CN-122021347-A - Urban economic horizontal space simulation method and equipment

CN122021347ACN 122021347 ACN122021347 ACN 122021347ACN-122021347-A

Abstract

The application provides a city economic level space simulation method and equipment, which comprises the steps of firstly obtaining multi-source space data, dividing a research area into a plurality of subareas according to space distribution characteristics of city economic levels, constructing a driving factor data set by taking regular space units as basic simulation units, then constructing a total quantity constraint model of city economic level evolution along time on a subarea scale based on historical city economic level total quantity data, constructing a space unit city economic level grade change probability model by combining space unit attribute information, calculating the probability of city economic level grade change of each space unit in a given time period, introducing a city economic level grade system dynamic expansion mechanism under the city economic level total quantity constraint condition, distributing and updating the city economic level of each space unit, and integrating the space of each subarea simulation result to ensure that the simulation result meets the total quantity constraint of the whole scale, thereby obtaining a city economic level space simulation result.

Inventors

LIU BAOJU
San Zhiji
TAN XIAOYONG
DENG MIN

Assignees

中南大学

Dates

Publication Date: 20260512
Application Date: 20260409

Claims (9)

1. A method for simulating urban economic horizontal space, comprising: Obtaining urban economic level multisource driving factor data and economic level statistical data of each space unit in time sequence, wherein a plurality of space units form a subarea, and all the subareas form an integral area of a city; Obtaining a driving factor characteristic vector from the driving factor data, and obtaining an economic level grade from the economic level statistical data; based on the economic level statistical data, constructing an economic level total amount time sequence prediction model for representing an economic level total amount evolution rule on a subarea scale and an area overall scale; constructing a space unit economic level grade development probability model for representing the probability of the economic level grade change of the space unit in a given time period based on the economic level grade; Obtaining a space distribution result of the economic level grade of each space unit based on the constructed multi-scale coordinated cellular automaton model, wherein the cellular automaton model takes the total economic level as a constraint and takes the economic level grade change probability as the space unit state transition probability input, and And on the basis of the space distribution result of the economic level grades of the space units, converting the economic level grades of the space units into continuous economic level values of the space units, and simulating the total urban economic level by the continuous economic level values of the space units.
2. The method for simulating urban economic horizontal space according to claim 1, wherein the driving factor feature vector is obtained by normalizing the driving factor data, and the economic horizontal grade is obtained by using the economic horizontal statistical data, and the method specifically comprises the following steps: Let the economic level of space unit i at time t be G i (t), let it be in the sub-region to which it belongs, the discretized hierarchical threshold sequence be: discretized economic level grade Expressed as: wherein the hierarchical threshold sequences collectively comprise A threshold value is correspondingly formed An economic level class interval.
3. The urban economic horizontal space simulation method according to claim 1, wherein the economic horizontal total amount time sequence prediction model is an S-shaped curve model: Wherein, the Time of presentation A corresponding total amount of economic level; The numerical value of the upper-bound parameter is obtained by fitting historical statistical data, wherein the upper-bound parameter is used for describing the total evolution trend of the economic level; And The method is a curve form control parameter and is used for adjusting the growth direction and the growth speed of the total economic level in the time evolution process.
4. The urban economic horizontal space simulation method according to claim 1, wherein the space unit economic horizontal level development probability model is a random forest model: Wherein, the Is a space unit At the time of D is the number of driving factors; the current prediction time node; developing probability for the economic level of the space unit; the number of base learners; Represent the first A classification output result of the individual base learner; To indicate a function, the corresponding base learner determines that the space element has a value of 1 when the economic level changes, and otherwise the corresponding base learner determines that the space element has a value of 0.
5. The urban economic horizontal space simulation method according to claim 1, wherein the method for obtaining the space distribution result of the economic horizontal level of each space unit based on the constructed multi-scale coordinated cellular automaton model specifically comprises the following steps: dynamically expanding the highest-level upper bound of the economic level system according to the economic level total amount time sequence prediction model; On the premise of meeting the total amount constraint of the economic level, sequencing the space units in the subarea according to the comprehensive development probability of the space units, wherein the comprehensive development probability is defined by the economic level grade development probability of the space units, a neighborhood effect factor and a development inhibition factor; And sequentially selecting space units according to the sequence from high to low of the comprehensive development probability to carry out iterative updating of the economic level grade.
6. The urban economic horizontal space simulation method according to claim 5, wherein the dynamic expansion of the highest-level upper bound of the economic horizontal level system is performed according to the economic horizontal total amount time sequence prediction model, and the method specifically comprises the following steps: Setting an economic level system according to preset time intervals Performing an update in which Representing the time span between two adjacent level expansion operations at a time node In this case, the economic level hierarchy in each sub-region is composed of a limited number of discrete levels, space units The economic level grade value range of (2) is: Wherein, the Representing time nodes The corresponding highest level number; At each preset time interval At the end, a dynamic expansion operation is executed on the economic level hierarchy, and the first is set The time nodes corresponding to the secondary level expansion moments are as follows: Set the highest-level upper bound corresponding to the expansion time of the upper level as The highest-level upper bound corresponding to the current level expansion time The update is as follows: Wherein, the For time interval [ The total increase rate of urban economic level in the interior is specifically expressed as: Wherein, the Representing at a time node The corresponding total amount of the target economic level, Representing at a time node A corresponding target economic level total; When meeting the requirements At the time of highest grade of the original economic level grade system Newly adds a highest grade on the basis of The value range corresponding to the new added level is defined as: 。
7. The urban economic horizontal space simulation method according to claim 6, wherein the space units are sequentially selected from high to low according to the comprehensive development probability to perform iterative updating of the economic horizontal level, and the method specifically comprises: For the selected space unit At the highest level not exceeding the current year On the constraint that the economic level is classified by Finishing primary grade updating; After each space unit is selected and updated, calculating the total economic level in the current subarea in real time based on the updated space unit level result, and setting the current annual economic level as Grade (grade) The corresponding upper and lower threshold values are respectively And (3) with The level representative value thereof is defined as: record in the current subregion, time node Belonging to the class The number of space units is: The total amount of economic level corresponding to the sub-region of the current year is expressed as: total current economic level of subareas Time node of total economic level time sequence prediction model Target economic level total amount of corresponding subareas Comparing, defining errors as: When the error is Less than a preset tolerance threshold When the current year is met, determining that the subarea meets the total amount constraint of the economic level, and stopping updating the space unit level in the subarea; otherwise, the next space unit is selected from the ordered sequence and the process is repeated.
8. The urban economic level space simulation method according to claim 1, wherein the economic level of each space unit is converted into a continuous economic level value of the space unit based on a spatial distribution result of the economic level of each space unit, and the urban economic level simulation total amount is calculated from the continuous economic level value of each space unit, and the method specifically comprises: Set the first Sub-region at time node The economic level class threshold sequence of (2) is: Wherein, the For the threshold number, if space units Belonging to the first Sub-region, which is at time node The urban economic level of (2) is: the continuous economic level value corresponding to the space unit is defined as: Wherein, the Representing space units At the time node Is a continuous value of economic level of (2); Summarizing the economic level continuous values of all the space units, and calculating the urban economic level simulation total amount: Wherein, the The total number of space units in the investigation region; calculating a proportionality coefficient between the urban economic level simulation total amount and the urban economic level target total amount: Wherein, the To be at a time node Is a city economic level target total amount; When (when) And (3) uniformly and proportionally adjusting the economic level continuous values of all the space units: Wherein, the Representing the economic level continuous value of the space unit after the proportion adjustment; through the proportion adjustment, the urban economic level simulation total quantity meets the overall scale target constraint, namely: Wherein M is the number of space units in the range of the research area.
9. An electronic device comprising a processor, and a memory coupled to the processor, The memory is used for storing a computer program; The processor for executing the computer program stored in the memory to cause the electronic device to perform the urban economic horizontal space simulation method according to any one of claims 1-8.

Description

Urban economic horizontal space simulation method and equipment Technical Field The application relates to the technical field of data processing, in particular to a city economy horizontal space simulation method and equipment. Background The urban economic level is used for representing the economic development state of urban space units in a certain period, reflecting the economic activity intensity and the economic scale level thereof, and has important significance in urban planning, regional economic analysis and space management decision. With the continuous advancement of the urban process, urban economic activities show remarkable aggregation and diversity in space, and the economic level distribution among different space units has obvious unbalanced characteristics. In the actual urban economic development process, urban economic activities show different development characteristics on different spatial scales under the influence of factors such as regional conditions, resource endowment, foundation facility level and the like. However, in the existing urban economic horizontal space simulation method, only a single scale of a space unit is considered, a unified transfer rule is constructed to model an economic evolution process of the space unit, and differences and synergistic effects among economic driving mechanisms under different space scales are difficult to effectively describe, so that the urban economic development space-time process is difficult to accurately reflect. In addition, the existing urban economic level space simulation method focuses on short-term simulation, generally assumes that the numerical interval of the urban economic level is fixed within the extremum range of the historical statistical data, and constructs a relatively static economic grade system according to the numerical interval. However, in the background of continuous and rapid development of urban economy, the total urban economy is continuously increased, and the economic levels of different areas gradually break through the value range of the historical statistics stage, so that the numerical interval of the urban economic level shows an expansion trend along with time. In the long-term urban economic level space simulation process, if a fixed numerical value interval or a static grade system is still adopted to restrict the economic level, the simulation result is easily limited by an artificial upper limit in a later stage, and an unreasonable cut-off or saturation phenomenon is generated, so that the space evolution characteristic of the urban economic level after the historical interval is broken through is difficult to be truly reflected. Disclosure of Invention The application provides a city economy horizontal space simulation method and equipment, which can solve one of the problems in the background technology. In order to achieve the above purpose, the application adopts the following technical scheme: in a first aspect, a method for simulating urban economy horizontal space is provided, which comprises the following steps: Obtaining urban economic level multisource driving factor data and economic level statistical data of each space unit in time sequence, wherein a plurality of space units form a subarea, and all the subareas form an integral area of a city; Obtaining a driving factor characteristic vector from the driving factor data, and obtaining an economic level grade from the economic level statistical data; based on the economic level statistical data, constructing an economic level total amount time sequence prediction model for representing an economic level total amount evolution rule on a subarea scale and an area overall scale; constructing a space unit economic level grade development probability model for representing the probability of the economic level grade change of the space unit in a given time period based on the economic level grade; Obtaining a space distribution result of the economic level grade of each space unit based on the constructed multi-scale coordinated cellular automaton model, wherein the cellular automaton model takes the total economic level as a constraint and takes the economic level grade change probability as the space unit state transition probability input, and And on the basis of the space distribution result of the economic level grades of the space units, converting the economic level grades of the space units into continuous economic level values of the space units, and simulating the total urban economic level by the continuous economic level values of the space units. Based on the technical scheme, firstly, multi-source space data are acquired and preprocessed, a research area is divided into a plurality of subareas according to space distribution characteristics of urban economic levels, a driving factor data set is constructed by taking regular space units as basic simulation units, then, a total quantity constraint model of urban economic l