A method for hierarchical and directional transformation of low-quality poplar plantations

Abstract

The present invention relates to the technical field of directional transformation of plantation forests, and specifically to a method for hierarchical directional transformation of low-quality poplar plantation forests, including deploying a plurality of sensors at different positions in the plantation forest area to construct a sensor network for data collection, and using a fusion algorithm to process the data collected by the sensors; constructing a model for classifying the degradation levels of plantation forests, respectively extracting index data and sub-index data from the fused data, constructing a constraint function in the constraint layer for constraint, and constructing a stability index function and a degradation index function under the optimal weight coefficient, and realizing the classification of the degradation levels of plantation forests by setting an undegraded index threshold in the target layer; constructing a comprehensive transformation objective function, and performing directional transformation according to the classified degradation levels of plantation forests, and realizing different degrees of directional transformation at different levels by adjusting the adjustment coefficients at different levels.

Description

Technical Field

[0001] The present invention relates to the technical field of the directional transformation of plantation forests, and specifically to a method for the hierarchical directional transformation of low-quality poplar plantation forests. Background Art

[0002] Traditional methods for the transformation of plantation forests usually only focus on the planting of a single tree species and lack the introduction of a diverse vegetation structure, resulting in a single ecological function and poor stress resistance. Although some transformation plans attempt to introduce multiple plants, most of them fail to achieve an effective ecological structure reconstruction, and in complex environments, they have poor stability and are difficult to maintain long-term ecological benefits;

[0003] Traditional methods for the transformation of forest land lack pertinence and a dynamic adjustment mechanism and usually rely only on a fixed transformation means, resulting in an unclear transformation effect, especially in severely degraded forest stands, where the effect is not as expected. In the prior art, differentiated measures have not been taken according to different degradation degrees, and the complexity and sustainability of the ecosystem have not been fully considered. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] To solve the above technical problems, the present invention provides the following technical solution: A method for the hierarchical directional transformation of low-quality poplar plantation forests, including the following steps,

[0006] Deploy a plurality of sensors at different positions in the plantation forest area to construct a sensor network for data collection, and use a fusion algorithm to process the data collected by the sensors, including,

[0007] Limit the deployment of sensors according to the sensor network coverage rate of the plantation forest area, and dynamically adjust the weight coefficient of the sensors based on the confidence of each sensor;

[0008] Construct a model for classifying the degradation level of the plantation forest, which is composed of an input layer, a constraint layer, and a target layer. Extract index data and sub-index data from the fused data respectively, and construct a constraint function in the constraint layer for constraint. At the same time, select the adjustment result of the optimal weight coefficient through a reward algorithm, and construct a stability index function and a degradation index function under the optimal weight coefficient. In the target layer, classify the degradation level of the plantation forest by setting an undegraded index threshold;

[0009] Carry out directional transformation according to the classified degradation level of the plantation forest, specifically:

[0010] Construct a comprehensive transformation objective function, and carry out directional transformation according to the classified degradation level of the plantation forest. By adjusting the adjustment coefficient at different levels, different degrees of directional transformation at different levels are realized.

[0011] As a preferred solution of the method for grading and directional transformation of low-quality poplar plantations according to the present invention, the deployment of sensors is limited according to the sensor network coverage rate in the plantation area, specifically as follows:

[0012] Set the size of the plantation area as , deploy sensor nodes in the current plantation area, and the coordinates of each sensor node are , then there is,

[0013]

[0014] Among them, represents the coordinates of the th sensor in the plantation area, represents the coordinates of the th sensor in the plantation area, represents the constructed sensor network;

[0015] Set the coverage radius of each sensor to achieve omnidirectional data monitoring of the entire plantation area, then there is,

[0016] For any coordinate point in the plantation, the coverage condition by the sensor is:

[0017]

[0018] And, for the sensor coordinate point satisfies the formula,

[0019]

[0020] At the same time, in order to achieve dynamic optimization of the sensor coverage radius, the coverage area of the sensor network is secondarily limited, then there is,

[0021]

[0022] Among them, represents the coverage radius of the th sensor, represents the area of the current plantation area, represents the total number of sensors deployed in the current plantation area, represents the sensor network coverage rate of the plantation area.

[0023] As a preferred solution of the method for grading and directional transformation of low-quality poplar plantations according to the present invention, the weight coefficient of the sensor is dynamically adjusted based on the confidence of each sensor, specifically as follows:

[0024] Highlight the confidence of the corresponding sensor through the signal-to-noise ratio,

[0025]

[0026] where, represents the data collected by the -th sensor, represents the mean value of the data collected by the -th sensor, represents the standard deviation of the data collected by the -th sensor, represents the confidence corresponding to the -th sensor, which is used to initialize the weight coefficient of the sensor. Then,

[0027] Initialize the weight coefficient of the sensor, specifically:

[0028]

[0029] where, represents the confidence corresponding to the -th sensor, represents the confidence corresponding to the -th sensor except the -th sensor, represents the total number of sensors, represents the -th sensor's initialized weight coefficient;

[0030] For the sensor with the initialized weight coefficient, when the sensor collects new data, the weight coefficient is dynamically updated according to the collected data. Then,

[0031]

[0032] where, represents the weight coefficient of the -th sensor after the -th iteration, represents the learning rate in the process of updating the weight coefficient, represents the confidence corresponding to the -th sensor in the -th iteration, represents the confidence corresponding to the -th sensor except the -th sensor in the -th iteration, represents the weight coefficient of the -th sensor after the -th iteration.

[0033] As a preferred embodiment of the method for grading and directional transformation of low-quality poplar plantations according to the present invention, wherein: for the update of the weight coefficient, by setting a weight coefficient change threshold , the number of updates of the weight coefficient is limited, specifically:

[0034] For the update of the weight coefficient of the th sensor, the difference between the weight coefficients of two consecutive times and is compared with the weight coefficient change threshold, and according to the comparison result, it is judged whether the update of the weight coefficient corresponding to the current sensor reaches the maximum number of iterations. Then,

[0035] If the comparison result satisfies the formula , it means that the change in the weight coefficient iteration results of the current sensor for two consecutive times is less than the weight coefficient change threshold, and the number of weight coefficient iterations corresponding to the current sensor reaches the maximum number. Stop the update of the weight coefficient of the current sensor. For the subsequent collected data, the weight coefficient of the current sensor is the weight coefficient of the last update;

[0036] If the comparison result satisfies the formula , it means that the change in the weight coefficient iteration results of the current sensor for two consecutive times is greater than the weight coefficient change threshold, and the number of weight coefficient iterations corresponding to the current sensor has not reached the maximum number. Continue to update the weight coefficient of the current sensor until the change in the weight coefficient iteration results for two consecutive times is less than the weight coefficient change threshold .

[0037] As a preferred embodiment of the method for grading and directional transformation of low-quality poplar plantations according to the present invention, wherein: the construction of the constraint function in the constraint layer for constraint is specifically as follows:

[0038] Based on the input data, a stability index function and a degradation index function are respectively constructed for constraint. The specific constraint process is as follows:

[0039] Stability index function,

[0040]

[0041] Degradation index function,

[0042]

[0043] At the same time, set the maximum value , minimum value of the stability index and the maximum value of the degradation index, and set constraint conditions for constraint. Then,

[0044]

[0045]

[0046] Among them, represents the th index data, represents the th sub-index data, represents the weight coefficient corresponding to the th index data, represents the weight coefficient corresponding to the th index data, represents the constructed stability index function, represents the constructed degradation index function.

[0047] As a preferred solution of the method for grading and directional transformation of low-quality poplar plantations described in the present invention, wherein: the adjustment result of selecting the best weight coefficient through the reward algorithm is specifically as follows:

[0048] Set the standard state of the plantation according to historical data , and at the same time, based on the plantation in the standard state, set the standard weight coefficients corresponding to the index data and sub-index data and ;

[0049] Calculate the initial state of the plantation according to the set index data and the standard weight coefficients corresponding to the sub-index data;

[0050] Define the weight coefficient adjustment action , which is to adjust the weight coefficients corresponding to the index data and sub-index data based on the current state of the plantation, then there is,

[0051]

[0052]

[0053] Among them, 、 respectively represent the adjustment results of the weight coefficients at time , and is the adjustment action taken at time ;

[0054] Calculate the state of the plantation at time after the adjustment action according to the taken adjustment action , and calculate the difference between the state of the plantation after the adjustment action at time and the initial state, and at the same time, calculate the time The state of the plantation after adjusting the actions at each moment , and calculate the time The difference between the state of the plantation after adjusting the actions at each moment and the initial state .

[0055] As a preferred scheme of the method for grading and directional transformation of low-quality poplar plantations described in the present invention, wherein: if the state comparison after two adjustment actions satisfies the formula , it means that the adjustment action taken at the latter moment causes the state of the plantation to approach the initial state, then it means that the adjustment action taken at the latter moment is a positive adjustment action;

[0056] If the state comparison after two adjustment actions satisfies the formula , it means that the adjustment action taken at the latter moment causes the state of the plantation to deviate from the initial state, then it means that the adjustment action taken at the latter moment is a reverse adjustment action, record the current reverse adjustment action, and delete this action;

[0057] For the positive adjustment actions taken, calculate the state of the plantation after all positive actions, select the state of the plantation closest to the initial state, the corresponding adjustment action is the optimal adjustment action, and select the corresponding weight coefficient and , and construct a stability index function according to the adjusted weight coefficient and a degradation index function , and perform constraint condition verification according to the constructed stability index function and degradation index function. If the verification is successful, it means that the current weight coefficient is satisfied, otherwise it is not satisfied. Re-select the state of the plantation closest to the initial state and the weight coefficient under the corresponding adjustment action, and also perform constraint condition verification until the verification result is satisfied.

[0058] As a preferred scheme of the method for grading and directional transformation of low-quality poplar plantations described in the present invention, wherein: the specific method for realizing the grading of plantation degradation by setting the non-degradation index threshold is as follows:

[0059] According to the constructed stability index function and the degradation index function carry out the grading of plantation degradation, specifically:

[0060] According to the constructed stability index function and the degradation index function , and through the determined weight coefficient and , calculate the degradation index through weighted summation , then there is

[0061]

[0062] Among them, and represent the weight coefficients under the optimal adjustment action, and represent the stability index function and the degradation index function constructed by the weight coefficients under the optimal adjustment action, represents the calculated degradation index, which is used to realize the classification of the degradation level of the plantation forest. Specifically:

[0063] Set the threshold of the non-degradation index of the plantation forest , and classify the degradation level of the plantation forest according to the set index threshold. Then,

[0064] If the comparison result between the calculated degradation index and the index threshold satisfies the formula , it means that the current degradation level of the plantation forest is non-degraded;

[0065] If the comparison result between the calculated degradation index and the index threshold satisfies the formula , it means that the degradation level of the plantation forest is the first-level degradation level;

[0066] If the comparison result between the calculated degradation index and the index threshold satisfies the formula , it means that the current degradation level of the plantation forest is the second-level degradation level;

[0067] If the comparison result between the calculated degradation index and the index threshold satisfies the formula , it means that the current degradation level of the plantation forest is the third-level degradation level.

[0068] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method for grading and directional transformation of low-quality poplar plantations are implemented.

[0069] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for grading and directional transformation of low-quality poplar plantations are implemented.

[0070] Advantages of the present invention:

[0071] The present invention maximizes the stability of the forest ecosystem (such as tree growth, species diversity, carbon storage, etc.) through reinforcement learning, and at the same time effectively reduces the degradation index (such as the mortality rate, the withered tip rate, etc.). Through the reward mechanism, the system can identify and optimize the weight combinations that are beneficial to the health and sustainable development of the ecosystem, thereby improving the long-term stability and reducing the degradation risk;

[0072] By dynamically adjusting the weight coefficients corresponding to the sensors and performing data fusion based on the confidence levels of each sensor, not only can the accuracy of the data be improved, but also the problem of varying data quality among different sensors can be addressed;

[0073] Through the sensor network and fusion algorithm, sensor errors can be eliminated, providing an accurate data basis and a reliable foundation for subsequent classification of poplar quality grades and targeted transformation;

[0074] By constructing a classification model for the degradation level of plantation forests and combining stability indicators and degradation indicators, the degradation level of plantation forests can be accurately classified, providing a scientific basis for targeted transformation;

[0075] Based on trees in different degradation levels, different transformation plans (such as felling, replanting, grass species restoration, tending management, etc.) are formulated to ensure the effectiveness and pertinence of the transformation measures;

[0076] Through targeted transformation, the ecological functions of plantation forests can be gradually restored, enhancing the ecological stability of forest land and its ability to resist wind and fix sand, and promoting the positive succession of poplar plantation forests towards coniferous and broad-leaved mixed forests. Description of the Drawings

[0077] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0078] Figure 1 It is a schematic structural diagram of the overall method steps of a method for grading and targeted transformation of low-quality poplar plantation forests of the present invention. Detailed Embodiments

[0079] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed embodiments of the present invention with reference to the drawings of the specification. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0080] Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0081] Secondly, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not all refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments.

[0082] The present invention will be described in detail with reference to schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally in a non-general proportion, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention here. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0083] At the same time, in the description of the present invention, it should be noted that the terms "first, second, or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0084] Unless otherwise clearly defined and limited in the present invention, the terms "installation, connection, and coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0085] Embodiment 1

[0086] Referring to Figure 1 , for an embodiment of the present invention, a method for grading and directional transformation of low-quality poplar plantations is provided, including the following steps:

[0087] S1: Collect data related to poplars in the current plantation area and process the collected data.

[0088] Specifically, the collection of data related to poplars in the current area is achieved by constructing a sensor network to collect data related to poplars. The sensor network is formed by deploying multiple sensors at different positions in the plantation area, and different data are collected respectively using the sensor characteristics, so as to achieve the comprehensive collection of poplar quality grade data. The processing of the collected data is to process the data collected by the sensor network through a fusion algorithm to eliminate the data collected due to sensor errors and provide an accurate data basis for the subsequent division of poplar quality grades.

[0089] Furthermore, the collection of different data using the sensor characteristics is achieved by deploying a sensor network composed of multiple types of sensors in the poplar plantation and collecting different types of poplar data in real time through wireless sensor nodes, specifically as follows:

[0090] The multi-type sensors include an environmental sensor for monitoring the impact of the external environment on the growth of poplar trees, a growth monitoring sensor for measuring the tree height, a wood structure sensor for monitoring the tree density, a leaf spectrum sensor for monitoring the growth health of the trees, and a soil sensor for monitoring the impact of the soil on the growth of the trees;

[0091] According to the size of the plantation area, a sensor network is constructed to achieve real-time monitoring of poplar tree data, specifically:

[0092] Set the size of the plantation area as , deploy sensor nodes within the current plantation area, and the coordinates of each sensor node are , then there is,

[0093]

[0094] Among them, represents the coordinates of the rd sensor in the plantation area, represents the coordinates of the th sensor in the plantation area, represents the constructed sensor network;

[0095] Set the coverage radius of each sensor to achieve all-round data monitoring of the entire plantation area, then there is,

[0096] For any coordinate point in the plantation, the coverage condition by the sensor is:

[0097]

[0098] And, for the sensor coordinate point it satisfies the formula,

[0099]

[0100] At the same time, in order to achieve dynamic optimization of the sensor coverage radius, a secondary limitation is imposed on the coverage area of the sensor network, then there is,

[0101]

[0102] Among them, represents the coverage radius of the th sensor, represents the area of the current plantation area, represents the total number of sensors deployed within the area of the current plantation area, It represents the coverage rate of the sensor network in the plantation area. By controlling the coverage rate, a secondary limitation of the sensor network is achieved. In this embodiment, it is limited by In practical applications, it is set by the implementer according to the actual application scenario.

[0103] Furthermore, the data processing of the collected data is to use a fusion algorithm to process the data collected by different sensors in the sensor network respectively to improve the accuracy of the collected data. The specific processing is as follows:

[0104] Set the data collected by the sensor as , where represents the data collected by the th sensor, represents the set of data collected by all sensors;

[0105] According to the sensor weights, data fusion is performed on the data collected by different sensors. Then,

[0106]

[0107]

[0108] where represents the weight coefficient corresponding to the th sensor, represents the data collected by the th sensor, represents the data related to poplar trees in the plantation after fusion.

[0109] It should be noted that in order to improve the accuracy of each sensor in the data fusion process, the weight coefficient of each sensor is dynamically adjusted as follows:

[0110] Since the sensors collect time series data, the signal-to-noise ratio is used to highlight the confidence of the corresponding sensors.

[0111]

[0112] where represents the data collected by the th sensor, represents the mean value of the data collected by the th sensor, represents the standard deviation of the data collected by the th sensor, represents the confidence of the th sensor, which is used to initialize the weight coefficient of the sensor. Then,

[0113] Initialize the weight coefficients of the sensors, specifically:

[0114]

[0115] Among them, represents the confidence level corresponding to the -th sensor, represents the confidence level corresponding to the -th sensor except for the -th sensor, represents the total number of sensors, represents the initial weight coefficient of the -th sensor;

[0116] For the sensors after initializing the weight coefficients, when the sensors collect new data, the weight coefficients are dynamically updated according to the collected data. Then,

[0117]

[0118] Among them, represents the weight coefficient after the -th iteration of the -th sensor, represents the learning rate in the process of updating the weight coefficients, represents the confidence level corresponding to the -th sensor in the -th iteration, represents the confidence level corresponding to the -th sensor except for the -th sensor in the -th iteration, represents the weight coefficient after the -th iteration of the -th sensor;

[0119] For the update of the weight coefficients, by setting the weight coefficient change threshold , the number of times of updating the weight coefficients is limited. Specifically:

[0120] For the update of the weight coefficient of the -th sensor, compare the difference between the weight coefficients and in two consecutive times with the weight coefficient change threshold, and judge whether the update of the weight coefficient corresponding to the current sensor reaches the maximum number of iterations according to the comparison result. Then,

[0121] If the comparison result satisfies the formula , it indicates that the change in the weight coefficient iteration results of the current sensor for two consecutive times is less than the weight coefficient change threshold, and the number of weight coefficient iterations corresponding to the current sensor reaches the maximum number. The update of the weight coefficient of the current sensor is stopped, and for the subsequently collected data, the weight coefficient of the current sensor is the weight coefficient of the last update;

[0122] If the comparison result satisfies the formula , it indicates that the change in the weight coefficient iteration results of the current sensor for two consecutive times is greater than the weight coefficient change threshold, and the number of weight coefficient iterations corresponding to the current sensor has not reached the maximum number. Continue to update the weight coefficient of the current sensor until the change in the weight coefficient iteration results for two consecutive times is less than the weight coefficient change threshold up to.

[0123] It should be noted that by dynamically adjusting the weight coefficient corresponding to the sensor and performing data fusion according to the confidence of each sensor, not only can the accuracy of the data be improved, but also the problem of changes in the data quality of different sensors can be addressed.

[0124] S2: Implement the classification of the degradation level of the planted forest based on the processed data.

[0125] Specifically, the implementation of the classification of the degradation level of the planted forest based on the processed data is achieved by constructing a classification model for the degradation level of the planted forest, taking the fused data as the input quantity of the model, and realizing the classification of the degradation level of the planted forest according to the output result of the model.

[0126] Furthermore, the classification model for the degradation level of the planted forest is a classification model composed of multiple layers, including an input layer, a constraint layer, and a target layer. The input layer extracts index data and sub-index data based on the fused data and inputs the extracted data into the model. The constraint layer constrains the data of the input layer by constructing evaluation indicators. The evaluation indicators include a stability indicator and a degradation indicator. The target layer classifies the degradation degree of the planted forest based on the evaluation indicators of the constraint layer. The specific implementation is as follows:

[0127] For the fused data , respectively extract index data from the data, where represents the index data set, represents the th index data, including stand structure, site conditions of the stand, species diversity, dead tree rate, and dead shoot rate;

[0128] Sub-index data , where represents the sub-index data set, represents the Sub-index data, including forest age, average tree height, average breast height diameter, density, canopy density, groundwater depth, soil water content, soil bulk density, soil porosity, and soil organic matter, and input the extracted data as input data into the artificial forest degradation level classification model;

[0129] Based on the input data, construct a stability index function and a degradation index function for constraint respectively. The specific constraint process is as follows:

[0130] Stability index function,

[0131]

[0132] Degradation index function,

[0133]

[0134] At the same time, set the maximum value of the stability index , minimum value and the maximum value of the degradation index , and set constraint conditions for constraint, then there are,

[0135]

[0136]

[0137] Among them, represents the th index data, represents the th sub-index data, represents the th weight coefficient corresponding to the index data, represents the th weight coefficient corresponding to the index data, represents the constructed stability index function, represents the constructed degradation index function;

[0138] For the constructed constraint function, use the reward algorithm to update the weight coefficient corresponding to the input data to ensure that the updated weight coefficient is the optimal weight coefficient, providing an accurate data basis for the subsequent artificial forest degradation level classification. The specific implementation is as follows:

[0139] Set the standard state of the artificial forest according to historical data . At the same time, based on the artificial forest in the standard state, set the standard weight coefficients corresponding to the index data and sub-index data and ;

[0140] Calculate the initial state of the plantation according to the standard weight coefficient corresponding to the set indicator data and sub-indicator data ;

[0141] Define weight coefficient adjustment actions , is the weight coefficient corresponding to the index data and sub-index data adjusted based on the current state of artificial forests, then,

[0142]

[0143]

[0144] in, , Respectively expressed in time The weight coefficient adjustment result of the moment is time Adjustment actions taken at all times;

[0145] Calculate the time based on the adjustment actions taken The state of the plantation after constant adjustment , and calculate the time The difference between the state of the plantation after the adjustment action and the initial state , while calculating the time The state of the plantation after constant adjustment , and calculate the time The difference between the state of the plantation after the adjustment action and the initial state ;

[0146] According to the comparison results between two consecutive adjustment actions, it is judged whether the adjustment action at the next moment is a positive adjustment action, specifically:

[0147] If the state comparison after two adjustment actions satisfies the formula , indicating that the adjustment action taken at the next moment causes the state of the plantation to be close to the initial state, then the adjustment action taken at the next moment is a positive adjustment action;

[0148] If the state comparison after two adjustment actions satisfies the formula , indicating that the adjustment action taken at the next moment causes the state of the plantation to deviate from the initial state, then the adjustment action taken at the next moment is a reverse adjustment action, the current reverse adjustment action is recorded, and this action is deleted;

[0149] For the positive adjustment actions taken, calculate the plantation state after all positive actions, select the plantation state closest to the initial state, the corresponding adjustment action is the optimal adjustment action, and select the corresponding weight coefficient as well as , and construct a stability index function according to the adjusted weight coefficient and a degradation index function , and perform constraint condition verification based on the constructed stability index function and degradation index function. If the verification is successful, it means that the current weight coefficient is satisfied; otherwise, it is not satisfied. Re-select the state of the artificial forest closest to the initial state, and the weight coefficient under the corresponding adjustment action is also used for constraint condition verification until the verification result is satisfied;

[0150] According to the constructed stability index function and a degradation index function perform the classification of the degradation level of the artificial forest, specifically:

[0151] According to the constructed stability index function and a degradation index function , and through the determined weight coefficient and , calculate the degradation index through weighted summation , then there is

[0152]

[0153] wherein , represent the weight coefficients under the optimal adjustment actions , represent the stability index function and degradation index function constructed by the weight coefficients under the optimal adjustment actions represents the calculated degradation index, which is used to realize the classification of the degradation level of the artificial forest, specifically:

[0154] Set the threshold of the non-degradation index of the artificial forest , and perform the classification of the degradation level of the artificial forest according to the set index threshold, then there is

[0155] If the comparison result between the calculated degradation index and the index threshold satisfies the formula , it means that the current degradation level of the artificial forest is non-degraded;

[0156] If the comparison result between the calculated degradation index and the index threshold satisfies the formula , it means that the degradation level of the artificial forest is the first-level degradation level;

[0157] If the comparison result between the calculated degradation index and the index threshold satisfies the formula , it means that the current degradation level of the artificial forest is the second-level degradation level;

[0158] If the comparison result between the calculated degradation index and the index threshold satisfies the formula , indicating that the current degradation level of the plantation is the third-level degradation level.

[0159] S3: Conduct targeted transformation according to the classified degradation levels of the plantation.

[0160] Specifically, the targeted transformation according to the classified degradation levels of the plantation is based on the output results of the target layer of the plantation degradation level classification model, and targeted transformation is carried out on trees of different quality levels respectively. The specific transformation is as follows:

[0161] Construct a comprehensive transformation objective function

[0162]

[0163] Among them, represents the classified degradation levels of the plantation, including non-degraded, first-level degradation level, second-level degradation level, and third-level degradation level, and is used to achieve the classification of the degradation levels of the plantation. represents the comprehensive transformation objective function. represents the felling operation function. represents the tree replanting operation function. represents the grass species restoration operation function. represents the tending management function.

[0164] It should be noted that the targeted transformation of trees of different quality levels is to formulate different operation functions for targeted transformation for trees of different quality levels respectively. Specifically:

[0165] Felling operation function

[0166]

[0167] Among them, represents the felling operation function. represents the calculated degradation index. represents the felling operation coefficient, which takes different values according to trees of different quality levels. Specifically:

[0168] For non-degraded plantations, the value is 0;

[0169] For first-level degraded plantations, the value is , is a constant term, and in this embodiment, the value is 0.2 for illustration;

[0170] For second-level degraded plantations, the value is ,

[0171] For third-level degraded plantations, the value is ;

[0172] Tree replanting operation function,

[0173]

[0174] Wherein, represents the tree replanting operation function, represents the calculated degradation index, represents the tree replanting operation coefficient, which takes different values according to the trees of different quality grades, specifically:

[0175] For the artificial forest with non-degraded quality grade, the value is 0;

[0176] For the artificial forest with the first-level degradation grade, the value is , is a constant term, and in this embodiment, the value is 0.3 for illustration;

[0177] For the artificial forest with the second-level degradation grade, the value is ,

[0178] For the artificial forest with the third-level degradation grade, the value is ;

[0179] Grass seed restoration operation function,

[0180]

[0181] Wherein, represents the grass seed restoration operation function, represents the calculated degradation index, represents the tree replanting operation coefficient, which takes different values according to the trees of different quality grades, specifically:

[0182] For the artificial forest with non-degraded quality grade, the value is 0;

[0183] For the artificial forest with the first-level degradation grade, the value is , is a constant term, and in this embodiment, the value is 0.4 for illustration;

[0184] For the artificial forest with the second-level degradation grade, the value is ,

[0185] For the artificial forest with the third-level degradation grade, the value is ;

[0186] Cultivation and management function,

[0187]

[0188] represents the cultivation and management function, represents the calculated degradation index, Represents the tending management operation coefficient, which takes different values according to the trees of different quality grades. Specifically:

[0189] For the artificial forest with non-degraded quality grade, the value is 0;

[0190] For the artificial forest with the first-level degradation grade, the value is , is a constant term, and in this embodiment, the value is 0.5 for illustration;

[0191] For the artificial forest with the second-level degradation grade, the value is ,

[0192] For the artificial forest with the third-level degradation grade, the value is .

[0193] It should be noted that in order to strengthen the understanding of the directional transformation of artificial forests, a specific actual application scenario is used for supplementary description as follows:

[0194] For the non-degraded artificial forest, there is no need for transformation, and only the growth status of the trees needs to be monitored in real time;

[0195] For the trees in the artificial forest with the first-level degradation grade, the specific transformation is as follows:

[0196] In August - October of the current year, use the degradation type evaluation system to judge the degradation type of the poplar artificial forest in the target area, and clarify that its degradation degree is mild degradation. Then, adopt the tending method of sanitary felling for this degradation degree, and start the directional transformation work in the spring of the next year. In the forest stand, fell the standing dead trees, retain the trees with survival potential, and for the weakened trees such as windthrown trees and pest-infected trees, deal with them by the method of coppicing and rejuvenation, that is, truncate the poplar main trunk at 0.1 m above the ground, and apply 2% - 5% copper sulfate solution to the truncated part for disinfection, and at the same time apply a protective agent. In the spring of the same year, adopt the method of aerial seeding to restore the understory vegetation, select Chinese wildrye, alfalfa, and buffalograss, and evenly broadcast them at a seeding rate of 1 - 1.5 kg per mu; in mid-late May, replant coniferous tree species, select 4 - 6-year-old Pinus sylvestris var. mongolica seedlings, and replant 80 - 100 plants per mu according to the planting standard of row spacing 2×3 m, ensuring that the replanted seedlings are evenly distributed to form a good growth space.

[0197] After the replanting is completed, carry out continuous 2 - 3 years of tending management for the newly planted seedlings, including regular weeding, watering, and pest control every year, to ensure the survival rate and healthy growth of the seedlings, and gradually form a coniferous and broad-leaved mixed forest structure.

[0198] In the 1 - 2 years after implementation, the number of understory ground cover species increased by 2. In the 3 - 5 years after implementation, a multi - layer vegetation structure combining trees, shrubs and herbs was formed, further enhancing the ecological function and stability of the forest land. In the 6 - 8 years after implementation, the survival rate of the replanted Pinus sylvestris var. mongolica seedlings reached over 90%. The stand structure was gradually optimized, the strip - cutting area was effectively integrated with the reserved stand, and the trend of stand degradation was curbed, promoting the positive succession of the poplar plantation towards a coniferous - broadleaved mixed forest.

[0199] For the artificial forest trees at the secondary degradation level, the specific transformation is as follows:

[0200] In August - September of the current growing season, use the degradation type evaluation index system to conduct a systematic assessment of the poplar plantation in the target area, and clarify that its degradation degree is moderate degradation. For the moderately degraded stand, adopt the strip - renewal cutting method and start the transformation work in the current autumn. In the stand, strip - cutting is carried out every 20 meters, forming a structure with strip - shaped open spaces and reserved trees arranged alternately; in mid - September, coniferous trees are replanted in the cut - over area. Select 6 - 8 - year - old Pinus sylvestris var. mongolica and 3 - 4 - year - old Pinus tabuliformis seedlings for mixed planting. According to the standard of plant spacing 2 * 3m, 90 - 100 plants are replanted per mu to ensure uniform distribution of the seedlings. The reclamation hole specification is 0.5 * 0.5m. The excavated soil is piled at the bottom of the seedling hole, at the same height as the above - ground height of the seedlings, reducing the harm of cold wind, providing sufficient growth space, and promoting the balanced development of the subsequent stand. After replanting, continuous 6 - 8 - year tending management is carried out for the newly planted seedlings. Specifically, in June of each year, weeding, watering and pest control are carried out within 1 meter of the seedling hole, and 0.5kg of compound fertilizer is applied to replant the dead seedlings in time; at the same time, in the replanting area, combined with the restoration of understory vegetation, the method of sowing grass seeds is adopted. Select a mixture of grey - green hedge, buffalograss, Chinese wildrye and alfalfa grass seeds and evenly sow them at a sowing rate of 3kg per mu.

[0201] In the 1 - 2 years after implementation, the number of understory ground cover species increased by 3. In the 3 - 7 years after implementation, Caragana korshinskii and Hippophae rhamnoides are planted in the forest gaps, forming a multi - layer vegetation structure combining trees, shrubs and herbs between the strip - shaped open spaces and the reserved forest, further enhancing the ecological function and stability of the forest land. In the 8 - 10 years after implementation, the survival rate of the replanted Pinus sylvestris var. mongolica seedlings reached over 85%. The stand structure was gradually optimized, the strip - cutting area was effectively integrated with the reserved stand, and the trend of stand degradation was curbed, promoting the positive succession of the poplar plantation towards a coniferous - broadleaved mixed forest.

[0202] For the artificial forest trees at the tertiary degradation level, the specific transformation is as follows:

[0203] From August to October of the same year, a systematic evaluation was carried out on the poplar plantation in the target area using the degradation type evaluation index system, and it was determined that the degree of degradation was severe. In the following spring, clear-cutting was carried out on the poplar plantation in this area to completely remove the degraded trees. Planting work was carried out after the rain in the middle and late May. Local Scotch pine seedlings with an age of at least 8 years were selected as the main tree species for afforestation. In the cleared forest land, strip land preparation was carried out, and the land preparation was carried out in strict accordance with the reclamation hole specifications of 0.35*0.35*0.4m.

[0204] The planting density was arranged according to the standard of plant spacing (3.5 - 4m) * (3.5 - 4m) to ensure uniform distribution of the seedlings and provide sufficient space for the growth of the seedlings to adapt to the local difficult site conditions. To improve the ecological stability and structural diversity of the forest stand, Caragana korshinskii and Haloxylon ammodendron were mixed-sown in the gaps between the Scotch pines. Before the spring rain in the third year, a mixed grass seed of alfalfa, Leymus chinensis, and Buchloe dactyloides was aerial-seeded at a seeding rate of 5 kg per mu to form a multi-layer forest stand structure combining trees, shrubs, and grasses, enhancing the windbreak and sand fixation and soil and water conservation capabilities of the forest stand. After planting, compound fertilizer granules were broadcast by drone at a rate of 10 - 15 kg per mu before the spring rain every year for 3 consecutive years. In the spring of each year for 5 consecutive years, soil loosening and weed removal were carried out within 1 m of the young trees, and then closed forest management was continued for 5 - 8 consecutive years.

[0205] Furthermore, if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0206] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0207] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0208] Furthermore, in order to provide a concise description of the exemplary embodiments, all features of the actual embodiments may not be described (i.e., those features that are not relevant to the currently considered best mode of implementing the present invention, or those features that are not relevant to the implementation of the present invention).

[0209] It should be understood that in the development of any actual implementation, as in any engineering or design project, numerous specific implementation decisions can be made. Such development efforts may be complex and time-consuming, but for those of ordinary skill in the art who benefit from this disclosure, without excessive experimentation, such development efforts will be a routine task of design, manufacturing, and production.

[0210] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered by the scope of the claims of the present invention.

Claims

1. A method for hierarchical and directional transformation of low-quality poplar plantations, characterized in that: The following steps are included: Deploy multiple sensors at different locations in the artificial forest area to build a sensor network for data collection, and use fusion algorithms to process the data collected by the sensors, including: Deployment of sensors is limited according to the sensor network coverage of the artificial forest area, and weight coefficients of sensors are dynamically adjusted based on the confidence of each sensor; A plantation forest degradation classification model composed of an input layer, a constraint layer, and a target layer was constructed. The index data and sub-index data were extracted from the fused data, and a constraint function was constructed in the constraint layer for constraint. At the same time, the adjustment result of the optimal weight coefficient was selected through a reward algorithm, and a stability index function and a degradation index function under the optimal weight coefficient were constructed. The classification of plantation forest degradation was achieved by setting a non-degraded index threshold in the target layer. The construction of constraint functions in the constraint layer to constrain is as follows: Based on the input data, the stability index function and the degradation index function are constructed for constraints. The specific constraint process is as follows: Stability index function, ; Degradation index function, ; At the same time, set the maximum value of the stability index , minimum And the maximum degradation index , and set constraints to constrain, then we have, ; ; in, Indicates Indicator data, Indicates Sub-indicator data, Indicates The weight coefficient corresponding to the indicator data is: Indicates The weight coefficient corresponding to the indicator data is: represents the constructed stability index function, represents the constructed degradation index function; The classification of plantation forest degradation by setting the non-degraded index threshold is as follows: According to the constructed stability index function And the degradation index function The degradation levels of artificial forests are classified as follows: According to the constructed stability index function And the degradation index function , and by determining the weight coefficient as well as , the degradation index is calculated by weighted summation , then there is, ; in, , represents the weight coefficient under the optimal adjustment action, , represents the stability index function and degradation index function constructed by the weight coefficients under the optimal adjustment action, Represents the calculated degradation index, which is used to achieve the classification of plantation forest degradation, specifically: Setting thresholds for plantation non-degradation index , according to the set index threshold, the artificial forest degradation level is divided into: If the calculated degradation index and the index threshold value are compared to satisfy the formula , indicating that the current level of plantation degradation is not degraded; If the calculated degradation index and the index threshold value are compared to satisfy the formula , indicating that the level of degradation of artificial forests is the first level of degradation; If the calculated degradation index and the index threshold value are compared to satisfy the formula , indicating that the current degradation level of artificial forests is the second level of degradation; If the calculated degradation index and the index threshold value are compared to satisfy the formula , indicating that the current level of plantation degradation is the third level of degradation; Targeted transformation is carried out according to the level of plantation forest degradation, specifically: A comprehensive transformation objective function is constructed, and targeted transformation is carried out according to the divided degradation levels of artificial forests. By adjusting the adjustment coefficients at different levels, targeted transformation of different degrees at different levels can be achieved.

2. A method for grading and directional transformation of low-quality poplar plantations as claimed in claim 1, characterized in that: The deployment of sensors is limited according to the sensor network coverage rate of the artificial forest area, as follows: Set the size of the plantation area to , deployed in the current plantation area sensor nodes, the coordinates of each sensor node are , then there is, ; in, Indicates that in the artificial forest area The coordinates of the sensors, Indicates that in the artificial forest area The coordinates of the sensors, represents the constructed sensor network; Set the coverage radius of each sensor To achieve all-round data monitoring of the entire artificial forest area, there are, For any coordinate point in the artificial forest , the coverage condition of the sensor is: ; And, for the sensor coordinate point Satisfies the formula, ; At the same time, in order to achieve dynamic optimization of the sensor coverage radius, the coverage area of ​​the sensor network is restricted twice, then, ; in, Indicates The coverage radius of the sensor, Indicates the current area of ​​artificial forests, Indicates the total number of sensors deployed in the current artificial forest area, Represents the sensor network coverage of the plantation area.

3. A method for grading and directional transformation of low-quality poplar plantations as claimed in claim 2, characterized in that: The specific method of dynamically adjusting the weight coefficient of the sensor based on the confidence level of each sensor is as follows: The signal-to-noise ratio highlights the confidence level of the corresponding sensor. ; in, Indicates The data collected by the sensors, Indicates The average value of the data collected by the sensors, Indicates The standard deviation of the data collected by each sensor is Indicates The confidence level corresponding to each sensor is used to initialize the sensor weight coefficient, then, Initialize the weight coefficient of the sensor, specifically: ; in, Indicates The confidence level corresponding to each sensor is Indicates to remove The sensor outside The confidence level corresponding to each sensor is Represents the total number of sensors, Indicates Initialization weight coefficients of sensors; For sensors with initialized weight coefficients, when the sensor collects new data, the weight coefficients are dynamically updated according to the collected data, then, ; in, Indicates The sensor The weight coefficient after iterations, represents the learning rate during the weight coefficient update process, Indicates The sensor in The confidence level corresponding to the iteration is Indicates to remove The sensor outside The sensor in The confidence level corresponding to the iteration is Indicates The sensor The weight coefficient after iterations.

4. A method for grading and directional transformation of low-quality poplar plantations as claimed in claim 3, characterized in that: For updating the weight coefficient, by setting the weight coefficient change threshold , to limit the number of weight coefficient updates, specifically: For The weight coefficient of each sensor is updated, and the weight coefficient of two consecutive as well as The difference between is compared with the weight coefficient change threshold, and the weight coefficient update corresponding to the current sensor is judged according to the comparison result to determine whether it reaches the maximum number of iterations. Then, If the comparison result satisfies the formula , indicating that the change in the weight coefficient iteration results of the current sensor for two consecutive times is less than the weight coefficient change threshold, and the number of weight coefficient iterations corresponding to the current sensor has reached the maximum number, and the weight coefficient update of the current sensor is stopped. For the subsequent collected data, the weight coefficient of the current sensor is the weight coefficient of the last update; If the comparison result satisfies the formula , indicating that the change in the weight coefficient iteration results of the current sensor for two consecutive times is greater than the weight coefficient change threshold, and the number of weight coefficient iterations corresponding to the current sensor has not reached the maximum number of times, and the weight coefficient of the current sensor continues to be updated until the change in the weight coefficient iteration results of two consecutive times is less than the weight coefficient change threshold until.

5. A method for grading and directional transformation of low-quality poplar plantations as claimed in claim 4, characterized in that: The adjustment result of selecting the best weight coefficient through the reward algorithm is as follows: Setting standard status for plantation forests based on historical data At the same time, based on the standard state of the plantation, set the standard weight coefficients corresponding to the index data and sub-index data as well as ; Calculate the initial state of the plantation according to the standard weight coefficient corresponding to the set indicator data and sub-indicator data ; Define weight coefficient adjustment actions , is the weight coefficient corresponding to the index data and sub-index data adjusted based on the current state of artificial forests, then, ; ; in, , Respectively, in time The weight coefficient adjustment result of the moment is time Adjustment actions taken at all times; Calculate the time based on the adjustment actions taken The state of the plantation after constant adjustment , and calculate the time The difference between the state of the plantation after the adjustment action and the initial state , while calculating the time The state of the plantation after constant adjustment , and calculate the time The difference between the state of the plantation after the adjustment action and the initial state .

6. A method for grading and directional transformation of low-quality poplar plantations as claimed in claim 5, characterized in that: If the state comparison after two adjustment actions satisfies the formula , indicating that the adjustment action taken at the next moment causes the state of the plantation to be close to the initial state, then the adjustment action taken at the next moment is a positive adjustment action; If the state comparison after two adjustment actions satisfies the formula , indicating that the adjustment action taken at the next moment causes the state of the plantation to deviate from the initial state, then the adjustment action taken at the next moment is a reverse adjustment action, the current reverse adjustment action is recorded, and this action is deleted; For the positive adjustment actions taken, calculate the plantation state after all positive actions, select the plantation state closest to the initial state, the corresponding adjustment action is the optimal adjustment action, and select the corresponding weight coefficient as well as , and construct a stability index function based on the adjusted weight coefficient And the degradation index function , and verify the constraints according to the constructed stability index function and degradation index function. If the verification is successful, it means that the current weight coefficient meets the requirements. Otherwise, it does not meet the requirements. The artificial forest state closest to the initial state is reselected, and the corresponding weight coefficient under the adjustment action is also verified until the verification result is met.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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