Vegetation data display method and device, computer equipment and storage medium

Abstract

The embodiment of the invention belongs to the field of agricultural growth monitoring, and relates to a vegetation data display method, which comprises the steps of acquiring a to-be-processed vegetation data set, dynamically segmenting target vegetation data to obtain target vegetation features, performing data pairing on a historical data set and the target vegetation features through a dynamic time window to obtain target vegetation variation, and displaying the target vegetation variation. And performing layer construction based on the target vegetation data, generating a target vegetation distribution grid layer, and importing the target vegetation variation into the target vegetation distribution grid layer to generate a target vegetation data display graph. The invention further provides a vegetation data display device, computer equipment and a storage medium. According to the method, the operation process and visual display of processing the vegetation data are simplified, so that the user can intuitively compare the vegetation indexes in different periods on the same interface, the abnormal region and change trend of vegetation growth can be quickly identified, the timeliness and accuracy of decision making can be effectively improved, and the working efficiency and experience of the user are optimized.

Description

Technical Field

[0001] The present application relates to the technical field of agricultural growth monitoring, and in particular to a vegetation data display method, device, computer equipment and storage medium. Background Art

[0002] In the fields of agriculture, forestry, natural resource management, environmental monitoring, etc., the NDVI index has become one of the widely used remote sensing indicators. The NDVI index is based on the ratio between the reflectance of infrared and visible light bands. It can measure the green plant coverage and health status of the land surface, and can monitor and analyze the growth of vegetation, land use and coverage changes.

[0003] In the traditional NDVI data display method, the NDVI data selector, time axis, and time selector are mainly used to obtain deviation charts, year-on-year charts, and quarter-on-quarter charts, which respectively display the NDVI indicators of satellite image data in the agricultural growth monitoring scenario, so as to assist users in monitoring the growth of crops.

[0004] However, since the NDVI data selector, time axis, and time selector selections are usually scattered in different locations on the interface, users need to frequently switch operation areas, which results in cumbersome operations, inconvenient data type switching, and limited time selection, which reduces work efficiency and causes users to have a poor experience when conducting crop growth analysis and find it difficult to quickly obtain the required information. Summary of the invention

[0005] The purpose of the embodiments of the present application is to propose a vegetation data display method, device, computer equipment and storage medium to solve the technical problem that it is difficult for users to quickly compare NDVI data of different periods in the same interface and cannot intuitively grasp the dynamic changes in crop growth conditions.

[0006] In order to solve the above technical problems, the present application embodiment provides a vegetation data display method, which adopts the following technical solutions:

[0007] Acquire a vegetation data set to be processed, wherein the vegetation data set to be processed includes historical vegetation data and target vegetation data;

[0008] Dynamically segmenting the target vegetation data to obtain target vegetation features, wherein the target vegetation features are stored in a dynamic time window;

[0009] Pairing the historical data set with the target vegetation features through the dynamic time window to obtain a target vegetation change amount;

[0010] Performing layer construction based on the target vegetation data to generate a target vegetation distribution raster layer;

[0011] The target vegetation change amount is imported into the target vegetation distribution grid layer to generate a target vegetation data display map.

[0012] Furthermore, the step of obtaining the vegetation data set to be processed specifically includes:

[0013] Acquiring a time range and a data type selected by a user, and pulling out the historical vegetation data and the real-time vegetation data from the vegetation database based on the time range and the data type selected by the user;

[0014] The target vegetation data is obtained by performing time series decomposition on the actual vegetation data using a seasonal decomposition time series analysis algorithm.

[0015] Furthermore, the step of dynamically segmenting the target vegetation data to obtain target vegetation features specifically includes:

[0016] Get the preset time granularity;

[0017] Based on the preset time granularity, a plurality of the dynamic time windows are generated by a sliding window technology;

[0018] The target vegetation data is imported into the dynamic window for feature statistics to obtain the target vegetation features.

[0019] Furthermore, the step of dynamically segmenting the target vegetation data to obtain target vegetation features specifically includes:

[0020] Obtaining a target matching strategy, wherein the target matching strategy includes a year-on-year matching strategy and a month-on-month matching strategy;

[0021] Importing the historical data set into the dynamic time window;

[0022] Data pairing is performed on the target vegetation features based on the target matching strategy to obtain the target vegetation change amount.

[0023] Furthermore, the step of performing data pairing on the target vegetation features based on the target matching strategy to obtain the target vegetation change amount specifically includes:

[0024] Determining the target matching strategy based on the number of the historical data sets imported into the dynamic time window;

[0025] If the historical data set is imported into only one dynamic time window, the same-year matching strategy is executed: matching the target vegetation feature with the historical data set to obtain the target vegetation change amount;

[0026] If the historical data set is imported into several dynamic time windows, the year-on-year matching strategy is executed: the target vegetation features are matched with the historical data set to obtain the changes in all branch vegetation; the branch vegetation changes in all dynamic time windows are differenced to obtain the target vegetation changes.

[0027] Furthermore, the step of constructing a layer based on the target vegetation data to generate a target vegetation distribution grid layer specifically includes:

[0028] The target vegetation data is interpolated by using an inverse distance weighted interpolation algorithm to generate a continuous standard distribution grid layer;

[0029] The standard distribution grid layer is color mapped according to the target vegetation characteristics to obtain the target vegetation distribution grid layer.

[0030] Furthermore, the step of importing the target vegetation change amount into the target vegetation distribution grid layer to generate a target vegetation data display map specifically includes:

[0031] Performing anomaly detection on the target vegetation change amount;

[0032] If the target vegetation change exceeds a preset abnormal threshold, the abnormal score of the target vegetation change is calculated, and the abnormal score is imported into the target vegetation distribution grid layer to generate a target vegetation data display map;

[0033] If the target vegetation change amount does not exceed the preset abnormal threshold, the target vegetation change amount is directly imported into the target vegetation distribution grid layer to generate a target vegetation data display map.

[0034] In order to solve the above technical problems, the embodiment of the present application also provides a vegetation data display device, which adopts the following technical solution:

[0035] An acquisition module, used for acquiring a vegetation data set to be processed, wherein the vegetation data set to be processed includes historical vegetation data and target vegetation data;

[0036] A segmentation module, used for dynamically segmenting the target vegetation data to obtain target vegetation features, wherein the target vegetation features are stored in a dynamic time window;

[0037] A data pairing module, used to pair the historical data set with the target vegetation features through the dynamic time window to obtain a target vegetation change amount;

[0038] A construction module, used to construct a layer based on the target vegetation data to generate a target vegetation distribution grid layer;

[0039] The import module is used to import the target vegetation change amount into the target vegetation distribution grid layer to generate a target vegetation data display map.

[0040] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0041] A computer device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the vegetation data display method when executing the computer-readable instructions.

[0042] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0043] A computer-readable storage medium, characterized in that computer-readable instructions are stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the steps of the vegetation data display method are implemented.

[0044] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0045] The embodiment of the present application obtains a vegetation data set to be processed, dynamically segments the target vegetation data, obtains target vegetation features, pairs the historical data set with the target vegetation features through a dynamic time window, obtains the target vegetation change, constructs a layer based on the target vegetation data, generates a target vegetation distribution grid layer, imports the target vegetation change into the target vegetation distribution grid layer, generates a target vegetation data display diagram, and achieves accurate comparison between historical vegetation data and target vegetation data through a dynamic time window and data pairing technology, thereby generating a distribution grid layer containing the target vegetation change, simplifying the operation process and visual display of vegetation data, allowing users to intuitively compare vegetation indices of different periods on the same interface, and quickly identify abnormal areas and change trends of vegetation growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0048] Figure 2is a flow chart of an embodiment of a vegetation data display method according to the present application;

[0049] Figure 3 is a structural schematic diagram of an embodiment of a vegetation data display device according to the present application;

[0050] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0052] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0053] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0054] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0055] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0056] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0057] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0058] It should be noted that the vegetation data display method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the vegetation data display device is generally arranged in the server / terminal device.

[0059] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0060] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for displaying vegetation data according to the present application. The method for displaying vegetation data comprises the following steps:

[0061] Step S201, obtaining a vegetation dataset to be processed.

[0062] In this embodiment, the vegetation data display method can be deployed in a vegetation data display platform. The vegetation data display platform can be constructed by a server or a server cluster. The server or server cluster can be any electronic device with image recognition, image processing, data transmission, data storage and other functions. The vegetation data display method runs on the electronic device (for example, Figure 1 The server / terminal device shown in the figure can receive the vegetation dataset to be processed through a wired connection or a wireless connection. It should be noted that the wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0063] In this embodiment, the vegetation dataset to be processed may be an NDVI dataset, which may be a dataset containing a Normalized Difference Vegetation Index (NDVI), and the NDVI dataset is used to analyze remote sensing data to evaluate and monitor the health status and density of vegetation on the ground. The NDVI index may be calculated by comparing the near-infrared light strongly reflected by the ground plants when healthy and the red light absorbed by the plants in the visible light, and the specific calculation formula is as follows:

[0064]

[0065] Among them, the above NIR represents the reflectivity of the near-infrared band, the above Red represents the reflectivity of the red light band, and the value range of the above NDVI index is from -1 to +1. When the NDVI is close to a value of +1, it indicates that the area has strong vegetation reflection and healthy green vegetation. When the NDVI is close to a value of 0, it indicates that the area is bare land or non-vegetated area. When the NDVI is close to a value of -1, it indicates that the area is a water area.

[0066] Specifically, the vegetation data set to be processed includes historical vegetation data and target vegetation data. The historical vegetation data may be NDVI data of the same area in past years, NDVI data of previous periods, long-term historical data, and NDVI data before and after extreme events, etc., which refers to NDVI data sets of one or more periods in the past;

[0067] The above-mentioned target vegetation data can be the latest collected NDVI data selected by the user, the NDVI data within a specific time range, the NDVI data of a specific area, etc., or it can be an NDVI data set obtained after cleaning and normalizing the historical NDVI data. The above-mentioned cleaning process includes removing outliers, filling missing values ​​and smoothing. The above-mentioned normalization can be a minimum-maximum standardization method.

[0068] Specifically, regarding the specific implementation process of obtaining the vegetation dataset to be processed, this application will provide further detailed descriptions in subsequent specific embodiments, and will not be elaborated on here.

[0069] Step S202: dynamically segment the target vegetation data to obtain target vegetation features.

[0070] In this embodiment, the target vegetation characteristics may include indicator data such as the mean, variance, skewness, and kurtosis of NDVI data. The target vegetation characteristics may be used to describe the growth status and health level of vegetation within a specific time.

[0071] In this embodiment, the target vegetation features can be stored in a dynamic time window, and the vegetation data display platform supports the simultaneous display of multiple dynamic time windows. Specifically, the dynamic time window can be a sliding window implemented based on sliding window technology, and the dynamic time window can be a series of continuous observation windows defined in a given time series data. The observation window has a fixed length, such as 7 days, 30 days or a quarter, and moves forward with time by a certain step length, such as one day or one week. In the dynamic time window, the target vegetation features can be extracted by moving the window along the time axis according to the step length of the target vegetation data.

[0072] Specifically, regarding the specific implementation process of dynamically segmenting the target vegetation data to obtain the target vegetation characteristics, this application will provide further detailed descriptions in subsequent specific embodiments, and will not be elaborated on here.

[0073] Step S203, pairing the historical data set with the target vegetation features through a dynamic time window to obtain the target vegetation change amount.

[0074] In this embodiment, the above target vegetation change amount can be used to analyze changes in vegetation health and growth conditions, and reveal the dynamic process of vegetation responding to environmental changes or management measures. Specifically, the above target vegetation change amount can include data such as absolute change amount, relative change amount, and annual change amount. The above absolute change amount can be the difference in NDVI values ​​between two time points; the above relative change amount can be the difference in NDVI values ​​between two different dynamic time windows expressed in percentage form; the above annual change amount can be the difference between the NDVI value of the current dynamic time window and the NDVI value displayed in the dynamic time window at the same time last year.

[0075] Specifically, regarding the specific implementation process of pairing the historical data set with the target vegetation characteristics through the dynamic time window to obtain the target vegetation change, this application will provide further details in subsequent specific embodiments and will not be elaborated on here.

[0076] Step S204: constructing a layer based on the target vegetation data to generate a target vegetation distribution raster layer.

[0077] In this embodiment, the target vegetation distribution raster layer can be a distribution raster layer (Raster Layer) for displaying vegetation spatial data. Specifically, the target vegetation distribution raster layer is composed of regularly arranged grid cells, each grid cell is called a "pixel" or "grid", and each pixel contains one or more NDVI values.

[0078] Specifically, regarding the specific implementation process of constructing the layer based on the target vegetation data to generate the target vegetation distribution grid layer, this application will provide further detailed descriptions in subsequent specific embodiments, which will not be elaborated here.

[0079] Step S205, importing the target vegetation change amount into the target vegetation distribution grid layer to generate a target vegetation data display map.

[0080] In this embodiment, the target vegetation data display diagram can be a multi-level visualization interface. The multi-level visualization integrates controls such as NDVI data selector, timeline, and time selector, supports browsing NDVI data changes in different periods by dragging the timeline, and integrates and displays trend analysis results, seasonal change patterns, and abnormal area information in the interface. Specifically, adjusting the NDVI data selector will cause changes in the timeline and time selector. The target vegetation data display diagram can perform data selection and time selection in one stop, generate dashboards and map content, and the NDVI data selector is a single-select selector, including "deviation from the mean", "year-on-year", "month-on-month", and "NDVI" functions. The timeline contains three controls, a left button, a timeline control, and a right button. The time selector provides year selection and specific date selection.

[0081] Specifically, regarding the specific implementation process of importing the target vegetation change amount into the target vegetation distribution grid layer to generate the target vegetation data display map, this application will provide further details in subsequent specific embodiments, and will not be elaborated here.

[0082] The present application obtains a vegetation data set to be processed, dynamically segments the target vegetation data, obtains target vegetation features, pairs the historical data set with the target vegetation features through a dynamic time window, obtains the target vegetation change, constructs a layer based on the target vegetation data, generates a target vegetation distribution grid layer, imports the target vegetation change into the target vegetation distribution grid layer, generates a target vegetation data display diagram, and achieves accurate comparison between historical vegetation data and target vegetation data through dynamic time windows and data pairing technology, thereby generating a distribution grid layer containing the target vegetation change, simplifying the operation process and visual display of vegetation data, allowing users to intuitively compare vegetation indices of different periods on the same interface, and quickly identify abnormal areas and change trends of vegetation growth.

[0083] In some optional implementations, step S201 includes the following steps:

[0084] Obtain the time range and data type selected by the user, and based on the time range and data type selected by the user, extract historical vegetation data and live vegetation data from the vegetation database;

[0085] The actual vegetation data is decomposed into time series by seasonal decomposition time series analysis algorithm to obtain the target vegetation data.

[0086] In this embodiment, the vegetation database may be a data source for storing and managing vegetation-related NDVI data, and the data source may be a relational database such as MYSQL, Oracle Database, PostgreSQL, or a non-relational database such as MongoDB, Cassandra, and CouchDB. Specifically, the vegetation database includes historical vegetation data and live vegetation data, and the live vegetation data may be NDVI data currently or recently extracted and collected from satellite images or aerial photography, and used for real-time monitoring and analysis of the health status of vegetation.

[0087] In this embodiment, the seasonal decomposition time series analysis algorithm may include time series decomposition algorithms such as Classical Seasonal Decomposition, X-11 decomposition method, STL (Seasonal and Trend decomposition using Loess) and SEATS (Seasonal Extraction in ARIMA Time Series). The seasonal decomposition time series analysis algorithm may be used to decompose NDVI data into trend, seasonal and random components to help analyze the periodic changes and long-term trends of vegetation.

[0088] In this embodiment, the user can select the time range and data type by selection or manual input on the above-mentioned vegetation data display platform, for example, the time range is today and today of last year, the data type is NDVI, and historical vegetation data and real-time vegetation data are pulled out from the vegetation database.

[0089] In a possible embodiment, the STL algorithm is used as an example of a seasonal decomposition time series analysis algorithm. The NDVI data within the time range selected by the user is used as the input time series, the parameters of the STL algorithm are determined, the annual vegetation growth cycle is selected as the seasonal window, and the annual vegetation growth cycle can be 12 months. A trend window is set to smooth the long-term trend, and the trend window can be 24 months. If the data contains more outliers, the number of robust iterations can be increased to improve the decomposition quality; then, local weighted regression (Loess) is applied to the NDVI data to estimate the trend component and the seasonal component respectively, and the trend component, the seasonal component and the random component are separated from the NDVI data; then, the trend component is analyzed to identify the long-term change trend of vegetation growth, such as whether it shows an increase or decrease, the seasonal component is observed, the seasonal pattern and periodic fluctuations of vegetation growth are determined, and the random component is evaluated to identify fluctuations that are not explained by the trend and seasonality. The fluctuations can be related to unconventional climate events or other external factors. Finally, the trend component, the seasonal component and the random component are used as the target vegetation data.

[0090] This application extracts historical and actual vegetation data from the vegetation database through the time range and data type specified by the user, and decomposes the actual data using the seasonal decomposition time series analysis algorithm, thereby achieving in-depth analysis of vegetation NDVI data, and can effectively separate vegetation growth trends, seasonal patterns and random fluctuations, thereby enhancing the understanding of dynamic changes in vegetation, and improving the accuracy and responsiveness of the decision support system, making environmental monitoring and vegetation management more scientific and accurate.

[0091] In some optional implementations, step S202 includes the following steps:

[0092] Get the preset time granularity;

[0093] Based on the preset time granularity, several dynamic time windows are generated through sliding window technology;

[0094] The target vegetation data is imported into the dynamic window for feature statistics to obtain the target vegetation features.

[0095] In this embodiment, the preset time granularity can be a time interval that can be selected by the user for analyzing and displaying data, and the preset time granularity can include daily granularity, weekly granularity, monthly granularity, seasonal granularity and annual granularity. Specifically, the daily granularity can be used to provide daily NDVI data, which is suitable for scenarios that require intensive monitoring and rapid response, such as disaster monitoring or specific event analysis. The weekly granularity can be used to summarize NDVI data within a week, which is suitable for capturing short-term changes in vegetation, such as rapidly changing crop conditions or changes at the beginning of the season. The monthly granularity can be used to analyze seasonal changes and monthly vegetation health status, which is often used in long-term environmental and agricultural planning. The seasonal granularity can be used to reflect seasonal changes, especially suitable for analyzing areas significantly affected by seasons, such as vegetation in monsoon climate zones. The annual granularity can be used to provide an overview of vegetation each year, which is suitable for long-term trend analysis, such as climate change research or evaluation of the effectiveness of vegetation restoration projects. The preset time granularity can be obtained by the user selecting or manually entering it on the vegetation data display platform.

[0096] In a possible embodiment, a user selects a monthly granularity by selection or manual input on the vegetation data display platform, and matches the window size and window step according to the monthly granularity. For example, if the time granularity is a monthly granularity, each window covers one month, and each time window is completely independent and non-overlapping, so as to obtain the dynamic time window. Then, the target vegetation data is grouped according to the dynamic time window, and each dynamic time window contains the locations of all NDVI data within the time period. The locations of all NDVI data within each dynamic time window are subjected to feature calculation, such as calculating the average NDVI value in each dynamic time window to reflect the average vegetation health status of the time period, calculating the standard deviation of each dynamic time window to measure the degree of variation of the NDVI value in each window, analyzing the shape characteristics of the NDVI distribution, identifying the skewness or abnormal distribution of the vegetation status, and obtaining the target vegetation features.

[0097] This application effectively realizes fine-grained time series analysis of vegetation data by setting a predetermined time granularity and using sliding window technology to generate dynamic time windows, and then imports the target vegetation data into these windows for detailed feature statistics. It allows continuous monitoring of the health status and growth patterns of vegetation, provides detailed information on vegetation changes, and reveals short-term and long-term trends in vegetation growth, enhancing the availability and resolution of data, making vegetation management and environmental monitoring decisions more accurate and timely.

[0098] In some optional implementations, step S203 includes the following steps:

[0099] Get target matching strategy;

[0100] Import historical data sets into dynamic time windows;

[0101] Based on the target matching strategy, data pairing is performed on the target vegetation features to obtain the target vegetation change.

[0102] In an embodiment of the present application, the above-mentioned target matching strategy includes a year-on-year matching strategy and a month-on-month matching strategy. Specifically, the above-mentioned year-on-year matching strategy can be to compare the vegetation data of the current dynamic time window with the data of the same dynamic time window in the previous year. For example, if the current NDVI data is from July 2024, then this year-on-year matching strategy will compare it with the NDVI data of the same months such as July 2023 and July 2022, which will help identify the annual change trends of vegetation growth, such as seasonal changes, long-term ecological changes, or the effects of specific intervention measures;

[0103] The above-mentioned month-on-month matching strategy can be to compare the vegetation data of the current dynamic time window with the data of the immediately previous dynamic time window. For example, using the month-on-month strategy, if the current dynamic time window is July 2024, it will be compared with the NDVI data of the dynamic time window in June 2024, which is suitable for monitoring vegetation changes in the short term, such as changes caused by seasonal transitions, climate anomalies or other environmental factors.

[0104] This application sets a target matching strategy, imports historical data sets into a dynamic time window, and performs data matching based on a year-on-year or month-on-month strategy, thereby achieving accurate measurement of vegetation changes. It can systematically analyze and compare vegetation characteristics in different time periods, and effectively identify and quantify changing trends in vegetation health.

[0105] In some optional implementations, the above step of "matching data of target vegetation features based on a target matching strategy to obtain a target vegetation change amount" includes the following steps:

[0106] Determine the target matching strategy based on the number of dynamic time windows imported into the historical data set;

[0107] If the historical data set is imported into only one dynamic time window, the same-year matching strategy is implemented: the target vegetation characteristics are matched with the historical data set to obtain the target vegetation change;

[0108] If the historical data set is imported into several dynamic time windows, the ring-matching strategy is implemented: the target vegetation features are matched with the historical data set to obtain the vegetation changes of all branches;

[0109] The branch vegetation changes in all dynamic time windows are interpolated to obtain the target vegetation changes.

[0110] In this embodiment, when the historical data set is only imported into one dynamic time window, the above-mentioned vegetation data display platform compares the vegetation data of the current dynamic time window with the data of the same historical period, for example, comparing the NDVI data of July 2024 with the NDVI data of July 2023 and July 2022, and obtaining the target vegetation change by calculating the difference between the NDVI data of the two. The above-mentioned difference can be the increase or decrease, the rate of change, etc. of the NDVI value.

[0111] In this embodiment, when the historical data set is imported into multiple dynamic time windows, the vegetation data display platform system compares the NDVI data of the current dynamic time window with the NDVI data of the previous or several consecutive dynamic time windows. For example, the data of July 2024 is compared with the NDVI data of June and May 2024, and the branch vegetation change amount is obtained by calculating the difference between the NDVI data of each of the two. The branch vegetation change amount can be the increase or decrease of the NDVI value, the change rate, etc., and then the secondary difference calculation is performed on all branch vegetation change amounts to obtain the target vegetation change amount.

[0112] This application determines the target matching strategy by the number of dynamic time windows imported based on historical data sets, and executes year-on-year or month-on-month matching strategies. The year-on-year matching strategy evaluates long-term trends by comparing historical and current data at the same time point, while the month-on-month matching strategy observes shorter-term changes by analyzing data in continuous time windows. It achieves comprehensive tracking and precise analysis of vegetation changes, improves the flexibility and response speed of environmental monitoring, and helps to timely detect environmental changes and take appropriate measures.

[0113] In some optional implementations, step S204 includes the following steps:

[0114] The target vegetation data is interpolated by using the inverse distance weighted interpolation algorithm to generate a continuous standard distribution raster layer;

[0115] The standard distribution raster layer is color mapped according to the target vegetation characteristics to obtain the target vegetation distribution raster layer.

[0116] In this embodiment, the above-mentioned Inverse Distance Weighting (IDW) interpolation algorithm may be a geographic data interpolation method for estimating the value of an unknown point based on the values ​​of its surrounding known points.

[0117] In this embodiment, data points of target vegetation data are collected, and these data points include the geographical location (latitude and longitude) and corresponding NDVI data of each point. The range of interpolation is determined according to the above geographical location and the distribution of data points, which will define the coverage area of ​​the final raster layer. The power parameter p is set to 2 to determine the search radius, that is, the maximum distance considered when calculating the interpolation of each grid point. For each data point in the raster layer, the weight formula of IDW is used to calculate the target vegetation data. The weight formula of IDW is as follows:

[0118]

[0119] Among them, the above W i is the weight of the i-th known point to the unknown point. i Represents the distance between the i-th known point and the unknown point. The above p represents the power parameter, which is usually taken as 2 and is used to adjust the sensitivity of the distance effect.

[0120] According to the above weight formula, all known data points within the search radius are considered, and the weighted average vegetation index of these points to the current grid point is calculated. Then, the size of the grid is determined based on the pixel value of the original remote sensing image, and the above weighted average vegetation index value is filled into the corresponding grid position to form the above standard distribution grid layer.

[0121] In this embodiment, color mapping rules can be defined for the above-mentioned standard distribution raster layer according to the vegetation range in the target vegetation feature, and the vegetation index can be converted into color to obtain the above-mentioned target vegetation distribution raster layer for easy visual interpretation and analysis. The color gradient usually transitions from a color representing a lower vegetation density (such as brown) to a color representing a high vegetation density (such as green).

[0122] This application uses an inverse distance weighted interpolation algorithm to interpolate the target vegetation data and generate a continuous standard distribution raster layer, and then performs color mapping on the layer to reflect different target vegetation characteristics, thereby converting point data into a visually continuous and intuitive geographic information layer, making complex spatial data easier to interpret and understand, greatly enhancing the accessibility and applicability of the data, and supporting more accurate environmental monitoring and management decisions.

[0123] In some optional implementations, step S205 includes the following steps:

[0124] Perform anomaly detection on target vegetation changes;

[0125] If the target vegetation change exceeds the preset abnormal threshold, the abnormal score of the target vegetation change is calculated, and the abnormal score is imported into the target vegetation distribution raster layer to generate a target vegetation data display map;

[0126] If the target vegetation change does not exceed the preset abnormal threshold, the target vegetation change is directly imported into the target vegetation distribution raster layer to generate a target vegetation data display map.

[0127] In this embodiment, the preset abnormal threshold can be determined by adding or subtracting three times the standard deviation of the mean of historical vegetation data.

[0128] In this embodiment, if the target vegetation change exceeds a preset abnormal threshold, the abnormal score of the calculated target vegetation change is quickly evaluated by a simple difference method, for example: abnormal score = |actual change - threshold|, the abnormal score is imported into the target vegetation distribution grid layer, a target vegetation data display diagram is generated, and color mapping is applied to distinguish normal and abnormal areas based on the vegetation change and the abnormal score, for example, a gradient from green (normal) to red (high abnormality) can be used; if the target vegetation change does not exceed the preset abnormal threshold, the target vegetation change is directly imported into the target vegetation distribution grid layer to generate a target vegetation data display diagram.

[0129] In a possible embodiment, controls such as NDVI data selector, time axis, time selector, etc. can also be integrated into the target vegetation data display diagram through an API interface, so as to support browsing NDVI data changes in different periods by dragging the time axis. The above NDVI data selector is a single-select selector, including "anomaly", "year-on-year", "month-on-month", and "NDVI" functions. The above time axis includes three parts of controls, a left button, a time axis control, and a right button. The above time selector provides year selection and specific date selection. .

[0130] This application achieves fine monitoring and dynamic visualization of the health status of vegetation by performing anomaly detection on the target vegetation change and adjusting the data display according to the detection results. When the target vegetation change exceeds the preset threshold, the anomaly score is calculated and displayed, which increases the ability to identify potential environmental risks and vegetation health problems. For data that does not exceed the threshold, direct display ensures the continuity and integrity of the information, and improves the efficiency and response speed of vegetation management.

[0131] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned target vegetation data display map, the above-mentioned target vegetation data display map can also be stored in a node of a blockchain.

[0132] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0134] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0135] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a vegetation data display device, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0136] like Figure 3 As shown, the vegetation data display device 300 described in this embodiment includes: an acquisition module 301, a segmentation module 302, a data matching module 303, a construction module 304 and an import module 305. Among them:

[0137] An acquisition module 301 is used to acquire a vegetation data set to be processed, wherein the vegetation data set to be processed includes historical vegetation data and target vegetation data;

[0138] A segmentation module 302, used to dynamically segment the target vegetation data to obtain target vegetation features, and the target vegetation features are stored in a dynamic time window;

[0139] A data pairing module 303 is used to pair the historical data set with the target vegetation feature through the dynamic time window to obtain a target vegetation change amount;

[0140] A construction module 304 is used to construct a layer based on the target vegetation data to generate a target vegetation distribution grid layer;

[0141] The import module 305 is used to import the target vegetation change amount into the target vegetation distribution grid layer to generate a target vegetation data display map.

[0142] The acquisition module 301 includes:

[0143] A first acquisition submodule, used for acquiring a time range and a data type selected by a user, and pulling out the historical vegetation data and the live vegetation data from the vegetation database based on the time range and the data type selected by the user;

[0144] The time series decomposition submodule is used to perform time series decomposition on the actual vegetation data by using a seasonal decomposition time series analysis algorithm to obtain the target vegetation data.

[0145] The segmentation module 302 includes:

[0146] The second acquisition submodule is used to acquire a preset time granularity;

[0147] A first generating submodule, configured to generate a plurality of the dynamic time windows by a sliding window technique based on the preset time granularity;

[0148] The feature statistics submodule is used to import the target vegetation data into the dynamic window to perform feature statistics to obtain the target vegetation features.

[0149] The data pairing module 303 includes:

[0150] A third acquisition submodule is used to acquire a target matching strategy, wherein the target matching strategy includes a year-on-year matching strategy and a month-on-month matching strategy;

[0151] An import submodule, used for importing the historical data set into the dynamic time window;

[0152] The data pairing submodule is used to perform data pairing on the target vegetation features based on the target matching strategy to obtain the target vegetation change amount.

[0153] The data pairing submodule includes:

[0154] A determination unit, configured to determine the target matching strategy based on the number of the historical data set imported into the dynamic time window;

[0155] A first judgment unit is used to execute the same-year matching strategy if the historical data set is imported into only one dynamic time window: matching the target vegetation feature with the historical data set to obtain the target vegetation change amount;

[0156] The second judgment unit is used to execute the ring-matching strategy if the historical data set is imported into a plurality of the dynamic time windows: matching the target vegetation features with the historical data set to obtain the vegetation change amounts of all branches;

[0157] The difference calculation unit is used to perform difference calculation on the branch vegetation change amounts of all dynamic time windows to obtain the target vegetation change amount.

[0158] The building block 304 includes:

[0159] An interpolation processing submodule is used to perform interpolation processing on the target vegetation data by using an inverse distance weighted interpolation algorithm to generate a continuous standard distribution grid layer;

[0160] The color mapping submodule is used to perform color mapping on the standard distribution grid layer according to the target vegetation characteristics to obtain the target vegetation distribution grid layer.

[0161] The import module 305 includes:

[0162] An anomaly detection submodule, used for performing anomaly detection on the target vegetation change amount;

[0163] The second generation submodule is used to calculate the abnormal score of the target vegetation change amount if the target vegetation change amount exceeds a preset abnormal threshold, import the abnormal score into the target vegetation distribution grid layer, and generate a target vegetation data display map;

[0164] The third generating submodule is used to directly import the target vegetation change amount into the target vegetation distribution grid layer to generate a target vegetation data display map if the target vegetation change amount does not exceed a preset abnormal threshold.

[0165] In this embodiment, by acquiring a vegetation data set to be processed, dynamically segmenting the target vegetation data to obtain target vegetation features, pairing the historical data set with the target vegetation features through a dynamic time window to obtain the target vegetation change, constructing a layer based on the target vegetation data to generate a target vegetation distribution grid layer, importing the target vegetation change into the target vegetation distribution grid layer, and generating a target vegetation data display diagram. Through the dynamic time window and data pairing technology, an accurate comparison between the historical vegetation data and the target vegetation data is achieved, thereby generating a distribution grid layer containing the target vegetation change, simplifying the operation process and visual display of the vegetation data, and enabling users to intuitively compare vegetation indices of different periods on the same interface, and quickly identify abnormal areas and change trends of vegetation growth.

[0166] In this embodiment, the operations performed by the above units or modules correspond one-to-one to the steps of the vegetation data display method in the above implementation mode, and will not be repeated here.

[0167] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0168] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0169] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0170] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the vegetation data display method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0171] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions or process data stored in the memory 41, such as computer-readable instructions for running the vegetation data display method.

[0172] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0173] The present embodiment provides a computer device, which obtains a vegetation data set to be processed, dynamically segments the target vegetation data, obtains target vegetation features, pairs the historical data set with the target vegetation features through a dynamic time window, obtains the target vegetation change, constructs a layer based on the target vegetation data, generates a target vegetation distribution grid layer, imports the target vegetation change into the target vegetation distribution grid layer, generates a target vegetation data display diagram, and achieves accurate comparison between historical vegetation data and target vegetation data through a dynamic time window and data pairing technology, thereby generating a distribution grid layer containing the target vegetation change, simplifying the operation process and visual display of vegetation data, allowing users to intuitively compare vegetation indexes of different periods on the same interface, and quickly identify abnormal areas and change trends of vegetation growth.

[0174] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the vegetation data display method as described above.

[0175] The present embodiment provides a computer-readable storage medium, which obtains a vegetation data set to be processed, dynamically segments the target vegetation data, obtains target vegetation features, performs data pairing between the historical data set and the target vegetation features through a dynamic time window, obtains the target vegetation change amount, constructs a layer based on the target vegetation data, generates a target vegetation distribution grid layer, imports the target vegetation change amount into the target vegetation distribution grid layer, generates a target vegetation data display diagram, and achieves accurate comparison between the historical vegetation data and the target vegetation data through the dynamic time window and data pairing technology, thereby generating a distribution grid layer containing the target vegetation change amount, simplifying the operation process and visual display of the vegetation data, allowing users to intuitively compare vegetation indexes of different periods on the same interface, and quickly identify abnormal areas and change trends of vegetation growth.

[0176] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0177] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A vegetation data display method, characterized in that: The steps include: Acquire a vegetation data set to be processed, wherein the vegetation data set to be processed includes historical vegetation data and target vegetation data; Dynamically segmenting the target vegetation data to obtain target vegetation features, wherein the target vegetation features are stored in a dynamic time window; Pairing the historical data set with the target vegetation features through the dynamic time window to obtain a target vegetation change amount; Performing layer construction based on the target vegetation data to generate a target vegetation distribution raster layer; The target vegetation change amount is imported into the target vegetation distribution grid layer to generate a target vegetation data display map.

2. The vegetation data display method according to claim 1, characterized in that: The step of obtaining the vegetation data set to be processed specifically includes: Acquiring a time range and a data type selected by a user, and pulling out the historical vegetation data and the real-time vegetation data from the vegetation database based on the time range and the data type selected by the user; The target vegetation data is obtained by performing time series decomposition on the actual vegetation data using a seasonal decomposition time series analysis algorithm.

3. The vegetation data display method according to claim 1, characterized in that: The step of dynamically segmenting the target vegetation data to obtain target vegetation features specifically includes: Get the preset time granularity; Based on the preset time granularity, a plurality of the dynamic time windows are generated by a sliding window technology; The target vegetation data is imported into the dynamic window for feature statistics to obtain the target vegetation features.

4. The vegetation data display method according to claim 1, characterized in that: The step of pairing the historical data set with the target vegetation feature through the dynamic time window to obtain the target vegetation change specifically includes: Obtaining a target matching strategy, wherein the target matching strategy includes a year-on-year matching strategy and a month-on-month matching strategy; Importing the historical data set into the dynamic time window; Data pairing is performed on the target vegetation features based on the target matching strategy to obtain the target vegetation change amount.

5. The vegetation data display method according to claim 4, characterized in that: The step of performing data pairing on the target vegetation features based on the target matching strategy to obtain the target vegetation change amount specifically includes: Determining the target matching strategy based on the number of the historical data sets imported into the dynamic time window; If the historical data set is imported into only one dynamic time window, the same-year matching strategy is executed: matching the target vegetation feature with the historical data set to obtain the target vegetation change amount; If the historical data set is imported into a plurality of the dynamic time windows, the ring-matching strategy is executed: the target vegetation feature is matched with the historical data set to obtain the vegetation change amount of all branches; The branch vegetation change amounts of all dynamic time windows are difference calculated to obtain the target vegetation change amount.

6. The vegetation data display method according to claim 1, characterized in that: The step of constructing a layer based on the target vegetation data to generate a target vegetation distribution grid layer specifically includes: The target vegetation data is interpolated by using an inverse distance weighted interpolation algorithm to generate a continuous standard distribution grid layer; The standard distribution grid layer is color mapped according to the target vegetation characteristics to obtain the target vegetation distribution grid layer.

7. The vegetation data display method according to claim 1, characterized in that: The step of importing the target vegetation change amount into the target vegetation distribution grid layer to generate a target vegetation data display map specifically includes: Performing anomaly detection on the target vegetation change amount; If the target vegetation change exceeds a preset abnormal threshold, the abnormal score of the target vegetation change is calculated, and the abnormal score is imported into the target vegetation distribution grid layer to generate a target vegetation data display map; If the target vegetation change amount does not exceed the preset abnormal threshold, the target vegetation change amount is directly imported into the target vegetation distribution grid layer to generate a target vegetation data display map.

8. A vegetation data display device, characterized in that: include: An acquisition module, used for acquiring a vegetation data set to be processed, wherein the vegetation data set to be processed includes historical vegetation data and target vegetation data; A segmentation module, used for dynamically segmenting the target vegetation data to obtain target vegetation features, wherein the target vegetation features are stored in a dynamic time window; A data pairing module, used to pair the historical data set with the target vegetation features through the dynamic time window to obtain a target vegetation change amount; A construction module, used to construct a layer based on the target vegetation data to generate a target vegetation distribution grid layer; The import module is used to import the target vegetation change amount into the target vegetation distribution grid layer to generate a target vegetation data display map.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the vegetation data display method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the vegetation data display method according to any one of claims 1 to 7 are implemented.

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