CN117575086A - Forestry district ecological management method and system based on risk prediction - Google Patents
Forestry district ecological management method and system based on risk prediction Download PDFInfo
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Abstract
The invention discloses a forestry district ecological management method and system based on risk prediction, which relates to the technical field of data processing, and the method comprises the following steps: reading an ecological database of a target forest zone, excavating regular supervision nodes, and supervising and training a risk prediction model by taking a comprehensive ecological system as a support; determining a pre-management node, positioning a mapped precursor node, performing remote sensing data retrieval, and determining real-time ecological information; based on the risk prediction model, performing forestry risk prediction, and determining a risk prediction result; and taking the configured decision baseline as a constraint, carrying out management scheme decision in an ecological management module, determining a pre-management scheme, and carrying out ecological management and feedback adjustment of the target forest zone. The invention solves the technical problem of poor ecological management effect caused by unreasonable formulation of the forest ecological management scheme in the prior art, and achieves the technical effect of obtaining the optimal management scheme and improving the ecological management effect through periodical supervision and staged scheme optimization.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a forestry district ecological management method and system based on risk prediction.
Background
Forestry is an important component of human society development, and forestry ecosystem management and protection aim at reasonable utilization and protection of forest resources, maintain stability and sustainable development of ecosystem, have important significance for maintaining ecological balance, protecting biodiversity, slowing down climate change and the like, however, when carrying out forestry ecosystem management and protection, the problem that ecological management effect is poor and even secondary damage is caused to ecology due to unreasonable management scheme formulation still exists.
Disclosure of Invention
The application provides a forest district ecological management method and system based on risk prediction, which are used for solving the technical problem of poor ecological management effect caused by unreasonable forest ecological management scheme in the prior art.
In a first aspect of the present application, there is provided a method for ecological management of forestry jurisdictions based on risk prediction, the method comprising: determining a comprehensive ecological system of a target forest zone, and reading an ecological database of the target forest zone, wherein the ecological database comprises forest-related event records and natural event records with time node identifiers; excavating regular supervision nodes based on the ecological database, wherein the ecological database has aging updating property, and each regular supervision node has a precursor node and a subsequent node based on a differential time zone; based on the comprehensive ecological system, monitoring and training a risk prediction model based on the ecological database and the regular supervision nodes; determining a pre-management node based on the regular supervision node, positioning a mapped precursor node, performing remote sensing data retrieval, and determining real-time ecological information; based on the risk prediction model, performing forestry risk prediction based on the real-time ecological information, and determining a risk prediction result; taking the configured decision baseline as a constraint, carrying out management scheme decision in an ecological management module, executing staged scheme expansion and competitive assimilation optimizing, and determining a pre-management scheme, wherein the decision baseline is based on protection, recovery, prevention and control and development collaborative setting at least comprising balanced forestry, and is marked with distributed weights; and carrying out ecological management and feedback adjustment and modification of the target forest zone based on the pre-management scheme.
In a second aspect of the present application, there is provided a risk prediction-based forestry jurisdiction ecological management system, the system comprising: the ecological database reading module is used for determining a comprehensive ecological system of the target forest zone and reading an ecological database of the target forest zone, and the ecological database comprises forest-related event records and natural event records with time node identifiers; the regular supervision node mining module is used for mining regular supervision nodes based on the ecological database, wherein the ecological database has timeliness updating property, and each regular supervision node has a precursor node and a subsequent node based on a differential time zone; the risk prediction model training module is used for monitoring and training a risk prediction model based on the ecological database and the regular supervision nodes on the basis of the comprehensive ecological system; the real-time ecological information determining module is used for determining a pre-management node based on the regular supervision node, positioning a mapped precursor node and performing remote sensing data retrieval to determine real-time ecological information; the risk prediction result determining module is used for performing forestry risk prediction based on the real-time ecological information based on the risk prediction model to determine a risk prediction result; the pre-management scheme determining module is used for making management scheme decisions in the ecological management module by taking a configured decision baseline as a constraint, executing staged scheme expansion and competitive assimilation optimizing, and determining a pre-management scheme, wherein the decision baseline is based on protection, recovery, prevention and development collaborative setting at least comprising balanced forestry, and is marked with distributed weights; and the ecological management module is used for carrying out ecological management and feedback adjustment and modification of the target forest zone based on the pre-management scheme.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the forestry district ecological management method based on risk prediction provided by the application relates to the technical field of data processing, remote sensing data of a precursor node is acquired by excavating a regular supervision node, real-time ecological information is determined, forest risk prediction is executed based on a risk prediction model, management scheme decision is carried out by taking a configured decision base line as a constraint, a pre-management scheme is determined, ecological management and feedback adjustment and modification of a target forest district are carried out, the technical problem that in the prior art, due to unreasonable forest ecological management scheme, ecological management effect is poor is solved, and the technical effects that an optimal management scheme is acquired and ecological management effect is improved through regular supervision and staged scheme optimization are realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a forest district ecological management method based on risk prediction according to an embodiment of the present application;
fig. 2 is a schematic flow chart of determining real-time ecological information in a forest district ecological management method based on risk prediction according to an embodiment of the present application;
fig. 3 is a schematic flow chart of determining a pre-management scheme in a forest district ecological management method based on risk prediction according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a forestry district ecological management system based on risk prediction according to an embodiment of the present application.
Reference numerals illustrate: the system comprises an ecological database reading module 11, a periodic supervision node mining module 12, a risk prediction model training module 13, a real-time ecological information determining module 14, a risk prediction result determining module 15, a pre-management scheme determining module 16 and an ecological management module 17.
Detailed Description
The application provides a forest district ecological management method based on risk prediction, which is used for solving the technical problem of poor ecological management effect caused by unreasonable forest ecological management scheme in the prior art.
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. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that, the terms "first," "second," and the like in the description of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Embodiment one: as shown in fig. 1, the present application provides a forest district ecological management method based on risk prediction, the method includes:
p10: determining a comprehensive ecological system of a target forest zone, and reading an ecological database of the target forest zone, wherein the ecological database comprises forest-related event records and natural event records with time node identifiers;
the method comprises the steps of determining the comprehensive ecological system of a target forest zone, namely the composition and characteristics of a forestry ecological system of the target area, including vegetation types, biological types, climate environment types and the like, as well as ecological system functions, material circulation, energy flow and the like, reading an ecological database of the target forest zone, namely a database for recording various ecological events of the target forest zone, including forest-related event records with time node identifiers and natural event records, wherein the forest-related event records refer to forestry damage event records caused by human activities, and the natural event records refer to forest disaster events caused by natural disasters, and can be used for carrying out forest disaster law analysis and determining supervision emphasis of the forest disasters.
P20: excavating regular supervision nodes based on the ecological database, wherein the ecological database has aging updating property, and each regular supervision node has a precursor node and a subsequent node based on a differential time zone;
optionally, the ecological database is used for analyzing the forest disaster rule, and regular supervision nodes, namely risk nodes needing to be supervised, are mined, wherein the regular inevitable risk nodes and random accidental risk nodes are included, for example, artificial activities such as travelling according to holidays, activities of surrounding residents and the like, seasonal natural disasters and the like. Setting a plurality of supervision time points, wherein the ecological database has timeliness updating property, the content of the ecological database is updated according to a certain period, each periodical supervision node is provided with a precursor node and a subsequent node based on a differentiated time zone, the precursor node and the subsequent node are nodes before and after each supervision node, for example, the time when the initial characteristic of a supervision event exists can be used as the precursor node, and the interval between the precursor node and the subsequent node can be determined based on scene live and evolution trend and risk degree of each supervision node and can be used for risk early prediction and feedback analysis.
P30: based on the comprehensive ecological system, monitoring and training a risk prediction model based on the ecological database and the regular supervision nodes;
it should be understood that, based on the comprehensive ecological system, sample supervision data of each periodic supervision node is extracted from the ecological database, including sample disaster events, corresponding sample risk features and sample risk grades, then a risk prediction sub-model of each periodic supervision node is constructed by combining a fully-connected neural network principle, and the risk prediction sub-model is supervised and trained by using the sample supervision data as training data until the output of the model reaches convergence and meets the preset accuracy requirement, and the risk prediction model is formed by a plurality of risk prediction sub-models and can be used for predicting forest disaster risks through ecological monitoring data.
P40: determining a pre-management node based on the regular supervision node, positioning a mapped precursor node, performing remote sensing data retrieval, and determining real-time ecological information;
further, as shown in fig. 2, step P40 in the embodiment of the present application further includes:
p41: mining and integrating a precursor risk feature library based on the ecological database;
p42: performing continuous remote sensing monitoring of the target forest zone, performing global identification by taking the precursor risk feature library as a reference, and generating a temporary early warning instruction if target risk features exist;
p43: generating a temporary management node along with the receiving of the temporary early warning instruction;
p44: and calling remote sensing data based on the temporary management node to serve as the real-time ecological information.
Specifically, the pre-management node based on the regular supervision node, that is, the precursor node located before the supervision node is determined, further, from the ecological database, a precursor risk feature library, that is, a database formed by risk features of a plurality of precursor nodes, is mined and integrated, forest environment features before a disaster occur, such as continuous high temperature before a forest fire, bark cracking and the like, can be reflected, a remote sensing monitoring technology is used for continuous remote sensing monitoring of the target forest area, the remote sensing monitoring technology is a technology for monitoring and identifying an environmental target by collecting electromagnetic wave information of the environment through aviation, satellite and the like, global risk identification is performed by taking precursor risk features in the precursor risk feature library as a reference, if the target risk features are matched with the precursor risk features, a temporary disaster warning instruction is generated, after the temporary disaster warning instruction is received, remote sensing data of the temporary management node is generated, and the temporary management node is called as real-time ecological information, and the risk prediction of the supervision node can be performed.
P50: based on the risk prediction model, performing forestry risk prediction based on the real-time ecological information, and determining a risk prediction result;
it should be understood that the real-time ecological information is input into the risk prediction model, risk prediction of the target forest zone is performed, risk characteristics in the real-time ecological information are identified through the risk prediction model, and corresponding risk types and risk grades are matched according to the risk characteristics to serve as risk prediction results, so that the risk prediction results can be used as risk early warning management references.
P60: taking the configured decision baseline as a constraint, carrying out management scheme decision in an ecological management module, executing staged scheme expansion and competitive assimilation optimizing, and determining a pre-management scheme, wherein the decision baseline is based on protection, recovery, prevention and control and development collaborative setting at least comprising balanced forestry, and is marked with distributed weights;
further, as shown in fig. 3, step P60 in the embodiment of the present application further includes:
p61: performing stepwise division of forestry wind control, and determining a plurality of connection stages, wherein the connection stages at least comprise a prevention stage, an initial stage, a diffusion stage and a recovery stage;
p62: determining an optimizing constraint space based on the decision baseline;
p63: taking the real-time ecological information as an index, and carrying out big data homologous retrieval to determine a target management scheme;
p64: dividing the target management scheme based on the plurality of continuing stages, performing step-by-step expansion optimization of the staged scheme based on the optimizing constraint space, and determining a plurality of staged pre-management schemes;
p65: and performing order splicing on the plurality of staged pre-management schemes to generate the pre-management scheme.
The management scheme decision is performed in the ecological management module by taking a configured decision baseline as a constraint, wherein the decision baseline refers to an intervention range constraint of each intervention measure of ecological management, such as a manual intervention degree constraint on forest insect pests, at least needs to be cooperatively set based on multiple aspects of protection, recovery, prevention, control, development and the like of balanced forestry, and a distributed weight is identified, such as a weight distribution mainly for protection and development as an auxiliary. The ecological management module is used for optimizing a risk management scheme through a risk prediction result, and can be obtained by acquiring sample risk prediction data and a sample risk management scheme and performing supervised training by combining a neural network.
Further, a plurality of continuing stages are determined by performing stepwise division of forestry wind control, the continuing stages at least comprise a prevention stage, an initial stage, a diffusion stage and a recovery stage, an optimizing constraint space of a pre-management scheme, namely an allowable adjustment range of the scheme, is determined according to the decision base line, the target management scheme is divided by referring to the continuing stages to obtain a plurality of stepwise schemes, and each stepwise expansion optimizing of each stepwise scheme is performed based on the optimizing constraint space, namely optimizing each stepwise scheme successively, and a plurality of stepwise pre-management schemes are determined to ensure the pertinence of optimizing, avoid influencing the accuracy of local management due to the limitation of the whole scheme, and further, the stepwise pre-management schemes are sequentially spliced to generate the pre-management scheme.
Further, step P64 of the embodiment of the present application further includes:
p64-1: extracting a one-stage scheme based on the target management scheme, performing space local demarcation in the optimizing constraint space, and performing scheme random disturbance to determine a one-stage expansion scheme set;
p64-2: dividing the one-stage expansion scheme set, and determining a preferred scheme set, an assimilation scheme set and a variation scheme set;
p64-3: selecting an optimal solution of the optimal scheme set, carrying out homodromous optimization adjustment on the assimilation scheme set, and determining a group of optimization scheme sets;
p64-4: selecting any one of the preferred scheme set and the group of optimization scheme sets, carrying out homodromous optimization adjustment on the variation scheme set, and determining two groups of optimization scheme sets, wherein the mapping optimization directions of all variation schemes are different;
p64-5: and checking the optimized scheme set, the group of optimized scheme sets and the two groups of optimized scheme sets, and selecting the optimal solution as a one-stage pre-management scheme.
The method comprises the steps of extracting a one-stage scheme of the target management scheme, defining a spatial local area of the one-stage scheme, namely a parameter adjustment range through the optimizing constraint space, and obtaining a plurality of one-stage expansion schemes through multiple parameter random adjustment to form a one-stage expansion scheme set. Further, obtaining scheme optimizing parameters, such as implementation cost, execution efficiency, environmental protection coefficient and the like, constructing a fitness optimizing function according to the scheme optimizing parameters, further calculating fitness of a plurality of one-stage expansion schemes according to the fitness optimizing function, sorting according to the fitness, and dividing the one-stage expansion scheme set into a preferred scheme set, an assimilation scheme set and a variation scheme set according to a dividing ratio, wherein the dividing ratio can be: 2:3:5.
Further, an optimal solution of the preferred scheme set, that is, a solution with the greatest fitness is selected, and the optimal solution is taken as an adjustment direction, so that one-stage expansion schemes in the assimilation scheme set are subjected to homodromous optimization adjustment to obtain a group of optimization scheme sets. Further, any one of the preferred scheme set and the optimized scheme set is selected as an adjustment direction, the schemes in the variation scheme set are subjected to homodromous optimization adjustment, two optimized scheme sets are determined, the mapping optimization directions of all variation schemes are different, and the obtained optimized schemes are also different. And finally, selecting an optimal solution from the optimal solution set, the group of optimal solution sets and the two groups of optimal solution sets as a one-stage pre-management solution.
Further, step P60 of the embodiment of the present application further includes:
p65: reading basic information of the target forest zone, and carrying out twin modeling by combining the comprehensive ecological system to determine a forest zone simulation model, wherein the forest zone simulation model is in communication connection with the ecological management module;
p66: based on the forest region simulation model, debugging a simulation mechanism, executing scene simulation based on the pre-management scheme, and determining scheme simulation data;
p67: and judging whether the scheme simulation data meets the expected ecological standard or not, and if not, generating an iterative optimization instruction and transmitting the iterative optimization instruction to the ecological management module.
Optionally, basic information of the target forest area is read, including geographic information, occupied area and the like, twin modeling is performed in combination with the comprehensive ecological system, a forest area simulation model is determined, and communication connection is established between the forest area simulation model and the ecological management module, so that feasibility of a preprocessing scheme output by the ecological management module can be checked. And using the forest region simulation model to debug a simulation mechanism, for example, performing forest region scene switching, time flow speed adjustment and the like, executing a scene simulation test of the pre-management scheme, acquiring scheme simulation data, and analyzing the executing effect of the scheme.
The method includes the steps of determining whether ecological intervention effects in the scheme simulation data meet expected ecological standards, for example, whether the ecological intervention effects meet pest safety standards, if the ecological intervention effects do not meet the pest safety standards, indicating that the ecological management effects cannot be achieved by using the pre-management scheme, generating iterative optimization instructions, transmitting the iterative optimization instructions to the ecological management module, and carrying out pre-management scheme optimization again by the ecological management module until a pre-management scheme capable of meeting the expected ecological standards is obtained.
P70: and carrying out ecological management and feedback adjustment and modification of the target forest zone based on the pre-management scheme.
Further, step P70 of the embodiment of the present application further includes:
p71: determining expected ecological standards based on the pre-management scheme by combining the forest region simulation model, wherein the expected ecological standards correspond to the plurality of continuing stages one by one, and each expected ecological standard has a tolerance interval based on ecological influence factors;
p72: and executing ecological management based on the pre-management scheme on the target forest zone, and performing stepping feedback analysis based on the expected ecological standard in successive stages.
Further, step P72 of the embodiment of the present application further includes:
p72-1: finishing ecological management in the first continuing stage, and collecting ecological feedback data;
p72-2: judging whether the ecological feedback data meets the first-stage expected ecological standard, if not, generating a feedback adjustment instruction by taking the current node as an inflection point;
p72-3: determining an ecological evolution trend of the first continuing stage by combining the ecological feedback data, wherein the ecological evolution trend is marked with degradation characteristics based on negative evolution;
p72-4: and based on the ecological evolution trend and the expected deviation, carrying out extension and scheme adjustment of the first continuing stage.
It should be understood that, with reference to the pre-management scheme, ecological management of the target forest zone is implemented, and scheme adjustment is performed according to management effect feedback. Firstly, combining the forest region simulation model, determining expected ecological standards based on the pre-management scheme, such as expected air quality, expected vegetation quantity, expected biological diversity and the like, wherein the expected ecological standards are in one-to-one correspondence with the plurality of continuous stages, namely each stage scheme has corresponding expected ecological standards, and each expected ecological standard is influenced by ecological influence factors, so that a certain tolerance interval exists, namely each expected ecological standard is a range constraint.
Further, when the ecological management based on the pre-management scheme is executed on the target forest area, step-by-step feedback analysis is needed to be performed in successive stages according to the expected ecological standard, that is, the scheme adjustment in the next stage is performed according to the effect feedback of each stage, specifically, when the ecological management in the first successive stage is completed, the ecological feedback data in the first successive stage is collected, whether the ecological feedback data meets the first-stage expected ecological standard is determined, if not, a feedback adjustment instruction is generated by taking the current node as an inflection point, and the ecological evolution trend analysis in the first successive stage is performed through the ecological feedback data, that is, whether the ecological intervention effect is positive or negative is determined, so that the ecological evolution trend is marked with a degradation characteristic based on negative evolution, the extension and the scheme adjustment in the first successive stage are performed according to the ecological evolution trend and the expected evolution degree, and if the ecological evolution trend is positive, the evolution speed is slow, the first stage is properly prolonged according to the expected evolution trend, and if the ecological evolution trend is negative, the scheme adjustment is needed.
In summary, the embodiments of the present application have at least the following technical effects:
according to the method, remote sensing data of the precursor node are acquired by excavating the regular supervision nodes, real-time ecological information is determined, forest risk prediction is executed based on a risk prediction model, management scheme decision is conducted by taking a configured decision base line as a constraint, a pre-management scheme is determined, and ecological management and feedback adjustment and modification of a target forest zone are conducted.
The technical effects of obtaining the optimal management scheme through regular supervision and periodical scheme optimizing and improving the ecological management effect are achieved.
Embodiment two: based on the same inventive concept as the forest district ecological management method based on risk prediction in the foregoing embodiments, as shown in fig. 4, the present application provides a forest district ecological management system based on risk prediction, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the ecological database reading module 11 is used for determining a comprehensive ecological system of the target forest zone and reading an ecological database of the target forest zone, wherein the ecological database comprises forest-related event records and natural event records with time node identifiers;
the periodic supervision node mining module 12 is configured to mine periodic supervision nodes based on the ecological database, where the ecological database has timeliness updating property, and each periodic supervision node has a predecessor node and a successor node based on a differential time zone;
the risk prediction model training module 13 is used for monitoring and training a risk prediction model based on the ecological database and the regular supervision nodes on the basis of the comprehensive ecological system by the aid of the risk prediction model training module 13;
the real-time ecological information determining module 14 is used for determining a pre-management node based on the regular supervision node, positioning a mapped precursor node and performing remote sensing data retrieval to determine real-time ecological information;
a risk prediction result determining module 15, where the risk prediction result determining module 15 is configured to perform forest risk prediction based on the real-time ecological information based on the risk prediction model, and determine a risk prediction result;
the pre-management scheme determining module 16, wherein the pre-management scheme determining module 16 is configured to perform management scheme decision in the ecological management module with a configured decision baseline as a constraint, execute staged scheme expansion and competitive assimilation optimizing, and determine a pre-management scheme, and the decision baseline is based on protection, recovery, prevention and development collaborative setting at least comprising balanced forestry, and is identified with distributed weights;
the ecological management module 17, the ecological management module 17 is used for carrying out ecological management and feedback adjustment of the target forest zone based on the pre-management scheme.
Further, the real-time ecological information determining module 14 is further configured to perform the following steps:
mining and integrating a precursor risk feature library based on the ecological database;
performing continuous remote sensing monitoring of the target forest zone, performing global identification by taking the precursor risk feature library as a reference, and generating a temporary early warning instruction if target risk features exist;
generating a temporary management node along with the receiving of the temporary early warning instruction;
and calling remote sensing data based on the temporary management node to serve as the real-time ecological information.
Further, the pre-management scheme determination module 16 is further configured to perform the following steps:
performing stepwise division of forestry wind control, and determining a plurality of connection stages, wherein the connection stages at least comprise a prevention stage, an initial stage, a diffusion stage and a recovery stage;
determining an optimizing constraint space based on the decision baseline;
taking the real-time ecological information as an index, and carrying out big data homologous retrieval to determine a target management scheme;
dividing the target management scheme based on the plurality of continuing stages, performing step-by-step expansion optimization of the staged scheme based on the optimizing constraint space, and determining a plurality of staged pre-management schemes;
and performing order splicing on the plurality of staged pre-management schemes to generate the pre-management scheme.
Further, the pre-management scheme determination module 16 is further configured to perform the following steps:
extracting a one-stage scheme based on the target management scheme, performing space local demarcation in the optimizing constraint space, and performing scheme random disturbance to determine a one-stage expansion scheme set;
dividing the one-stage expansion scheme set, and determining a preferred scheme set, an assimilation scheme set and a variation scheme set;
selecting an optimal solution of the optimal scheme set, carrying out homodromous optimization adjustment on the assimilation scheme set, and determining a group of optimization scheme sets;
selecting any one of the preferred scheme set and the group of optimization scheme sets, carrying out homodromous optimization adjustment on the variation scheme set, and determining two groups of optimization scheme sets, wherein the mapping optimization directions of all variation schemes are different;
and checking the optimized scheme set, the group of optimized scheme sets and the two groups of optimized scheme sets, and selecting the optimal solution as a one-stage pre-management scheme.
Further, the pre-management scheme determination module 16 is further configured to perform the following steps:
reading basic information of the target forest zone, and carrying out twin modeling by combining the comprehensive ecological system to determine a forest zone simulation model, wherein the forest zone simulation model is in communication connection with the ecological management module;
based on the forest region simulation model, debugging a simulation mechanism, executing scene simulation based on the pre-management scheme, and determining scheme simulation data;
and judging whether the scheme simulation data meets the expected ecological standard or not, and if not, generating an iterative optimization instruction and transmitting the iterative optimization instruction to the ecological management module.
Further, the ecological management module 17 is further configured to perform the following steps:
determining expected ecological standards based on the pre-management scheme by combining the forest region simulation model, wherein the expected ecological standards correspond to the plurality of continuing stages one by one, and each expected ecological standard has a tolerance interval based on ecological influence factors;
and executing ecological management based on the pre-management scheme on the target forest zone, and performing stepping feedback analysis based on the expected ecological standard in successive stages.
Further, the ecological management module 17 is further configured to perform the following steps:
finishing ecological management in the first continuing stage, and collecting ecological feedback data;
judging whether the ecological feedback data meets the first-stage expected ecological standard, if not, generating a feedback adjustment instruction by taking the current node as an inflection point;
determining an ecological evolution trend of the first continuing stage by combining the ecological feedback data, wherein the ecological evolution trend is marked with degradation characteristics based on negative evolution;
and based on the ecological evolution trend and the expected deviation, carrying out extension and scheme adjustment of the first continuing stage.
It should be noted that the sequence of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
The specification and drawings are merely exemplary of the application and are to be regarded as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (8)
1. The forestry district ecological management method based on risk prediction is characterized by comprising the following steps:
determining a comprehensive ecological system of a target forest zone, and reading an ecological database of the target forest zone, wherein the ecological database comprises forest-related event records and natural event records with time node identifiers;
excavating regular supervision nodes based on the ecological database, wherein the ecological database has aging updating property, and each regular supervision node has a precursor node and a subsequent node based on a differential time zone;
based on the comprehensive ecological system, monitoring and training a risk prediction model based on the ecological database and the regular supervision nodes;
determining a pre-management node based on the regular supervision node, positioning a mapped precursor node, performing remote sensing data retrieval, and determining real-time ecological information;
based on the risk prediction model, performing forestry risk prediction based on the real-time ecological information, and determining a risk prediction result;
taking the configured decision baseline as a constraint, carrying out management scheme decision in an ecological management module, executing staged scheme expansion and competitive assimilation optimizing, and determining a pre-management scheme, wherein the decision baseline is based on protection, recovery, prevention and control and development collaborative setting at least comprising balanced forestry, and is marked with distributed weights;
and carrying out ecological management and feedback adjustment and modification of the target forest zone based on the pre-management scheme.
2. The method of claim 1, characterized in that the method comprises:
mining and integrating a precursor risk feature library based on the ecological database;
performing continuous remote sensing monitoring of the target forest zone, performing global identification by taking the precursor risk feature library as a reference, and generating a temporary early warning instruction if target risk features exist;
generating a temporary management node along with the receiving of the temporary early warning instruction;
and calling remote sensing data based on the temporary management node to serve as the real-time ecological information.
3. The method of claim 1, characterized in that the method comprises:
performing stepwise division of forestry wind control, and determining a plurality of connection stages, wherein the connection stages at least comprise a prevention stage, an initial stage, a diffusion stage and a recovery stage;
determining an optimizing constraint space based on the decision baseline;
taking the real-time ecological information as an index, and carrying out big data homologous retrieval to determine a target management scheme;
dividing the target management scheme based on the plurality of continuing stages, performing step-by-step expansion optimization of the staged scheme based on the optimizing constraint space, and determining a plurality of staged pre-management schemes;
and performing order splicing on the plurality of staged pre-management schemes to generate the pre-management scheme.
4. A method as claimed in claim 3, wherein step-wise extended optimization of the staged approach is performed, the method comprising:
extracting a one-stage scheme based on the target management scheme, performing space local demarcation in the optimizing constraint space, and performing scheme random disturbance to determine a one-stage expansion scheme set;
dividing the one-stage expansion scheme set, and determining a preferred scheme set, an assimilation scheme set and a variation scheme set;
selecting an optimal solution of the optimal scheme set, carrying out homodromous optimization adjustment on the assimilation scheme set, and determining a group of optimization scheme sets;
selecting any one of the preferred scheme set and the group of optimization scheme sets, carrying out homodromous optimization adjustment on the variation scheme set, and determining two groups of optimization scheme sets, wherein the mapping optimization directions of all variation schemes are different;
and checking the optimized scheme set, the group of optimized scheme sets and the two groups of optimized scheme sets, and selecting the optimal solution as a one-stage pre-management scheme.
5. The method of claim 4, wherein after determining the pre-management scheme, the method comprises:
reading basic information of the target forest zone, and carrying out twin modeling by combining the comprehensive ecological system to determine a forest zone simulation model, wherein the forest zone simulation model is in communication connection with the ecological management module;
based on the forest region simulation model, debugging a simulation mechanism, executing scene simulation based on the pre-management scheme, and determining scheme simulation data;
and judging whether the scheme simulation data meets the expected ecological standard or not, and if not, generating an iterative optimization instruction and transmitting the iterative optimization instruction to the ecological management module.
6. The method of claim 5, wherein the ecological management and feedback modification of the target forest area is performed based on the pre-management scheme, the method comprising:
determining expected ecological standards based on the pre-management scheme by combining the forest region simulation model, wherein the expected ecological standards correspond to the plurality of continuing stages one by one, and each expected ecological standard has a tolerance interval based on ecological influence factors;
and executing ecological management based on the pre-management scheme on the target forest zone, and performing stepping feedback analysis based on the expected ecological standard in successive stages.
7. The method of claim 6, wherein the step-wise feedback analysis based on the desired ecological criteria is performed from one stage to the next, the method comprising:
finishing ecological management in the first continuing stage, and collecting ecological feedback data;
judging whether the ecological feedback data meets the first-stage expected ecological standard, if not, generating a feedback adjustment instruction by taking the current node as an inflection point;
determining an ecological evolution trend of the first continuing stage by combining the ecological feedback data, wherein the ecological evolution trend is marked with degradation characteristics based on negative evolution;
and based on the ecological evolution trend and the expected deviation, carrying out extension and scheme adjustment of the first continuing stage.
8. Forestry district ecological management system based on risk prediction, characterized in that, the system includes:
the ecological database reading module is used for determining a comprehensive ecological system of the target forest zone and reading an ecological database of the target forest zone, and the ecological database comprises forest-related event records and natural event records with time node identifiers;
the regular supervision node mining module is used for mining regular supervision nodes based on the ecological database, wherein the ecological database has timeliness updating property, and each regular supervision node has a precursor node and a subsequent node based on a differential time zone;
the risk prediction model training module is used for monitoring and training a risk prediction model based on the ecological database and the regular supervision nodes on the basis of the comprehensive ecological system;
the real-time ecological information determining module is used for determining a pre-management node based on the regular supervision node, positioning a mapped precursor node and performing remote sensing data retrieval to determine real-time ecological information;
the risk prediction result determining module is used for performing forestry risk prediction based on the real-time ecological information based on the risk prediction model to determine a risk prediction result;
the pre-management scheme determining module is used for making management scheme decisions in the ecological management module by taking a configured decision baseline as a constraint, executing staged scheme expansion and competitive assimilation optimizing, and determining a pre-management scheme, wherein the decision baseline is based on protection, recovery, prevention and development collaborative setting at least comprising balanced forestry, and is marked with distributed weights;
and the ecological management module is used for carrying out ecological management and feedback adjustment and modification of the target forest zone based on the pre-management scheme.
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