A method and device for analyzing the impact of urbanization process of urban agglomerations on water system patterns

Abstract

The present invention provides a method and device for analyzing the impact of the urbanization process of an urban circle on the water system pattern. The method comprises selecting an urban circle, determining a comprehensive evaluation index system for the urbanization process for target cities and corresponding research periods, obtaining, for each target city, the urbanization level of each target city in each time period during the research period and the corresponding urban spatial change situation, dividing the city into multiple urbanization levels, extracting urban water system data for each target city, selecting multiple water system spatiotemporal change quantitative indicators and calculating the results of the spatiotemporal change quantitative indicators of each water system, and obtaining the correlation between the urbanization level of each target city in each time period and the spatiotemporal change quantitative indicators of each water system. By determining the comprehensive evaluation index system for the urbanization process and dividing urbanization into multiple stages, the present invention can explore the correlation between the water system and urbanization in a sample with relatively few water system data.

Description

Technical Field

[0001] The present invention relates to the technical field of water system pattern impact analysis, and in particular to a method and device for analyzing the impact of the urbanization process of a city circle on the water system pattern. Background Art

[0002] At present, the urbanization process of urban agglomerations is getting faster and faster, and the urbanization process of urban agglomerations has an impact on the water system pattern. The urbanization process may cause changes in the number, length, and river network density of rivers, which will have a key impact on the morphology of rivers and lakes, the structure of the water system, and the storage capacity, causing the decline of the water system and restricting the sustainable development of cities and river basins. Therefore, it is necessary to analyze the impact on the water system pattern.

[0003] Study the impact of the urbanization process of urban agglomerations on the water system pattern, that is, conduct a correlation analysis between the two.

[0004] Traditional correlation analysis methods include regression analysis and correlation analysis. Regression analysis requires numerous statistical assumptions and calculations. Samples that do not conform to the assumptions must undergo specific mathematical transformations, which is relatively tedious and results in a large amount of computation. This extensive computation further leads to the accumulation of errors, which in turn affects the reliability of the regression relationship. Correlation analysis uses the correlation coefficient to reflect the correlation between sample sequences. On the one hand, the correlation coefficient only reflects the linear correlation between samples and fails to capture other correlations between samples. On the other hand, the correlation coefficient only reflects the numerical correlation between sample sequences and cannot reveal the true nature of the correlation between variables. In other words, it cannot determine whether the variables truly contain internal correlations or causal relationships. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a method and device for analyzing the impact of the urbanization process of urban agglomerations on the water system pattern. By selecting highly representative urbanization level indicators, a comprehensive evaluation index system for the urbanization process is determined, and urbanization is divided into multiple stages. In the case of samples with less water system data, the grey correlation analysis model is used to explore the correlation between the water system and urbanization.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for analyzing the impact of urbanization on water system patterns in a city circle comprises the following steps:

[0008] Step 1: Select an urban agglomeration, which includes one or more target cities. For each target city and the corresponding research period, select a highly representative urbanization level indicator to determine a comprehensive evaluation indicator system for the urbanization process.

[0009] Step 2: For each target city, the entropy-Topsis method was used as a model to measure the urbanization process. The weight matrix was calculated to obtain the urbanization level of each target city in each period during the study period and the corresponding urban spatial changes. The urbanization level was divided into the period before urbanization scale was formed, the urbanization establishment stage-urbanization radiation stage, the urbanization radiation stage-lower-level urbanization stage, and the lower-level urbanization-high-level urbanization stage.

[0010] Step 3: Extract urban water system data for each target city: Analyze the hydrological information of the digital elevation data, establish a surface water flow model, and determine the linear water system data to the urban area;

[0011] Step 4: Based on the landscape ecology index table and the quantitative characteristics, morphological structure and complexity of landscape parameters, multiple water system spatiotemporal variation quantitative indicators are selected and the results of the spatiotemporal variation quantitative indicators of each water system are calculated to analyze the spatiotemporal evolution of the water system pattern;

[0012] Step 5: Select the grey correlation model to perform correlation analysis to obtain the correlation between the urbanization level of each target city in each period and the quantitative indicators of spatiotemporal changes of each water system.

[0013] Furthermore, in step 1, the urbanization level indicators include indicators of five aspects: population urbanization, economic urbanization, social urbanization, spatial urbanization and green urbanization. Population urbanization includes urban population density, total employment rate, proportion of non-agricultural employment, and natural population growth rate; economic urbanization includes per capita GDP, urban economic density, proportion of industrial added value in GDP, and fixed asset investment density; social urbanization includes the number of high school students and above per 10,000 people, per capita disposable income of urban residents, number of beds in health institutions per 10,000 people, and number of transportation vehicles per 10,000 people; spatial urbanization includes land urbanization rate, per capita residential building area, and per capita road area; green and innovative cities include the growth value of high-tech industries per 10,000 people, the number of high-value invention patents per 10,000 people, reduction of energy consumption per unit GDP, and green coverage rate of built-up areas.

[0014] Furthermore, the specific processes of measuring urbanization include:

[0015] Data standardization: Assuming that the comprehensive evaluation index system of urbanization process is m urbanization level indicators within n years, the basic data matrix R is:

[0016] R={r ij} n×m (i=1,2,...,m)

[0017] Among them, r ij represents the data of the jth urbanization level indicator in the i-th year;

[0018] The formula for Z-score standardization is:

[0019]

[0020] Among them, μ j is the overall average value of the jth urbanization level indicator, σ j is the standard deviation of the j-th urbanization level indicator;

[0021] Then define the standardized matrix Y of the original data as:

[0022] Y={y ij} n×m

[0023]

[0024] From the data matrix Y, we can get the positive ideal solution Y consisting of the maximum values of each urbanization level indicator: + The negative ideal solution Y consisting of the minimum value - :

[0025] Y + ={y max1 ,y max2 ,...,y maxn} n×m

[0026] Y - ={y min1 ,y min2 ,...,y minn} n×m

[0027] Among them, y max1 、y max2 ,...,y maxn is the maximum value of each urbanization level indicator, y min1 、y min2 ,...,y minn is the minimum value of each urbanization level indicator;

[0028] Calculate the weight and entropy, the weight of the j-th urbanization level indicator in the index system ω j and entropy H j The calculation formula is as follows:

[0029]

[0030]

[0031] Calculate the weighted distance, the positive ideal solution is and the negative ideal solution is. The weighted distance from the i-th urbanization level indicator to the positive ideal solution and the negative ideal solution is calculated by the following formula:

[0032]

[0033]

[0034] Calculate the evaluation results, and the degree of closeness between the i-th urbanization level index and the negative ideal solution is:

[0035]

[0036] If the evaluated urbanization level index is farthest from the negative ideal solution, the evaluation is the highest, otherwise the evaluation is the worst.

[0037] Furthermore, in step 4, the quantitative characteristics of landscape parameters include the number of patches, boundary density, patch type area and the proportion of patch area in the landscape; the morphological structure of landscape parameters includes the area-weighted average patch shape index and the area-weighted average patch fractal dimension; and the complexity of landscape parameters includes the average patch area and connectivity.

[0038] Furthermore, in step 3, Arcgis software is used to perform depression filling processing, generate flow direction data, generate flow data, extract linear water system data, and expand linear water system data.

[0039] Furthermore, in step 4, the linear water system data is processed, including clipping the water system data, converting the coordinate system, converting the format, and setting parameters.

[0040] Furthermore, the establishment process of the grey relational model is as follows:

[0041] The reference sequence was determined as:

[0042] X0={X0(1),X0(2),X0(3),...,X0(n)}

[0043] The comparison sequence is:

[0044] X i ={X i (1),X i (2),X i (3),...,X i (n)}(i=1,2,...,m)

[0045] The result sequence is set as the reference sequence, and the factor sequence is set as the comparison sequence;

[0046] Data preprocessing and dimension unification:

[0047]

[0048] Compute the difference sequence, maximum difference, and minimum difference:

[0049] Δ i (k)=|X'0(k)-X' i (k)|

[0050]

[0051]

[0052] Among them, Δ i (k) is the difference sequence; M is the maximum difference; m is the minimum difference;

[0053] Calculate the point k correlation coefficient. The correlation coefficient calculation formula is:

[0054]

[0055] Among them, ρ is the resolution coefficient, which is generally set to 0.5;

[0056] Calculate the correlation between sequences. The correlation formula is:

[0057]

[0058] A device for analyzing the impact of urbanization on water system patterns in a city circle, comprising:

[0059] The urbanization level indicator selection module is used to select urban circles, which include one or more target cities. For each target city and the corresponding research period, a highly representative urbanization level indicator is selected to determine the comprehensive evaluation indicator system of the urbanization process;

[0060] The urbanization level acquisition module is used to calculate the weight matrix for each target city using the entropy-Topsis method as a model for measuring the urbanization process, obtain the urbanization level of each target city in each period of the study period and the corresponding urban spatial changes, and divide the urbanization level into the period before the formation of urbanization scale, the urbanization establishment stage-urbanization radiation period, the urbanization radiation period-lower-level urbanization period, and the lower-level urbanization-high-level urbanization period;

[0061] The water system data extraction module is used to extract urban water system data for each target city: analyzing the hydrological information of digital elevation data, establishing a surface water flow model, and determining the linear water system data to the urban circle;

[0062] The module for obtaining quantitative indicators of spatiotemporal changes in water systems is used to select multiple quantitative indicators of spatiotemporal changes in water systems based on the landscape ecology indicator table and the quantitative characteristics, morphological structure and complexity of landscape parameters, and calculate the results of each quantitative indicator of spatiotemporal changes in water systems to analyze the spatiotemporal evolution of water system patterns.

[0063] The correlation analysis module is used to select the grey correlation model for correlation analysis to obtain the correlation between the urbanization level of each target city in each period and the quantitative indicators of spatiotemporal changes of each water system.

[0064] A device for analyzing the impact of the urbanization process of a city circle on the water system pattern includes a processor and a memory for storing a computer program that can be run on the processor. When the processor is used to run the computer program, it executes the steps of any of the above-mentioned methods for analyzing the impact of the urbanization process of a city circle on the water system pattern.

[0065] A computer storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for analyzing the impact of the urbanization process of a city circle on the water system pattern.

[0066] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0067] (1) The present invention provides a method and device for analyzing the impact of the urbanization process of a city circle on the water system pattern. By dividing the urbanization level into the period before the formation of urbanization scale, the urbanization establishment stage-urbanization radiation period, the urbanization radiation period-lower-level urbanization period, and the lower-level urbanization-high-level urbanization period, and selecting a gray correlation model for correlation analysis, the correlation between the urbanization level of a target city circle in each period and the quantitative indicators of spatiotemporal changes of each water system is obtained. The coordination degree between regional urbanization development and water system pattern can be judged, and guidance can be provided for current urban development. Based on the research of existing regions, reference significance can be provided for the impact of urbanization process on water system pattern in the future development of other cities.

[0068] (2) The present invention provides a method and device for analyzing the impact of the urbanization process of a city circle on the water system pattern. The method uses a gray correlation model to perform correlation analysis. Gray correlation analysis is based on the evolution law of the evaluation object and reflects the geometric similarity of the object evolution sequence. There is no special requirement for the sample size. In addition, gray correlation analysis uses the analysis of the overall first-order slope difference, the overall second-order slope difference, and the overall displacement difference to reflect the consistency of the development trend of the sample sequence, analyze and depict the connotation and essence of the relationship between things, and reflect the actual causal relationship between samples. Finally, gray correlation analysis has the characteristics of low computational complexity and high efficiency.

[0069] (3) The present invention provides a method and device for analyzing the impact of the urbanization process of a city circle on the water system pattern. For samples with less water system data, the grey correlation analysis model can be used to preliminarily explore the correlation between the water system and urbanization. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0071] Figure 1 This is a flow chart of the analysis of the impact of the urbanization process of the urban circle on the water system pattern of the present invention. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0073] The present invention provides a method for analyzing the impact of urbanization process of urban agglomeration on water system pattern. Figure 1 As shown, the following steps are included:

[0074] Step 1: Select an urban agglomeration, which includes one or more target cities. For each target city and the corresponding research period, select a highly representative urbanization level indicator to determine a comprehensive evaluation indicator system for the urbanization process.

[0075] Step 2: For each target city, the entropy-Topsis method was used as a model to measure the urbanization process. The weight matrix was calculated to obtain the urbanization level of each target city in each period during the study period and the corresponding urban spatial changes. The urbanization level was divided into the period before urbanization scale was formed, the urbanization establishment stage-urbanization radiation stage, the urbanization radiation stage-lower-level urbanization stage, and the lower-level urbanization-high-level urbanization stage.

[0076] Step 3: Extract urban water system data for each target city: Analyze the hydrological information of the digital elevation data, establish a surface water flow model, and determine the linear water system data to the urban area;

[0077] Step 4: Based on the landscape ecology index table and the quantitative characteristics, morphological structure and complexity of landscape parameters, multiple water system spatiotemporal variation quantitative indicators are selected and the results of the spatiotemporal variation quantitative indicators of each water system are calculated to analyze the spatiotemporal evolution of the water system pattern;

[0078] Step 5: Select the grey correlation model to perform correlation analysis to obtain the correlation between the urbanization level of each target city in each period and the quantitative indicators of spatiotemporal changes of each water system.

[0079] The present invention provides a method for analyzing the impact of the urbanization process of an urban agglomeration on the water system pattern. By dividing the urbanization level into the period before the formation of the urbanization scale, the urbanization establishment stage-the urbanization radiation period, the urbanization radiation period-the lower-level urbanization period, and the lower-level urbanization-the advanced urbanization period, and selecting a grey correlation model for correlation analysis, the correlation between the urbanization level of a target city agglomeration in each period and the quantitative indicators of the spatiotemporal changes of each water system is obtained. The method can judge the coordination between regional urbanization development and the water system pattern, provide guidance for current urban development, and provide reference significance for the impact of the urbanization process on the water system pattern of future development of other cities based on existing regional research.

[0080] This paper provides a method for analyzing the impact of urbanization on water patterns in urban agglomerations. The method uses a gray correlation model for correlation analysis. Gray correlation analysis is based on the evolutionary patterns of the evaluated objects, reflecting the geometric similarity of their evolutionary sequences. It does not specify a specific sample size. Furthermore, gray correlation analysis uses the overall first-order slope difference, overall second-order slope difference, and overall displacement difference to reflect the consistency of the sample sequence's development trend. This analysis analyzes and depicts the connotation and essence of the relationships between objects, reflecting the substantive causal relationships between samples. Finally, gray correlation analysis is characterized by low computational complexity and high efficiency.

[0081] The water system data changes little from year to year, so there is no need to extract water system data year by year. This study extracts water system data every 5 years. Therefore, compared with the data on urbanization level (calculated annually), the water system data is relatively less.

[0082] Therefore, for samples with less water system data, the present invention can use the grey correlation analysis model to preliminarily explore the correlation between water systems and urbanization.

[0083] In this paper, the urban spatial changes are spatially mapped according to the urbanization stages divided by the overall urbanization measurement using Arcgis software to obtain a spatial change map of the urbanization level of the target city and analyze its spatial distribution and changes.

[0084] In one embodiment of the present invention, taking a certain city circle as an example, a method for analyzing the impact of the urbanization process of the city circle on the water system pattern is provided, and the specific steps are as follows: according to the current status of the water system of the receiving lake and the planning of the river-lake connection, two water diversion routes are selected; according to the index system proposed by scholars, some indicators with weak representation are eliminated, and multiple indicators are combined and calculated to determine the specific indicators of the comprehensive evaluation index system of the urbanization process, as shown in Table 1. In step 1, the urbanization level indicators include indicators of population urbanization, economic urbanization, social urbanization, spatial urbanization and green urbanization. Population urbanization includes urban population density, total land use, land use, and land use. Employment rate, proportion of population employed in non-agricultural products, and natural population growth rate; economic urbanization includes per capita GDP, urban economic density, proportion of industrial added value in GDP, and fixed asset investment density; social urbanization includes the number of high school students and above per 10,000 people, per capita disposable income of urban residents, number of beds in health institutions per 10,000 people, and number of transportation vehicles per 10,000 people; spatial urbanization includes land urbanization rate, per capita residential building area, and per capita road area; green and innovative cities include the growth value of high-tech industries per 10,000 people, the number of high-value invention patents per 10,000 people, reduction of energy consumption per unit GDP, and green coverage rate of built-up areas.

[0085] Table 1 Urbanization level indicators

[0086]

[0087] In step 2, the specific process of measuring urbanization includes:

[0088] Data standardization: Assuming that the comprehensive evaluation index system of urbanization process is m urbanization level indicators within n years, the basic data matrix R is:

[0089] R={r ij} n×m (i=1,2,...,m)

[0090] Among them, r ij represents the data of the jth urbanization level indicator in the i-th year;

[0091] The formula for Z-score standardization is:

[0092]

[0093] Among them, μ j is the overall average value of the jth urbanization level indicator, σ j is the standard deviation of the j-th urbanization level indicator;

[0094] Then define the standardized matrix Y of the original data as:

[0095] Y={yij} n×m

[0096]

[0097] From the data matrix Y, we can get the positive ideal solution Y consisting of the maximum values of each urbanization level indicator: + The negative ideal solution Y consisting of the minimum value - :

[0098] Y + ={y max1 ,y max2 ,...,y maxn} n×m

[0099] Y - ={y min1 ,y min2 ,...,y minn} n×m

[0100] Among them, y max1 、y max2 ,...,y maxn is the maximum value of each urbanization level indicator, y min1 、y min2 ,...,y minn is the minimum value of each urbanization level indicator;

[0101] Among them, the positive ideal solution refers to the optimal vector composed of the maximum values of each urbanization level indicator in each city during the study period, and the negative ideal solution refers to the worst vector composed of the minimum values of each urbanization level indicator in each city during the study period;

[0102] Calculate the weight and entropy, the weight of the j-th urbanization level indicator in the index system ω j and entropy H j The calculation formula is as follows:

[0103]

[0104]

[0105] Calculate the weighted distance. The weighted distance from the i-th urbanization level indicator to the positive ideal solution and the negative ideal solution is calculated by the following formula:

[0106]

[0107] Calculate the evaluation results, and the degree of closeness between the i-th urbanization level index and the negative ideal solution is:

[0108]

[0109] If the evaluated urbanization level index is farthest from the negative ideal solution, the evaluation is the highest, otherwise the evaluation is the worst.

[0110] In the embodiment of the present invention, as shown in Table 2, the weights of the urbanization level indicators of a target city, y max 、y min , weight and entropy.

[0111] Table 1 Weights and y of urbanization level indicators of a target city max 、y min , weight and entropy

[0112]

[0113] In the embodiment of the present invention, Arcgis is used to analyze the hydrological information of digital elevation data, establish a surface water flow model, and finally determine the linear water system data of the metropolitan area. The required digital elevation data is obtained through the geospatial data cloud platform built by the Chinese Academy of Sciences. The required digital elevation data is obtained through the geospatial data cloud platform built by the Chinese Academy of Sciences. In 2000, the DEM data with a resolution of 90m under the SRTM-DEM database was selected, and the DEM data with a resolution of 30m under the ASTER GDEM database was selected in 2010 and 2019.

[0114] In step 3, the urban water system data is extracted and processed in Arcgis software, including filling processing, making flow direction data, making flow data, extracting linear water system data, expanding linear water system data and visual interpretation.

[0115] In step 4, the quantitative characteristics of landscape parameters include the number of patches, boundary density, patch type area, and the proportion of patch area in the landscape. The morphological structure of landscape parameters includes the area-weighted average patch shape index and the area-weighted average patch fractal dimension. The complexity of landscape parameters includes the average patch area and connectivity, as shown in Table 2.

[0116] Table 2 Quantitative index diagram of spatiotemporal changes in water systems

[0117]

[0118] In step 4, the calculation of specific indicators requires the application of the landscape ecology software Fragstats4.2. However, this software has certain limitations on the calculation data, and the linear water system data extracted in step 3 needs to be processed, including: clipping water system data, converting coordinate systems, converting formats, and setting parameters.

[0119] Specifically, clip the water system data: clip it into separate water system data for each city in Arcgis according to the administrative divisions of each city.

[0120] Convert the coordinate system: Import the planar water system data (.shp format) into the ArcGIS software, and use the feature projection tool to convert the GCS_WGS_1984 coordinate system to the WGS_1984_UTM_Zone_50N coordinate system.

[0121] Conversion format: Since Fragstats 4.2 does not support Arcgis .shp files during calculations, this format is converted to raster format (.tiff). This conversion is done directly in Arcgis.

[0122] In step 5, the establishment process of the grey relational model is as follows:

[0123] The reference sequence was determined as:

[0124] X0={X0(1),X0(2),X0(3),...,X0(n)}

[0125] The comparison sequence is:

[0126] X i ={X i (1),X i (2),X i (3),...,X i (n)}(i=1,2,...,m)

[0127] The result sequence is set as the reference sequence, and the factor sequence is set as the comparison sequence;

[0128] Data preprocessing and dimension unification:

[0129]

[0130] Compute the difference sequence, maximum difference, and minimum difference:

[0131] Δ i (k)=|X'0(k)-X' i (k)|

[0132]

[0133]

[0134] Among them, Δ i (k) is the difference sequence; M is the maximum difference; m is the minimum difference.

[0135] Calculate the point k correlation coefficient. The correlation coefficient calculation formula is:

[0136]

[0137] Among them, ρ is the resolution coefficient, which is generally set to 0.5;

[0138] Calculate the correlation between sequences. The correlation formula is:

[0139]

[0140] In this embodiment of the present invention, the correlation between the urbanization level of each target city and the quantitative indicators of spatiotemporal changes of each water system in each time period is obtained according to step 5. The results are shown in Table 3; r(i) represents the correlation between the i-th water system indicator and the urbanization level.

[0141] The present invention also provides a device for analyzing the impact of urbanization on water system patterns in urban areas, comprising:

[0142] The urbanization level indicator selection module is used to select urban circles, which include one or more target cities. For each target city and the corresponding research period, a highly representative urbanization level indicator is selected to determine the comprehensive evaluation indicator system of the urbanization process;

[0143] The urbanization level acquisition module is used for each target city. It uses the entropy-Topsis method as a model for measuring the urbanization process, calculates the weight matrix, obtains the urbanization level of each target city in each period of the study period and obtains the corresponding urban spatial changes. The urbanization level is divided into the period before the formation of urbanization scale, the urbanization establishment stage-urbanization radiation period, the urbanization radiation period-lower-level urbanization period, and the lower-level urbanization-high-level urbanization period;

[0144] The water system data extraction module is used to extract urban water system data for each target city: analyzing the hydrological information of digital elevation data, establishing a surface water flow model, and determining the linear water system data to the urban circle;

[0145] The module for obtaining quantitative indicators of spatiotemporal changes in water systems is used to select multiple quantitative indicators of spatiotemporal changes in water systems based on the landscape ecology indicator table and the quantitative characteristics, morphological structure and complexity of landscape parameters, and calculate the results of each quantitative indicator of spatiotemporal changes in water systems to analyze the spatiotemporal evolution of water system patterns.

[0146] The correlation analysis module is used to select the grey correlation model for correlation analysis to obtain the correlation between the urbanization level of each target city in each period and the quantitative indicators of spatiotemporal changes of each water system.

[0147] The present invention also provides a device for analyzing the impact of the urbanization process of an urban circle on the water system pattern, comprising a processor and a memory for storing a computer program that can be run on the processor. When the processor is used to run the computer program, it executes the steps of any of the above-mentioned methods for analyzing the impact of the urbanization process of an urban circle on the water system pattern.

[0148] The memory in the embodiment of the present invention is used to store various types of data to support the operation of the device for analyzing the impact of urbanization on water system patterns. Examples of such data include any computer program for operating the device for analyzing the impact of urbanization on water system patterns.

[0149] The method for analyzing the impact of urbanization on water patterns in urban agglomerations disclosed in the embodiments of the present invention can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the method for analyzing the impact of urbanization on water patterns in urban agglomerations can be completed by hardware integrated logic circuits or software instructions in the processor. The processor may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium located in a memory. The processor reads information from the memory and, in conjunction with its hardware, completes the steps of the method for analyzing the impact of urbanization on water patterns in urban agglomerations provided in the embodiments of the present invention.

[0150] In an exemplary embodiment, the device for analyzing the impact of the urbanization process of a city circle on the water system pattern can be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), FPGAs, general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to execute the aforementioned method.

[0151] It is understood that the memory can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disk, or compact disc read-only memory (CD-ROM); magnetic surface memory can be magnetic disk memory or tape memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). The memory described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0152] The present invention also provides a computer storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods for analyzing the impact of the urbanization process of a city circle on the water system pattern are implemented.

[0153] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for analyzing the impact of urbanization on water system patterns in urban agglomerations, characterized by: The following steps are involved: Step 1: Select an urban agglomeration, which includes one or more target cities. For each target city and the corresponding research period, select a highly representative urbanization level indicator to determine a comprehensive evaluation indicator system for the urbanization process. Step 2: For each target city, the entropy-Topsis method was used as a model to measure the urbanization process. The weight matrix was calculated to obtain the urbanization level of each target city in each period during the study period and the corresponding urban spatial changes. The urbanization level was divided into the period before urbanization scale was formed, the urbanization establishment stage-urbanization radiation stage, the urbanization radiation stage-lower-level urbanization stage, and the lower-level urbanization-high-level urbanization stage. Step 3: Extract urban water system data for each target city: Analyze the hydrological information of the digital elevation data, establish a surface water flow model, and determine the linear water system data to the urban area; Step 4: Based on the landscape ecology index table and the quantitative characteristics, morphological structure and complexity of landscape parameters, multiple water system spatiotemporal variation quantitative indicators are selected and the results of the spatiotemporal variation quantitative indicators of each water system are calculated to analyze the spatiotemporal evolution of the water system pattern; Step 5: Select the grey correlation model to conduct correlation analysis to obtain the correlation between the urbanization level of each target city in each period and the quantitative indicators of spatiotemporal changes of each water system; Specific processes for measuring urbanization include: Data standardization: Assuming that the comprehensive evaluation index system of urbanization process is m urbanization level indicators within n years, the basic data matrix R is: R={r ij } n×m (i=1,2,...,m) Among them, r ij represents the data of the jth urbanization level indicator in the i-th year; The formula for Z-score standardization is: Among them, μ j is the overall average value of the jth urbanization level indicator, σ j is the standard deviation of the j-th urbanization level indicator; Then define the standardized matrix Y of the original data as: And={and ij } n×m From the data matrix Y, we can get the positive ideal solution Y consisting of the maximum values of each urbanization level indicator: + The negative ideal solution Y consisting of the minimum value - : AND + ={and max1 ,and max2 ,...,and maxn } n×m AND - ={and min1 ,and min2 ,...,and minn } n×m Among them, y max1 、y max2 ,...,y maxn is the maximum value of each urbanization level indicator, y min1 、y min2 ,...,y minn is the minimum value of each urbanization level indicator; Calculate the weight and entropy, the weight of the j-th urbanization level indicator in the index system ω j and entropy H j The calculation formula is as follows: Calculate the weighted distance, the positive ideal solution is and the negative ideal solution is. The weighted distance from the i-th urbanization level indicator to the positive ideal solution and the negative ideal solution is calculated by the following formula: Calculate the evaluation results, and the degree of closeness between the i-th urbanization level index and the negative ideal solution is: If the evaluated urbanization level index is farthest from the negative ideal solution, the evaluation is the highest, otherwise the evaluation is the worst.

2. The method for analyzing the impact of urbanization on water system patterns in a city circle according to claim 1, characterized in that: In step 1, the urbanization level indicators include indicators of population urbanization, economic urbanization, social urbanization, spatial urbanization and green urbanization. Population urbanization includes urban population density, total employment rate, proportion of non-agricultural employment, and natural population growth rate; economic urbanization includes per capita GDP, urban economic density, proportion of industrial added value in GDP, and fixed asset investment density; social urbanization includes the number of high school students and above per 10,000 people, per capita disposable income of urban residents, number of beds in health institutions per 10,000 people, and number of transportation vehicles per 10,000 people; spatial urbanization includes land urbanization rate, per capita residential building area, and per capita road area; green and innovative cities include the growth value of high-tech industries per 10,000 people, the number of high-value invention patents per 10,000 people, reduction of energy consumption per unit GDP, and green coverage rate of built-up areas.

3. The method for analyzing the impact of urbanization on water system patterns in a city circle according to claim 1, characterized in that: In step 4, the quantitative characteristics of landscape parameters include the number of patches, boundary density, patch type area and the proportion of patch area in the landscape. The morphological structure of landscape parameters includes the area-weighted average patch shape index and the area-weighted average patch fractal dimension. The complexity of landscape parameters includes the average patch area and connectivity.

4. The method for analyzing the impact of urbanization on water system patterns in a city circle according to claim 1, characterized in that: In step 3, Arcgis software is used to fill depressions, produce flow direction data, produce flow data, extract linear water system data, and expand linear water system data.

5. The method for analyzing the impact of urbanization on water system patterns in a city circle according to claim 1 is characterized by: In step 4, the linear water system data is processed, including clipping the water system data, converting the coordinate system, converting the format, and setting parameters.

6. The method for analyzing the impact of urbanization on water system patterns in a city circle according to claim 1, characterized in that: The establishment process of the grey relational model is as follows: The reference sequence was determined as: X0={X0(1),X0(2),X0(3),...,X0(n)} The comparison sequence is: X i ={X i (1),X i (2),X i (3),...,X i (n)}(i=1,2,...,m) The result sequence is set as the reference sequence, and the factor sequence is set as the comparison sequence; Data preprocessing and dimension unification: Compute the difference sequence, maximum difference, and minimum difference: Δ i (k)=|X'0(k)-X' i (k)| Among them, Δ i (k) is the difference sequence; M is the maximum difference; m is the minimum difference; Calculate the point k correlation coefficient. The correlation coefficient calculation formula is: Where, ρ is the resolution coefficient, which is set to 0.5; Calculate the correlation between sequences. The correlation formula is:

7. A device for analyzing the impact of urbanization on water system patterns in urban areas, characterized by: include: The urbanization level indicator selection module is used to select urban circles, which include one or more target cities. For each target city and the corresponding research period, a highly representative urbanization level indicator is selected to determine the comprehensive evaluation indicator system of the urbanization process; The urbanization level acquisition module is used to calculate the weight matrix for each target city using the entropy-Topsis method as a model for measuring the urbanization process, obtain the urbanization level of each target city in each period of the study period and the corresponding urban spatial changes, and divide the urbanization level into the period before the formation of urbanization scale, the urbanization establishment stage-urbanization radiation period, the urbanization radiation period-lower-level urbanization period, and the lower-level urbanization-high-level urbanization period; The water system data extraction module is used to extract urban water system data for each target city: analyzing the hydrological information of digital elevation data, establishing a surface water flow model, and determining the linear water system data to the urban circle; The module for obtaining quantitative indicators of spatiotemporal changes in water systems is used to select multiple quantitative indicators of spatiotemporal changes in water systems based on the landscape ecology indicator table and the quantitative characteristics, morphological structure and complexity of landscape parameters, and calculate the results of each quantitative indicator of spatiotemporal changes in water systems to analyze the spatiotemporal evolution of water system patterns. The correlation analysis module is used to select the grey correlation model for correlation analysis to obtain the correlation between the urbanization level of each target city at each period and the quantitative indicators of spatiotemporal changes of each water system; The urbanization level acquisition module is used to measure the specific process of urbanization, including: Data standardization: Assuming that the comprehensive evaluation index system of urbanization process is m urbanization level indicators within n years, the basic data matrix R is: R={r ij } n×m (i=1,2,...,m) Among them, r ij represents the data of the jth urbanization level indicator in the i-th year; The formula for Z-score standardization is: Among them, μ j is the overall average value of the jth urbanization level indicator, σ j is the standard deviation of the j-th urbanization level indicator; Then define the standardized matrix Y of the original data as: And={and ij } n×m From the data matrix Y, we can get the positive ideal solution Y consisting of the maximum values of each urbanization level indicator: + The negative ideal solution Y consisting of the minimum value - : AND + ={and max1 ,and max2 ,...,and maxn } n×m AND - ={and min1 ,and min2 ,...,and minn } n×m Among them, y max1 、y max2 ,...,y maxn is the maximum value of each urbanization level indicator, y min1 、y min2 ,...,y minn is the minimum value of each urbanization level indicator; Calculate the weight and entropy, the weight of the j-th urbanization level indicator in the index system ω j and entropy H j The calculation formula is as follows: Calculate the weighted distance, the positive ideal solution is and the negative ideal solution is. The weighted distance from the i-th urbanization level indicator to the positive ideal solution and the negative ideal solution is calculated by the following formula: Calculate the evaluation results, and the degree of closeness between the i-th urbanization level index and the negative ideal solution is: If the evaluated urbanization level index is farthest from the negative ideal solution, the evaluation is the highest, otherwise the evaluation is the worst.

8. A device for analyzing the impact of urbanization on water system patterns in urban areas, characterized by: It comprises a processor and a memory for storing a computer program that can be run on the processor. When the processor is used to run the computer program, it executes the steps of the method for analyzing the impact of the urbanization process of the urban circle on the water system pattern as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for analyzing the impact of the urbanization process of the urban circle on the water system pattern as described in any one of claims 1 to 6 are implemented.

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