WO2021103323A1 - 一种历史城市保护发展协同控制方案辅助设计系统 - Google Patents

一种历史城市保护发展协同控制方案辅助设计系统 Download PDF

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WO2021103323A1
WO2021103323A1 PCT/CN2020/075750 CN2020075750W WO2021103323A1 WO 2021103323 A1 WO2021103323 A1 WO 2021103323A1 CN 2020075750 W CN2020075750 W CN 2020075750W WO 2021103323 A1 WO2021103323 A1 WO 2021103323A1
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population
data
development
city
area
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庞峰
董海韬
张鹏
孙波
晁潇潇
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青岛理工大学
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Priority to ZA2021/02648A priority patent/ZA202102648B/en
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F30/00—Computer-aided design [CAD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

Definitions

  • the invention belongs to the technical field of data visualization, and particularly relates to an auxiliary design system for a collaborative control scheme for the protection and development of historical cities.
  • the data assignment process is very cumbersome; subsequent GIS platforms need to perform a large number of overlay operations to obtain the weight layer, and these overlay operations involve the minimum overlap method , It also involves the superposition method of taking the maximum value, which is prone to errors.
  • the present invention provides an auxiliary design system for a collaborative control scheme for the protection and development of historical cities. It can automatically obtain relevant data, data preprocessing and analysis methods according to the needs of users, effectively reducing the large amount of data preparation and preprocessing workload in the early stage of the project.
  • one or more embodiments of the present invention provide the following technical solutions:
  • a server that includes:
  • the data storage subsystem is used to obtain and store various city factor data
  • Method management subsystem used to pre-encapsulate data processing methods, related analysis methods and calculation formulas for multiple urban factor data
  • Data analysis subsystem including:
  • the data acquisition module is used to receive the user's designation of the data area range and data requirements, obtain the relevant city factor data from the data storage subsystem, and obtain the corresponding data processing method from the method management subsystem to process these city factor data;
  • the old city population forecast and environmental population carrying capacity estimation module is used to obtain the urban factor data of the corresponding area in the old city, and match according to the spatial location parameters; receive the user's designation of the basic statistical unit, and obtain the relevant analysis methods for population prediction and environment respectively Population carrying capacity estimation;
  • the new urban area development intensity prediction module obtains the urban factor data of the corresponding area in the suburbs, and matches according to the spatial location parameters; selects the site according to the total population pre-accommodated in the new urban area and the evaluation results of development suitability; In the suburban area, receive the user's designation of basic statistical units, obtain relevant analysis methods to estimate the environmental population carrying capacity, and predict the development intensity based on the population carrying capacity distribution;
  • the old city protection plan formulation module including: according to the old city population forecast results and the environmental population carrying capacity, the estimated population overrun time is obtained, and the development and construction period is predicted according to the new urban development intensity forecast value; according to the old city population's estimated overrun time and the new urban area Development and construction period, calculate the time when the new area starts to be constructed
  • the new urban development intensity prediction module includes:
  • the new urban area auxiliary site selection unit is used to receive the total population pre-accommodated in the new urban area and calculate the area of the pre-opened new area; obtain the city factor data of the corresponding area in the suburbs, and match it according to the spatial location parameters; receive the user’s restrictive factors and the development Designation of non-restrictive factors to conduct development suitability evaluation; combined with the development suitability evaluation result map, generate new urban candidate addresses based on the area of pre-opened new districts; receive user adjustments to candidate addresses to determine the new urban construction area;
  • the new urban area development intensity prediction unit estimates the environmental resource capacity of each basic statistical unit in the new urban area, and estimates the environmental population carrying capacity of the new urban area based on the environmental resource capacity estimation results; according to the distribution map of the environmental population carrying capacity of the new urban area, predict Estimate the total amount of urban development and the scale of construction of various facilities.
  • modules for the formulation of the protection plan for the old city include:
  • the population control index calculation unit based on the population forecast results of the old urban areas of each basic statistical unit and the environmental population carrying capacity, the estimated population overtime value and population decommissioning control indicators;
  • the new urban area development and construction cycle forecast unit which combines the total urban development and the scale of various facilities to predict the development cycle
  • the difference between the population over-limit time value and the new urban area development and construction cycle is the starting time for the construction of the new urban area based on the current time.
  • the urban factor data includes: basic geographic data, socio-demographic data, socio-economic data, natural resources and environmental data, infrastructure design, and spatial system data.
  • the server also includes a rights management subsystem for managing user information and corresponding rights.
  • the data acquisition module also receives a weight assignment file, which is used to assign weights to a series of city factor layers, and obtain multiple raster layers with weight values as pixel values for population prediction analysis and development suitability analysis .
  • the population prediction analysis method includes a population prediction method, an environmental capacity estimation method, and an environmental population carrying capacity estimation method.
  • One or more embodiments provide a user terminal, which is connected to the server in communication, including:
  • City factor data editing module used to upload local city factor data or city factor data in the server to the server after secondary processing
  • the basic statistical unit designation module is used to designate the basic statistical unit for the old city and the new city and send it to the server;
  • Method selection module used to select analysis methods and calculation formulas
  • the visualization module is used to obtain and visualize the data generated in the analysis process.
  • a weight editing module which is used to receive the user's designation of the influence weight value, restrictive factor, and non-restrictive factor of each city factor, and generate a weight assignment file.
  • One or more embodiments provide an auxiliary design system for a collaborative control scheme for the protection and development of historical cities, including the server and the user terminal.
  • the present invention pre-packages the population forecast of the old city, the inherent data of the system and related analysis algorithms on the server side, and cannot be downloaded at will, ensuring the safety of the data; the user uploads the data and related analysis algorithms edited and created by the user through the user terminal. After the server, only the user can use it without authorizing others. On the premise of ensuring data security, it also protects personal intellectual property rights.
  • the server of the present invention can easily and fully obtain the data required for planning and design by establishing a communication connection with the server of the relevant department, avoiding a large amount of data preparation workload in the early stage of the planning project; and it is pre-configured with data preprocessing and layer overlay stages.
  • Commonly used algorithms, and these algorithms can be customized and modified to meet the individual needs of users, avoiding a large amount of data preparation and preprocessing workload in the early stage of the project.
  • the data analysis of the present invention can only be performed on the server side, which effectively prevents data leakage, and also reduces the hardware configuration requirements of the user terminal.
  • the server of the present invention provides a variety of population prediction algorithms for the population prediction stage. Users can select and modify according to the specific conditions of the city. At the same time, in order to reduce the negative impact caused by the city dynamics, it also provides law analysis and result verification. A series of methods are used to assist users in revising the prediction model to obtain a model that can objectively and accurately predict the population.
  • the present invention estimates the environmental population capacity of the old urban area and the new urban area respectively; based on the environmental population capacity and population prediction of the old urban area, determines the population overrun time, and estimates the development intensity of the new urban area based on the environmental population capacity of the new urban area.
  • the start time of the development and construction of the new urban area was estimated to realize the protection of the old urban area.
  • the present invention takes the urban environmental population carrying capacity as the quantitative basis for the collaborative control of the historic city protection planning, constructs the historic city protection planning collaborative control system, and uses the simulation model construction method to logically derive the urban population capacity as the protection of the old city and the new city Quantitative basis for collaborative control of district development.
  • the use of environmental carrying capacity to estimate the time sequence and countermeasures for the overall protection and development of historical cities is scientific and manipulable.
  • Figure 1 is a schematic diagram of a system framework in one or more embodiments of the present invention.
  • Figure 2 is a schematic diagram of a population prediction process in one or more embodiments of the present invention.
  • FIG. 3 is a schematic diagram of the hierarchical division of the old urban area based on the statistical unit of "street neighborhood committee" in one or more embodiments of the present invention
  • Figure 4 shows the population density heat map of the new city development area based on the "geographic grid residential area” unit.
  • This embodiment discloses an auxiliary design system for a collaborative control scheme for the protection and development of historical cities. Including: server and user terminal.
  • the server includes:
  • the data storage subsystem is used for the six major factors of the city. Specifically, it includes storing basic geographic data, social demographic data, socioeconomic data, natural resources and environmental data, infrastructure design, and spatial system data. specifically,
  • Basic geographic data includes administrative division data, digital elevation data, high-resolution remote sensing images, and land use data.
  • the vector graphics data is divided into layers according to land types. In this embodiment, it includes: water bodies, roads, vegetation coverage areas, residential land, park land, etc.
  • the above-mentioned layers are among the three element forms of points, lines, and areas.
  • Each layer of the vector graphics data corresponds to an attribute table, which is used to record all the attributes of each graphics unit on the layer.
  • Socio-demographic data include: total urban population size at the end of the target year, population size of each urban area, average annual population growth rate, urban population density, population spatial distribution, population age structure, population gender structure, population ethnic structure, labor force composition, family population Composition, industrial population composition, population cultural composition, urban floating population scale. All kinds of population including total population and urban population and related basic data, including current and historical series of data within the planning scope, should be based on official statistical data. It mainly includes "Statistical Yearbook", statistical bulletin, census announcement, population sampling survey bulletin, etc.; other relevant data such as public security and family planning departments can be used as the basis and reference for verification.
  • Natural resources and environmental data include: total urban ecological land area, annual standard value of ecological land area per capita, total urban available water resources, standard per capita water consumption, topography, roads, rivers, lakes, nature reserves, basic farmland, and geology , Plants, minerals, climate and other data; specifically can be divided into: land resource data, including agricultural land quality classification, soil database, etc.; water resource data, including the distribution of water resources in the old city, etc.; environmental data, including Environmental pollutant statistics, atmospheric and water environmental quality monitoring data, etc.; ecological data, including the spatial distribution of various vegetation coverage, parks, natural reserves, scenic spots, etc.; climate and meteorological data, Including the coordinates of the city and its surrounding weather stations, as well as data such as average wind speed, strong wind days, quiet wind days, precipitation, and temperature for many years.
  • Infrastructure settings include: total urban road area, target value of per capita road area, total number of primary and secondary school degrees, target value of per capita number of primary and secondary school degrees, total number of hospital beds in medical facilities, target value of per capita hospital beds, total annual electricity supply in the city, and annual per capita Standard amount of electricity.
  • Spatial system data includes: total urban construction land area, urban land classification and planning construction land standards, urban individual construction land standards; per capita urban construction land area, urban construction land structure, per capita total urban construction land quota index, urban per capita land use classification index , Per capita housing area in urban areas.
  • the server establishes a communication connection with the servers of the resources and resources department, the agricultural department, the water conservancy department, the ecological environment department, the meteorological department and other relevant departments, and regularly obtains the latest data from the servers of the corresponding departments.
  • the authority management subsystem is used to manage the access authority of the user terminal. This system can be used for decision-making assistance of government departments, analysis of scientific research projects of planning and design units and universities or research institutes. Therefore, the authority management subsystem receives and stores the registration information of the user terminal.
  • the registration information includes the unit, name, and certificate. Number and other information.
  • the analysis method management subsystem is used to store the preprocessing (missing data processing, normalization, spatial interpolation algorithm, etc.) of the factor data for the six major cities, associating with spatial data, layer overlay, population prediction, environmental carrying capacity, and environment Those skilled in the art can understand the relevant analysis and calculation formulas of population limit carrying capacity, development suitability analysis, urban construction scale, etc., these methods can be stored in the form of code files, and the file name is used to indicate which method is for a certain type of data.
  • the population prediction methods specifically include: two types of mathematical predictions, including comprehensive growth rate method and regression model method; one type of socioeconomic prediction, that is, economic correlation analysis method; and one type of BP neural network model method.
  • P t is the population size of the basic statistical unit in the forecast target year
  • P o is the population size of the basic statistical unit in the base year
  • r is the average annual comprehensive growth rate of the basic statistical unit population
  • n is the predicted number of years (when n>5, take (5 years is a timing period)
  • P t is the predicted population size in the target year t ;
  • Y t is the total GDP predicted in the target year;
  • a and b are parameters
  • the six census data in the study area are used as the original data, and the population size prediction model is established based on the relationship between the changes in the data sets. Iterate repeatedly to obtain a prediction model that conforms to the actual conditions of the research city.
  • Calculation method of environmental carrying capacity Based on the social and environmental conditions of the old city, six models are provided for the estimation of environmental capacity for multi-angle combination. There are three types of capacity research: water resources carrying capacity method, land resource carrying capacity method and environmental capacity method; three types of infrastructure carrying capacity research types: road carrying capacity method, educational facility carrying capacity method, and medical facility carrying capacity method. Specifically:
  • P t is the population scale at the end of the forecast target year; St is the ecological land area in the forecast target year; st is the ecological land area per capita in the forecast target year
  • P t is the population size at the end of the forecast target year
  • D t is the total road area of the forecast target year
  • d t is the road land area per capita in the forecast target year
  • P t set the goal at the end of population size; S t to predict the total number of primary and secondary school degree target at the end; s t to predict the number of primary and secondary school places per capita late goal
  • P t is the population size at the end of the forecast target year
  • B t is the total number of hospital beds at the end of the forecast target year
  • b t is the number of hospital beds per capita in the forecast target year.
  • Result verification method Provide mutual check of multiple prediction models to judge the accuracy of the results. Specifically, two results verification methods are provided: comparative verification method and water resource capacity method.
  • P K is the sample element of the sequence number K;
  • P 1 is the sample element ranked first, also known as the top Urban elements;
  • q is the rank scale index.
  • y is a measure of the local or subsystem
  • x is a measure of the system as a whole
  • b is the allometric growth coefficient
  • Equation 9 the relationship between urban population and area allometric growth can be expressed as:
  • n is the total number of regional units involved in the analysis; x i and x j are respectively the observation values of a certain phenomenon x or a certain attribute characteristic x on spatial and regional units i and j; X is the average value of the research object x; W ij is the spatial weight matrix.
  • n is the total number of regional units involved in the analysis; x i and x j are respectively the observation values of a certain phenomenon x or a certain attribute characteristic x on spatial and regional units i and j; X is the average value of the research object x; W ij is the spatial weight matrix.
  • the remaining population carried by this unit can be calculated. According to the average annual growth rate of the urban population of this unit provided by the census, the time for the population of this unit to reach the environmental limit can be calculated;
  • Su is the population surplus carried by the basic statistical unit
  • St1 is the ultimate environmental population carrying capacity of the basic statistical unit
  • S 0 is the actual population of the basic statistical unit.
  • the average annual growth rate of the urban population in this unit provided by the census is expressed as:
  • the calculation can get the time value t1 for the population of this statistical unit to reach the environmental limit carrying capacity:
  • S t1 is the total population in t1; S 0 is the initial population; ⁇ is the average annual population growth rate; t1 is the time.
  • Se is the population depopulation of the basic statistical unit
  • S 0 is the actual population of the basic statistical unit
  • S t1 is the ultimate environmental population carrying capacity of the basic statistical unit.
  • TF is the comprehensive evaluation value of all non-linear factors
  • Wi is the weight of a single non-restrictive factor
  • Fi is the specific grading assignment of a single factor.
  • the difference between the population over-limit time value t 1 and the new city development and construction period value t 2 is the starting point t development at which the construction of the new city should start based on the current time, which can coordinate the construction sequence of the new and old city as a whole.
  • the formula is expressed as:
  • t 1 is the time value of population over-limit
  • t 2 is the time value of the new city development and construction cycle
  • t development is the time starting point for the construction of the new city.
  • the data analysis subsystem receives data analysis requests from the user terminal, creates analysis tasks for the user terminal, and performs corresponding analysis, including:
  • the city factor data retrieval module is used to retrieve relevant city factor data in the designated area according to the selection of the user terminal, and after receiving the confirmation message from the user terminal, associate the relevant city factor data with the analysis task of the user terminal;
  • the city factor data preprocessing module is used to preprocess the retrieved city factor data. Specifically, for population, socioeconomic data, retrieve the corresponding preprocessing method in the analysis algorithm management subsystem for preprocessing, mainly Including the filling of missing data and data normalization, etc.; for environmental (air pollution, etc.) and meteorological data that only have point values, because these data are spatially continuous, the preprocessing methods mainly include data normalization, spatial Interpolation processing, etc.;
  • the spatial data preparation module is used to associate socio-economic data with administrative division data based on the received high-precision land use data and/or administrative division data, or based on the land use data and/or administrative division data that comes with the system
  • the natural resource environment data is associated with the attribute data of the corresponding layer of the land use data according to the geographic coordinate information;
  • the weighting module is used to receive a weighting file sent by the user terminal, and generating multiple raster layers with weight values as pixel values based on the weighting rules; the weighting file includes weighting for each layer A rule, the weight assignment rule includes the corresponding relationship between the condition to be satisfied and the weight value;
  • the population prediction module of the old city area including:
  • the population prediction unit of the old city area receives the basic statistical unit specified by the user via the user terminal and the year used for population prediction, associates the population data of the corresponding year with the corresponding basic statistical unit, and calls one or more populations specified by the user Prediction method to predict the population size in a specified year; and to receive model modifications made by the user terminal based on the verification result;
  • the result verification unit receives the reference demographic data sent by the user terminal and the designation of the verification method, calculates the verification result, and feeds the verification result back to the user terminal;
  • the environmental population carrying capacity estimation module includes:
  • the environmental capacity estimation unit of the old urban area is used to estimate the corresponding environmental resource capacity of each basic statistical unit in the old urban area;
  • the environmental population carrying capacity estimation unit is used to estimate the environmental population carrying capacity of the old urban area according to the estimation result of the environmental resource capacity
  • the new urban development intensity prediction module includes:
  • the area estimation unit of the new area receives the total population capacity of the new area sent by the user terminal, and calculates the area of the pre-opened new area;
  • the urban resource population capacity estimation unit uses the urban resource population capacity estimation model to estimate the population capacity of the administrative districts around the old city, and ranks and ranks them. Specifically, according to the urban natural basic geographic information data, use the urban resource population capacity estimation theoretical model and GIS application model to conduct a comprehensive assessment of the environmental population carrying capacity;
  • the new urban development suitability evaluation unit receives the user’s designation of restricted and non-restricted factors, conducts development suitability evaluation, and obtains a development suitability evaluation map with development suitability score as the pixel value;
  • the new urban area candidate address generation unit analyzes the quantitative results of "environmental population carrying capacity estimation + environmental development suitability evaluation" as the basis for site selection in the new urban area; comprehensively ranked according to the evaluation results of the environmental carrying capacity conditions in each major area , To obtain the overall candidate area suitable for new city development; comprehensively rank and sort according to the environmental carrying capacity of each basic statistical unit, correlate graphical data and model results, and use three-dimensional visualization methods for intuitive expression, and obtain the expansion area based on the basic statistical unit.
  • Schematic diagram of development suitability evaluation classification accept user's choice and delineate the red line of new urban construction;
  • the urban axis distribution calculation unit sends the distribution map of the ultimate environmental population carrying capacity to the user terminal and performs a three-dimensional visualization simulation, which can present a fuzzy urban axis distribution relationship (primary and secondary axis relationship).
  • This fuzzy evaluation result provides suitable for new district planning. Design quantitative basis for the survival and development needs of urban population;
  • the total urban construction and development of the pre-opened new area is calculated based on the per capita urban construction land area (i.e. urban land standard, m/person); according to the urban per capita land use classification index (m/ Person), can calculate the per capita area of various types of land such as residence, public facilities, industry, road square, external transportation, storage, municipal public facilities, green space, special land, etc., and the product of the population can calculate the construction scale of various functional areas .
  • the per capita urban construction land area i.e. urban land standard, m/person
  • m/ Person urban per capita land use classification index
  • the new area development intensity indicator calculation unit determines the total urban development and the scale of various facilities construction based on the urban population capacity distribution of each basic statistical unit in the new city development area, with the help of urban construction indicators. Form the planning logic of "population accounting + suitability evaluation” ⁇ “total urban development” ⁇ “facility development”. The ultimate carrying capacity of environmental population is positively correlated with the intensity of land and space development;
  • Formulation modules of the protection plan for the old city area including:
  • the population control index calculation unit is used to estimate the ultimate environmental population carrying capacity in each basic statistical unit of the old city. According to the population status of each basic unit, calculate the over-population time value and the population depopulation control index of each unit.
  • the new area development and construction cycle forecasting unit based on the general plan and sub-plans, predicts the development and construction cycle, construction investment, and total amount of materials according to the project construction plan, plans the construction process, and provides data support for the development of the new city;
  • the new area development time prediction module coordinates the overall time sequence of new and old city construction.
  • the difference between the population over-limit time value and the new city development period value is the starting time for the construction of the new city area based on the current time.
  • the server adopts a cloud server.
  • the data upload and data retrieval of the relevant departments are through the encryption and decryption mechanism.
  • the data is only used on the server side. No user can download it at will. This protects the security of the original data;
  • the user opens up an independent storage space to store the data uploaded by the user or processed by the user, the analysis method and the data obtained during the analysis process, so that the user can trace the analysis process.
  • the storage space of each user is limited to the user's own access, and may not be accessed by other users without authorization.
  • the city factor data visualization module is used to retrieve and visualize city factor data from the server according to user requests.
  • City factor data editing module used for secondary processing of city factor data and uploading to the server, for example, to retrieve the data processed by the server for review and revision; to digitize and visualize the retrieved high-resolution remote sensing image data Interpret and obtain high-precision land use data.
  • land use data may also be prepared in advance, and directly uploaded to the server through this module for related subsequent analysis.
  • the weight editing module uses the weight of evidence model to calculate the influence weight value of each city factor, as well as the designation of restrictive factors and non-restrictive factors, and generates weight assignment files.
  • the basic statistical unit designation module is used to designate the basic unit for statistics.
  • the administrative management unit-"street neighborhood committee" is defined as the basic statistical unit.
  • the method selection module is used to select the calculation method used in the analysis process.
  • the model editing module is used to modify the model parameters according to the verification results of the population prediction
  • the visualization module is used to visualize the population prediction results, law analysis results, verification results, environmental resource capacity estimation results, environmental population carrying capacity estimation results, and population control indicators obtained in the analysis process.
  • the visualization can set different visualization forms according to the content to be visualized.
  • this embodiment uses the sixth population census data as the benchmark data to predict the population size in the future years.
  • two or more different prediction methods are selected for prediction respectively, and multiple prediction schemes are obtained by adjusting the parameter assignment in the formula. Since there are many population forecasting methods, each forecasting method has its adaptability, advantages and limitations. It is necessary to choose a forecasting method that conforms to the characteristics of urban population and environmental resources, and according to the principles of easy operation and generalization. Generally choose two models: 1. Universal model; 2. Specific model. The comparative verification method is a universal method with strong adaptability and is suitable for all census regions.
  • the rank-scale index analysis method is used to explore the correlation between elements and sequences in a certain area, reflect the distribution characteristics of urban elements at different levels, and also reflect the concentration or equilibrium degree of elements, and understand the distribution of urban population size and structure. Loose, ideal or concentrated, evaluate whether this population distribution is suitable for urban development; population-area allometric growth analysis method: predict the urban population and area, and calculate the urban population-area allometric scale factor year by year based on the forecast data.
  • the change value of the scaling factor can obtain the change of urban population and area on the time axis; spatial autocorrelation analysis method: reveals the spatial distribution law and internal correlation of the static population distribution in the entire study area and various internal regions.
  • the global index is used to verify the spatial pattern of the entire study area, and the calculation results indicate the overall characteristics of the spatial distribution of the population in the area (that is, the adjacent trend of the high and low density areas of the population distribution); while the local index is used to reflect a certain area on a regional unit.
  • the calculation results indicate the characteristics of the population distribution in each local area (that is, the high and low density of specific areas of population distribution).
  • the comparison verification method is to compare and modify models based on actual deviations, and the water resources capacity method uses the "water resources capacity method" (Equation 6) model, and introduces the relevant values of the target unit for calculation. See whether the calculation result is the same or similar to the model derivation results of other modules to judge the correctness of the multi-dimensional construction of the collaborative control model system.
  • PCPM model multi-angle urban population prediction model
  • one or more of the six models are selected to calculate the environmental capacity and combine them from multiple perspectives to construct a theoretical model (UECE model) for estimating the environmental resource capacity of the old city.
  • UECE model a theoretical model
  • the development suitability evaluation factors are divided into two categories: restrictive factors and non-restrictive factors. Extract roads, rivers, lakes, nature reserves, DEM and land use data from the basic geographic database, convert them into raster data, and then evaluate the suitability of development according to the technical process.
  • the main considerations are the areas that are close to roads, water sources (river, lake) and slopes, and then assign values according to the pros and cons of conditions, and use the Delphi scoring method to comprehensively summarize. Find out the candidates that can be used as the location of the new city through spatial query. Finally, perform overlay analysis based on the distribution maps of restricted factors and non-restricted factors to obtain the development suitability evaluation chart, which provides a reference for the site selection of the new city (DAEM model).
  • the present invention pre-packages the population forecast of the old city, the inherent data of the system and related analysis algorithms on the server side, and cannot be downloaded at will, ensuring the safety of the data; the user uploads the data and related analysis algorithms edited and created by the user through the user terminal. After the server, only the user can use it without authorizing others. On the premise of ensuring data security, it also protects personal intellectual property rights.
  • the server of the present invention can easily and fully obtain the data required for planning and design by establishing a communication connection with the server of the relevant department, avoiding a large amount of data preparation workload in the early stage of the planning project; and it is pre-configured with data preprocessing and layer overlay stages.
  • Commonly used algorithms, and these algorithms can be customized to meet the individual needs of users, avoiding a large amount of data preparation and preprocessing workload in the early stage of planning projects.
  • the data analysis of the present invention can only be performed on the server side, which effectively prevents data leakage, and also reduces the hardware configuration requirements of the user terminal.
  • the server of the present invention provides a variety of population prediction algorithms for the population prediction stage. Users can select and modify according to the specific conditions of the city. At the same time, in order to reduce the negative impact caused by the city dynamics, it also provides law analysis and result verification. A series of methods are used to assist users in revising the prediction model to obtain a model that can objectively and accurately predict the population.
  • modules or steps of the present invention can be implemented by a general-purpose computer device. Alternatively, they can be implemented by a program code executable by the computing device, so that they can be stored in a storage device. The device is executed by a computing device, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps in them are fabricated into a single integrated circuit module for implementation.
  • the present invention is not limited to any specific combination of hardware and software.

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Abstract

一种历史城市保护发展协同控制方案辅助设计系统，包括服务器和用户终端，所述服务器与相关部门服务器建立连接，基于用户需求获取城市因子数据，并且预先封装针对多种城市因子数据的数据处理方法、人口变化监测的相关分析方法；接收用户关于待研究区域和数据的需求，获取相关城市因子数据，并获取相应数据处理方法进行处理；将城市因子数据按照空间位置参数进行匹配；对于老城区和新城区分别进行环境人口容量估计；基于老城区的环境人口容量和人口预测，确定人口超限时间，基于新城区的环境人口容量，估算新城区开发强度，据此进行老城区保护方案的指定，分析过程中得到的数据均可进行可视化。能够降低项目前期的数据准备和预处理工作量。

Description

一种历史城市保护发展协同控制方案辅助设计系统 技术领域
本发明属于数据可视化技术领域，尤其涉及一种历史城市保护发展协同控制方案辅助设计系统。
背景技术
本部分的陈述仅仅是提供了与本公开相关的背景技术信息，不必然构成在先技术。
目前，中国108个国家级历史名城多是政治、经济、文化中心。多呈现出人口高密度、交通拥挤、建筑年久失修、生活服务和市政配套设施不足等严重城市问题。在所推行的“保护古城、开发新城”措施中，新城区选址、规划环节一直缺乏量化依据，“人为决策”导致城市规划缺乏科学性和前瞻性，甚至陷入重复建设的浪费窘态。
目前进行历史老城区保护相关项目工作中，存在以下问题：
尽管二十世纪九十年代已出现环境容量概念(Michael Jacobs,1997)，但国内外关于环境容量的研究都停留在宏观研究层面，未提出确定环境阈值量化指标的具体方法与步骤。
在“人口→用地→设施”的城市保护规划逻辑中，人口规模预测的准确性极大影响了保护规划编制的科学性和合理性。但目前的研究方法多为针对预测模型自身合理性及数理逻辑关系的探讨，缺少根据城市和人口现状及变化特点，进行量体裁衣的模型筛选组合过程。
而对于城市人口最优密度和空间分布等方面，已有的研究模型多是针对城市构成中单一因素的数理推导，未考虑城市各因子间的综合效应。
总而言之，在历史名城保护研究中，尚未出现采用基于新城发展控制模型进行量化研究的先例。
目前的涉及到新城区规划等的规划设计项目，前期均需做大量的数据准备和预处理工作，例如基础地理数据、社会经济数据等，均需到公开网站上进行查询和下载，并且，获取遥感影像、数字高程数据等基础地理数据在查询过程中还需手动设置要获取的数据范围(通过输入经纬度或框选)；获取社会经济数据后，还需人工的将社会经济数据赋予到数字化后的区划数据或土地利用数据上，由于数据类型多样，数据赋值过程十分繁琐；后续GIS平台中获取权重图层时需执行大量的叠加操作，而这些叠加操作中既涉及到取最小值的叠加方法，又涉及到取最大值的叠加方法，容易出错。
发明内容
为克服上述现有技术的不足，本发明提供了一种一种历史城市保护发展协同控制方案辅 助设计系统。能够针对用户的需求自动获取相关数据、数据预处理和分析方法，有效降低了项目前期的大量数据准备和预处理工作量。
为实现上述目的，本发明的一个或多个实施例提供了如下技术方案：
一种服务器，包括：
数据存储子系统，用于获取并存储多种城市因子数据；
方法管理子系统，用于预先封装针对多种城市因子数据的数据处理方法、相关分析方法和计算公式；
数据分析子系统，包括：
数据获取模块，用于接收用户关于数据区域范围和数据需求的指定，从数据存储子系统获取相关城市因子数据，从方法管理子系统获取相应数据处理方法对这些城市因子数据进行处理；
老城区人口预测和环境人口承载量估算模块，用于获取老城区相应区域的城市因子数据，按照空间位置参数进行匹配；接收用户关于基础统计单元的指定，获取相关分析方法分别进行人口预测和环境人口承载量估算；
新城区开发强度预测模块，获取城郊相应区域的城市因子数据，按照空间位置参数进行匹配；根据获取的新城区预容纳的人口总量和开发适宜性评价结果进行选址；对于选址范围内的城郊区域，接收用户关于基础统计单元的指定，获取相关分析方法分别进行环境人口承载量估算，并根据人口承载量分布预测开发强度；
老城区保护方案制定模块，包括：根据老城区人口预测结果和环境人口承载量得到人口预计超限时间，根据新城区开发强度预测值预测开发建设周期；根据老城区人口预计超限时间和新城区开发建设周期，计算新区开始建设时间。
进一步地，新城区开发强度预测模块，具体包括：
新城区辅助选址单元，用于接收新城区预容纳的人口总量，推算预开辟新区面积；获取城郊相应区域的城市因子数据，按照空间位置参数进行匹配；接收用户关于开发的限制性因子和非限制性因子的指定，进行开发适应性评价；结合开发适宜性评价结果图，根据预开辟新区面积，生成新城区候选地址；接收用户对候选地址的调整，确定新城区建设区域；
新城区开发强度预测单元，根据对新城区各基础统计单元相应的环境资源容量进行估算，以及根据环境资源容量估算结果，估算新城区环境人口承载量；根据新城区环境人口承载量分布图，预估城市开发总量及各类设施建设规模。
进一步地，老城区保护方案制定模块，具体包括：
人口调控指标计算单元，基于各基础统计单元老城区人口预测结果和环境人口承载量，预计人口超限时间值和人口疏解控制指标；
新城区开发建设周期预测单元，结合城市开发总量及各类设施建设规模，预测开发周期；
新城区开发时间预测单元，人口超限时间值与新城区开发建设周期的差值，即为从当前时间为基准，开始进行新城区建设的时间起点。
进一步地，所述城市因子数据包括：基础地理数据、社会人口数据、社会经济类数据、自然资源环境类数据、基础设施设局、空间系统数据。
进一步地，所述服务器还包括权限管理子系统，用于管理用户信息和相应权限。
进一步地，所述数据获取模块还接收权重赋予文件，用于对一系列城市因子图层赋予权重，得到以权重值为像素值的多个栅格图层用于人口预测分析和开发适宜性分析。
进一步地，所述人口预测分析方法包括人口预测方法、环境容量估计方法和环境人口承载量估计方法。
一个或多个实施例提供了一种用户终端，与所述服务器通信连接，包括：
城市因子数据编辑模块，用于将本地城市因子数据或针对服务器中城市因子数据经二次加工上传至服务器；
基础统计单元指定模块，用于针对老城区和新城区分别指定基础统计单元并发送至服务器；
方法选择模块，用于针对分析方法和计算公式进行选择；
可视化模块，用于获取分析过程中产生的数据并进行可视化。
进一步地，还包括权重编辑模块，用于接收用户针对各城市因子影响权重值和限制性因子、非限制性因子的指定，生成权重赋予文件。
一个或多个实施例提供了一种一种历史城市保护发展协同控制方案辅助设计系统，包括所述的服务器和所述的用户终端。
以上一个或多个技术方案存在以下有益效果：
本发明将老城区人口预测、系统固有的数据和相关分析算法均在服务器端预先封装，不能被随意下载，保证了数据的安全；用户通过用户终端自行编辑和创建的数据和相关分析算法上传到服务器后，再未对他人进行授权的前提下仅用户本人可以使用，在保证了数据安全的前提下，还保护了个人的知识产权。
本发明的服务器通过与相关部门服务器建立通信连接，能够方便且充分的获取规划设计所需数据，避免了规划项目前期的大量数据准备工作量；并且预先配置了数据预处理、图层 叠加阶段的常用算法，且这些算法均可进行自定义修改，以满足用户个性化的需求，避免了项目前期的大量数据准备和预处理工作量。
本发明的数据的分析仅能够在服务器端进行，有效防止了数据的泄露，并且，对用户终端的硬件配置要求也降低。
本发明的服务器为人口预测阶段提供了多种人口预测算法，用户可根据城市的具体情况进行选择和修正，同时，为了消减因城市动态性带来的消极影响，还提供了规律分析、结果验证等一系列方法，用于辅助用户对预测模型进行修正，以得到能够客观准确预测人口的模型。
本发明通过在划定了基础统计单元(街道居委会)的基础上，在各基础统计单元进行了人口预测、环境容量估计和环境人口承载量的估算，这种方法综合考虑了城市的影响因子，在实现了微观定量的人口预测、环境容量估计和环境人口承载量估算的同时，还实现了这些指标的空间分布。
本发明通过分别对于老城区和新城区进行环境人口容量估计；基于老城区的环境人口容量和人口预测，确定人口超限时间，基于新城区的环境人口容量，估算新城区开发强度，据此进行了新城区开发建设的开始时间估计，以实现老城区的保护。
本发明将城市环境人口承载量作为历史名城保护规划的协同控制的量化依据，构建了历史名城保护规划协同控制系统，将采用仿真模型构建法对城市人口容量进行逻辑推导，作为老城区保护与新城区发展协同控制的量化依据。利用环境承载量估算推演历史城市整体保护开发时序及对策，具有科学性和可操作性。
附图说明
构成本发明的一部分的说明书附图用来提供对本发明的进一步理解，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。
图1为本发明一个或多个实施例中系统框架示意图；
图2为本发明一个或多个实施例中人口预测流程示意图；
图3为本发明一个或多个实施例中老城区以“街道居委会”为基础统计单元的等级区划示意图；
图4为新城拓展区以“地理格网住区板块”为基础统计单元的人口密度热力图。
具体实施方式
应该指出，以下详细说明都是示例性的，旨在对本发明提供进一步的说明。除非另有指明，本文使用的所有技术和科学术语具有与本发明所属技术领域的普通技术人员通常理解的 相同含义。
需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本发明的示例性实施方式。如在这里所使用的，除非上下文另外明确指出，否则单数形式也意图包括复数形式，此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合。
在不冲突的情况下，本发明中的实施例及实施例中的特征可以相互组合。
实施例一
本实施例公开了一种历史城市保护发展协同控制方案辅助设计系统。包括：服务器和用户终端。
所述服务器包括：
数据存储子系统，用于城市六大因子数据。具体包括存储基础地理数据、社会人口数据、社会经济类数据、自然资源环境类数据、基础设施设局、空间系统数据。具体地，
基础地理类数据包括行政区划数据、数字高程数据、高分辨率遥感影像，以及土地利用数据。其中，所述矢量图形数据根据地类种类划分图层，本实施例中包括：水体、道路、植被覆盖区域、居住用地、公园用地等，上述图层为点、线、面三种要素形式中的一种或多种。所述矢量图形数据的每个图层均对应一个属性表，用于记录该图层上每个图形单元的所有属性。
社会人口数据包括：目标年末城市总人口规模、各城区人口规模、人口年均增长率、城区人口密度、人口空间分布状况、人口年龄结构、人口性别结构、人口民族结构、劳动人口构成、家庭人口构成、产业人口构成、人口文化构成、城市流动人口规模。针对规划范围的包括总人口、城市人口在内的各类人口以及相关基础数据，包括现状和历史系列数据，应以官方公布的统计数据为主。主要包括《统计年鉴》、统计公报、人口普查公告、人口抽样调查公报等；其它如公安和计生部门的有关数据，可作为校核的依据和参考。
社会经济类数据包括：城市年GDP总量、年人均GDP目标值、城市劳动力需求总量、产业结构系统数据。
自然资源环境类数据包括：城市生态用地总面积、年人均生态用地面积标准值、城市可供水资源总量、人均用水标准量、地形地貌、道路、河流、湖泊、自然保护区、基本农田、地质、植物、矿藏、气候等数据；具体可划分为：土地资源类数据，包括农用地质量分等、土壤数据库等；水资源类数据，包括该老城区的水资源分布等；环境类数据，包括环境污染物统计数据、大气和水环境质量监测数据等；生态类数据，包括各类植被覆盖的空间分布、 公园空间分布、自然保护区空间分布、风景名胜区空间分布数据等；气候气象类数据，包括所述城区及其周边气象站站点坐标，以及多年的平均风速、大风日数、静风日数、降水量、气温等数据。
基础设施设局包括：城市道路总面积、人均道路面积目标值、中小学学位总数、人均中小学学位数目标值、医疗设施的病床总数、人均病床数目标值、城市年供电总量、年人均用电标准量。
空间系统数据包括：城市建设用地总面积、城市用地分类与规划建设用地标准、城市单项建设用地标准；人均城市建设用地面积、城市建设用地结构、人均城市建设总用地定额指标、城市人均用地分类指标、城区人均住房面积。
所述服务器与资源资源部门、农业部门、水利部门、生态环境部门、气象部门等相关部门的服务器建立通信连接，定期从相应部门的服务器获取最新数据。
权限管理子系统，用于对用户终端的访问权限进行管理。本系统可用于政府部门的辅助决策、规划设计单位和高校或研究院所的科研项目分析，因此，权限管理子系统接收用户终端的注册信息并进行存储，所述注册信息包括单位、姓名、证件号码等信息。
分析方法管理子系统，用于存储针对六大城市因子数据的预处理(缺失数据处理、归一化、空间插值算法等)、关联到空间数据、图层叠加以及人口预测、环境承载力、环境人口极限承载量、开发适宜性分析、城市建设规模等的相关分析和计算公式，本领域技术人员可以理解，这些方法可以代码文件的形式存储，用文件名表示针对某类数据的何种方法。
其中，人口预测方法具体包括：其中2种数学预测类，包括综合增长率法和回归模型法；1种社会经济预测类，即经济相关分析法；1种BP神经网络模型法。
数学预测类：
(1)综合增长率法：
P t＝P 0×(1+r) n          (1)
式中：P t为预测目标年基础统计单元人口规模；P o为基准年基础统计单元人口规模；r为基础统计单元人口年均综合增长率，n为预测年限(当n＞5时，以5年为一个计时段)
(2)回归模型法：
P t＝a+b·T      (2)
P t＝A·e T+b     (3)
P t＝a+b·ln(T)      (4)
式中:为P t第T年人口预测规模；a和b为参数，一般采用最小二乘法进行拟合 社会经济预测类，即经济相关分析法：
P t＝a+b ln(Y t)      (5)
式中：P t为目标t年人口预测规模；Y t为预测目标年GDP总量；a、b为参数
BP神经网络预测模型法：
将研究区域六次人口普查数据作为原始数据,依据各次数据集间变化关系，建立人口规模预测模型。反复迭代，获得符合研究城市实际状况的预测模型。
环境承载力的计算方法：基于老城区社会环境状况，环境容量的估算提供了6种模型以便进行多角度组合。其中3种容量研究类：水资源承载力法、土地资源承载力法和环境容量法；3种基础设施承载力研究类：道路承载力法、教育设施承载力法、医疗设施承载力法。具体包括：
1.容量研究类
水资源承载力法：
P t＝W t/w t      (6)
土地资源承载力法:
Figure PCTCN2020075750-appb-000001
式中：P为人口容量；L为建设用地最大规模；L a为人均建设用地面积标准
环境容量法：
Figure PCTCN2020075750-appb-000002
式中：P t为预测目标年末人口规模；S t为预测目标年生态用地面积；s t为预测目标年人均生态用地面积
2.基础设施承载力研究类
道路承载力法：
Figure PCTCN2020075750-appb-000003
式中：P t为预测目标年末人口规模；D t为预测目标年道路总面积；d t为预测目标年人均道路用地面积
教育设施承载力法：
P t＝S t/s t     (10)
式中：P t为预测目标年末人口规模；S t为预测目标年末中小学学位总数；s t为预测目标年 末人均中小学学位数
医疗设施承载力法：
P t＝B t/b t     (11)
式中：P t为预测目标年末人口规模；B t为预测目标年末病床总数；b t为预测目标年人均病床数。
结果验证方法：提供多种预测模型的相互校核，判断结果的准确性。具体地，提供2种结果验证法：对比验证法和水资源容量法。
对比验证法
利用第四、五次人口普查数据关系构建数学模型，将运算结果同第五、六次人口普查结果数据进行比对，校验正确性。
水资源容量法：同式(6)。
规律分析模型的相关方法：提供了2种非线性几何数学分析类和1种空间统计分析类。
1)2种非线性几何分形数学分析类，包括位序-规模指标分析法、人口-面积异速生长分析法；
位序-规模指标分析法：
P k＝P 1K -q     (12)
式中：K为样本序号(K＝1，2，…，N，N为系统中样本总数)；P K为序号K的样本要素；P 1为排位第一的样本要素，亦称为首位城市要素；q为位序规模指数。
对式7取对数：
lnP (k)＝lnP 1-qlnk       (13)
式中：q为与区域条件和发展阶段有关的常数
人口-面积异速生长分析法：
Figure PCTCN2020075750-appb-000004
式中：y为局部或子系统的某种测定；x为系统整体的某种测度；b为异速生长系数。
根据式9可将城市人口-面积异速生长关系表示为:
A＝aP b    (15)
式中：A为城区面积；P为该城区人口；b为标度指数。
2)1种空间统计分析类，即人口空间自相关分析法(包括全域空间相关性指标Moran I、局域空间相关性指标Local Moran's I与Getis's Gi)。
(1)全域空间相关性指标Moran I：
Figure PCTCN2020075750-appb-000005
式中n为参与分析的区域单元的总数；x i和x j分别表示为某现象x或某属性特征x在空间地域单元i和j上的观测值；X为研究对象x的平均值；W ij是空间权重矩阵。
(2)局域空间相关性指标Local Moran's I与Getis's G i：
Figure PCTCN2020075750-appb-000006
Figure PCTCN2020075750-appb-000007
式中n为参与分析的区域单元的总数；x i和x j分别表示为某现象x或某属性特征x在空间地域单元i和j上的观测值；X为研究对象x的平均值；W ij是空间权重矩阵。
人口超限时间值和各单元的人口疏解控制指标计算方法：
(1)根据老城区各基础单元人口状况，计算人口超限时间值t1
将老城区各基础统计单元实际人口量与本单元环境人口极限承载量进行对比，可核算本单元承载人口余量。根据人口普查提供的本单元城市人口年均增长率，可计算本单元人口达到环境极限承载量时间；
将老城区各基础统计单元实际人口量S 0与本单元环境人口极限承载量S t1进行对比。当S t1＞S 0时，本统计单元承载人口余量为：
S u＝S t1-S 0      (19)
式中：S u为基础统计单元承载人口余量；S t1为基础统计单元环境人口极限承载量；S 0为基础统计单元实际人口量。
人口普查提供的本单元城市人口年均增长率表达为：
Figure PCTCN2020075750-appb-000008
式中：δ为人口年均增长率；t1为时间；S t1为t1年人口总量；S 0为初始人口
计算可得本统计单元人口达到环境极限承载量时间值t1：
S t1＝S 0(1+δ) t1      (21)
式中：S t1为t1年人口总量；S 0为初始人口；δ为人口年均增长率；t1为时间。
(2)可确定各基础统计单元人口疏解量指标Se值
通过4个阶段、5个步骤的逻辑推演，可对老城区人口疏解提供量化数据，因地制宜地制定城市保护策略。
S e＝S 0-S t1      (22)
式中：Se为基础统计单元人口疏解量；S 0为基础统计单元实际人口量；S t1为基础统计单元环境人口极限承载量。
基于城郊缺少人力开发的自然环境状况，选择2种容量研究类，即水资源承载力法和环境容量法。
水资源容量法：同式(6)。
环境容量法：同式(8)。
新城区开发适宜性评价公式：
根据自然、社会经济等因子对于选址影响程度，将新城开发适宜性评价因子分为限制性因子和非限制性因子。利用德尔菲打分法综合汇总：
Figure PCTCN2020075750-appb-000009
式中:TF为所有非线性因子综合评估值；Wi某单项非限制性因子权重；Fi为某单项因子具体分级赋值。
以老城区保护为目的的新城区开发实施措施公式集：
人口超限时间值t 1与新城开辟建设周期值t 2的差值，即为从当前时间为基准，应开始建设新城区的时间起点t开辟，可整体协调新、老城区建设时序。公式表达为：
t 1-t 2＝t 开辟      (24)
式中：t 1为人口超限时间值；t 2为新城开辟建设周期时间值；t开辟为建设新城区的时间起点。
数据分析子系统，接收用户终端的数据分析请求，为该用户终端创建分析任务，执行相应分析，具体包括：
城市因子数据调取模块，用于根据用户终端的选择调取指定区域内的相关城市因子数据，待接收到用户终端的确认消息后，将相关城市因子数据关联到该用户终端的分析任务；
城市因子数据预处理模块，用于对调取的城市因子数据进行预处理，具体地，针对人口类、社会经济类数据，调取分析算法管理子系统中相应的预处理方法进行预处理，主要包括缺失数据的填充和数据归一化等；对于只具有点值的环境类(大气污染等)、气象类数据，由于这些数据空间上具有连续性，预处理方法主要包括数据归一化、空间插值处理等；
空间数据准备模块，用于基于接收的高精度土地利用数据和/或行政区划数据，或者基于 系统自带的土地利用数据和/或行政区划数据，将社会经济类数据根据区划关联到行政区划数据的属性数据中，将自然资源环境类数据根据地理坐标信息关联到土地利用数据相应图层的属性数据中；
权重赋予模块，用于接收用户终端发送的权重赋予文件，基于所述权重赋予规则生成以权重值为像素值的多个栅格图层；所述权重赋予文件中包括针对各图层的权重赋予规则，所述权重赋予规则包括需满足的条件与权重值的对应关系；
老城区人口预测模块，包括：
老城区人口预测单元，接收用户经由用户终端指定的基础统计单元，以及用于进行人口预测的年份，将相应年份的人口数据关联到相应的基础统计单元中，调用用户指定的一个或多个人口预测方法，对指定年份的人口规模进行预测；以及，接收用户终端根据验证结果进行的模型修改；
结果验证单元，接收用户终端发送的参考人口统计数据，以及关于验证方法的指定，计算验证结果，并将验证结果反馈至用户终端；
环境人口承载量估算模块，包括：
老城区环境容量估算单元，用于对老城区各基础统计单元相应的环境资源容量进行估算；
环境人口承载量估算单元，用于根据环境资源容量估算结果，估算老城区环境人口承载量；
新城区开发强度预测模块，包括：
新区面积估算单元，接收用户终端发送的新区容纳人口总量，推算预开辟新区面积；
城市资源人口容量估算单元，运用城市资源人口容量估算模型，对老城区周边行政区进行人口容量估算，并分级、排序。具体地，根据的城市自然基础地理信息数据，运用城市资源人口容量估算理论模型和GIS应用模型，进行环境人口承载量综合评估；
新城区开发适宜性评价单元，接收用户关于限制因子和非限制因子的指定，进行开发适宜性评价，得到以开发适宜性评分为像素值的开发适宜性评价图；
新城区候选地址生成单元，以“环境人口承载量估算+环境开发适宜性评价”的量化结果分析，作为新城区选址依据；根据各大区域环境承载量条件优劣评价结果，进行综合分级排序，获取适宜新城开发的候选总体区域；根据各基础统计单元环境承载量条件优劣综合分级排序，关联图形数据与模型结果，采用三维可视化手段进行直观表达，获取以基础统计单元为单位的拓展区开发适宜性评价分级示意图；接收用户的选择，划定新城区建设红线；
围内各基础统计单元环境人口极限承载量；
城市轴线分布计算单元，将环境人口极限承载量分布图发送至用户终端并进行三维可视化模拟，可呈现模糊的城市轴线分布关系(主、次轴线关系)，此模糊评价结果为新区规划提供了适合城市人口生存发展需求的设计量化依据；
具体地，得到拓展区容纳人口总量预测值后，依据人均城市建设用地面积(即城市用地标准，m/人)，推算预开辟新区城市建设开发总量；按照城市人均用地分类指标(m/人)，可计算居住、公共设施、工业、道路广场、对外交通、仓储、市政公共设施、绿地、特殊用地等各类用地的人均占有面积，与人口数量的乘积可核算各类功能区域建设规模。
新区开发强度指标测算单元，根据新城拓展区各基础统计单元的城市人口容量分布状况，借助城市建设指标，确定城市开发总量及各类设施建设规模。形成“人口核算+适宜性评价”→“城市开发总量”→“设施开发”的规划逻辑。环境人口极限承载量和土地、空间开发强度量呈正相关；
老城区保护方案制定模块，包括：
人口调控指标计算单元，用于估算老城区各基础统计单元内环境人口极限承载量。根据各基础单元人口状况，计算人口超限时间值和各单元的人口疏解控制指标。
新区开发建设周期预测单元，在总规、分规基础上，根据工程建设方案，对开发建设周期、建设投资、材料总量进行预测，对建设工序进行计划安排，为新城开发提供数据支持；
新区开发时间预测模块，整体协调新、老城区建设时序，人口超限时间值与新城开辟建设周期值的差值，即为从当前时间为基准，开始进行新城区建设的时间起点。
本实施例中，所述服务器采用云服务器。相关部门的数据的上传和数据的调取均通过加解密机制，数据仅在服务器端使用，任何用户不能随意下载，保护了原始数据的安全；并且，针对各个用户终端的个性化分析，为各个用户开辟独立的存储空间，存储用户上传或经用户加工的数据、分析方法以及分析过程中所得到的数据，以便于用户追溯分析过程。并且，各个用户的存储空间仅限于该用户自己访问，未经授权不得由其他用户访问。
用户终端，包括：
城市因子数据可视化模块，用于根据用户请求从服务器调取城市因子数据并进行可视化。
城市因子数据编辑模块，用于针对城市因子数据进行二次加工并上传至服务器，例如，调取经服务器处理的数据进行审核和修订；基于调取的高分辨率遥感影像数据进行数字化、目视解译，得到高精度土地利用数据。本领域技术人员可以理解，所述土地利用数据也可以是预先准备好的，通过该模块直接上传至服务器用于相关的后续分析。
权重编辑模块，运用证据权法模型计算各城市因子影响权重值，以及限制性因子和非限 制性因子的指定，并生成权重赋予文件。
表1
Figure PCTCN2020075750-appb-000010
基础统计单元指定模块，用于指定用于统计的基础单元，本实施例界定以行政管理单元——“街道居委会”为基础统计单元。
方法选定模块，用于选择分析过程中所采用的计算方法。
模型编辑模块，用于根据人口预测的验证结果，对模型参数进行修改；
可视化模块，用于对分析过程中得到的人口预测结果、规律分析结果、验证结果、环境资源容量估计结果、环境人口承载量估算结果和人口调控指标进行可视化。
所述可视化可根据待可视化的内容分别设定不同的可视化形式。
通过对分析过程中产生的数据并进行可视化，用户可以方便的对比不同分析方法和不同参数之间的差异，了解或学习不同分析方法和不同参数对人口预测或环境资源容量估算的影响，有助于选择和修正得到更准确的模型。
作为一个实例，本实施例以第六次人口普查数据作为基准数据，对未来年份人口规模预测。根据待分析老城区选取两类以上不同预测方法分别进行预测，并且通过调整公式中的参数赋值，获得多个预测方案。由于人口预测方法很多，每种预测方法都有它的适应条件、优点和局限性，需要选择符合城市人口、环境资源特点，按照易于操作、可推广原则，选择预测方法。一般选择两种模型：1.普适性模型；2.专属性模型。对比验证法是一个普适性方法，适应性强，适合于所有人口普查地区，属于普适性模型；专属性模型，比如水资源容量法适用于受水资源条件约束较大的城市(本次研究欲以新疆喀什为样本)。而土地资源容量法就不适合新疆这种地广人稀的城市环境。所以根据不同拟定的城市环境要有所调整，选择适合 的模型。
采用多种模型进行人口预测后，为确保模型推导结论的全面性和正确性，还对人口分布变化规律进行分析。其中，位序～规模指标分析法，用于探寻一定区域内要素与序列的相关关系，反映城市要素在不同级别中的分布特征，也可以反映要素集中或均衡程度，了解城市人口规模结构分布是松散、理想还是集中，评价这种人口分布情况是否适合城市发展；人口-面积异速生长分析法：对城市人口、面积进行预测，根据预测数据逐年计算城市人口～面积异速标度因子，根据标度因子变化值，可获取城市人口、面积在时间轴上的变化情况；空间自相关性分析法：揭示整个研究区以及内部各个区域之间人口静态分布的空间分布规律及内在相互关联性。全域指标用于验证整个研究区域的空间模式，计算结果表明该区域人口空间分布的整体特征(即人口分布高、低密度区域的邻接趋势)；而局域指标用于反映一个区域单元上的某种地理现象或某一属性值与邻近区域单元上同一现象或属性值的相关程度，计算结果表明各局部区域人口分布特征(即人口分布的具体区块的高、低密度状况)。
采用多种模型进行人口预测后，还通过对比验证法或水资源容量法对各预测结果进行验证。具体地，对比验证法是根据实际偏差进行模型对比和修正，水资源容量法是用“水资源容量法”的(式6)模型，引入目标单元的相关数值进行计算。看计算结果是否与其他模块的模型推导结果相同或相近，来评判多维度构建协同控制模型体系的正确性。
通过上述定量预测(外因分析)、规律分析(内因分析)和结果验证三个过程，得到多角度城市人口预测模型(PCPM模型)。
基于老城区社会环境状况，从6种模型选取一个或多个分别计算环境容量进行多角度组合，构建城市老城区环境资源容量估算理论模型(UECE模型)。
根据自然、社会经济等因子对于选址的影响程度，将开发适宜性评价因子分为两大类：限制性因子和非限制性因子。从基础地理数据库中提取道路、河流、湖泊、自然保护区、DEM及土地利用数据，将其转换为栅格数据，然后根据技术流程对开发适宜性进行评价。对于非限制性因子，主要考虑与道路、水源地(河流、湖泊)距离近以及坡度较缓的区域，然后根据条件优劣分级赋值，利用德尔菲打分法综合汇总。通过空间查询方式找出可作为新城选址的候选项。最后根据限制因子和非限制因子分布图进行叠置分析，获取开发适宜性评价图，为新城选址提供参考依据(DAEM模型)。
本发明将老城区人口预测、系统固有的数据和相关分析算法均在服务器端预先封装，不能被随意下载，保证了数据的安全；用户通过用户终端自行编辑和创建的数据和相关分析算法上传到服务器后，再未对他人进行授权的前提下仅用户本人可以使用，在保证了数据安全 的前提下，还保护了个人的知识产权。
本发明的服务器通过与相关部门服务器建立通信连接，能够方便且充分的获取规划设计所需数据，避免了规划项目前期的大量数据准备工作量；并且预先配置了数据预处理、图层叠加阶段的常用算法，且这些算法均可进行自定义修改，以满足用户个性化的需求，避免了规划项目前期的大量数据准备和预处理工作量。
本发明的数据的分析仅能够在服务器端进行，有效防止了数据的泄露，并且，对用户终端的硬件配置要求也降低。
本发明的服务器为人口预测阶段提供了多种人口预测算法，用户可根据城市的具体情况进行选择和修正，同时，为了消减因城市动态性带来的消极影响，还提供了规律分析、结果验证等一系列方法，用于辅助用户对预测模型进行修正，以得到能够客观准确预测人口的模型。
本领域技术人员应该明白，上述本发明的各模块或各步骤可以用通用的计算机装置来实现，可选地，它们可以用计算装置可执行的程序代码来实现，从而，可以将它们存储在存储装置中由计算装置来执行，或者将它们分别制作成各个集成电路模块，或者将它们中的多个模块或步骤制作成单个集成电路模块来实现。本发明不限制于任何特定的硬件和软件的结合。
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。
上述虽然结合附图对本发明的具体实施方式进行了描述，但并非对本发明保护范围的限制，所属领域技术人员应该明白，在本发明的技术方案的基础上，本领域技术人员不需要付出创造性劳动即可做出的各种修改或变形仍在本发明的保护范围以内。

Claims (10)

  1. 一种服务器，其特征在于，包括：
    数据存储子系统，用于获取并存储多种城市因子数据；
    方法管理子系统，用于预先封装针对多种城市因子数据的数据处理方法、相关分析方法和计算公式；
    数据分析子系统，包括：
    数据获取模块，用于接收用户关于数据区域范围和数据需求的指定，从数据存储子系统获取相关城市因子数据，从方法管理子系统获取相应数据处理方法对这些城市因子数据进行处理；
    老城区人口预测和环境人口承载量估算模块，用于获取老城区相应区域的城市因子数据，按照空间位置参数进行匹配；接收用户关于基础统计单元的指定，获取相关分析方法分别进行人口预测和环境人口承载量估算；
    新城区开发强度预测模块，获取城郊相应区域的城市因子数据，按照空间位置参数进行匹配；根据获取的新城区预容纳的人口总量和开发适宜性评价结果进行选址；对于选址范围内的城郊区域，接收用户关于基础统计单元的指定，获取相关分析方法分别进行环境人口承载量估算，并根据人口承载量分布预测开发强度；
    老城区保护方案制定模块，包括：根据老城区人口预测结果和环境人口承载量得到人口预计超限时间，根据新城区开发强度预测值预测开发建设周期；根据老城区人口预计超限时间和新城区开发建设周期，计算新区开始建设时间。
  2. 如权利要求1所述的服务器，其特征在于，新城区开发强度预测模块，具体包括：
    新城区辅助选址单元，用于接收新城区预容纳的人口总量，推算预开辟新区面积；获取城郊相应区域的城市因子数据，按照空间位置参数进行匹配；接收用户关于开发的限制性因子和非限制性因子的指定，进行开发适应性评价；结合开发适宜性评价结果图，根据预开辟新区面积，生成新城区候选地址；接收用户对候选地址的调整，确定新城区建设区域；
    新城区开发强度预测单元，根据对新城区各基础统计单元相应的环境资源容量进行估算，以及根据环境资源容量估算结果，估算新城区环境人口承载量；根据新城区环境人口承载量分布图，预估城市开发总量及各类设施建设规模。
  3. 如权利要求1所述的服务器，其特征在于，老城区保护方案制定模块，具体包括：
    人口调控指标计算单元，基于各基础统计单元老城区人口预测结果和环境人口承载量，预计人口超限时间值和人口疏解控制指标；
    新城区开发建设周期预测单元，结合城市开发总量及各类设施建设规模，预测开发周期；
    新城区开发时间预测单元，人口超限时间值与新城区开发建设周期的差值，即为从当前时间为基准，开始进行新城区建设的时间起点。
  4. 如权利要求1所述的一种服务器，其特征在于，所述城市因子数据包括：基础地理数据、社会人口数据、社会经济类数据、自然资源环境类数据、基础设施设局、空间系统数据。
  5. 如权利要求1所述的一种服务器，其特征在于，所述服务器还包括权限管理子系统，用于管理用户信息和相应权限。
  6. 如权利要求1所述的一种服务器，其特征在于，所述数据获取模块还接收权重赋予文件，用于对一系列城市因子图层赋予权重，得到以权重值为像素值的多个栅格图层用于人口预测分析和开发适宜性分析。
  7. 如权利要求1所述的一种服务器，其特征在于，所述人口预测分析方法包括人口预测方法、环境容量估计方法和环境人口承载量估计方法。
  8. 一种用户终端，其特征在于，与权利要求1-7任一项所述服务器通信连接，包括：
    城市因子数据编辑模块，用于将本地城市因子数据或针对服务器中城市因子数据经二次加工上传至服务器；
    基础统计单元指定模块，用于针对老城区和新城区分别指定基础统计单元并发送至服务器；
    方法选择模块，用于针对分析方法和计算公式进行选择；
    可视化模块，用于获取分析过程中产生的数据并进行可视化。
  9. 如权利要求6所述的一种用户终端，其特征在于，还包括权重编辑模块，用于接收用户针对各城市因子影响权重值和限制性因子、非限制性因子的指定，生成权重赋予文件。
  10. 一种一种历史城市保护发展协同控制方案辅助设计系统，其特征在于，包括如权利要求1-7任一项所述的服务器和如权利要求8-9任一项所述的用户终端。
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