Intelligent territorial space planning system and method based on space-time big data

Abstract

The invention discloses an intelligent territorial space planning system and method based on space-time big data, particularly relates to the technical field of space planning, and can timely discover the problem of excessive utilization possibly occurring in water resource exploitation by analyzing and monitoring the change speed of underground water resources. Therefore, unsustainable use of water resources and damage to the ecological environment are prevented. Secondly, by evaluating the water utilization efficiency of the land, the opportunity of improving agricultural and industrial water utilization methods can be identified, in addition, by comprehensively evaluating the water resource dependence degree and supply and demand fluctuation of the region, the dynamic change of the water resource can be better understood, and a scientific basis is provided for implementing an effective water resource management strategy and space planning. Finally, the method not only enhances the sustainable management capability of regional water resources, but also helps to guarantee the stable development of social and economic activities, improve the living quality of residents, ensure the long-term safety and ecological balance of the water resources, and support the realization of the sustainable development goal.

Description

Technical Field

[0001] The present invention relates to the field of spatial planning technology, and more specifically, to a smart national land space planning system and method based on spatiotemporal big data. Background Art

[0002] National land space planning is a comprehensive plan covering land use, water resources management, urban and rural construction, environmental protection and other aspects; Spatial temporal big data refers to a collection of big data containing time and space attributes. This type of data is not only huge in volume and diverse in types, but also has strong temporal dynamics and spatial correlation; Spatiotemporal big data comes from a wide range of sources, covering information collected from multiple channels such as geographic information systems (GIS), satellite remote sensing, mobile devices, sensor networks, social media, traffic flow data, etc. The common feature of these data is that they are associated with specific time points and geographical locations, allowing analysts to understand and parse the data from both time and space dimensions.

[0003] Water resources management is an important part of national land space planning, which is directly related to the sustainable development of the region and the quality of life of residents. Although spatiotemporal big data technology has begun to be applied to the field of national land space planning, the existing application frameworks and methods often lack systematicity and comprehensiveness, and it is difficult to fully reflect and deal with complex water resources management issues. Traditional water resources management planning methods rely on static data collection and linear decision-making processes, and usually face problems such as untimely data updates, slow decision-making responses, and inefficient resource allocation. The timeliness of assessing the stability of water resource use is not enough.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a smart national land space planning system and method based on spatiotemporal big data to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A smart national land space planning method based on spatiotemporal big data includes the following steps:

[0008] S1: Analyze the change rate of groundwater resources in the target area and evaluate the change rate of groundwater resources in the target area;

[0009] S2: Analyze the relationship between historical production data and water resource consumption in the target area to measure the water use efficiency of the land;

[0010] S3: Evaluate the potential risk of over-exploitation of water resources in the target area by the rate of change of groundwater resources and the water utilization efficiency of the land in the target area;

[0011] S4: If the risk of excessive water exploitation in the target area is serious, an alarm is issued; otherwise, the target area’s dependence on the current water resource allocation model and the degree of fluctuation in water resource supply and demand are evaluated;

[0012] S5: Conduct a comprehensive analysis of the target area’s dependence on the current water resource allocation model and the fluctuation of water resource supply and demand, and evaluate the stability of water resource use in the target area; conduct spatial planning for the target area based on the stability of water resource use in the target area.

[0013] In a preferred embodiment, the change rate of groundwater resources in the target area is analyzed to evaluate the change rate of groundwater resources in the target area, specifically:

[0014] Set a preset time interval; evenly divide the preset time interval into multiple time periods;

[0015] Obtain the recharge volume for each time period. The recharge volume refers to the total amount of water supplied to the groundwater system in the target area during the time period.

[0016] Obtain the evaporation in each time period. The evaporation refers to the amount of water that evaporates directly from the surface of the target area and the soil surface into the atmosphere during the time period.

[0017] Obtain the output for each time period, where the output refers to the total amount of water removed from the groundwater system in the target area during the time period;

[0018] Calculate the groundwater level change rate, the expression is: Where Dsv is the rate of change of groundwater level, n is the number of time periods in the preset time interval, i is the number of time periods in the preset time interval, i = 1, 2, 3, ..., n, i is a positive integer, R i is the supply amount in the i-th time period, E i is the evaporation amount in the i-th time period, Q i is the output in the i-th time period.

[0019] In a preferred embodiment, the relationship between the historical production data and water resource consumption of the target area is analyzed to measure the water utilization efficiency of the land, specifically:

[0020] Collect historical production data and water consumption data within the target area;

[0021] The Cobb-Douglas production function model is used in economics, and the model is defined as:

[0022] Y T =A*W T α*L T β*K T γ*eξ;

[0023] Among them, Y T is the output, A is the overall technical efficiency level, W T is the water resource input, L T Represents the labor input, K T represents the amount of capital input, e is a natural number base, α, β, γ are the output elasticities of water resources input, labor input and capital input respectively, ξ is the error term;

[0024] Calculate the land water use efficiency index, the expression is: Among them, WUE is the land water use efficiency index.

[0025] In a preferred embodiment, the risk level of excessive water resource exploitation in the target area is assessed by the change rate of groundwater resources and the water utilization efficiency of the land in the target area, specifically:

[0026] Set the groundwater level change rate threshold and compare the groundwater level change rate with the groundwater level change rate threshold:

[0027] When the groundwater level change rate is greater than the groundwater level change rate threshold, it is determined that the water resource exploitation situation is normal; when the groundwater level change rate is less than or equal to the groundwater level change rate threshold, it is determined that there is a risk of excessive water resource exploitation;

[0028] Set a land water use efficiency threshold and compare the land water use efficiency index with the land water use efficiency threshold:

[0029] When the land water utilization efficiency index is greater than the land water utilization efficiency threshold, the water resource utilization efficiency is judged to be normal; when the land water utilization efficiency index is less than or equal to the land water utilization efficiency threshold, the water resource utilization efficiency is judged to be low;

[0030] When the water resource exploitation situation is normal and the water resource utilization efficiency is normal, it is judged that the hidden danger level of excessive water resource exploitation in the target area is low; otherwise, it is judged that the hidden danger level of excessive water resource exploitation in the target area is serious.

[0031] In a preferred embodiment, if the risk of excessive water resource exploitation in the target area is low, the target area's reliance on the current water resource allocation model is assessed, specifically:

[0032] PCA was used to assess the target area's dependence on the current water resource allocation pattern;

[0033] The data matrix X is first centered, and then the covariance matrix C of the data is calculated, which is defined as: Among them, X T is the transpose of X, m is the number of observations;

[0034] Calculate the eigenvalues ​​and eigenvectors of the covariance matrix C. The eigenvectors define the directions of the principal components in the data, and the eigenvalues ​​represent the variance in the directions of the principal components.

[0035] Collect data on water consumption, groundwater levels, precipitation, population, and economic activity;

[0036] Standardize the data so that the mean of each variable is 0 and the standard deviation is 1; fill or delete observations with missing values;

[0037] Compute the covariance matrix between variables;

[0038] Perform eigendecomposition on the covariance matrix and extract eigenvalues ​​and eigenvectors;

[0039] Select the principal component with large eigenvalue and analyze the loading of each principal component to determine the contribution of each variable to the principal component;

[0040] Based on the selected principal components, the water resource dependence index is calculated as follows: Among them, WDY is the water resource dependence index, λw is the variance contribution rate of the w-th principal component, pw is the score of the w-th principal component, k represents the number of principal components selected for use in PCA, and w is the number of the principal component selected for use in PCA, w = 1, 2, 3, ..., k, where w is a positive integer.

[0041] In a preferred embodiment, the fluctuation degree of water supply and demand is assessed, specifically:

[0042] Obtain water supply data Su, which refers to the total water supply at each observation time point u;

[0043] Obtain water demand data Du, which refers to the total water demand at the same observation time point u;

[0044] Calculate the supply-demand ratio Ru:

[0045] Calculate the average supply-demand ratio, which is the average of the supply-demand ratios of all observation points;

[0046] The supply-demand ratio volatility index is calculated by standard deviation, and its expression is: Wherein, Gxb is the supply-demand ratio fluctuation index, J is the number of observation time points, u is the number of observation time points, u=1, 2, 3, ..., J, u is a positive integer, and Rpj is the average value of the supply-demand ratio.

[0047] In a preferred embodiment, in S5, a comprehensive analysis is conducted on the degree of dependence of the target area on the current water resource allocation mode and the degree of fluctuation of water resource supply and demand to evaluate the stability of water resource use in the target area, specifically:

[0048] The water resource dependence index and the supply-demand ratio fluctuation index are normalized, and the normalized water resource dependence index and the supply-demand ratio fluctuation index are assigned preset proportional coefficients respectively, and the water resource use stability coefficient is calculated to evaluate the stability of water resource use in the target area;

[0049] From the calculation formula of the water resource use stability coefficient, it can be seen that the larger the water resource use stability coefficient, the worse the stability of water resource use in the target area;

[0050] According to the stability of water resources use in the target area, spatial planning is carried out for the target area, specifically:

[0051] Set a water resource use stability coefficient threshold; compare the water resource use stability coefficient with the water resource use stability coefficient threshold:

[0052] When the water resource use stability coefficient is less than or equal to the water resource use stability coefficient threshold, it is determined that a water resource use stability signal is generated;

[0053] When the water resource usage stability coefficient is greater than the water resource usage stability coefficient threshold, it is determined that a water resource usage instability signal is generated.

[0054] On the other hand, the present invention provides a smart national land space planning system based on spatiotemporal big data, including a resource change assessment module, a utilization efficiency assessment module, an over-exploitation assessment module, an exploitation hidden danger alarm module, a use stability analysis module, and a use stability assessment module;

[0055] The resource change assessment module analyzes the change rate of groundwater resources in the target area and assesses the change rate of groundwater resources in the target area;

[0056] The utilization efficiency assessment module analyzes the relationship between historical production data and water resource consumption in the target area to measure the water utilization efficiency of the land;

[0057] The over-exploitation assessment module assesses the potential risk of over-exploitation of water resources in the target area through the rate of change of groundwater resources and the water utilization efficiency of the land in the target area.

[0058] If the risk of excessive water resource exploitation in the target area is serious, the exploitation risk alarm module will sound an alarm; otherwise, the stability analysis module will be used to evaluate the target area's dependence on the current water resource allocation model and the fluctuation of water resource supply and demand;

[0059] Use the stability assessment module to conduct a comprehensive analysis of the target area's dependence on the current water resource allocation model and the fluctuation of water resource supply and demand, and evaluate the stability of water resource use in the target area; based on the stability of water resource use in the target area, carry out spatial planning of the target area.

[0060] The technical effects and advantages of the intelligent national land space planning system and method based on spatiotemporal big data of the present invention are as follows:

[0061] By analyzing and monitoring the rate of change of groundwater resources, it is possible to timely discover the possible over-use problems in water resource exploitation, thereby preventing the unsustainable use of water resources and the destruction of the ecological environment. Secondly, by evaluating the water use efficiency of land, it helps to identify opportunities to improve agricultural and industrial water use methods, thereby promoting the conservation and efficient use of water resources. In addition, by comprehensively evaluating the degree of regional water resource dependence and supply and demand fluctuations, comprehensive data support is provided to better understand the dynamic changes of water resources and provide a scientific basis for the implementation of effective water resource management strategies and spatial planning. Ultimately, this approach not only enhances the sustainable management capacity of regional water resources, but also helps to ensure the stable development of social and economic activities, improve the quality of life of residents, ensure the long-term security of water resources and ecological balance, and support the realization of sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of a smart national land space planning method based on spatiotemporal big data according to the present invention;

[0063] Figure 2 This is a structural schematic diagram of a smart national land space planning system based on spatiotemporal big data in the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Example 1

[0066] Figure 1 The present invention provides a smart national land space planning method based on spatiotemporal big data, which includes the following steps:

[0067] S1: Analyze the changing speed of groundwater resources in the target area and evaluate the changing rate of groundwater resources in the target area.

[0068] S2: Analyze the relationship between historical production data and water resource consumption in the target area to measure the water use efficiency of the land.

[0069] S3: Assess the potential risk of over-exploitation of water resources in the target area by measuring the rate of change of groundwater resources and the water utilization efficiency of the land in the target area.

[0070] S4: If the risk of excessive water exploitation in the target area is serious, an alarm is issued; otherwise, the target area’s dependence on the current water resource allocation model and the degree of fluctuation in water resource supply and demand are evaluated.

[0071] S5: Conduct a comprehensive analysis of the target area’s dependence on the current water resource allocation model and the fluctuation of water resource supply and demand, and evaluate the stability of water resource use in the target area; conduct spatial planning for the target area based on the stability of water resource use in the target area.

[0072] Among them, the target area refers to the land area where water resources analysis and assessment are required.

[0073] In S1, the change rate of groundwater resources in the target area is analyzed and the change rate of groundwater resources in the target area is evaluated, specifically:

[0074] A preset time interval is set, wherein the time length corresponding to the preset time interval is set according to actual conditions.

[0075] Divide the preset time interval evenly into multiple time periods.

[0076] Obtain recharge for each time period. Recharge is the total amount of water supplied to the groundwater system in the target area during the time period, including all natural and artificial recharge sources; natural and artificial recharge sources include:

[0077] Rainfall Infiltration: The amount of water that penetrates into the ground during a precipitation event.

[0078] River infiltration: The natural infiltration of surface water bodies such as rivers and lakes into the groundwater system.

[0079] Irrigation infiltration: Water that is not absorbed by crops during irrigation seeps into groundwater through the soil layer.

[0080] Artificial recharge: such as direct recharge from injection wells.

[0081] Recharge is obtained from rainfall records and river flow data collected by meteorological and hydrological stations, irrigation statistics provided by the agricultural sector or relevant water resources management bureaus, and the use of groundwater simulation software (such as MODFLOW) to simulate the effects of natural and artificial recharge.

[0082] The evaporation amount in each time period is obtained. The evaporation amount refers to the amount of water that evaporates directly from the surface and soil surface of the target area into the atmosphere during the time period. The evaporation amount includes the water that evaporates directly from the surface into the atmosphere and the water that plants release into the atmosphere through their leaves.

[0083] The evaporation rate is obtained by measuring the evaporation rate of the water surface through evaporation dishes, which is a common method used in meteorological stations. Remote sensing data is used to estimate vegetation transpiration and soil evaporation in large areas.

[0084] Get the output for each time period, which is the total amount of water removed from the groundwater system in the target area during the time period.

[0085] Outputs include:

[0086] Pumped water volume: The amount of water directly extracted from groundwater through facilities such as wells.

[0087] Base flow: The amount of groundwater that naturally flows into rivers and lakes, maintaining the water levels of rivers and lakes.

[0088] Artificial discharge: For example, drainage to lower the water table.

[0089] Output acquisition methods include:

[0090] Pumping statistics: Data provided by the local water authority or well owner.

[0091] Hydrogeological investigations: estimation of baseflow and other natural discharges through groundwater modelling.

[0092] Remote sensing and ground monitoring: Combine remote sensing data with ground monitoring facilities to monitor artificial emissions and baseflow.

[0093] Calculate the groundwater level change rate, the expression is: Where Dsv is the rate of change of groundwater level, n is the number of time periods in the preset time interval, i is the number of time periods in the preset time interval, i = 1, 2, 3, ..., n, i is a positive integer, R i is the supply amount in the i-th time period, E i is the evaporation amount in the i-th time period, Q i is the output in the i-th time period.

[0094] A large positive value of the groundwater level change rate (i.e. a rapid rise in groundwater level) may indicate:

[0095] High precipitation: During rainy seasons or floods, large amounts of precipitation can quickly recharge groundwater, causing water levels to rise significantly.

[0096] Increased recharge areas: For example, infiltration from rivers and lakes, or the impact of artificial recharge measures (such as recharge), may lead to rapid replenishment of groundwater.

[0097] Reduced pumping: Reduced groundwater extraction during the agricultural off-season or periods of reduced industrial water use can also lead to rising water levels.

[0098] If the groundwater level change rate is a large negative value (i.e. the groundwater level is falling rapidly), it may indicate:

[0099] Over-abstraction: Excessive withdrawal of water for agricultural irrigation, industrial use or domestic use, especially during dry seasons, can lead to a rapid decline in groundwater levels.

[0100] Reduced rainfall: During drought conditions, reduced precipitation slows the natural recharge rate of groundwater and accelerates the decline in water levels.

[0101] Changes in surface water bodies: If the water level of nearby rivers and lakes decreases, it may reduce the recharge to groundwater, thereby affecting the groundwater level.

[0102] In S2, the relationship between historical production data and water resource consumption in the target area is analyzed to measure the water use efficiency of the land, specifically:

[0103] Collect historical production data and water consumption data in the target area:

[0104] Historical production data: This includes data on crop production, forest product production or pasture output over different time periods. These data are usually obtained from local agricultural departments, forestry bureaus or related enterprises.

[0105] Water consumption data: including irrigation water, processing water, drinking water, etc. The data may come from local water bureaus, farm operation records and other relevant water resource monitoring sites.

[0106] The relationship between water resources and land output can be modeled using the Cobb-Douglas production function in economics, which is widely used to describe the relationship between input and output.

[0107] The model is defined as: T =A*W T α*L T β*K T γ*eξ.

[0108] Among them, Y TIt is the output quantity (such as total output), which represents the total output of the target area in year T, including crop output, forest product output, pasture output, etc., depending on the main economic activities in the target area.

[0109] A is the overall technical efficiency level. The overall technical efficiency level is a coefficient that reflects the technical efficiency in the production process. It affects the efficiency of all production factors. The overall technical efficiency level is estimated by fitting historical data into the production function model.

[0110] W T is the water input, which is the total water consumption in the target area in year T, including irrigation water, industrial water, domestic water, etc. Water input can be collected through records from local water bureaus, farm operation records, and other relevant water resource monitoring stations.

[0111] L T Represents the labor input; the labor input represents the labor input used in production activities in year T, which can be measured in labor hours or the number of workers. The labor input can be obtained from the local labor statistics bureau or directly from the human resources department of the enterprise.

[0112] K T Represents the amount of capital input, which represents the amount of capital input used for production in year T, including the value or quantity of machinery, equipment, buildings, etc. The amount of capital input usually comes from the company's financial statements or asset management records, and sometimes needs to be estimated through depreciation or investment records.

[0113] e is the natural number base.

[0114] α, β, and γ are the output elasticities of water resource input, labor input, and capital input, respectively. Output elasticity indicates the percentage change in output when the corresponding input changes by 1%. α, β, and γ are estimated based on historical data through statistical analysis methods (such as regression analysis).

[0115] ξ is the error term, which reflects the impact of other unobservable factors and is used to include the impact of all random factors not explained by the model.

[0116] Calculate the land water use efficiency index, the expression is: Among them, WUE is the land water use efficiency index.

[0117] When the land water use efficiency index is high, it means that more output can be produced per unit of water resources, which means that water resources are used efficiently. In areas where water resources are tight or water conservation is needed, a higher land water use efficiency index is the focus of goal and pursuit because it can reduce water consumption while maintaining or even increasing production.

[0118] When the LWEI is low, it indicates that water resources are used inefficiently, that is, a large amount of water is consumed but only a small amount of output is produced. This may be due to a variety of reasons, including inappropriate irrigation methods, poor soil conditions leading to inefficient water use, or crop varieties that are not suitable for the local environment.

[0119] In S3, the risk level of excessive water resource exploitation in the target area is assessed through the change rate of groundwater resources and the water utilization efficiency of the land in the target area, specifically:

[0120] A groundwater level change rate threshold is set. The groundwater level change rate threshold is set by professional and technical personnel in this field based on the magnitude of the groundwater level change rate, the requirement standard for the groundwater level change rate and other actual conditions. The groundwater level change rate threshold is generally a negative value. The smaller the groundwater level change rate, the greater the risk of excessive exploitation of water resources in the target area.

[0121] Compare the rate of groundwater level change to the rate of groundwater level change threshold:

[0122] When the groundwater level change rate is greater than the groundwater level change rate threshold, it is determined that the water resource exploitation situation is normal.

[0123] When the groundwater level change rate is less than or equal to the groundwater level change rate threshold, it is determined that there is a risk of over-exploitation of water resources.

[0124] A land water utilization efficiency threshold is set. The land water utilization efficiency threshold is set by professional and technical personnel in this field based on the size of the land water utilization efficiency index and other actual conditions such as the requirements for water resource utilization efficiency.

[0125] Compare the land water use efficiency index to the land water use efficiency threshold:

[0126] When the land water utilization efficiency index is greater than the land water utilization efficiency threshold, the water resource utilization efficiency is judged to be normal;

[0127] When the land water use efficiency index is less than or equal to the land water use efficiency threshold, it is judged that the water resource utilization efficiency is low; at this time, there is room for improving the water resource utilization efficiency or there is a risk of over-use.

[0128] Therefore, when the water resource exploitation situation is normal and the water resource utilization efficiency is normal, it is judged that the hidden danger level of excessive water resource exploitation in the target area is low; otherwise, it is judged that the hidden danger level of excessive water resource exploitation in the target area is serious.

[0129] The low risk of over-exploitation of water resources in the target areas indicates that water resource extraction and utilization are sustainable and the risks are low.

[0130] The high risk of over-exploitation of water resources in the target area indicates that there may be problems with over-exploitation or inefficient use of water resources, and the risk of over-exploitation of water resources is considered to be high.

[0131] In S4, if the hidden danger of excessive water resources in the target area is serious, an alarm is triggered; specifically:

[0132] Once an alarm is triggered, relevant management departments and decision makers should be notified immediately:

[0133] Email and SMS Notification: Automatically send emails and text messages containing alarm details to a preset list of recipients.

[0134] Sound and light signals: Alarm systems set up at key locations send out sound or light signals to alert on-site personnel.

[0135] The alarm system should also be linked to the local water authority’s emergency response plan to ensure that the following measures can be taken quickly:

[0136] Immediately inspect and assess water resource conditions: Send a technical team to the site for on-site inspection.

[0137] Adjust water resource management strategies: temporarily adjust water use rights to optimize water resource allocation.

[0138] Public Information Release: Issue water shortage warnings to the public, provide water conservation advice and information on possible alternative water sources.

[0139] Through real-time monitoring, rapid alarm and effective response, the risk of over-exploitation of water resources can be minimized, and regional water resource security and ecological balance can be protected and maintained.

[0140] If the risk of over-exploitation of water resources in the target area is low, assess the target area's reliance on the current water resource allocation model, specifically:

[0141] Principal component analysis (PCA) is a statistical technique used to simplify the complexity of a dataset while preserving as much variability and structure as possible in the original data; this is done by finding the main directions (principal components) in the data and using these directions to explain most of the data variability.

[0142] Here, principal component analysis (PCA) is used to assess the degree of dependence of the target areas on the current water resource allocation pattern.

[0143] In PCA, the data matrix X (where rows represent observations and columns represent variables) is first centered by subtracting the mean of each variable from its value. Then the covariance matrix C of the data is calculated and is defined as: Among them, X T is the transpose of X, and m is the number of observations.

[0144] The eigenvalues ​​and eigenvectors of the covariance matrix C are calculated. The eigenvectors define the directions of the principal components in the data, while the eigenvalues ​​represent the magnitude of the variance in the directions of the principal components, that is, the importance of that direction.

[0145] Collect data, including:

[0146] Water consumption (agricultural, industrial, residential), groundwater level, precipitation, population data and economic activity data (agricultural output value, industrial output value).

[0147] Since PCA is sensitive to the scale of variables, the data needs to be standardized so that the mean of each variable is 0 and the standard deviation is 1.

[0148] Fill in or remove observations with missing values.

[0149] Based on the preprocessed data, the covariance matrix between the variables is calculated.

[0150] Perform eigendecomposition on the covariance matrix to extract eigenvalues ​​and eigenvectors. These eigenvectors (principal components) indicate the direction of the most important sources of variance in the data.

[0151] Select principal components with large eigenvalues, which are usually the components that explain most of the data variation. This can be determined by calculating the variance contribution of each principal component, and the principal components are usually selected to have a total variance contribution of more than 85%.

[0152] The loadings (the coefficients of the eigenvectors) of each principal component are analyzed to determine the extent to which each variable contributes to the principal component.

[0153] Based on the selected principal components, the water resource dependence index is calculated. The water resource dependence index is the weighted sum of the selected principal components, and the weight is the variance contribution rate of each principal component. The formula is as follows: Among them, WDY is the water resource dependence index, λw is the variance contribution rate of the w-th principal component, pw is the score of the w-th principal component, k represents the number of principal components selected for use in PCA, and w is the number of the principal component selected for use in PCA, w = 1, 2, 3, ..., k, where w is a positive integer.

[0154] By analyzing the water resource dependence index, we can assess the degree of dependence of the target area on the current water resource allocation pattern. A larger water resource dependence index indicates that the region is highly dependent on water resources, and measures such as increasing water supply and improving water resource management strategies may need to be taken to reduce risks and ensure the sustainable use of water resources.

[0155] The principal component analysis (PCA) was used to quantitatively assess the target region's dependence on the current water resource allocation pattern. First, this method can reveal the multidimensional factors that affect water resource dependence, such as water consumption, groundwater level, population, and economic activities, thereby helping managers to fully understand the complexity and dynamics of regional water resource use. By identifying the principal components, it is possible to determine which factors have the greatest impact on water resource dependence, which is essential for developing targeted strategies to optimize water resource management. For example, if the analysis results show that economic activities contribute more to water resource dependence, it may be necessary to consider implementing stricter industrial water management measures or promoting the application of water efficiency technologies; in addition, PCA provides a clear perspective by simplifying complex data sets, enabling decision makers to quickly identify and respond to key issues in water resource allocation; this data-driven approach enhances the scientificity and effectiveness of policy making, making water resource management more precise and adaptable, especially in the face of climate change and changing environmental pressures. This method provides a powerful tool to ensure the sustainable use of water resources and prevent socioeconomic problems that may be caused by water shortages. Principal component analysis not only improves the efficiency of data processing and the transparency of management, but also enhances the understanding of the complexity of water resources management and supports the formulation of more scientific and effective water resources management strategies. This strategy can effectively respond to possible water resource challenges in the future while ensuring regional development and ecological security, and maintain the long-term stability and sustainable development of society.

[0156] Assess the volatility of water supply and demand, specifically:

[0157] Obtain water supply data Su, where the water supply data Su refers to the total water supply at each observation time point u (for example, daily, monthly, or annually). The water supply may include water from all sources, such as surface water, groundwater, and recycled water, etc., and is usually measured in cubic meters.

[0158] Obtain water demand data Du, which refers to the total water demand at the same observation time point u, covering water consumption for agriculture, industry, household and other purposes, also in cubic meters.

[0159] Both the water supply data Su and the water demand data need to be cleaned and standardized to ensure that each data point is comparable on the same time scale and to eliminate any outliers or errors.

[0160] Calculate the supply-demand ratio Ru: The supply-demand ratio Ru provides a view of the supply-demand balance at a single observation time point. A supply-demand ratio greater than 1 means that the water supply is greater than the water demand, otherwise it is less than 1.

[0161] Calculate the average supply-demand ratio, which is the average of the supply-demand ratios of all observation points, indicating the average state of supply-demand balance during the entire observation period.

[0162] The supply-demand ratio volatility index is calculated by standard deviation, and its expression is: Wherein, Gxb is the supply-demand ratio fluctuation index, J is the number of observation time points, u is the number of observation time points, u=1, 2, 3, ..., J, u is a positive integer, and Rpj is the average value of the supply-demand ratio.

[0163] A higher supply-demand ratio fluctuation index indicates that the supply-demand ratio fluctuates greatly at different time points. The larger the supply-demand ratio fluctuation index, the greater the fluctuation in water supply and demand, which may be due to supply-demand imbalance caused by seasonal changes, climate conditions, changes in human activities or other external factors.

[0164] In S5, a comprehensive analysis is conducted on the target area’s dependence on the current water resource allocation model and the fluctuation of water resource supply and demand to assess the stability of water resource use in the target area, specifically:

[0165] The water resource dependence index and the supply-demand ratio fluctuation index are normalized, and the normalized water resource dependence index and the supply-demand ratio fluctuation index are respectively assigned preset proportional coefficients. The water resource utilization stability coefficient is calculated, and the stability of water resource utilization in the target area is evaluated through the water resource utilization stability coefficient.

[0166] The specific implementation method of calculating the water resource utilization stability coefficient is not specifically limited here. Any method that can calculate the water resource utilization stability coefficient by assigning preset proportional coefficients to the normalized water resource dependence index and the supply-demand ratio fluctuation index respectively is sufficient. In order to implement the technical solution of the present invention, the present invention provides a specific method for calculating the water resource utilization stability coefficient.

[0167] The expression of water resource use stability coefficient is: Among them, Szh is the water resource use stability coefficient, θ1 and θ2 are the preset proportional coefficients of the water resource dependence index and the supply-demand ratio fluctuation index, respectively, and θ1 and θ2 are both greater than 0.

[0168] Among them, the setting of preset proportionality coefficients determines the relative importance and contribution of different indicators (such as water resource dependence index and supply-demand ratio fluctuation index) when calculating the water resource use stability coefficient. The setting of these proportionality coefficients is usually based on the following considerations:

[0169] Different regions may have different water resource management strategies and goals. For example, if a region pays more attention to the sustainable use of water resources, it may give a higher weight to the water resource dependence index; while for those regions that attach more importance to the stability of short-term water resource supply, they may pay more attention to the impact of fluctuations in the supply-demand ratio. By analyzing historical data, the performance and fluctuations of various factors in water resource management can be evaluated. This analysis helps to determine which factors have had a greater impact on water resource stability in the past, thereby providing a basis for setting the proportional coefficient. When determining the proportional coefficient, it is also necessary to consider the contribution of each factor to the potential risks of the water resource system. For example, if the impact of supply and demand fluctuations on the water resource system is more serious, it may be necessary to increase the proportional coefficient of the index.

[0170] It can be seen from the calculation formula of the water resource utilization stability coefficient that the larger the water resource utilization stability coefficient, the worse the stability of water resource utilization in the target area.

[0171] According to the stability of water resources use in the target area, spatial planning is carried out for the target area, specifically:

[0172] The water resource utilization stability coefficient threshold is set. The water resource utilization stability coefficient threshold is set by professional and technical personnel in this field based on the size of the water resource utilization stability coefficient calculated above and other actual conditions such as the actual requirements for the stability of water resource utilization in the target area, and will not be repeated here.

[0173] Compare the water resource use stability factor with the water resource use stability factor threshold:

[0174] When the water resource use stability coefficient is less than or equal to the water resource use stability coefficient threshold, a water resource use stability signal is generated; this indicates that the water resource use in the target area is stable during the current assessment period. This means that the supply and demand of water resources is good, there is no significant imbalance between supply and demand, and the management and allocation efficiency of water resources meets the expected standards.

[0175] When the water resource use stability coefficient is greater than the water resource use stability coefficient threshold, a water resource use instability signal is generated; this indicates that the water resource use in the target area is unstable during the current assessment period, and there may be an imbalance between supply and demand, inefficient resource utilization, or water resource shortages caused by environmental changes.

[0176] When a signal of unstable water use is generated, the measures taken include:

[0177] Urgent Assessment and Response: Immediately conduct a detailed water resources assessment to identify specific factors causing instability (e.g., extreme weather events, population growth, increased industrial water use, etc.).

[0178] Adjust and reallocate resources: Based on the assessment results, it may be necessary to temporarily adjust the allocation of water resources to prioritize water supply for domestic use and key industries, while potentially limiting water consumption for certain non-critical uses.

[0179] Implement contingency plans: Activate water emergency response plans, which may include activating alternate water sources, implementing temporary water restrictions, and deploying additional technical and human resources.

[0180] Notification and Communication: Broadly inform the public and relevant stakeholders about the current water resource situation and response measures taken to ensure transparency and public cooperation.

[0181] Adjustment of long-term strategies: If the instability is caused by long-term factors, long-term water resource management and protection strategies need to be reassessed and adjusted.

[0182] Example 2

[0183] The difference between Example 2 of the present invention and Example 1 is that this example introduces a smart national land space planning system based on spatiotemporal big data.

[0184] Figure 2 A structural schematic diagram of a smart national land space planning system based on spatiotemporal big data is given in the present invention. A smart national land space planning system based on spatiotemporal big data includes a resource change assessment module, an utilization efficiency assessment module, an over-exploitation assessment module, an exploitation hidden danger alarm module, a usage stability analysis module and a usage stability assessment module.

[0185] The resource change assessment module analyzes the changing speed of groundwater resources in the target area and assesses the rate of change of groundwater resources in the target area.

[0186] The utilization efficiency assessment module analyzes the relationship between historical production data and water resource consumption in the target area to measure the water utilization efficiency of the land.

[0187] The over-exploitation assessment module evaluates the potential risk of over-exploitation of water resources in the target area through the rate of change of groundwater resources and the water utilization efficiency of the land in the target area.

[0188] If the risk of excessive water exploitation in the target area is serious, the exploitation risk alarm module will sound an alarm; otherwise, the stability analysis module will be used to evaluate the target area's dependence on the current water resource allocation model and the fluctuation of water resource supply and demand.

[0189] Use the stability assessment module to conduct a comprehensive analysis of the target area's dependence on the current water resource allocation model and the fluctuation of water resource supply and demand, and evaluate the stability of water resource use in the target area; based on the stability of water resource use in the target area, carry out spatial planning of the target area.

[0190] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0191] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0192] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0193] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0194] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0195] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0196] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0197] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0198] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0199] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart national land space planning method based on spatiotemporal big data, characterized in that: The steps include: S1: Analyze the change rate of groundwater resources in the target area and evaluate the change rate of groundwater resources in the target area; S2: Analyze the relationship between historical production data and water resource consumption in the target area to measure the water use efficiency of the land; S3: Evaluate the potential risk of over-exploitation of water resources in the target area by the rate of change of groundwater resources and the water utilization efficiency of the land in the target area; S4: If the risk of excessive water exploitation in the target area is serious, an alarm is issued; otherwise, the target area’s dependence on the current water resource allocation model and the degree of fluctuation in water resource supply and demand are evaluated; S5: Conduct a comprehensive analysis of the target area’s dependence on the current water resource allocation model and the degree of fluctuation in water resource supply and demand, and assess the stability of water resource use in the target area; Spatial planning of the target area is carried out based on the stability of water resource use in the target area.

2. According to claim 1, a smart land space planning method based on spatiotemporal big data is characterized in that: Analyze the change rate of groundwater resources in the target area and evaluate the change rate of groundwater resources in the target area, specifically: Set a preset time interval; evenly divide the preset time interval into multiple time periods; Obtain the recharge volume for each time period. The recharge volume refers to the total amount of water supplied to the groundwater system in the target area during the time period. Obtain the evaporation in each time period. The evaporation refers to the amount of water that evaporates directly from the surface of the target area and the soil surface into the atmosphere during the time period. Obtain the output for each time period, where the output refers to the total amount of water removed from the groundwater system in the target area during the time period; Calculate the groundwater level change rate, the expression is: Where Dsv is the rate of change of groundwater level, n is the number of time periods in the preset time interval, i is the number of time periods in the preset time interval, i = 1, 2, 3, ..., n, i is a positive integer, R i is the supply amount in the i-th time period, E i is the evaporation amount in the i-th time period, Q i is the output in the i-th time period.

3. According to claim 2, a smart land space planning method based on spatiotemporal big data is characterized in that: Analyze the relationship between historical production data and water resource consumption in the target area to measure the water use efficiency of the land, specifically: Collect historical production data and water consumption data within the target area; The Cobb-Douglas production function model is used in economics. The model is defined as: T =A*W T α*L T β*K T γ*eξ; Among them, Y T is the output, A is the overall technical efficiency level, W T is the water resource input, L T Represents the labor input, K T represents the amount of capital input, e is the natural number base, α, β, γ are the output elasticities of water resources input, labor input and capital input respectively, ξ is the error term; Calculate the land water use efficiency index, the expression is: Among them, WUE is the land water use efficiency index.

4. According to claim 3, a method for smart land space planning based on spatiotemporal big data is characterized in that: The potential risk of excessive water resource exploitation in the target area is assessed through the change rate of groundwater resources and the water utilization efficiency of the land in the target area. Specifically: Set the groundwater level change rate threshold and compare the groundwater level change rate with the groundwater level change rate threshold: When the groundwater level change rate is greater than the groundwater level change rate threshold, it is determined that the water resource exploitation situation is normal; when the groundwater level change rate is less than or equal to the groundwater level change rate threshold, it is determined that there is a risk of excessive water resource exploitation; Set a land water use efficiency threshold and compare the land water use efficiency index with the land water use efficiency threshold: When the land water utilization efficiency index is greater than the land water utilization efficiency threshold, the water resource utilization efficiency is judged to be normal; when the land water utilization efficiency index is less than or equal to the land water utilization efficiency threshold, the water resource utilization efficiency is judged to be low; When the water resource exploitation situation is normal and the water resource utilization efficiency is normal, it is judged that the risk of excessive water resource exploitation in the target area is low; Otherwise, it is determined that the risk of excessive exploitation of water resources in the target area is serious.

5. According to claim 4, a smart land space planning method based on spatiotemporal big data is characterized in that: If the risk of over-exploitation of water resources in the target area is low, assess the target area's reliance on the current water resource allocation model, specifically: PCA was used to assess the target area's dependence on the current water resource allocation pattern; The data matrix X is first centered, and then the covariance matrix C of the data is calculated, which is defined as: Among them, X T is the transpose of X, m is the number of observations; Calculate the eigenvalues ​​and eigenvectors of the covariance matrix C. The eigenvectors define the directions of the principal components in the data, and the eigenvalues ​​represent the variance in the directions of the principal components. Collect data on water consumption, groundwater levels, precipitation, population, and economic activity; Standardize the data so that the mean of each variable is 0 and the standard deviation is 1; fill or delete observations with missing values; Compute the covariance matrix between variables; Perform eigendecomposition on the covariance matrix and extract eigenvalues ​​and eigenvectors; Select the principal component with large eigenvalue and analyze the loading of each principal component to determine the contribution of each variable to the principal component; Based on the selected principal components, the water resource dependence index is calculated as follows: Among them, WDY is the water resource dependence index, λw is the variance contribution rate of the w-th principal component, pw is the score of the w-th principal component, k represents the number of principal components selected for use in PCA, and w is the number of the principal component selected for use in PCA, w = 1, 2, 3, ..., k, where w is a positive integer.

6. According to claim 5, a method for smart land space planning based on spatiotemporal big data is characterized in that: Assess the volatility of water supply and demand, specifically: Obtain water supply data Su, which refers to the total water supply at each observation time point u; Obtain water demand data Du, which refers to the total water demand at the same observation time point u; Calculate the supply-demand ratio Ru: Calculate the average supply-demand ratio, which is the average of the supply-demand ratios of all observation points; The supply-demand ratio volatility index is calculated by standard deviation, and its expression is: Wherein, Gxb is the supply-demand ratio fluctuation index, J is the number of observation time points, u is the number of observation time points, u=1, 2, 3, ..., J, u is a positive integer, and Rpj is the average value of the supply-demand ratio.

7. The method for smart land space planning based on spatiotemporal big data according to claim 6 is characterized in that: In S5, a comprehensive analysis is conducted on the target area’s dependence on the current water resource allocation model and the fluctuation of water resource supply and demand to assess the stability of water resource use in the target area, specifically: The water resource dependence index and the supply-demand ratio fluctuation index are normalized, and the normalized water resource dependence index and the supply-demand ratio fluctuation index are assigned preset proportional coefficients respectively, and the water resource use stability coefficient is calculated to evaluate the stability of water resource use in the target area; From the calculation formula of the water resource use stability coefficient, it can be seen that the larger the water resource use stability coefficient, the worse the stability of water resource use in the target area; According to the stability of water resources use in the target area, spatial planning is carried out for the target area, specifically: Set a water resource use stability coefficient threshold; compare the water resource use stability coefficient with the water resource use stability coefficient threshold: When the water resource use stability coefficient is less than or equal to the water resource use stability coefficient threshold, it is determined that a water resource use stability signal is generated; When the water resource usage stability coefficient is greater than the water resource usage stability coefficient threshold, it is determined that a water resource usage instability signal is generated.

8. A smart national space planning system based on spatiotemporal big data, used to implement a smart national space planning method based on spatiotemporal big data as described in any one of claims 1 to 7, characterized in that: It includes resource change assessment module, utilization efficiency assessment module, over-exploitation assessment module, mining hidden danger alarm module, use stability analysis module and use stability assessment module; The resource change assessment module analyzes the change rate of groundwater resources in the target area and assesses the change rate of groundwater resources in the target area; The utilization efficiency assessment module analyzes the relationship between historical production data and water resource consumption in the target area to measure the water utilization efficiency of the land; The over-exploitation assessment module assesses the potential risk of over-exploitation of water resources in the target area through the rate of change of groundwater resources and the water utilization efficiency of the land in the target area. If the risk of excessive water resource exploitation in the target area is serious, the exploitation risk alarm module will sound an alarm; otherwise, the stability analysis module will be used to evaluate the target area's dependence on the current water resource allocation model and the fluctuation of water resource supply and demand; Use the stability assessment module to conduct a comprehensive analysis of the target area's dependence on the current water resource allocation model and the fluctuation of water resource supply and demand, and evaluate the stability of water resource use in the target area; Spatial planning of the target area is carried out based on the stability of water resource use in the target area.

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