Ecological red line area dynamic division method and device, medium and product

Abstract

The invention discloses an ecological red line area dynamic division method and device, a medium and a product. The method comprises the following steps: constructing ecological indexes of an ecological protection data system according to collected ecological environment data; calculating the index weight of each ecological index through a preset fuzzy logic algorithm, and determining an ecological environment index score; according to the ecological environment data and the obtained ecological environment historical data, adopting an uncertain environment dynamic decision-making method to simulate the dynamic change of each ecological index, and adjusting the score of the ecological environment index; and determining an ecological red line division region according to the adjusted ecological environment index score. According to the method, the dynamic adjustment of the ecological red line can be realized according to the dynamic change of the ecological system, and the ecological red line division is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a method, device, medium and product for dynamically dividing ecological red line areas. Background Art

[0002] The ecological red line is a strict control boundary drawn in key ecological function areas, ecological environment sensitive areas and fragile areas, and is the bottom line of regional ecological security. Drawing and strictly observing the ecological protection red line is an important measure to implement the main functional area system and implement ecological space use control. It is an effective means to improve the supply capacity of ecological products and the service function of the ecosystem and build an ecological security pattern.

[0003] The existing ecological red line demarcation method has a long update cycle and is difficult to adapt to the dynamic changes in the ecological environment, resulting in inaccurate demarcation results and inability to effectively protect the ecological environment. Summary of the invention

[0004] Compared with the prior art, the present invention proposes a method, device, medium and product for dynamically dividing ecological red line areas, which can realize dynamic adjustment of the ecological red line according to the dynamic changes of the ecosystem, making the ecological red line division more accurate.

[0005] An embodiment of the present invention provides a method for dynamically dividing an ecological red line area, the method comprising:

[0006] Construct ecological indicators of the ecological protection data system based on the collected ecological environment data;

[0007] The indicator weight of each ecological indicator is calculated through a preset fuzzy logic algorithm to determine the ecological environment indicator score;

[0008] According to the ecological environment data and the acquired historical ecological environment data, a dynamic decision-making method in an uncertain environment is adopted to simulate the dynamic changes of various ecological indicators and adjust the scores of ecological environment indicators;

[0009] The ecological red line demarcation area will be determined based on the adjusted ecological environment indicator scores.

[0010] Preferably, the method further comprises:

[0011] According to the ecological red line division area, a preset coverage hybrid optimization algorithm is used to optimize the position of the pre-deployed monitoring equipment to determine the optimal deployment position and maximum coverage range of the monitoring equipment.

[0012] Furthermore, the positions of pre-deployed monitoring devices are optimized by a preset coverage hybrid optimization algorithm to determine the optimal deployment position and maximum coverage of the monitoring devices, including:

[0013] Calculate the total coverage rate of the monitoring range of several monitoring devices randomly deployed in advance to the ecological red line area;

[0014] When the coverage rate does not meet the preset requirements, the position of the monitoring equipment is optimized by the coverage rate hybrid optimization algorithm for iterative update, and the total coverage rate of the monitoring range to the ecological red line area is recalculated;

[0015] When the number of iterations reaches a preset upper limit, or the total coverage reaches a preset coverage threshold, the current position of the monitoring device is determined to be the optimal deployment position, and the maximum coverage range is output.

[0016] Further, the location of the monitoring device is optimized by the coverage hybrid optimization algorithm for iterative update, including:

[0017] Based on the coverage hybrid optimization algorithm, a preset speed update model is used to calculate the update speed of the monitoring device;

[0018] Perform a local area search near the current optimal solution, update the position of the monitoring device in the local area, and obtain a first updated position;

[0019] A global search is performed in the deployment area, the global search of the deployment area is calculated, and the position of the monitoring device is updated again to obtain a second updated position as the position of the monitoring device after the current iterative update.

[0020] Preferably, the indicator weight of each ecological indicator is calculated by a preset fuzzy logic algorithm to determine the ecological environment indicator score, including:

[0021] The fuzzy logic weight of each ecological indicator is determined through a preset fuzzy logic weight model;

[0022] The posterior weight of each indicator is calculated based on the conditional dependency relationship between ecological indicators;

[0023] Calculating the indicator weight of each ecological indicator according to the fuzzy logic indicator and the posterior weight;

[0024] The ecological environment index score is determined based on the weighted sum of each ecological index and the corresponding index weight.

[0025] As a preferred solution, based on the ecological environment data and the acquired historical ecological environment data, an uncertain environment dynamic decision-making method is adopted to simulate the dynamic changes of various ecological indicators and adjust the ecological environment indicator scores, including:

[0026] Determine the element layer that affects the current state of each ecological indicator based on the ecological environment data, and determine the influencing factors;

[0027] According to the influencing factors of each ecological indicator, a dynamic decision-making method in an uncertain environment is adopted to build a change trend prediction model;

[0028] According to the change trend prediction model and the acquired ecological environment historical data, the dynamic changes of various ecological indicators are simulated and the ecological environment indicator scores are adjusted.

[0029] Preferably, the ecological indicators of the ecological protection data system are constructed based on the collected ecological environment data, including:

[0030] Collect data values ​​of different factors in each element layer of the ecological environment;

[0031] Use preset statistical methods to detect and identify outliers on the data values ​​of different factors, mark abnormal values ​​and missing values, and calculate missing values ​​using preset interpolation methods;

[0032] Normalize the data values ​​of different factors in each element layer of the ecological environment;

[0033] The ecological indicators of different ecological protection data systems are calculated based on the data values ​​processed by different factors of each element layer.

[0034] Preferably, the ecological indicators include aridity index, vegetation growth trend index and vegetation coverage.

[0035] The embodiment of the present invention further provides a device for dynamically dividing an ecological red line area, the device comprising:

[0036] The indicator determination module is used to construct ecological indicators of the ecological protection data system based on the collected ecological environment data;

[0037] The score calculation module is used to calculate the indicator weight of each ecological indicator through a preset fuzzy logic algorithm to determine the ecological environment indicator score;

[0038] A dynamic adjustment module is used to simulate the dynamic changes of various ecological indicators and adjust the ecological environment indicator scores by using an uncertain environment dynamic decision-making method according to the ecological environment data and the acquired ecological environment historical data;

[0039] The regional division module is used to determine the ecological red line division area based on the adjusted ecological environment indicator scores.

[0040] Preferably, the device further comprises a location optimization module, configured to:

[0041] According to the ecological red line division area, a preset coverage hybrid optimization algorithm is used to optimize the position of the pre-deployed monitoring equipment to determine the optimal deployment position and maximum coverage range of the monitoring equipment.

[0042] Preferably, the location optimization module is specifically used for:

[0043] Calculate the total coverage rate of the monitoring range of several monitoring devices randomly deployed in advance to the ecological red line area;

[0044] When the coverage rate does not meet the preset requirements, the position of the monitoring equipment is optimized by the coverage rate hybrid optimization algorithm for iterative update, and the total coverage rate of the monitoring range to the ecological red line area is recalculated;

[0045] When the number of iterations reaches a preset upper limit, or the total coverage reaches a preset coverage threshold, the current position of the monitoring device is determined to be the optimal deployment position, and the maximum coverage range is output.

[0046] Preferably, the location optimization module is specifically used for:

[0047] Based on the coverage hybrid optimization algorithm, a preset speed update model is used to calculate the update speed of the monitoring device;

[0048] Perform a local area search near the current optimal solution, update the position of the monitoring device in the local area, and obtain a first updated position;

[0049] A global search is performed in the deployment area, the global search of the deployment area is calculated, and the position of the monitoring device is updated again to obtain a second updated position as the position of the monitoring device after the current iterative update.

[0050] Preferably, the score calculation module is specifically used for:

[0051] The fuzzy logic weight of each ecological indicator is determined through a preset fuzzy logic weight model;

[0052] The posterior weight of each indicator is calculated based on the conditional dependency relationship between ecological indicators;

[0053] Calculating the indicator weight of each ecological indicator according to the fuzzy logic indicator and the posterior weight;

[0054] The ecological environment index score is determined based on the weighted sum of each ecological index and the corresponding index weight.

[0055] Preferably, the dynamic adjustment module is specifically used for:

[0056] According to the ecological environment data and the historical ecological environment data obtained, the dynamic decision-making method in an uncertain environment is adopted to simulate the dynamic changes of various ecological indicators and adjust the scores of ecological environment indicators, including:

[0057] Determine the element layer that affects the current state of each ecological indicator based on the ecological environment data, and determine the influencing factors;

[0058] According to the influencing factors of each ecological indicator, a dynamic decision-making method in an uncertain environment is adopted to build a change trend prediction model;

[0059] According to the change trend prediction model and the acquired ecological environment historical data, the dynamic changes of various ecological indicators are simulated and the ecological environment indicator scores are adjusted.

[0060] Preferably, the indicator determination module is used for:

[0061] Collect data values ​​of different factors in each element layer of the ecological environment;

[0062] Use preset statistical methods to detect and identify outliers on the data values ​​of different factors, mark abnormal values ​​and missing values, and calculate missing values ​​using preset interpolation methods;

[0063] Normalize the data values ​​of different factors in each element layer of the ecological environment;

[0064] The ecological indicators of different ecological protection data systems are calculated based on the data values ​​processed by different factors of each element layer.

[0065] Preferably, the ecological indicators include aridity index, vegetation growth trend index and vegetation coverage.

[0066] An embodiment of the present invention also provides a device for dynamically dividing ecological red line areas, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a method for dynamically dividing ecological red line areas as described in any of the above embodiments.

[0067] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a method for dynamically dividing an ecological red line area as described in any of the above embodiments.

[0068] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which implements the steps of the method described in any of the above embodiments when executed by a processor.

[0069] Compared with the prior art, the present invention provides a method, device, medium and product for dynamically dividing ecological red line areas, which constructs ecological indicators of ecological protection data system according to collected ecological environment data; calculates the indicator weight of each ecological indicator through a preset fuzzy logic algorithm to determine the ecological environment indicator score; according to the ecological environment data and the acquired ecological environment historical data, adopts an uncertain environment dynamic decision-making method to simulate the dynamic changes of each ecological indicator and adjust the ecological environment indicator score; determines the ecological red line division area according to the adjusted ecological environment indicator score. This application can realize the dynamic adjustment of ecological red line according to the dynamic changes of the ecosystem, and the ecological red line division is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of a flow chart of a method for dynamically dividing an ecological red line area provided by an embodiment of the present invention;

[0071] Figure 2 is another flow chart of the method for dynamically dividing ecological red line areas provided by an embodiment of the present invention;

[0072] Figure 3 It is a structural schematic diagram of a device for dynamically dividing an ecological red line area provided by an embodiment of the present invention;

[0073] Figure 4 It is another structural schematic diagram of a device for dynamically dividing an ecological red line area provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] The ecological red line demarcation method of the existing scheme has a long update cycle and is difficult to adapt to the dynamic changes of the ecological environment, resulting in inaccurate demarcation results and inability to effectively protect the ecological environment. This application proposes a dynamic demarcation method for ecological red line areas, see Figure 1 , is a flow chart of a method for dynamically dividing an ecological red line area provided by an embodiment of the present invention, the method comprising steps S1 to S4;

[0076] S1, construct ecological indicators of the ecological protection data system based on the collected ecological environment data;

[0077] S2, calculating the indicator weight of each ecological indicator through a preset fuzzy logic algorithm to determine the ecological environment indicator score;

[0078] S3, based on the ecological environment data and the acquired historical ecological environment data, using an uncertain environment dynamic decision-making method to simulate the dynamic changes of each ecological indicator and adjust the ecological environment indicator score;

[0079] S4. Determine the ecological red line demarcation area based on the adjusted ecological environment indicator scores.

[0080] In the specific implementation of this embodiment, the case establishes data indicators based on the collected ecological and environmental data, specifically by performing feature processing on the ecological and environmental data and generating preliminary ecological red line area division results.

[0081] After determining the ecological indicators, an ecological environment assessment is carried out to process complex and uncertain ecological protection area data. It is able to handle the ambiguity and uncertainty in ecological protection areas, such as the uncertainty of ecological data and the ambiguity of indicators, and handle the complex dependencies between indicators.

[0082] Through the preset fuzzy logic algorithm, the criteria for dividing fuzzy sets are used to combine the membership degree with the rules to calculate the indicator weights of each ecological indicator.

[0083] Based on the indicator weights and different ecological indicators, a fusion calculation is performed to determine the total score of different ecological environmental indicators, which corresponds to the ecological environmental evaluation value at this moment.

[0084] By analyzing regional signaling data, water conservation, soil and water conservation, biodiversity, windbreak and sand fixation, regional weather interface, and regional production and economic utilization status indicators, the weighted summation is performed according to the above calculated weights to determine the final score U i Based on the score U i The proportion of the scores of the four element layers (related to water conservation, soil and water conservation, biodiversity, and windbreak and sand fixation) divides the ecological protection area into four types: water conservation, soil and water conservation, biodiversity, and windbreak and sand fixation. Each type is divided into five levels according to the score (slightly sensitive, slightly sensitive, moderately sensitive, highly sensitive, and extremely sensitive). The areas with extremely strong sensitivity in the ecological protection area are divided into ecological protection red line areas; the areas with slightly sensitive, moderately sensitive, and highly sensitive ecological protection areas are divided into ecological protection yellow line areas; the insensitive areas of the ecological protection area are divided into ecological protection green line areas, so as to construct the ecological protection red line area system.

[0085] Based on ecological and environmental assessment, this plan adopts the FBDWA model of dynamic decision-making in uncertain environments to simulate the dynamic changes of ecosystems, combine historical data with real-time data, and dynamically adjust the ecological red line division.

[0086] Assuming that the indicator Ui(t) of each region represents the score at time t, the state changes of these indicators at different time points are modeled, and the transition probability is calculated by the dynamic decision-making method in an uncertain environment to describe the changes from one time step to the next time step. Assuming U i (t) represents the index score of the ith region at time t, U i (t+1) represents the index score at time t+1, thereby simulating the dynamic changes of various ecological indicators and adjusting the ecological environment index scores.

[0087] According to the adjusted ecological environment index score, based on U i (t) Classify them according to their types and levels and determine the ecological red line demarcation areas.

[0088] This plan can take into account a variety of ecological and environmental factors and simulate the dynamic changes of the ecosystem, so as to more accurately delineate the ecological red line.

[0089] In another embodiment provided by the present invention, the method further includes:

[0090] According to the ecological red line division area, a preset coverage hybrid optimization algorithm is used to optimize the position of the pre-deployed monitoring equipment to determine the optimal deployment position and maximum coverage range of the monitoring equipment.

[0091] During the specific implementation of this embodiment, the existing base station deployment strategies often ignore the importance of ecological protection, resulting in the inability to fully cover the base station layout and the ecological red line area, limiting the detection range, and affecting the protection of the ecological environment. Therefore, after determining the dynamic division of the ecological red line area, this case optimizes the coverage range and deployment location of the base station based on the dynamic division results of the ecological red line area.

[0092] The existing monitoring emergency mechanism, which uses patrols, remote sensing, and cameras, has problems such as high cost, small monitoring coverage, and inability to conduct refined monitoring, and cannot quickly reach all users within the red line. However, mobile phone signaling data from telecommunications operators has the ability to quickly reach a wide range of users.

[0093] According to the coverage characteristics of the wireless communication network and the need for the wireless communication network to provide continuous services to mobile phone users, mobile phone signaling is used to maintain contact with the wireless communication network. In the area covered by the operator's base station signal, detection equipment is used to monitor the activities of people within the ecological protection red line.

[0094] It should be noted that the detection equipment can use portable mobile devices such as drones to adjust the detection range more quickly and flexibly.

[0095] In this case, the principle of calculating the area of ​​the ecological protection red line area covered by the base station is to associate the base station covering the map with the map according to the base station positioning, base station type and coverage radius, and to perform coverage according to demand based on the base station coverage deployment optimization method based on HWPO (Hybrid Whale-Particle Optimization coverage rate hybrid optimization algorithm). The cooperation of multiple drone base stations can detect the uncovered ecological protection red line area in a larger range, thereby determining the optimal deployment location and maximum coverage range of the monitoring equipment.

[0096] The optimization algorithm is used to determine the optimal deployment location and coverage of the base station. During the optimization process, the communication needs and ecological protection requirements are comprehensively considered to achieve the reasonable deployment of base stations and ecological red line areas.

[0097] In another embodiment provided by the present invention, the positions of pre-deployed monitoring devices are optimized by a preset coverage hybrid optimization algorithm to determine the optimal deployment position and maximum coverage range of the monitoring devices, including:

[0098] Calculate the total coverage rate of the monitoring range of several monitoring devices randomly deployed in advance to the ecological red line area;

[0099] When the coverage rate does not meet the preset requirements, the position of the monitoring equipment is optimized by the coverage rate hybrid optimization algorithm for iterative update, and the total coverage rate of the monitoring range to the ecological red line area is recalculated;

[0100] When the number of iterations reaches a preset upper limit, or the total coverage reaches a preset coverage threshold, the current position of the monitoring device is determined to be the optimal deployment position, and the maximum coverage range is output.

[0101] When adjusting the position of the monitoring equipment, perform the following steps:

[0102] The principle for calculating the area of ​​ecological protection red line area covered by base stations is to associate the base stations covering the map spots with the map spots according to the base station positioning, base station type and coverage radius. According to the typical values ​​of the coverage distance of 2G, 4G and 5G base stations, the coverage radius of each type of base station is taken. According to the location and type of a single base station covering the map spots of the ecological protection red line area, the coverage range of a single base station can be calculated. By superimposing the coverage range of all base stations covering the map spots of the ecological protection red line area, the corresponding relationship between the overall base station coverage range and the map spot range can be obtained.

[0103] Since the location and type of base stations are sensitive data, the specific base station locations cannot be presented. Only the overall range after the superposition of all base station coverage can be obtained. Since some map spots belong to sandy land, desert or forest areas, there is no base station coverage.

[0104] Based on the currently available patch information and base station coverage area information, etc., it is calculated that the proportion of the boundary length of the patches covered by the base stations in the whole region is 78.38%, and the proportion of the boundary length of the patches not covered by the base stations is 21.62%. For the areas of the patches not covered by the base stations, in an emergency scenario in a complex environment (such as sandy land, desert or forest) (during periods such as grazing ban and restricted grazing, wildfire prevention, restricted tree felling, fishing ban, etc.), a drone carrying a micro base station as an auxiliary aerial base station first needs to deploy a number of drone base stations within the benchmark uncovered area to maximize its coverage area. The coverage area can be divided into grids with equal side lengths. Each grid cell can be regarded as a small area, defined as G = {g1, g2, … g n}, the uncovered area is divided into N×N grid cells, and each grid cell is represented by g ij , where i and j represent the row and column indices respectively. The position of the drone is represented by two-dimensional coordinates (x k , y k ), and the coverage range of each drone is a circular area with a radius of r.

[0105] See Figure 2 , which is a schematic flow chart of the monitoring device position update process provided by an embodiment of the present invention. First, the drones are randomly distributed over the task area. For each drone k and each grid cell g ij , it is determined whether the grid cell is covered by the drone, and the Euclidean distance from the position (x k , y k ) of the drone k to the center position (x ij , y ij ) of the grid cell is calculated:

[0106] where, if the distance d kij < r, the grid cell g ij is covered by the drone k.

[0107] When calculating the coverage area of the drones, for the entire drone group, the number of covered grid cells is calculated. To avoid double-counting the grid cells covered by multiple drones, a boolean set can be used to represent the coverage situation. Define the coverage indication variable

[0108] where k is the index of the drone, and the total coverage area S cover can be calculated by summation, and the total coverage area

[0109] where, A grid is the area of each grid cell, is the number of covered grid cells.

[0110] When the coverage rate does not meet the preset requirements, the position of the monitoring device is optimized by the coverage rate hybrid optimization algorithm for iterative update. The optimization goal is to maximize the coverage area:

[0111]

[0112] Determine whether the target coverage or maximum number of iterations is met.

[0113] If not, by using the HWPO algorithm, the deployment position of the drone is continuously adjusted to gradually increase the number of covered grid cells, and the total coverage of the monitoring range to the ecological red line area is recalculated until the optimal solution is reached.

[0114] When the number of iterations reaches a preset upper limit, or the total coverage reaches a preset coverage threshold, the current position of the monitoring device is determined to be the optimal deployment position, and the maximum coverage range is output.

[0115] In another embodiment provided by the present invention, the process of iterative updating specifically includes:

[0116] Based on the coverage hybrid optimization algorithm, a preset speed update model is used to calculate the update speed of the monitoring device;

[0117] Perform a local area search near the current optimal solution, update the position of the monitoring device in the local area, and obtain a first updated position;

[0118] A global search is performed in the deployment area, the global search of the deployment area is calculated, and the position of the monitoring device is updated again to obtain a second updated position as the position of the monitoring device after the current iterative update.

[0119] Specifically, the position of each drone base station is randomly generated and speed v k , use the preset speed update model to update the speed:

[0120] Updated speed

[0121] Among them, v i (t) is the speed of drone i, It is the drone location. is the optimal position of the individual, g * is the global optimal position, r1 and r2 are random numbers, w is the inertia weight, c1 and c2 are acceleration factors.

[0122] See also Figure 2When updating the position, first update the current drone to v(t+1), that is, update the speed, calculate the local search of the deployment area, and change the position. The drone performs a local search near the current optimal solution, tries to find a better deployment solution in the local area, updates the position, and obtains the first updated position.

[0123] in, is the current optimal solution, C and A are coefficient vectors, is the current position, the distance vector

[0124] Then calculate the global exploration of the deployment area and change the position x2. The global exploration of the drone in the deployment area. In the optimization of drone deployment, this behavior makes the drone's position not only limited to the vicinity of the optimal solution, but also able to explore a wider area, thereby avoiding falling into the local optimum, spiral update behavior, and obtain the second updated position

[0125] Among them, b is a constant, l is a random number, and the updated distance vector

[0126] By calculating the new position of each drone, f(x k ,y k ) fitness value. Update the individual optimal position of each drone and the global optimal position g * The hybrid optimization steps of HWPO are repeated until the maximum number of iterations is reached or the convergence condition is met, that is, the total coverage reaches the preset coverage threshold.

[0127] In another embodiment provided by the present invention, when the step S2 is implemented, the following steps are specifically performed:

[0128] The fuzzy logic index of each ecological index is determined through a preset fuzzy logic weight model;

[0129] The posterior weight of each indicator is calculated based on the conditional dependency relationship between ecological indicators;

[0130] Calculating the indicator weight of each ecological indicator according to the fuzzy logic indicator and the posterior weight;

[0131] The ecological environment index score is determined based on the weighted sum of each ecological index and the corresponding index weight.

[0132] In the specific implementation of this embodiment, ecological protection needs to comprehensively consider multiple indicators, such as the dryness index I calculated in step 1. i, vegetation growth trend index NPP (x, t), water conservation, soil and water conservation, biodiversity, wind and sand fixation, regional weather conditions and regional production economy, etc. By integrating multiple data sources, a comprehensive weight allocation scheme is provided. Ecological protection areas often need to be dynamically adjusted according to environmental changes. FBDWA (dynamic decision-making method in uncertain environment) can update weights and adjust regional ecological sensitivity levels in real time to ensure that protection measures match actual environmental conditions.

[0133] First, set the index value z ij represents the jth indicator of the i-th factor layer Z. Fuzzy logic is used to determine the importance of this indicator in the indicator system. For each indicator z ij , construct the membership function μ A (z ij ), indicating the degree of membership of the indicator to a specific level

[0134] Among them, a is the starting point of the membership function, b is the upper limit of the membership function, and the standard for dividing the fuzzy set. The membership degree is combined with the rule to generate the output weight w k Assume that there are the following fuzzy rules, μ A (z ij ) is high, the weight is large, μ A (z ij ) is low, the weight is small.

[0135] The fuzzy output (weight) W is generated using the following fuzzy inference rules: k , and then the final fuzzy logic weight is obtained through fuzzy defuzzification

[0136] Among them, the defuzzification function is the process of converting the result of fuzzy reasoning into an exact value, and it is the defuzzification of the weight calculated by fuzzy rules. k is the weighted output of each fuzzy rule, Represents the weight generated by fuzzy logic.

[0137] Each indicator z ij corresponds to a node in the network. The edge represents the conditional dependency between indicators. Set the probability table to indicate that the indicator z ij The probability distribution of

[0138] The parent node in the network corresponds to the index z ij Related other feature layers, these variables affect or determine z in the network ij The value of , and then calculate the posterior weight of each indicator

[0139] Among them, Observed Data refers to the data that the model has observed at the current and past time points.

[0140] The fuzzy logic weights and network weights Perform mean fusion to obtain the final comprehensive index weight

[0141] Calculate the ecological environment index score at this moment

[0142] Among them, w ij is the comprehensive indicator weight, z ij is the normalized data index, U i (t) is the ecological environment evaluation value corresponding to this moment, based on U j (t) Classify them into types and levels.

[0143] For example:

[0144]

[0145] According to the specific application requirements and the distribution of indicators, Type (U j (t)) Classify the ecological protection red line type, Grade (U j (t)) The ecological protection red line level is divided into four levels. P1, P2, P3, and P4 are the proportions of four element layers (related to water conservation, soil and water conservation, biodiversity, and windbreak and sand fixation). The thresholds T1, T2, T3, and T4 can be adjusted to adapt to different division standards. These thresholds can be determined through data analysis and historical records.

[0146] By analyzing regional signaling data, water conservation, soil and water conservation, biodiversity, windbreak and sand fixation, regional weather interface, and regional production and economic utilization status indicators, the weighted summation is performed according to the above calculated weights to determine the final score U j Based on the score U jThe proportion of the scores of the four element layers (related to water conservation, soil and water conservation, biodiversity, and windbreak and sand fixation) divides the ecological protection area into four types: water conservation, soil and water conservation, biodiversity, and windbreak and sand fixation. Each type is divided into five levels according to the score (slightly sensitive, slightly sensitive, moderately sensitive, highly sensitive, and extremely sensitive). The areas with extremely strong sensitivity in the ecological protection area are divided into ecological protection red line areas; the areas with slightly sensitive, moderately sensitive, and highly sensitive ecological protection areas are divided into ecological protection yellow line areas; the insensitive areas of the ecological protection area are divided into ecological protection green line areas, so as to construct the ecological protection red line area system.

[0147] In another embodiment provided by the present invention, based on the ecological environment data and the acquired ecological environment historical data, an uncertain environment dynamic decision-making method is adopted to simulate the dynamic changes of each ecological indicator and adjust the ecological environment indicator score, including:

[0148] Determine the element layer that affects the current state of each ecological indicator based on the ecological environment data, and determine the influencing factors;

[0149] According to the influencing factors of each ecological indicator, a dynamic decision-making method in an uncertain environment is adopted to build a change trend prediction model;

[0150] According to the change trend prediction model and the acquired ecological environment historical data, the dynamic changes of various ecological indicators are simulated and the ecological environment indicator scores are adjusted.

[0151] In the specific implementation of this embodiment, the future trend of ecological red line areas is predicted, time series data is processed and the dynamic relationship between variables is modeled. In the prediction of ecological red line areas, the element layer affecting the current state of each ecological indicator is determined according to the ecological environment data, and the influencing factors are determined. Each variable (such as U i Score) is modeled as a time series. Assume that the indicator U of each region i (t) represents the score at time t, modeling the state changes of these indicators at different time points.

[0152] The transition probability is used to describe the change from one time step to the next time step. Assume that U i (t) represents the index score of the ith region at time t, U i (t+1) represents the indicator score at time t+1.

[0153] Z(U i (t)) are external factors that affect the current state. These factors may include climate change, soil and water conservation, biodiversity and other factors. i (t) Produce an impact, thereby indirectly affecting U i (t+1). Influencing factors Z(Ui (t)) can usually be further decomposed into specific factors Z k (t), for example:

[0154] Z(U i (t))={Z1(t), Z2(t),...,Z m (t)};

[0155] Each Z1(t) is a specific external influencing factor, such as climate temperature, rainfall, etc. The regional change trend prediction model can be expressed as:

[0156] P(U i (t+1)|U i (t), Z(U i (t)))=f(U i (t), Z(U i (t)))+∈;

[0157] Among them, Z(U i (t)) represents the calculation of Z(U i (t)) factors, f(U i (t), Z(U i (t))) is the prediction function, describing the i The influence change of (t+1), ∈ is the error term.

[0158] When predicting the future state, it is necessary to calculate based on the current state and transition probability. The prediction formula is: 1))×P(U j (t+k-1)).

[0159] Among them, k is the time step of prediction, and Observed Data refers to the data that the model has observed at the current and past time points.

[0160] According to the change trend prediction model and the acquired ecological environment historical data, the dynamic changes of various ecological indicators are simulated and the ecological environment indicator scores are adjusted.

[0161] In another embodiment provided by the present invention, the step S1 specifically includes:

[0162] Collect data values ​​of different factors in each element layer of the ecological environment;

[0163] Use preset statistical methods to detect and identify outliers on the data values ​​of different factors, mark abnormal values ​​and missing values, and calculate missing values ​​using preset interpolation methods;

[0164] Normalize the data values ​​of different factors in each element layer of the ecological environment;

[0165] The ecological indicators of different ecological protection data systems are calculated based on the data values ​​processed by different factors of each element layer.

[0166] In the specific implementation of this embodiment, the delineation of the ecological protection red line is carried out on the basis of taking into account the factors such as environment, ecology and resources, and the important areas of water conservation function, soil and water conservation, and biodiversity protection are taken as the delineation objects. Its main purpose is to protect the important ecological function areas, maintain the ecosystem service function, and promote the sustainable development of the economy; ensure the safety of the national ecological pattern and the safety of the human living environment; protect natural resources and key species, maintain biodiversity, ensure the balanced development of the ecosystem, and promote the sustainable use and development of biological resources.

[0167] Collect data values ​​of different factors in each element layer of ecological environment, see Table 1;

[0168] Table 1 Example of ecological environment data

[0169]

[0170]

[0171] The preset statistical methods are used to detect and identify outliers on the data values ​​of different factors, mark abnormal values ​​and missing values, and calculate missing values ​​through the preset interpolation method.

[0172] In the process of data collection, outliers and missing values ​​may be caused by measurement errors, data input errors or other reasons. Statistical methods such as IQR are used to detect and identify outliers and missing values. The values ​​of missing data can be inferred by interpolating existing data through linear interpolation, Lagrange interpolation and spline interpolation.

[0173] Since the units of the various factors of the data indicators are different and the size ranges are different, they need to be normalized and unified to the same size range to facilitate the subsequent weighted assignment processing. Positive indicators: Negative indicators: The data values ​​of different factors in each element layer of the ecological environment are normalized.

[0174] The ecological indicators of different ecological protection data systems are calculated based on the data values ​​processed by different factors of each element layer.

[0175] Based on the preprocessed data, feature engineering is performed to extract key features that affect the demarcation of ecological red lines and construct a feature set for model training.

[0176] In another embodiment provided by the present invention, the ecological indicators include a dryness index, a vegetation growth trend index and a vegetation coverage.

[0177] Specifically, the dryness index I j It can indicate the degree of aridity of the climate in the study area to a certain extent, and reflect the comparative relationship between regional precipitation and evaporation capacity. When the annual precipitation is greater than the evaporation rate, it indicates that the climate in the area is humid and suitable for vegetation growth; on the contrary, when the evaporation rate is greater than the annual precipitation, it indicates that the degree of aridity in the area is obvious, vegetation growth is difficult, the surface vegetation is sparse, and the possibility of land desertification increases. The aridity index and the humidity index are inversely proportional to each other. The aridity index is proportional to the humidity index.

[0178] The dryness index of the study area is I j , T j is the average temperature (temperature greater than ten degrees), R j is the total rainfall (when the temperature is greater than ten degrees).

[0179] Vegetation growth state index NPP(x,t), the growth state index refers to the total amount of organic dry matter produced by green plants through photosynthesis per unit time and per unit area, minus the remaining part after respiration. On the one hand, the vegetation growth state index can indicate the natural production capacity of vegetation communities and characterize the response degree of grassland ecosystems to environmental factors; on the other hand, it can indicate the strength of grassland ecosystem carbon sequestration capacity. Vegetation growth state index is mainly determined by light energy conversion rate (ε) and photosynthetically active radiation (APAR) absorbed by grassland: NPP(x,t) = APAR(x,t) × ε(x,t).

[0180] Among them, NPP(x,t) is the vegetation production capacity in area x within time period t, APAR(x,t) is the absorbed photosynthetically active radiation, and ε(x,t) is the light energy conversion rate.

[0181] This case provides a method based on coverage hybrid optimization combined with dynamic division of ecological red line areas to optimize the coverage range and deployment location of base stations. The algorithm can provide coverage on demand in emergency scenarios (grazing ban and restriction, wildfire prevention, restriction on deforestation, fishing ban, etc.) in complex environments (sand, desert or forest) to protect the ecological environment to the greatest extent.

[0182] It should be noted that when the dynamic division method of the ecological red line area of ​​this case is specific, by building a division system, the overall architecture of the system can be divided into: display layer, interface layer, service layer, computing layer, storage layer, and data source layer. The VUE+ElementUi front-end framework is used, which is mainly used for the front-end page display of this system project. The Http protocol + JSON data format is used, which is mainly used for the definition and specification of the data format transmission of this system project. The Spring Cloud microservice architecture is used, combined with Nacos distributed service management, with service registration discovery, dynamic configuration management, and service health monitoring.

[0183] The DACP big data development platform of the yarn queue is used for offline batch task data processing, and the Flink+Spark big data framework is used for real-time data processing, providing computing power and framework support for this system project, and the final results are stored in the rds relational database. Kafka is used to consume the real-time raw data of the operator signaling, and HIVE+HDFS is used to store historical signaling data, providing a storage data source layer for real-time and historical data for this system project: based on mobile location signaling, full user tags, content insight data, and ecological red line map information, the upper-level model and system project application are developed.

[0184] The underlying model and computing links are deployed in the mobile big data platform, where the front-end, back-end and data separation ensures data security, and two database servers, application servers and Web servers each ensure high availability.

[0185] See also Figure 3 , is a schematic diagram of a structure of a device for dynamically dividing an ecological red line area provided by an embodiment of the present invention, the device comprising:

[0186] The indicator determination module is used to construct ecological indicators of the ecological protection data system based on the collected ecological environment data;

[0187] The score calculation module is used to calculate the indicator weight of each ecological indicator through a preset fuzzy logic algorithm to determine the ecological environment indicator score;

[0188] A dynamic adjustment module is used to simulate the dynamic changes of various ecological indicators and adjust the ecological environment indicator scores by using an uncertain environment dynamic decision-making method according to the ecological environment data and the acquired ecological environment historical data;

[0189] The regional division module is used to determine the ecological red line division area based on the adjusted ecological environment indicator scores.

[0190] It should be noted that the device for dynamically dividing ecological red line areas provided in this embodiment can execute all the steps and functions of the method for dynamically dividing ecological red line areas provided in any of the above embodiments, and the specific functions of the device will not be described in detail here.

[0191] See also Figure 4 , is another structural diagram of a device for dynamically dividing an ecological redline area provided in an embodiment of the present invention. The device for dynamically dividing an ecological redline area includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for dynamically dividing an ecological redline area. When the processor executes the computer program, the steps in each of the above-mentioned embodiments of the method for dynamically dividing an ecological redline area are implemented, such as Figure 1 Alternatively, the processor implements the functions of each module in the above-mentioned device embodiments when executing the computer program.

[0192] Exemplarily, the computer program may be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program in the device for dynamically dividing ecological red line areas. For example, the computer program may be divided into several modules, and the specific functions of each module have been described in detail in a method for dynamically dividing ecological red line areas provided in any of the above embodiments, and the specific functions of the device will not be repeated here.

[0193] The device for dynamically dividing an ecological redline area may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The device for dynamically dividing an ecological redline area may include, but not limited to, a processor and a memory. A person skilled in the art may understand that the schematic diagram is only an example of a device for dynamically dividing an ecological redline area, and does not constitute a limitation on a device for dynamically dividing an ecological redline area. The device may include more or fewer components than shown in the diagram, or a combination of certain components, or different components. For example, the device for dynamically dividing an ecological redline area may also include input and output devices, network access devices, buses, etc.

[0194] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device for dynamically dividing an ecological red line area, and uses various interfaces and lines to connect the various parts of the device for dynamically dividing an ecological red line area.

[0195] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the dynamic demarcation device of ecological red line area by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0196] Wherein, if the module integrated in the dynamic demarcation device of the ecological red line area 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 such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium.

[0197] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which implements the steps of the method described in any of the above embodiments when executed by a processor.

[0198] It should be noted that the computer program product provided in this embodiment can execute all the steps and functions of a method for dynamically dividing an ecological red line area provided in any of the above embodiments, and the specific functions of the device will not be described in detail here.

[0199] It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for dynamically dividing ecological red line areas, characterized in that: The method comprises: Construct ecological indicators of the ecological protection data system based on the collected ecological environment data; The indicator weight of each ecological indicator is calculated through a preset fuzzy logic algorithm to determine the ecological environment indicator score; According to the ecological environment data and the acquired historical ecological environment data, a dynamic decision-making method in an uncertain environment is adopted to simulate the dynamic changes of various ecological indicators and adjust the scores of ecological environment indicators; The ecological red line demarcation area will be determined based on the adjusted ecological environment indicator scores.

2. According to claim 1, a method for dynamically dividing ecological red line areas is characterized in that: The method further comprises: According to the ecological red line division area, a preset coverage hybrid optimization algorithm is used to optimize the position of the pre-deployed monitoring equipment to determine the optimal deployment position and maximum coverage range of the monitoring equipment.

3. The method for dynamically dividing ecological red line areas according to claim 2 is characterized in that: The preset coverage hybrid optimization algorithm is used to optimize the locations of pre-deployed monitoring devices and determine the best deployment locations and maximum coverage of monitoring devices, including: Calculate the total coverage rate of the monitoring range of several monitoring devices randomly deployed in advance to the ecological red line area; When the coverage rate does not meet the preset requirements, the position of the monitoring equipment is optimized by the coverage rate hybrid optimization algorithm for iterative update, and the total coverage rate of the monitoring range to the ecological red line area is recalculated; When the number of iterations reaches a preset upper limit, or the total coverage reaches a preset coverage threshold, the current position of the monitoring device is determined to be the optimal deployment position, and the maximum coverage range is output.

4. The method for dynamically dividing ecological red line areas according to claim 3 is characterized in that: The position of the monitoring device is optimized by the coverage hybrid optimization algorithm for iterative update, including: Based on the coverage hybrid optimization algorithm, a preset speed update model is used to calculate the update speed of the monitoring device; Perform a local area search near the current optimal solution, update the position of the monitoring device in the local area, and obtain a first updated position; A global search is performed in the deployment area, the global search of the deployment area is calculated, and the position of the monitoring device is updated again to obtain a second updated position as the position of the monitoring device after the current iterative update.

5. The method for dynamically dividing ecological red line areas according to claim 1 is characterized in that: The weight of each ecological indicator is calculated through a preset fuzzy logic algorithm to determine the ecological environment indicator score, including: The fuzzy logic weight of each ecological indicator is determined through a preset fuzzy logic weight model; The posterior weight of each indicator is calculated based on the conditional dependency relationship between ecological indicators; Calculating the indicator weight of each ecological indicator according to the fuzzy logic indicator and the posterior weight; The ecological environment index score is determined based on the weighted sum of each ecological index and the corresponding index weight.

6. The method for dynamically dividing ecological redline areas according to claim 1 is characterized in that: According to the ecological environment data and the historical ecological environment data obtained, the dynamic decision-making method in an uncertain environment is adopted to simulate the dynamic changes of various ecological indicators and adjust the scores of ecological environment indicators, including: Determine the element layer that affects the current state of each ecological indicator based on the ecological environment data, and determine the influencing factors; According to the influencing factors of each ecological indicator, a dynamic decision-making method in an uncertain environment is adopted to build a change trend prediction model; According to the change trend prediction model and the acquired ecological environment historical data, the dynamic changes of various ecological indicators are simulated and the ecological environment indicator scores are adjusted.

7. The method for dynamically dividing ecological red line areas according to claim 1 is characterized in that: Ecological indicators of the ecological protection data system are constructed based on the collected ecological environment data, including: Collect data values ​​of different factors in each element layer of the ecological environment; Use preset statistical methods to detect and identify outliers on the data values ​​of different factors, mark abnormal values ​​and missing values, and calculate missing values ​​using preset interpolation methods; Normalize the data values ​​of different factors in each element layer of the ecological environment; The ecological indicators of different ecological protection data systems are calculated based on the data values ​​processed by different factors of each element layer.

8. The method for dynamically dividing ecological red line areas according to claim 1 is characterized in that: The ecological indicators include aridity index, vegetation growth trend index and vegetation coverage.

9. A device for dynamically dividing ecological red line areas, characterized in that: The device comprises: The indicator determination module is used to construct ecological indicators of the ecological protection data system based on the collected ecological environment data; The score calculation module is used to calculate the indicator weight of each ecological indicator through a preset fuzzy logic algorithm to determine the ecological environment indicator score; A dynamic adjustment module is used to simulate the dynamic changes of various ecological indicators and adjust the ecological environment indicator scores by using an uncertain environment dynamic decision-making method according to the ecological environment data and the acquired ecological environment historical data; The regional division module is used to determine the ecological red line division area based on the adjusted ecological environment indicator scores.

10. A device for dynamically dividing ecological red line areas, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for dynamically dividing an ecological red line area as described in any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a method for dynamically dividing an ecological red line area as described in any one of claims 1 to 8.

12. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method described in any one of claims 1 to 8 are implemented.

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