CN112115413B - Termite quantity monitoring method based on iteration method - Google Patents
Termite quantity monitoring method based on iteration method Download PDFInfo
- Publication number
- CN112115413B CN112115413B CN202010930230.3A CN202010930230A CN112115413B CN 112115413 B CN112115413 B CN 112115413B CN 202010930230 A CN202010930230 A CN 202010930230A CN 112115413 B CN112115413 B CN 112115413B
- Authority
- CN
- China
- Prior art keywords
- matrix
- data
- weight
- termite
- consistency
- 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.)
- Active
Links
- 241000256602 Isoptera Species 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000012544 monitoring process Methods 0.000 title claims abstract description 17
- 239000011159 matrix material Substances 0.000 claims abstract description 79
- 238000012545 processing Methods 0.000 claims abstract description 24
- 238000001514 detection method Methods 0.000 claims abstract description 15
- 230000000295 complement effect Effects 0.000 claims abstract description 13
- 238000010606 normalization Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 4
- 238000012804 iterative process Methods 0.000 claims description 4
- 238000012423 maintenance Methods 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 description 5
- 239000003814 drug Substances 0.000 description 4
- 230000004888 barrier function Effects 0.000 description 3
- 229910000831 Steel Inorganic materials 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- BDAGIHXWWSANSR-UHFFFAOYSA-N methanoic acid Natural products OC=O BDAGIHXWWSANSR-UHFFFAOYSA-N 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- OSWFIVFLDKOXQC-UHFFFAOYSA-N 4-(3-methoxyphenyl)aniline Chemical compound COC1=CC=CC(C=2C=CC(N)=CC=2)=C1 OSWFIVFLDKOXQC-UHFFFAOYSA-N 0.000 description 1
- 241000238631 Hexapoda Species 0.000 description 1
- 241000607479 Yersinia pestis Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 238000004925 denaturation Methods 0.000 description 1
- 230000036425 denaturation Effects 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 235000019253 formic acid Nutrition 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 239000000575 pesticide Substances 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
Abstract
The invention provides a termite quantity monitoring method based on an iteration method, which comprises the following steps: acquiring relevant index data monitored by a termite detection terminal, and initializing the weight of the relevant index data to obtain a weight initial value; establishing a hierarchical structure model for the actual application scene based on the related index data and the weight initial value; constructing a fuzzy complementary judgment matrix according to a scale principle of an FAHP weighting method; traversing matrix data in the fuzzy complementary judgment matrix, and carrying out uniform processing to obtain a uniform matrix; calculating a weight vector of each row of single line according to the consistency matrix, and accumulating and calculating the total weight of each layer to obtain the total weight of each element data; the method and the device can effectively monitor the termites and provide accurate data for termite control, and greatly reduce equipment maintenance cost and labor cost.
Description
Technical Field
The invention relates to the technical field of termite control, in particular to a termite quantity monitoring method based on an iteration method.
Background
Termites are one of five world pests with wide hazard and extremely high destructiveness. Termites are soft in body, light-afraid in habit, firm in nest and capable of supplying millions of termites to live together. The termite bait can be used for eating plants in dark environment of nest-built termite road for a long time, and termites can secrete formic acid. The concrete is greatly damaged by corrosion of steel bars, and concrete denaturation is caused.
At present, termite control technology mainly depends on trapping and killing methods, and physical barrier technology can inhibit termite damage to a certain extent. The trapping and killing method mainly adopts pesticide which can pollute the environment, especially underground water and threaten human health. In the physical barrier technology, a series of termite control products such as a steel sheet, a sand barrier, a PVC board and the like cannot be applied to various control scenes.
Termites belong to social population living insects and have complex organization and division. In this case, it is particularly important to detect the number of termites and the termite status level. The termite detection device has the advantages that termite detection technology is more efficient and accurate, working efficiency can be improved, and equipment maintenance cost and labor cost can be greatly reduced.
In summary, the termite quantity monitoring method based on the iteration method, which can effectively monitor termites, gives more effective termite-related data directly in the termite control process and improves the working efficiency, is a problem which needs to be solved by those skilled in the art.
Disclosure of Invention
The technical scheme aims at the problems and the needs, and provides an iteration-based termite quantity monitoring method which can solve the technical problems due to the adoption of the following technical scheme.
In order to achieve the above purpose, the present invention provides the following technical solutions: an iterative method-based termite number monitoring method comprises the following steps: s1, acquiring related index data monitored by a termite detection terminal, and initializing the weight of the related index data to obtain a weight initial value;
s2, establishing a hierarchical structure model aiming at an actual application scene based on the related index data and the weight initial value;
s3, scoring importance degrees of index detection values of every two termites in different time periods according to a scale principle of a FAHP weighting method, and constructing a fuzzy complementary judgment matrix;
s4, traversing the matrix data in the fuzzy complementary judgment matrix, and carrying out uniform processing to obtain a uniform matrix;
s5, calculating the maximum characteristic root of the consistency matrix and normalizing the consistency matrix to obtain a characteristic vector, wherein the characteristic vector is a weight vector of a single row of each row, calculating the total weight of each layer in an accumulated way, and finally carrying out weight normalization to obtain the total weight of each element data;
and S6, traversing the related index data in different time periods, calculating respective weights, carrying out data filtering according to the weight values, recording the data processing result of each time, and carrying out iterative processing as one of indexes of the next data processing, thereby improving the reliability of the data result and finally obtaining the number and change condition of the termites in the control period.
Preferably, the related index data includes temperature sensing information and humidity sensing information.
More preferably, the scale principle comprises a triangular fuzzy scale evaluation principle.
More preferably, the scoring the importance degree of the index detection values of every two termites in different time periods, and constructing the fuzzy complementary judgment matrix includes: constructing a fuzzy complementary judgment matrix by adopting triangular fuzzy numbers according to the results of pairwise comparison between indexes of each layer in an index systemWherein h is ij Representing the ambiguous relationship of the underlying i-th element relative to the j-th element, the quantitative representation will be given using a 1-9 scale.
More preferably, the performing the unification process includes: calculating a consistency index CR of a judgment matrix H, and when the CR is smaller than 0.1, the judgment matrix H meets consistency and directly outputs a consistency matrix; otherwise, the judgment matrix H is adjusted, and the adjusted judgment matrix is output; recalculating the consistency index CR of the adjusted judgment matrix, and if CR is smaller than 0.1, ending the adjustment and outputting a consistency matrix; otherwise, the judgment matrix H is repeatedly adjusted until the output judgment matrix meets the consistency requirement.
More preferably, the calculating the maximum feature root of the consistency matrix and normalizing the consistency matrix to obtain a feature vector includes: calculating the maximum characteristic root lambda of the consistency matrix by adopting a mathematical algorithm method max Multiplying each element of each row of the consistency matrix, calculating the n-th root of each multiplication result to obtain m evolution results,wherein n is the number of elements in each row, m is the number of elements in each column of the consistency matrix, and the sum of the square results divided by m is the weight vector w= (W) of each row 1 ,w 2 ,…,w m ,) T I.e., the weight vector of the corresponding evaluation unit, wherein,
more preferably, the calculating the total weight of each layer cumulatively and finally performing weight normalization includes: according to the weight vector w= (W 1 ,w 2 ,…,w m ,) T And obtaining the combined weights of all layers, and normalizing the combined weights of all layers to obtain the total weight of all element data.
More preferably, the temperature sensing information and the humidity sensing information in different time periods are traversed, the corresponding index weights are calculated, data filtering is carried out according to the weight values of the index weights, element data with larger influence on a target result is deleted, the result of each data processing is recorded and used as one of indexes of the next data processing, and the iteration processing is carried out to obtain the number and the change condition of the termites in the control period.
Preferably, the index data next in the iterative process includes the temperature sensing information, the humidity sensing information and the total weight value of each element data last time.
From the technical scheme, the beneficial effects of the invention are as follows: the invention provides a data processing algorithm for a remote termite detection terminal with data storage, which can give more effective and more direct termite related data in the termite control process, can effectively monitor termites, provides accurate data for termite control, improves the working efficiency, and greatly reduces the equipment maintenance cost and the labor cost.
In addition to the objects, features and advantages described above, preferred embodiments for carrying out the present invention will be described in more detail below with reference to the accompanying drawings so that the features and advantages of the present invention can be readily understood.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will briefly describe the drawings that are required to be used in the description of the embodiments of the present invention, wherein the drawings are only for illustrating some embodiments of the present invention, and not limiting all embodiments of the present invention thereto.
Fig. 1 is a schematic diagram of steps of an iterative termite number monitoring method of the present invention.
FIG. 2 is a flow chart of the unification process in the present invention.
Fig. 3 is a schematic structural diagram of a hierarchical structure model in this embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the technical solutions of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of specific embodiments of the present invention. Like reference numerals in the drawings denote like parts. It should be noted that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
The existing termite detection technology mainly selects food preferred by termites as a bait device, and then places the device on the periphery of a building for periodic inspection. If there is evidence of termite activity or feeding, the control is performed using the drug. In this control mode, the termite number at different time periods cannot be known, and thus the amount of the used medicine is inaccurate. The control medicine amount is small, and the control effect can not be achieved; the medicine has large dosage and causes pollution to the environment. The present method is directed to overcoming the shortcomings of the prior art and is directed to providing a data processing algorithm for a remote termite detection terminal with data storage that provides more efficient and direct termite related data during termite control. The termite quantity monitoring method based on the iterative method can effectively monitor termites, provides accurate data for termite control, improves working efficiency, and greatly reduces equipment maintenance cost and labor cost.
As shown in fig. 1 to 3, the method includes:
s1, acquiring related index data monitored by a termite detection terminal, and initializing the weight of the related index data to obtain a weight initial value.
In this embodiment, the related index data includes temperature sensing information and humidity sensing information.
S2, establishing a hierarchical structure model for the actual application scene based on the related index data and the weight initial value.
In this embodiment, the hierarchical model includes a target layer including termite numbers and variations in different time periods during a control period, a criterion layer including elements that affect the termite numbers and variations, and a protocol layer including a method of controlling the elements.
In the method, a triangle fuzzy scale judgment principle is adopted as a scale principle.
And S3, scoring importance degrees of index detection values of every two termites in different time periods according to a scale principle of an FAHP weighting method, and constructing a fuzzy complementary judgment matrix.
In the method, the scoring the importance degree of the index detection values of every two termites in different time periods, and constructing the fuzzy complementary judgment matrix comprises the following steps: constructing a fuzzy complementary judgment matrix by adopting triangular fuzzy numbers according to the results of pairwise comparison between indexes of each layer in an index systemWherein h is ij Representing the ambiguous relationship of the underlying i-th element relative to the j-th element, the quantitative representation will be given using a 1-9 scale.
S4, traversing the matrix data in the fuzzy complementary judgment matrix, and carrying out uniform processing to obtain a uniform matrix;
s5, calculating the maximum characteristic root of the consistency matrix and normalizing the consistency matrix to obtain a characteristic vector, wherein the characteristic vector is a weight vector of a single row of each row, calculating the total weight of each layer in an accumulated way, and finally carrying out weight normalization to obtain the total weight of each element data;
and S6, traversing the related index data in different time periods, calculating respective weights, carrying out data filtering according to the weight values, recording the data processing result of each time, and carrying out iterative processing as one of indexes of the next data processing, thereby improving the reliability of the data result and finally obtaining the number and change condition of the termites in the control period.
As shown in fig. 2, the performing the unification process includes: calculating a consistency index CR of a judgment matrix H, and when the CR is smaller than 0.1, the judgment matrix H meets consistency and directly outputs a consistency matrix; otherwise, the judgment matrix H is adjusted, and the adjusted judgment matrix is output; recalculating the consistency index CR of the adjusted judgment matrix, and if CR is smaller than 0.1, ending the adjustment and outputting a consistency matrix; otherwise, the judgment matrix H is repeatedly adjusted until the output judgment matrix meets the consistency requirement. The adjustment process of the judgment matrix H is as follows: normalizing the judgment matrix H to obtain a normalized matrix H ', dividing each component of any column vector by the corresponding component in each column vector of the matrix H ' to obtain a matrix H '; (0) Taking the smallest h ", if the h corresponding to the smallest h ij Adjusting, turning (3), otherwise turning (1); (1) Taking the largest h ", if the corresponding h to the largest h ij And (3) adjusting, namely turning to (2), otherwise, adjusting the rule as follows when h ij When the number is an integer, a new h ij ＝h ij -1, corresponding h ji ＝1/(h ij -1), turn (5), when h ij When the integer is the reciprocal of the integer, a new h ij ＝1/(1/h ij +1), corresponding to h ji ＝1/h ij +1, turn (5); other unadjusted elements being unchanged, i.e. new h ij ＝h ij The method comprises the steps of carrying out a first treatment on the surface of the (2) Judgment person considers h ij When the adjustment is not needed, selecting the element corresponding to h 'with the smallest column of h' as the object to be adjusted, if the newly selected smallest h ij "(at this time, h ij "also less than 1) the corresponding element is adjusted, turn (3), otherwise, turn (4); (3) This isTime h ij "less than 1, when h ij When the h is an integer, a new h after adjustment ij ＝h ij +1, corresponding h ji ＝1/(h ij +1), turn (5), when h ij When the integer is the reciprocal of the integer, a new h is adjusted ji ＝1/h ij -1, turn (5); other unadjusted elements being unchanged, i.e. new h ij ＝h ij The method comprises the steps of carrying out a first treatment on the surface of the (4) When the judger considers h ij When not to be adjusted, the next largest h can be reselected ij ", and excludes the maximum h that has been selected before ij "when a new maximum h ij "greater than 1, turn (1); when the new maximum h ij When the value is less than 1, turning (3); when the new maximum h ij "equal to 1, turn (5); (5) Outputting the adjusted judgment matrix, recalculating the consistency index CR of the adjusted judgment matrix, if CR is smaller than 0.1, finishing adjustment, outputting the consistency matrix, otherwise, continuing adjustment.
In this embodiment, the calculating the maximum feature root of the consistency matrix and normalizing the consistency matrix to obtain the feature vector includes: calculating the maximum characteristic root lambda of the consistency matrix by adopting a mathematical algorithm method max Multiplying each element of each row of the consistency matrix, and calculating the n-th root of each multiplication result to obtain m evolution results, wherein n is the number of elements of each row, m is the number of elements of each column of the consistency matrix, and each evolution result is divided by the sum of the m evolution results to obtain a weight vector W= (W) of a single row of each row 1 ,w 2 ,…,w m ,) T I.e., the weight vector of the corresponding evaluation unit, wherein,
the step of cumulatively calculating the total weight of each layer, and the step of finally carrying out weight normalization comprises the following steps: according to the weight vector w= (W 1 ,w 2 ,…,w m ,) T And obtaining the combined weights of all layers, and normalizing the combined weights of all layers to obtain the total weight of all element data.
And traversing the temperature sensing information and the humidity sensing information in different time periods, calculating the corresponding index weights, carrying out data filtering according to the weight values of the index weights, deleting element data with larger influence on a target result, recording the data processing result each time as one of indexes of the next data processing, and carrying out iterative processing to obtain the number and change condition of the termites in the control period. And the index data of the next time in the iterative process comprises the temperature sensing information, the humidity sensing information and the total weight value of the data of each element of the last time.
It should be noted that the embodiments of the present invention are only preferred modes for implementing the present invention, and only obvious modifications are included in the overall concept of the present invention, and should be considered as falling within the scope of the present invention.
Claims (9)
1. An iterative termite number monitoring method, comprising:
s1, acquiring related index data monitored by a termite detection terminal, and initializing the weight of the related index data to obtain a weight initial value;
s2, establishing a hierarchical structure model aiming at an actual application scene based on the related index data and the weight initial value;
s3, scoring importance degrees of index detection values of every two termites in different time periods according to a scale principle of a FAHP weighting method, and constructing a fuzzy complementary judgment matrix;
s4, traversing the matrix data in the fuzzy complementary judgment matrix, and carrying out uniform processing to obtain a uniform matrix;
s5, calculating the maximum characteristic root of the consistency matrix and normalizing the consistency matrix to obtain a characteristic vector, wherein the characteristic vector is a weight vector of a single row of each row, calculating the total weight of each layer in an accumulated way, and finally carrying out weight normalization to obtain the total weight of each element data;
and S6, traversing the related index data in different time periods, calculating respective weights, carrying out data filtering according to the weight values, recording the data processing result of each time, and carrying out iterative processing as one of indexes of the next data processing, thereby improving the reliability of the data result and finally obtaining the number and change condition of the termites in the control period.
2. The iterative termite number monitoring method of claim 1 wherein the correlation index data comprises temperature sensing information and humidity sensing information.
3. The iterative termite number monitoring method of claim 2 wherein the scale principle comprises a triangular fuzzy scale judgment principle.
4. The method for monitoring termite number based on the iterative method as claimed in claim 3, wherein the step of scoring importance of the index detection values of every two termites in different time periods, and the step of constructing the fuzzy complementation judgment matrix comprises the steps of: constructing a fuzzy complementary judgment matrix by adopting triangular fuzzy numbers according to the results of pairwise comparison between indexes of each layer in an index systemWherein h is ij Representing the ambiguous relationship of the underlying i-th element relative to the j-th element, the quantitative representation will be given using a 1-9 scale.
5. The iterative termite number monitoring method of claim 4 wherein the performing of the conforming process comprises: calculating a consistency index CR of a judgment matrix H, and when the CR is smaller than 0.1, the judgment matrix H meets consistency and directly outputs a consistency matrix; otherwise, the judgment matrix H is adjusted, and the adjusted judgment matrix is output; recalculating the consistency index CR of the adjusted judgment matrix, and if CR is smaller than 0.1, ending the adjustment and outputting a consistency matrix; otherwise, the judgment matrix H is repeatedly adjusted until the output judgment matrix meets the consistency requirement.
6. The iterative termite number monitoring method of claim 5 wherein the calculating means calculates the termite numberThe maximum feature root of the consistency matrix and the normalization of the consistency matrix to obtain the feature vector comprise the following steps: calculating the maximum characteristic root lambda of the consistency matrix by adopting a mathematical algorithm method max Multiplying each element of each row of the consistency matrix, and calculating the n-th root of each multiplication result to obtain m evolution results, wherein n is the number of elements of each row, m is the number of elements of each column of the consistency matrix, and each evolution result is divided by the sum of the m evolution results to obtain a weight vector W= (W) of a single row of each row 1 ,w 2 ,…,w m ,) T I.e., the weight vector of the corresponding evaluation unit, wherein,
7. the method of claim 6, wherein the step of cumulatively calculating total weight of each level and finally performing weight normalization comprises: according to the weight vector w= (W 1 ,w 2 ,…,w m ,) T And obtaining the combined weights of all layers, and normalizing the combined weights of all layers to obtain the total weight of all element data.
8. The termite number monitoring method based on the iterative method according to claim 7, wherein the temperature sensing information and the humidity sensing information in different time periods are traversed, the respective corresponding index weights are calculated, data filtering is carried out according to the weight values of the index weights, element data with larger influence on a target result is deleted, the result of each data processing is recorded and used as one of indexes of the next data processing, and the number and the change condition of termites in the control period are obtained through iterative processing.
9. The termite number monitoring method based on the iterative process as set forth in claim 1, wherein the next index data in the iterative process includes the temperature sensing information and the humidity sensing information and the last element data total weight value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010930230.3A CN112115413B (en) | 2020-09-07 | 2020-09-07 | Termite quantity monitoring method based on iteration method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010930230.3A CN112115413B (en) | 2020-09-07 | 2020-09-07 | Termite quantity monitoring method based on iteration method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112115413A CN112115413A (en) | 2020-12-22 |
| CN112115413B true CN112115413B (en) | 2023-11-24 |
Family
ID=73803310
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010930230.3A Active CN112115413B (en) | 2020-09-07 | 2020-09-07 | Termite quantity monitoring method based on iteration method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112115413B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015034759A1 (en) * | 2013-09-04 | 2015-03-12 | Neural Id Llc | Pattern recognition system |
| GB201520368D0 (en) * | 2014-11-20 | 2016-01-06 | Petrochina Co Ltd | Evaluation method and evaluation device for water breakthrough risk of production wells in aquifer drive gas reservoirs |
| CN106295911A (en) * | 2016-08-30 | 2017-01-04 | 东方电子股份有限公司 | A kind of grid branch parameter evaluation method based on chromatographic assays |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120137367A1 (en) * | 2009-11-06 | 2012-05-31 | Cataphora, Inc. | Continuous anomaly detection based on behavior modeling and heterogeneous information analysis |
-
2020
- 2020-09-07 CN CN202010930230.3A patent/CN112115413B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015034759A1 (en) * | 2013-09-04 | 2015-03-12 | Neural Id Llc | Pattern recognition system |
| GB201520368D0 (en) * | 2014-11-20 | 2016-01-06 | Petrochina Co Ltd | Evaluation method and evaluation device for water breakthrough risk of production wells in aquifer drive gas reservoirs |
| CN106295911A (en) * | 2016-08-30 | 2017-01-04 | 东方电子股份有限公司 | A kind of grid branch parameter evaluation method based on chromatographic assays |
Non-Patent Citations (2)
| Title |
|---|
| 基于模糊聚类和模糊推理的电网连锁故障预警方法;吴旭;张建华;赵天阳;宋晓磊;;电网技术(06);全文 * |
| 基于粒子群算法的模糊层次分析模型;李壮阔;薛有添;;计算机应用研究(12);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112115413A (en) | 2020-12-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Møller | Flight distance of urban birds, predation, and selection for urban life | |
| Skalak et al. | Sampling period, size and duration influence measures of bat species richness from acoustic surveys | |
| Hovick et al. | Weather constrains the influence of fire and grazing on nesting greater prairie-chickens | |
| CN115879773B (en) | Intelligent disease and pest early warning method and system based on Internet of things | |
| CN111882138B (en) | Water quality prediction method, device, equipment and storage medium based on space-time fusion | |
| Johnson et al. | Bird community responses to cattle stocking rates in a Pacific Northwest bunchgrass prairie | |
| Scolamacchia et al. | Principal climatic and edaphic determinants of Culicoides biting midge abundance during the 2007–2008 bluetongue epidemic in the N etherlands, based on OVI light trap data | |
| Chandler et al. | Estimating recruitment from capture–recapture data by modelling spatio‐temporal variation in birth and age‐specific survival rates | |
| Maiorano | A physiologically based approach for degree-day calculation in pest phenology models: the case of the European corn borer (Ostrinia nubilalis Hbn.) in Northern Italy | |
| Ihwagi et al. | Poaching lowers elephant path tortuosity: implications for conservation | |
| Distiller et al. | Using Continuous‐Time Spatial Capture–Recapture models to make inference about animal activity patterns | |
| CN112001809A (en) | Method for acquiring farmland returning information of agriculture and forestry area | |
| Cypher et al. | Potential habitat and carrying capacity of endangered San Joaquin kit foxes in an urban environment: Implications for conservation and recovery | |
| Ballard et al. | Factors affecting wetland use by spring migrating ducks in the southern Prairie Pothole Region | |
| CN112115413B (en) | Termite quantity monitoring method based on iteration method | |
| CN107392398A (en) | A kind of agricultural management method, mist calculating platform and system | |
| Beeton et al. | Using the spatial population abundance dynamics engine for conservation management | |
| Ramesh et al. | Dry season factors determining habitat use and distribution of mouse deer (Moschiola indica) in the Western Ghats | |
| CN116523182B (en) | Ecological garden construction and production management method, system and storage medium | |
| Webster et al. | Spinner dolphins Stenella longirostris off south-west Mauritius: abundance and residency | |
| Madsen et al. | Weather and exposure period affect coyote detection at camera traps | |
| Dart et al. | The effect of scent lures on detection is not equitable among sympatric species | |
| Ramsey et al. | A management experiment reveals the difficulty of altering seedling growth and palatable plant biomass by culling invasive deer | |
| Smart et al. | Changes in the frequency of common plant species across linear features in Wales from 1990 to 2016: implications for potential delivery of ecosystem services | |
| Pacioni et al. | A novel modelling framework to explicitly simulate predator interaction with poison baits |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |