort of numerous scholars and scientific worker, in terms of unconventional accident research, achieve many
Achievement.In existing achievement in research, unconventional accident recognition methodss have a common trait with the research of model aspect,
The focus point being exactly these achievements in research is algorithm and model itself, how existing achievement in research is efficiently applied to believe online
Breath is processed, and corresponding research has also lacked.
Content of the invention
Goal of the invention：For problems of the prior art, the present invention calls pass the data of unconventional accident
Key data, critical data correspond to key message, emphasis process around unconventional accident online information in data flow knowledge
Not, the frequency that data weighting, data occur and priority carry out analysis, research, and proposition is a kind of based on Model Matching very
Rule accident dynamic priority method, using method proposed by the present invention, is that unconventional accident is applied at online information
Reason provides a kind of alternative thinking and scheme.
Technical scheme：A kind of unconventional accident dynamic priority method based on Model Matching, knows including critical data
Not, the process that key message weight calculation, the calculating of key message frequency and key message priority calculate.
Critical data identification process
Key message model KMODEL：The set of the various key message rules of mark key message, to real-time processing number
Mated according to stream, filtered out the key message meeting rule by coupling.
KMODEL=M (KRULE)=x | x ∈ KRULE } (4)
Real-time stream is mated by key message model, is calculated and identification, mistake by key message logic rule
Leach the data acquisition system meeting key message rule.
Key message model is a dynamic model, and in different system, the recognition rule of key message is different, passes through
Model configuration file record, description key message rule, build key message model.
Before identification critical data, first key message model configuration file is loaded and parsed, obtain and close
Key information model data MS, key message model data is a key message regular collection, a key message rule parsing
Result corresponds to a key message mark.
Key message weight
Key message weight：Different types of key message, significance level in systems is different, to different crucial letters
Breath gives different numerical value, represents key message significance level in systems by the size giving numerical value.
Initial data is mated through key message model, identifies key message, and key message is mapped to be had
The interval of different weights.
Key message weight dynamic change, in different system, different business, the weighted value of key message is different, leads to
Cross the relation of key message weight configuration file record, description key message data weighting and key message.
Before identification critical data, load weight configuration file, weight configuration file is parsed, calculate weight mark
Will, obtains weight mark set WS, and WS is put into internal memory.Implement step as follows：
1) load weight configuration file；
2) parsing is entered to weight configuration file；
3) calculate weight mark；
4) weight mark puts into internal memory list.
A kind of weight type corresponds to a weight mark, represents different weight marks by different numerical value (weighted value), leads to
Cross the weight type that different weighted values represent different key messages, the relationship of the two corresponds.
Weight mark configuration file and key message configuration file are associated by key message rule flag.
In conjunction with MS and WS, the data flow after original data stream, process is identified, recognition result passes through 1,0 two-value table
Show, 1 expression identification data belongs to key message, 0 represents that identification data is non-critical information, if data meets key message rule
Then, then the data being identified is key message, is not otherwise, algorithm such as (5)：
Wherein：
KSnIt is that key message rule starts in current time in recognition cycle, every class key message that system identification goes out
Number of times set, DATA is identified data, and MS is model data.
Key message frequency
Key message frequency：The number of times occurring in the key message unit interval.I.e. key message rule is in key message
Between in window, every class key message frequency of occurrences.Frequency formula such as (6):
Pre=FFrequency (KSn, t)=KSn/t (6)
Wherein：
KSnThe set of the number of times that every class key message identifies, t be key message rule recognition cycle start to
The time span of current time.
Key message priority
Key message priority：Arithmetic average, key message frequency and weight that key message frequency is amassed with weight, certainly
Determine key message significance level in systems.
In a time slice, change over time, the continual analysis of mass data, the priority of key message is not
Break and change, Mobile state adjustment is entered by the key message priority that visualization is shown, real-time, preferential display priority is high
Data, by being averaged with the product of weight to the frequency of key message, average is big, and priority is high, and priority data calculates
It is a lasting process, priority dynamically adjusts formula such as (7)：
Wherein：
Pre is the frequency of key message, is weight initial value, and WS is weight mark.
In real-time analyzer, in real time, continually enter system, Pri value is big, then key message is excellent for mass data
First level is higher, and data is more crucial.
Brief description
Fig. 1 is key message rule and key message relationship model figure；
Fig. 2 is model, key message is regular and key message rule flag；
Fig. 3 maps for key message to weight；
Fig. 4 identifies internal memory logic chart for key message；
Fig. 5 is clock memory graph of a relation；
Fig. 6 is key message priority forming process relationship memory figure；
Fig. 7 is threshold method result figure；
Fig. 8 is key message frequency；
Fig. 9 is key message priority.
Specific embodiment
With reference to specific embodiment, it is further elucidated with the present invention it should be understood that these embodiments are merely to illustrate the present invention
Rather than restriction the scope of the present invention, after having read the present invention, the various equivalences to the present invention for the those skilled in the art
The modification of form all falls within the application claims limited range.
A kind of unconventional accident dynamic priority method based on Model Matching, abbreviation Model Matching dynamic priority algorithm
(PMADP), calculate and key message priority meter including critical data identification, key message weight calculation, key message frequency
The process calculated.Wherein critical data identification link, existing algorithm and model can be applied to this as data recognition rule
Bright method.
Critical data identification process
Key message time window：Key message starts to calculate to key message the time interval being treated to end point, this
It it is the vital stage of key message, key message is once processed (carry out identification data using key message recognition rule to obtain respectively
Class key message, calculates the frequency of every class key message by key message frequency computing formula, designs every class key message
Weight, by critical data weight, calculates the priority that the frequency obtaining to calculate every class key message.) after, the mark of key message
Note will reset to original state.
Key message rule KRULE：The logical relation of mark key message, logical relation contains number in real-time stream
According to calculated relationship, incidence relation, judgment criterion, algorithm and the computation model with independent function.Logical relation can be divided into：
Simple logic relation：The logical relation of only single logical operationss relation.
DATA=data | data ∈ R } (1)
In formula (1), R is a kind of simple relation, and DATA is all data acquisition systems meeting certain relation R, and formula (1) represents full
All data acquisition systems of sufficient relation R.
The production logic of formula (1) is represented by：
R—>DATA IF R THEN DATA (2)
Wherein：R is unity logic relation, and DATA is the data meeting R relation.
Compound logic relation：Passed through by multiple simple logic operation relations or and, the complex relation that constitutes such as XOR.
In formula (3), R1, R2, R3, R4 are a kind of simple relations, and DATAC is all data acquisition systems meeting these relations,
Formula (3) represents all and meets relation R1, R2, all data acquisition systems of the various combination of R3, R4.
Key message model KMODEL：The set of the various key message rules of mark key message, to real-time processing number
Mated according to stream, filtered out the key message meeting rule by coupling.
KMODEL=x | x ∈ KRULE } (4)
Key message rule is a point total relation with key message model, the relation of key message rule and key message model
As Fig. 1.Real-time stream is mated by key message model, is calculated and identification by key message logic rule, filters
Go out to meet the data acquisition system of key message rule.
Key message model is a dynamic model, and in different system, the recognition rule of key message is different, passes through
Model configuration file record, description key message rule, build key message model.
Before identification critical data, first key message model configuration file is loaded and parsed, obtain and close
Key information model data MS, key message model data is a key message regular collection, a key message rule parsing
Result corresponds to a key message mark, resolution logic such as Fig. 2.
Key message model property：
Key message model is key message regular collection, that is, key message model by numerous key messages rule with
The rule composition of these key message rule association relations is described；
One key message rule corresponds to a key message rule flag, after model configuration file loads, regular and mark
The one-to-one corresponding known is indicated in calculator memory.
Key message weight
Key message weight：Different types of key message, significance level in systems is different, to different crucial letters
Breath gives different numerical value, represents key message significance level in systems by the size giving numerical value.
Initial data is mated through key message model, identifies key message, and key message is mapped to be had
The interval of different weights, such as Fig. 3.
Key message weight dynamic change, in different system, different business, the weighted value of key message is different, leads to
Cross the relation of key message weight configuration file record, description key message data weighting and key message.
Before identification critical data, load weight configuration file, weight configuration file is parsed, calculate weight mark
Will, obtains weight mark set WS, and WS is put into internal memory.Implement step as follows：
1) load weight configuration file；
2) parsing is entered to weight configuration file；
3) calculate weight mark；
4) weight mark puts into internal memory list.
A kind of weight type corresponds to a weight mark, represents different weight marks by different numerical value (weighted value), leads to
Cross the weight type that different weighted values represent different key messages, the relationship of the two corresponds.Key message and weight type, power
Weight values relation such as Fig. 4.
Weight mark configuration file and key message configuration file are associated by key message rule flag.
In conjunction with MS and WS, the data flow after original data stream, process is identified, recognition result passes through 1,0 two-value table
Show, 1 expression identification data belongs to key message, 0 represents that identification data is non-critical information, if data meets key message rule
Then, then the data being identified is key message, is not otherwise, algorithm such as (5)：
Wherein：
KSnIt is that key message rule starts in current time in recognition cycle, every class key message that system identification goes out
Number of times set, DATA is identified data, and MS is model data.Key message identification internal memory logic is shown in Fig. 4.
Key message frequency
Key message frequency：The number of times occurring in the key message unit interval.I.e. key message rule is in key message
Between in window, every class key message frequency of occurrences.Frequency formula such as (6):
Pre=KSn/t (6)
Wherein：
KSnThe set of the number of times that every class key message identifies, t be key message rule recognition cycle start to
The time span of current time, clock memory relation is shown in Fig. 5.
Key message priority
Key message priority：Arithmetic average, key message frequency and weight that key message frequency is amassed with weight, certainly
Determine key message significance level in systems.
In a time slice, change over time, the continual analysis of mass data, the priority of key message is not
Break and change, Mobile state adjustment is entered by the key message priority that visualization is shown, real-time, preferential display priority is high
Data, by being averaged with the product of weight to the frequency of key message, average is big, and priority is high, and priority data calculates
It is a lasting process, priority dynamically adjusts formula such as (7)：
Wherein：
Pre is the frequency of key message, is weight initial value, and WS is weight mark, PreiRepresent i-th key message
Frequency.
In real-time analyzer, in real time, continually enter system, Pri value is big, then key message is excellent for mass data
First level is higher, and data is more crucial, and key message priority forming process relationship memory is shown in Fig. 6.
Experimental analysiss
According to the achievement in Nsfc Major project " unconventional sudden incidents report research "
Statistics, in 2001 to 2010 years especially great natural disasters of China, forest fire tops the list.Threshold method is to grind in warning system
Study carefully, apply one of more method, experiment is compared analysis for references object to this algorithm and threshold method with forest fire.
The process logic such as formula (8) of threshold method
IF R>=X THEN Doing (8)
If R is more than or equal to X, then do logical process.In formula 8, X is the threshold value of setting, and R is initial data or various calculation
The end value that method calculates.
Air humidity, temperature, the hydrocarbonaceous amount of in the air, wind-force, thunder and lightning are to determine the key factor that forest fire occurs.Gloomy
Cigarette in woods, flame are the direct embodiments of forest fires.
Data configuration situation explanation：
Air humidity：It may occur however that fire during less than 61.6%；
Area and temperature coefficient：R=0.367, P>0.01 it may occur however that fire；
The hydrocarbonaceous amount of in the air：During more than 0.2%, easily there is fire；
Wind-force：It may occur however that fire during less than 2 grades；
Thunder and lightning：During more than 50 kilo-ampere, easy initiation fire；
Cigarette：It is fiery point；
Flame：It is fiery point.
Area of woods is big, and temperature, humidity, wind-force, gas concentration, cigarette, fiery data change at any time, and this is accomplished by sensor
Quantity is many, and acquisition time interval is short, therefore contains much information, forest fire is effectively monitored, and application system needs to accomplish：
Alarm interference is few；
The high integration stress of priority, preferential display.
Data type, weight and threshold value relation are shown in Table 1.
Air humidity, area and temperature coefficient, hydrocarbonaceous amount, wind-force, thunder and lightning, cigarette, fiery data are built by analog form.Phase
Hope that experimental data can be tried one's best in the time dimension of 200 minutes and reflect the actual data change situation of a long period, because
This experimental data has larger undulatory property.
Fig. 7 is air humidity, area and temperature coefficient, hydrocarbonaceous amount, wind-force, thunder and lightning, cigarette, the result of fiery threshold method.
According to formula 8, using the identical data of Fig. 7, air humidity, area and temperature coefficient, hydrocarbonaceous amount, wind-force, thunder and lightning,
Cigarette, the frequency of fire are shown in Fig. 8.
According to formula 9, using the identical data of Fig. 7, air humidity, area and temperature coefficient, hydrocarbonaceous amount, wind-force, thunder and lightning,
Cigarette, the priority of fire are shown in Fig. 9.
As seen in Figure 7, using threshold method, alarm interference is many, and key message emphasis does not project.
By Fig. 8,9 as can be seen that using new algorithm, invalid warning information reduces, the information priorities highest of most critical,
It is displayed by priority and pay close attention to.
Table 3.1 data type, weight and threshold value relation
Claims (5)
1. a kind of unconventional accident dynamic priority method based on Model Matching it is characterised in that：Know including critical data
Not, the process that key message weight calculation, the calculating of key message frequency and key message priority calculate；
Critical data identification process
Key message model KMODEL：The set of the various key message rules of mark key message, to real-time processing data stream
Mated, filtered out the key message meeting rule by coupling；
Key message weight
Different types of key message, significance level in systems is different, gives different numerical value to different key messages,
Key message significance level in systems is represented by the size giving numerical value；
Key message frequency
Key message frequency, the number of times occurring in the key message unit interval；I.e. key message rule is in key message time window
Interior, every class key message frequency of occurrences.
Key message priority
Key message priority, arithmetic average, key message frequency and weight that key message frequency is amassed with weight, determine
Key message significance level in systems.
2. the unconventional accident dynamic priority method based on Model Matching as claimed in claim 1 it is characterised in that：?
Before identification critical data, first key message model configuration file is loaded and parsed, obtained key message model
Data MS, key message model data is a key message regular collection, a key message rule parsing result corresponding
Individual key message mark.
3. the unconventional accident dynamic priority method based on Model Matching as claimed in claim 1 it is characterised in that：Former
Beginning data is mated through key message model, identifies key message, and key message is mapped to there is different weights
Interval；Key message weight dynamic change, in different system, different business, the weighted value of key message is different, by closing
The relation of key information weight configuration file record, description key message data weighting and key message.
Before identification critical data, load weight configuration file, weight configuration file is parsed, calculate weight mark, obtain
Obtain weight mark set WS, and WS is put into internal memory.Implement step as follows：
1) load weight configuration file；
2) parsing is entered to weight configuration file；
3) calculate weight mark；
4) weight mark puts into internal memory list.
A kind of weight type corresponds to a weight mark, represents different weight marks by different numerical value (weighted value), by not
Represent the weight type of different key messages with weighted value, the relationship of the two corresponds；
Weight mark configuration file and key message configuration file are associated by key message rule flag；
In conjunction with MS and WS, the data flow after original data stream, process is identified, recognition result is represented by 1,0 two-value, 1
Represent that identification data belongs to key message, 0 represents that identification data is non-critical information, if data meets key message rule,
The data being then identified is key message, is not otherwise, algorithm such as (5)：
Wherein：
KSnIt is that key message rule starts in current time in recognition cycle, the number of times of every class key message that system identification goes out
Set, DATA is identified data, and MS is model data.
4. the unconventional accident dynamic priority method based on Model Matching as claimed in claim 3 it is characterised in that：Frequently
Rate formula such as (6):
Pre=FFrequency (KSn, t)=KSn/t (6)
Wherein：
KSnIt is the set of the number of times that every class key message identifies, t is that key message rule starts to current in recognition cycle
The time span of time.
5. the unconventional accident dynamic priority method based on Model Matching as claimed in claim 4 it is characterised in that：
In a time slice, change over time, the continual analysis of mass data, the priority of key message is constantly sent out
Changing, by entering Mobile state adjustment, real-time, preferential display priority is high number to the key message priority of visualization display
According to by being averaged with the product of weight to the frequency of key message, average is big, and priority is high, and it is one that priority data calculates
Individual lasting process, priority dynamically adjusts formula such as (7)：
Wherein：
Pre is the frequency of key message, is weight initial value, and WS is weight mark；
In real-time analyzer, in real time, continually enter system, Pri value is big, then key message priority for mass data
Higher, data is more crucial.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610826499.0A CN106446941B (en) | 2016-09-14 | 2016-09-14 | Unconventional emergency event dynamic priority method based on Model Matching |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610826499.0A CN106446941B (en) | 2016-09-14 | 2016-09-14 | Unconventional emergency event dynamic priority method based on Model Matching |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106446941A true CN106446941A (en) | 2017-02-22 |
| CN106446941B CN106446941B (en) | 2019-05-24 |
Family
ID=58169018
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610826499.0A Active CN106446941B (en) | 2016-09-14 | 2016-09-14 | Unconventional emergency event dynamic priority method based on Model Matching |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106446941B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018166339A1 (en) * | 2017-03-13 | 2018-09-20 | 中兴通讯股份有限公司 | Information processing method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6356664B1 (en) * | 1999-02-24 | 2002-03-12 | International Business Machines Corporation | Selective reduction of video data using variable sampling rates based on importance within the image |
| US20060080736A1 (en) * | 2004-10-07 | 2006-04-13 | Electronics And Telecommunications Research Institute | Method for analyzing security grade of information property |
| CN101021857A (en) * | 2006-10-20 | 2007-08-22 | 鲍东山 | Video searching system based on content analysis |
| CN102708168A (en) * | 2012-04-27 | 2012-10-03 | 北京邮电大学 | System and method for sorting search results of teaching resources |
| CN103813347A (en) * | 2014-02-28 | 2014-05-21 | 电信科学技术研究院 | Base station frequency resource allocation method and network device |
| CN104125087A (en) * | 2013-04-28 | 2014-10-29 | 中国移动通信集团设计院有限公司 | Alarm information processing method and device |
-
2016
- 2016-09-14 CN CN201610826499.0A patent/CN106446941B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6356664B1 (en) * | 1999-02-24 | 2002-03-12 | International Business Machines Corporation | Selective reduction of video data using variable sampling rates based on importance within the image |
| US20060080736A1 (en) * | 2004-10-07 | 2006-04-13 | Electronics And Telecommunications Research Institute | Method for analyzing security grade of information property |
| CN101021857A (en) * | 2006-10-20 | 2007-08-22 | 鲍东山 | Video searching system based on content analysis |
| CN102708168A (en) * | 2012-04-27 | 2012-10-03 | 北京邮电大学 | System and method for sorting search results of teaching resources |
| CN104125087A (en) * | 2013-04-28 | 2014-10-29 | 中国移动通信集团设计院有限公司 | Alarm information processing method and device |
| CN103813347A (en) * | 2014-02-28 | 2014-05-21 | 电信科学技术研究院 | Base station frequency resource allocation method and network device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018166339A1 (en) * | 2017-03-13 | 2018-09-20 | 中兴通讯股份有限公司 | Information processing method |
| CN108574624A (en) * | 2017-03-13 | 2018-09-25 | 中兴通讯股份有限公司 | A kind of processing method of group chatting information, apparatus and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106446941B (en) | 2019-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103985057B (en) | Safety of coal mines risk assessment or earthquake loss estimation methodology and device | |
| CN103337043B (en) | The method for early warning of electric power communication device running status and system | |
| CN104915600B (en) | A kind of Android application securitys methods of risk assessment and device | |
| CN107544450B (en) | Process industry network model construction method and system based on data | |
| CN107370766A (en) | A kind of network flow abnormal detecting method and system | |
| CN110046812A (en) | The integrated evaluating method of city safety development level | |
| CN109947806A (en) | A kind of Super High construction safety accident emergency aid decision-making method of case-based reasioning | |
| CN115222303B (en) | Industry risk data analysis method and system based on big data and storage medium | |
| CN106408140A (en) | Grading and classifying model method based on power grid enterprise data | |
| CN106446941A (en) | Unconventional emergency dynamic priority method based on model matching | |
| Shen et al. | Does industrial air pollution increase health care expenditure? Evidence from China | |
| CN114048952A (en) | Iron works safety situation perception method based on edge internet of things technology and neural network | |
| CN116030607A (en) | Intelligent power plant safety supervision reminding and early warning system | |
| CN112000055A (en) | System and method for controlling total amount of volatile organic pollutants | |
| Fang et al. | Human capital, energy and economic growth in China–Evidence from multivariate nonlinear Granger causality | |
| CN103606111A (en) | Integrated voltage qualified rate evaluation method | |
| CN104601567B (en) | A kind of indexing security measure method excavated based on information network security of power system event | |
| CN110544014A (en) | Aerospace safety risk classification and deep cause analysis method and model | |
| CN115987692A (en) | Safety protection system and method based on flow backtracking analysis | |
| CN106971255A (en) | A kind of distribution automation schemes synthesis assessment system | |
| CN114580946A (en) | Dynamic classification method, device and equipment for operation risk in limited space | |
| Iyengar | On the standard error of the Lorenz concentration ratio | |
| Zhang et al. | Transmission dynamics of stochastic Svir influenza models with media coverage | |
| CN115170078A (en) | Industrial park carbon emission monitoring system and method based on 5G network | |
| CN113341894A (en) | Accident rule data generation method and device and terminal equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |