A Dynamic Collaborative Governance Method for Migrant Population Based on the Transformation of Five Roles

Abstract

This invention discloses a dynamic collaborative governance method for the floating population based on the transformation of five roles, belonging to the field of community governance technology. Specifically, it includes: collecting information on floating population events and the spatial coordinates and functional attributes of governance entities; activating corresponding role attributes based on the matching relationship between event type and spatial coordinates; constructing a spatiotemporal simulation sandbox based on historical data; injecting role behavior characteristic parameters; simulating collaborative paths using a Monte Carlo algorithm; and outputting a collaborative chain containing execution order and priority weights; generating role-specific instruction sets based on the collaborative chain; distributing adapted tasks to each role; comparing instruction logic relationships in real time; freezing the execution flow and initiating a phased arbitration process when conflicts are detected; recording role operation trajectories to generate a behavior deviation rate matrix; and updating sandbox parameters and role binding conditions through a dynamic learning algorithm. This invention achieves precise collaboration and continuous optimization in the governance of the floating population through dynamic role transformation and intelligent collaborative simulation.

Description

Technical Field

[0001] This invention relates to the field of community governance technology, specifically to a dynamic collaborative governance method for the floating population based on the transformation of five roles. Background Technology

[0002] With rapid economic and social development, the scale of population mobility continues to expand, making the service and management of the floating population an important component of grassroots social governance. As the process of new urbanization accelerates, population mobility exhibits characteristics such as high frequency, wide scope, and complex structure. The traditional static management model based on household registration is insufficient to meet the needs of dynamic governance. How to achieve collaborative participation from multiple stakeholders and accurately respond to the service and security management needs of the floating population has become a key issue in improving the effectiveness of grassroots governance. Against this backdrop, integrating the forces of government departments, community organizations, and residents to build a dynamic and collaborative governance system has become an important direction for addressing the challenges of population mobility governance.

[0003] Currently, existing technological solutions for managing the floating population largely revolve around information technology. Examples include using government service platforms to register and share floating population information, utilizing geographic information systems to delineate management grids, and relying on big data analysis to predict population flow trends. Some solutions introduce the concept of multiple governance stakeholders, such as establishing collaborative mechanisms involving landlords, community workers, and police officers, improving management efficiency by clearly defining responsibilities and authority. Other technologies employ virtual simulation methods to model governance processes, providing decision support for resource allocation. These technologies, to a certain extent, have driven the transformation of floating population management from manual to digital, and from a single entity to multiple stakeholders.

[0004] However, existing technical solutions lack dynamic adaptability in the collaboration of governance entities. Governance roles are mostly fixed and cannot be changed in real time according to factors such as event type and spatial location, resulting in low efficiency in cross-scenario response. Secondly, the collaboration process lacks refined simulation and relies on preset rules to allocate tasks without considering dynamic factors such as the behavioral characteristics of the entities and spatiotemporal constraints, which can easily lead to resource mismatch or process conflict. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic collaborative governance method for the floating population based on the transformation of five roles, and to solve the following technical problems:

[0006] In existing technologies, the collaboration of governance entities lacks dynamic adaptation. Roles are fixed and it is difficult to change functions in real time according to event type and spatial location. Furthermore, the collaboration process lacks refined simulation, relies on preset rules, and does not consider dynamic factors.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] The dynamic collaborative governance method for the floating population based on the transformation of five roles includes the following steps:

[0009] Collect information on incidents involving the floating population and the spatial coordinates and functional attributes of the five governance entities, including landlords and business owners, community police officers, auxiliary police officers, police station chiefs, and residents of the jurisdiction;

[0010] The five roles are activated based on the matching relationship between event type and spatial coordinates. A spatiotemporal simulation sandbox with timestamps is constructed based on historical data. Behavioral feature parameters of the five roles are injected. The Monte Carlo algorithm is used to simulate the role collaboration path. The simulation output includes a collaboration chain containing execution order and priority weight.

[0011] Based on the collaboration chain, a set of instructions specific to each role is generated, an information verification list is sent to the assistant administrator role, a data anomaly prompt is pushed to the supervisor role, a conflict handling plan is distributed to the referee role, a resource distribution heat map is transmitted to the commander role, and behavior identification rules are issued to the intelligence officer role.

[0012] Real-time comparison of the logical relationships between the instructions of each role; when a conflict is detected between the commander’s resource allocation instructions and the referee’s on-site handling instructions, the execution process is frozen and a phased arbitration process is initiated.

[0013] The operation trajectories of the five roles are recorded to generate a behavior deviation rate matrix, and the sandbox role parameters and role binding conditions are updated through a dynamic learning algorithm.

[0014] As a further aspect of the present invention: the activation of the five role attributes based on the matching relationship between event type and spatial coordinates includes:

[0015] Rental housing management incidents activate the landlord's role as an assistant manager; security patrol incidents activate the police auxiliary personnel's role as inspectors; public service consultation incidents activate the community police officers' role as referees; resource allocation incidents activate the police station chief's role as a commander; and abnormal behavior reporting incidents activate the residents' role as informants.

[0016] Role transformation is triggered by the geofence. When the governing entity enters the preset geofence, the corresponding role operation module is automatically loaded. When an event triggers both resource scheduling and conflict mediation instructions, a permission stacking mechanism is set up, allowing the referee role to temporarily call the commander role's data interface during execution.

[0017] As a further aspect of the present invention: when the original role is unable to respond due to the failure of the geofence, the functional similarity and spatial accessibility of the surrounding governance entities are scanned, the spherical distance weight between the governance entity and the event point is calculated, and the historical operation accuracy score of the governance entity is superimposed to generate a role inheritance priority list.

[0018] After the commander confirms the final inheritance plan, he pushes the role conversion instruction and permission package to the target governance entity, and updates the role distribution parameters in the sand table simulation.

[0019] As a further aspect of the present invention: the process of spatiotemporal simulation sand table simulation is as follows:

[0020] By aggregating population migration trajectory density data and police deployment response path data from historical events, a three-dimensional virtual space model with timestamps is formed. The dynamic behavioral parameter set of five roles is injected into the virtual space model, including the normal distribution function of the registration delay rate of assistant administrators, the decay curve of the verification accuracy of supervisors, and the fluctuation coefficient of the mediation success rate of referees.

[0021] The Monte Carlo random path simulation algorithm is used, with role response delay rate and decision bias as input variables, to calculate the governance efficiency of different collaboration sequences of five roles, and output the percentile value of event resolution time and resource consumption coefficient; and a real-time data calibration channel is established to input the traffic network congestion index into the spatial mobility resistance model in real time, and dynamically update the geographic grid travel time cost parameters.

[0022] As a further aspect of the present invention: when the resolution time of the collaborative chain output by the sand table exceeds the historical benchmark value, the scene repair process is activated, the abnormal population density value and resource coverage gap value of the current population event are extracted, extreme weather interference factors or sudden mass event variables are injected into the spatiotemporal simulation sand table, the collaborative path of the five roles under pressure scenario is re-simulated, the repaired collaborative chain is compared with the original chain, and a role stress resistance assessment report is generated.

[0023] As a further aspect of the present invention: the generation of role-specific instruction sets based on the collaboration chain includes:

[0024] The assistant administrator's instructions include a structured tenant identity verification field library and a registration deadline countdown reminder module triggered by geographical location; the inspector's instructions integrate an automatic data contradiction marking engine and a cross-departmental review query path planner; the referee's instructions embed a legal clause intelligent matching matrix and a multi-branch mediation process decision tree; the commander's instructions overlay a real-time resource distribution heat map layer and a scheduling priority identification algorithm based on event urgency; the intelligence officer's instructions encapsulate an abnormal behavior feature vector library and a standardized reporting format validator. Each instruction component is dynamically sorted according to the priority of the collaboration chain output by the sand table simulation.

[0025] As a further aspect of the present invention: the phased arbitration process specifically comprises:

[0026] The system analyzes the resource call identifiers and action codes in the five-member instructions in real time. When it detects that there is a logical mutual exclusion between the commander's resource blocking instruction and the referee's on-site intervention instruction, it immediately freezes the instruction execution thread and initiates a phased arbitration process.

[0027] The first stage calls the sandbox engine to regenerate and optimize the collaboration chain with the current event state as input. The second stage hands over to the referee role for manual decision-making and marks the basis for conflict resolution. The third stage pushes electronic voting requests to the intelligence officer role and calculates the confidence level of the voting results according to the preset weight formula. A conflict knowledge base is established to store the spatiotemporal feature matrix of the contradiction points of the instructions and the arbitration path, which is used to train the conflict probability prediction model of the sandbox simulation engine.

[0028] As a further aspect of the present invention: the process of generating a behavior deviation rate matrix by recording the operation trajectories of the five roles is as follows:

[0029] Construct a three-dimensional matrix of the behavior deviation rate of five members. The vertical dimension records the role response time offset, the horizontal dimension counts the operation accuracy fluctuation value, and the depth dimension analyzes the instruction execution completeness.

[0030] The Bayesian inference algorithm is used to transform the excessive delay rate of the assistant administrator's registration into a time cost correction coefficient in the sand table model, and the peak error rate of the supervisor's verification is mapped to the inflection point threshold of the model's accuracy decay curve.

[0031] A dynamic learning feedback loop is established. When the referee's mediation success rate falls below the benchmark value for an extended period, the skill retraining process in the role binding module is automatically triggered, and the decision preference parameters of that role in the sand table simulation are updated.

[0032] As a further aspect of the present invention: the updating of sandbox character parameters and character binding includes:

[0033] The error rate of assistant administrator information verification exceeding the standard is converted into an adjustment coefficient for the registration delay rate weight in the sand table model. The conversion formula is established based on the Pearson correlation between historical error rate and delay rate.

[0034] The time deviation of the inspector's data review is mapped to a dynamic decay factor of the accuracy threshold of the sand table inspection, and the mapping relationship is fitted by a time series regression model.

[0035] The updated sandbox parameters are fed back to the character attribute activation process in real time, triggering the assistant administrator identity authentication enhancement process or the supervisor skill assessment mechanism.

[0036] The beneficial effects of this invention are:

[0037] This invention addresses the issues of fixed role allocation and difficulty in real-time function conversion by collecting information on five governance entities and dynamically activating their role attributes based on event type and spatial coordinates. It improves cross-scenario response efficiency by leveraging geofencing to trigger role conversion and permission stacking mechanisms. Furthermore, by constructing a timestamped virtual sandbox, injecting role behavior characteristic parameters, and employing a Monte Carlo algorithm to simulate collaboration paths, combined with real-time data calibration to dynamically update spatiotemporal parameters, it solves the problems of lacking refined deduction of collaboration processes and relying on preset rules, achieving precise collaboration chain planning that considers entity behavior characteristics and spatiotemporal constraints. By generating role-specific instruction sets and pushing customized tasks to different roles, it ensures precise matching of instructions with role functions. A phased arbitration process effectively resolves instruction logic conflicts, and training a prediction model using a conflict knowledge base reduces resource misallocation. Finally, by recording role operation trajectories to generate a behavior deviation rate matrix and using a dynamic learning algorithm to update sandbox parameters and role binding conditions, a closed-loop optimization mechanism is formed, continuously improving governance efficiency and ultimately achieving precise, efficient, and adaptive optimization of dynamic collaborative governance of the floating population. Attached Figure Description

[0038] The invention will now be further described with reference to the accompanying drawings.

[0039] Figure 1 This is a flowchart illustrating the present invention. Detailed Implementation

[0040] 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.

[0041] Please see Figure 1 As shown, this invention is a dynamic collaborative governance method for the floating population based on the transformation of five roles, including the following steps:

[0042] Step 1: Collect information on incidents involving the floating population and the spatial coordinates and functional attributes of the five governance entities, specifically covering the housing rental-related information of landlords and business owners, the patrol duties of auxiliary police officers, the jurisdictional service authority of community police officers, the resource allocation authority of police station chiefs, and the residential areas of residents in the jurisdiction, while recording key information such as the type of incident, location of occurrence, and personnel involved.

[0043] Step 2: Activate the attributes of the five roles based on the matching relationship between event type and spatial coordinates. For example, in rental housing management events, activate the assistant administrator attribute of landlords and property owners; in public security events, activate the inspector attribute of police auxiliary personnel. Construct a spatiotemporal simulation sandbox with timestamps based on historical governance data, and inject behavioral characteristic parameters of the five roles, including the information registration habits of assistant administrators, the verification response speed of inspectors, and the dispute mediation tendency of referees. Use the Monte Carlo algorithm to simulate the role collaboration path in different scenarios, comprehensively consider factors such as spatial distance and functional matching degree, and output a collaboration chain that includes execution order and priority weight.

[0044] Step 3: Generate a set of instructions specific to each role based on the collaboration chain, send an information verification list containing tenant identity, length of stay and other information to the assistant administrators, push abnormal prompts such as inconsistent data and delayed updates to the supervisors, distribute step-by-step conflict handling plans to the referees, transmit resource heat maps such as police force distribution and material reserves to the commanders, and issue identification rules such as suspicious behavior characteristics and reporting standards to the intelligence officers.

[0045] Step 4: Compare the logical relationships of the instructions of each role in real time. When a conflict is detected between the resource dispatch instructions of the commander and the on-site handling instructions of the referee, the execution flow is immediately frozen and a phased arbitration process is initiated. First, the collaboration path is optimized by re-deducing through a sand table. Then, the referee makes a decision manually and marks the basis. Finally, the final solution is formed by combining the feedback from the residents in the jurisdiction.

[0046] Step 5: Record the operation trajectory of the five roles, generate a behavior deviation rate matrix covering dimensions such as response time offset and operation accuracy fluctuation, and use a dynamic learning algorithm to transform this data into the basis for adjusting the sandbox role parameters. At the same time, update the role binding conditions to ensure that the role attributes are continuously adapted to the actual governance needs.

[0047] In step 2, activating the five role attributes based on the matching relationship between event type and spatial coordinates includes:

[0048] The five roles are activated based on the matching relationship between event type and spatial coordinates, specifically by precisely binding different event scenarios with the functions of the corresponding governance entities. When rental housing management events occur, such as tenant registration and updates to rental information, the landlord's role as an assistant administrator is activated. The landlord then assumes responsibility for collecting basic tenant information and recording occupancy dynamics, becoming a frontline force in collecting information on the floating population. If security patrol events occur, including routine community patrols and security checks in key areas, the auxiliary police officer's role as a supervisor is activated, responsible for verifying the authenticity of registered information and investigating potential safety hazards in rental housing. When public service consultation events occur, such as answering questions about social security procedures for the floating population and residence permit applications, the community police officer's role as a referee is triggered, requiring them to provide standardized guidance based on policies and regulations, while also mediating disputes arising from service requests. In resource allocation events, such as the need to deploy additional police force for sudden security incidents or allocate security supplies for large-scale events, the police station chief's role as a commander is activated, coordinating the allocation and deployment of various governance resources. If abnormal behavior is reported, such as frequent gatherings of strangers or the storage of suspicious items, the residents of the jurisdiction will be able to report abnormal information through dedicated channels, thus becoming the last tentacles of public security prevention and control.

[0049] By triggering role transformation through geofencing, electronic fences are pre-defined within the jurisdiction to delineate different functional areas, such as residential communities, commercial streets, and industrial parks, into independent fence units. When a governance entity enters a pre-defined electronic fence, its terminal device automatically loads the corresponding role's operation module. For example, when a police auxiliary officer enters a residential community fence, the terminal will display the area's migrant population registration ledger, list of key personnel, and other supervisor-specific functions; when a community police officer enters a government service center fence, the terminal will automatically display the public service policy database, mediation document templates, and other referee-specific tools. When an event simultaneously triggers resource allocation and conflict mediation instructions, such as a rental housing dispute leading to a mass conflict, requiring both conflict mediation and the deployment of surrounding police forces, a permission stacking mechanism is set up. During the mediation process, the referee role can temporarily access the commander role's data interface to view real-time police force distribution, material reserves, and other information, ensuring that the mediation plan matches the resource allocation pace and avoiding inefficiencies due to information fragmentation.

[0050] When a designated role becomes unresponsive due to geofencing failure—for example, a coordinator responsible for a certain area temporarily leaving the geofence or a terminal signal interruption—a role inheritance mechanism is activated. First, other governance entities within a certain radius are scanned, comparing the similarity between their original functions and the functions required to handle the event. Simultaneously, the spatial accessibility of these entities to the event point is assessed, including factors such as traffic conditions and distance. A spherical distance weight is calculated between the governance entity and the event point, with closer distances resulting in higher weights. Then, the governance entity's historical operational accuracy score is overlaid; higher accuracy in handling similar events in the past results in a higher score. These two factors combined generate a role inheritance priority list, clearly displaying the matching degree ranking of each potential successor entity.

[0051] The commander reviews the priority list, considering factors such as the urgency of the event and the current workload of the entity, to confirm the final succession plan. Then, a role conversion instruction and permission package are pushed to the target governance entity. The permission package includes information query permissions and instruction issuance permissions required for temporary duties. For example, if a coordinator originally responsible for a commercial district is assigned to take over the responsibilities of a community coordinator, their terminal will receive a conversion instruction and simultaneously obtain housing information and tenant registration permissions for that community. At the same time, the role distribution parameters in the spatiotemporal simulation sandbox are updated synchronously, injecting the new role allocation and permission scope into the sandbox to ensure that subsequent collaborative path simulations are based on the latest role configuration and maintain the continuity of the governance process.

[0052] In step 2, the process of the spatiotemporal simulation sand table is as follows:

[0053] The simulation process of the spatiotemporal model begins with the aggregation of historical data. Population migration trajectory density data and police deployment response path data from historical events are automatically compiled. The former includes records of the migration direction and aggregation areas of the floating population within the jurisdiction at different times, while the latter contains archives such as police response routes and police deployment trajectories in various events. These data are marked in chronological order, naturally forming a three-dimensional virtual space model that includes geographical coordinates, time nodes, and event types. The model can intuitively present the population flow trends and the distribution of governance resources at different historical moments.

[0054] Subsequently, the dynamic behavioral parameter set of the five roles was injected into the virtual space model. Among them, the normal distribution function of the assistant administrator's registration delay rate reflects the deviation between the completion time and the standard duration when registering landlord / owner information, such as the common range of registration delays under different time periods and event types; the inspector's verification accuracy decay curve reflects the trend of accuracy changes with time or workload when auxiliary police officers continuously perform verification tasks, such as the fluctuation of verification error rate after long working hours; the referee's mediation success rate fluctuation coefficient is related to the fluctuation characteristics of the success rate when community police officers handle different conflicts, such as rental disputes and neighborhood conflicts, and is related to factors such as the complexity of the event and the number of people involved. These parameters together constitute a digital profile of the role behavior, providing a foundation for collaborative simulation.

[0055] The simulation employed a Monte Carlo random path algorithm, using role response delay rate and decision deviation as input variables. Role response delay rate refers to the time difference between receiving an instruction and taking actual action, while decision deviation represents the degree of deviation between the actual operation and the standard procedure. The algorithm randomly generates multiple possible collaborative sequences, considering the participation order and coordination methods of different roles, and calculates the governance effectiveness of each sequence. The process comprehensively evaluates the smoothness of role coordination and the rationality of resource utilization, ultimately outputting the percentile value of event resolution time and the resource consumption coefficient. The former reflects the position of the resolution time of the collaborative sequence within the historical processing time of similar events, while the latter reflects the efficiency of the use of human, material, and other resources within the sequence.

[0056] Simultaneously, a real-time data calibration channel is established, through which traffic network congestion indices are fed into the simulation process in real time. These indices are derived from traffic monitoring records and road condition reports within the jurisdiction. The congestion index is input into the spatial mobility resistance model, dynamically adjusting the travel time cost parameters of the geographic grid. For example, when congestion occurs on a certain road segment, the travel time of the grid in that area will increase accordingly, ensuring that the estimated movement time of characters in the sandbox is consistent with real-world road conditions and that the simulation results closely match actual traffic conditions.

[0057] When the resolution time of the collaborative chain output by the sand table exceeds the historical benchmark, the scenario repair process is automatically activated. Anomalies in population density (such as a sudden increase in the number of migrant workers in a region exceeding the historical average for the same period) and resource coverage gaps (i.e., the difference between existing police force, resources, etc., and the needs of the event) are extracted from the current population event. Then, extreme weather interference factors (such as heavy rain causing traffic difficulties in some areas) or sudden group event variables (such as disputes in a rental property) are injected into the spatiotemporal simulation sand table to re-simulate the collaborative paths of the five roles under these stressful scenarios. The repaired collaborative chain is compared with the original chain to analyze differences in role response speed, resource allocation methods, and process connections. A role stress resistance assessment report is generated, containing the performance characteristics of each role under stress, such as changes in the information collection efficiency of assistant administrators and the resource scheduling flexibility of commanders. This report is then sent to step 5 for deviation optimization.

[0058] In step 3, generating a role-specific instruction set based on the collaboration chain includes:

[0059] Based on the role-specific instruction set generated by the collaboration chain, differentiated components are designed for the functional characteristics of different roles. The assistant administrator's instructions include a structured tenant identity verification field library, covering core information items such as name, ID number, place of residence, check-in time, and contact information. Each field has preset standardized filling requirements. At the same time, a registration time countdown reminder module triggered by geolocation is built in. When the assistant administrator enters the geographical area of ​​the rental property, the reminder is automatically activated, and the remaining valid time for information registration is displayed in real time to avoid omissions or delays.

[0060] The inspector's instructions integrate an automatic data contradiction marking engine, which can automatically identify inconsistencies in information during the verification process, such as discrepancies between registered address and actual residence address, or conflicts between identity information and historical records. Combined with a cross-departmental review query path planner, it clearly marks the source department of the data to be verified and the query steps, such as guiding the connection to the social security system to verify employment information, and linking to housing and construction archives to confirm the property type, thereby improving the efficiency of review.

[0061] The referee's instructions are embedded in a smart matching matrix of legal clauses, which automatically associates the corresponding legal entries according to the type of conflict. For example, rental disputes are matched with relevant clauses of the Contract Law, and neighborhood conflicts are matched with the content of the Public Security Administration Punishment Law. At the same time, a multi-branch mediation process decision tree is built in, which divides the handling path according to the severity of the event and the number of people involved, and provides tiered operation guidance from fact verification, communication of demands to negotiation of solutions.

[0062] Commander instructions are overlaid with a real-time resource distribution heatmap, which intuitively presents the real-time distribution status of resources such as police force locations, material reserve points, and emergency vehicles within the jurisdiction; combined with dispatch priority indicators based on the urgency of the event, the order of handling is automatically marked according to the nature of the event, ensuring that critical resources are prioritized for emergency scenarios.

[0063] The intelligence officer's instructions encapsulate an abnormal behavior feature vector library, including typical characteristic descriptions such as frequent late-night visitors, multiple changes of address in a short period, and deliberate concealment of identity; it is equipped with a standardized reporting format validator, which automatically checks whether the reported content contains required elements such as time, location, and specific behavior, ensuring that the information is complete and standardized. All instruction components are dynamically sorted according to the priority of the collaboration chain output by the sand table simulation, with core task modules displayed first, and auxiliary functions displayed later.

[0064] In step 4, the phased arbitration process is specifically as follows:

[0065] The phased arbitration process begins with real-time instruction parsing, continuously identifying resource mobilization identifiers and action codes within the five-person instruction set. Resource mobilization identifiers clearly indicate specific resource information such as required police force allocation, material types, and scope of authority. Action codes correspond to various operational instructions such as resource blocking, on-site intervention, and cross-departmental coordination. When a logical conflict is detected between a commander's resource blocking instruction and a referee's on-site intervention instruction—for example, a commander requests a temporary blockade of police resources in a certain area to prepare for an upcoming large-scale event, while a referee needs to immediately mobilize police resources in that area for on-site mediation due to a sudden rental dispute—the instruction execution thread will immediately pause, and the phased arbitration process will begin simultaneously.

[0066] The first phase utilizes the sandbox engine, taking real-time information such as the specific location of the current event, the number of people involved, the current distribution of resources, and surrounding traffic conditions as input parameters. Multiple rounds of collaborative path simulation are then conducted to generate an optimized collaborative chain. This new collaborative chain comprehensively balances resource scheduling efficiency with on-site handling needs, providing data-driven path references for conflict resolution.

[0067] The second phase involves transferring conflict resolution authority to referees for manual decision-making. Referees, considering the actual situation on-site, relevant legal provisions, experience in handling similar historical cases, and optimization suggestions from scenario simulations, formulate specific conflict solutions. They meticulously document the rationale behind their decisions, including reasons for prioritizing certain needs, specific clauses of the management regulations cited, and assessments of potential impacts, ensuring a transparent and systematic decision-making process.

[0068] The third phase involves sending electronic voting requests to intelligence officers to gather their feedback on conflict resolution solutions. The voting results are then comprehensively calculated based on preset weights, such as the intelligence officers' historical accuracy in judgment and their familiarity with similar events, to form a confidence assessment of the voting results. This assessment serves as a supplementary reference for the referees' decisions, enhancing the objectivity and applicability of the solutions.

[0069] During this process, a conflict knowledge base is established simultaneously. The system records the spatiotemporal feature matrix of each command conflict point, covering information such as the specific geographical location, time node, event type, and resource type involved in the conflict. It also stores the corresponding arbitration path, decision basis, and final processing result. This data is continuously used to train the conflict probability prediction model of the sand table simulation engine, constantly improving the engine's ability to predict potential command conflicts and reducing the frequency of subsequent similar conflicts.

[0070] In step 5, the specific process of recording the operation trajectories of the five characters to generate a behavior deviation rate matrix, and updating the sandbox character parameters and character binding conditions through a dynamic learning algorithm is as follows:

[0071] The process of recording the operational trajectories of five roles to generate a behavioral deviation rate matrix begins with constructing a three-dimensional behavioral deviation rate matrix for the five roles. The vertical dimension specifically records the role's response time offset, i.e., the difference between the time from receiving the instruction to actually initiating the operation and the standard response time, clearly showing the fluctuations in each role's response speed under different event types and time periods. The horizontal dimension statistically analyzes the fluctuation values ​​of operational accuracy, quantifying the changes in accuracy of each role in information registration, data verification, and conflict mediation by comparing actual operation results with standard process requirements. The depth dimension analyzes the completeness of instruction execution, considering whether the role has completed the proportion of each task in the instruction, including whether key steps were omitted and whether all operations were completed within the specified time. These three dimensions are interconnected and together constitute a comprehensive evaluation framework reflecting role behavioral deviations.

[0072] Using a Bayesian inference algorithm, events indicating excessive delays in assistant administrator registration are transformed into time cost correction coefficients in the sandbox model. When assistant administrators exceed the standard time limit in multiple rental information registrations, these events are processed by the algorithm as adjustment parameters to correct the sandbox model's estimation of assistant administrator operation time, making the model more closely reflect actual execution efficiency. Simultaneously, the peak error rate of inspectors is mapped to the inflection point threshold of the model's accuracy decay curve. When inspectors experience a sudden increase in error rate during continuous inspection tasks, this peak is marked as the inflection point of the curve, used to adjust the model's simulation of accuracy changes after prolonged work, ensuring that the simulation results reflect real-world working conditions.

[0073] A dynamic learning feedback loop is established to continuously track the performance of each role. When a referee's mediation success rate consistently falls below the benchmark, it indicates a potential deficiency in that role's conflict resolution capabilities. In this case, the skill retraining process in the role-binding module is automatically triggered, pushing targeted training content such as complex dispute mediation techniques and interpretations of the latest regulations. Simultaneously, the role's decision-making preference parameters are updated during the simulation exercise, such as adjusting the estimated mediation time and modifying the coordination weights when collaborating with other roles, making the simulation results more consistent with the current actual capability level.

[0074] The process of updating the sandbox role parameters and role binding is closely integrated with deviation data from actual role operations. Events where the information verification error rate of assistant administrators exceeds the standard are converted into an adjustment coefficient for the registration delay rate weight in the sandbox model. This conversion is based on the Pearson correlation between historical error rates and delay rates; that is, by analyzing the correlation patterns between the two in past data, it determines how to adjust the weight ratio of the delay rate in the model when the error rate increases, so that the model can more accurately reflect the relationship between information verification quality and time consumption.

[0075] The deviation in the data review time of the inspectors is mapped to a dynamic decay factor of the accuracy threshold of the sand table inspection. This mapping relationship is fitted by a time series regression model. That is, according to the changing pattern of the review time deviation and the inspection accuracy of the inspectors in different time periods, the correlation pattern between the two is established. When the review time deviation occurs, the decay rate of the accuracy threshold can be automatically adjusted, so that the model can simulate the working status of the inspectors more closely with reality.

[0076] The updated sandbox parameters are fed back in real time to the role attribute activation step in step 2, forming a closed-loop adjustment. When the information verification error rate of assistant administrators continues to exceed the standard, parameter changes will trigger an enhanced identity authentication process for assistant administrators, adding identity verification steps during information verification, such as requiring the uploading of tenant ID photos and real-time comparison with the public security system, to improve information accuracy. When the data review time of supervisors deviates significantly, a supervisor skills assessment mechanism will be triggered, testing their verification capabilities through simulated tasks. The assessment results will affect the permission configuration during subsequent role attribute activation, such as temporarily restricting the allocation of complex tasks, and gradually relaxing restrictions as capabilities improve. This dynamic adjustment mechanism ensures that the sandbox model and the actual performance of the roles remain synchronized, enabling the entire collaborative governance process to continuously adapt to changes in actual needs.

[0077] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A dynamic collaborative governance method for the floating population based on the transformation of five roles, characterized in that: Includes the following steps: Collect information on incidents involving the floating population and the spatial coordinates and functional attributes of the five governance entities, including landlords and business owners, community police officers, auxiliary police officers, police station chiefs, and residents of the jurisdiction; The five roles are activated based on the matching relationship between event type and spatial coordinates. A spatiotemporal simulation sandbox with timestamps is constructed based on historical data. Behavioral feature parameters of the five roles are injected. The Monte Carlo algorithm is used to simulate the role collaboration path. The simulation output includes a collaboration chain containing execution order and priority weight. Based on the collaboration chain, a set of instructions specific to each role is generated, an information verification list is sent to the assistant administrator role, a data anomaly prompt is pushed to the supervisor role, a conflict handling plan is distributed to the referee role, a resource distribution heat map is transmitted to the commander role, and behavior identification rules are issued to the intelligence officer role. Real-time comparison of the logical relationships between the instructions of each role; when a conflict is detected between the commander’s resource allocation instructions and the referee’s on-site handling instructions, the execution process is frozen and a phased arbitration process is initiated. The operation trajectories of the five roles are recorded to generate a behavior deviation rate matrix, and the sandbox role parameters and role binding conditions are updated through a dynamic learning algorithm.

2. The method for dynamic collaborative governance of the floating population based on the transformation of five roles as described in claim 1, characterized in that, The activation of the five role attributes based on the matching relationship between event type and spatial coordinates includes: Rental housing management incidents activate the landlord's role as an assistant manager; security patrol incidents activate the police auxiliary personnel's role as inspectors; public service consultation incidents activate the community police officers' role as referees; resource allocation incidents activate the police station chief's role as a commander; and abnormal behavior reporting incidents activate the residents' role as informants. Role transformation is triggered by the geofence. When the governing entity enters the preset geofence, the corresponding role operation module is automatically loaded. When an event triggers both resource scheduling and conflict mediation instructions, a permission stacking mechanism is set up, allowing the referee role to temporarily call the commander role's data interface during execution.

3. The dynamic collaborative governance method for the floating population based on the transformation of five roles, as described in claim 2, is characterized in that... When the original role is unable to respond due to the failure of the geofence, scan the functional similarity and spatial accessibility of the surrounding governance entities, calculate the spherical distance weight between the governance entity and the event point, superimpose the historical operation accuracy score of the governance entity, and generate a role inheritance priority list. After the commander confirms the final inheritance plan, he pushes the role conversion instruction and permission package to the target governance entity, and updates the role distribution parameters in the sand table simulation.

4. The method for dynamic collaborative governance of the floating population based on the transformation of five roles as described in claim 1, characterized in that, The process of spacetime simulation sand table simulation is as follows: By aggregating population migration trajectory density data and police deployment response path data from historical events, a three-dimensional virtual space model with timestamps is formed. The dynamic behavioral parameter set of five roles is injected into the virtual space model, including the normal distribution function of the registration delay rate of assistant administrators, the decay curve of the verification accuracy of supervisors, and the fluctuation coefficient of the mediation success rate of referees. The Monte Carlo random path simulation algorithm is used, with role response delay rate and decision bias as input variables, to calculate the governance efficiency of different collaboration sequences of five roles, and output the percentile value of event resolution time and resource consumption coefficient; and a real-time data calibration channel is established to input the traffic network congestion index into the spatial mobility resistance model in real time, and dynamically update the geographic grid travel time cost parameters.

5. The dynamic collaborative governance method for the floating population based on the transformation of five roles, as described in claim 4, is characterized in that... When the resolution time of the collaborative chain output by the sand table exceeds the historical benchmark value, the scenario repair process is activated. The population density anomaly value and resource coverage gap value of the current population event are extracted. Extreme weather interference factors or sudden mass event variables are injected into the spatiotemporal simulation sand table. The collaborative path of the five roles under stress scenarios is re-simulated. The differences between the repaired collaborative chain and the original chain are compared to generate a role stress resistance assessment report.

6. The method for dynamic collaborative governance of the floating population based on the transformation of five roles as described in claim 1, characterized in that, The generation of role-specific instruction sets based on the collaboration chain includes: The assistant administrator's instructions include a structured tenant identity verification field library and a registration deadline countdown reminder module triggered by geographical location; the inspector's instructions integrate an automatic data contradiction marking engine and a cross-departmental review query path planner; the referee's instructions embed a legal clause intelligent matching matrix and a multi-branch mediation process decision tree; the commander's instructions overlay a real-time resource distribution heat map layer and a scheduling priority identification algorithm based on event urgency; the intelligence officer's instructions encapsulate an abnormal behavior feature vector library and a standardized reporting format validator. Each instruction component is dynamically sorted according to the priority of the collaboration chain output by the sand table simulation.

7. The method for dynamic collaborative governance of the floating population based on the transformation of five roles as described in claim 1, characterized in that, The phased arbitration process is as follows: The system analyzes the resource call identifiers and action codes in the five-member instructions in real time. When it detects that there is a logical mutual exclusion between the commander's resource blocking instruction and the referee's on-site intervention instruction, it immediately freezes the instruction execution thread and initiates a phased arbitration process. The first stage calls the sandbox engine to regenerate and optimize the collaboration chain with the current event state as input. The second stage hands over to the referee role for manual decision-making and marks the basis for conflict resolution. The third stage pushes electronic voting requests to the intelligence officer role and calculates the confidence level of the voting results according to the preset weight formula. A conflict knowledge base is established to store the spatiotemporal feature matrix of the contradiction points of the instructions and the arbitration path, which is used to train the conflict probability prediction model of the sandbox simulation engine.

8. The method for dynamic collaborative governance of the floating population based on the transformation of five roles as described in claim 1, characterized in that, The process of recording the operation trajectories of the five roles to generate the behavior deviation rate matrix is ​​as follows: Construct a three-dimensional matrix of the behavior deviation rate of five members. The vertical dimension records the role response time offset, the horizontal dimension counts the operation accuracy fluctuation value, and the depth dimension analyzes the instruction execution completeness. The Bayesian inference algorithm is used to transform the excessive delay rate of the assistant administrator's registration into a time cost correction coefficient in the sand table model, and the peak error rate of the supervisor's verification is mapped to the inflection point threshold of the model's accuracy decay curve. Establish a dynamic learning feedback loop. When the referee's mediation success rate falls below the benchmark value for an extended period, the skill retraining process in the role binding module is automatically triggered, and the decision preference parameters of that role in the sand table simulation are updated.

9. The dynamic collaborative governance method for the floating population based on the transformation of five roles as described in claim 8, characterized in that, The updated sandbox character parameters and character binding include: The error rate of assistant administrator information verification exceeding the standard is converted into an adjustment coefficient for the registration delay rate weight in the sand table model. The conversion formula is established based on the Pearson correlation between historical error rate and delay rate. The time deviation of the inspector's data review is mapped to a dynamic decay factor of the accuracy threshold of the sand table verification, and the mapping relationship is fitted by a time series regression model. The updated sandbox parameters are fed back to the character attribute activation process in real time, triggering the assistant administrator identity authentication enhancement process or the supervisor skill assessment mechanism.

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