CN113570489B - Ecological space analysis method and system based on statistical unit self-adaption - Google Patents
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Abstract
The invention provides an ecological space analysis method and system based on statistical unit self-adaptation, comprising the following steps: determining and receiving an analysis range and input of analysis target data and indexes; judging the division mode of the subtask unit according to the analysis range and the analysis target data and indexes to obtain the range of each analysis unit; using the analysis unit range to perform spatial superposition on analysis target data, extracting target data elements in each analysis unit range, and adding the same analysis unit identification to the target data elements falling into the same analysis unit range to obtain a subtask unit; and distributing each subtask unit to different computing nodes, and executing respective subtasks by each node. The invention automatically splits the analysis data into a plurality of proper subtasks according to different analysis ranges, analysis data and indexes and analysis result statistical expression requirements, can utilize computing resources in a parallel computing environment to the maximum extent, and obtains better analysis statistical efficiency.
Description
Technical Field
The invention relates to the technical field of geographic information spatial analysis, in particular to an ecological space analysis method and system based on statistical unit self-adaptation.
Background
In the ecological environment protection business, the spatial analysis and statistics work has the following characteristics:
(1) diversified analysis range and large area difference
The analysis range is divided into a standard administrative division range and a special area range: analyzing and counting by using a standard administrative division range, wherein different levels of provinces, cities and counties are distinguished; analyzing and counting by using special area range, such as an ecological protection red line area, a natural protection area, an important drainage basin and the like; custom range analysis statistics were used.
The area difference is large or even very different between different types of analysis ranges or between different areas of the same type of range, which results in large difference of calculation workload of analysis scenes in different ranges.
(2) Various analysis scenes and different complexity
And analyzing target data, including ecological relevant element data such as ecological protection red lines, human activities, ecological land classification and the like, and various red line thematic monitoring evaluation data such as vegetation conditions, water conservation, water and soil conservation, wind prevention and sand fixation, living maintenance and the like.
In the aspect of analysis statistic types, the statistics of a single time phase of certain data is provided, the transverse superposition analysis statistics of two special data is provided, and the longitudinal superposition analysis statistics of two time phases of certain data is provided.
Due to the difference of the data quantity of the analysis target data and the difference of the analysis statistic types, the complexity and the calculation workload of different analysis scenes are greatly different. For example, in the case of selecting a natural reserve of a certain country level as an analysis range, the following three analysis scenarios have large differences in calculation workload:
counting human activity types in the national-level natural preservation area: the method is the statistics of a single time phase of the first-class data, target data are not fully covered, the data volume is small, and the overall space analysis calculation amount is small;
human activity analysis in the national natural reserve of the ecological protection red line: the two thematic data are transversely overlapped, analyzed and counted, the two data are not fully covered, one of the data volume is large, and the overall space analysis calculation amount is medium;
and (3) classified change analysis of an ecosystem in a certain time interval in a national-level natural conservation area: the method is used for analyzing and counting two/multiple time phases of the same data in a superposition mode, the two data are all full-covered and large in data quantity, and the whole space analysis calculated quantity is large.
With the development of big data and cloud computing technologies, more and more industry fields begin to use a spatial analysis statistical method of parallel computing. In the ecological environment protection business, the common method is as follows:
in a real-time analysis scene of a specified range and an analysis index of the front end of an application system, no matter what the analysis range, target data, a statistical index and a statistical requirement are, most of the tasks are executed as a whole. Under the method, most analysis takes longer time and has lower efficiency unless three conditions of small analysis range, small target data amount and simple analysis type are simultaneously met.
In the pre-analysis and statistics work of the common data and indexes of the ecological space, technicians are usually relied on to manually separate the common areas and the related data in each area, and then separate analysis and calculation tasks are created one by one. In the process, complicated data preprocessing and sorting procedures are involved, and in addition, a technician can only split the subtasks within the range of the analysis area per se, when the analysis area of a certain subtask is greatly different from that of other subtasks, the time consumption of the subtask is far longer than that of other subtasks, and finally, the whole execution period of the index is long.
Therefore, the technical staff in the field needs to solve the problem of how to provide a method for automatically splitting the analysis data and indexes into a plurality of suitable subtasks and performing ecological space analysis according to different analysis ranges, analysis data and indexes and analysis result statistical expression requirements.
Disclosure of Invention
In view of this, the present invention provides an adaptive ecological space analysis method and system based on statistical units, which mainly relate to analyzing input analysis scene parameters, automatic partitioning of analysis units, segmentation of analysis target data, allocation of analysis subtasks, parallel execution of each subtask, and result merging and summarizing. By the method, computing resources in the parallel computing environment can be utilized to the maximum extent, and better analysis statistical efficiency is obtained.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention firstly provides an ecological space analysis method based on statistical unit self-adaptation, which comprises the following steps:
s1, determining and receiving the analysis range and the input of the analysis target data and indexes;
s2, judging the dividing mode of the subtask unit according to the analysis range and the analysis target data and indexes, and splitting the analysis unit to obtain the range of each analysis unit;
s3, performing spatial superposition on analysis target data by using the analysis unit ranges, extracting target data elements in the analysis unit ranges, and adding the same analysis unit identification to the target data elements falling into the same analysis unit range to obtain analysis target data subsets, namely subtask units, of the analysis units;
and S4, combining the number of the subtask units and the number of the computing nodes, distributing each subtask unit to different computing nodes, and executing each subtask by each node.
Preferably, in S1, the analysis range includes an administrative division, an ecological protection red line, a natural protection area, a designated drainage basin, or a custom range; the analysis target data and indexes comprise human activity types, human activity areas, human activity quantity, ecosystem classification change types, ecosystem classification areas, ecosystem classification quantity or ecosystem service functions; and S1 also inputs a superposition range map layer which comprises administrative districts, ecological protection red lines or human activity ranges and the like.
Preferably, the S2 further includes: analyzing the analysis target data and the index, judging whether subtask splitting can be performed, and if so, entering S3; wherein resolving the analysis target data and the index comprises:
judging whether the analysis calculation of the analysis target data and indexes of each position in the analysis range needs to utilize the analysis target data and indexes of adjacent positions, if so, taking the analysis range as a whole task to execute spatial analysis and statistics, and not splitting subtasks; and if not, splitting the subtasks.
Preferably, the determining the dividing manner of the subtask unit in S2 includes:
when the analysis range contains the designated analysis area, dividing the subtask by adopting the regular geographic grid unit, and comprising the following steps:
s211, automatically generating a square geographic grid according to the coordinate span of the analysis range, and distributing the divided geographic grids to each analysis calculation node in a balanced manner;
s212, overlapping the analysis range and the geographic grids, dividing the geographic grids falling on the boundary of the analysis range according to the boundary of the analysis range, bringing each geographic grid graph falling in the same analysis range into the same analysis unit, and adding the ID of the falling grid into the unit identification of the graph;
s213, splitting each geography grid graph in the analysis range according to the unit identification to obtain the range data of each analysis unit for splitting the molecule task;
when the analysis range does not contain the designated analysis area, dividing the subtask by using the designated analysis area unit, wherein the designated analysis area comprises N-level areas according to the division priority level, and N is more than or equal to 1, and the method comprises the following steps:
s221, based on the analysis range, splitting the analysis unit according to the first-level division, and adding the first-level division as a unit identifier;
and S223, splitting the analysis range graph according to the unit identification to obtain the range data of each analysis unit for splitting the subtasks.
Preferably, in S221, if the area of the analysis unit after splitting in the first stage is greater than the preset threshold, the analysis unit continues to be split in the next stage until the area of each analysis unit does not exceed the preset threshold. And after the next-level partition is split, adding the next-level partition as a unit identifier.
Preferably, if the area of the analysis unit of the secondary compartment exceeds the preset threshold, the sub-task is divided by combining the designated analysis compartment unit with the regular geographic grid unit, and the method includes the following steps:
s231, adopting the step of S221 to obtain the range data of each analysis unit with a primary division as a unit identifier;
s232, screening analysis units of other analysis ranges with the difference of the areas of the two-level regional analysis units in a given range, further splitting the analysis units according to the regular geographic grid unit division method of the other analysis ranges, and adding geographic grid IDs as unit identifiers;
and S233, splitting the analysis range graph according to the unit identifier to obtain analysis unit range data for splitting the subtasks, wherein the analysis unit range data is formed by combining the designated partition unit identifier and the geographic grid ID unit identifier.
Preferably, the method further comprises the following steps: an input step of determining and receiving a statistical result display form; and adding a return mode identifier to each subtask according to the statistical result display form:
aiming at the subtask with the first return mode identifier, returning a subtask result after the execution is finished;
and aiming at the subtask with the second returning mode identifier, after the execution is completed, after other subtasks of the same task are completely executed, combining and calculating to obtain an aggregated result, and returning the aggregated result.
Preferably, based on the returned result, the visualization display is performed, and the specific steps include:
returning the analysis task result of the first identifier, displaying the overall data of the statistical indexes in the analysis range, and/or selecting a subtask unit, and displaying the statistical index data in the subtask unit;
and returning the analysis task result of the identifier two, and displaying the overall data of the statistical indexes in the analysis range.
The invention also provides an ecological space analysis system based on statistical unit self-adaptation, which comprises:
the input module is used for determining and receiving the analysis range and the input of the analysis target data and indexes;
the analysis unit range division module is used for judging the division mode of the subtask unit according to the analysis range and the analysis target data and indexes, and splitting the analysis unit to obtain the range of each analysis unit;
the subtask unit generation module is used for performing spatial superposition on analysis target data by using the analysis unit ranges, extracting target data elements in the analysis unit ranges, and adding the same analysis unit identification to the target data elements falling into the same analysis unit range so as to obtain an analysis target data subset of each analysis unit, namely a subtask unit;
an allocation calculation module: the method is used for combining the number of the subtask units and the number of the computing nodes, distributing each subtask unit to different computing nodes, and executing each subtask by each node.
Preferably, the device further includes an analysis result display unit, configured to return an identifier according to the analysis result to display different statistical index data, including:
aiming at the analysis task result of the returned identifier I, displaying the overall data of the statistical indexes in the analysis range, and/or selecting a subtask unit and displaying the statistical index data in the subtask unit;
and displaying the overall data of the statistical indexes in the analysis range aiming at the analysis task result of the returned identifier II.
Through the technical scheme, compared with the prior art, the invention has the beneficial effects that:
in the field of ecological environment protection, in the past, aiming at real-time analysis and statistics of input conditions of an application end or pre-analysis and statistics of common indexes of a common area, no matter whether the analysis range is a standard administrative area or a special unit or a custom range such as an ecological protection red line, a natural protection place, a key area, a key drainage basin and the like, no matter what type of analysis target data and statistical indexes are, most of the analysis target data and the statistical indexes are executed as a whole task or are partially split by manpower, but the split units are not suitable, so that most of analysis scenes cannot fully utilize computing resources and the analysis efficiency is not high. The method fully considers various analysis ranges of ecological environment protection and types and characteristics of analysis target data and indexes, automatically provides the most appropriate strategy for dividing the statistical units and analyzing the split subtasks according to the conditions of the computing resources by analyzing parameters of various analysis scenes, and determines the return mode of the calculation results of the subtasks according to the requirements of result statistics. Under the method, the real-time statistical scene related to the ecological protection red line can utilize the existing computing resources to the maximum extent, and higher statistical efficiency and better statistical effect are obtained.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts;
FIG. 1 is a logic diagram of an adaptive ecological space analysis method based on statistical units according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a unit splitting performed on an analysis range of a river buffer area by using a regular geographic grid according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a subset of two analysis target data extracted by dividing two analysis target data using a split river buffer analysis range graph according to an embodiment of the present invention;
fig. 4 is a schematic diagram of performing unit splitting on an analysis range by using an administrative partition according to an embodiment of the present invention;
fig. 5 is a schematic diagram of extracting an analysis target data subset by dividing two analysis target data using a split administrative division analysis scope graph according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method and the system for ecological space analysis based on statistical unit self-adaptation, which are disclosed by the embodiment, consider the diversity of analysis scenes in ecological environment protection services and also consider the requirement of later expansion, and no matter whether the method is directed to real-time analysis statistics or the pre-analysis statistics of common indexes of common areas, how to automatically divide the appropriate analysis statistical units and split a plurality of subtasks with appropriate calculation amount aiming at different analysis ranges, analysis data and indexes and analysis result statistical expression requirements, have important significance for maximally utilizing calculation resources, releasing manual workload, improving various analysis application efficiencies of ecological protection red lines and the like.
The embodiment mainly relates to analyzing input analysis scene parameters, automatically dividing an analysis unit, dividing analysis target data, distributing analysis subtasks, executing each subtask in parallel and merging and summarizing results. By the method, computing resources in the parallel computing environment can be utilized to the maximum extent, and better analysis statistical efficiency is obtained.
A first aspect of the present embodiment discloses an ecological space analysis method based on statistical unit adaptation, and fig. 1 shows a logic diagram of an analysis scene analysis and statistical unit adaptation method, which specifically includes the following steps:
and S1, determining and receiving the analysis range and the input of the analysis target data and indexes.
In one embodiment, the analysis scope includes administrative divisions, natural conservation, key areas, key watersheds, or custom scopes; the analysis target data and the index include a human activity type, a human activity area, a human activity amount, an ecosystem classification change type, an ecosystem classification area or an ecosystem classification amount.
In this embodiment, the human activity data and the ecosystem classification data can be divided and calculated according to the year.
In one embodiment, the overlay range layer may also be input, including an ecological protection red line or a human activity range, etc. The step can be skipped, and if the step is skipped, no superposed layer is defaulted.
In one embodiment, a selection statistics presentation form may also be input, including selecting "global summary presentation only" or "global summary presentation and secondary area drill presentation".
And S2, judging the dividing mode of the subtask unit according to the analysis range, the analysis target data and the index, and splitting the analysis unit to obtain the range of each analysis unit.
In one embodiment, whether the sub-task splitting needs to be performed is determined as follows:
analyzing and analyzing the target data and the indexes, judging whether the subtask splitting can be carried out, and if so, entering S3; wherein analyzing the target data and the index comprises:
judging whether the analysis calculation of the analysis target data and the index of each position in the analysis range is influenced by the data of the adjacent position or not, and if the analysis result needs to be subjected to global adjustment, the analysis scene must be used as an integral task to perform spatial analysis and statistics, and the subtask splitting cannot be performed; and if not, performing sub-task splitting.
In one embodiment, the determining the dividing manner of the subtask unit includes: dividing the subtasks by adopting the regular geographic grid unit, dividing the subtasks by adopting the specified analysis zone unit and dividing the subtasks by adopting the mode of combining the specified analysis zone unit and the regular geographic grid unit. The following detailed description is provided for the selection steps of different division modes:
the first method is as follows: when the analysis range contains the designated analysis area, dividing the subtask by adopting the regular geographic grid unit, and comprising the following steps:
s211, automatically generating a square geographic grid according to the coordinate span of the analysis range, and distributing the divided geographic grid to each analysis calculation node in a balanced manner;
s212, overlapping the analysis range and the geographic grids, dividing the geographic grids falling on the boundary of the analysis range according to the boundary of the analysis range, bringing each geographic grid graph falling in the same analysis range into the same analysis unit, and adding the ID of the falling grid into the unit identification of the graph;
s213, splitting each geography grid graph in the analysis range according to the unit identification to obtain the range data of each analysis unit for splitting the molecule task.
In this way, the span of a single geographic grid can be dynamically adjusted according to the analysis target data and the analysis index, and the target position ensures that the number of the divided overall grids can be evenly distributed to each computing node. The specific span mediation criteria may employ the following two methods:
if the analysis target data is full coverage and the data volume is large, and the analysis type is the comparative superposition of two time phases of one type of data or the transverse superposition of two types of data, the span of a single grid is controlled within 20km multiplied by 20 km;
if the analysis target data is not fully covered and the data volume is small, and the analysis type is statistics of a single time phase of the data, the single grid span does not exceed 100km multiplied by 100 km.
The second method comprises the following steps: when the analysis range does not contain the designated analysis area, dividing the subtask by using the designated analysis area unit, wherein the designated analysis area comprises N-level areas according to the division priority level, and N is more than or equal to 1, and the method comprises the following steps:
s221, based on the analysis range, splitting the analysis unit according to the first-level division, and adding the first-level division as a unit identifier; and if the area of the analysis unit after the first-stage division is split is larger than the preset threshold, continuing to split the next-stage division until the area of each analysis unit does not exceed the preset threshold. After the next-level partition is split, adding the next-level partition as a unit identifier;
and S223, splitting the analysis range graph according to the unit identification to obtain the range data of each analysis unit for splitting the subtasks.
The designated analysis area in the present embodiment may be an analysis area with an administrative division attribute. The preset threshold value is determined by combining the characteristics and the analysis type of the analysis target data, and two conditions in the mode 1 regular geographic grid unit division subtasks are referred.
The third method comprises the following steps: and dividing the molecular tasks in a mode of combining administrative division units and geographic grid units. This method is suitable for the case where the analysis area input in the method 2 has the administrative division attribute, but the area of the unit divided by the second-level administrative division still exceeds the threshold, and includes the following steps:
s231, adopting the step of S221 to obtain the range data of each analysis unit with a primary division as a unit identifier;
s232, screening analysis units of other analysis ranges with the difference of the areas of the two-level region analysis units in the given range, further splitting the analysis units according to a regular geographic grid unit division method of the other analysis ranges, and adding a geographic grid ID as a unit identifier;
and S233, splitting the analysis range graph according to the unit identifier to obtain analysis unit range data for splitting the subtasks, wherein the analysis unit range data is formed by combining the designated partition unit identifier and the geographic grid ID unit identifier.
And S3, performing spatial superposition on the analysis target data by using each analysis unit range, extracting target data elements in each analysis unit range, and adding the same analysis unit identification to the target data elements falling into the same analysis unit range, thereby obtaining the analysis target data subset of each analysis unit, namely the subtask unit.
In one embodiment, according to the selection requirement of the statistical result display form, adding a return mode identifier to each subtask:
returning a subtask result after the execution is finished aiming at the subtask with the first return mode identifier;
and aiming at the subtask with the second returning mode identifier, after the execution is completed, after other subtasks of the same task are completely executed, combining and calculating to obtain an aggregated result, and returning the aggregated result.
The return mode identification comprises the following steps: "1" indicates that each subtask result is returned; "0" indicates that the subtask results are not returned.
And S4, combining the number of the subtask units and the number of the computing nodes, distributing each subtask unit to different computing nodes, and executing each subtask by each node.
In one embodiment, based on the returned results, the visualization is displayed, and the specific steps include:
returning the analysis task result marked as '1', displaying the overall data of the statistical indexes in the analysis range, and/or selecting the name of a subtask unit (such as the name of an administrative division), displaying the statistical index data in the subtask unit, and quickly drilling and displaying the statistical index condition in the unit;
and returning an analysis task result marked as '0', and only displaying the overall data of the statistical indexes in the analysis range.
The second aspect of this embodiment further discloses an adaptive ecological space analysis system based on statistical units, where the system is configured to perform the method disclosed in the first aspect of this embodiment, and the method includes:
the input module is used for determining and receiving the analysis range and the input of the analysis target data and indexes;
the analysis unit range division module is used for judging the division mode of the subtask unit according to the analysis range and the analysis target data and indexes, and splitting the analysis unit to obtain the range of each analysis unit;
the subtask unit generation module is used for performing spatial superposition on analysis target data by using each analysis unit range, extracting target data elements in each analysis unit range, and adding the same analysis unit identification to the target data elements falling into the same analysis unit range so as to obtain an analysis target data subset of each analysis unit, namely a subtask unit;
an allocation calculation module: the method is used for combining the number of the subtask units and the number of the computing nodes, distributing each subtask unit to different computing nodes, and executing each subtask by each node.
In one embodiment, the method further includes an analysis result presentation unit, configured to present different statistical indicator data according to the returned identifier of the analysis result, including:
aiming at the analysis task result of the returned identifier I, displaying the overall data of the statistical indexes in the analysis range, and/or selecting a subtask unit and displaying the statistical index data in the subtask unit;
and displaying the overall data of the statistical indexes in the analysis range aiming at the analysis task result of the returned identifier two.
The following describes the implementation of the present embodiment with reference to a specific analysis scenario.
Example one
Carrying out classification change analysis on 2015-2019 ecological systems within 10 kilometers of a certain drainage basin:
firstly, analyzing statistical analysis parameters:
analysis scope: the area distribution range is not provided with the standard administrative division attribute.
Analyzing a scene: the method has the characteristics of full coverage, large data volume and high data precision; the analysis type is longitudinal superposition analysis of two time phases, and the analysis and calculation complexity is relatively high.
Analysis results statistical requirements: only the summary result within 10 km of a certain basin needs to be counted.
And secondly, dividing subtasks by adopting a regular geographic grid unit. The key steps are as follows:
s1: and generating a regular geographic grid with a proper size according to the area of the 10 kilometer range of the drainage basin. Referring to fig. 2, a regular geographic grid is used to perform unit splitting on an analysis range of a river 10 km buffer area, so as to generate a plurality of analysis units.
S2: and segmenting the data within 10 kilometers of the drainage basin according to the geographic grids, extracting drainage basin range elements of each grid, and adding the ID of the grid where the drainage basin range elements are located as unit identifiers. Referring to fig. 3, the split analysis unit graph is used to divide two analysis target data, and element subsets are extracted in units. The right side of the figure is an analysis target data set 1, and the left side is an analysis target data set 2.
S3: taking the analysis range of the grid basin with grid ID as a unit, respectively performing statistical target data segmentation on the ecosystem classification data in 2015 and the ecosystem classification data in 2019, and adding an analysis unit identifier to obtain statistical target data subsets in 2015 and 2019 of the sub-units. Referring to fig. 3, two analysis target data element sets in the same unit are used as a subtask to perform overlay analysis and statistics.
S4: distributing subtasks of each analysis unit to each node of a computing environment, distributing analysis target data subsets in 2015 and 2019 identified by the same analysis unit to the same node for the same task, and performing analysis calculation according to requirements to obtain a statistical index result;
s5: and after the calculation of each node subtask is completed, combining the results, and returning a statistical index summarizing result within 10 kilometers of a certain basin.
S6: and performing visual display based on a statistical index summary result of 'within 10 kilometers of a certain river basin'.
Example two
Human activity condition analysis in 2020 ecological protection red line of certain mountain region:
firstly, analyzing statistical analysis parameters:
analysis scope: in 39 counties (regions) in a certain mountain region, the area of each county exceeds 100 square kilometers, and 353 villages and towns are administered in 39 counties (regions).
Analyzing a scene: for the horizontal superposition analysis statistics of the two thematic data of the ecological protection red line image layer and the human activity graphic spots, the two data are not fully covered, but the quantity of the ecological protection red line graphic spots is large and the data quantity is large;
analysis result statistical requirements: the method not only statistically displays the summary of human activities in the ecological red line of a certain mountain area, but also drills and displays the conditions in the respective range of the 39 county areas.
And secondly, dividing subtasks by adopting an administrative division unit. The key steps are as follows:
s1: splitting a regional range map layer of a certain mountain land according to 39 counties; and further splitting the tasks of 39 counties according to the respective rural administrative division ranges, and adding the names of the rural administrative divisions as analysis unit identifications. Specifically, referring to the table I, the analysis range table of each unit is extracted according to administrative divisions.
S2: 353 county ranges with analysis unit identifications are used for segmenting the 2020 ecological protection red line data and the 2020 human activity pattern spot data, and the analysis unit identifications are added to obtain 353 superposed analysis data sets with respective unit identifications.
S3: and taking the segmented ecological red line pattern spots and human activity pattern spots with the same rural unit identification as the same analysis subtasks, adding a return mode identification '1' to each subtask, distributing the subtasks of each unit to each node in the computing environment, and performing analysis calculation according to requirements to obtain statistical indexes.
S4: and after the subtask calculation of each node is completed, returning the index results of the respective analysis units immediately for drilling and displaying the 39 county area analysis condition. After the subtasks on each node are executed, all subtask results are integrally combined to obtain a summary result, and the summary result is used for displaying the overall analysis condition of a certain mountain area.
Example three was performed: ecological land analysis of human activity occupation in 2020 year in a certain designated area
Analyzing statistical analysis parameters:
analysis scope: the area of a second-level administrative district under the three administrative districts mostly exceeds 100 square kilometers, and the area difference among the areas is large.
Analyzing a scene: transversely overlapping, analyzing and counting two thematic data for classifying the human activity pattern spots and the ecosystem, wherein one of the two data is fully covered, the other data is not fully covered, and the data volume of the classified data of the ecosystem is large;
statistical requirements for analysis results: the method not only can count and display the ecological land occupation situation of human activities in a specified area, but also can drill and display the respective situations of three provincial administrative divisions.
In the embodiment, a combined mode of administrative division units and geographic grid units is adopted to divide subtasks. The key steps are as follows:
s1: splitting the designated area range according to 3 provincial administrative regions;
s2: referring to fig. 4, for 3 provincial administrative district unit ranges, generating a regular geographic grid according to the respective ranges, further splitting subtask units, and adding administrative district names and grid IDs as subtask unit identifiers;
s3: referring to fig. 5, the analysis unit range with administrative divisions and regular geographic grid ID combination identifiers is used to segment two superimposed analysis data of human activity pattern spots and ecological land classification, so as to obtain each subtask analysis target data with analysis unit identifiers. The right side of the figure is an analysis target data set 1, and the left side is an analysis target data set 2.
S4: and taking the target data subset identified by the same analysis unit as a task, adding a return mode identifier '1' to each task, distributing the subtasks of each analysis unit to each node in the computing environment, and executing analysis and calculation according to requirements.
S6: in each parallel computing subtask, the execution results of all subtasks with the same 'administrative division' in the analysis unit identification are merged and are summarized as the analysis result of the administrative division unit and returned. By adopting the method, the analysis results of the three administrative areas are returned in sequence and are used for drilling and displaying the respective analysis conditions of the three administrative areas; and after all the subtasks are completely executed, combining the results of the three administrative districts to obtain a summary result of the specified area, and displaying the total analysis condition of the specified area.
The statistical unit adaptive-based ecological space analysis method and system provided by the invention are introduced in detail, specific examples are applied in the method to explain the principle and the implementation mode of the invention, and the description of the above embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. An ecological space analysis method based on statistical unit self-adaptation is characterized by comprising the following steps:
s1, determining and receiving the analysis range and the input of the analysis target data and indexes;
s2, judging the dividing mode of the subtask unit according to the analysis range and the analysis target data and indexes, and splitting the analysis unit to obtain the range of each analysis unit; the method comprises the following steps:
when the analysis range contains the designated analysis area, dividing the subtask by adopting the regular geographic grid unit, and comprising the following steps:
s211, automatically generating a square geographic grid according to the coordinate span of the analysis range, and distributing the divided geographic grids to each analysis calculation node in a balanced manner;
s212, overlapping the analysis range with the geographic grids, dividing the geographic grids falling on the boundary of the analysis range according to the boundary of the analysis range, bringing each geographic grid graph falling in the same analysis range into the same analysis unit, and adding the ID of the falling grid into the unit identifier of the graph;
s213, splitting each geography grid graph in the analysis range according to the unit identification to obtain the range data of each analysis unit for splitting the molecule task;
when the analysis range does not contain the designated analysis area, dividing the subtask by using the designated analysis area unit, wherein the designated analysis area comprises N-level areas according to the division priority level, and N is more than or equal to 1, and the method comprises the following steps:
s221, based on the analysis range, splitting the analysis unit according to the first-level division, and adding the first-level division as a unit identifier;
s223, splitting the analysis range graph according to the unit identification to obtain range data of each analysis unit for splitting the subtasks;
s3, using the analysis unit ranges to spatially superimpose the analysis target data, extracting target data elements in the analysis unit ranges, adding the same analysis unit identification to the target data elements falling into the same analysis unit range, and thus obtaining the analysis target data subsets, namely subtask units, of the analysis units;
and S4, combining the number of the subtask units and the number of the computing nodes, distributing each subtask unit to different computing nodes, and executing each subtask by each node.
2. The adaptive ecological space analysis method based on statistical units according to claim 1, wherein in S1, the analysis range comprises administrative division, ecological protection red line, natural protection ground, designated area, designated watershed or custom range; the analysis target data and indexes comprise human activity types, human activity areas, human activity quantity, ecosystem classification change types, ecosystem classification areas or ecosystem classification quantity and ecosystem service functions; and the S1 also inputs a superposition range map layer which comprises an administrative region, an ecological protection red line or a human activity range.
3. The adaptive subspace analysis method based on statistical units of claim 1, wherein said S2 is preceded by: analyzing the analysis target data and the index, judging whether subtask splitting can be performed, and if so, entering S3; wherein resolving the analysis target data and the index comprises:
judging whether the analysis calculation of the analysis target data and indexes of each position in the analysis range needs to utilize the analysis target data and indexes of adjacent positions, if so, taking the analysis range as a whole task to execute spatial analysis and statistics, and not splitting subtasks; and if not, splitting the subtasks.
4. The adaptive subspace analysis method according to claim 1, wherein in S221, if the analysis unit area after the first-stage partition splitting is greater than the preset threshold, continuing to split the next-stage partition until each analysis unit area does not exceed the preset threshold; and after the next-level partition is split, adding the next-level partition as a unit identifier.
5. The adaptive subspace analysis method according to claim 4, wherein if the analysis unit area of the secondary partition exceeds a predetermined threshold, the sub-task is divided by combining the designated analysis partition unit with the regular geogrid grid unit, comprising the following steps:
s231, adopting the step of S221 to obtain the range data of each analysis unit with a primary division as a unit identifier;
s232, screening analysis units of other analysis ranges with the difference of the areas of the two-level regional analysis units in a given range, further splitting the analysis units according to the regular geographic grid unit division method of the other analysis ranges, and adding geographic grid IDs as unit identifiers;
and S233, splitting the analysis range graph according to the unit identifier to obtain analysis unit range data for splitting the subtasks, wherein the analysis unit range data is formed by combining the designated partition unit identifier and the geographic grid ID unit identifier.
6. The adaptive subspace analysis method based on statistical unit of claim 1, further comprising: an input step of determining and receiving a statistical result display form; and adding a return mode identifier to each subtask according to the statistical result display form:
aiming at the subtask with the first return mode identifier, returning a subtask result after the execution is finished;
and aiming at the subtask with the second returning mode identifier, after the execution is completed, after other subtasks of the same task are completely executed, combining and calculating to obtain an aggregated result, and returning the aggregated result.
7. The adaptive ecological space analysis method based on statistical units according to claim 6, wherein the visualization presentation is performed based on the returned results, and the specific steps include:
returning the analysis task result of the first identifier, displaying the overall data of the statistical indexes in the analysis range, and/or selecting a subtask unit, and displaying the statistical index data in the subtask unit;
and returning the analysis task result of the identifier two, and displaying the overall data of the statistical indexes in the analysis range.
8. An ecological space analysis system based on statistical unit adaptation, comprising:
the input module is used for determining and receiving the analysis range and the input of the analysis target data and indexes;
the analysis unit range division module is used for judging the division mode of the subtask unit according to the analysis range and the analysis target data and indexes, and splitting the analysis unit to obtain the range of each analysis unit; the method comprises the following steps:
when the analysis range contains the designated analysis area, dividing the subtask by adopting the regular geographic grid unit, and comprising the following steps:
s211, automatically generating a square geographic grid according to the coordinate span of the analysis range, and distributing the divided geographic grids to each analysis calculation node in a balanced manner;
s212, overlapping the analysis range with the geographic grids, dividing the geographic grids falling on the boundary of the analysis range according to the boundary of the analysis range, bringing each geographic grid graph falling in the same analysis range into the same analysis unit, and adding the ID of the falling grid into the unit identifier of the graph;
s213, splitting each geography grid graph in the analysis range according to the unit identification to obtain the range data of each analysis unit for splitting the molecule task;
when the analysis range does not contain the designated analysis area, dividing the subtask by using the designated analysis area unit, wherein the designated analysis area comprises N-level areas according to the division priority level, and N is more than or equal to 1, and the method comprises the following steps:
s221, based on the analysis range, splitting the analysis unit according to the first-level division, and adding the first-level division as a unit identifier;
s223, splitting the analysis range graph according to the unit identification to obtain range data of each analysis unit for splitting the subtasks;
the subtask unit generation module is used for performing spatial superposition on analysis target data by using the analysis unit ranges, extracting target data elements in the analysis unit ranges, and adding the same analysis unit identification to the target data elements falling into the same analysis unit range so as to obtain an analysis target data subset of each analysis unit, namely a subtask unit;
an allocation calculation module: the method is used for combining the number of the subtask units and the number of the computing nodes, distributing each subtask unit to different computing nodes, and executing each subtask by each node.
9. The adaptive ecological space analysis system based on statistical units according to claim 8, further comprising an analysis result display unit for displaying different statistical index data according to the returned identification of the analysis result, comprising:
aiming at the analysis task result of the returned identifier I, displaying the overall data of the statistical indexes in the analysis range, and/or selecting a subtask unit and displaying the statistical index data in the subtask unit;
and displaying the overall data of the statistical indexes in the analysis range aiming at the analysis task result of the returned identifier II.
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