CN115131678B - Block chain architecture construction method and system for digital intelligent forestry - Google Patents

Block chain architecture construction method and system for digital intelligent forestry Download PDF

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CN115131678B
CN115131678B CN202210769267.1A CN202210769267A CN115131678B CN 115131678 B CN115131678 B CN 115131678B CN 202210769267 A CN202210769267 A CN 202210769267A CN 115131678 B CN115131678 B CN 115131678B
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白颢
沈扬
邹辉晖
李雄威
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Hangzhou Wanlin Digital Chain Technology Service Co ltd
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Abstract

The invention discloses a block chain architecture construction method and a block chain architecture construction system for digital intelligent forestry, which belong to the technical field of digital intelligent forestry and comprise the following steps: acquiring forestry data and forming digital model data; separating the data of the individual plants of the trees; constructing a multi-level digital block chain model and isolating blocks; monitoring the block abnormity through a moving window screening algorithm; and (4) data uplink. The block chain architecture construction method and system for the digital intelligent forestry are effectively applied to the management of the active wood assets, the protection of forestry resources, the monitoring of forest states, the monitoring of ecological environment and climate, the evaluation of carbon sequestration and the like, and a brand-new management method is provided for a forestry management department, a right-to-forest trading department and a double-carbon evaluation trading platform by combining a block chain trust system on the basis of the digitization of the forest assets.

Description

Block chain architecture construction method and system for digital intelligent forestry
Technical Field
The invention belongs to the technical field of digital intelligent forestry, and particularly relates to a block chain architecture construction method and a block chain architecture construction system for digital intelligent forestry.
Background
The forest is an important ecological resource closely related to human survival, once forest diseases and insect pests and forest fires occur in a large scale, the greening results for many years can be destroyed once, so that the serious threat to the ecological civilization construction of China is formed, many adverse consequences of the forest fires cannot be calculated strictly by using numbers, soil erosion and atmospheric and water pollution caused by burning substances are avoided, smoke generated by the fires has obvious negative effects on the global atmospheric system, the greenhouse effect is intensified, the sugar metabolism is realized, and the carbohydrate metabolism is destroyed;
in order to protect difficult greening results and guarantee sustainable development of ecological construction, a healthy forest fire prevention and pest prevention long-acting mechanism needs to be established from a system, and forest intelligent management capacity construction such as law curing forests, scientific management forests, early warning responses, emergency treatment and basic guarantee needs to be improved practically by applying technical means, so that forest disasters can be effectively prevented and actively suppressed, forest resources and life and property safety of people are protected, but the existing management system is a centralized system and mainly depends on a certain mechanism or enterprise, and related data can face risks of being tampered; because the system modes are different and certain system barriers exist among the system modes, data are difficult to share, and meanwhile, the system cannot realize distributed storage, is easy to tamper, cannot be traced and the like, a block chain architecture construction method and a block chain architecture construction system for digital intelligent forestry need to be developed to solve the existing problems.
Disclosure of Invention
The invention aims to provide a block chain architecture construction method and a block chain architecture construction system for digital intelligent forestry, which are used for solving the problem that a data confidence interval cannot be established between intelligent forestry management systems.
In order to achieve the purpose, the invention provides the following technical scheme: a block chain architecture construction method for digital intelligent forestry comprises the following steps:
acquiring forestry basic data through an unmanned aerial vehicle laser radar, an image and a multispectral sensor, and forming digital model data;
separating the data of the individual plants of the trees;
establishing a tree individual plant data model and associating a corresponding attribute database;
constructing a multi-level digital block chain model and isolating blocks; monitoring the block abnormity through a moving window screening algorithm;
and managing data uplink.
Preferably, the forestry basic data includes: laser radar data, hyperspectral data, and high-precision visible image data.
Preferably, the data of the isolated tree single plant comprises:
accurately subdividing the crown breadth of a single tree crown through laser point cloud, hyperspectral imaging and high-precision image;
analyzing the tree crown shielding and fuzzy area, establishing single plant separation algorithm parameters, and evaluating the tree species adaptability of the algorithm model parameters;
and determining the position coordinates, the tree height, the crown size and the total number of the active wood single plants to finish the right determination of the active wood single plants.
Preferably, the laser point cloud includes:
after preprocessing forest moving laser point cloud acquisition data, calculating forest land elevation data and carrying out spatial filtering on the forest land elevation data, denoising the arbor layer data, obtaining multi-scale local extreme points through orthographic projection, estimating the crown central point location degree after back projection, and obtaining single tree crown amplitude accurate subdivision through point cloud characteristic analysis.
Preferably, the hyperspectral imaging comprises:
acquiring airborne forest spectral data, preprocessing the airborne forest spectral data to obtain effective spectral features of various trees, building a forest spectral big database through data source accumulation, and automatically classifying different trees through semi-supervised learning.
Preferably, the high-precision image includes:
acquiring an airborne forest image, processing a digital image of the airborne forest image, calculating different tree species textures of the forest, building a large forest image texture database through data source accumulation, and obtaining a high-dimensional sample and sparse representation through machine learning.
Preferably, the multi-level digital blockchain model includes: sensing layer, deposition layer, block chain layer.
Preferably, the data uplink step includes:
establishing a confusion matrix, wherein TP represents a sample positive value and a predicted value is also a positive value, FP represents that the sample is a negative value and the predicted value is a positive value, FN represents that the sample is a positive value and the predicted value is a negative value, and TN represents that the sample is a negative value and the predicted value is also a negative value;
accuracy is calculated by the following formula,
Figure BDA0003726772980000031
the Precision value Precision is calculated by the following formula,
Figure BDA0003726772980000032
the withdrawal rate Recall is calculated by the following formula,
Figure BDA0003726772980000033
wherein: TP represents that the sample is positive and the predicted value is also positive, FP represents that the sample is negative but the predicted value is positive, FN represents that the sample is positive but the predicted value is negative, TN represents that the sample is negative and the predicted value is also negative;
calculating the relation F1_ score between the accurate value and the withdrawal rate;
Figure BDA0003726772980000034
a blockchain architecture system for digital intelligent forestry, comprising:
the forestry data acquisition module is used for acquiring forestry data;
the single tree data separation module is used for separating single tree data;
the multilevel digital blockchain model building module is used for building a base multilevel digital blockchain model;
isolation Block Module for isolation Block representation
The block abnormity monitoring module is used for carrying out abnormity monitoring through a screening algorithm based on a moving window;
a data uplink module for data uplink;
the visualization module is used for visually displaying the tree data and relevant attributes of the active woods, the forest region geographic information basic data, the ecological environment data, the climate data and the tree growth trend data;
and the forestry asset full life cycle management module is used for establishing a block chain management system and completely managing forest asset full life cycle full variable data.
The invention has the technical effects and advantages that: the block chain architecture construction method and system for the digital intelligent forestry are effectively applied to the management of the active wood assets, the protection of forestry resources, the monitoring of forest states, the monitoring of ecological environment and climate, the evaluation of carbon sequestration and the like, and a brand-new management method is provided for a forestry management department, a right-to-forest trading department and a double-carbon evaluation trading platform by combining a block chain trust system on the basis of the digitization of the forest assets; the method is fully established on the basis of a block chain core technology, solves the problem of trust system construction in data acquisition, processing, circulation, evaluation and comprehensive data management, ensures the uniqueness and non-tampering property of all data, and greatly improves the data confidence interval in an intelligent forestry management system.
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FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a flow chart of the steps of the present invention for separating individual tree data;
FIG. 3 is a schematic diagram of a frame of a forest individual plant separation algorithm of the present invention;
FIG. 4 is a diagram showing the effect of the present invention on the separation and digitization of a single forest plant;
FIG. 5 is a diagram b showing the effect of the present invention on the separation and digitization of a single plant of a forest;
FIG. 6 is a diagram of the effect of the present invention on the isolation and digitization of a single plant of a forest;
FIG. 7 is a block chain digital management and data monitoring architecture diagram of the present invention;
FIG. 8 is a schematic diagram of a quarantine tree structure according to the present invention;
FIG. 9 is a block filtering algorithm based on moving window according to the present invention;
FIG. 10 is a schematic view of a visualization display according to the present invention;
FIG. 11 is a schematic view of an asset full lifecycle management architecture of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a block chain architecture construction method for digital intelligent forestry, which is shown in figure 1 and comprises the following steps:
acquiring forestry basic data through an unmanned aerial vehicle laser radar, an image and a multispectral sensor, and forming digital model data; the forestry basic data comprises: the method comprises the following steps that laser radar data, hyperspectral data and high-precision visible light image data are combined, active wood single plants are separated based on an artificial intelligence data analysis algorithm, and digitized model data are formed;
separating the data of the individual plants of the trees; as shown in fig. 2, the data of the isolated forest individual plants includes:
accurately subdividing the crown breadth of a single tree crown through laser point cloud, hyperspectral imaging and high-precision image; the laser point cloud comprises:
after preprocessing forest moving laser point cloud acquisition data, calculating forest land elevation data and carrying out spatial filtering on the forest land elevation data, carrying out denoising processing on arbor layer data, obtaining multi-scale local extreme points through orthographic projection, carrying out crown central point location estimation after back projection, and obtaining single-plant crown amplitude accurate subdivision through point cloud characteristic analysis;
the hyperspectral imaging includes:
acquiring airborne forest spectral data, preprocessing the airborne forest spectral data to obtain effective spectral features of various trees which are manually calibrated, building a forest spectral big database through data source accumulation, and automatically classifying different trees through semi-supervised learning;
the high-precision image comprises:
acquiring an airborne forest image, processing a digital image of the airborne forest image, calculating different tree species textures of the forest, building a large forest image texture database through data source accumulation, and obtaining a high-dimensional sample and sparse representation through machine learning.
Analyzing the tree crown shielding and fuzzy area, establishing single plant separation algorithm parameters, and evaluating the tree species adaptability of the algorithm model parameters; as shown in figure 3 of the drawings,
determining the position coordinates, the tree height, the crown size and the total number of the single active wood plants to finish the right determination of the single active wood plants; as shown in figures 4, 5 and 6,
and positioning the number, height and space of crowns of each tree to ensure the right to live standing trees.
Establishing a tree individual plant data model and associating a corresponding attribute database;
constructing a multi-level digital block chain model and isolating blocks; monitoring the block abnormity through a moving window screening algorithm; as shown in fig. 9, in the present embodiment, the multi-level digital blockchain model includes: sensing layer, deposition layer, block chain layer.
The isolation block includes a time stamp, a block ID, existing hash values and prefix hash values (hashvalues), a node ID, and an isolation tree, expressed by the following formula:
Block＝[timeStamp||blockchainI D||hashPre||hashCur||NID||iTree].
wherein, block represents an isolation Block,
the timestamp represents a time stamp of the time-series,
the blockchainID represents a block chain number,
hashPre represents the pre-hash value and,
hashCur represents the current hash value,
NID-Node denotes an ID Node number,
the iTree-isolated tree represents an isolation tree;
the isolation tree comprises a plurality of nodes, each node comprises a conclusion threshold beta, and a left node T l And right node T r (ii) a As shown in fig. 8, the isolation tree structure is expressed as follows:
iTree＝[iTree left ||β||iTree right ]wherein:
iTree represents a quarantine tree;
iTree left representing the left barrier tree;
iTree right representing the right isolation tree.
And (4) data uplink.
The data uplink step includes:
establishing a confusion matrix, and displaying a confusion matrix schematic (confluencymatrix) in data management, as shown in the following table;
LABEL (mark POSITIVE NEGATIVE value)
Figure BDA0003726772980000071
Wherein TP represents that the sample is a positive value and the predicted value is also a positive value, FP represents that the sample is a negative value but the predicted value is a positive value, FN represents that the sample is a positive value but the predicted value is a negative value, and TN represents that the sample is a negative value and the predicted value is also a negative value;
accuracy is calculated by the following formula,
Figure BDA0003726772980000081
calculating a Precision value Precision by the following formula;
Figure BDA0003726772980000082
the withdrawal rate Recall is calculated by the following formula,
Figure BDA0003726772980000083
TP represents that the positive value of the sample and the predicted value are also positive values, FP represents that the sample is a negative value but the predicted value is a positive value, FN represents that the sample is a positive value but the predicted value is a negative value, and TN represents that the sample is a negative value and the predicted value is also a negative value;
calculating the relation F1_ score between the accurate value and the withdrawal rate;
Figure BDA0003726772980000084
the subject blockchain algorithm comprises two main algorithms, the following being the basic blockchain algorithm,
Figure BDA0003726772980000091
the following is a block chain algorithm architecture after optimizing the initialization structure,
Figure BDA0003726772980000092
finally completing the construction of a block chain management architecture of the digital forestry data through the algorithm architecture and the evaluation system;
the invention also provides a block chain architecture system for digital intelligent forestry, comprising: the forestry data acquisition module is used for acquiring forestry data;
the single tree data separation module is used for separating single tree data;
the multilevel digital blockchain model building module is used for building a base multilevel digital blockchain model;
isolation Block Module for isolation Block representation
The block abnormity monitoring module is used for carrying out abnormity monitoring through a screening algorithm based on a moving window;
a data uplink module for data uplink;
a visualization module, as shown in fig. 10, configured to visually display the tree vigor data and the related attributes, the forest region geographic information basic data, the ecological environment data, the climate data, and the tree growth data; performing platform visual expression on various comprehensive data in digital forestry management, as shown in fig. 7, including vitality forest plant data and related attributes, forest geographical information basic data, ecological environment data, climate data, forest growth data and the like
The forestry asset life-cycle management module, as shown in fig. 11, is used to establish a block chain management system and perform complete management on the forest asset life-cycle fully-variable data, in this embodiment, on the basis of the vitality tree data block chain management, a set of block chain management system of reference digital currency issuing system is established for the whole forestry asset life-cycle dynamic data, and the forest asset life-cycle fully-variable data is completely managed,
finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.

Claims (3)

1. A block chain architecture construction method for digital intelligent forestry is characterized by comprising the following steps: the method comprises the following steps: acquiring forestry basic data through an unmanned aerial vehicle laser radar, an image and a multispectral sensor, and forming digital model data; separating the data of the individual plants of the trees; establishing a tree individual plant data model and associating a corresponding attribute database; constructing a multi-level digital block chain model and isolating blocks; monitoring the block abnormity through a moving window screening algorithm; data uplink management;the data of the isolated forest individual plants comprises the following steps: accurately subdividing the crown width of a single tree crown by laser point cloud, hyperspectral imaging and high-precision image; analyzing the tree crown shielding and fuzzy area, establishing single plant separation algorithm parameters, and evaluating the tree species adaptability of the algorithm model parameters; determining the position coordinates, the tree height, the crown size and the total number of the single active wood plants to finish the right determination of the single active wood plants; the isolation block comprises a time stamp, a block ID, existing hash values and pre-hash values hashvalues, a node ID and an isolation tree, and is expressed by the following formula:
Figure 865856DEST_PATH_IMAGE002
wherein Block represents an isolation Block, timestamp represents a time stamp, blockchainID represents a Block chain number, hashPre represents a previous hash value, hashCur represents a current hash value, NID-Node represents an ID Node number, and iTree-isolated tree represents an isolation tree; the isolation tree comprises a plurality of nodes, each node comprises a conclusion threshold value beta, and a left node T 1 And right node T r (ii) a The isolation tree structure is expressed as follows:
Figure 379177DEST_PATH_IMAGE002
wherein:
Figure DEST_PATH_IMAGE003
representing a quarantine tree;
Figure 968422DEST_PATH_IMAGE004
showing a left isolation tree;
Figure DEST_PATH_IMAGE005
representing a right treelet; the forestry basic data comprises: laser radar data, hyperspectral data and high-precision visible light image data; the laser point cloud comprises: preprocessing forest moving laser point cloud acquisition data, calculating forest land elevation data, performing spatial filtering, denoising arbor layer data, obtaining multi-scale local extreme points through orthographic projection, and performing reverse projection on the multi-scale local extreme points in a crownEstimating the position of a center point, and obtaining accurate subdivision of the crown amplitude of the single tree crown through point cloud characteristic analysis; the hyperspectral imaging includes: acquiring airborne forest spectral data, preprocessing the airborne forest spectral data to obtain effective spectral features of various trees which are manually calibrated, building a forest spectral big database through data source accumulation, and automatically classifying different trees through semi-supervised learning;
acquiring an onboard forest image, processing a digital image of the onboard forest image, calculating different tree species textures of the forest, building a large forest image texture database through data source accumulation, and obtaining a high-dimensional sample and sparse representation through machine learning; the data uplink step includes: establishing a confusion matrix, and calculating the accuracy by the following formula
Figure 154683DEST_PATH_IMAGE006
，
Figure DEST_PATH_IMAGE007
Calculating an accurate value by the following formula
Figure 346630DEST_PATH_IMAGE008
，
Figure DEST_PATH_IMAGE009
Calculating the withdrawal rate by the following formula
Figure 567527DEST_PATH_IMAGE010
，
Figure DEST_PATH_IMAGE011
Wherein:
Figure 560629DEST_PATH_IMAGE012
indicating that the sample is positive while the prediction value is also positive,
Figure DEST_PATH_IMAGE013
indicating that the sample is negative but the prediction value is positive,
Figure 624400DEST_PATH_IMAGE014
indicating that the sample is positive but the prediction value is negative,
Figure DEST_PATH_IMAGE015
the sample is a negative value, and the predicted value is also a negative value; calculating the relationship between the precision value and the withdrawal rate
Figure 229825DEST_PATH_IMAGE016
；
Figure 164283DEST_PATH_IMAGE017
2. The method of claim 1, wherein the method comprises: the multilevel digitized blockchain model comprises: sensing layer, deposition layer, block chain layer.
3. A construction system based on the block chain architecture construction method for digital intelligent forestry claimed in claim 1 or 2, comprising: the forestry data acquisition module is used for acquiring forestry data; the single tree data separation module is used for separating single tree data; the multilevel digital blockchain model building module is used for building a base multilevel digital blockchain model; the isolation block module is used for isolating the block expression block abnormity monitoring module and carrying out abnormity monitoring through a screening algorithm based on a moving window; a data uplink module for data uplink; the visualization module is used for visually displaying the tree data and relevant attributes of the active woods, the forest region geographic information basic data, the ecological environment data, the climate data and the tree growth trend data; and the forestry asset full life cycle management module is used for establishing a block chain management system and completely managing the forest asset full life cycle full variable data.
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