CN109885600B - Forest vegetation growth rate analysis method and system - Google Patents
Forest vegetation growth rate analysis method and system Download PDFInfo
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Abstract
The embodiment of the application provides a forest vegetation growth rate analysis method and system, and a clustering center mean value of all-dimensional statistical data of all forest vegetation areas is obtained by obtaining the multi-dimensional statistical data of all forest vegetation areas in each fixed statistical period and carrying out gravity center clustering in at least one iteration period. And finally, determining whether vegetation growth processing needs to be carried out on the forest vegetation region according to the vegetation growth evaluation coefficient of the forest vegetation region. Therefore, multidimensional statistical data of all forest vegetation areas can be analyzed and excavated effectively and objectively, and the forest vegetation areas are classified and subdivided, so that vegetation growth of each forest vegetation area is evaluated, the efficiency is greatly improved, and manpower and material resources are saved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a forest vegetation growth rate analysis method and system.
Background
At present, forests are used as lungs of the earth and are continuously supplied with oxygen and a large amount of organic matters needed by people. Human survival development is closely related to the forest ecosystem. However, since recently, the forest has been greatly reduced directly due to the unregulated felling by human beings. Nowadays, how to efficiently and quickly make reasonable use of and protect natural resources of forests is very important. The forest protection work is the key work in the resource development link, but along with the development of economy and the increase of population, the phenomenon of illegal cutting of forests occurs sometimes, and although individual cutting events cannot destroy vegetation in a large area, forest paths and small-area open ground surfaces are easy to form, so that the settlement and the reclamation planting of people can be further promoted. And the cut vegetation is less and more, so that the growth speed of the vegetation is lower than the cutting speed, and the damage of the forest vegetation is caused finally.
Based on the above technical problems, how to accurately analyze which forest vegetation areas need vegetation growth processing is an urgent problem to be solved by technical personnel in the field, however, the actual situation is that the statistical data of the forest vegetation areas are many, the statistical data corresponding to different statistical periods are different, if only relevant personnel rely on empirical analysis, not only a large amount of manpower and material resources are consumed, but also objective analysis results are difficult to form, and further more subsequent manpower and material resources are consumed.
Disclosure of Invention
In view of this, an object of the embodiment of the present application is to provide a method and a system for analyzing forest vegetation growth rate, which can effectively and objectively analyze and mine multidimensional statistical data of each forest vegetation area and classify and subdivide the forest vegetation area, so as to evaluate vegetation growth of each forest vegetation area, greatly improve efficiency, and save manpower and material resources.
According to an aspect of embodiments of the present application, there is provided an electronic device that may include one or more storage media and one or more processors in communication with the storage media. One or more storage media store machine-readable instructions executable by a processor. When the electronic equipment runs, the processor is communicated with the storage medium through the bus, and the processor executes the machine readable instructions to execute the forest vegetation growth rate analysis method.
According to another aspect of the embodiments of the present application, there is provided a forest vegetation growth rate analysis method, applied to a server, the method including:
acquiring multi-dimensional statistical data of each forest vegetation area in each fixed statistical period, wherein the multi-dimensional statistical data at least comprise forest vegetation growth rate, forest vegetation utilization rate, forest vegetation coverage rate and forest vegetation destruction rate;
based on the multi-dimensional statistical data of each forest vegetation area in each fixed statistical period, obtaining a clustering center mean value of each dimensional statistical data of each forest vegetation area through gravity center clustering of at least one iteration period;
calculating vegetation growth evaluation coefficients of all forest vegetation areas according to the clustering center mean value of all dimensional statistical data of all forest vegetation areas;
and determining whether vegetation growth treatment needs to be carried out on the forest vegetation area or not according to the vegetation growth evaluation coefficient of the forest vegetation area.
In a possible embodiment, the step of obtaining a cluster center mean value of each dimension statistical data of each forest vegetation area through gravity center clustering of at least one iteration cycle based on the multidimensional statistical data of each forest vegetation area in each fixed statistical cycle includes:
aiming at each iteration cycle, obtaining a clustered first cluster through iterative clustering in the iteration cycle based on multi-dimensional statistical data of a plurality of forest vegetation areas in each fixed statistical cycle;
calculating the distance between each fixed statistical period and the clustering center mean value of the clustered first cluster in the multi-dimensional statistical data of each fixed statistical period of the first cluster;
traversing each fixed statistical period, and if the calculated distance is smaller than the set distance, adding the fixed statistical period into a second cluster to obtain a new second cluster;
in the second cluster, calculating the distance between each fixed statistical period and the cluster center mean value of the clustered second cluster;
traversing each fixed statistical period, and if the calculated distance is smaller than the set distance, adding the fixed statistical period into a third cluster to obtain a new third cluster;
and taking the third cluster as a new second cluster, returning to the second cluster, calculating the distance between each fixed statistical period and the clustering center mean of the clustered second cluster until an iteration stop condition is met, and taking the finally obtained clustering center mean of the third cluster as the clustering center mean of each dimension of statistical data of each forest vegetation area.
In a possible embodiment, the iteration stop condition comprises at least one of the following conditions:
the fixed statistical period in the third cluster does not change any more;
the iteration times reach the set times;
and the moving distance of the gravity center of the third cluster is less than the set distance.
In a possible embodiment, the step of calculating the vegetation growth estimation coefficient of each forest vegetation area according to the cluster center mean of the statistical data of each dimension of each forest vegetation area includes:
based on multivariate regression modeling, taking the mean value of the clustering centers of all-dimensional statistical data of all forest vegetation areas as independent variables, taking the vegetation growth assessment coefficient of each forest vegetation area as dependent variables, and constructing a forest vegetation growth rate prediction model of each forest vegetation area according to input area influence factors of each forest vegetation area, wherein the area influence factors at least comprise forest vegetation maintenance scale, forest vegetation maintenance process, forest vegetation floor area, forest vegetation historical maintenance level, forest vegetation historical management level and vegetation types of forest vegetation;
respectively inputting the clustering center mean value of each dimension statistical data of each forest vegetation area into a forest vegetation growth rate prediction model of the corresponding forest vegetation area, and calculating vegetation growth evaluation values corresponding to the clustering center mean value of each dimension statistical data of each forest vegetation area under the influence of each area influence factor;
and calculating vegetation growth evaluation sub-coefficients corresponding to vegetation growth evaluation values corresponding to the mean value of the clustering centers of all dimensional statistical data of all forest vegetation areas according to the influence weight of each area influence factor, and taking the sum of all the vegetation growth evaluation sub-coefficients as the vegetation growth evaluation coefficient of each forest vegetation area.
In one possible embodiment, the step of determining whether vegetation growth treatment for the forest vegetation area is required according to the vegetation growth evaluation coefficient for the forest vegetation area includes:
and judging whether the vegetation growth evaluation coefficient of the forest vegetation area is greater than a preset vegetation growth evaluation coefficient, if so, determining that vegetation growth processing needs to be carried out on the forest vegetation area, and if not, determining that vegetation growth processing does not need to be carried out on the forest vegetation area.
In one possible embodiment, after the step of determining whether vegetation growth treatment for the forest vegetation region is required according to the vegetation growth assessment coefficients for the forest vegetation region, the method further comprises:
if it is determined that vegetation growth processing needs to be performed on the forest vegetation region, finding a vegetation growth processing strategy corresponding to the vegetation growth evaluation coefficient section where the vegetation growth evaluation coefficient is located from a pre-stored vegetation growth processing strategy library, and sending the vegetation growth processing strategy to a monitoring platform server where the forest vegetation region is located.
In one possible embodiment, after the step of determining whether vegetation growth treatment for the forest vegetation region is required according to the vegetation growth assessment coefficients for the forest vegetation region, the method further comprises:
and if it is determined that vegetation growth processing is not required for the forest vegetation area, continuously monitoring the multidimensional statistical data of the forest vegetation area in each next fixed statistical period, returning the multidimensional statistical data based on each forest vegetation area in each fixed statistical period when the fact that the multidimensional statistical data of the forest vegetation area in any next fixed statistical period is abnormal is monitored, and obtaining a clustering center mean value of each multidimensional statistical data of each forest vegetation area through gravity center clustering of at least one iteration period.
According to another aspect of the embodiments of the present application, there is provided a forest vegetation growth rate analyzing apparatus, applied to a server, the apparatus including:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring multi-dimensional statistical data of each forest vegetation area in each fixed statistical period, and the multi-dimensional statistical data at least comprises a forest vegetation growth rate, a forest vegetation utilization rate, a forest vegetation coverage rate and a forest vegetation destruction rate;
the gravity center clustering module is used for obtaining a clustering center mean value of each dimension statistical data of each forest vegetation area through gravity center clustering of at least one iteration cycle based on the multi-dimension statistical data of each forest vegetation area in each fixed statistical cycle;
the calculation module is used for calculating vegetation growth evaluation coefficients of all the forest vegetation areas according to the clustering center mean value of all the dimensional statistical data of all the forest vegetation areas;
and the determining module is used for determining whether vegetation growth processing needs to be carried out on the forest vegetation area or not according to the vegetation growth evaluation coefficient of the forest vegetation area for each forest vegetation area.
According to another aspect of the embodiments of the present application, there is provided a readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, may perform the steps of the forest vegetation growth rate analysis method described above.
Based on any one of the above aspects, the embodiment of the application obtains the clustering center mean value of each dimension statistical data of each forest vegetation area through obtaining the multi-dimension statistical data of each forest vegetation area in each fixed statistical period and through gravity center clustering of at least one iteration period. And finally, determining whether vegetation growth processing needs to be carried out on the forest vegetation region according to the vegetation growth evaluation coefficient of the forest vegetation region. Therefore, multidimensional statistical data of all forest vegetation areas can be analyzed and excavated effectively and objectively, and the forest vegetation areas are classified and subdivided, so that vegetation growth of each forest vegetation area is evaluated, the efficiency is greatly improved, and manpower and material resources are saved.
In order to make the aforementioned objects, features and advantages of the embodiments of the present application more comprehensible, embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 illustrates a schematic diagram of exemplary hardware and software components of a server provided by embodiments of the present application;
fig. 2 shows one of the flow diagrams of the forest vegetation growth rate analysis method provided in the embodiment of the present application;
fig. 3 is a second schematic flowchart of a forest vegetation growth rate analysis method provided in the embodiment of the present application;
fig. 4 shows a third flow chart of a forest vegetation growth rate analysis method provided in the embodiment of the present application;
fig. 5 shows a functional block diagram of a forest vegetation growth rate analysis apparatus according to an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some of the embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 illustrates a schematic diagram of exemplary hardware and software components of a server 100 provided by some embodiments of the present application. For example, the processor 120 may be used on the server 100 and to perform the functions in the present application.
The server 100 may be a general purpose computer or a special purpose computer, both of which may be used to implement the forest vegetation growth rate analysis method of the present application. Although only a single computer is shown, for convenience, the functions described herein may be implemented in a distributed fashion across multiple similar platforms to balance processing loads.
For example, the server 100 may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as disks, ROM, or RAM, or any combination thereof. Illustratively, the computer platform may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented in accordance with these program instructions. The server 100 also includes an Input/Output (I/O) interface 150 between the computer and other Input/Output devices (e.g., keyboard, display screen).
For ease of illustration, only one processor is depicted in server 100. However, it should be noted that the server 100 in the present application may also include a plurality of processors, and thus the steps performed by one processor described in the present application may also be performed by a plurality of processors in combination or individually. For example, if the processor of the server 100 executes step a and step B, it should be understood that step a and step B may also be executed by two different processors together or executed in one processor separately. For example, a first processor performs step a and a second processor performs step B, or the first processor and the second processor perform steps a and B together.
Fig. 2 is a flow chart of a forest vegetation growth rate analysis method provided by some embodiments of the present application, which may be executed by the server 100 shown in fig. 1. It should be understood that, in other embodiments, the order of some steps in the forest vegetation growth rate analysis method according to this embodiment may be interchanged according to actual needs, or some steps may be omitted or deleted. The detailed steps of the forest vegetation growth rate analysis method are introduced as follows.
And S110, acquiring multi-dimensional statistical data of each forest vegetation area in each fixed statistical period.
In one possible example, the multi-dimensional statistics may include at least forest vegetation growth rate, forest vegetation utilization rate, forest vegetation coverage rate, and forest vegetation destruction rate. Of course, it can be understood by those skilled in the art that more multidimensional statistical data may be added on the basis of the foregoing in practical implementation, and the embodiment does not limit this.
And S120, based on the multi-dimensional statistical data of each forest vegetation area in each fixed statistical period, obtaining a clustering center mean value of each dimensional statistical data of each forest vegetation area through gravity center clustering of at least one iteration period.
In one possible example, first, for each iteration cycle, based on multi-dimensional statistical data of a plurality of forest vegetation areas in each fixed statistical cycle, a clustered first cluster is obtained through iterative clustering in the iteration cycle.
Then, calculating the distance between each fixed statistical period and the clustering center mean value of the clustered first cluster in the multi-dimensional statistical data of each fixed statistical period of the first cluster;
then, traversing each fixed statistical period, and if the calculated distance is smaller than the set distance, adding the fixed statistical period into a second cluster to obtain a new second cluster;
then, in the second cluster, calculating the distance between each fixed statistical period and the cluster center mean value of the clustered second cluster;
then traversing each fixed statistical period, and if the calculated distance is smaller than the set distance, adding the fixed statistical period into a third cluster to obtain a new third cluster;
and finally, taking the third cluster as a new second cluster, returning to the second cluster, calculating the distance between each fixed statistical period and the clustering center mean value of the clustered second cluster until an iteration stop condition is met, and taking the finally obtained clustering center mean value of the third cluster as the clustering center mean value of each dimension of statistical data of each forest vegetation area.
Therefore, based on the iterative clustering process, the multi-dimensional statistical data of each forest vegetation area can be analyzed and mined effectively and objectively, the finally generated clustering center mean value of each dimensional statistical data of each forest vegetation area more and more conforms to the actual characteristic value of the forest vegetation area, and therefore a theoretical basis is provided for planning of follow-up forest vegetation.
Wherein the iteration stop condition may include at least one of the following conditions:
the fixed statistical period in the third cluster does not change any more;
the iteration times reach the set times;
and the moving distance of the gravity center of the third cluster is less than the set distance.
And S130, calculating a vegetation growth evaluation coefficient of each forest vegetation area according to the clustering center mean value of each dimension of statistical data of each forest vegetation area.
In one possible example, firstly, based on multiple regression modeling, with the mean value of the clustering centers of the statistical data of the dimensions of the forest vegetation areas as independent variables and the vegetation growth assessment coefficient of each forest vegetation area as dependent variables, a forest vegetation growth rate prediction model of each forest vegetation area is constructed according to the input area influence factors of each forest vegetation area, wherein the area influence factors at least comprise forest vegetation maintenance scale, forest vegetation maintenance process, forest vegetation floor area, forest vegetation historical maintenance level, forest vegetation historical management level and vegetation type of forest vegetation.
And then, respectively inputting the cluster center mean value of each dimension statistical data of each forest vegetation area into the forest vegetation growth rate prediction model of the corresponding forest vegetation area, and calculating the vegetation growth evaluation value corresponding to the cluster center mean value of each dimension statistical data of each forest vegetation area under the influence of each area influence factor.
And finally, calculating vegetation growth assessment sub-coefficients corresponding to vegetation growth assessment values corresponding to the mean value of the clustering centers of all dimensions of statistical data of all forest vegetation areas according to the influence weight of each area influence factor, and taking the sum of all the vegetation growth assessment sub-coefficients as the vegetation growth assessment coefficient of each forest vegetation area.
Therefore, the present embodiment considers the area influence factor of each actual forest vegetation area, and combines the area influence factor with the cluster center mean of the dimensional statistical data of each forest vegetation area in step S120 to determine the vegetation growth evaluation coefficient of each forest vegetation area, so as to evaluate the vegetation growth of each forest vegetation area more accurately.
And S140, determining whether vegetation growth treatment needs to be carried out on the forest vegetation area or not according to the vegetation growth evaluation coefficient of the forest vegetation area for each forest vegetation area.
In this embodiment, it may be determined whether the vegetation growth evaluation coefficient of the forest vegetation region is greater than a preset vegetation growth evaluation coefficient, and if so, it is determined that vegetation growth processing needs to be performed on the forest vegetation region, and if not, it is determined that vegetation growth processing does not need to be performed on the forest vegetation region.
On the basis, as an example, please further refer to fig. 3, the method for analyzing forest vegetation growth rate according to the embodiment may further include the following steps:
and S150, if it is determined that vegetation growth processing needs to be carried out on the forest vegetation region, searching a vegetation growth processing strategy corresponding to the vegetation growth evaluation coefficient section in which the vegetation growth evaluation coefficient is located from a pre-stored vegetation growth processing strategy library, and sending the vegetation growth processing strategy to a monitoring platform server in which the forest vegetation region is located.
As another example, please further refer to fig. 4, the method for analyzing forest vegetation growth rate according to the present embodiment may further include the following steps:
and S160, if it is determined that vegetation growth processing is not required to be performed on the forest vegetation area, continuously monitoring the multidimensional statistical data of the forest vegetation area in each next fixed statistical period, and when the fact that the multidimensional statistical data of the forest vegetation area in any next fixed statistical period are abnormal is monitored, returning the multidimensional statistical data based on each forest vegetation area in each fixed statistical period, and obtaining a clustering center mean value of each multidimensional statistical data of each forest vegetation area through gravity center clustering of at least one iteration period.
Therefore, the multi-dimensional statistical data of all the forest vegetation areas can be analyzed and excavated effectively and objectively, the forest vegetation areas are classified and subdivided, vegetation growth of each forest vegetation area is evaluated, efficiency is greatly improved, and manpower and material resources are saved.
Fig. 5 is a functional block diagram of a forest vegetation growth rate analysis apparatus 200 according to some embodiments of the present disclosure, where the functions implemented by the forest vegetation growth rate analysis apparatus 200 may correspond to the steps executed by the above method. The forest vegetation growth rate analysis apparatus 200 may be understood as the server 100, or a processor of the server 100, or may be an independent component from the server 100 or the processor, which implements the functions of the present application under the control of the server 100, as shown in fig. 5, the forest vegetation growth rate analysis apparatus 200 may include an acquisition module 210, a center-of-gravity clustering module 220, a calculation module 230, and a determination module 240, and the functions of the functional modules of the forest vegetation growth rate analysis apparatus 200 are described in detail below.
The obtaining module 210 is configured to obtain multidimensional statistical data of each forest vegetation area in each fixed statistical period, where the multidimensional statistical data at least includes a forest vegetation growth rate, a forest vegetation utilization rate, a forest vegetation coverage rate, and a forest vegetation destruction rate. It is understood that the obtaining module 210 can be configured to perform the step S110, and for a detailed implementation of the obtaining module 210, reference may be made to the content related to the step S110.
And the gravity center clustering module 220 is configured to obtain a clustering center mean value of each dimension statistical data of each forest vegetation area through gravity center clustering of at least one iteration cycle based on the multi-dimension statistical data of each forest vegetation area in each fixed statistical cycle. It is understood that the centroid clustering module 220 can be used to perform the above step S120, and for the detailed implementation of the centroid clustering module 220, reference can be made to the above description regarding step S120.
And the calculating module 230 is configured to calculate a vegetation growth evaluation coefficient of each forest vegetation region according to the cluster center mean of each dimension of statistical data of each forest vegetation region. It is understood that the calculating module 230 can be used to execute the step S130, and for the detailed implementation of the calculating module 230, reference can be made to the above-mentioned contents related to the step S130.
And the determining module 240 is used for determining whether vegetation growth processing needs to be carried out on the forest vegetation area according to the vegetation growth evaluation coefficient of the forest vegetation area for each forest vegetation area. It is understood that the determining module 240 may be configured to perform the step S140, and for detailed implementation of the determining module 240, reference may be made to the content related to the step S140.
In a possible implementation manner, the center-of-gravity clustering module 220 may specifically obtain a cluster center mean of each dimension of statistical data of each forest vegetation area by:
aiming at each iteration cycle, obtaining a clustered first cluster through iterative clustering in the iteration cycle based on multi-dimensional statistical data of a plurality of forest vegetation areas in each fixed statistical cycle;
calculating the distance between each fixed statistical period and the clustering center mean value of the clustered first cluster in the multi-dimensional statistical data of each fixed statistical period of the first cluster;
traversing each fixed statistical period, and if the calculated distance is smaller than the set distance, adding the fixed statistical period into a second cluster to obtain a new second cluster;
in the second cluster, calculating the distance between each fixed statistical period and the cluster center mean value of the clustered second cluster;
traversing each fixed statistical period, and if the calculated distance is smaller than the set distance, adding the fixed statistical period into a third cluster to obtain a new third cluster;
and taking the third cluster as a new second cluster, returning to the second cluster, calculating the distance between each fixed statistical period and the clustering center mean of the clustered second cluster until an iteration stop condition is met, and taking the finally obtained clustering center mean of the third cluster as the clustering center mean of each dimension of statistical data of each forest vegetation area.
In one embodiment, the calculation module 230 may specifically calculate the vegetation growth estimation coefficient for each forest vegetation area by:
based on multivariate regression modeling, taking the mean value of the clustering centers of all-dimensional statistical data of all forest vegetation areas as independent variables, taking the vegetation growth assessment coefficient of each forest vegetation area as dependent variables, and constructing a forest vegetation growth rate prediction model of each forest vegetation area according to input area influence factors of each forest vegetation area, wherein the area influence factors at least comprise forest vegetation maintenance scale, forest vegetation maintenance process, forest vegetation floor area, forest vegetation historical maintenance level, forest vegetation historical management level and vegetation types of forest vegetation;
respectively inputting the clustering center mean value of each dimension statistical data of each forest vegetation area into a forest vegetation growth rate prediction model of the corresponding forest vegetation area, and calculating vegetation growth evaluation values corresponding to the clustering center mean value of each dimension statistical data of each forest vegetation area under the influence of each area influence factor;
and calculating vegetation growth evaluation sub-coefficients corresponding to vegetation growth evaluation values corresponding to the mean value of the clustering centers of all dimensional statistical data of all forest vegetation areas according to the influence weight of each area influence factor, and taking the sum of all the vegetation growth evaluation sub-coefficients as the vegetation growth evaluation coefficient of each forest vegetation area.
The modules may be connected or in communication with each other via a wired or wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, etc., or any combination thereof. The wireless connection may comprise a connection over a LAN, WAN, bluetooth, ZigBee, NFC, or the like, or any combination thereof. Two or more modules may be combined into a single module, and any one module may be divided into two or more units.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to corresponding processes in the method embodiments, and are not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. A forest vegetation growth rate analysis method is applied to a server and comprises the following steps:
acquiring multi-dimensional statistical data of each forest vegetation area in each fixed statistical period, wherein the multi-dimensional statistical data at least comprise forest vegetation growth rate, forest vegetation utilization rate, forest vegetation coverage rate and forest vegetation destruction rate;
based on the multi-dimensional statistical data of each forest vegetation area in each fixed statistical period, obtaining a clustering center mean value of each dimensional statistical data of each forest vegetation area through gravity center clustering of at least one iteration period;
calculating vegetation growth evaluation coefficients of all forest vegetation areas according to the clustering center mean value of all dimensional statistical data of all forest vegetation areas;
determining whether vegetation growth processing needs to be carried out on the forest vegetation area or not according to the vegetation growth evaluation coefficient of the forest vegetation area aiming at each forest vegetation area;
the step of calculating the vegetation growth evaluation coefficient of each forest vegetation area according to the clustering center mean value of each dimension statistical data of each forest vegetation area comprises the following steps:
based on multivariate regression modeling, taking the mean value of the clustering centers of all-dimensional statistical data of all forest vegetation areas as independent variables, taking the vegetation growth assessment coefficient of each forest vegetation area as dependent variables, and constructing a forest vegetation growth rate prediction model of each forest vegetation area according to input area influence factors of each forest vegetation area, wherein the area influence factors at least comprise forest vegetation maintenance scale, forest vegetation maintenance process, forest vegetation floor area, forest vegetation historical maintenance level, forest vegetation historical management level and vegetation types of forest vegetation;
respectively inputting the clustering center mean value of each dimension statistical data of each forest vegetation area into a forest vegetation growth rate prediction model of the corresponding forest vegetation area, and calculating vegetation growth evaluation values corresponding to the clustering center mean value of each dimension statistical data of each forest vegetation area under the influence of each area influence factor;
calculating vegetation growth evaluation sub-coefficients corresponding to vegetation growth evaluation values corresponding to the mean value of the clustering centers of all dimensional statistical data of all forest vegetation areas according to the influence weight of each area influence factor, and taking the sum of all vegetation growth evaluation sub-coefficients as the vegetation growth evaluation coefficient of each forest vegetation area;
the step of determining whether vegetation growth processing needs to be carried out on the forest vegetation area according to the vegetation growth evaluation coefficient of the forest vegetation area comprises the following steps:
and judging whether the vegetation growth evaluation coefficient of the forest vegetation area is greater than a preset vegetation growth evaluation coefficient, if so, determining that vegetation growth processing needs to be carried out on the forest vegetation area, and if not, determining that vegetation growth processing does not need to be carried out on the forest vegetation area.
2. The forest vegetation growth rate analysis method of claim 1, wherein the step of obtaining a cluster center mean of the multi-dimensional statistical data of each forest vegetation area through gravity center clustering of at least one iteration cycle based on the multi-dimensional statistical data of each forest vegetation area in each fixed statistical cycle comprises:
aiming at each iteration cycle, obtaining a clustered first cluster through iterative clustering in the iteration cycle based on multi-dimensional statistical data of a plurality of forest vegetation areas in each fixed statistical cycle;
calculating the distance between each fixed statistical period and the clustering center mean value of the clustered first cluster in the multi-dimensional statistical data of each fixed statistical period of the first cluster;
traversing each fixed statistical period, and if the calculated distance is smaller than the set distance, adding the fixed statistical period into a second cluster to obtain a new second cluster;
in the second cluster, calculating the distance between each fixed statistical period and the cluster center mean value of the clustered second cluster;
traversing each fixed statistical period, and if the calculated distance is smaller than the set distance, adding the fixed statistical period into a third cluster to obtain a new third cluster;
and taking the third cluster as a new second cluster, returning to the second cluster, calculating the distance between each fixed statistical period and the clustering center mean of the clustered second cluster until an iteration stop condition is met, and taking the finally obtained clustering center mean of the third cluster as the clustering center mean of each dimension of statistical data of each forest vegetation area.
3. The method of forest vegetation growth rate analysis of claim 2, wherein the iteration stop condition comprises at least one of:
the fixed statistical period in the third cluster does not change any more;
the iteration times reach the set times;
and the moving distance of the gravity center of the third cluster is less than the set distance.
4. The method of any one of claims 1-3, wherein after the step of determining whether vegetation growth treatment for the forest vegetation region is required based on the vegetation growth estimation coefficient for the forest vegetation region, the method further comprises:
if it is determined that vegetation growth processing needs to be performed on the forest vegetation region, finding a vegetation growth processing strategy corresponding to the vegetation growth evaluation coefficient section where the vegetation growth evaluation coefficient is located from a pre-stored vegetation growth processing strategy library, and sending the vegetation growth processing strategy to a monitoring platform server where the forest vegetation region is located.
5. The method of any one of claims 1-3, wherein after the step of determining whether vegetation growth treatment for the forest vegetation region is required based on the vegetation growth estimation coefficient for the forest vegetation region, the method further comprises:
and if it is determined that vegetation growth processing is not required for the forest vegetation area, continuously monitoring the multidimensional statistical data of the forest vegetation area in each next fixed statistical period, returning the multidimensional statistical data based on each forest vegetation area in each fixed statistical period when the fact that the multidimensional statistical data of the forest vegetation area in any next fixed statistical period is abnormal is monitored, and obtaining a clustering center mean value of each multidimensional statistical data of each forest vegetation area through gravity center clustering of at least one iteration period.
6. The utility model provides a forest vegetation growth rate analytical equipment which characterized in that is applied to the server, the device includes:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring multi-dimensional statistical data of each forest vegetation area in each fixed statistical period, and the multi-dimensional statistical data at least comprises a forest vegetation growth rate, a forest vegetation utilization rate, a forest vegetation coverage rate and a forest vegetation destruction rate;
the gravity center clustering module is used for obtaining a clustering center mean value of each dimension statistical data of each forest vegetation area through gravity center clustering of at least one iteration cycle based on the multi-dimension statistical data of each forest vegetation area in each fixed statistical cycle;
the calculation module is used for calculating vegetation growth evaluation coefficients of all the forest vegetation areas according to the clustering center mean value of all the dimensional statistical data of all the forest vegetation areas;
the determining module is used for determining whether vegetation growth processing needs to be carried out on the forest vegetation area or not according to the vegetation growth evaluation coefficient of the forest vegetation area aiming at each forest vegetation area;
the calculating module calculates the vegetation growth evaluation coefficient of each forest vegetation area by the following method:
based on multivariate regression modeling, taking the mean value of the clustering centers of all-dimensional statistical data of all forest vegetation areas as independent variables, taking the vegetation growth assessment coefficient of each forest vegetation area as dependent variables, and constructing a forest vegetation growth rate prediction model of each forest vegetation area according to input area influence factors of each forest vegetation area, wherein the area influence factors at least comprise forest vegetation maintenance scale, forest vegetation maintenance process, forest vegetation floor area, forest vegetation historical maintenance level, forest vegetation historical management level and vegetation types of forest vegetation;
respectively inputting the clustering center mean value of each dimension statistical data of each forest vegetation area into a forest vegetation growth rate prediction model of the corresponding forest vegetation area, and calculating vegetation growth evaluation values corresponding to the clustering center mean value of each dimension statistical data of each forest vegetation area under the influence of each area influence factor;
calculating vegetation growth evaluation sub-coefficients corresponding to vegetation growth evaluation values corresponding to the mean value of the clustering centers of all dimensional statistical data of all forest vegetation areas according to the influence weight of each area influence factor, and taking the sum of all vegetation growth evaluation sub-coefficients as the vegetation growth evaluation coefficient of each forest vegetation area;
the method for determining whether vegetation growth processing needs to be carried out on the forest vegetation region according to the vegetation growth evaluation coefficient of the forest vegetation region comprises the following steps:
and judging whether the vegetation growth evaluation coefficient of the forest vegetation area is greater than a preset vegetation growth evaluation coefficient, if so, determining that vegetation growth processing needs to be carried out on the forest vegetation area, and if not, determining that vegetation growth processing does not need to be carried out on the forest vegetation area.
7. The forest vegetation growth rate analysis device of claim 6, wherein the center of gravity clustering module obtains a cluster center mean of each dimension of statistical data of each forest vegetation area by specifically:
aiming at each iteration cycle, obtaining a clustered first cluster through iterative clustering in the iteration cycle based on multi-dimensional statistical data of a plurality of forest vegetation areas in each fixed statistical cycle;
calculating the distance between each fixed statistical period and the clustering center mean value of the clustered first cluster in the multi-dimensional statistical data of each fixed statistical period of the first cluster;
traversing each fixed statistical period, and if the calculated distance is smaller than the set distance, adding the fixed statistical period into a second cluster to obtain a new second cluster;
in the second cluster, calculating the distance between each fixed statistical period and the cluster center mean value of the clustered second cluster;
traversing each fixed statistical period, and if the calculated distance is smaller than the set distance, adding the fixed statistical period into a third cluster to obtain a new third cluster;
and taking the third cluster as a new second cluster, returning to the second cluster, calculating the distance between each fixed statistical period and the clustering center mean of the clustered second cluster until an iteration stop condition is met, and taking the finally obtained clustering center mean of the third cluster as the clustering center mean of each dimension of statistical data of each forest vegetation area.
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