Floating population dynamic collaborative governance method based on five-member role conversion

Abstract

The invention discloses a floating population dynamic collaborative governance method based on five-member role conversion, and belongs to the technical field of community governance, and the method specifically comprises the steps: collecting floating population event information and space coordinates and function attributes of a governance subject, activating corresponding role attributes according to the matching relation between event types and the space coordinates, and carrying out the dynamic collaborative governance of the floating population; constructing a space-time deduction sand table based on historical data, injecting role behavior characteristic parameters, simulating a cooperation path by adopting a Monte Carlo algorithm, and outputting a cooperation chain containing an execution sequence and a priority weight; generating a role exclusive instruction set according to the cooperation chain, and distributing an adaptive task to each role; comparing the instruction logic relationship in real time, freezing an execution stream when a conflict is detected, and starting a staged arbitration process; recording a role operation track to generate a behavior deviation rate matrix, and updating sand table parameters and role binding conditions through a dynamic learning algorithm; according to the method, through dynamic role conversion and intelligent collaborative deduction, precise collaboration and continuous optimization of floating population management are realized.

Description

Technical Field

[0001] The present invention relates to the field of community governance technology, and in particular to a method for dynamic collaborative governance of migrant population based on the transformation of five roles. Background Art

[0002] With the rapid development of the economy and society, the scale of population mobility continues to expand, and the management and service of migrant workers has become a crucial component of grassroots social governance. With the acceleration of new urbanization, population mobility has become characterized by high frequency, wide scope, and complex structure. The traditional static management model based on household registration is difficult to adapt to the dynamic governance needs. How to achieve the coordinated participation of multiple stakeholders and accurately respond to the service and public security management needs of migrant workers has become a key issue in improving the effectiveness of grassroots governance. In this context, 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 technical solutions for managing migrant populations primarily focus on information technology. These include registering and sharing migrant population information through government service platforms, demarcating management grids using geographic information systems, and predicting population mobility trends through big data analysis. Some solutions incorporate the concept of multiple governance entities, such as establishing collaborative mechanisms involving landlords, community workers, and police officers to improve management efficiency by dividing responsibilities and authorities. Other technologies employ virtual simulation to simulate governance processes and provide decision-making support for resource allocation. These technologies are, to a certain extent, promoting the shift in migrant population management from manual to digital, and from a single entity to a multi-faceted approach.

[0004] However, the existing technical solutions lack dynamic adaptability in the collaboration of governance entities, and governance roles are mostly fixedly assigned, making it difficult to convert functions in real time based on factors such as event type and spatial location, resulting in inefficient cross-scenario responses. Secondly, the collaborative process lacks refined deduction and relies heavily on preset rules to assign tasks. Dynamic factors such as subject behavior characteristics and time and space constraints are not taken into account, which can easily lead to resource mismatches or process conflicts. Summary of the Invention

[0005] The purpose of this invention is to provide a method for dynamic collaborative governance of migrant population based on the transformation of five roles, and to solve the following technical problems: In existing technologies, the collaboration among governance entities lacks dynamic adaptation, roles are fixed and it is difficult to convert functions in real time according to event types and spatial locations. In addition, the collaborative process lacks refined deduction, relies on preset rules and does not consider dynamic factors.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The dynamic collaborative governance method for migrant population based on the transformation of the five members' roles includes the following steps: Collect information on migrant population events and the spatial coordinates and functional attributes of the five governance entities, including landlords, community police officers, auxiliary police officers, police station chiefs, and residents in the jurisdiction; Activate the attributes of the five roles based on the matching relationship between event types and spatial coordinates, build a time-stamped spatiotemporal simulation sandbox based on historical data, inject behavioral characteristic parameters of the five roles, use the Monte Carlo algorithm to simulate the role collaboration path, and deduce the output of the collaboration chain including execution order and priority weights; Generate role-specific instruction sets based on the collaboration chain, send information verification checklists to the coordinator role, push data anomaly alerts to the inspector role, distribute conflict resolution plans to the referee role, transmit resource distribution heat maps to the commander role, and issue behavior recognition rules to the intelligence officer role; Compare the logical relationships between the commands of each role in real time. When a conflict is detected between the commander's resource dispatch command and the referee's on-site handling command, freeze the execution process and start the phased arbitration process. The operation trajectories of the five characters are recorded to generate a behavior deviation rate matrix, and the sandbox character parameters and character binding conditions are updated through a dynamic learning algorithm.

[0007] As a further solution of the present invention: activating the attributes of the five characters according to the matching relationship between the event type and the spatial coordinates includes: Rental housing management events activate the assistant manager attributes of landlords and business owners, public security patrol events activate the inspector attributes of police auxiliary personnel, public service consultation events activate the referee attributes of community police officers, resource scheduling events activate the commander attributes of police station chiefs, and abnormal behavior reporting events activate the informant attributes of residents in the jurisdiction. Role transformation is triggered through the geographic fence range. When the governance subject enters the preset electronic fence, the corresponding role operation module is automatically loaded. When the event triggers resource scheduling and conflict mediation instructions at the same time, a permission superposition mechanism is set up. The referee role can temporarily call the commander role's data interface during the execution process.

[0008] As a further solution of the present invention: when the original role cannot respond due to the failure of the geo-fence, 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 entity is superimposed to generate a role inheritance priority list; After the commander role confirms the final inheritance plan, the role conversion instructions and permission package are pushed to the target entity, and the role distribution parameters in the sandbox simulation are updated at the same time.

[0009] As a further solution of the present invention: the process of time-space deduction sand table deduction is: Aggregating population migration trajectory density data and police deployment response path data from historical events to form a timestamp-labeled three-dimensional virtual space model, this model is then fed with dynamic behavioral parameter sets for the five roles, including a normal distribution function for the registration delay rate of assistants, a decay curve for the verification accuracy of inspectors, and a fluctuation coefficient for the mediation success rate of referees. The Monte Carlo random path simulation algorithm is adopted, with the role response delay rate and decision deviation value 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.

[0010] As a further solution of the present invention: when the resolution time of the collaboration chain output by the sandbox exceeds the historical benchmark value, the scenario repair process is activated, the population density anomaly and resource coverage gap value of the current population event are extracted, extreme weather interference factors or sudden group event variables are injected into the space-time deduction sandbox, and the collaboration path of the five characters in the stress scenario is re-simulated. The repaired collaboration chain is compared with the original chain to generate a role stress resistance assessment report.

[0011] As a further solution of the present invention: generating a role-specific instruction set based on the collaboration chain includes: The assistant manager's instructions include a structured tenant identity verification field library and a registration time countdown reminder module triggered by geographic location; the inspector's instructions integrate an automatic data conflict markup engine and a cross-departmental review query path planner; the referee's instructions embed an intelligent matching matrix of legal terms and a multi-branch mediation process decision tree; the commander's instructions superimpose a real-time resource distribution heat map hierarchy 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, and each instruction component is dynamically sorted according to the collaboration chain priority output by the sandbox simulation.

[0012] As a further solution of the present invention: the staged arbitration process is specifically as follows: The system parses the resource call identifiers and handling action codes in the five-member team's instructions in real time. When it detects that the commander's resource blocking instruction and the referee's on-site intervention instruction are logically mutually exclusive, it immediately freezes the instruction execution thread and starts the phased arbitration process. In the first stage, the sandbox engine is called to regenerate the optimized collaboration chain with the current event status as input. In the second stage, the referee role is handed over to make manual decisions and mark the basis for conflict resolution. In the third stage, an electronic voting request is pushed to the intelligence role and the confidence of the voting result is calculated according to the preset weight formula; a conflict knowledge base is established to store the spatiotemporal feature matrix and arbitration path of the instruction conflict points, which is used to train the conflict probability prediction model of the sandbox simulation engine.

[0013] As a further solution of the present invention: the process of recording the operation trajectories of the five characters and generating the behavior deviation rate matrix is ​​as follows: Construct a three-dimensional matrix of the five-player behavior deviation rates. The vertical dimension records the role response time offset, the horizontal dimension counts the fluctuation value of the operation accuracy, and the depth dimension analyzes the completeness of instruction execution. The Bayesian inference algorithm is used to convert the excessive registration delay rate of assistant managers into the time cost correction coefficient in the sandbox model, and the peak error rate of supervisors is mapped to the inflection point threshold of the model accuracy decay curve. A dynamic learning feedback loop is established. When the referee's mediation success rate is continuously lower than the benchmark value, the skill retraining process in the role binding module is automatically triggered, and the decision preference parameters of the role in the sandbox simulation are updated.

[0014] As a further solution of the present invention: the updating of sandbox role parameters and role binding includes: The event of the assistant manager's information verification error rate exceeding the standard is converted into an adjustment coefficient for the registration delay rate weight in the sandbox model. The conversion formula is established based on the Pearson correlation between the historical error rate and delay rate. The inspector data review time deviation is mapped to the dynamic attenuation factor of the sandbox verification accuracy threshold, and the mapping relationship is fitted through the time series regression model; The updated sandbox parameters are fed back to the role attribute activation step in real time, triggering the assistant manager's identity authentication enhancement process or the inspector's skill assessment mechanism.

[0015] Beneficial effects of the present invention: The present invention collects information on the five governance subjects and dynamically activates the role attributes of the five members according to the event type and spatial coordinates, thereby solving the problem of fixed allocation of governance roles and difficulty in real-time conversion of functions. It uses geographic fences to trigger role conversion and authority superposition mechanism, thereby improving cross-scenario response efficiency. By constructing a virtual sandbox with timestamps, injecting role behavior characteristic parameters and using Monte Carlo algorithm to simulate the collaboration path, and combining real-time data calibration to dynamically update spatiotemporal parameters, it solves the problem of lack of refined deduction and reliance on preset rules in the collaboration process, and realizes accurate collaboration chain planning that considers the subject behavior characteristics and spatiotemporal constraints. By generating role-specific instruction sets, customized tasks are pushed to different roles to ensure accurate matching of instructions and role functions. Setting a phased arbitration process can effectively resolve instruction logic conflicts, and combining the conflict knowledge base to train the prediction model to reduce resource mismatch. By recording the role operation trajectory to generate a behavior deviation rate matrix, the dynamic learning algorithm is used to update the sandbox parameters and role binding conditions, forming a closed-loop optimization mechanism, continuously improving governance efficiency, and ultimately achieving precise, efficient and adaptive optimization of dynamic collaborative governance of migrant populations. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 It is a schematic flow diagram of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, the present invention is a method for dynamic collaborative governance of floating population based on the transformation of five roles, comprising the following steps: Step 1: Collect information on incidents involving migrant workers and the spatial coordinates and functional attributes of the five governance entities. Specifically, this includes information on the rental of landlords and business owners, the patrol responsibilities of auxiliary police officers, the jurisdictional service authority of community police officers, the resource dispatch authority of police station chiefs, and the residential areas of residents within their jurisdiction. Key information such as the type of incident, location, and individuals involved should also be recorded. Step 2: Activate the attributes of the five roles based on the matching relationship between event type and spatial coordinates. For example, a rental housing management event activates the assistant manager attribute of the landlord, and a public security event activates the inspector attribute of the police auxiliary. A time-stamped spatiotemporal simulation sandbox is constructed based on historical governance data, and the behavioral characteristic parameters of the five roles are injected, including the information registration habits of assistant managers, the verification response speed of inspectors, and the dispute mediation tendency of referees. A Monte Carlo algorithm is used to simulate the role collaboration paths in different scenarios, comprehensively considering factors such as spatial distance and functional matching, and the deduction output includes the execution order and priority weights of the collaboration chain. Step 3: Generate a role-specific instruction set based on the collaboration chain, send an information verification list containing tenant identity, length of stay, etc. to the assistant manager, push abnormal notifications such as data inconsistency and update delays to the inspector, distribute step-by-step conflict resolution plans to the referee, transmit resource heat maps such as police force distribution and material reserves to the commander, and issue identification rules such as suspicious behavior characteristics and reporting standards to the intelligence officer; Step 4: Compare the logical relationships between the commands of each role in real time. When a conflict is detected between the commander's resource dispatch command and the referee's on-site disposal command, the execution flow is immediately frozen and a phased arbitration process is initiated. First, the collaborative path is optimized through sandbox simulation, then the referee manually decides and marks the basis. Finally, the final solution is formed based on the feedback from residents in the jurisdiction. Step 5: Record the operation trajectories of the five roles and generate a behavioral deviation rate matrix covering dimensions such as response time offset and operation accuracy fluctuation. Use a dynamic learning algorithm to convert this data into a basis for adjusting the sandbox role parameters. At the same time, update the role binding conditions to ensure that the role attributes continue to adapt to actual governance needs.

[0020] In step 2, activating the attributes of the five characters according to the matching relationship between the event type and the spatial coordinates includes: The five role attributes are activated based on the matching relationship between event type and spatial coordinates, specifically by precisely binding different event scenarios to the functions of corresponding governance entities. When rental housing management events occur, such as tenant check-in registration or rental information updates, the landlord's assistant attribute is activated. Landlords are now responsible for collecting basic tenant information and recording living dynamics, becoming the front-end for collecting information on migrant workers. When security patrols occur, including routine community patrols and safety inspections of key areas, the inspector attribute of auxiliary police officers is activated, responsible for verifying the authenticity of registered information and identifying safety hazards in rental housing. When public service consultations occur, such as answering questions about social security and residence permit applications for migrant workers, the referee attribute of community police officers is triggered. They are required to provide standardized guidance based on policies and regulations and mediate disputes arising from service requests. When resource allocation events occur, such as sudden public security incidents requiring additional police force or large-scale events requiring the deployment of security supplies, the police station chief's commander attribute is activated, coordinating the allocation and dispatch of various governance resources. If any abnormal behavior is reported, such as frequent gatherings of strangers or storage of suspicious items, the intelligence attributes of residents in the jurisdiction will be activated, and they can feedback abnormal information through dedicated channels, becoming the terminal tentacles of public security control.

[0021] Geofencing triggers role transitions. Pre-defined geofences are established within the jurisdiction to define different functional areas, such as residential communities, commercial districts, and industrial parks. When a governance entity enters a pre-defined geofence, their terminal automatically loads the corresponding role's operational module. For example, when a police auxiliary enters a residential community's geofence, their terminal will access the area's migrant population registration ledger and a list of individuals under special scrutiny. When a community police officer enters the government service center's geofence, their terminal automatically displays referee tools such as the public service policy library and mediation document templates. When an event simultaneously triggers resource dispatch and conflict mediation, such as a rental dispute sparking a collective conflict requiring both conflict mediation and the deployment of surrounding police forces, a permission-overlay mechanism is implemented. During the mediation process, the referee role can temporarily access the commander role's data interface to view real-time information such as police force deployment and material reserves. This ensures that the mediation plan aligns with the resource dispatch schedule and avoids inefficient resolution due to information fragmentation.

[0022] When the original role cannot respond due to the failure of the geographic fence, such as the assistant manager in charge of a certain area temporarily leaving the fence range, the terminal signal is interrupted, etc., the role inheritance mechanism will be activated. First, scan other governance entities within a certain range around, compare the similarity between their original functions and the functions required for the event to be handled, and evaluate the spatial accessibility of these entities to the event point, including factors such as traffic conditions and distance. Calculate the spherical distance weight between the governance entity and the event point. The closer the distance, the higher the weight. Then add the historical operation accuracy score of the entity. The higher the accuracy rate of handling similar events in the past, the higher the score. Combining these two factors generates a role inheritance priority list, which clearly presents the matching ranking of each potential inheritance entity.

[0023] The commander role reviews the priority list and confirms the final succession plan based on the actual situation such as the urgency of the incident and the current load of the subject. The role conversion instruction and permission package are then pushed to the target subject. The permission package contains the information query permission and instruction issuance permission required for temporary performance of duties. For example, the assistant manager originally in charge of the commercial district is designated to inherit the assistant manager duties of a certain community. His terminal will receive the conversion instruction and obtain the housing information and tenant registration permissions of the community. At the same time, the role distribution parameters in the space-time simulation sandbox will be updated synchronously, and the new role allocation, permission range and other data will be injected into the sandbox to ensure that the subsequent collaborative path simulation is based on the latest role configuration and maintain the consistency of the governance process.

[0024] In step 2, the process of space-time simulation sandbox simulation is as follows: The simulation process for the spatiotemporal simulation sandbox begins with the aggregation of historical data. Data on population migration density and police deployment response paths from historical events are automatically compiled. The former records the migration directions and concentration areas of migrant populations within the jurisdiction at different time periods, while the latter includes police response routes and force deployment trajectories for various incidents. This data is chronologically labeled, naturally forming a three-dimensional virtual spatial model that includes geographic coordinates, time nodes, and event types. This model can intuitively present population mobility trends and the distribution of governance resources at different historical moments.

[0025] Subsequently, dynamic behavioral parameter sets for the five roles were injected into the virtual space model. The normal distribution function for the assistant's registration delay rate reflects the deviation between the completion time and the standard duration for landlord registration, such as the typical range of registration delays across different time periods and event types. The inspector's verification accuracy decay curve reflects the changing accuracy of auxiliary police officers as they continuously perform verification tasks, as time passes or as workload increases. For example, the fluctuation in verification error rates after extended work periods. The referee's mediation success rate fluctuation coefficient correlates with the fluctuations in the success rate of community police officers handling various conflicts, such as rental disputes and neighborhood clashes, and is related to factors such as the complexity of the incident and the number of individuals involved. Together, these parameters form a digital portrait of the role's behavior, providing a foundation for collaborative simulation.

[0026] The Monte Carlo random path simulation algorithm is used during the simulation, with the role response delay rate and decision deviation value as input variables. The role response delay rate refers to the time difference between receiving instructions and actual action, while the decision deviation value represents the degree of deviation between actual operations and standard procedures. The algorithm randomly generates multiple possible collaboration sequences, considers the order of participation and coordination of different roles, and calculates the governance effectiveness of each sequence. The process comprehensively evaluates the smoothness of role connection and the rationality of resource utilization, and ultimately outputs the event resolution time percentile value and resource consumption coefficient. The former reflects the position of the resolution time of the collaboration sequence in the historical processing time of similar events, and the latter reflects the efficiency of the use of human, material and other resources in the sequence.

[0027] A real-time data calibration channel was also established, through which traffic network congestion indices were fed into the simulation process in real time. These indices were derived from traffic monitoring records and road condition reports within the jurisdiction. The congestion indices were then fed into the spatial mobility resistance model, dynamically adjusting the travel time cost parameters of the geographic grid. For example, if a certain road section experiences congestion, the travel time for the grid in that area would be increased accordingly. This ensures that the character's travel time estimate in the sandbox matches the actual road conditions, ensuring that the simulation results are consistent with actual traffic conditions.

[0028] When the resolution time of a collaboration chain output by the sandbox exceeds the historical benchmark, the scenario repair process is automatically activated. Population density anomalies (e.g., a sudden increase in the number of migrant workers in a certain area compared to the historical average) and resource coverage gaps (i.e., the gap between the available police force and supplies and the event's requirements) are extracted. Then, extreme weather disruptions (e.g., heavy rain causing limited access to certain areas) or sudden mass incidents (e.g., a dispute at a rental house) are injected into the spatiotemporal simulation sandbox to re-simulate the collaboration paths of the five characters under these stressful scenarios. The repaired collaboration chain is compared with the original chain, analyzing differences in character response speed, resource allocation, and process integration. A stress tolerance assessment report is generated, which includes characteristics of each character's performance under stressful conditions, such as changes in the information collection efficiency of assistants and the resource allocation flexibility of commanders. This report is then sent to step 5 for deviation optimization.

[0029] In step 3, generating a role-specific instruction set based on the collaboration chain includes: Based on the role-specific instruction sets generated by the collaboration chain, differentiated components are designed for different roles' functional characteristics. The assistant manager's instructions include a structured library of tenant identity verification fields, covering core information items such as name, ID number, place of residence, check-in date, and contact information. Each field has pre-set standard filling requirements. A location-based registration timeout countdown reminder module is also built in. When the assistant manager enters the geographical area of ​​the rental property, a reminder automatically triggers, displaying the remaining validity period of the information registration in real time to avoid omissions or delays.

[0030] The inspector's instructions integrate an automatic data inconsistency marking engine, which can automatically identify information inconsistencies during the verification process, such as discrepancies between the registered address and the actual residential address, conflicts between identity information and historical records, etc.; combined with a cross-departmental review and query path planner, it clearly marks the source departments and query steps of the data that need to be verified, such as guiding the connection to the social security system to verify employment information, linking housing and construction archives to confirm the nature of the house, etc., to improve review efficiency.

[0031] The referee's instructions are embedded in the intelligent matching matrix of legal terms, which automatically associates corresponding regulatory entries according to the type of conflict. For example, rental disputes are matched with relevant provisions of the Contract Law, and neighborhood conflicts are linked to the content of the Public Security Administration Punishment Law. A multi-branch mediation process decision tree is built in simultaneously, which divides the processing paths according to the severity of the incident and the number of people involved, and provides step-by-step operational guidance from fact verification, demand communication to solution negotiation.

[0032] The commander's instructions are superimposed on the real-time resource distribution heat map level to intuitively present the real-time distribution status of resources such as police positions, material storage points, and emergency vehicles within the jurisdiction; combined with the dispatch priority identification based on the urgency of the incident, the handling order is automatically marked according to the nature of the incident to ensure that key resources are prioritized for emergency scenarios.

[0033] The intelligence officer's instructions encapsulate a library of characteristic vectors for abnormal behavior, including descriptions of typical characteristics such as frequent late-night visitors, frequent changes of address within a short period of time, and deliberate concealment of identity. A standardized reporting format verifier automatically checks that reports include required elements such as time, location, and specific behavior, ensuring complete and standardized information. Each instruction component is dynamically sorted by the priority of the collaboration chain output from sandbox simulations, with core task modules displayed first, followed by auxiliary functions.

[0034] In step 4, the staged arbitration process is specifically as follows: The phased arbitration process starts with real-time parsing of instructions and continuously identifies resource call identifiers and handling action codes in the instructions of the five members. The resource call identifier clearly marks the specific resource information such as the required police force configuration, material types, and authority scope, and the handling action code corresponds to various operational instructions such as resource blocking, on-site intervention, and cross-departmental coordination. When it is detected that the resource blocking instruction issued by the commander and the on-site intervention instruction issued by the referee are logically mutually exclusive, for example, the commander requires a temporary blockade of police resources in a certain area to cope with the security of an upcoming large-scale event, and the referee needs to immediately mobilize the police force in the area for on-site mediation due to a sudden rental dispute in the area, the instruction execution thread will be immediately suspended and the phased arbitration process will be started synchronously.

[0035] In the first phase, a sandbox engine is used to input real-time information such as the specific location of the current incident, the number of migrant workers involved, the distribution of existing resources, and surrounding traffic conditions. Multiple rounds of collaborative path simulations are then re-run to generate an optimized collaborative chain. This new collaborative chain balances resource scheduling efficiency with on-site response requirements, providing a data-based path reference for conflict resolution.

[0036] In the second phase, conflict resolution authority is transferred to the referee role for manual decision-making. Based on the actual situation on site, relevant laws and regulations, historical experience with similar cases, and optimization suggestions from sandbox simulations, the referee formulates a specific conflict resolution plan and details the basis for the decision, including the reasons for prioritizing certain needs, the specific provisions of the management regulations cited, and an assessment of potential impacts, ensuring a transparent and well-regulated decision-making process.

[0037] In the third phase, electronic voting requests are sent to the intelligence agents to collect their feedback on conflict resolution. The voting results are then combined with pre-set weights, such as the intelligence agent's historical judgment accuracy and familiarity with the incident, to form a confidence assessment of the voting results. This serves as a supplementary reference for the referee's decision, enhancing the objectivity and applicability of the solution.

[0038] During this process, a conflict knowledge base is simultaneously established. The system records the spatiotemporal feature matrix of each instruction conflict, including the specific location, time point, event type, and resource type of the conflict. It also stores the corresponding arbitration path, decision-making basis, and final resolution. This data is continuously used to train the sandbox simulation engine's conflict probability prediction model, continuously improving the engine's ability to predict potential instruction conflicts and reducing the frequency of subsequent similar conflicts.

[0039] In step 5, the operation trajectories of the five characters are recorded to generate a behavior deviation rate matrix. The specific process of updating the sandbox character parameters and character binding conditions through the dynamic learning algorithm is as follows: The process of recording the operational trajectories of the five characters to generate a behavioral deviation rate matrix begins with constructing a three-dimensional matrix of the five characters' behavioral deviation rates. The vertical dimension specifically records the character's response time offset—the difference between the time it takes to receive a command and actually initiate the action and the standard response time—to clearly demonstrate the fluctuations in each character's response speed across different event types and time periods. The horizontal dimension measures fluctuations in operational accuracy, comparing actual operational results with standard process requirements to quantify the changes in accuracy across each character in aspects such as information registration, data verification, and conflict mediation. The depth dimension analyzes the completeness of command execution, considering the completion rate of each character's tasks within the command, including whether key steps were omitted and whether all operations were completed within the specified timeframe. These three interrelated dimensions together form a three-dimensional assessment framework reflecting character behavioral deviations.

[0040] Through the Bayesian inference algorithm, the events of the assistant manager's registration delay rate exceeding the standard are converted into the time cost correction coefficient in the sandbox model. When the assistant manager exceeds the standard time in multiple rental house information registrations, these events will be processed by the algorithm as adjustment parameters to correct the estimation of the assistant manager's operation time in the sandbox model, making the model more in line with the actual execution efficiency. At the same time, the peak of the inspector's verification error rate is mapped to the inflection point threshold of the model's accuracy decay curve. When the inspector's error rate suddenly increases during continuous verification tasks, this peak will be marked as the turning point of the curve, which will be used to adjust the model's simulation of the accuracy change trend of the inspector after a long period of work, to ensure that the deduction results can reflect the actual working status.

[0041] A dynamic learning feedback loop is established to continuously track the operational performance of each character. When a referee's mediation success rate continuously falls below the baseline, it indicates that the character may be lacking in conflict resolution capabilities. This automatically triggers the skill refresher process in the character binding module, delivering targeted training content such as complex dispute mediation techniques and interpretations of the latest regulations. Simultaneously, the character's decision-making preferences are updated during the sandbox simulation, such as adjustments to the estimated mediation duration and modifications to the coordination weights when collaborating with other characters, ensuring that simulation results are more consistent with the character's current capabilities.

[0042] The process of updating sandbox role parameters and role binding closely integrates with deviation data from actual role operations. Events where the assistant's information verification error rate exceeds the standard are converted into an adjustment factor 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. Specifically, by analyzing the correlation between the two in past data, the weight of the delay rate in the model should be adjusted as the error rate increases, allowing the model to more accurately reflect the relationship between information verification quality and time consumption.

[0043] The inspector's data review time deviation is mapped to the dynamic attenuation factor of the sandbox verification accuracy threshold. This mapping relationship is fitted through a time series regression model. That is, according to the changing rules of the inspector's review time deviation and verification accuracy in different time periods, a correlation model between the two is established. When there is a deviation in the review time, the attenuation rate of the accuracy threshold can be automatically adjusted, making the model's simulation of the inspector's working status closer to reality.

[0044] The updated sandbox parameters will be fed back to the role attribute activation link in step 2 in real time, forming a closed-loop adjustment. When the information verification error rate of the assistant manager continues to exceed the standard, the parameter change will trigger the assistant manager's identity authentication enhancement process, adding identity authentication steps during information verification, such as requiring the upload of photos of tenants' ID cards, real-time comparison with the public security system, etc., to improve information accuracy; when the inspector's data review time deviation is large, the inspector's skill assessment mechanism will be triggered to test 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 then gradually releasing them after the capabilities are improved. This dynamic adjustment mechanism ensures that the sandbox model is always synchronized with the actual performance of the role, so that the entire collaborative governance process can continue to adapt to changes in actual needs.

[0045] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A dynamic collaborative governance method for migrant population based on the transformation of the roles of five members, characterized by: The following steps are involved: Collect information on migrant population events and the spatial coordinates and functional attributes of the five governance entities, including landlords, community police officers, auxiliary police officers, police station chiefs, and residents in the jurisdiction; Activate the attributes of the five roles based on the matching relationship between event types and spatial coordinates, build a time-stamped spatiotemporal simulation sandbox based on historical data, inject behavioral characteristic parameters of the five roles, use the Monte Carlo algorithm to simulate the role collaboration path, and deduce the output of the collaboration chain including execution order and priority weights; Generate role-specific instruction sets based on the collaboration chain, send information verification lists to the coordinator role, push data anomaly prompts to the inspector role, distribute conflict resolution plans to the referee role, transmit resource distribution heat maps to the commander role, and issue behavior recognition rules to the intelligence officer role; Compare the logical relationships between the commands of each role in real time. When a conflict is detected between the commander's resource dispatch command and the referee's on-site handling command, freeze the execution process and start the phased arbitration process. The operation trajectories of the five characters are recorded to generate a behavior deviation rate matrix, and the sandbox character parameters and character binding conditions are updated through a dynamic learning algorithm.

2. The method for dynamic collaborative governance of floating population based on the transformation of five roles according to claim 1 is characterized in that: The activation of the five-character attributes according to the matching relationship between the event type and the spatial coordinates includes: Rental housing management events activate the assistant manager attributes of landlords and business owners, public security patrol events activate the inspector attributes of police auxiliary personnel, public service consultation events activate the referee attributes of community police officers, resource scheduling events activate the commander attributes of police station chiefs, and abnormal behavior reporting events activate the informant attributes of residents in the jurisdiction. Role transformation is triggered through the geographic fence range. When the governance subject enters the preset electronic fence, the corresponding role operation module is automatically loaded. When the event triggers resource scheduling and conflict mediation instructions at the same time, a permission superposition mechanism is set up. The referee role can temporarily call the commander role's data interface during the execution process.

3. The method for dynamic collaborative governance of floating population based on the transformation of five roles according to claim 2 is characterized in that: When the original role cannot respond due to the failure of the geo-fence, 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 entity is superimposed to generate a role inheritance priority list; After the commander role confirms the final inheritance plan, the role conversion instructions and permission package are pushed to the target entity, and the role distribution parameters in the sandbox simulation are updated at the same time.

4. The method for dynamic collaborative governance of floating population based on the transformation of five roles according to claim 1 is characterized in that: The process of space-time deduction sand table deduction is as follows: Aggregating population migration trajectory density data and police deployment response path data from historical events to form a time-stamped three-dimensional virtual space model, this model is then fed with dynamic behavioral parameter sets for the five roles, including a normal distribution function for the registration delay rate of assistants, a decay curve for the verification accuracy of inspectors, and a fluctuation coefficient for the mediation success rate of referees. The Monte Carlo random path simulation algorithm is adopted, with the role response delay rate and decision deviation value 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 method for dynamic collaborative governance of floating population based on the transformation of five roles according to claim 4 is characterized in that: When the resolution time of the collaboration chain output by the sandbox exceeds the historical benchmark value, the scenario repair process is activated, the population density anomaly and resource coverage gap value of the current population event are extracted, extreme weather interference factors or sudden group event variables are injected into the space-time deduction sandbox, and the collaboration path of the five roles in the stress scenario is re-simulated. The repaired collaboration chain is compared with the original chain to generate a role stress resistance assessment report.

6. The method for dynamic collaborative governance of floating population based on the transformation of five roles according to claim 1 is characterized in that: Generating a role-specific instruction set based on the collaboration chain includes: The assistant manager's instructions include a structured tenant identity verification field library and a registration time countdown reminder module triggered by geographic location; the inspector's instructions integrate an automatic data conflict markup engine and a cross-departmental review query path planner; the referee's instructions embed an intelligent matching matrix of legal terms and a multi-branch mediation process decision tree; the commander's instructions superimpose a real-time resource distribution heat map hierarchy 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, and each instruction component is dynamically sorted according to the collaboration chain priority output by the sandbox simulation.

7. The method for dynamic collaborative governance of floating population based on the transformation of five roles according to claim 1 is characterized in that: The staged arbitration process is as follows: The system parses the resource call identifiers and handling action codes in the five-member team's instructions in real time. When it detects that the commander's resource blocking instruction and the referee's on-site intervention instruction are logically mutually exclusive, it immediately freezes the instruction execution thread and starts the phased arbitration process. In the first stage, the sandbox engine is called to regenerate the optimized collaboration chain with the current event status as input. In the second stage, the referee role is handed over to make manual decisions and mark the basis for conflict resolution. In the third stage, an electronic voting request is pushed to the intelligence role and the confidence of the voting result is calculated according to the preset weight formula; a conflict knowledge base is established to store the spatiotemporal feature matrix and arbitration path of the instruction conflict points, which is used to train the conflict probability prediction model of the sandbox simulation engine.

8. The method for dynamic collaborative governance of floating population based on the transformation of five roles according to claim 1 is characterized in that: The process of recording the operation trajectories of the five characters and generating the behavior deviation rate matrix is ​​as follows: Construct a three-dimensional matrix of the five-player behavior deviation rates. The vertical dimension records the role response time offset, the horizontal dimension counts the fluctuation value of the operation accuracy, and the depth dimension analyzes the completeness of instruction execution. The Bayesian inference algorithm is used to convert the excessive registration delay rate of assistant managers into the time cost correction coefficient in the sandbox model, and the peak error rate of supervisors is mapped to the inflection point threshold of the model accuracy decay curve. A dynamic learning feedback loop is established. When the referee's mediation success rate is continuously lower than the benchmark value, the skill retraining process in the role binding module is automatically triggered, and the decision preference parameters of the role in the sandbox simulation are updated.

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

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