CN102034023A - Evidence theory-based multi-source information fusion risk analysis method - Google Patents
Evidence theory-based multi-source information fusion risk analysis method Download PDFInfo
- Publication number
- CN102034023A CN102034023A CN201010575275XA CN201010575275A CN102034023A CN 102034023 A CN102034023 A CN 102034023A CN 201010575275X A CN201010575275X A CN 201010575275XA CN 201010575275 A CN201010575275 A CN 201010575275A CN 102034023 A CN102034023 A CN 102034023A
- Authority
- CN
- China
- Prior art keywords
- risk
- index
- elementary probability
- risk class
- fusion
- 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
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an evidence theory-based multi-source information fusion risk analysis method, which belongs to the technical field of information processing. The method comprises the following steps of: determining an index and a weight of a risk system by using an analytic hierarchy process; dividing a risk level by using a mean principle in a statistical method and generating basic probability assignment; fusing basic probability assignment by a weighted fusion method which effectively processes conflicting evidence; determining a main focal element of a fusion result; and obtaining the risk level of the risk system according to the corresponding relationship between the risk level and a basic probability assignment function focal element so as to realize risk analysis. The method has high generality and the advantages of low calculation complexity and high instantaneity and can be conveniently applied to risk evaluation of the system.
Description
Technical field
What the present invention relates to is a kind of method of technical field of information processing, specifically is a kind of Multi-source Information Fusion risk analysis method based on evidence theory.
Background technology
Venture analysis is a kind of identification and measuring and calculating risk, and exploitation, selection Managed Solution solve the means of these risks.It comprises the content of risk identification, risk assessment and risk management three aspects.Wherein, risk assessment is that risk and consequence thereof that potential problems may cause are carried out quantification, and determines its order of severity.It involves the integrated application of multiple model, obtains the comprehensive impression of system risk at last.The method of risk assessment has a variety of, summarizes to get up can be divided into three major types: quantitative methods of risk assessment, the appraisal procedure that combines of methods of risk assessment, qualitative and quantitative qualitatively.Wherein, quantitative analysis method mainly contains analytical hierarchy process, fuzzy comprehensive evaluation method, BP neural network, grey system forecasting model etc.; Way of qualitative analysis mainly contains expert assessment method, fault tree analysis process, causal method.Venture analysis relates to many uncertain factors, and system architecture is comparatively complicated, therefore adopts risk assessment stable, that the high-efficiency information fusion method is carried out system very necessary.
Information fusion technology is a kind of automated information comprehensive treatment technique that development in recent years is got up, and by to handling from the information of different indexs or sensor, draws the analysis-by-synthesis assessment result, in order to improve quality of information, improves the precision of information.Data fusion method has a lot, and common have clustering methodology, artificial neural network method, gray system method, a D-S evidence theory etc.Wherein related to the present invention is the D-S evidence theory.
As a kind of uncertain inference method, yet the D-S evidence theory has obtained widespread use, particularly the risk assessment field for the expression and synthetic the providing from strong method of uncertain information in uncertain inference and data fusion.In the application of D-S evidence theory, relate to two basic problems: first problem is the generation of elementary probability assignment function (BPA); Second problem is the blending algorithm of BPA.Whether whether the generation of BPA rationally is directly connected to fusion results correct.In existing BPA generation and fusion method, often have the characteristics that professional limitation is strong, computation complexity is high.For example: determine that based on the FCM classification results method of pixel BPA is used to classification of multi-source Medical image fusion and image segmentation problem; The method that generates BPA according to the sorter confusion matrix is used in Handwritten Digits Recognition problem etc.This can't satisfy present information and merge application fields, requires also can't be applied than higher place for some real-times.
Summary of the invention
The present invention is directed to the prior art above shortcomings, a kind of Multi-source Information Fusion risk analysis method based on evidence theory is provided, have good versatility, and it is little to have a computation complexity, the advantage that real-time is good can conveniently be used for the risk assessment of system.
The present invention is achieved by the following technical solutions, the present invention includes following steps:
Determining and weight of the first step, risk system index
In risk assessment, at first determine risk indicator, this is a pith of correctly setting up the risk system model, is called risk identification again.When setting up the index system of risk evaluating system, should follow three principles: 1) representativeness principle; 2) comprehensive principle; 3) operability principle.
Secondly, to the percentage contribution of this risk system, it is carried out the division of weight according to each risk indicator (being the risk source).The present invention adopts analytical hierarchy process (AHP) to calculate the weight of each index, and concrete calculation procedure is as follows:
1.1) set up hierarchy Model, this structural model comprises destination layer, rule layer, solution layer.
1.2) the structure judgment matrix.
1.3) calculate single preface weight vector and do consistency check: each judgment matrix is calculated eigenvalue of maximum and characteristic of correspondence vector thereof, utilize coincident indicator, coincident indicator and Consistency Ratio are done consistency check at random.If upcheck, proper vector (after the normalization) is weight vector; If do not pass through, need re-construct judgment matrix.
1.4) calculate total ordering weight vector and do consistency check.
The division of second step, risk class
The method that risk class is divided comprises two classes: statistical method and systems approach, wherein statistical method is divided into majority principle, half principle, mean principle, minority principle and mode principle again.The present invention mainly adopts the mean principle in the statistical method to divide risk class.Specifically be divided into following 3 steps:
2.1) determine the directivity of risk indicator: in risk assessment, each index all has the directivity preference of oneself.When for example the consumer bought product, the risk partiality of price guideline was the smaller the better.Have only the directivity of having determined index, could correct qualitative or quantitative data correctly handle index.
2.2) processing of achievement data.Because the present invention adopts mean principle divided rank, therefore need obtain this achievement data maximal value max over a period to come, minimum value min, and difference DELTA, difference average p etc., wherein:
Δ＝max-min (1)
p＝Δ/4 (2)
2.3) obtaining the critical value of risk class according to each achievement data: the difference according to the index directivity is divided into two kinds of risk class division methods.Fig. 1 provides the risk class division methods of two kinds of directivity indexs: the risk class division methods of positive tropism's index is shown in Fig. 1 (a), and the risk class division methods of negative tropism index is shown in Fig. 1 (b).
The 3rd step, generation elementary probability are assigned:
3.1) the burnt unit of risk class and elementary probability assignment function corresponding: it is fixed that the burnt unit of elementary probability assignment function is come by the number of risk class.The risk system of general five risk class adopts the burnt unit of element to get final product.Run into risk system achievement data deletion condition, can carry out the complete or collected works Jiao Yuan that itself and elementary probability are assigned corresponding.
3.2) the synthetic elementary probability assignment function of weighting: so-called weighting is synthetic to be meant according to the percentage contribution of each risk indicator to this risk system, give different weights to it respectively, again the burnt unit of its correspondence is multiplied by addition after the weight, generates the elementary probability assignment function.Suppose the number of m for the burnt unit that occurs, n is the index number, it is weighted synthetic, that is:
Just obtain the BPA of this risk system thus.
The fusion method of the 4th step, BPA: employing Deng Yong (Deng Yong, Shi Wenkang, Zhu Zhenfu. a kind of combined method of effective processing conflicting evidence [J]. infrared and millimeter wave journal, 2004,23 (1): weighting fusion method 27-32.) merges the BPA that generates.When risk system had n index, the elementary probability assignment function with weighting after synthetic was by following Dempster rule of combination combination n-1 time.
Wherein,
M (Φ)=0.
The 5th step, risk Comprehensive Assessment: the BPA fusion results that obtains in the 4th step is analyzed, find out the main burnt unit (Jiao Yuanzhong of elementary probability assignment function occupies the burnt unit of clear superiority) of merging back BPA, corresponding relation by risk class and the burnt unit of elementary probability assignment function, draw the risk class of risk system, realized Multi-source Information Fusion risk analysis method based on evidence theory.
The invention has the beneficial effects as follows:
1) the present invention by the corresponding relation of risk class with burnt unit, can realize that the BPA function generates automatically under the prerequisite that provides certain risk indicator sample data;
2) application prerequisite of the present invention is the qualitative or quantitative sample data of risk system index, and this is very easy to realize having good versatility in the venture analysis system;
3) by risk class and the synthetic BPA of burnt first corresponding relation, and by the weighting synthetic method it is merged, computation complexity is low, has good real-time performance;
4) the present invention assigns the part elementary probability and distributes to unknown burnt first complete or collected works Θ, even the data of a certain like this index disappearance, its fusion results can not be 0 also always, and this is more rational, and under some strong jamming situation, this method has certain robustness.
Description of drawings
Fig. 1 is positive and negative directivity index risk class division methods;
Wherein: (a) be the risk class division methods of positive tropism's index; (b) be the risk class division methods of negative tropism index.
Fig. 2 is risk analysis method operation synoptic diagram.
Embodiment
Below embodiments of the invention are elaborated, present embodiment is being to implement under the prerequisite with the technical solution of the present invention, provided detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.Present embodiment is that Chinese grain security risk assessment process is given an example.The detailed process of present embodiment comprises following step:
(1) the risk system index determines and weight
The index of at first determining grain security risk assessment system is as follows: x1 grain yield (ten thousand tons), x2 grain acreage (thousand hectares), the equal sown area of x3 people (square metre/people), the x4 added value of agriculture accounts for the proportion (%) of gross domestic product (GDP), x5 arable land effective irrigation area (thousand hectares), x6 provision price index (%), x7 per capita grain possession (kilogram), x8 export of farm produce volume accounts for the proportion (%) of total export, x9 farm imports volume accounts for the proportion (%) of total import value, x10 disaster area (thousand hectares), the x11 area that causes disaster accounts for the proportion (%) in disaster area.According to China Statistical Yearbook-2007 data, it is as shown in table 1 to obtain each achievement data:
Table 1.1995-2007 grain security achievement data
Secondly, to the percentage contribution of this risk system, it is carried out the division of weight according to each risk indicator (being the risk source).The present invention adopts analytical hierarchy process (AHP) to calculate the weight such as the table 1 of each index:
The total proportion of table 2. grain security index
| The grain security index | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 |
| Total proportion | 0.14 | 0.1 | 0.11 | 0.05 | 0.08 | 0.09 | 0.17 | 0.05 | 0.05 | 0.08 | 0.08 |
(2) division of risk class
Determine the risk class of grain security risk assessment system, specific implementation process is divided into following 3 steps:
Step1. determine the directivity of grain security risk indicator.
In risk assessment, each index all has the directivity preference of oneself.The directivity of grain security risk indicator is as shown in table 2:
The directivity of table 3. grain security index
Step2. the processing of achievement data.According to the data of table 1, the deal with data that can draw the every index of grain security is as shown in table 4:
The processing of table 4. achievement data
Step3. obtain the critical value of risk class according to each achievement data.
Difference according to the index directivity is divided into two kinds of risk class division methods.Fig. 1 provides the risk class division methods of two kinds of directivity indexs: the risk class division methods of positive tropism's index is shown in Fig. 1 (a), and the risk class division methods of negative tropism index is shown in Fig. 1 (b).
(3) generating elementary probability assigns
The step that generates BPA is as follows:
1. the burnt unit of risk class and elementary probability assignment function is corresponding.
Here, the risk class of supposing grain security risk assessment system is divided into 5 grades: excessive risk, high risk, middle risk, than low-risk, low-risk.Then the burnt unit of corresponding elementary probability assignment function is as shown in table 5:
The burnt first mapping table of table 5. risk class and elementary probability assignment function
In like manner, according to the critical value of risk, can obtain the burnt unit of grain security evaluation index at the corresponding respectively elementary probability assignment function of 1995-2007.
2. the elementary probability assignment function is synthesized in weighting.
Assign (BPA) as shown in table 6 by the elementary probability of the automatic grain security evaluation system that generates of formula (3):
The elementary probability assignment function is synthesized in the weighting of table 6. grain security achievement data
(4) fusion method of BPA
Adopt the Dempster rule of combination that the BPA that generates is merged, because grain security risk assessment system relates to 11 indexs, therefore it is merged 10 times, the fusion results that obtains is as shown in table 7:
The synthetic elementary probability assignment function of table 7. weighting merges the result who obtains after 10 times
(5) risk Comprehensive Assessment
The BPA fusion results that obtains in the step (4) is analyzed, find out the main burnt unit (Jiao Yuanzhong of elementary probability assignment function occupies the burnt unit of clear superiority) of merging back BPA, by the corresponding relation of risk class and the burnt unit of elementary probability assignment function, draw the risk class of risk system.The result is as shown in table 8:
Table 8. grain security risk class result of determination
| Time | Main burnt unit | Risk class |
| 1995 | m(a) | Low-risk |
| 1996 | m(a) | Low-risk |
| 1997 | m(a) | Low-risk |
| 1998 | m(a) | Low-risk |
| 1999 | m(a) | Low-risk |
| 2000 | m(a) | Low-risk |
| 2001 | m(b) | Middle risk |
| 2002 | m(b) | Middle risk |
| 2003 | m(c) | Excessive risk |
| 2004 | m(b) | Middle risk |
| 2005 | m(a) | Low-risk |
| 2006 | m(a) | Low-risk |
| 2007 | m(a) | Low-risk |
By table 8 result of determination as can be known, the grain security risk class is the low-risk time to have 1995,1996,1997,1998,1999,2000,2005,2006,2007, risk class is the time of middle risk to have 2001,2002,2004, and risk class is the high risk time to have 2003.The analysis showed that by searching of pertinent literature: this grain security risk assessment system can correctly objectively reflect the grain security risk class of 1995-2007.
Claims (5)
1. Multi-source Information Fusion risk analysis method based on evidence theory, it is characterized in that, determine risk system index and weight thereof by analytical hierarchy process successively, adopt the mean principle in the statistical method to divide risk class and generate the elementary probability appointment, adopt then elementary probability to be assigned and merge based on the weighting fusion method of effective processing conflicting evidence, determine the main burnt unit of fusion results at last, draw the risk class of risk system then by the corresponding relation of risk class and the burnt unit of elementary probability assignment function, realize venture analysis.
2. the Multi-source Information Fusion risk analysis method based on evidence theory according to claim 1 is characterized in that described analytical hierarchy process is meant:
1.1) set up the hierarchy Model comprise destination layer, rule layer and solution layer;
1.2) the structure judgment matrix;
1.3) each judgment matrix is calculated eigenvalue of maximum and characteristic of correspondence vector thereof, utilize coincident indicator, coincident indicator and Consistency Ratio are done consistency check at random, when upchecking, proper vector is weight vector after normalization; Otherwise re-construct judgment matrix;
1.4) calculate total ordering weight vector and do consistency check.
3. the Multi-source Information Fusion risk analysis method based on evidence theory according to claim 1 is characterized in that, described division risk class is meant:
2.1) determine the directivity of risk indicator;
2.2) processing of achievement data: adopt the mean principle to determine the maximal value max of achievement data in the cycle, minimum value min, and difference DELTA, difference average p, wherein: Δ=max-min, p=Δ/4;
2.3) divide risk class according to the different mining of index directivity with the risk class division risk class of positive tropism's index or the risk class of negative tropism index.
4. the Multi-source Information Fusion risk analysis method based on evidence theory according to claim 1 is characterized in that, described elementary probability is assigned in the following manner and obtained:
3.1) the burnt unit of elementary probability assignment function come by the number of risk class fixed: the risk system of five risk class adopts the burnt unit of element to determine, then carries out the complete or collected works Jiao Yuan of itself and elementary probability appointment corresponding when the risk system achievement data lack;
3.2) the synthetic elementary probability assignment function of weighting: according to the percentage contribution of each risk indicator, give different weights to it respectively, the more burnt unit of its correspondence is multiplied by addition after the weight, generate the elementary probability assignment function, be specially this risk system:
J=1,2 ..., m, wherein: the burnt first number of m for occurring, n is the index number, the elementary probability that just obtains this risk system is thus assigned.
5. the Multi-source Information Fusion risk analysis method based on evidence theory according to claim 1, it is characterized in that, described weighting fusion method is meant: when risk system had n index, the elementary probability assignment function with weighting after synthetic press the Dempster rule of combination and is made up n-1 time:
Wherein:
M (Φ)=0.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201010575275XA CN102034023A (en) | 2010-12-07 | 2010-12-07 | Evidence theory-based multi-source information fusion risk analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201010575275XA CN102034023A (en) | 2010-12-07 | 2010-12-07 | Evidence theory-based multi-source information fusion risk analysis method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102034023A true CN102034023A (en) | 2011-04-27 |
Family
ID=43886908
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201010575275XA Pending CN102034023A (en) | 2010-12-07 | 2010-12-07 | Evidence theory-based multi-source information fusion risk analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102034023A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102779316A (en) * | 2011-05-12 | 2012-11-14 | 株式会社东芝 | Information processing system and method |
| CN103020450A (en) * | 2012-12-12 | 2013-04-03 | 西安工程大学 | Combined measuring method for multi-evidence source conflict |
| CN103530525A (en) * | 2013-10-26 | 2014-01-22 | 中北大学 | Method for improving risk evaluation accuracy of tailing dam based on reservoir water level |
| CN104408324A (en) * | 2014-12-11 | 2015-03-11 | 云南师范大学 | D-S evidence theory based multi-sensor information fusion method |
| CN105320993A (en) * | 2014-07-11 | 2016-02-10 | 西北工业大学 | Water source pollution detection method based on evidence theory |
| CN105391694A (en) * | 2015-10-20 | 2016-03-09 | 中国人民解放军信息工程大学 | Multi-source situation information fusion method |
| CN107835153A (en) * | 2017-09-29 | 2018-03-23 | 桂林电子科技大学 | A kind of fragility situation data fusion method |
| CN108364136A (en) * | 2018-02-28 | 2018-08-03 | 南昌工程学院 | A kind of shortage of water resources risk analysis method and system based on evidential reasoning |
| CN110388315A (en) * | 2019-07-05 | 2019-10-29 | 中国石油大学(北京) | Oil transfer pump fault recognition method, apparatus and system based on Multi-source Information Fusion |
| CN110598966A (en) * | 2019-03-28 | 2019-12-20 | 中国辐射防护研究院 | Method for grading equipment in post-processing facility based on risk guidance |
| WO2021184825A1 (en) * | 2020-03-20 | 2021-09-23 | 支付宝(杭州)信息技术有限公司 | Data processing method and apparatus, electronic device, and storage medium |
| CN113487223A (en) * | 2021-07-23 | 2021-10-08 | 深圳市城市公共安全技术研究院有限公司 | Risk assessment method and risk assessment system based on information fusion |
| CN114374561A (en) * | 2022-01-13 | 2022-04-19 | 潍坊学院 | Network security state evaluation method and device and storage medium |
-
2010
- 2010-12-07 CN CN201010575275XA patent/CN102034023A/en active Pending
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102779316A (en) * | 2011-05-12 | 2012-11-14 | 株式会社东芝 | Information processing system and method |
| CN103020450A (en) * | 2012-12-12 | 2013-04-03 | 西安工程大学 | Combined measuring method for multi-evidence source conflict |
| CN103020450B (en) * | 2012-12-12 | 2016-04-20 | 西安工程大学 | The combination metric method of many evidences source conflict |
| CN103530525A (en) * | 2013-10-26 | 2014-01-22 | 中北大学 | Method for improving risk evaluation accuracy of tailing dam based on reservoir water level |
| CN103530525B (en) * | 2013-10-26 | 2016-07-20 | 中北大学 | A kind of improve the tailing dam method based on the risk assessment accuracy of reservoir level |
| CN105320993A (en) * | 2014-07-11 | 2016-02-10 | 西北工业大学 | Water source pollution detection method based on evidence theory |
| CN104408324A (en) * | 2014-12-11 | 2015-03-11 | 云南师范大学 | D-S evidence theory based multi-sensor information fusion method |
| CN104408324B (en) * | 2014-12-11 | 2017-06-13 | 云南师范大学 | Multiple sensor information amalgamation method based on D S evidence theories |
| CN105391694A (en) * | 2015-10-20 | 2016-03-09 | 中国人民解放军信息工程大学 | Multi-source situation information fusion method |
| CN107835153A (en) * | 2017-09-29 | 2018-03-23 | 桂林电子科技大学 | A kind of fragility situation data fusion method |
| CN108364136A (en) * | 2018-02-28 | 2018-08-03 | 南昌工程学院 | A kind of shortage of water resources risk analysis method and system based on evidential reasoning |
| CN108364136B (en) * | 2018-02-28 | 2021-08-06 | 南昌工程学院 | Water resource shortage risk analysis method and system based on evidence reasoning |
| CN110598966A (en) * | 2019-03-28 | 2019-12-20 | 中国辐射防护研究院 | Method for grading equipment in post-processing facility based on risk guidance |
| CN110598966B (en) * | 2019-03-28 | 2024-01-26 | 中国辐射防护研究院 | Grading method of equipment in risk-guided post-processing facility |
| CN110388315A (en) * | 2019-07-05 | 2019-10-29 | 中国石油大学(北京) | Oil transfer pump fault recognition method, apparatus and system based on Multi-source Information Fusion |
| WO2021184825A1 (en) * | 2020-03-20 | 2021-09-23 | 支付宝(杭州)信息技术有限公司 | Data processing method and apparatus, electronic device, and storage medium |
| CN113487223A (en) * | 2021-07-23 | 2021-10-08 | 深圳市城市公共安全技术研究院有限公司 | Risk assessment method and risk assessment system based on information fusion |
| CN113487223B (en) * | 2021-07-23 | 2024-04-30 | 深圳市城市公共安全技术研究院有限公司 | Risk assessment method and system based on information fusion |
| CN114374561A (en) * | 2022-01-13 | 2022-04-19 | 潍坊学院 | Network security state evaluation method and device and storage medium |
| CN114374561B (en) * | 2022-01-13 | 2023-10-24 | 潍坊学院 | Network security state evaluation method, device and storable medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102034023A (en) | Evidence theory-based multi-source information fusion risk analysis method | |
| Ding et al. | Analysis of feature matrix in machine learning algorithms to predict energy consumption of public buildings | |
| You et al. | Sustainable livelihoods and rural sustainability in China: Ecologically secure, economically efficient or socially equitable? | |
| Hsu | Investment decision making using a combined factor analysis and entropy-based topsis model | |
| CN104881609A (en) | Credibility evaluation method of software unit of complex software system | |
| CN107967487A (en) | A kind of colliding data fusion method based on evidence distance and uncertainty | |
| CN108154094A (en) | Hyperspectral image unsupervised waveband selection method based on subinterval division | |
| CN102930275A (en) | Remote sensing image feature selection method based on Cramer's V index | |
| CN108762503A (en) | A kind of man-machine interactive system based on multi-modal data acquisition | |
| Xiao et al. | Dynamic multi-attribute evaluation of digital economy development in China: A perspective from interaction effect | |
| CN104881852A (en) | Image segmentation method based on immune clone algorithm and fuzzy kernel-clustering algorithm | |
| CN117670527B (en) | Method and system for determining peasant household loan credit limit based on land parcel data | |
| CN105303302A (en) | Power grid evaluating indicator correlation analysis method, apparatus and computing apparatus | |
| Balcı et al. | Hierarchies in communities of UK stock market from the perspective of Brexit | |
| CN117710096A (en) | Post-loan risk monitoring method and system for agricultural loan based on land parcel information | |
| CN112381422A (en) | Method and device for determining performance of photovoltaic power station | |
| CN117195114A (en) | Chemical production line identification method and system | |
| CN115630964B (en) | Construction method of high-dimensional private data-oriented correlation data transaction framework | |
| CN117036933A (en) | Intelligent crop pest control system and method | |
| Svabova et al. | Prediction of default of small companies in the Slovak Republic | |
| CN106651630A (en) | Key electricity utilization industry identification method and system | |
| CN110084428A (en) | The Water Resources Allocation method and system calculated based on decisionmaker's preference scheme | |
| CN116433062A (en) | Enterprise innovation capability assessment method, system and readable storage medium based on big data | |
| CN114048967A (en) | Big data-based value evaluation method for higher education system | |
| CN113705920A (en) | Generation method of water data sample set for thermal power plant 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 | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20110427 |