Dynamic simulation method and system for smart city planning based on spatiotemporal big data

Abstract

The application discloses a smart city planning dynamic simulation method and system based on space-time big data, and particularly relates to the technical field of smart city planning deduction and space-time data processing, and comprises the following steps: acquiring population records, facility records, planning action records and qualification rule records for a target region in a current planning round; forming a planning base table comprising object identification, object location, belonging stage, object category and associated rules through object unification, location unification and stage unification; unifying the population records, the facility records, the planning action records and the qualification rule records to the same stage chain, expanding object state changes stage by stage according to the planning action effective process, and simultaneously solving public service qualification formation, qualification switching, qualification invalidation and their undertaking registration results, and then performing reverse back-reference attribution to the undertaking discontinuity.

Description

Technical Field

[0001] This invention relates to the field of smart city planning simulation and spatiotemporal data processing technology, and more specifically, to a method and system for dynamic simulation of smart city planning based on spatiotemporal big data. Background Technology

[0002] In smart city planning and simulation, existing technologies mainly focus on determining whether various public services can continue to accommodate new or relocated populations after the planning scheme is implemented. In practice, information such as population distribution, facility location, service area, road conditions, resource scale, and phased construction arrangements are generally collected. Coverage calculations, accessibility analyses, and quantity verifications are carried out for resources such as schools, medical care, parking, elderly care, childcare, and affordable housing. Based on this, a judgment is made on whether the planning arrangements for each stage are valid. Taking the area where comprehensive development around rail stations and urban renewal are carried out in parallel as an example, the planning and implementation process often requires the introduction of residents in batches, adjustment of school districts, relocation of community health service points, reduction of some ground parking and simultaneous delivery of affordable housing. This process must follow the existing rules such as school enrollment, designated access, parking registration, waiting list review and service recipient identification. It is not possible to make separate assumptions about the implementation path that deviates from the actual management standards in order to complete the simulation. In this situation, existing methods repeatedly encounter a problem that can be directly verified: the model results show that in a certain stage, the surrounding schools, medical facilities, parking resources, or affordable housing can cover the target group in terms of spatial location and quantity. However, when it comes to actual organization, there are continuous phenomena such as inability to enroll, access, register, or wait in line. The root cause is that existing simulations mostly stay at the level of location, accessibility, and quantity, without putting the formation, change, and connection of access qualifications for various public services into the same continuous simulation chain, causing the connection relationship on the map to be disconnected from the access relationship in reality. The technical problem this application aims to solve is: how to incorporate the formation, transfer, expiration, and continuation of public service recipients' qualifications at each stage into the dynamic simulation of smart city planning based on spatiotemporal big data, so as to avoid gaps in the connection that are spatially covered but cannot be actually accessed. Summary of the Invention

[0003] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for dynamic simulation of smart city planning based on spatiotemporal big data. By unifying population records, facility records, planning action records, and qualification rule records onto the same stage chain, the changes in object status are unfolded stage by stage according to the effective process of planning actions, and the formation, transfer, invalidation, and registration results of public service qualifications are solved simultaneously. Then, reverse attribution is performed on the gaps in the acceptance process to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a dynamic simulation method for smart city planning based on spatiotemporal big data, comprising: S1. For the current planning cycle of the target area, obtain population records, facility records, planning action records and qualification rule records, and form a basic planning table including object identifier, object location, stage, object category and association rules through object unification, location unification and stage unification; S2. Based on the planning action records in the planning base table, execute the effective deployment, generate the corresponding object change results according to the implementation stage, target and scope of impact, and write them into the planning base table in order of stage to form a stage deployment table. S3. For each population object in the stage expansion table, combine the corresponding qualification rule records to execute qualification generation, qualification transfer and qualification cancellation, form the qualification results of each population object in each stage, and construct a qualification transfer dynamic model between population objects and facility objects based on the qualification results of each stage, and output the stage qualification table and qualification transfer dynamic model. S4. In the stage qualification table, based on the qualification acceptance dynamic model, the population objects with corresponding service qualifications are written into the corresponding facility records in sequence according to the qualification category, and the acceptance registration is completed one by one according to the available locations in the facility records. When multiple population objects correspond to the same available location, the population object written first is retained, and the remaining population objects are continued to be written into the next available location. When there are no remaining available locations in the corresponding facility records, it is recorded that the population object forms a gap in acceptance in the current stage, and the stage acceptance table is output.

[0005] In a preferred embodiment, it further includes: S5. Perform continuous comparison according to the stage acceptance table in stages, and perform back-and-forth pointers on the acceptance results of the same population object in adjacent stages to determine the failure qualification, the stage of the failure, and the planned action that triggers the failure qualification for each acceptance gap, forming a dynamic simulation result including the acceptance results of each stage, the record of the acceptance gap, and the record of the source of the gap.

[0006] In a preferred embodiment, S1 includes: S11. For population records, facility records, planning action records, and qualification rule records, extract the object name, object type, region, associated objects, and record time. Perform the first round of merging on records with the same name, type, and region. Perform the continuation merging on records with different names but the same associated objects and the record time corresponding to the same planning round to form a unified object result table. S12. Based on the unified result table of objects, perform position rewriting on the address description, coordinate description and region description in each record. Write the position description that can directly correspond to the coordinates into the unified coordinate position, and map the position description that cannot directly correspond to the coordinates to the unified region position according to the region and associated object. Perform back-pointing on multiple positions of the same object in different records, retain the target position corresponding to the recording time, and form a unified position result table. S13. Based on the record time, planning action effective time, and qualification rule applicable time in the location unification result table, the execution stage is rearranged. Population and facility records earlier than the planning action effective time are written into the preceding stage, records corresponding to the planning action effective time are written into the current stage, and records later than the current stage and directly triggered by the planning action in the current stage are written into the subsequent stage to form the planning base table.

[0007] In a preferred embodiment, S2 includes: S21. For each planning action record in the planning base table, extract the action effective time, action duration interval, target object and scope of influence, sort each planning action record according to the action effective time, and perform a succession search on the planning action records before and after the same target object to form an action sequence table. S22. Based on the action priority table, each planned action record is expanded according to the target and scope of influence. The addition, deletion, migration, replacement and scope change corresponding to the planned action record are written as object change items. Multiple object change items corresponding to the same target in the same implementation stage are merged before and after execution to form a stage change table. S23. Based on the phase change table, write the changes of each object into the planning base table one by one according to the implementation phase sequence, perform overwrite update on the object status formed in the current implementation phase, and perform inheritance write for subsequent implementation phases to form a phase expansion table.

[0008] In a preferred embodiment, S3 includes: S31. For each population object in the stage expansion table, extract the stage order, rule-affected object, qualification category, qualification source, and qualification destination by combining the corresponding qualification rule record. Perform qualification candidate expansion for each population object between adjacent stages to construct a population object qualification candidate graph. In the population object qualification candidate graph, only retain the candidate edges that simultaneously satisfy the following conditions: continuous stage order, consistent object identifier, corresponding rule-affected object, consistent qualification category, and the same qualification source not written to the same population object. Connect the retained candidate edges end to end according to the stage order to form a candidate qualification linked list. S32. Based on the candidate qualification chain, perform forward succession verification, backward back-pointing verification, and same-stage circumstantial verification on each candidate qualification chain for each population object; when the qualification source of a candidate qualification chain in the current stage corresponds to the qualification destination in the previous stage, the qualification node in the current stage can back-point to generate the planning action record of the qualification node, and there is a facility object record corresponding to the qualification category in the same stage, write the candidate qualification chain into the target qualification chain; when there are multiple target qualification chains for the same population object, retain a unique target qualification chain in the order of rule effective time, qualification source record order, and facility object record order, and perform qualification cancellation or qualification transfer rewriting on the remaining target qualification chains to form a stage qualification table.

[0009] In a preferred embodiment, S3 further includes: S33. Based on the stage eligibility table, extract the eligibility category, eligibility destination, and stage acceptance order of each population object in each stage. Combine this with the facility object's facility category, service scope, acceptance position order, and position occupancy status in the corresponding stage to construct a dynamic eligibility acceptance diagram between population objects and facility objects. In the eligibility acceptance dynamic diagram, perform acceptance connections between population objects and facility objects that correspond in eligibility category, stage, and eligibility destination. For cases where the same population object corresponds to multiple facility object connections, retain a unique acceptance connection according to the facility object's recording order. For cases where the same facility object has multiple acceptance connections at the same acceptance position, retain a unique acceptance connection according to the population object's recording order when entering the current stage. Write the remaining unretained acceptance connections back to the next acceptance position or the next stage to continue until all population objects in the current stage have completed acceptance registration or a gap in acceptance has been formed, thus obtaining the eligibility acceptance dynamic model.

[0010] In a preferred embodiment, S4 includes: S41. For each population object in the stage qualification table, extract the stage identifier, qualification category, qualification destination, entry order and previous acceptance result. Combine the facility objects, acceptance position order, position occupancy status and stage connection relationship in the qualification acceptance dynamic model. According to the rules of qualification category correspondence, stage identifier correspondence, qualification destination correspondence and previous acceptance result continuation, generate an acceptance request sequence consisting of target facility objects and their acceptance position order for each population object. At the same time, generate a stage position status table consisting of each acceptance position of each facility object. S42. Based on the acceptance request sequence and the stage position status table, execute acceptance writing in the order of population object entry; when the current acceptance position is an empty position, write the population object to the acceptance position and register the acceptance result; when the current acceptance position has other population objects registered, extract the next acceptance position of the registered population object in the acceptance request sequence, form a position transfer chain with the current population object and the registered population object, and search for an empty position along the position transfer chain; when an empty position is found, rewrite the registered population objects in each acceptance position in reverse order of the position transfer chain; when no empty position is found in all searchable acceptance positions, maintain the original registration result in the stage position status table and write the current population object to the acceptance gap queue.

[0011] In a preferred embodiment, S4 further includes: S43. Based on the stage position status table after acceptance and writing, perform acceptance closure verification, position uniqueness verification, and stage continuation verification on each population object; when the same population object corresponds to multiple acceptance positions, retain the acceptance registration with the earliest record time and delete the rest of the acceptance registrations; when the same acceptance position corresponds to multiple acceptance registrations, retain the acceptance registration that remains at the acceptance position after being rewritten in reverse by the position transfer chain and delete the rest of the acceptance registrations; when a population object has not formed an acceptance registration and there is a corresponding record in the acceptance gap queue, record the population object as the current stage acceptance gap, and form a stage acceptance table by combining the retained acceptance registrations and acceptance gap records.

[0012] In a preferred embodiment, S5 includes: S51. For each population object in the phase transfer table, extract the phase identifier, transfer result, transfer facility object, transfer location, qualification category, qualification source and corresponding planning action identifier, and perform front and back connection for the same population object between adjacent phases according to the phase sequence to form a phase transfer chain list including continuous transfer phase, transfer transfer phase and transfer gap phase. S52. Based on the stage-based connection list, perform reverse backfinding for each connection gap segment, and retrieve the changes in eligibility sources, connection facility objects, and planning actions of the same population object stage by stage forward along the gap stage, and write them into the records; when there is a qualification category deletion record in a certain preceding stage, record the qualification corresponding to the qualification category deletion record as invalid qualification; when there is a connection facility object withdrawal record or connection location deletion record in a certain preceding stage, record the planning action corresponding to the withdrawal record or deletion record as triggered planning action; when there are no qualification category deletion records, connection facility object withdrawal records, or connection location deletion records between adjacent stages, record the population object corresponding to the gap stage as not having generated connection eligibility, forming a gap source table; S53. Based on the phase succession list and the gap source list, the continuous succession segment, succession transfer segment, succession gap segment, failure qualification and triggering planning action are collected for each population object in phase order to form a dynamic simulation result. Among them, the gap source record in the dynamic simulation result includes at least the population object identifier, gap stage, failure qualification identifier and triggering planning action identifier.

[0013] A smart city planning dynamic simulation system based on spatiotemporal big data includes a planning table creation module, a phase unfolding module, an eligibility modeling module, a registration module, and a gap indexing module. The planning and table building module is used to obtain population records, facility records, planning action records, and qualification rule records for the current planning cycle of the target area. Through object unification, location unification, and stage unification, it forms a basic planning table that includes object identifier, object location, stage, object category, and association rules. The phase expansion module is used to execute effective expansion based on the planning action records in the planning base table, generate the corresponding object change results according to the implementation stage, target, and scope of impact, and write them into the planning base table in the order of stages to form the phase expansion table. The qualification modeling module is used to perform qualification generation, qualification transfer and qualification cancellation for each population object in the phase expansion table, combined with the corresponding qualification rule records, to form the qualification results of each population object in each phase, and to build a dynamic model of qualification transfer between population objects and facility objects based on the qualification results of each phase, and output the phase qualification table and the qualification transfer dynamic model. The service registration module is used to write eligible population objects into the corresponding facility records in the stage qualification table according to qualification category and based on the qualification acceptance dynamic model. It completes the service registration one by one according to the available locations in the facility records. When multiple population objects correspond to the same available location, the population objects written first are retained and the remaining population objects are written to the next available location. When there are no remaining available locations in the corresponding facility records, it records that the population object forms a service gap in the current stage and outputs the stage service table. The gap indexing module is used to perform continuous comparisons according to the phase acceptance table in phase order, and to perform back-and-forth indexing on the acceptance results of the same population object in adjacent phases. It determines the failure qualification, gap phase and the planned action to trigger the failure qualification corresponding to each acceptance gap, and forms a dynamic simulation result including the acceptance results of each phase, acceptance gap records and gap source records.

[0014] The technical effects and advantages of this invention are as follows: 1. This solution incorporates the relationship between qualification formation, qualification transfer, qualification expiration and acceptance continuation into the same stage of the deduction chain, which can further realize the spatial coverage relationship into the actual access relationship, thereby relatively reducing the situation where the map shows that it can be accepted but the actual acceptance gap occurs. 2. By unifying the objects, locations, and stages of implementation of population records, facility records, planning action records, and qualification rule records, a consistent planning basis table can be formed, thereby relatively improving the consistency of stage extrapolation under the condition of multiple source records coexisting. 3. Expand the planning action record into object change items corresponding to addition, deletion, migration, replacement and scope change, and write them according to the implementation stage sequence, which can more accurately reflect the continuous impact of planning actions on the status of objects at each stage. 4. By constructing a candidate qualification chain and performing forward acceptance verification, backward back-pointing verification, and same-stage corroboration verification, cross-constraints can be applied to the qualification results at each stage, thereby relatively improving the verifiability of the qualification acceptance deduction results. 5. By performing acceptance writing, position transfer chain rewriting, and acceptance gap registration based on the qualification acceptance dynamic model, acceptance conflicts can be resolved under the condition of limited acceptance position, thereby relatively improving the continuity of stage acceptance results. 6. By performing reverse indexing on the gaps in the project and locating the failure qualifications and triggering planning actions, a record of the source of the gaps can be generated, thus providing a clearer basis for stage-based attribution for adjustments to the planning scheme. Attached Figure Description

[0015] Figure 1 This is a flowchart outlining the method steps of the present invention; Figure 2 This is a schematic diagram of the system module structure of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Refer to the instruction manual appendix Figure 1-2 The present invention provides a dynamic simulation method for smart city planning based on spatiotemporal big data, comprising: S1. For the current planning cycle of the target area, obtain population records, facility records, planning action records and qualification rule records, and form a basic planning table including object identifier, object location, stage, object category and association rules through object unification, location unification and stage unification; This implementation method is used to organize original records from different sources and with different expressions within the same planning round into a unified input result that can directly participate in subsequent planning actions. The processing order is as follows: first, object merging is completed; then, position rewriting is completed; and finally, stage rearrangement is completed to form a planning base table for S2 to read. The implementation process includes the following steps: In S11, the goal is to eliminate duplicate records of the same object and form a unified object identifier. The input includes the object name, object type, region, associated objects, and record time from population records, facility records, planning action records, and qualification rule records. Records are first sorted by object type, then by region and record time. Records with the same name, object type, and region are merged in the first round, written into the same object merge group, and an object identifier is generated. For records that have not completed the first round of merging, continued merging is performed on records with different names but the same associated objects and record times corresponding to the same planning round, inheriting the object identifiers already written to the records. When a record corresponds to multiple continued merge candidates, a unique merge result is retained sequentially according to the record time order, associated object record order, and input record order. Records that do not meet the merging conditions generate separate object identifiers. The unified object result table is output and written to S12 for reading. Records missing object names, object types, or record times are not merged, written into the unmerged record set, and a missing field marker is registered. In S12, the goal is to rewrite different location expressions into a unified location expression, forming a target location that can be directly called by the same object at each stage. The input quantities are the object identifier, address description, coordinate description, region description, region, associated object, and record time in the unified result table of the object. For records with coordinate descriptions, the coordinate descriptions are written into the unified coordinate location, and the address description and region description are retained as lookup fields. For records without coordinate descriptions but with address descriptions, the location correspondence is performed according to the region and associated object, and the records are written into the unified region location. For records with only region descriptions, the records are written into the unified region location according to the region and associated object. Subsequently, for multiple location records under the same object identifier, back-pointing is performed according to the recording time; when the same object has multiple locations in the same stage, the location corresponding to the record with the later recording time is retained as the target location, and its preceding location is written as the location source record; when the same object corresponds to different locations in different stages, the target locations of the corresponding stages are retained respectively, without cross-stage overwriting; the unified location result table is output and written to S13 for reading; records that are missing address description, coordinate description, and region description are not subject to location rewriting, are written to the location supplement record set, and an empty location mark is registered; In S13, the purpose is to write the unified object records into the corresponding stages based on the planning action effectiveness relationship and rule application relationship, forming the stage foundation required for subsequent development. The input quantities are the object identifier, target location, and recording time in the location unification result table; the planning action identifier, action effectiveness time, and target object in the planning action record; and the rule identifier, rule application time, and target object in the qualification rule record. First, the stage corresponding to the action effectiveness time of each planning action record is taken as the current stage. Population records and facility records with recording times earlier than the action effectiveness time are written into the previous stage, and records with recording times corresponding to the action effectiveness time are written into the current stage. For records with recording times later than the current stage, it is checked whether they can be directly referenced by the target object of the planning action in the current stage. If they can be referenced, they are written into the subsequent stage; if they cannot be referenced, they are not written into the subsequent stage chain corresponding to the planning action. Qualification rule records are written into the corresponding stages according to the rule application time and are written into the corresponding target object of the rule in the same stage. The planning foundation table is output and written into S2 for reading. Records with missing action effectiveness time or rule application time are not subject to stage rearrangement, are written into the stage pending record set, and the stage pending mark is registered. Through the above processing, duplicate records of the same object are unified, the location expression of the same object is rewritten, and the writing relationship of the same object in each stage is fixed, so that the planning base table has a unified object identifier, a unified target location, and a unified stage. In practical applications: For example, if the same resident appears in both the residence registration record and the affordable housing waiting list record, or if the same school is recorded simultaneously with its name, coordinates, and street, and if school expansion corresponds to the current stage and the new capacity corresponds to the subsequent stage, this implementation method first merges the resident record and the school record, then rewrites the target location of the school, and finally rearranges the execution stage according to the effective time of the school expansion action. The school record before the expansion is written into the preceding stage, the expansion action is written into the current stage, and the capacity record after the expansion is written into the subsequent stage, resulting in a planning basic table that can be directly called by S2.

[0018] S2. Based on the planning action records in the planning base table, execute the effective deployment, generate the corresponding object change results according to the implementation stage, target and scope of impact, and write them into the planning base table in order of stage to form a stage deployment table. This implementation method converts planning action records in the planning base table into object change results that are continuously expanded in stages, so that subsequent qualification generation, qualification transfer, and qualification cancellation are directly established on the already effective object status. The processing order is as follows: first, an action sequence table is generated; then, a stage change table is generated; finally, the stage change table is written into the planning base table stage by stage, and a stage expansion table is generated. This implementation process includes the following steps: In S21, the purpose is to determine the order of effectiveness of each planning action record and the succession relationship on the same target object, so that the expansion of subsequent object change items has a fixed order. The input quantities are the planning action identifier, action effective time, action duration interval, target object, scope of influence, and writing stage from the planning base table. First, the planning action records are sorted in order according to the action effective time. If the action effective times are the same, they are sorted according to the record order of the planning action identifier. Then, a succession search is performed on adjacent planning action records corresponding to the same target object. When the action effective time of the later planning action record is later than that of the earlier planning action record and the two are linked, the succession search is performed. When the objects are consistent, the subsequent planned action record is written as the successor action of the previous planned action record; when the objects of two planned action records are consistent and the action duration intervals are connected end to end, the two planned action records are written into the same successor chain; when the scope of influence of two planned action records has an inclusion relationship, the planned action record with the more complete set of objects corresponding to the scope of influence is retained as the master record, and the remaining planned action records are written as slave records; the action sequence table is output and written to S22 for reading; planned action records with missing action effective time, objects, or scope of influence do not participate in the successor retrieval, are written into the action record set to be supplemented and the missing field mark is registered; In S22, the purpose is to break down each planning action record into object change items that can be written to stage by stage according to the target and scope of influence, so that the effective results of the planning action can be directly applied to the object status. The input quantities are the planning action identifier, action effective time, action duration interval, target, scope of influence and succession relationship in the action sequence table, and the object identifier, object category, object location and stage in the planning base table. Each planning action record is expanded one by one. When a planning action record is added, a new object change item is generated and the new object identifier, entry position, entry stage and source action identifier are written. When a planning action record is deleted, a deleted object change item is generated and the exit object identifier, exit stage and source action identifier are written. When a planned action record corresponds to a migration, an exit object change item and a target object entry object change item are generated simultaneously. When a planned action record corresponds to a replacement, an exit object change item and a replacement object entry object change item are generated simultaneously. When a planned action record corresponds to a range change, a range change object change item is generated, and the range before and after the change are written. Subsequently, multiple object change items corresponding to the same object in the same implementation phase are merged. The rules for merging are as follows: when a deleted object change item and a subsequently added object change item act on the same object identifier, they are merged into a replacement object change item; when an exit object change item and an entry object change item correspond end to end, they are merged into a migration object change item; when multiple range change object change items act on the same object identifier, they are retained sequentially according to the action order and form a continuous range change chain. The phase change table is output and written to S23 for reading. Object change items that cannot be matched to an object identifier are not merged, but are written separately into the unmerged change item set and the source action identifier is registered. In S23, the purpose is to write the object change items in the phase change table into the planning base table according to the implementation phase sequence, forming the phase object status required for subsequent qualification acceptance calculations. The input quantities are the object change items, implementation phase, source action identifier, object identifier, object location, and scope of influence in the phase change table, as well as the existing object status in the planning base table. First, the phase change table is divided into columns according to the implementation phase sequence. Within each implementation phase, the object change items are written into the planning base table in order of action sequence. For newly added object change items, the newly added object status is written to the current implementation phase. For deleted object change items, an object exit record is written to the current implementation phase. For relocated object change items, an original location exit record and a target location entry record are written to the current implementation phase. For replaced object change items, an original object exit record and a replaced object entry record are written to the current implementation phase. For scope change object change items, the changed scope of influence status is written to the current implementation phase. If there are existing object states with the same object identifier and the same stage in the current implementation phase, the object state corresponding to the object change item is used to perform an overwrite update; if the object state formed in the current implementation phase needs to enter the subsequent implementation phase, the object state is inherited and written according to the stage sequence until a subsequent object change item with the same object identifier is encountered; the stage expansion table is output and written to S3 for reading; when the object state corresponding to the object change item can neither complete the writing in the current implementation phase nor form the inheritance writing in the subsequent implementation phase, the object change item is written to the status pending record set and the reason for not writing is recorded; Through the above processing, the planning action record is first fixed as a sequential action chain, then broken down into writable object change items, and finally written into the planning base table stage by stage to form a stage expansion table, so that the objects, sources and destinations of subsequent qualification rules are all based on the state of the already effective stage objects. In practical applications, for example, in the comprehensive development around rail stations, there are three types of planning actions: school expansion, parking facility relocation, and community health service point replacement. This implementation method first arranges the records of the three types of planning actions according to the effective time of the actions, and performs a succession search on the records before and after school expansion and parking facility relocation. Then, school expansion is written as a new location change item, parking facility relocation is written as an original location exit change item and a target location entry change item, and community health service point replacement is written as an original object exit change item and a replacement object entry change item. Finally, according to the implementation stage, the above object change items are written into the planning base table stage by stage. The new school acceptance status, new parking facility location status, and community health service point replacement status formed in the current stage are overwritten, updated, and subsequently written, resulting in a stage expansion table that can be directly read by S3.

[0019] S3. For each population object in the stage expansion table, combine the corresponding qualification rule records to execute qualification generation, qualification transfer and qualification cancellation, form the qualification results of each population object in each stage, and construct a qualification transfer dynamic model between population objects and facility objects based on the qualification results of each stage, and output the stage qualification table and qualification transfer dynamic model. This implementation method is used to further solve for the valid eligibility results of each population object in each stage based on the stage expansion table that has already formed the stage object status, and to establish a dynamic relationship between population objects and facility objects that can be continuously transferred. The processing sequence is as follows: first, expand the eligibility candidates for population objects in adjacent stages and form candidate eligibility chains; then, perform multiple checks on the candidate eligibility chains and retain the unique target eligibility chain; finally, construct a qualification transfer dynamic diagram based on the stage eligibility table and facility object status, for S4 to perform transfer registration; the implementation process includes the following steps: In S31, the goal is to organize the possible eligibility relationships scattered in adjacent stages into a continuously verifiable candidate eligibility chain. The mechanism is to first generate candidate edges, and then connect them end-to-end according to the stage order, thereby transforming single-stage eligibility nodes into a cross-stage continuous eligibility structure. The inputs are the population object identifier, stage, stage order, and object category in the stage expansion table, as well as the rule-affected object, eligibility category, eligibility source, eligibility destination, and rule effective time in the eligibility rule record. First, the population object records are sorted by population object identifier, and then arranged by stage order. For each population object, eligibility candidate expansion is performed between adjacent stages. When the population object identifiers, rule-affected objects, and eligibility categories in two adjacent stages are consistent, and the eligibility destination in the previous stage corresponds to the eligibility source in the next stage, a candidate edge is generated between the eligibility nodes in the two stages. If the same eligibility source has already been written to a certain eligibility node of the same population object, candidate edges will not be generated again for other eligibility nodes of the same population object; candidate edges that meet the requirements of continuous stage order, consistent object identifier, corresponding rule-acting objects, consistent eligibility category, and that the same eligibility source has not been written to two eligibility nodes of the same population object will be retained, and the remaining candidate edges will be deleted; then the retained candidate edges will be connected end to end according to the stage order to form a candidate eligibility chain, and single-stage eligibility nodes that cannot be connected to subsequent stages will be written separately as single-node candidate eligibility chains; the candidate eligibility chain list will be output and written to S32 for reading; when a population object is missing any field of stage order, eligibility category, eligibility source, or eligibility destination, eligibility candidate expansion will not be performed, the corresponding record will be written to the eligibility pending record set and a missing field marker will be registered; In S32, the goal is to filter out valid qualification chains from candidate qualification chains that can be supported by the states of objects in the preceding and following stages as well as objects in the same stage. Its mechanism is to filter each chain one by one through forward acceptance verification, backward back-pointing verification, and same-stage circumstantial verification, and to uniquely retain multiple valid qualification chains for the same population object, thereby forming a stage qualification table that can be directly called upon in subsequent acceptance calculations. The input quantities are the population object identifier, stage order, qualification category, qualification source, qualification destination, and rule effective time in the candidate qualification chain list, the planning action records in the stage expansion table, and the facility object records in the same stage. First, a forward acceptance verification is performed on each candidate qualification chain, and the verification content is whether the qualification source of the current stage corresponds to the qualification destination of the previous stage. Then, perform a backward pointer check to verify whether the current stage qualification node can generate the planning action record for that qualification node; then perform a same-stage circumstantial verification check to verify whether there is a facility object record corresponding to the qualification category in the current stage; if all three checks are met, write the candidate qualification chain into the target qualification chain; if there are multiple target qualification chains for the same population object, retain a unique target qualification chain in the order of rule effective time, qualification source record, and facility object record; if the previous order has already formed a difference in priority, do not continue to compare the next order; if all three orders are the same, retain a unique target qualification chain in the order in which the target qualification chain is written; for the target qualification chain that is not retained, if there is a previous stage qualification destination that can be inherited in the current stage, perform qualification transfer rewriting; if there is no previous stage qualification destination that can be inherited in the current stage, perform qualification cancellation; output the stage qualification table and write it to S33 for reading; when the candidate qualification chain cannot complete any of the three checks, it is not written into the target qualification chain, and the reason for failing the check is recorded in the stage qualification table; In S33, the purpose is to convert the qualification results in the stage qualification table into facility acceptance relationships that can be directly called upon for acceptance registration. Its mechanism is to first generate acceptance links between population objects and facility objects according to the correspondence between qualification categories and stages, and then perform unique retention and priority write-back on conflicting links, thereby forming a dynamic qualification acceptance graph. The input quantities are the population object identifier, stage, qualification category, qualification destination and stage acceptance priority in the stage qualification table, and the facility object identifier, facility category, service scope, acceptance position order and position occupancy status in the facility object record. First, population objects and facility objects are listed according to their stage. For population objects and facility objects with corresponding qualification categories, corresponding stages and qualification destinations of the same type, acceptance links are generated. Among them, if the facility category is the same but the population object's location is not within the service scope of the facility object, no acceptance link is generated. For the same population object corresponding to multiple facility object acceptance links, a unique acceptance link is retained according to the order of the facility object records, and the remaining acceptance links are first written back to the next acceptance position in the same stage. If there is no next receiving location in the same stage, write back to the next stage to continue the process; if there are multiple receiving edges corresponding to the same receiving location of the same facility object, retain the unique receiving edge according to the record order of the population object entering the current stage, and continue to write back the remaining receiving edges according to the receiving location order; if there is no corresponding facility object in the next receiving location and the next stage, write the unretained receiving edge as an unretained edge; continue to execute the above writing back until all population objects in the current stage have completed the retention of receiving edges or written as unretained edges; output the qualification receiving dynamic model and write it to S4 for reading; when a facility object is missing any field of facility category, service scope, receiving location order or location occupancy status, it will not participate in the generation of receiving edges, and the corresponding facility object will be written into the facility to be supplemented record set and the missing field mark will be registered; Through the above processing, the population objects in the phase expansion table are first organized into candidate qualification chains, then filtered into unique target qualification chains, and finally mapped into facility acceptance relationships, so that the phase qualification table and qualification acceptance dynamic model have continuity, recursiveness, and registrability. In this way, S4 does not need to repeatedly judge the qualification source, qualification destination, and facility correspondence, but only needs to perform acceptance writing based on the qualification acceptance dynamic model. In practical applications: For example, in the scenario of comprehensive development around rail transit stations, a certain household was eligible for the original school district school in the previous stage. In the current stage, due to school expansion and school district adjustment, the household becomes eligible for a new school district school. At the same time, in the current stage, there is also the eligibility for the childcare facilities配套to the affordable housing. In this implementation method, first, a candidate eligibility chain for this household is generated between adjacent stages based on the eligibility source and destination. Then, the school district adjustment action is recorded back through the planning action, and collateral verification is completed through the records of schools and childcare facilities in the same stage. The only target eligibility chain that can be supported by both the previous and next stages and the same stage is retained. Subsequently, the connection edges between this household and the school facility objects are generated according to the facility category, service scope, and order of the承接位置of the new school district school facilities. For multiple school connection edges that appear in the same stage, only the connection edge is retained according to the order of the facility object records, and the un-retained connection edges are written back to the next承接位置or the next stage. Finally, a stage eligibility table and an eligibility承接动态图that can be directly used for S4 to perform承接登记are formed.

[0020] S4. In the stage eligibility table, the population objects with corresponding service eligibility are sequentially written into the corresponding facility records based on the eligibility category according to the eligibility承接动态模型, and the承接登记is completed one by one according to the可承接位置in the facility records. When multiple population objects correspond to the same可承接位置, the population object written first is retained, and the remaining population objects are continued to be written into the next可承接位置. When there is no remaining可承接位置in the corresponding facility record, it is recorded that this population object has a承接断档in the current stage, and the stage承接表is output. This implementation method is used to implement the eligibility results in the stage eligibility table as the actual承接登记results. Under the condition of limited承接位置, through the generation of承接请求, the rewriting of位置让渡, and the verification of results, a stage承接表that can be directly used for subsequent承接断档回指is formed. The processing sequence is: first, generate the承接请求sequence and the stage位置状态表for each population object, then perform承接写入and位置让渡according to the entry order, and finally perform closed verification, unique verification, and续接校核on the承接结果. This implementation process includes the following steps: In S41, the purpose is to convert the eligibility results in the stage eligibility table into承接请求that can be written one by one, so that the subsequent承接登记has a fixed writing order and a fixed target位置. The input quantities are the population object identifier, stage identifier, eligibility category, eligibility destination, entry order, and previous承接结果in the stage eligibility table, as well as the facility object identifier,承接位置顺序,位置占用状态, and stage连边关系in the eligibility承接动态模型. First, each population object is separated according to the stage identifier, and then arranged according to the entry order. For each population object, the facility object corresponding to its eligibility category, stage identifier, and eligibility destination is retrieved. When the previous承接结果exists, first retrieve the stage连边of the facility object of the same category corresponding to the previous承接结果, and then retrieve the remaining stage连边. Subsequently, according to the record order of facility objects and the order of acceptance positions within each facility object, a sequence of acceptance requests consisting of the target facility object and its acceptance position order is generated sequentially; simultaneously, the position occupancy status of each acceptance position of each facility object is extracted to generate a stage position status table; the acceptance request sequence and stage position status table are output and written to S42 for reading; when a population object is missing any field in eligibility category, eligibility destination, or entry priority, no acceptance request sequence is generated, the population object is written to the acceptance reservation record set and a missing field mark is registered; when a facility object is missing an acceptance position order or position occupancy status, it is not written to the stage position status table, the facility object is written to the facility pending record set; In S42, the objective is to complete the sequential acceptance and writing based on the acceptance request sequence and the stage position status table, and to rearrange the acceptance results through the position transfer chain when the current position is occupied, so that the limited acceptance positions can continue to accommodate subsequent population objects. The input quantities are the population object identifier, target facility object, acceptance position order and entry order in the acceptance request sequence, and the facility object identifier, acceptance position, position occupancy status and registered population objects in the stage position status table. First, the acceptance and writing are performed sequentially according to the entry order of the population objects. When the current acceptance position is a free position, the current population object is written to the acceptance position, and the population object identifier, facility object identifier, acceptance position and registration time are registered. When the current acceptance position has been registered by other population objects, the next acceptance position of the registered population object in its acceptance request sequence is extracted, the current population object and the registered population object form a position transfer chain, and a free position is searched sequentially along the position transfer chain. The starting point of the location transfer chain is the current receiving position, and the next node is the next receiving position corresponding to the registered population object in the current receiving position. The same receiving position cannot be entered repeatedly in the same location transfer chain. When an empty position is found, starting from the empty position, the registered population objects in each receiving position are rewritten in reverse order of the location transfer chain until the starting receiving position is rewritten. Then, the current population object is written to the starting receiving position. When all searchable receiving positions in the location transfer chain are occupied, or when there is no next receiving position for the population object in the chain, the transfer retrieval stops, the original registration result in the stage position status table is maintained, and the current population object is written to the receiving gap queue. The stage position status table and the receiving gap queue after receiving and writing are output and written to S43 for reading. When the receiving request sequence is empty, receiving and writing is not performed. The corresponding population object is directly written to the receiving gap queue and the gap writing time is recorded. In S43, the purpose is to perform consistency processing on the acceptance results after acceptance writing, forming a unique, closed, and resumable stage acceptance table; the input quantities are the stage position status table after acceptance writing, the acceptance gap queue, population object identifier, facility object identifier, acceptance position, recording time, and previous acceptance results; first, an acceptance closure check is performed on each population object, the check content is whether the population object has formed an acceptance registration, or whether there is a corresponding record in the acceptance gap queue; then, a position uniqueness check is performed, when the same population object corresponds to multiple acceptance positions, the acceptance registration with the earlier recording time is retained and the other acceptance registrations are deleted; Then, a unique verification of the receiving location is performed. When multiple receiving registrations correspond to the same receiving location, the receiving registration that remains at that receiving location after being reverse-written through the location transfer chain is retained, and the remaining receiving registrations are deleted. Next, a phase continuation verification is performed, which verifies whether the facility object and receiving location corresponding to the receiving registration in the current phase can serve as the preceding receiving result of the receiving request sequence in the next phase. When a population object has not formed a receiving registration and there is a corresponding record in the receiving gap queue, the population object is recorded as the receiving gap in the current phase. The phase receiving table is output, which includes at least the population object identifier, phase identifier, receiving facility object, receiving location, qualification category, receiving result, and gap marker, and is written to S5 for reading. When receiving registration and receiving gap records exist simultaneously, the receiving registration is retained, the receiving gap records of the same population object in the same phase are deleted, and the reason for deletion is recorded. Through the above processing, the qualification results in the stage qualification table are converted into executable acceptance requests in positional order. Conflicts under limited acceptance positions are rearranged through positional transfer chains, and finally a unique and referential stage acceptance table is obtained, thus providing a stable input for subsequent acceptance gap source identification. In practical applications: For example, if a new resident in a certain phase simultaneously requests acceptance from three types of facilities—schools, childcare facilities, and parking facilities—and the first acceptance position for a certain school has already been occupied by a previous resident, this implementation method first generates a school acceptance request sequence for the new resident based on the eligibility category, eligibility destination, and previous acceptance results. Then, when the first acceptance position is occupied, the next acceptance position for the original registered resident is extracted, forming a position transfer chain. If a free acceptance position is subsequently found in the transfer chain, the acceptance positions of the original registered resident and the new resident are rewritten in reverse order. If all searchable acceptance positions are occupied, the new resident is added to the acceptance gap queue. Finally, a closed-loop check, a unique check, and a continuation check are performed on all acceptance registrations for schools, childcare facilities, and parking facilities to form a phase acceptance table that can be directly used by S5 to perform gap pointers.

[0021] S5. Perform continuous comparison according to the stage acceptance table in stage order, and perform back-and-forth pointing for the acceptance results of the same population object in adjacent stages to determine the failure qualification, the stage of the failure, and the planning action that triggers the failure qualification for each acceptance gap, and form a dynamic simulation result including the acceptance results of each stage, the acceptance gap record, and the gap source record. This implementation method is used to perform continuity processing and attribution of gap sources in the transition table, forming dynamic simulation results that can directly characterize the transition evolution process of each population object in each stage. The processing order is as follows: first, the transition results of the same population object in adjacent stages are connected to form a stage transition chain; then, the transition gap segments are reversed and the source of the gap is determined; finally, various transition segments and source results are aggregated according to stage order to form dynamic simulation results. This implementation process includes the following steps: In S51, the goal is to organize the discrete acceptance results in the stage acceptance table into a stage acceptance chain arranged continuously by stage, so as to perform stage-by-stage back-pointing for subsequent acceptance gaps; the input quantities are the population object identifier, stage identifier, stage order, acceptance result, acceptance facility object, acceptance location, qualification category, qualification source and corresponding planning action identifier in the stage acceptance table; first, all acceptance records are listed by population object identifier, and then sorted by stage order; for the same population object, the front and back docking is performed between adjacent stages. When there are acceptance results in adjacent stages and the acceptance facility objects are consistent and the acceptance locations are continuously corresponding, the records of the two stages are written as a continuous acceptance segment; When adjacent stages both have acceptance results but the receiving facility object or the acceptance location changes, the records of these two stages are written as acceptance transfer segments; when the current stage has no acceptance results and there is a gap mark, the record of this stage is written as an acceptance gap segment; for multiple consecutive stages of the same population object that meet the same writing condition, the adjacent writing results are connected end to end to form a stage acceptance linked list; the stage acceptance linked list is output and written to S52 for reading; when the population object is missing any field in stage order, acceptance result or qualification source, the front and back docking is not performed, the corresponding record is written to the acceptance chain pending record set and the missing field mark is registered; In S52, the objective is to trace the cause of the gap along the transition gap segment, so that each transition gap record corresponds to a clear source of qualification failure or planning action. The input quantities are the population object identifier, gap stage, qualification source, transition facility object, transition location and corresponding planning action identifier in the stage transition chain list, as well as qualification category deletion records in the stage qualification table, transition facility object exit records, transition location deletion records and planning action writing records in the stage expansion table. Reverse backfinding is performed on each transition gap segment, with the backfinding starting point being the gap stage and the backfinding direction being the adjacent preceding stage before the gap stage. In each preceding stage, it is first checked whether there is a qualification category deletion record for the same population object. If there is, the qualification corresponding to the qualification category deletion record is written as invalid qualification, and the stage corresponding to the record is written as invalid stage. If no qualification category deletion record is found, then check if there is a record of facility withdrawal. If it exists, write the planning action corresponding to the withdrawal record as a triggered planning action. If it still does not exist, then check if there is a record of location deletion. If it exists, write the planning action corresponding to the deletion record as a triggered planning action. If none of the three types of records exist, continue to backtrack to the previous stage. If no qualification category deletion record, facility withdrawal record, or location deletion record is found after backtracking to the earliest previous stage, write the population object corresponding to the gap stage as not having generated a qualification. Output the gap source table and write it to S53 for reading. The gap source table includes at least the population object identifier, gap stage, invalid qualification identifier, triggered planning action identifier, and source type. When multiple records of the same type of source are found in the same gap stage, retain the unique source record according to the stage order, record time order, and planning action writing order. In S53, the goal is to unify the continuous acceptance results, transfer results, and gap attribution results in the stage acceptance chain list and the gap source table into a dynamic simulation result oriented towards planning and deduction output. The input quantities are the continuous acceptance segments, acceptance transfer segments, and acceptance gap segments in the stage acceptance chain list, as well as the failure qualifications, triggering planning actions, and source types in the gap source table. First, they are sorted by population object identifier, and then the continuous acceptance segments, acceptance transfer segments, and acceptance gap segments corresponding to each population object are gathered according to stage order. For acceptance gap segments with gap source records, the corresponding failure qualifications and triggering planning actions are written into the same gap source record. For acceptance gap segments without triggering planning actions and whose source type is "no acceptance qualification generated", the "no acceptance qualification generated" is written into the gap source record. Subsequently, the continuous acceptance segments, acceptance transfer segments, acceptance gap segments, invalidation qualifications, and triggering planning actions of each population object are integrated in phase order to form dynamic simulation results; the dynamic simulation results are output, wherein the gap source record in the dynamic simulation results includes at least the population object identifier, gap stage, invalidation qualification identifier, and triggering planning action identifier, and is written into the result release table or for subsequent display modules to read; when the source records of the same population object and the gap source table are inconsistent for the same gap stage, the source record in the gap source table is retained, and the inconsistent results are written into the result review record set; Through the above processing, the discrete acceptance results in the stage acceptance table are organized into a continuous acceptance chain that unfolds in stages. The source of the acceptance gap is pointed back to the failure qualification or the triggering planning action in each stage, and finally a dynamic simulation result that can directly represent the impact path of the planning action is formed. In practical applications: For example, if a resident took over school facilities in a previous stage but failed to register the takeover in a later stage due to the school's withdrawal, this implementation method first connects the resident's takeover record in the previous stage with the gap record in the later stage to form a stage takeover chain. Then, it traces backward from the gap stage to retrieve the planning action identifier corresponding to the school withdrawal record and writes the planning action as a triggered planning action. If no school withdrawal record is found but a qualification category deletion record is found, the qualification is written as invalid. If neither type of record exists, the resident is recorded as not having generated takeover qualification in the gap stage. Finally, the resident's continuous takeover segments, takeover gap segments, invalid qualifications, or triggered planning actions are aggregated into a dynamic simulation result, thereby clearly showing the resident's takeover change process in each stage and the source of the gap.

[0022] Furthermore, the present invention also includes a smart city planning dynamic simulation system based on spatiotemporal big data, the system comprising a planning table creation module, a phase unfolding module, a qualification modeling module, a registration module, and a gap indexing module: The planning and table building module is used to obtain population records, facility records, planning action records, and qualification rule records for the current planning cycle of the target area. Through object unification, location unification, and stage unification, it forms a basic planning table that includes object identifier, object location, stage, object category, and association rules. The phase expansion module is used to execute effective expansion based on the planning action records in the planning base table, generate the corresponding object change results according to the implementation stage, target, and scope of impact, and write them into the planning base table in the order of stages to form the phase expansion table. The qualification modeling module is used to perform qualification generation, qualification transfer and qualification cancellation for each population object in the phase expansion table, combined with the corresponding qualification rule records, to form the qualification results of each population object in each phase, and to build a dynamic model of qualification transfer between population objects and facility objects based on the qualification results of each phase, and output the phase qualification table and the qualification transfer dynamic model. The service registration module is used to write eligible population objects into the corresponding facility records in the stage qualification table according to qualification category and based on the qualification acceptance dynamic model. It completes the service registration one by one according to the available locations in the facility records. When multiple population objects correspond to the same available location, the population objects written first are retained and the remaining population objects are written to the next available location. When there are no remaining available locations in the corresponding facility records, it records that the population object forms a service gap in the current stage and outputs the stage service table. The gap indexing module is used to perform continuous comparisons according to the phase acceptance table in phase order, and to perform back-and-forth indexing on the acceptance results of the same population object in adjacent phases. It determines the failure qualification, gap phase and the planned action to trigger the failure qualification corresponding to each acceptance gap, and forms a dynamic simulation result including the acceptance results of each phase, acceptance gap records and gap source records.

[0023] Working Principle: This scheme first organizes various raw data related to urban planning into a unified base table that can be continuously extrapolated. Then, it unfolds step by step in the order of planning action taking effect, object status change, qualification formation and transfer, facility acceptance registration, and gap attribution. Specifically, it first unifies population records, facility records, planning action records, and qualification rule records under the same object, location, and stage. Then, planning actions such as road adjustments, school expansions, facility relocations, and service replacements are unfolded into object change results in stages. Subsequently, based on the qualification rules, it determines what service qualifications each population object has at each stage and establishes the acceptance relationship between them and facility objects. Next, it writes the qualified population objects into the corresponding facility acceptance locations in sequence, recording which objects have been accepted and which objects have experienced acceptance gaps in the current stage. Finally, it traces back along the stage sequence to find out whether the acceptance gap was caused by qualification failure, facility withdrawal, or planning action triggering, thus obtaining a dynamic simulation result that can not only show whether the acceptance was successful, but also why there was a gap, where it was, and what caused it. For example, in scenarios where comprehensive development around rail stations and urban renewal are carried out simultaneously, existing schools, community health service points, and parking facilities may be expanded, relocated, replaced, or withdrawn as the plan is implemented. The enrollment, medical care, and parking arrangements for new and existing residents will also change at different stages. This plan will first organize these residents, facilities, planning actions, and eligibility rules into the same stage chain, then determine whether each group of residents has the corresponding enrollment, medical care, or parking eligibility at each stage, and then write these residents into the corresponding school, health service point, or parking facility's location in sequence. If a stage's location is full, it will continue to the next location or the next stage. If it is impossible to accommodate all residents, it will be marked as a gap. In this way, planners can not only see who has been accommodated and who has not been accommodated at a certain stage, but also directly trace the gap to whether it was caused by the withdrawal of schools, cancellation of eligibility, or deletion of facility locations, thus more accurately judging whether the planning scheme is truly feasible in actual implementation.

[0024] The above description is merely 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 within the protection scope of the present invention.

Claims

1. A dynamic simulation method for smart city planning based on spatiotemporal big data, characterized in that, include: S1. For the current planning cycle of the target area, obtain population records, facility records, planning action records and qualification rule records, and form a basic planning table including object identifier, object location, stage, object category and association rules through object unification, location unification and stage unification; S2. Based on the planning action records in the planning base table, execute the effective deployment, generate the corresponding object change results according to the implementation stage, target and scope of impact, and write them into the planning base table in order of stage to form a stage deployment table. S3. For each population object in the stage expansion table, combine the corresponding qualification rule records to execute qualification generation, qualification transfer and qualification cancellation, form the qualification results of each population object in each stage, and construct a qualification transfer dynamic model between population objects and facility objects based on the qualification results of each stage, and output the stage qualification table and qualification transfer dynamic model. S4. In the stage qualification table, based on the qualification acceptance dynamic model, the population objects with corresponding service qualifications are written into the corresponding facility records in sequence according to the qualification category, and the acceptance registration is completed one by one according to the available locations in the facility records. When multiple population objects correspond to the same available location, the population object written first is retained, and the remaining population objects are continued to be written into the next available location. When there are no remaining available locations in the corresponding facility records, it is recorded that the population object forms a gap in acceptance in the current stage, and the stage acceptance table is output.

2. The method for dynamic simulation of smart city planning based on spatiotemporal big data according to claim 1, characterized in that: Also includes: S5. Perform continuous comparison according to the stage acceptance table in stages, and perform back-and-forth pointers on the acceptance results of the same population object in adjacent stages to determine the failure qualification, the stage of the failure, and the planned action that triggers the failure qualification for each acceptance gap, forming a dynamic simulation result including the acceptance results of each stage, the record of the acceptance gap, and the record of the source of the gap.

3. The method for dynamic simulation of smart city planning based on spatiotemporal big data according to claim 2, characterized in that: S1 includes: S11. For population records, facility records, planning action records, and qualification rule records, extract the object name, object type, region, associated objects, and record time. Perform the first round of merging on records with the same name, type, and region. Perform the continuation merging on records with different names but the same associated objects and the record time corresponding to the same planning round to form a unified object result table. S12. Based on the unified result table of objects, perform position rewriting on the address description, coordinate description and region description in each record. Write the position description that can directly correspond to the coordinates into the unified coordinate position, and map the position description that cannot directly correspond to the coordinates to the unified region position according to the region and associated object. Perform back-pointing on multiple positions of the same object in different records, retain the target position corresponding to the recording time, and form a unified position result table. S13. Based on the record time, planning action effective time, and qualification rule applicable time in the location unification result table, the execution stage is rearranged. Population and facility records earlier than the planning action effective time are written into the preceding stage, records corresponding to the planning action effective time are written into the current stage, and records later than the current stage and directly triggered by the planning action in the current stage are written into the subsequent stage to form the planning base table.

4. The method for dynamic simulation of smart city planning based on spatiotemporal big data according to claim 3, characterized in that: S2 includes: S21. For each planning action record in the planning base table, extract the action effective time, action duration interval, target object and scope of influence, sort each planning action record according to the action effective time, and perform a succession search on the planning action records before and after the same target object to form an action sequence table. S22. Based on the action priority table, each planned action record is expanded according to the target and scope of influence. The addition, deletion, migration, replacement and scope change corresponding to the planned action record are written as object change items. Multiple object change items corresponding to the same target in the same implementation stage are merged before and after execution to form a stage change table. S23. Based on the phase change table, write the changes of each object into the planning base table one by one according to the implementation phase sequence, perform overwrite update on the object status formed in the current implementation phase, and perform inheritance write for subsequent implementation phases to form a phase expansion table.

5. The method for dynamic simulation of smart city planning based on spatiotemporal big data according to claim 4, characterized in that: S3 includes: S31. For each population object in the stage expansion table, extract the stage order, rule-affected object, qualification category, qualification source, and qualification destination by combining the corresponding qualification rule record. Perform qualification candidate expansion for each population object between adjacent stages to construct a population object qualification candidate graph. In the population object qualification candidate graph, only retain the candidate edges that simultaneously satisfy the following conditions: continuous stage order, consistent object identifier, corresponding rule-affected object, consistent qualification category, and the same qualification source not written to the same population object. Connect the retained candidate edges end to end according to the stage order to form a candidate qualification linked list. S32. Based on the candidate qualification chain, perform forward succession verification, backward back-pointing verification, and same-stage circumstantial verification on each candidate qualification chain for each population object; when the qualification source of a candidate qualification chain in the current stage corresponds to the qualification destination in the previous stage, the qualification node in the current stage can back-point to generate the planning action record of the qualification node, and there is a facility object record corresponding to the qualification category in the same stage, write the candidate qualification chain into the target qualification chain; when there are multiple target qualification chains for the same population object, retain a unique target qualification chain in the order of rule effective time, qualification source record order, and facility object record order, and perform qualification cancellation or qualification transfer rewriting on the remaining target qualification chains to form a stage qualification table.

6. The method for dynamic simulation of smart city planning based on spatiotemporal big data according to claim 5, characterized in that: S3 also includes: S33. Based on the stage eligibility table, extract the eligibility category, eligibility destination, and stage acceptance order of each population object in each stage. Combine this with the facility object's facility category, service scope, acceptance position order, and position occupancy status in the corresponding stage to construct a dynamic eligibility acceptance diagram between population objects and facility objects. In the eligibility acceptance dynamic diagram, perform acceptance connections between population objects and facility objects that correspond in eligibility category, stage, and eligibility destination. For cases where the same population object corresponds to multiple facility object connections, retain a unique acceptance connection according to the facility object's recording order. For cases where the same facility object has multiple acceptance connections at the same acceptance position, retain a unique acceptance connection according to the population object's recording order when entering the current stage. Write the remaining unretained acceptance connections back to the next acceptance position or the next stage to continue until all population objects in the current stage have completed acceptance registration or a gap in acceptance has been formed, thus obtaining the eligibility acceptance dynamic model.

7. The method for dynamic simulation of smart city planning based on spatiotemporal big data according to claim 6, characterized in that: S4 includes: S41. For each population object in the stage qualification table, extract the stage identifier, qualification category, qualification destination, entry order and previous acceptance result. Combine the facility objects, acceptance position order, position occupancy status and stage connection relationship in the qualification acceptance dynamic model. According to the rules of qualification category correspondence, stage identifier correspondence, qualification destination correspondence and previous acceptance result continuation, generate an acceptance request sequence consisting of target facility objects and their acceptance position order for each population object. At the same time, generate a stage position status table consisting of each acceptance position of each facility object. S42. Based on the acceptance request sequence and the stage position status table, execute acceptance writing in the order of population object entry; when the current acceptance position is an empty position, write the population object to the acceptance position and register the acceptance result; when the current acceptance position has other population objects registered, extract the next acceptance position of the registered population object in the acceptance request sequence, form a position transfer chain with the current population object and the registered population object, and search for an empty position along the position transfer chain; when an empty position is found, rewrite the registered population objects in each acceptance position in reverse order of the position transfer chain; when no empty position is found in all searchable acceptance positions, maintain the original registration result in the stage position status table and write the current population object to the acceptance gap queue.

8. The method for dynamic simulation of smart city planning based on spatiotemporal big data according to claim 7, characterized in that: S4 also includes: S43. Based on the stage position status table after acceptance and writing, perform acceptance closure verification, position uniqueness verification, and stage continuation verification on each population object; when the same population object corresponds to multiple acceptance positions, retain the acceptance registration with the earliest record time and delete the rest of the acceptance registrations; when the same acceptance position corresponds to multiple acceptance registrations, retain the acceptance registration that remains at the acceptance position after being rewritten in reverse by the position transfer chain and delete the rest of the acceptance registrations; when a population object has not formed an acceptance registration and there is a corresponding record in the acceptance gap queue, record the population object as the current stage acceptance gap, and form a stage acceptance table by combining the retained acceptance registrations and acceptance gap records.

9. The method for dynamic simulation of smart city planning based on spatiotemporal big data according to claim 8, characterized in that: S5 includes: S51. For each population object in the phase transfer table, extract the phase identifier, transfer result, transfer facility object, transfer location, qualification category, qualification source and corresponding planning action identifier, and perform front and back connection for the same population object between adjacent phases according to the phase sequence to form a phase transfer chain list including continuous transfer phase, transfer transfer phase and transfer gap phase. S52. Based on the stage-based connection list, perform reverse backfinding for each connection gap segment, and retrieve the changes in eligibility sources, connection facility objects, and planning actions of the same population object stage by stage forward along the gap stage, and write them into the records; when there is a qualification category deletion record in a certain preceding stage, record the qualification corresponding to the qualification category deletion record as invalid qualification; when there is a connection facility object withdrawal record or connection location deletion record in a certain preceding stage, record the planning action corresponding to the withdrawal record or deletion record as triggered planning action; when there are no qualification category deletion records, connection facility object withdrawal records, or connection location deletion records between adjacent stages, record the population object corresponding to the gap stage as not having generated connection eligibility, forming a gap source table; S53. Based on the phase succession list and the gap source list, the continuous succession segment, succession transfer segment, succession gap segment, failure qualification and triggering planning action are collected for each population object in phase order to form a dynamic simulation result. Among them, the gap source record in the dynamic simulation result includes at least the population object identifier, gap stage, failure qualification identifier and triggering planning action identifier.

10. A smart city planning dynamic simulation system based on spatiotemporal big data, used to implement the smart city planning dynamic simulation method based on spatiotemporal big data as described in any one of claims 1-9, the system comprising a planning table creation module, a phase expansion module, a qualification modeling module, a registration module, and a gap indexing module, characterized in that: The planning and table building module is used to obtain population records, facility records, planning action records, and qualification rule records for the current planning cycle of the target area. Through object unification, location unification, and stage unification, it forms a basic planning table that includes object identifier, object location, stage, object category, and association rules. The phase expansion module is used to execute effective expansion based on the planning action records in the planning base table, generate the corresponding object change results according to the implementation stage, target, and scope of impact, and write them into the planning base table in the order of stages to form the phase expansion table. The qualification modeling module is used to perform qualification generation, qualification transfer and qualification cancellation for each population object in the phase expansion table, combined with the corresponding qualification rule records, to form the qualification results of each population object in each phase, and to build a dynamic model of qualification transfer between population objects and facility objects based on the qualification results of each phase, and output the phase qualification table and the qualification transfer dynamic model. The service registration module is used to write eligible population objects into the corresponding facility records in the stage qualification table according to qualification category and based on the qualification acceptance dynamic model. It completes the service registration one by one according to the available locations in the facility records. When multiple population objects correspond to the same available location, the population objects written first are retained and the remaining population objects are written to the next available location. When there are no remaining available locations in the corresponding facility records, it records that the population object forms a service gap in the current stage and outputs the stage service table. The gap indexing module is used to perform continuous comparisons according to the phase acceptance table in phase order, and to perform back-and-forth indexing on the acceptance results of the same population object in adjacent phases. It determines the failure qualification, gap phase and the planned action to trigger the failure qualification corresponding to each acceptance gap, and forms a dynamic simulation result including the acceptance results of each phase, acceptance gap records and gap source records.

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