CN109885600A - Forest cover growth rate analysis method and system - Google Patents
Forest cover growth rate analysis method and system Download PDFInfo
- Publication number
- CN109885600A CN109885600A CN201910138882.0A CN201910138882A CN109885600A CN 109885600 A CN109885600 A CN 109885600A CN 201910138882 A CN201910138882 A CN 201910138882A CN 109885600 A CN109885600 A CN 109885600A
- Authority
- CN
- China
- Prior art keywords
- forest cover
- cluster
- cover region
- forest
- region
- 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
- 238000004458 analytical method Methods 0.000 title claims abstract description 32
- 238000011156 evaluation Methods 0.000 claims abstract description 67
- 238000005259 measurement Methods 0.000 claims abstract description 60
- 238000012545 processing Methods 0.000 claims abstract description 35
- 238000000034 method Methods 0.000 claims description 20
- 230000000694 effects Effects 0.000 claims description 17
- 238000012423 maintenance Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 230000001419 dependent effect Effects 0.000 claims description 5
- 238000005303 weighing Methods 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000001066 destructive effect Effects 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 239000000463 material Substances 0.000 abstract description 6
- 238000005065 mining Methods 0.000 abstract description 5
- 238000003860 storage Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 210000004072 lung Anatomy 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
Abstract
The embodiment of the present application provides a kind of forest cover growth rate analysis method and system, by obtaining each forest cover region in the multidimensional statistics data of each fixed measurement period, and by the center cluster of at least one iteration cycle, the cluster centre mean value of each dimension statistical data in each forest cover region is obtained.Then according to the cluster centre mean value of each dimension statistical data in each forest cover region, calculate the vegetation Growth Evaluation coefficient in each forest cover region, it is finally directed to each forest cover region, is determined the need for carrying out vegetation growth processing to the forest cover region according to the vegetation Growth Evaluation coefficient in the forest cover region.Thereby, it is possible to the effectively objectively multidimensional statistics data in each forest cover region of analysis mining, carry out classification subdivision to forest cover region, assess to increase to the vegetation in each forest cover region, greatly improve efficiency, use manpower and material resources sparingly.
Description
Technical field
This application involves field of computer technology, in particular to a kind of forest cover growth rate analysis method and are
System.
Background technique
Currently, lung of the forest as the earth, the oxygen and a large amount of organic matter being continuously conveyed to needed for us.People
The survival and development of class and forest ecosystem are closely bound up.However, since modern times, due to the intemperate felling of the mankind, directly
Forest is caused to largely reduce.Nowadays, how efficiently and rapidly the reasonable of the natural resources of forest to be utilized and protection to Guan Chong
It wants.Forest protection work is the major tasks in development of resources link, but with the growth of expanding economy and population, forest is non-
Method felling phenomenon happens occasionally, though a other felling event is unable to extensive damage vegetation, easily forms forest-road and small area
Spacious earth's surface, what this can further contribute to people migrates and opens up wasteland plantation.And taking care of the pence for vegetation is cut down, make the growth of vegetation
Speed will cause the destruction of forest cover lower than felling speed eventually.
Based on above-mentioned technical problem, it is this which forest cover region, which how accurately analyzed, to need to carry out vegetation growth processing
Field technical staff's urgent problem to be solved, however reality is, the statistical data in forest cover region is many, different systems
The meter period, corresponding statistical data was had nothing in common with each other, if only relying on empirical analysis by related personnel, not only consumed a large amount of manpower
Material resources, and it is difficult to be formed objectively analysis as a result, to cause the subsequent more manpower and material resources of consumption.
Summary of the invention
In view of this, the embodiment of the present application is designed to provide a kind of forest cover growth rate analysis method and system,
Can the effective objectively multidimensional statistics data in each forest cover region of analysis mining, forest cover region sort out thin
Point, it is assessed to increase to the vegetation in each forest cover region, greatly improves efficiency, use manpower and material resources sparingly.
According to the one aspect of the embodiment of the present application, a kind of electronic equipment is provided, may include that one or more storages are situated between
Matter and one or more processors communicated with storage medium.One or more storage mediums are stored with the executable machine of processor
Device readable instruction.When electronic equipment operation, by bus communication between processor and storage medium, processor executes the machine
Device readable instruction, to execute forest cover growth rate analysis method.
According to the another aspect of the embodiment of the present application, a kind of forest cover growth rate analysis method is provided, is applied to service
Device, which comprises
Each forest cover region is obtained in the multidimensional statistics data of each fixed measurement period, the multidimensional statistics data are at least
Including forest cover growth rate, forest cover utilization rate, forestry vegetation bestrewing rate and forest cover destructive rate；
Based on each forest cover region each fixed measurement period multidimensional statistics data, by least one iteration cycle
Center cluster, obtain the cluster centre mean value of each dimension statistical data in each forest cover region；
According to the cluster centre mean value of each dimension statistical data in each forest cover region, each forest cover region is calculated
Vegetation Growth Evaluation coefficient；
For each forest cover region, determined the need for according to the vegetation Growth Evaluation coefficient in the forest cover region to this
Forest cover region carries out vegetation growth processing.
In a kind of possible embodiment, it is described based on each forest cover region in the more of each fixed measurement period
It ties up statistical data and obtains each dimension statistical data in each forest cover region by the center cluster of at least one iteration cycle
Cluster centre mean value the step of, comprising:
It is passed through for each iteration cycle based on multiple forest cover regions in the multidimensional statistics data of each fixed measurement period
Cross the iteration cluster in the iteration cycle, the first cluster after obtaining a cluster；
In the multidimensional statistics data of each fixed measurement period of first cluster, calculates each fixed measurement period and gather with described
The distance between cluster centre mean value of the first cluster after class；
Each fixed measurement period is traversed, if calculated distance is less than set distance, which is added
Into the second cluster, the second new cluster is obtained with cluster；
In second cluster, between the cluster centre mean value of the second cluster after calculating each fixed measurement period and the cluster
Distance；
Each fixed measurement period is traversed, if calculated distance is less than set distance, which is added
Into third cluster, new third cluster is obtained with cluster；
Using the third cluster as the second new cluster, and return it is described in second cluster, calculate each fixed measurement period with
The step of the distance between cluster centre mean value of the second cluster after the cluster, will be last until meeting iteration stopping condition
Cluster centre mean value of the cluster centre mean value of obtained third cluster as each dimension statistical data in each forest cover region.
In a kind of possible embodiment, the iteration stopping condition includes at least one of the following conditions:
Fixation measurement period in the third cluster is no longer changed；
The number of iterations reaches setting number；
The gravity motion distance of the third cluster is less than set distance.
In a kind of possible embodiment, each dimension statistical data according to each forest cover region is gathered
Class central mean, the step of calculating the vegetation Growth Evaluation coefficient in each forest cover region, comprising:
It is modeled based on multiple regression, is from change with the cluster centre mean value of each dimension statistical data in each forest cover region
The vegetation Growth Evaluation coefficient of amount, each forest cover region is dependent variable, according to the area in each forest cover region of input
Domain influence factor constructs the forest cover growth rate prediction model in each forest cover region, wherein the regional effect factor
Water is safeguarded including at least forest cover maintenance scale, forest cover service procedure, forest cover occupied area, forest cover history
Flat, forest cover history management level, the vegetation type of forest cover；
The cluster centre mean value of each dimension statistical data in each forest cover region is separately input to corresponding forest cover area
In the forest cover growth rate prediction model in domain, each forest cover region under the influence of each regional effect factor is calculated
The corresponding vegetation Growth Evaluation value of cluster centre mean value of each dimension statistical data；
The poly- of each dimension statistical data in each forest cover region is calculated separately according to the weighing factor of each regional effect factor
The corresponding vegetation Growth Evaluation subsystem number of the corresponding vegetation Growth Evaluation value of class central mean, and each vegetation Growth Evaluation is sub
Vegetation Growth Evaluation coefficient of the sum of the coefficient as each forest cover region.
It is described to be according to the vegetation Growth Evaluation coefficient determination in the forest cover region in a kind of possible embodiment
It is no to need to carry out the step of vegetation increases processing to the forest cover region, comprising:
Judge whether the vegetation Growth Evaluation coefficient in the forest cover region is greater than default vegetation Growth Evaluation coefficient, if more than,
It then determines and needs to carry out vegetation growth processing to the forest cover region, if being not more than, it is determined that do not need to the forest cover
Region carries out vegetation growth processing.
It is described to be according to the vegetation Growth Evaluation coefficient determination in the forest cover region in a kind of possible embodiment
After no the step of needing to carry out the forest cover region vegetation growth processing, the method also includes:
If it is determined that needing to carry out vegetation growth processing to the forest cover region, then it will increase processing plan from pre-stored vegetation
The corresponding vegetation of vegetation Growth Evaluation coefficient segment where slightly searching the vegetation Growth Evaluation coefficient in library increases processing plan
Slightly, and by the vegetation growth processing strategie monitoring platform server being sent to where the forest cover region.
It is described to be according to the vegetation Growth Evaluation coefficient determination in the forest cover region in a kind of possible embodiment
After no the step of needing to carry out the forest cover region vegetation growth processing, the method also includes:
If it is determined that not needing to carry out vegetation growth processing to the forest cover region, then continues to monitor the forest cover region and connecing
Each of get off the multidimensional statistics data of fixed measurement period, and is monitoring the forest cover region next any one
The multidimensional statistics data of a fixed measurement period are deposited when abnormal, are returned based on each forest cover region in each fixed statistics
The multidimensional statistics data in period obtain each dimension in each forest cover region by the center cluster of at least one iteration cycle
The step of cluster centre mean value of statistical data.
According to the another aspect of the embodiment of the present application, a kind of forest cover growth rate analytical equipment is provided, is applied to service
Device, described device include:
Module is obtained, it is described more for obtaining each forest cover region in the multidimensional statistics data of each fixed measurement period
It is broken including at least forest cover growth rate, forest cover utilization rate, forestry vegetation bestrewing rate and forest cover to tie up statistical data
Bad rate；
Center cluster module, for the multidimensional statistics data based on each forest cover region in each fixed measurement period, warp
The center cluster for crossing at least one iteration cycle, the cluster centre for obtaining each dimension statistical data in each forest cover region are equal
Value；
Computing module calculates every for the cluster centre mean value according to each dimension statistical data in each forest cover region
The vegetation Growth Evaluation coefficient in a forest cover region；
Determining module, it is true according to the vegetation Growth Evaluation coefficient in the forest cover region for being directed to each forest cover region
It is fixed whether to need to carry out vegetation growth processing to the forest cover region.
According to the another aspect of the embodiment of the present application, a kind of readable storage medium storing program for executing is provided, is stored on the readable storage medium storing program for executing
There is computer program, above-mentioned forest cover growth rate analysis method can be executed when which is run by processor
Step.
Based on any of the above-described aspect, the embodiment of the present application is by obtaining each forest cover region in each fixed statistics week
The multidimensional statistics data of phase, and by the center cluster of at least one iteration cycle, obtain each dimension in each forest cover region
The cluster centre mean value of statistical data.Then according to the cluster centre mean value of each dimension statistical data in each forest cover region,
The vegetation Growth Evaluation coefficient in each forest cover region is calculated, each forest cover region is finally directed to, is planted according to the forest
It is determined the need for carrying out vegetation growth processing to the forest cover region by the vegetation Growth Evaluation coefficient in region.As a result, can
The multidimensional statistics data in enough effective objectively each forest cover regions of analysis mining sort out to forest cover region thin
Point, it is assessed to increase to the vegetation in each forest cover region, greatly improves efficiency, use manpower and material resources sparingly.
To enable the above objects, features, and advantages of the embodiment of the present application to be clearer and more comprehensible, below in conjunction with embodiment, and
Cooperate appended attached drawing, elaborates.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 shows the example hardware of server provided by the embodiment of the present application and the schematic diagram of component software；
Fig. 2 shows one of the flow diagrams of forest cover growth rate analysis method provided by the embodiment of the present application；
Fig. 3 shows two of the flow diagram of forest cover growth rate analysis method provided by the embodiment of the present application；
Fig. 4 shows three of the flow diagram of forest cover growth rate analysis method provided by the embodiment of the present application；
Fig. 5 shows the functional block diagram of forest cover growth rate analytical equipment provided by the embodiment of the present application.
Specific embodiment
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
In attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it should be understood that attached drawing in the application
The purpose of illustration and description is only played, is not used to limit the protection scope of the application.In addition, it will be appreciated that schematical attached
Figure does not press scale.Process used herein shows real according to some embodiments of the embodiment of the present application
Existing operation.It should be understood that the operation of flow chart can be realized out of order, the step of context relation of logic can be with
Reversal order is implemented simultaneously.In addition, those skilled in the art under the guide of teachings herein, can add to flow chart
Other one or more operations, can also remove one or more operations from flow chart.
In addition, described embodiments are only a part of embodiments of the present application, instead of all the embodiments.Usually exist
The component of the embodiment of the present application described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause
This, is not intended to limit claimed the application's to the detailed description of the embodiments herein provided in the accompanying drawings below
Range, but it is merely representative of the selected embodiment of the application.Based on embodiments herein, those skilled in the art are not being done
Every other embodiment obtained under the premise of creative work out, shall fall in the protection scope of this application.
Fig. 1 shows the example hardware for the server 100 that some embodiments of the application provide and the signal of component software
Figure.For example, processor 120 can be used on server 100, and for executing the function in the application.
Server 100 can be the computer of general purpose computer or specific use, both can be used to implement the application
Forest cover growth rate analysis method.The application is although illustrate only a computer, for convenience's sake, Ke Yi
Function described herein is realized in a distributed way on multiple similar platforms, is loaded with equilibrium treatment.
For example, server 100 may include the network port 110 for being connected to network, one for executing program instructions
Or multiple processors 120, communication bus 130 and various forms of storage mediums 140, for example, disk, ROM or RAM or its
Any combination.Illustratively, computer platform can also include being stored in ROM, RAM or the storage of other kinds of non-transitory
Medium, or any combination thereof in program instruction.The present processes may be implemented according to these program instructions.Server 100
It further include input/output (Input/Output, the I between computer and other input-output equipment (such as keyboard, display screen)
/ O) interface 150.
For ease of description, a processor is only described in server 100.It should be noted, however, that in the application
Server 100 can also include multiple processors, therefore a step of processor described in this application executes can also be by
Multiple processor joints are executed or are individually performed.For example, if the processor of server 100 executes step A and step B, it should
Understand, step A and step B can also be executed jointly by two different processors or be individually performed in a processor.
For example, first processor executes step A, second processor executes step B or first processor and second processor is held jointly
Row step A and B.
Fig. 2 shows the flow diagrams for the forest cover growth rate analysis method that some embodiments of the application provide, should
Forest cover growth rate analysis method can the server 100 as shown in Fig. 1 execute.It should be appreciated that in other embodiments, this
The sequence of forest cover growth rate analysis method part step described in embodiment can be exchanged with each other according to actual needs,
Or part steps therein also can be omitted or delete.The detailed step of the forest cover growth rate analysis method is introduced such as
Under.
Step S110 obtains each forest cover region in the multidimensional statistics data of each fixed measurement period.
In a possible example, the multidimensional statistics data at least may include forest cover growth rate, forest plant
Utilization rate, forestry vegetation bestrewing rate and forest cover destructive rate.Certainly it will be appreciated by persons skilled in the art that in reality
Border can also increase more multidimensional statistics data when implementing on the basis of the above, and the present embodiment does not do any restrictions to this.
Step S120, based on each forest cover region each fixed measurement period multidimensional statistics data, by extremely
The center cluster of a few iteration cycle, obtains the cluster centre mean value of each dimension statistical data in each forest cover region.
In a possible example, firstly, each iteration cycle is directed to, based on multiple forest cover regions each solid
The multidimensional statistics data for determining measurement period are clustered by the iteration in the iteration cycle, the first cluster after obtaining a cluster.
Then, in the multidimensional statistics data of each fixed measurement period of first cluster, each fixed statistics week is calculated
The distance between the cluster centre mean value of the first cluster after phase and the cluster；
Then, each fixed measurement period is traversed, if calculated distance is less than set distance, by the fixation measurement period
It is added in the second cluster, the second new cluster is obtained with cluster；
Then, in second cluster, the cluster centre mean value of the second cluster after calculating each fixed measurement period and the cluster
The distance between；
Then, each fixed measurement period is traversed, if calculated distance is less than set distance, by the fixation measurement period
It is added in third cluster, new third cluster is obtained with cluster；
Finally, using the third cluster as the second new cluster, and return to described in second cluster, calculating each fixed statistics week
It the step of the distance between cluster centre mean value of the second cluster after phase and the cluster, will until meeting iteration stopping condition
The cluster centre mean value of the third cluster finally obtained is equal as the cluster centre of each dimension statistical data in each forest cover region
Value.
It is based on above-mentioned iteration cluster process as a result, is capable of the more of the effective objectively each forest cover region of analysis mining
Statistical data is tieed up, the cluster centre mean value of each dimension statistical data in each forest cover region ultimately generated increasingly meets this
Forest cover region actual characteristic value, so that the planning for subsequent forest cover provides theoretical foundation.
Wherein, above-mentioned iteration stopping condition may include at least one of the following conditions:
Fixation measurement period in the third cluster is no longer changed；
The number of iterations reaches setting number；
The gravity motion distance of the third cluster is less than set distance.
Step S130 is calculated every according to the cluster centre mean value of each dimension statistical data in each forest cover region
The vegetation Growth Evaluation coefficient in a forest cover region.
In a possible example, firstly, being modeled based on multiple regression, with each dimension in each forest cover region
The cluster centre mean value of statistical data is independent variable, and the vegetation Growth Evaluation coefficient in each forest cover region is dependent variable, root
The forest cover growth rate for constructing each forest cover region according to the regional effect factor in each forest cover region of input is pre-
Survey model, wherein the regional effect factor includes at least forest cover maintenance scale, forest cover service procedure, forest and plants
By occupied area, forest cover history maintenance levels, forest cover history management level, the vegetation type of forest cover.
Then, the cluster centre mean value of each dimension statistical data in each forest cover region is separately input to corresponding gloomy
In the forest cover growth rate prediction model in forest vegetation region, calculates each forest under the influence of each regional effect factor and plant
By the corresponding vegetation Growth Evaluation value of cluster centre mean value of each dimension statistical data in region.
Finally, being counted according to each dimension that the weighing factor of each regional effect factor calculates separately each forest cover region
The corresponding vegetation Growth Evaluation subsystem number of the corresponding vegetation Growth Evaluation value of the cluster centre mean value of data, and each vegetation is increased
Vegetation Growth Evaluation coefficient of the sum of the long assessment subsystem number as each forest cover region.
The present embodiment considers the regional effect factor in practical each forest cover region as a result, and with step S120
In the cluster centre mean value of each dimension statistical data in each forest cover region be combined with each forest cover region of determination
Vegetation Growth Evaluation coefficient, assessed to increase to the vegetation in each forest cover region more accurately.
Step S140, it is true according to the vegetation Growth Evaluation coefficient in the forest cover region for each forest cover region
It is fixed whether to need to carry out vegetation growth processing to the forest cover region.
In the present embodiment, it can be determined that whether the vegetation Growth Evaluation coefficient in the forest cover region, which is greater than default vegetation, increases
Long metewand, if more than, it is determined that it needs to carry out vegetation growth processing to the forest cover region, if being not more than, it is determined that
It does not need to carry out vegetation growth processing to the forest cover region.
On the basis of the above, as an example, please further refering to Fig. 3, forest cover provided in this embodiment increases
Long rate analysis method can with the following steps are included:
Step S150, however, it is determined that it needs to carry out vegetation growth processing to the forest cover region, then it will be from pre-stored vegetation
Increase the corresponding vegetation of vegetation Growth Evaluation coefficient segment searched where the vegetation Growth Evaluation coefficient in processing strategie library
Increase processing strategie, and vegetation growth processing strategie is sent to the monitoring platform service where the forest cover region
Device.
As another example, please further refering to Fig. 4, forest cover growth rate analysis method provided in this embodiment
Can with the following steps are included:
Step S160, however, it is determined that do not need to carry out vegetation growth processing to the forest cover region, then continue to monitor forest plant
By region in the multidimensional statistics data of next each fixed measurement period, and monitoring the forest cover region in the case where connecing
The multidimensional statistics data that any one come fixes measurement period are deposited when abnormal, are returned based on each forest cover region each
The multidimensional statistics data of a fixed measurement period obtain each forest cover by the center cluster of at least one iteration cycle
The step of cluster centre mean value of each dimension statistical data in region.
In this way, the present embodiment can the effective objectively multidimensional statistics data in each forest cover region of analysis mining, it is right
Forest cover region carries out classification subdivision, assesses, greatly improves to increase to the vegetation in each forest cover region
Efficiency uses manpower and material resources sparingly.
Fig. 5 shows the functional block diagram for the forest cover growth rate analytical equipment 200 that some embodiments of the application provide,
The function that the forest cover growth rate analytical equipment 200 is realized can correspond to the step of above method executes.The forest cover increases
Long rate analytical equipment 200 can be understood as the processor of above-mentioned server 100 or server 100, it is understood that for independently of
The component that the application function is realized under the control of server 100 except above-mentioned server 100 or processor, as shown in figure 5, should
Forest cover growth rate analytical equipment 200 may include obtain module 210, center cluster module 220, computing module 230 and
Determining module 240, the function of each functional module of the forest cover growth rate analytical equipment 200 is explained in detail separately below
It states.
Module 210 is obtained, for obtaining each forest cover region in the multidimensional statistics data of each fixed measurement period,
The multidimensional statistics data include at least forest cover growth rate, forest cover utilization rate, forestry vegetation bestrewing rate and forest
Vegetation deterioration rate.It is appreciated that the acquisition module 210 can be used for executing above-mentioned steps S110, about the acquisition module 210
Detailed implementation is referred to above-mentioned to the related content of step S110.
Center cluster module 220, for based on each forest cover region each fixed measurement period multidimensional statistics
Data obtain the cluster of each dimension statistical data in each forest cover region by the center cluster of at least one iteration cycle
Central mean.It is appreciated that the center cluster module 220 can be used for executing above-mentioned steps S120, about the center cluster mould
The detailed implementation of block 220 is referred to above-mentioned to the related content of step S120.
Computing module 230, for the cluster centre mean value according to each dimension statistical data in each forest cover region,
Calculate the vegetation Growth Evaluation coefficient in each forest cover region.It is appreciated that the computing module 230 can be used for executing it is above-mentioned
Step S130, the detailed implementation about the computing module 230 are referred to above-mentioned to the related content of step S130.
Determining module 240, for being directed to each forest cover region, according to the vegetation Growth Evaluation in the forest cover region
Coefficient determines the need for carrying out vegetation growth processing to the forest cover region.It is appreciated that the determining module 240 can be used
In executing above-mentioned steps S140, the detailed implementation about the determining module 240 is referred to above-mentioned related to step S140
Content.
In a kind of possible embodiment, the center cluster module 220 can specifically obtain respectively in the following manner
The cluster centre mean value of each dimension statistical data in a forest cover region:
It is passed through for each iteration cycle based on multiple forest cover regions in the multidimensional statistics data of each fixed measurement period
Cross the iteration cluster in the iteration cycle, the first cluster after obtaining a cluster；
In the multidimensional statistics data of each fixed measurement period of first cluster, calculates each fixed measurement period and gather with described
The distance between cluster centre mean value of the first cluster after class；
Each fixed measurement period is traversed, if calculated distance is less than set distance, which is added
Into the second cluster, the second new cluster is obtained with cluster；
In second cluster, between the cluster centre mean value of the second cluster after calculating each fixed measurement period and the cluster
Distance；
Each fixed measurement period is traversed, if calculated distance is less than set distance, which is added
Into third cluster, new third cluster is obtained with cluster；
Using the third cluster as the second new cluster, and return it is described in second cluster, calculate each fixed measurement period with
The step of the distance between cluster centre mean value of the second cluster after the cluster, will be last until meeting iteration stopping condition
Cluster centre mean value of the cluster centre mean value of obtained third cluster as each dimension statistical data in each forest cover region.
In one embodiment, the computing module 230 can specifically calculate each forest cover in the following manner
The vegetation Growth Evaluation coefficient in region:
It is modeled based on multiple regression, is from change with the cluster centre mean value of each dimension statistical data in each forest cover region
The vegetation Growth Evaluation coefficient of amount, each forest cover region is dependent variable, according to the area in each forest cover region of input
Domain influence factor constructs the forest cover growth rate prediction model in each forest cover region, wherein the regional effect factor
Water is safeguarded including at least forest cover maintenance scale, forest cover service procedure, forest cover occupied area, forest cover history
Flat, forest cover history management level, the vegetation type of forest cover；
The cluster centre mean value of each dimension statistical data in each forest cover region is separately input to corresponding forest cover area
In the forest cover growth rate prediction model in domain, each forest cover region under the influence of each regional effect factor is calculated
The corresponding vegetation Growth Evaluation value of cluster centre mean value of each dimension statistical data；
The poly- of each dimension statistical data in each forest cover region is calculated separately according to the weighing factor of each regional effect factor
The corresponding vegetation Growth Evaluation subsystem number of the corresponding vegetation Growth Evaluation value of class central mean, and each vegetation Growth Evaluation is sub
Vegetation Growth Evaluation coefficient of the sum of the coefficient as each forest cover region.
Above-mentioned module can be connected to each other or communicate via wired connection or wireless connection.Wired connection may include gold
Belong to cable, optical cable, mixing cable etc., or any combination thereof.Wireless connection may include by LAN, WAN, bluetooth, ZigBee,
Or the connection of the forms such as NFC, or any combination thereof.Two or more modules can be combined into individual module, and any one
A module is segmented into two or more units.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description
With the specific work process of device, the corresponding process in embodiment of the method can be referred to, is repeated no more in the application.In the application
In provided several embodiments, it should be understood that disclosed systems, devices and methods, it can be real by another way
It is existing.The apparatus embodiments described above are merely exemplary, for example, the division of the module, only a kind of logic function
It can divide, there may be another division manner in actual implementation, in another example, multiple module or components can combine or can collect
At another system is arrived, or some features can be ignored or not executed.Another point, shown or discussed mutual coupling
Conjunction or direct-coupling or communication connection can be the indirect coupling or communication connection by some communication interfaces, device or module,
It can be electrical property, mechanical or other forms.
The module as illustrated by the separation member may or may not be physically separated, aobvious as module
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in the executable non-volatile computer-readable storage medium of a processor.Based on this understanding, the application
Technical solution substantially the part of the part that contributes to existing technology or the technical solution can be with software in other words
The form of product embodies, which is stored in a storage medium, including some instructions use so that
One computer equipment (can be personal computer, server or the network equipment etc.) executes each embodiment institute of the application
State all or part of the steps of method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, ROM, RAM, magnetic or disk
Etc. the various media that can store program code.
The above is only the protection scopes of the specific embodiment of the application, but the application to be not limited thereto, any to be familiar with
Those skilled in the art within the technical scope of the present application, can easily think of the change or the replacement, and should all cover
Within the protection scope of the application.Therefore, the protection scope of the application should be subject to the protection scope in claims.
Claims (10)
1. a kind of forest cover growth rate analysis method, which is characterized in that be applied to server, which comprises
Each forest cover region is obtained in the multidimensional statistics data of each fixed measurement period, the multidimensional statistics data are at least
Including forest cover growth rate, forest cover utilization rate, forestry vegetation bestrewing rate and forest cover destructive rate；
Based on each forest cover region each fixed measurement period multidimensional statistics data, by least one iteration cycle
Center cluster, obtain the cluster centre mean value of each dimension statistical data in each forest cover region；
According to the cluster centre mean value of each dimension statistical data in each forest cover region, each forest cover region is calculated
Vegetation Growth Evaluation coefficient；
For each forest cover region, determined the need for according to the vegetation Growth Evaluation coefficient in the forest cover region to this
Forest cover region carries out vegetation growth processing.
2. forest cover growth rate analysis method according to claim 1, which is characterized in that described to be planted based on each forest
Multidimensional statistics data by region in each fixed measurement period obtain each by the center cluster of at least one iteration cycle
The step of cluster centre mean value of each dimension statistical data in a forest cover region, comprising:
It is passed through for each iteration cycle based on multiple forest cover regions in the multidimensional statistics data of each fixed measurement period
Cross the iteration cluster in the iteration cycle, the first cluster after obtaining a cluster；
In the multidimensional statistics data of each fixed measurement period of first cluster, calculates each fixed measurement period and gather with described
The distance between cluster centre mean value of the first cluster after class；
Each fixed measurement period is traversed, if calculated distance is less than set distance, which is added
Into the second cluster, the second new cluster is obtained with cluster；
In second cluster, between the cluster centre mean value of the second cluster after calculating each fixed measurement period and the cluster
Distance；
Each fixed measurement period is traversed, if calculated distance is less than set distance, which is added
Into third cluster, new third cluster is obtained with cluster；
Using the third cluster as the second new cluster, and return it is described in second cluster, calculate each fixed measurement period with
The step of the distance between cluster centre mean value of the second cluster after the cluster, will be last until meeting iteration stopping condition
Cluster centre mean value of the cluster centre mean value of obtained third cluster as each dimension statistical data in each forest cover region.
3. forest cover growth rate analysis method according to claim 2, which is characterized in that the iteration stopping condition packet
Include at least one of the following conditions:
Fixation measurement period in the third cluster is no longer changed；
The number of iterations reaches setting number；
The gravity motion distance of the third cluster is less than set distance.
4. forest cover growth rate analysis method according to claim 1, which is characterized in that described according to described each gloomy
The cluster centre mean value of each dimension statistical data in forest vegetation region calculates the vegetation Growth Evaluation coefficient in each forest cover region
The step of, comprising:
It is modeled based on multiple regression, is from change with the cluster centre mean value of each dimension statistical data in each forest cover region
The vegetation Growth Evaluation coefficient of amount, each forest cover region is dependent variable, according to the area in each forest cover region of input
Domain influence factor constructs the forest cover growth rate prediction model in each forest cover region, wherein the regional effect factor
Water is safeguarded including at least forest cover maintenance scale, forest cover service procedure, forest cover occupied area, forest cover history
Flat, forest cover history management level, the vegetation type of forest cover；
The cluster centre mean value of each dimension statistical data in each forest cover region is separately input to corresponding forest cover area
In the forest cover growth rate prediction model in domain, each forest cover region under the influence of each regional effect factor is calculated
The corresponding vegetation Growth Evaluation value of cluster centre mean value of each dimension statistical data；
The poly- of each dimension statistical data in each forest cover region is calculated separately according to the weighing factor of each regional effect factor
The corresponding vegetation Growth Evaluation subsystem number of the corresponding vegetation Growth Evaluation value of class central mean, and each vegetation Growth Evaluation is sub
Vegetation Growth Evaluation coefficient of the sum of the coefficient as each forest cover region.
5. forest cover growth rate analysis method according to claim 1, which is characterized in that described according to the forest cover
The vegetation Growth Evaluation coefficient in region determines the need for carrying out the forest cover region the step of vegetation increases processing, packet
It includes:
Judge whether the vegetation Growth Evaluation coefficient in the forest cover region is greater than default vegetation Growth Evaluation coefficient, if more than,
It then determines and needs to carry out vegetation growth processing to the forest cover region, if being not more than, it is determined that do not need to the forest cover
Region carries out vegetation growth processing.
6. forest cover growth rate analysis method described in any one of -5 according to claim 1, which is characterized in that described
It determines the need for carrying out at vegetation growth the forest cover region according to the vegetation Growth Evaluation coefficient in the forest cover region
After the step of reason, the method also includes:
If it is determined that needing to carry out vegetation growth processing to the forest cover region, then it will increase processing plan from pre-stored vegetation
The corresponding vegetation of vegetation Growth Evaluation coefficient segment where slightly searching the vegetation Growth Evaluation coefficient in library increases processing plan
Slightly, and by the vegetation growth processing strategie monitoring platform server being sent to where the forest cover region.
7. forest cover growth rate analysis method described in any one of -5 according to claim 1, which is characterized in that described
It determines the need for carrying out at vegetation growth the forest cover region according to the vegetation Growth Evaluation coefficient in the forest cover region
After the step of reason, the method also includes:
If it is determined that not needing to carry out vegetation growth processing to the forest cover region, then continues to monitor the forest cover region and connecing
Each of get off the multidimensional statistics data of fixed measurement period, and is monitoring the forest cover region next any one
The multidimensional statistics data of a fixed measurement period are deposited when abnormal, are returned based on each forest cover region in each fixed statistics
The multidimensional statistics data in period obtain each dimension in each forest cover region by the center cluster of at least one iteration cycle
The step of cluster centre mean value of statistical data.
8. a kind of forest cover growth rate analytical equipment, which is characterized in that be applied to server, described device includes:
Module is obtained, it is described more for obtaining each forest cover region in the multidimensional statistics data of each fixed measurement period
It is broken including at least forest cover growth rate, forest cover utilization rate, forestry vegetation bestrewing rate and forest cover to tie up statistical data
Bad rate；
Center cluster module, for the multidimensional statistics data based on each forest cover region in each fixed measurement period, warp
The center cluster for crossing at least one iteration cycle, the cluster centre for obtaining each dimension statistical data in each forest cover region are equal
Value；
Computing module calculates every for the cluster centre mean value according to each dimension statistical data in each forest cover region
The vegetation Growth Evaluation coefficient in a forest cover region；
Determining module, it is true according to the vegetation Growth Evaluation coefficient in the forest cover region for being directed to each forest cover region
It is fixed whether to need to carry out vegetation growth processing to the forest cover region.
9. forest cover growth rate analytical equipment according to claim 8, which is characterized in that the center cluster module tool
Body obtains the cluster centre mean value of each dimension statistical data in each forest cover region in the following manner:
It is passed through for each iteration cycle based on multiple forest cover regions in the multidimensional statistics data of each fixed measurement period
Cross the iteration cluster in the iteration cycle, the first cluster after obtaining a cluster；
In the multidimensional statistics data of each fixed measurement period of first cluster, calculates each fixed measurement period and gather with described
The distance between cluster centre mean value of the first cluster after class；
Each fixed measurement period is traversed, if calculated distance is less than set distance, which is added
Into the second cluster, the second new cluster is obtained with cluster；
In second cluster, between the cluster centre mean value of the second cluster after calculating each fixed measurement period and the cluster
Distance；
Each fixed measurement period is traversed, if calculated distance is less than set distance, which is added
Into third cluster, new third cluster is obtained with cluster；
Using the third cluster as the second new cluster, and return it is described in second cluster, calculate each fixed measurement period with
The step of the distance between cluster centre mean value of the second cluster after the cluster, will be last until meeting iteration stopping condition
Cluster centre mean value of the cluster centre mean value of obtained third cluster as each dimension statistical data in each forest cover region.
10. forest cover growth rate analytical equipment according to claim 8, which is characterized in that the computing module is specific
The vegetation Growth Evaluation coefficient in each forest cover region is calculated in the following manner:
It is modeled based on multiple regression, is from change with the cluster centre mean value of each dimension statistical data in each forest cover region
The vegetation Growth Evaluation coefficient of amount, each forest cover region is dependent variable, according to the area in each forest cover region of input
Domain influence factor constructs the forest cover growth rate prediction model in each forest cover region, wherein the regional effect factor
Water is safeguarded including at least forest cover maintenance scale, forest cover service procedure, forest cover occupied area, forest cover history
Flat, forest cover history management level, the vegetation type of forest cover；
The cluster centre mean value of each dimension statistical data in each forest cover region is separately input to corresponding forest cover area
In the forest cover growth rate prediction model in domain, each forest cover region under the influence of each regional effect factor is calculated
The corresponding vegetation Growth Evaluation value of cluster centre mean value of each dimension statistical data；
The poly- of each dimension statistical data in each forest cover region is calculated separately according to the weighing factor of each regional effect factor
The corresponding vegetation Growth Evaluation subsystem number of the corresponding vegetation Growth Evaluation value of class central mean, and each vegetation Growth Evaluation is sub
Vegetation Growth Evaluation coefficient of the sum of the coefficient as each forest cover region.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910138882.0A CN109885600B (en) | 2019-02-25 | 2019-02-25 | Forest vegetation growth rate analysis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910138882.0A CN109885600B (en) | 2019-02-25 | 2019-02-25 | Forest vegetation growth rate analysis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109885600A true CN109885600A (en) | 2019-06-14 |
| CN109885600B CN109885600B (en) | 2022-04-12 |
Family
ID=66929233
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910138882.0A Expired - Fee Related CN109885600B (en) | 2019-02-25 | 2019-02-25 | Forest vegetation growth rate analysis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109885600B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111259963A (en) * | 2019-12-27 | 2020-06-09 | 南京林业大学 | Driving factor analysis method and device for regional vegetation index and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103488871A (en) * | 2013-08-27 | 2014-01-01 | 国家电网公司 | Flood forecasting method for area without runoff data |
| CN104050513A (en) * | 2014-04-15 | 2014-09-17 | 中国科学院遥感与数字地球研究所 | Space sampling scheme optimizing method for crop planting area monitoring |
| US20160306075A1 (en) * | 2015-04-14 | 2016-10-20 | International Business Machines Corporation | Weather-driven multi-category infrastructure impact forecasting |
| CN106815551A (en) * | 2016-12-08 | 2017-06-09 | 新疆农业大学 | A kind of optimization method of the variation function parameter fitting of forest inventory control |
| CN107133652A (en) * | 2017-05-17 | 2017-09-05 | 国网山东省电力公司烟台供电公司 | Electricity customers Valuation Method and system based on K means clustering algorithms |
| CN107145964A (en) * | 2017-04-10 | 2017-09-08 | 同济大学 | Multiple regression forecasting model optimization method based on genetic programming |
| CN109146158A (en) * | 2018-08-03 | 2019-01-04 | 青海大学 | A kind of Alpine Meadow ecosystem health analysis method, computer |
-
2019
- 2019-02-25 CN CN201910138882.0A patent/CN109885600B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103488871A (en) * | 2013-08-27 | 2014-01-01 | 国家电网公司 | Flood forecasting method for area without runoff data |
| CN104050513A (en) * | 2014-04-15 | 2014-09-17 | 中国科学院遥感与数字地球研究所 | Space sampling scheme optimizing method for crop planting area monitoring |
| US20160306075A1 (en) * | 2015-04-14 | 2016-10-20 | International Business Machines Corporation | Weather-driven multi-category infrastructure impact forecasting |
| CN106815551A (en) * | 2016-12-08 | 2017-06-09 | 新疆农业大学 | A kind of optimization method of the variation function parameter fitting of forest inventory control |
| CN107145964A (en) * | 2017-04-10 | 2017-09-08 | 同济大学 | Multiple regression forecasting model optimization method based on genetic programming |
| CN107133652A (en) * | 2017-05-17 | 2017-09-05 | 国网山东省电力公司烟台供电公司 | Electricity customers Valuation Method and system based on K means clustering algorithms |
| CN109146158A (en) * | 2018-08-03 | 2019-01-04 | 青海大学 | A kind of Alpine Meadow ecosystem health analysis method, computer |
Non-Patent Citations (2)
| Title |
|---|
| 王艳: "基于聚类分析的林分生长模型研究", 《国优秀硕士学位论文全文数据库 (农业科技辑)》 * |
| 高萌: "基于数据挖掘的区域森林乔木层生物量估算与评价研究", 《中国优秀博硕士学位论文全文数据库(博士)农业科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111259963A (en) * | 2019-12-27 | 2020-06-09 | 南京林业大学 | Driving factor analysis method and device for regional vegetation index and storage medium |
| CN111259963B (en) * | 2019-12-27 | 2023-10-13 | 南京林业大学 | Driving factor analysis method and device for regional vegetation index and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109885600B (en) | 2022-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11632422B2 (en) | Automated server workload management using machine learning | |
| US11050656B2 (en) | System and method to learn and prescribe network path for SDN | |
| CN105224458B (en) | A kind of data library test method and system | |
| US20170013003A1 (en) | Log Analysis Based on User Activity Volume | |
| CN107070692A (en) | A kind of cloud platform monitoring service system analyzed based on big data and method | |
| CN103235974A (en) | Method for improving processing efficiency of massive spatial data | |
| US20200089533A1 (en) | Methods and systems for cloud application optimization | |
| CN106502720B (en) | A kind of data processing method and device | |
| US7562410B2 (en) | System and method for managing a computer network | |
| Rees et al. | Graph theoretic and motif analyses of the hippocampal neuron type potential connectome | |
| Rogers et al. | Approaches to modeling natural regeneration in oak-dominated forests | |
| Gupta et al. | Long range dependence in cloud servers: a statistical analysis based on google workload trace | |
| CN109885600A (en) | Forest cover growth rate analysis method and system | |
| Hibbins et al. | Phylogenomic comparative methods: accurate evolutionary inferences in the presence of gene tree discordance | |
| CN106603661B (en) | A kind of dynamic resource balance dispatching method suitable for cloud platform | |
| CN113313304A (en) | Power grid accident abnormity analysis method and system based on big data decision tree | |
| CN106776309A (en) | A kind of testing time optimization method and system | |
| CN111967113B (en) | Cloud platform node topological graph layout method and device | |
| Hariharasubramanian | Improving application infrastructure provisioning using resource usage predictions from cloud metric data analysis | |
| Chen et al. | Low-power pest monitoring model based on cloud computing. | |
| Mitchell | A parallel random forest implementation for R | |
| CN110334813A (en) | Operation management method and operation management system | |
| Dennis et al. | High performance computing enabled simulation of the food-water-energy system: Simulation of intensively managed landscapes | |
| CN117435870B (en) | Load data real-time filling method, system, equipment and medium | |
| Gál et al. | Complex event processing in supercomputer environment sensor and neural network based analysis |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20220412 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |