CN117726194B - Forestry environment analysis system based on big data - Google Patents
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- 238000004458 analytical method Methods 0.000 title claims abstract description 41
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- 239000002689 soil Substances 0.000 claims abstract description 131
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Abstract
The invention discloses a forestry environment analysis system based on big data, which relates to the technical field of forestry environment analysis, and monitors plant information and animal information in a current forest, acquires corresponding plant information and animal information according to historical information each time, analyzes ecological changes in the forest, acquires soil information of each high layer of the forest, further analyzes the changes of the soil in the forest, monitors the pest information of each high layer of the forest, and further analyzes the changes of the pests in the forest, so that environmental health in the forest is analyzed, intelligent and automatic analysis of forest environment health is realized, changes of ecology, soil and pests in the forest are clearly reflected, timeliness found in forest environment abnormality is greatly improved, effective references are provided for subsequent forest environment management, and stability of forest ecological health is ensured.
Description
Technical Field
The invention relates to the technical field of forestry environment analysis, in particular to a forestry environment analysis system based on big data.
Background
The forest environment influences the ecological development of the forest, and the data of the forest environment are collected, processed and analyzed by utilizing a big data analysis technology to find potential abnormal conditions of the forest, so that a powerful tool is provided for forest management and environmental protection, a decision maker is helped to better understand forest resources and an ecological system, and accordingly more effective policies and management measures are formulated. At the same time, it helps to promote sustainable forestry and ecological conservation practices.
The traditional analysis of the forest environment mainly analyzes the ecological health of the forest according to the data of animals and plants in the forest, but does not analyze the change state of the animals and plants in the forest according to the animal and plant information and the animal and plant information collected by history, and further analyzes the ecological health in the forest according to the change state of the animals and plants, so that timely early warning and treatment cannot be performed when the ecological change in the forest is abnormal, and the effect and efficiency of forest ecological protection cannot be reflected; on one hand, the traditional forest soil environment monitoring and analysis mainly carries out soil random sampling in a forest so as to analyze the state of the soil in the forest, but the types of trees in different height layers in the forest are different, meanwhile, the soil environments suitable for growth of the trees in different types are also different, the traditional technology does not divide the height layers of the forest when the soil is sampled, and the soil state is analyzed, the forest soil environment monitoring and analysis accuracy cannot be reflected, and effective reference cannot be provided for the analysis of the subsequent forest environment; on the other hand, the plant growth in the forest is influenced by the plant diseases and insect pests in the forest, but most of the traditional technologies analyze the plant diseases and insect pests in the forest according to the type and the quantity of the plant diseases and insect pests, and the change of the plant diseases and insect pests in the forest is not analyzed according to the historical plant diseases and insect pests information, so that abnormal plant diseases and insect pests in the forest cannot be timely found, the timeliness of the plant diseases and insect pests treatment in the forest cannot be guaranteed, the plant growth in the forest is influenced, and the stability of a forest ecological system cannot be guaranteed.
Disclosure of Invention
Aiming at the technical defects, the invention aims to provide a forestry environment analysis system based on big data.
In order to solve the technical problems, the invention adopts the following technical scheme: the invention provides a forestry environment analysis system based on big data, which comprises the following modules: the forest model building module is used for collecting an image set corresponding to the current forest, further building a three-dimensional model corresponding to the forest, and simultaneously obtaining plant information and animal information corresponding to the current forest;
the forest ecological analysis module is used for extracting the number of plant types in the current forest, the number of plants corresponding to each plant type and the size of each plant in each plant type from the plant information corresponding to the current forest, extracting the number of animal types, the number of animals of each animal type and the active area of each animal type in the current forest from the animal information corresponding to the current forest, collecting the corresponding plant information and animal information based on the historical information of the forest each time stored in the cloud database, and calculating an ecological variation evaluation coefficient corresponding to the forest;
the soil information acquisition module is used for dividing a forest into all height layers according to preset heights, setting all acquisition points in all height layers of the forest according to preset horizontal intervals, and further acquiring soil information corresponding to all the acquisition points in all height layers of the current forest;
the soil health analysis module is used for acquiring each plant type corresponding to each height layer of the current forest according to the three-dimensional model corresponding to the forest, and simultaneously acquiring the soil information corresponding to each acquisition point in each height layer of the current forest based on the soil information corresponding to each acquisition point in each height layer of the current forest and the historical information stored in the cloud database for each time, and calculating a soil health change evaluation coefficient corresponding to the forest;
The plant diseases and insect pests acquisition module is used for acquiring plant diseases and insect pests information corresponding to each acquisition point in each height layer of the current forest;
the plant diseases and insect pests analysis module is used for extracting historical information of the forest for each time from the cloud database to acquire plant diseases and insect pests information corresponding to each acquisition point in each height layer, and calculating plant diseases and insect pests change evaluation coefficients corresponding to the forest based on the plant diseases and insect pests information corresponding to each acquisition point in each height layer of the current forest;
the forest environment analysis module is used for analyzing the environmental health evaluation coefficient corresponding to the forest according to the ecological change evaluation coefficient, the soil health change evaluation coefficient and the plant diseases and insect pests change evaluation coefficient corresponding to the forest and judging the environmental health state corresponding to the forest;
and the execution terminal is used for executing the weakening operation when the environmental health state corresponding to the forest is in the weakening state.
Preferably, the calculating the ecological variation evaluation coefficient corresponding to the forest specifically comprises the following steps: extracting the number of plant types corresponding to forest historical information collection, the number of plants corresponding to each plant type and the size of each plant in each plant type from plant information corresponding to forest historical information collection stored in a cloud database, comparing the sizes of each plant in each plant type corresponding to forest historical information collection, and selecting a mode from the comparison results as a plant reference size corresponding to each plant type of forest historical information collection; comparing the sizes of plants in the current forest plant types, selecting mode as the plant reference size of the current forest plant types, calculating to obtain plant change evaluation coefficients corresponding to the forest, and marking as；
Extracting the number of animal types, the number of animals of each animal type and the active area of each animal type corresponding to each historical information acquisition of the forest from animal information corresponding to each historical information acquisition of the forest stored in a cloud database, and further obtaining an animal change evaluation coefficient corresponding to the forest through calculation, and marking the animal change evaluation coefficient as；
According to the calculation formulaObtaining the ecological change evaluation coefficient/>, corresponding to the forestWherein/>、/>Respectively set plant change evaluation coefficients and weight factors corresponding to animal change evaluation coefficients.
Preferably, the calculation formula of the plant change evaluation coefficient corresponding to the forest is: Wherein zn represents the number of plant species in the current forest,/> Representing the number of plant species corresponding to the r-th historical information acquisition of a forest,/>、/>Respectively representing the number of plants and the reference size of the plants corresponding to the ith plant type in the current forest,/>、/>Respectively representing the number of plants and the reference size of the plants corresponding to the ith plant category in the r-th historical information acquisition of the forest, r represents the number corresponding to each historical information acquisition, r=1, 2..g..g., i represents the number corresponding to each plant species, i represents each plant species the corresponding number is used for the purpose of providing the corresponding codeAre all less than or equal to n,/>、/>、/>Respectively the set weight factors corresponding to the plant species number, the plant number and the plant reference size.
Preferably, the calculation formula of the animal change evaluation coefficient corresponding to the forest is as follows: Where dm represents the number of animal species in the current forest,/> Represents the number of animal species corresponding to the r-th historical information collection of the forest,、/>Respectively represent the number and active area of animals corresponding to the jth animal species in the current forest,/>、/>Respectively representing the number and active area of animals corresponding to the jth animal category in the forest r-th historical information collection, j represents the number corresponding to each animal category, j=1, 2Are all less than or equal to m,/>、/>、/>Respectively the weight factors corresponding to the set animal species number, animal number and active area.
Preferably, the soil information corresponding to each collecting point in each height layer of the current forest comprises trace element content, soil volume weight and ion content corresponding to each collecting point in each height layer.
The soil information of each collection point in each height layer corresponding to each time of historical information collection of the forest comprises trace element content, soil volume weight and ion content of each collection point in each height layer corresponding to each time of historical information collection.
Preferably, the calculating the soil health change evaluation coefficient corresponding to the forest comprises the following specific calculating process: comparing each plant type corresponding to each height layer of the current forest with the suitable soil information corresponding to each plant type stored in the cloud database to obtain the suitable soil information corresponding to each plant type of each height layer of the current forest;
Respectively calculating the trace element content, the soil volume weight and the ion content corresponding to each acquisition point in each height layer through an average value to obtain the average trace element content, the average soil volume weight and the average ion content corresponding to each height layer, wherein the average trace element content, the average soil volume weight and the average ion content are used as the trace element content, the soil volume weight and the ion content corresponding to each height layer; the trace element content, the soil volume weight and the ion content of each collection point in each height layer corresponding to each historical information collection are respectively calculated by means to obtain the average trace element content, the average soil volume weight and the average ion content corresponding to each height layer corresponding to each historical information collection, and the average trace element content, the soil volume weight and the ion content corresponding to each height layer corresponding to each historical information collection are respectively marked as 、/>、/>Y represents the number corresponding to each height layer, y=1, 2. The term p is used herein, p is any integer greater than 2;
Based on each time of history information acquisition, corresponding to each height layer, the trace element content, the soil volume weight and the ion content are calculated, and the average trace element content change rate, the average soil volume weight change rate and the average ion content change rate of each height layer are respectively recorded as 、/>、/>；
By calculation formulaObtaining the soil suitability evaluation coefficient/>, corresponding to the forestWherein/>、/>、/>Respectively representing the content of suitable microelements, the volume weight of suitable soil and the content of suitable ions of the ith plant species corresponding to the present y-th height layer of the forest,/>、/>、Respectively represents the content of trace elements, the volume weight of soil and the ion content corresponding to the y-th height layer,/>、/>、/>Respectively set permissible microelement content difference, soil volume weight difference and ion content difference,/>、/>、/>Respectively setting weight factors corresponding to the trace element content, the soil volume weight and the ion content;
According to the calculation formula Obtaining the soil change evaluation coefficient/>, corresponding to the forestWherein/>、/>、/>Respectively represents the trace element content, the soil volume weight and the ion content of the y-th high layer corresponding to the g-th historical information collection,/>、/>、/>Respectively the weight factors corresponding to the trace element content change rate, the soil volume weight change rate and the ion content change rate;
By calculation formula Obtaining the soil health change evaluation coefficient/>, corresponding to the forestWherein/>、/>Respectively set soil suitability evaluation coefficients and weight factors corresponding to soil change evaluation coefficients.
Preferably, the pest information corresponding to each collection point in each height layer includes the type of each pest and the number of the pests of each pest type.
Preferably, the calculating the plant diseases and insect pests change evaluation coefficient corresponding to the forest comprises the following specific calculating process: extracting the plant diseases and insect pests types and the plant diseases and insect pests numbers of the plant diseases and insect pests types of the plant diseases and insect pests of the plant diseases of each collection point in each height layer from the plant diseases and insect pests information of each collection point in each height layer of the forest, further counting the historical plant diseases and insect pests types of the forest, and accumulating the plant diseases and insect pests numbers of the plant diseases and insect pests types of each collection point in each height layer of each historical information collection to obtain the total plant diseases and insect pests number of each plant diseases and insect pests corresponding to each collection point of each historical information collection;
Counting the types of the plant diseases and insect pests of the forest according to the types of the plant diseases and insect pests corresponding to the collecting points in each height layer, and accumulating the number of the plant diseases and insect pests corresponding to the collecting points in each height layer to obtain the total number of the plant diseases and insect pests in the forest;
comparing each plant disease and insect pest type corresponding to the forest with each plant disease and insect pest type corresponding to the forest, if the plant disease and insect pest type corresponding to the forest is different from each plant disease and insect pest type corresponding to the forest, marking the plant disease and insect pest type as a newly increased plant disease and insect pest type, and counting the number of the newly increased plant disease and insect pest types corresponding to the forest;
acquiring historical information of each time, calculating the total number of the plant diseases and insect pests corresponding to each plant disease and insect pest type through a mean value to obtain the historical average total number of the plant diseases and insect pests of each plant disease and insect pest type of the forest, and substituting the historical average total number into a calculation formula Obtaining the plant diseases and insect pests change evaluation coefficient/>, corresponding to the forestWherein/>Representing the number of newly increased plant diseases and insect pests corresponding to forests,/>Newly increasing the number of plant diseases and insect pests for the set permission,/>、/>The average total number and the total number of pest histories of the f-th pest species of the forest are respectively represented, f represents the number corresponding to each pest species, f=1, 2、/>Respectively setting weight factors corresponding to the number of newly added plant diseases and insect pests and the total number of plant diseases and insect pests.
Preferably, the analyzing the environmental health assessment coefficient corresponding to the forest specifically includes the following steps: according to the calculation formulaObtaining the environmental health evaluation coefficient/>, corresponding to the forestWherein/>、/>、/>Respectively represent the ecological change evaluation coefficient, the soil health change evaluation coefficient and the plant diseases and insect pests change evaluation coefficient corresponding to the forest、/>、/>Respectively set ecological change evaluation coefficients, soil health change evaluation coefficients and weight factors corresponding to the plant diseases and insect pests change evaluation coefficients.
Preferably, the specific judging process for judging the environmental health state corresponding to the forest is as follows: comparing the environmental health assessment coefficient corresponding to the forest with an environmental health assessment coefficient threshold stored in the cloud database, if the environmental health assessment coefficient corresponding to the forest is greater than or equal to the environmental health assessment coefficient threshold, judging that the environmental health state corresponding to the broken forest is in a normal state, otherwise, judging that the environmental health state corresponding to the forest is in a weak state.
The invention has the beneficial effects that: according to the forest environment analysis system based on big data, the plant information and the animal information in the current forest are monitored, the corresponding plant information and the animal information are acquired according to the historical information, the ecological change in the forest is analyzed, meanwhile, the soil information of each high layer of the forest is acquired, the change of the soil in the forest is further analyzed, the pest and disease information of each high layer of the forest is monitored, and accordingly the change of the pest and disease in the forest is analyzed, environmental health in the forest is analyzed, the defect of the traditional technology in forest ecological health analysis is overcome, the intelligent and automatic analysis of forest environment health is realized, the change of ecology, soil and pests in the forest is clearly reflected, the timeliness found in abnormal forest environment is greatly improved, effective references are provided for the subsequent forest environment management, and the stability of forest ecological health is ensured.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the system structure of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides a forestry environment analysis system based on big data, which comprises the following modules: the system comprises a forest model building module, a forest ecological analysis module, a soil information acquisition module, a soil health analysis module, a plant disease and insect pest acquisition module, a plant disease and insect pest analysis module, a forest environment analysis module, an execution terminal and a cloud database.
The forest model building module is used for collecting an image set corresponding to the current forest, further building a three-dimensional model corresponding to the forest, and simultaneously obtaining plant information and animal information corresponding to the current forest;
the image set corresponding to the current forest is collected through the camera carried by the unmanned aerial vehicle.
The plant information corresponding to the forest comprises the number of plant types, the number of plants corresponding to each plant type and the size of each plant in each plant type;
the forest-corresponding animal information includes the number of animal species, the number of animals of each animal species, and the active area of each animal species.
The method comprises the steps of acquiring images corresponding to plants in a forest based on a three-dimensional model corresponding to the forest, comparing the images of the drink of the plants with characteristic image sets corresponding to the plant types stored in a cloud database to obtain plant types corresponding to the plants in the forest, counting the number of plant types and the number of plants corresponding to the plant types, acquiring the sizes corresponding to the plants from the images corresponding to the plants in the forest, and obtaining the sizes of the plants in the plant types according to the plant types corresponding to the plants.
Based on a three-dimensional model corresponding to a forest, acquiring images corresponding to animals in the forest, comparing the images with a characteristic image set corresponding to each animal type stored in a cloud database to acquire animal types corresponding to each animal, counting the number of animal types and the number of animals of each animal type, acquiring positions corresponding to each animal based on the three-dimensional model corresponding to the forest, sequentially connecting the animal positions with the same animal type, acquiring an active area corresponding to each animal type, and acquiring an active area corresponding to each animal type from the three-dimensional model corresponding to the forest as an active area corresponding to each animal type. And sending the plant information and the animal information corresponding to the current forest to a cloud database for storage.
The forest ecological analysis module is used for extracting the number of plant types in the current forest, the number of plants corresponding to each plant type and the size of each plant in each plant type from the plant information corresponding to the current forest, extracting the number of animal types, the number of animals of each animal type and the active area of each animal type in the current forest from the animal information corresponding to the current forest, collecting the corresponding plant information and animal information based on the historical information of the forest each time stored in the cloud database, and calculating an ecological variation evaluation coefficient corresponding to the forest;
In a specific embodiment, the calculating the ecological variation evaluation coefficient corresponding to the forest specifically includes the following steps: extracting the number of plant types corresponding to forest historical information collection, the number of plants corresponding to each plant type and the size of each plant in each plant type from plant information corresponding to forest historical information collection stored in a cloud database, comparing the sizes of each plant in each plant type corresponding to forest historical information collection, and selecting a mode from the comparison results as a plant reference size corresponding to each plant type of forest historical information collection; comparing the sizes of plants in the current forest plant types, selecting mode as the plant reference size of the current forest plant types, calculating to obtain plant change evaluation coefficients corresponding to the forest, and marking as ；
In the above, the calculation formula of the plant change evaluation coefficient corresponding to the forest is: Wherein zn represents the number of plant species in the current forest,/> Representing the number of plant species corresponding to the r-th historical information acquisition of a forest,/>、/>Respectively representing the number of plants and the reference size of the plants corresponding to the ith plant type in the current forest,/>、/>Respectively representing the number of plants and the reference size of the plants corresponding to the ith plant category in the r-th historical information acquisition of the forest, r represents the number corresponding to each historical information acquisition, r=1, 2..g..g., i represents the number corresponding to each plant species, i represents each plant species the corresponding number is used for the purpose of providing the corresponding codeAre all less than or equal to n,/>、/>、/>Respectively the set weight factors corresponding to the plant species number, the plant number and the plant reference size.
It should be noted that the number of the substrates,、/>、/>All greater than 0 and less than 1.
Extracting the number of animal types, the number of animals of each animal type and the active area of each animal type corresponding to each historical information acquisition of the forest from animal information corresponding to each historical information acquisition of the forest stored in a cloud database, and further obtaining an animal change evaluation coefficient corresponding to the forest through calculation, and marking the animal change evaluation coefficient as；
In the above, the calculation formula of the animal change evaluation coefficient corresponding to the forest is: Where dm represents the number of animal species in the current forest,/> Represents the number of animal species corresponding to the r-th historical information collection of the forest,、/>Respectively represent the number and active area of animals corresponding to the jth animal species in the current forest,/>、/>Respectively representing the number and active area of animals corresponding to the jth animal category in the forest r-th historical information collection, j represents the number corresponding to each animal category, j=1, 2Are all less than or equal to m,/>、/>、/>Respectively the weight factors corresponding to the set animal species number, animal number and active area.
It should be noted that the number of the substrates,、/>、/>All greater than 0 and less than 1.
According to the calculation formulaObtaining the ecological change evaluation coefficient/>, corresponding to the forestWherein/>、Respectively set plant change evaluation coefficients and weight factors corresponding to animal change evaluation coefficients.
It should be noted that the number of the substrates,、/>All greater than 0 and less than 1.
The soil information acquisition module is used for dividing a forest into all height layers according to preset heights, setting all acquisition points in all height layers of the forest according to preset horizontal intervals, and further acquiring soil information corresponding to all the acquisition points in all height layers of the current forest;
In the above, the soil information corresponding to each collecting point in each height layer of the current forest includes trace element content, soil volume weight and ion content corresponding to each collecting point in each height layer.
The soil sampling is carried out on each collecting point in each height layer of the current forest, and then the soil information corresponding to each collecting point in each height layer of the current forest is collected through a soil analyzer. And sending the soil information corresponding to each acquisition point in each height layer of the current forest to a cloud database for storage.
The soil health analysis module is used for acquiring each plant type corresponding to each height layer of the current forest according to the three-dimensional model corresponding to the forest, and simultaneously acquiring the soil information corresponding to each acquisition point in each height layer of the current forest based on the soil information corresponding to each acquisition point in each height layer of the current forest and the historical information stored in the cloud database for each time, and calculating a soil health change evaluation coefficient corresponding to the forest;
In a specific embodiment, the calculating the soil health change evaluation coefficient corresponding to the forest specifically includes the following steps: comparing each plant type corresponding to each height layer of the current forest with the suitable soil information corresponding to each plant type stored in the cloud database to obtain the suitable soil information corresponding to each plant type of each height layer of the current forest;
Respectively calculating the trace element content, the soil volume weight and the ion content corresponding to each acquisition point in each height layer through an average value to obtain the average trace element content, the average soil volume weight and the average ion content corresponding to each height layer, wherein the average trace element content, the average soil volume weight and the average ion content are used as the trace element content, the soil volume weight and the ion content corresponding to each height layer; the trace element content, the soil volume weight and the ion content of each collection point in each height layer corresponding to each historical information collection are respectively calculated by means to obtain the average trace element content, the average soil volume weight and the average ion content corresponding to each height layer corresponding to each historical information collection, and the average trace element content, the soil volume weight and the ion content corresponding to each height layer corresponding to each historical information collection are respectively marked as 、/>、/>Y represents the number corresponding to each height layer, y=1, 2. The term p is used herein, p is any integer greater than 2;
Based on each time of history information acquisition, corresponding to each height layer, the trace element content, the soil volume weight and the ion content are calculated, and the average trace element content change rate, the average soil volume weight change rate and the average ion content change rate of each height layer are respectively recorded as 、/>、/>；
In the above, the calculation formula of the average trace element content change rate of each height layer is as follows: wherein/> And (5) expressing the trace element content of the layer corresponding to the y-th high level acquired by the r-1 th historical information.
The average soil volume weight change rate and the average ion content change rate of each height layer are calculated according to the calculation mode of the average trace element content change rate of each height layer.
By calculation formulaObtaining the soil suitability evaluation coefficient/>, corresponding to the forestWherein/>、/>、/>Respectively representing the content of suitable microelements, the volume weight of suitable soil and the content of suitable ions of the ith plant species corresponding to the present y-th height layer of the forest,/>、/>、/>Respectively represents the content of trace elements, the volume weight of soil and the ion content corresponding to the y-th height layer,/>、/>、/>Respectively set permissible microelement content difference, soil volume weight difference and ion content difference,/>、/>、/>Respectively setting weight factors corresponding to the trace element content, the soil volume weight and the ion content;
It should be noted that the number of the substrates, 、/>、/>All greater than 0 and less than 1.
According to the calculation formulaObtaining the soil change evaluation coefficient/>, corresponding to the forestWherein/>、/>、/>Respectively represents the trace element content, the soil volume weight and the ion content of the y-th high layer corresponding to the g-th historical information collection,/>、/>、/>Respectively the weight factors corresponding to the trace element content change rate, the soil volume weight change rate and the ion content change rate;
It should be noted that the number of the substrates, 、/>、/>All greater than 0 and less than 1.
By calculation formulaObtaining the soil health change evaluation coefficient corresponding to the forestWherein/>、/>Respectively set soil suitability evaluation coefficients and weight factors corresponding to soil change evaluation coefficients.
It should be noted that the number of the substrates,、/>All greater than 0 and less than 1.
The plant diseases and insect pests acquisition module is used for acquiring plant diseases and insect pests information corresponding to each acquisition point in each height layer of the current forest;
in the above, the pest information corresponding to each collecting point in each height layer includes the type of each pest and the number of the pest of each pest type.
The pest and disease detector is arranged at each collecting point in each height layer, and pest and disease information corresponding to each collecting point in each height layer of the current forest is collected through the pest and disease detector at each collecting point in each height layer. And sending the pest and disease information corresponding to each acquisition point in each height layer of the current forest to a cloud database for storage.
The plant diseases and insect pests analysis module is used for extracting historical information of the forest for each time from the cloud database to acquire plant diseases and insect pests information corresponding to each acquisition point in each height layer, and calculating plant diseases and insect pests change evaluation coefficients corresponding to the forest based on the plant diseases and insect pests information corresponding to each acquisition point in each height layer of the current forest;
In a specific embodiment, the calculating the evaluation coefficient of the pest and disease damage variation corresponding to the forest specifically includes the following steps: extracting the plant diseases and insect pests types and the plant diseases and insect pests numbers of the plant diseases and insect pests types of the plant diseases and insect pests of the plant diseases of each collection point in each height layer from the plant diseases and insect pests information of each collection point in each height layer of the forest, further counting the historical plant diseases and insect pests types of the forest, and accumulating the plant diseases and insect pests numbers of the plant diseases and insect pests types of each collection point in each height layer of each historical information collection to obtain the total plant diseases and insect pests number of each plant diseases and insect pests corresponding to each collection point of each historical information collection;
Counting the types of the plant diseases and insect pests of the forest according to the types of the plant diseases and insect pests corresponding to the collecting points in each height layer, and accumulating the number of the plant diseases and insect pests corresponding to the collecting points in each height layer to obtain the total number of the plant diseases and insect pests in the forest;
comparing each plant disease and insect pest type corresponding to the forest with each plant disease and insect pest type corresponding to the forest, if the plant disease and insect pest type corresponding to the forest is different from each plant disease and insect pest type corresponding to the forest, marking the plant disease and insect pest type as a newly increased plant disease and insect pest type, and counting the number of the newly increased plant disease and insect pest types corresponding to the forest;
acquiring historical information of each time, calculating the total number of the plant diseases and insect pests corresponding to each plant disease and insect pest type through a mean value to obtain the historical average total number of the plant diseases and insect pests of each plant disease and insect pest type of the forest, and substituting the historical average total number into a calculation formula Obtaining the plant diseases and insect pests change evaluation coefficient/>, corresponding to the forestWherein/>Represents the number of newly-increased plant diseases and insect pests corresponding to the forest,Newly increasing the number of plant diseases and insect pests for the set permission,/>、/>The average total number and the total number of pest histories of the f-th pest species of the forest are respectively represented, f represents the number corresponding to each pest species, f=1, 2、/>Respectively setting weight factors corresponding to the number of newly added plant diseases and insect pests and the total number of plant diseases and insect pests.
It should be noted that the number of the substrates,、/>All greater than 0 and less than 1.
The forest environment analysis module is used for analyzing the environmental health evaluation coefficient corresponding to the forest according to the ecological change evaluation coefficient, the soil health change evaluation coefficient and the plant diseases and insect pests change evaluation coefficient corresponding to the forest and judging the environmental health state corresponding to the forest;
in a specific embodiment, the analyzing the environmental health assessment coefficient corresponding to the forest specifically includes the following steps: according to the calculation formula Obtaining the environmental health evaluation coefficient/>, corresponding to the forestWherein/>、/>、/>Respectively represent the ecological change evaluation coefficient, the soil health change evaluation coefficient and the plant diseases and insect pests change evaluation coefficient corresponding to the forest、/>、/>Respectively set ecological change evaluation coefficients, soil health change evaluation coefficients and weight factors corresponding to the plant diseases and insect pests change evaluation coefficients.
It should be noted that the number of the substrates,、/>、/>All greater than 0 and less than 1.
In another specific embodiment, the determining the environmental health status corresponding to the forest specifically includes the following steps: comparing the environmental health assessment coefficient corresponding to the forest with an environmental health assessment coefficient threshold stored in the cloud database, if the environmental health assessment coefficient corresponding to the forest is greater than or equal to the environmental health assessment coefficient threshold, judging that the environmental health state corresponding to the broken forest is in a normal state, otherwise, judging that the environmental health state corresponding to the forest is in a weak state.
And the execution terminal is used for executing the weakening operation when the environmental health state corresponding to the forest is in the weakening state.
In a specific embodiment, when the environmental health state corresponding to the forest is in a weakened state, an alarm prompt is given by an alarm of the execution terminal, and meanwhile, the environmental health weakened prompt is displayed in a display of the execution terminal, for example: the environment health state corresponding to the current forest is in a debilitating state, and a forest manager is requested to process.
The cloud database is used for storing plant information and animal information corresponding to forest historical information collection, storing soil information corresponding to forest historical information collection at each collection point in each height layer, storing plant disease and insect pest information corresponding to forest historical information collection at each collection point in each height layer, and storing proper soil information corresponding to each plant type and environmental health evaluation coefficient threshold.
According to the embodiment of the invention, the plant information and the animal information in the current forest are monitored, the corresponding plant information and animal information are acquired according to each time of historical information, the ecological change in the forest is analyzed, meanwhile, the soil information of each height layer of the forest is acquired, the change of the soil in the forest is further analyzed, and the pest information of each height layer in the forest is monitored, so that the change of the pests in the forest is analyzed, the environmental health in the forest is analyzed, the defect of the ecological health analysis of the forest in the traditional technology is overcome, the intelligent and automatic analysis of the environmental health of the forest is realized, the change of ecology, soil and pests in the forest is clearly reflected, the timeliness found in the case of forest environmental abnormality is greatly improved, an effective reference is provided for the subsequent forest environmental management, and the stability of the ecological health of the forest is ensured.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of the invention or beyond the scope of the invention as defined in the description.
Claims (7)
1. The forestry environment analysis system based on big data is characterized by comprising the following modules:
the forest model building module is used for collecting an image set corresponding to the current forest, further building a three-dimensional model corresponding to the forest, and simultaneously obtaining plant information and animal information corresponding to the current forest;
the forest ecological analysis module is used for extracting the number of plant types in the current forest, the number of plants corresponding to each plant type and the size of each plant in each plant type from the plant information corresponding to the current forest, extracting the number of animal types, the number of animals of each animal type and the active area of each animal type in the current forest from the animal information corresponding to the current forest, collecting the corresponding plant information and animal information based on the historical information of the forest each time stored in the cloud database, and calculating an ecological variation evaluation coefficient corresponding to the forest;
the ecological change evaluation coefficient corresponding to the forest is calculated, and the specific calculation process is as follows:
Extracting the number of plant types corresponding to forest historical information collection, the number of plants corresponding to each plant type and the size of each plant in each plant type from plant information corresponding to forest historical information collection stored in a cloud database, comparing the sizes of each plant in each plant type corresponding to forest historical information collection, and selecting a mode from the comparison results as a plant reference size corresponding to each plant type of forest historical information collection; comparing the sizes of plants in the current forest plant types, selecting mode as the plant reference size of the current forest plant types, calculating to obtain plant change evaluation coefficients corresponding to the forest, and marking as
Extracting the number of animal types, the number of animals of each animal type and the active area of each animal type corresponding to each historical information acquisition of the forest from animal information corresponding to each historical information acquisition of the forest stored in a cloud database, and further obtaining an animal change evaluation coefficient corresponding to the forest through calculation, and marking the animal change evaluation coefficient as
According to the calculation formulaObtaining the ecological change evaluation coefficient/>, corresponding to the forestWherein epsilon 1、ε2 is a set plant change evaluation coefficient and a weight factor corresponding to the animal change evaluation coefficient respectively;
the calculation formula of the plant change evaluation coefficient corresponding to the forest is as follows: Wherein zn represents the number of plant species in the current forest, zn r represents the number of plant species corresponding to the r-th historical information acquisition of the forest, an i、cni represents the number of plants and the plant reference size corresponding to the i-th plant species in the current forest, an ri、cnri represents the number of plants and the plant reference size corresponding to the i-th plant species in the r-th historical information acquisition of the forest, r represents the number corresponding to each historical information acquisition, r=1, 2..g, i represents the number corresponding to each plant species, i=1, 2..n, g and n are any integers greater than 2, zn and zn r are all less than or equal to n, and gamma 1、γ2、γ3 are weight factors corresponding to the set number of plant species, the number of plants and the plant reference size, respectively;
the calculation formula of the animal change evaluation coefficient corresponding to the forest is as follows: Wherein dm represents the number of animal species in the current forest, dm r represents the number of animal species corresponding to the r-th historical information acquisition of the forest, am j、smj represents the number of animals and the active area corresponding to the j-th animal species in the current forest, am rj、smrj represents the number of animals and the active area corresponding to the j-th animal species in the r-th historical information acquisition of the forest, j represents the number corresponding to each animal species, j=1, 2.
The soil information acquisition module is used for dividing a forest into all height layers according to preset heights, setting all acquisition points in all height layers of the forest according to preset horizontal intervals, and further acquiring soil information corresponding to all the acquisition points in all height layers of the current forest;
the soil health analysis module is used for acquiring each plant type corresponding to each height layer of the current forest according to the three-dimensional model corresponding to the forest, and simultaneously acquiring the soil information corresponding to each acquisition point in each height layer of the current forest based on the soil information corresponding to each acquisition point in each height layer of the current forest and the historical information stored in the cloud database for each time, and calculating a soil health change evaluation coefficient corresponding to the forest;
The plant diseases and insect pests acquisition module is used for acquiring plant diseases and insect pests information corresponding to each acquisition point in each height layer of the current forest;
the plant diseases and insect pests analysis module is used for extracting historical information of the forest for each time from the cloud database to acquire plant diseases and insect pests information corresponding to each acquisition point in each height layer, and calculating plant diseases and insect pests change evaluation coefficients corresponding to the forest based on the plant diseases and insect pests information corresponding to each acquisition point in each height layer of the current forest;
the forest environment analysis module is used for analyzing the environmental health evaluation coefficient corresponding to the forest according to the ecological change evaluation coefficient, the soil health change evaluation coefficient and the plant diseases and insect pests change evaluation coefficient corresponding to the forest and judging the environmental health state corresponding to the forest;
and the execution terminal is used for executing the weakening operation when the environmental health state corresponding to the forest is in the weakening state.
2. A big data based forestry environment analysis system according to claim 1, wherein the soil information corresponding to each collection point in each height layer of the current forest comprises trace element content, soil volume weight and ion content corresponding to each collection point in each height layer.
The soil information of each collection point in each height layer corresponding to each time of historical information collection of the forest comprises trace element content, soil volume weight and ion content of each collection point in each height layer corresponding to each time of historical information collection.
3. The big data-based forestry environment analysis system of claim 2, wherein the calculating the soil health change evaluation coefficient corresponding to the forest comprises the following specific calculation processes:
Comparing each plant type corresponding to each height layer of the current forest with the suitable soil information corresponding to each plant type stored in the cloud database to obtain the suitable soil information corresponding to each plant type of each height layer of the current forest;
Respectively calculating the trace element content, the soil volume weight and the ion content corresponding to each acquisition point in each height layer through an average value to obtain the average trace element content, the average soil volume weight and the average ion content corresponding to each height layer, wherein the average trace element content, the average soil volume weight and the average ion content are used as the trace element content, the soil volume weight and the ion content corresponding to each height layer; the trace element content, the soil volume weight and the ion content of each collection point in each height layer are respectively calculated by average value to obtain the average trace element content, the average soil volume weight and the average ion content corresponding to each height layer in each time of history information collection, the trace element content, the soil volume weight and the ion content corresponding to each height layer are respectively marked as wl ry、tlry、zlry as each time of history information collection, y represents the corresponding number of each height layer, y=1, 2.
Acquiring trace element content, soil volume weight and ion content corresponding to each height layer based on each time of history information, and calculating average trace element content change rate, average soil volume weight change rate and average ion content change rate of each height layer, wherein the average trace element content change rate, the average soil volume weight change rate and the average ion content change rate are respectively recorded as kappa 1 y、κ2y、κ3y;
By calculation formula Obtaining a soil suitability evaluation coefficient alpha 1 corresponding to a forest, wherein wl yi、tlyi、zlyi respectively represents the suitable trace element content, the suitable soil volume weight and the suitable ion content of the ith plant species corresponding to the y-th height layer of the current forest, wl y、tly、zly respectively represents the trace element content, the soil volume weight and the ion content corresponding to the y-th height layer, deltawl, deltazl respectively represent the set permissible trace element content difference, the soil volume weight difference and the ion content difference, and eta 1、η2、η3 respectively represent the set trace element content, the soil volume weight and the weight factors corresponding to the ion content;
According to the calculation formula
Obtaining a soil change evaluation coefficient alpha 2 corresponding to a forest, wherein wl gy、tlgy、zlgy respectively represents the trace element content, the soil volume weight and the ion content of a y-th height layer corresponding to the g-th historical information acquisition, and eta 4、η5、η6 respectively represents weight factors corresponding to the trace element content change rate, the soil volume weight change rate and the ion content change rate;
And obtaining a soil health change evaluation coefficient alpha corresponding to the forest through a calculation formula alpha=alpha 1x lambda 1+alpha 2 x lambda 2, wherein lambda 1、λ2 is a set soil suitability evaluation coefficient and a set weight factor corresponding to the soil change evaluation coefficient respectively.
4. A system for analyzing forestry environment based on big data according to claim 1, wherein the pest information corresponding to each collecting point in each height layer includes the type of each pest and the number of the pest and the pest of each pest and pest type;
5. A system for analyzing forestry environment based on big data according to claim 4, wherein the calculating the evaluation coefficient of the pest and disease damage variation corresponding to the forest comprises the following specific calculation process:
Extracting the plant diseases and insect pests types and the plant diseases and insect pests numbers of the plant diseases and insect pests types of the plant diseases and insect pests of the plant diseases of each collection point in each height layer from the plant diseases and insect pests information of each collection point in each height layer of the forest, further counting the historical plant diseases and insect pests types of the forest, and accumulating the plant diseases and insect pests numbers of the plant diseases and insect pests types of each collection point in each height layer of each historical information collection to obtain the total plant diseases and insect pests number of each plant diseases and insect pests corresponding to each collection point of each historical information collection;
Counting the types of the plant diseases and insect pests of the forest according to the types of the plant diseases and insect pests corresponding to the collecting points in each height layer, and accumulating the number of the plant diseases and insect pests corresponding to the collecting points in each height layer to obtain the total number of the plant diseases and insect pests in the forest;
comparing each plant disease and insect pest type corresponding to the forest with each plant disease and insect pest type corresponding to the forest, if the plant disease and insect pest type corresponding to the forest is different from each plant disease and insect pest type corresponding to the forest, marking the plant disease and insect pest type as a newly increased plant disease and insect pest type, and counting the number of the newly increased plant disease and insect pest types corresponding to the forest;
acquiring historical information of each time, calculating the total number of the plant diseases and insect pests corresponding to each plant disease and insect pest type through a mean value to obtain the historical average total number of the plant diseases and insect pests of each plant disease and insect pest type of the forest, and substituting the historical average total number into a calculation formula Obtaining the plant diseases and insect pests change evaluation coefficient/>, corresponding to the forestWherein Q represents the number of newly increased plant diseases and insect pests corresponding to the forest, Q' is the set number of newly increased plant diseases and insect pests permitted, and IX f represents the average total number and total number of pest and disease history of the f-th pest and disease species in the forest, f represents the numbers corresponding to the respective pest and disease species, f=1, 2.
6. A big data based forestry environment analysis system according to claim 1, wherein the analysis forest corresponds to an environmental health assessment coefficient, and the specific analysis process is as follows: according to the calculation formulaObtaining an environmental health evaluation coefficient phi corresponding to the forest, wherein/>α、Respectively representing an ecological change evaluation coefficient, a soil health change evaluation coefficient and a plant diseases and insect pests change evaluation coefficient corresponding to the forest, wherein tau 1、τ2、τ3 is respectively a set weight factor corresponding to the ecological change evaluation coefficient, the soil health change evaluation coefficient and the plant diseases and insect pests change evaluation coefficient.
7. The system for analyzing the forestry environment based on big data according to claim 1, wherein the specific judging process is as follows: comparing the environmental health assessment coefficient corresponding to the forest with an environmental health assessment coefficient threshold stored in the cloud database, if the environmental health assessment coefficient corresponding to the forest is greater than or equal to the environmental health assessment coefficient threshold, judging that the environmental health state corresponding to the broken forest is in a normal state, otherwise, judging that the environmental health state corresponding to the forest is in a weak state.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110738379A (en) * | 2019-11-07 | 2020-01-31 | 成都淞幸科技有限责任公司 | Large data prediction platform for main diseases and insect pests of angiosperms |
| CN116029616A (en) * | 2023-03-23 | 2023-04-28 | 四川省林业和草原调查规划院(四川省林业和草原生态环境监测中心) | Forest ecological system health evaluation method and device and electronic equipment |
| CN117196249A (en) * | 2023-09-27 | 2023-12-08 | 青海省交控建设工程集团有限公司 | Vegetation greening method for high-cold high-altitude areas |
| CN117371671A (en) * | 2023-12-07 | 2024-01-09 | 杨凌职业技术学院 | Agricultural consultation service system based on information acquisition reasonable distribution |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200202458A1 (en) * | 2018-12-24 | 2020-06-25 | The Climate Corporation | Predictive seed scripting for soybeans |
| EP4022556A1 (en) * | 2020-11-18 | 2022-07-06 | Hijo Ip Pte Ltd. | Crop disease prediction and treatment based on artificial intelligence (ai) and machine learning (ml) models |
-
2024
- 2024-02-07 CN CN202410175083.1A patent/CN117726194B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110738379A (en) * | 2019-11-07 | 2020-01-31 | 成都淞幸科技有限责任公司 | Large data prediction platform for main diseases and insect pests of angiosperms |
| CN116029616A (en) * | 2023-03-23 | 2023-04-28 | 四川省林业和草原调查规划院(四川省林业和草原生态环境监测中心) | Forest ecological system health evaluation method and device and electronic equipment |
| CN117196249A (en) * | 2023-09-27 | 2023-12-08 | 青海省交控建设工程集团有限公司 | Vegetation greening method for high-cold high-altitude areas |
| CN117371671A (en) * | 2023-12-07 | 2024-01-09 | 杨凌职业技术学院 | Agricultural consultation service system based on information acquisition reasonable distribution |
Non-Patent Citations (3)
| Title |
|---|
| Ecosystem services of forests for snail control and schistosomiasis prevention in the Yangtze River Basin;Ma Li;《Journal of Zhejiang A&F University》;20190604;第36卷;130-137 * |
| 北京市八达岭林场森林生态系统健康性评价;鲁绍伟;刘凤芹;余新晓;樊金栓;张振明;陈峻崎;赵广亮;;水土保持学报;20060630(第03期);全文 * |
| 生态林业建设视域下提高洮河自然保护区营造林质量的关键技术;张荣华;《南方农业》;20231231;第17卷(第14期);127-129 * |
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