A public rental housing right sunshine operation management method

Abstract

The application provides a public rental house right sunshine operation management method, which comprises the following steps: collecting public rental house whole-process approval data, intelligently analyzing time limit, approval opinion and manual operation according to supervision rules, and generating graded risk early warning; pushing the early warning and receiving supervision instructions, urging the handling subject to feed back the disposal; auditing the feedback materials, canceling the warning according to rules to form a supervision closed loop; and fusing public rental house data and approval data to dynamically monitor the living state, issuing disposal instructions and storing operation logs when an abnormality occurs. The public rental house right sunshine operation management method of the application embodiment realizes whole-process embedded intelligent supervision and closed-loop management of public rental house right operation, effectively solves the problems of non-transparent approval, lagging supervision and missing post-rent monitoring, improves the identification accuracy and management efficiency of illegal behaviors through multi-source data fusion and dynamic early warning mechanism, and guarantees the fairness and justice of the allocation process.

Description

Technical Field

[0001] This invention relates to the field of public rental housing supervision technology, and in particular to a method for transparent operation and management of public rental housing rights. Background Technology

[0002] With the development of digital governance technologies, relevant management systems have constructed a preliminary information system from application acceptance to allocation and occupancy through collaborative operations involving electronic business processes, basic data statistics, and simple rule-based early warning. Specifically, this system covers key aspects such as housing resource management, eligibility verification, and contract signing and registration, enabling online data transfer and basic archiving, and providing technical support for the standardized implementation of housing security work.

[0003] However, existing public rental housing supervision methods rely on static information entry and subsequent manual verification, failing to establish an embedded intelligent monitoring and multi-source data fusion mechanism for the entire power operation process. The lack of in-depth analysis and dynamic monitoring of the semantics of approval opinions, abnormal operational behaviors, and actual post-rental living conditions makes it difficult to promptly identify and prevent risks such as overdue processing, perfunctory approvals, and illegal subletting. This regulatory lag and information silos prevent early warning information from forming an effective supervisory loop, severely hindering the fairness, transparency, and management efficiency of housing security resource allocation. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] The purpose of this invention is to provide a method for the transparent operation and management of public rental housing rights.

[0006] To achieve the above objectives, this invention proposes a method for the transparent operation and management of public rental housing rights, comprising:

[0007] The system acquires approval behavior data throughout the entire public rental housing business process, and intelligently analyzes processing time limits, approval opinions, and manual intervention operations based on preset monitoring rules to generate early warning information that includes risk types and risk levels. The warning information is pushed to the preset responsible entities, and the supervisory instructions initiated by the superior regulatory authorities in response to the warning information are received, so as to trigger the responsible entities to perform feedback processing operations. Obtain feedback and supporting materials submitted by the handling entity, review and verify the feedback and supporting materials, and eliminate the corresponding warning information after the review is approved to form a regulatory closed loop; By integrating housing usage status data collected by IoT devices with business approval data, the actual occupancy status of houses is dynamically monitored. When an abnormal occupancy status is detected, a handling instruction is generated and a full-process operation log is recorded.

[0008] Optionally, the intelligent analysis of processing time limits, approval opinions, and manual intervention operations based on preset monitoring rules includes: Real-time monitoring of the remaining processing time for each business step; when the remaining time is less than a preset threshold, a time limit warning signal is generated. The approval opinion text of the current node is obtained. Natural language processing technology is used to calculate the semantic similarity between the approval opinion text and the standard examples in the preset valid opinion template library. The length of the approval opinion text and whether it contains high-frequency perfunctory words are detected. When the similarity is lower than the preset threshold, or the text length is insufficient, or it contains high-frequency perfunctory words, a content warning signal is generated. The system compares the machine review results with the manual approval results, and generates an abnormal operation warning signal when the two conflict. The time limit warning signal, content warning signal and abnormal operation warning signal are integrated into a warning information that includes risk type and risk level.

[0009] Optionally, obtaining the approval opinion text of the current node and calculating the semantic similarity between the approval opinion text and standard examples in a preset valid opinion template library using natural language processing technology includes: Use a pre-trained language model to process the approval opinion text. and a library of preset valid opinion templates Standard example in Convert to semantic vector and ; calculate and The cosine similarity is calculated using the following formula:

[0010] Take the maximum value among all similarity calculation results If the maximum value is less than the preset similarity threshold If the content of the approval opinion is deemed irrelevant to the business scenario, a red light warning will be triggered.

[0011] Optionally, detecting the length of the approval opinion text and whether it contains high-frequency perfunctory words includes: Statistical approval document character length ,like Less than the preset minimum effective length If the judgment is too simplistic, a red light warning will be triggered. Building a high-frequency perfunctory word library If the approval document Belongs to the high-frequency perfunctory word library And its character length Less than or equal to the maximum allowed length of perfunctory words If so, it will be judged as a perfunctory opinion and trigger a red light warning.

[0012] Optionally, pushing the warning information to a preset responsible entity includes: Based on the risk level and business process type of the early warning information, the corresponding person in charge and department head will be automatically matched as the responsible party. The system sends a notification message containing a near-expiration reminder, an overdue alarm, or a supervision instruction to the responsible entity via an SMS notification interface. The notification message carries a unique identifier for the warning item and a redirect link to trigger the responsible entity to fill in the processing instructions and upload supporting materials in the system to perform feedback processing.

[0013] Optionally, the review and verification of the feedback and supporting materials includes: The higher-level regulatory authorities retrieve the feedback explanation text and supporting documents associated with the early warning information through the management terminal; The feedback description text was reviewed for compliance, and the completeness and authenticity of the supporting documents were verified. After the review is approved, an approval instruction is generated. In response to the approval instruction, the system automatically turns off the warning light corresponding to the warning information, thus forming a regulatory closed loop of warning-supervision-feedback-completion.

[0014] Optionally, the data on housing usage status and business approvals collected by the integrated IoT devices includes: Data on entry and exit records from smart access control devices and usage curves from smart water and electricity meters are obtained through standardized data interfaces. The entry and exit record data and usage curve data are matched with the lessee information in the business approval data in a spatiotemporal correlation. The system analyzes the frequency of access control and water and electricity consumption within a preset time period. When it detects that there are no access control records for more than a preset number of days, or that water and electricity consumption is lower than the preset threshold for several consecutive months, or that non-tenants frequently enter and exit the house, it determines that the actual occupancy status of the house is abnormal and generates an abnormal occupancy status indicator.

[0015] Optionally, the step of generating a handling instruction and recording a full-process operation log when an abnormal living status is detected includes: Based on the risk level of the abnormal living status indicator, a tiered handling instruction is generated, which includes a remote door locking operation instruction or a manual intervention verification instruction. Execute the remote door locking command or send a manual intervention verification task to the patrol personnel via a mobile terminal; The system synchronously records the entire process of operation logs, from IoT data collection, anomaly identification and judgment, generation of handling instructions to confirmation of final execution results. The entire process operation logs include user behavior, approval opinions, early warning triggers, supervision instructions and feedback content, and support multi-dimensional retrospective auditing by time, personnel and business type.

[0016] Optionally, the method further includes: Construct an information overview and transparent regulatory view, and provide a dual-view switching function for pre-rental management and post-rental management; The early warning data dashboard displays the total number of early warnings, the number of individual early warnings, and the trend changes in the early warning structure across the entire jurisdiction. In response to user drill-down operations on statistical data, detailed regional data and individual household data are displayed sequentially, enabling a layer-by-layer penetrating query from macro-level summary data to specific business documents and responsible persons.

[0017] Optionally, the method further includes: Multi-dimensional statistical analysis is performed to construct a dual-track reporting system that includes early warning reports and business reports. The early warning reports include a general early warning report, a detailed early warning report, a feedback rate statistics report, and an annual review early warning report, used to evaluate regulatory effectiveness. The business reports include a housing distribution report, a guaranteed family structure report, a contract signing rate statistics report, a home visit inspection record report, and a report on the guarantee status of various groups, used to assist decision-making. All statistical indicators support drill-down to original business documents or resident files to achieve data searchability and process traceability.

[0018] The embodiments of the present invention have the following beneficial effects: by embedding intelligent monitoring and multi-source data fusion, proactive early warning and dynamic monitoring of abnormal approval of public rental housing and post-rental violations are realized; by constructing an early warning-supervision-feedback closed-loop management mechanism, the problems of regulatory lag and information silos are effectively solved, ensuring that the exercise of power is fully traceable and transparent. Attached Figure Description

[0019] The above-described and additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 A flowchart illustrating a method for the transparent operation and management of public rental housing rights, provided as an embodiment of the present invention. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0022] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0024] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0025] Example 1 Figure 1 This is a flowchart of a public rental housing power transparency management method according to an embodiment of the present invention.

[0026] like Figure 1 As shown, the transparent operation and management method for public rental housing includes the following steps: S1 acquires approval behavior data throughout the entire process of public rental housing business, and intelligently analyzes the processing time limit, approval opinion content and manual intervention operation based on preset monitoring rules to generate early warning information including risk type and risk level.

[0027] In one embodiment of the present invention, approval behavior data throughout the entire public rental housing business process is acquired, and intelligent analysis of data features is performed based on preset monitoring rules to generate early warning information. This step aims to identify compliance risks in the business process through automated monitoring of key nodes in the exercise of power. The approval behavior data includes timestamp information of business flow, textual opinions generated at approval nodes, and comparison records of automatic system review results and final human decisions.

[0028] Understandably, the pre-defined monitoring rules construct a multi-dimensional evaluation model for processing time limits, approval opinion quality, and abnormal human intervention. By calculating the deviation of actual business parameters from standard thresholds, the system determines the type of risk and classifies its risk level. In practical implementation, the system can monitor the relationship between the remaining time at each stage and pre-defined thresholds to trigger tiered warnings. Regarding the content of approval opinions, natural language processing technology is used to convert the text to be reviewed into a semantic vector, and its cosine similarity to standard examples in the pre-defined valid opinion template library is calculated. The formula is expressed as follows: In addition, by combining text length verification and high-frequency perfunctory word matching, it determines whether the opinion is brief or irrelevant; for manual intervention operations, it automatically compares the consistency between the machine review conclusion and the manual approval result, and marks the conflicting situations as high-risk operations.

[0029] The specific algorithms and logic described above are only one implementation method for intelligent analysis based on preset monitoring rules. The scope of protection of this step is not limited to this. All technical means that use rule matching to quantify the risk of approval behavior data and generate early warning information fall within the scope of this step.

[0030] This step, by embedding supervisory rules into the entire business process, shifts from passive post-event accountability to proactive pre-event and in-event early warning, effectively solving the problems of lagging supervision and arbitrary human intervention in traditional management. Through in-depth mining and intelligent analysis of multi-source approval data, it can accurately identify potential risks such as overdue processing, perfunctory approvals, and abnormal order modifications, significantly improving the transparency and standardization of power operation, and providing reliable data support and decision-making basis for the subsequent construction of a closed-loop supervision system.

[0031] S2, push the warning information to the preset responsible entity, and receive the supervision instruction initiated by the superior regulatory department for the warning information, so as to trigger the responsible entity to perform feedback processing operation.

[0032] This technical approach, which pushes early warning information to pre-defined responsible parties and triggers feedback processing, aims to address the problems in traditional supervision caused by delayed early warning information transmission, unclear accountability, and a lack of higher-level intervention mechanisms. This step achieves seamless integration from risk identification to execution feedback by establishing a multi-level, interconnected information distribution and instruction response mechanism.

[0033] Specifically, the system automatically matches and activates the corresponding responsible entity's receiving channel based on the risk type and level of the early warning information, ensuring that risk information accurately reaches the responsible personnel or relevant management departments. Simultaneously, this step establishes an interface for higher-level regulatory departments to proactively intervene in early warning matters, allowing them to initiate supervisory instructions based on overall regulatory needs. These instructions act as trigger signals, driving the responsible entity to initiate a standardized feedback processing procedure, thus forming a dynamic interactive logic of "early warning push - supervisory trigger - feedback execution." During this process, information push methods can encompass various forms, including in-system messages, mobile notifications, or external communication interfaces. The generation and issuance of supervisory instructions can be automatically triggered based on rules or manually initiated after manual assessment. As one implementation method, the system can send SMS notifications containing near-expiration reminders or overdue alerts to preset responsible personnel and department heads. When higher-level regulatory departments initiate supervision of specific early warning matters through management terminals, the system mandates that the responsible entity fill in a processing description and upload supporting materials in the system interface to respond to the supervisory instructions and execute feedback processing operations.

[0034] This step, by establishing a top-down, interconnected supervision and triggering mechanism, effectively breaks down information silos, ensures that early warnings can be promptly translated into concrete management actions, significantly improves the enforcement of regulatory instructions and the response speed to problem rectification, and lays a solid foundation for forming a complete regulatory closed loop.

[0035] S3. Obtain the feedback and supporting materials submitted by the handling entity, review and verify the feedback and supporting materials, and eliminate the corresponding warning information after the review is passed to form a regulatory closed loop.

[0036] Regarding the regulatory process triggered by the early warning information, this step aims to ensure the effectiveness and authenticity of risk handling by establishing an audit and verification mechanism, thereby forming a complete regulatory closed loop.

[0037] The core of this process lies in receiving feedback and supporting materials submitted by the responsible entity in response to the supervisory instructions, and automatically or through human-machine collaboration to verify the compliance, completeness, and authenticity of these materials based on pre-set review logic. The essence of the review and verification is to correlate and compare the submitted feedback with the original warning risk points to determine whether the risk has been substantially eliminated or reasonably explained. Only when the verification result meets the pre-set pass conditions will the system execute the operation to eliminate the corresponding warning information, thereby ending the lifecycle of that risk item; if the verification fails, the warning status is maintained and a new round of supervisory procedures may be triggered.

[0038] As a specific implementation method, the superior regulatory department can review the processing instructions filled in by the person in charge and the supporting materials uploaded in the system online. After the review is approved, the system will automatically change the status of the corresponding warning light from the warning color to the normal status, thereby realizing closed-loop management from the issuance of the warning to the completion of rectification.

[0039] By implementing the aforementioned review, verification, and early warning elimination mechanisms, we can effectively prevent early warning information from becoming a mere formality and ensure that every risk warning receives substantive processing and feedback. This step constructs a rigorous logical closed loop, which not only improves the enforceability and rectification rate of regulatory instructions but also, through clear criteria for determining status elimination, enables the traceability and quantification of the power operation supervision process, significantly enhancing the credibility and closed-loop control capabilities of the management system.

[0040] S4 integrates housing usage status data collected by IoT devices with business approval data to dynamically monitor the actual occupancy status of housing. When an abnormal occupancy status is detected, it generates a handling instruction and records the entire process operation log.

[0041] This step aims to achieve dynamic monitoring and anomaly handling of the actual living conditions of houses through multi-source data fusion technology. Its core lies in establishing a correlation analysis mechanism between IoT sensing data and business approval data.

[0042] Specifically, the system accesses housing usage status data collected by various IoT devices through standardized interfaces and maps it in real time to the corresponding business approval data records, constructing a cross-validation model between physical space usage behavior and administrative review information. Based on this, the system continuously analyzes the integrated data according to preset dynamic monitoring rules, identifying anomalies where the actual occupancy status differs from the declared information. Once an anomaly is detected, corresponding handling instructions are automatically generated to trigger subsequent control actions. Simultaneously, the entire operational information from data collection and anomaly identification to instruction generation is written into an immutable log record, ensuring the traceability of the power operation process.

[0043] As one implementation method, the housing usage status data may include smart access control entry and exit records and water and electricity usage curves. When it is detected that there are no access control entry and exit records for more than a preset number of days, water and electricity usage is lower than a preset threshold for several consecutive months, or non-tenants frequently enter and exit, it is determined to be an abnormal living status such as suspected vacancy or illegal subletting, and then a door locking operation command is issued and relevant logs are recorded.

[0044] By integrating housing usage status data collected by IoT devices with business approval data, this step overcomes the limitations of traditional post-rental management that relies on manual inspections, enabling real-time, dynamic, and objective monitoring of the actual occupancy status of properties. This monitoring mechanism, based on cross-validation of multi-source data, can effectively identify hidden violations such as subletting and vacancy, significantly improving the accuracy and response speed of supervision. Meanwhile, the recording of full-process operation logs ensures transparency and auditability of the handling process, forming a complete chain of evidence from anomaly detection to instruction execution.

[0045] Example 2 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S1 in the public rental housing power transparency operation management method: "obtain approval behavior data in the entire process of public rental housing business, perform intelligent analysis on processing time limits, approval opinion content and manual intervention operations based on preset monitoring rules, and generate early warning information containing risk types and risk levels".

[0046] In this embodiment, regarding the intelligent analysis process of the content of the approval comments and the manual intervention operations, the system first obtains the text of the approval comments submitted by the current handler from the business approval node in real time. As the input source, specifically, the system calls the pre-trained language model BERT to compare the approval opinion text T with a preset valid opinion template library. Examples of standards in Mapped to high-dimensional semantic vectors respectively and .

[0047] Subsequently, the processing action performs cosine similarity calculation, based on the formula... Calculate the similarity score between the current text and each standard example, and extract the maximum value from all calculated results. If the maximum value is less than the preset similarity threshold... (For example, if set to 0.6), the system determines that the content of the opinion is irrelevant to the business scenario. Simultaneously, the system calculates the character length of the approval opinion text T in parallel. ,like If the length is less than the preset minimum effective length L (e.g., 20 characters), the opinion is deemed too brief. Furthermore, the system searches the constructed high-frequency perfunctory word library W (containing words such as "agree" and "read"). If the approval opinion text T belongs to this word library and its character length is less than the preset minimum effective length L, the opinion is deemed too brief. If the length of a perfunctory comment is less than or equal to the maximum allowed length L0 (e.g., 10 characters), it is considered a perfunctory comment. When any of the above conditions are met, the system immediately generates a content warning signal and marks it as a red light level.

[0048] On the other hand, for manual intervention operations, the system input sources are the system's machine review result and the final manual approval result under the same business document. The processing action is a logical comparison. When a conflict is detected, such as the machine review result being "disagree" while the manual result is changed to "agree", an abnormal operation warning signal is generated. Finally, the system will monitor the time limit warning signal generated by the remaining processing time of the business process in real time, integrate it with the aforementioned content warning signal and abnormal operation warning signal, and output comprehensive warning information containing specific risk types and risk levels, thereby completing the closed-loop identification of the three types of key risk points.

[0049] This implementation method introduces a deep text analysis algorithm based on semantic vector space, which can accurately identify perfunctory, brief, and irrelevant behaviors in approval opinions. It effectively overcomes the shortcomings of traditional keyword matching in understanding semantic context, and significantly improves the ability to detect hidden violations in the exercise of power and the accuracy of supervision.

[0050] Example 3 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S2 in the public rental housing power transparency operation management method: "pushing the early warning information to the preset responsible entity and receiving the supervision instruction initiated by the superior regulatory department for the early warning information, so as to trigger the handling entity to perform feedback processing operation".

[0051] In this embodiment, the specific implementation process of pushing the early warning information to the preset responsible entities is as follows: First, the system's input source is the early warning information generated in step S1. This information data structure explicitly includes a risk level field and a business process type field. The processing action is to execute the automatic matching logic of the responsible entity. The system has a pre-set mapping rule table, which defines the correspondence between different risk levels and business process types and the responsible entities. Specifically, when the risk level is an imminent early warning, the system automatically matches the person in charge of the current business node as the responsible entity; when the risk level is an overdue alarm or a high-risk operation involving abnormal manual intervention, the system simultaneously matches the person in charge and the head of their department as joint responsible entities. The output of this processing action is a determined list of responsible entities, including the unique user identifier and associated communication contact information of each entity.

[0052] Next, using the aforementioned list of responsible entities and the original early warning information as input, the system calls the SMS notification interface to send a message. When constructing the notification message content, the system dynamically generates text based on the specific circumstances of the early warning. If the deadline is approaching, a near-expiration reminder is generated; if the deadline has passed, an overdue alarm is generated; and if it is a supervisory order initiated by a superior, a supervisory instruction is generated. Crucially, the system embeds a unique identifier for the early warning item and a specific redirect link to the system frontend in each notification message. This URL carries the unique identifier as a query parameter. After processing is complete, the output is a notification message sent to the responsible entity's mobile terminal.

[0053] Finally, the system triggers feedback processing by having the responsible party click the redirect link in the notification message. The system parses the unique identifier in the link, automatically locates and loads the corresponding warning details page, which forcibly displays a processing description form and a supporting document upload control. After the responsible party enters a text-based processing description and uploads supporting documents in image or document format, they click the submit button. The system then receives this feedback data and temporarily stores it in the database, completing the entire process from warning push to triggering feedback processing.

[0054] Through the above specific implementation methods, the early warning information is accurately and targeted based on the risk level, ensuring that risks of different severity can reach the corresponding management level. At the same time, the jump link with a unique identifier breaks down the barrier between mobile terminal notification and PC terminal business processing, significantly shortening the response time from risk discovery to manual intervention and ensuring the effective execution of supervisory instructions.

[0055] Example 4 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S3 in the public rental housing power transparency operation management method: "obtaining the feedback statement and supporting materials submitted by the handling entity, reviewing and verifying the feedback statement and supporting materials, and eliminating the corresponding early warning information after the review is passed to form a regulatory closed loop".

[0056] In this embodiment, the specific implementation process of reviewing and verifying the feedback description and supporting materials in step S3 is as follows: The operator of the superior regulatory department logs into the system through the management terminal. The system responds to the query request and extracts the feedback description text and supporting material file that are uniquely associated with the current pending warning information from the database of the full-process log recording module as input data. The feedback description text is a structured string filled in by the handling entity, and the supporting material file includes binary data streams such as on-site photos and scanned copies of lease contracts.

[0057] Subsequently, the system performs compliance review and verification. For the feedback text, it first calculates its character length and compares it with a preset minimum valid length threshold. If the length is insufficient, it is directly marked as non-compliant. Simultaneously, it uses natural language processing technology to convert the text into a semantic vector and calculates its cosine similarity with each template in the preset standard response template library. The formula is as follows: If the maximum similarity is lower than the set threshold or if the detected text completely matches the high-frequency perfunctory word library and the length is less than the maximum allowed length of perfunctory words, the content is deemed non-compliant. For supporting material files, the system verifies the file header information to confirm the format integrity and compares the file hash value with the upload record to verify authenticity and prevent the file from being tampered with or replaced.

[0058] After processing, if all the above review indicators meet the preset rules, the system generates an approval instruction. This instruction acts as a trigger signal to drive the intelligent monitoring module to perform a status update operation, automatically switching the warning light status of the corresponding warning information from red or yellow to off, and recording the timestamp of approval and the operator ID in the log, thereby closing the management loop of "early warning-supervision-feedback-completion" at the data level.

[0059] The above-described specific implementation method introduces a hybrid recognition algorithm based on semantic similarity and integrity verification, which realizes intelligent and standardized review of feedback content, effectively avoids the subjectivity and oversight of manual review, ensures the authenticity and effectiveness of the "completion" node in the regulatory closed loop, and significantly improves the supervisory efficiency of the operation of public rental housing power.

[0060] Example 5 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S4 in the public rental housing power transparency operation and management method: "Integrating housing usage status data collected by IoT devices with business approval data to dynamically monitor the actual occupancy status of the housing, generating disposal instructions and recording the entire process operation log when an abnormal occupancy status is identified."

[0061] In this embodiment, the specific implementation process of integrating the housing usage status data collected by IoT devices with business approval data in step S4 is as follows: The system first uses a standardized data interface as input to acquire in real time entry and exit timestamps and personnel identification data recorded by smart access control devices, as well as usage curve data collected by smart water and electricity meters at a fixed sampling frequency. The processing involves extracting key fields from the above entry and exit records and usage curve data, performing spatiotemporal correlation matching with tenant identity information and house codes stored in the business approval database, and constructing a fusion dataset indexed by the house. The output is a structured data record containing complete occupancy behavior characteristics of a specific house within a preset time period.

[0062] Next, the system uses the structured data record as input and performs processing actions to analyze the access control frequency and water and electricity consumption values ​​within a preset time period. Specifically, the system counts the number of access control card swipes for consecutive days and calculates the average daily water and electricity consumption value, comparing them with preset number of days thresholds and consumption thresholds respectively. When it detects that there are no access control records for more than a preset number of days, or that the water and electricity consumption is lower than the preset threshold for several consecutive months, or that non-tenants frequently enter and exit the house, it determines that the actual occupancy of the house is abnormal and generates an abnormal occupancy status identifier as the output result. This identifier carries a specific abnormality type code.

[0063] Furthermore, based on the generated abnormal residence status identifier, the system generates tiered handling instructions according to the risk level indicated by the identifier. If the risk level is determined to be high, a remote door-locking operation instruction is generated; if it is medium to low risk, a manual intervention verification instruction is generated. The handling action is to execute the remote door-locking operation instruction to directly control the access control controller to lock the door, or to send a manual intervention verification task notification to the patrol personnel via a mobile terminal. During this process, the system synchronously records the entire process operation log from IoT data collection, abnormal identification and judgment, handling instruction generation to final execution result confirmation. The entire process operation log includes user behavior, approval opinions, early warning triggers, supervision instructions and feedback content, and establishes an index to support multi-dimensional retrospective auditing by time, personnel and business type, thereby forming a complete data closed loop.

[0064] This implementation method deeply integrates IoT-sensing data with business approval data, enabling precise and dynamic identification of violations such as vacancy and subletting of public rental housing. This effectively solves the problem of lag caused by traditional post-rental supervision relying on manual inspections. Simultaneously, the combination of a tiered handling mechanism and full-process log recording ensures both rapid response to abnormal situations and traceability and auditability of the power operation process, significantly improving the transparency and enforceability of supervision.

[0065] Example 6 This embodiment will describe in detail the complete implementation of the transparent operation and management method of public rental housing rights, covering the specific implementation details from data collection, intelligent supervision, closed-loop supervision to post-rental dynamic monitoring and full-process traceability.

[0066] In this embodiment, the system first performs data acquisition and preprocessing of approval behavior throughout the entire public rental housing business process. Specifically, the system connects to the housing security business database through a standardized data interface, extracting business flow data covering the entire lifecycle from application acceptance, eligibility verification, allocation, and occupancy in real time. This data includes business document number, current processing stage, stage start time, preset processing time limit, handler's identity information, machine review results, and manual approval opinion text. Before the data enters the analysis engine, the system cleans the text data, removes invalid whitespace characters, and uniformly formats the timestamps to ensure the accuracy of subsequent time-series logic calculations. Simultaneously, the system establishes a preset monitoring rule base, which stores time limit thresholds for different business stages, a valid opinion template library, a high-frequency perfunctory word library, and machine review and manual comparison logic, providing a benchmark for subsequent intelligent analysis.

[0067] The core processing logic of this embodiment is intelligent analysis of processing time limits, approval content, and manual intervention. Regarding processing time limit monitoring, the system monitors the remaining processing time for each business step in real time, calculates the difference between the current time and the start time of the step, and compares it with the preset total time limit. When the remaining time is less than a preset threshold (e.g., 3 days), the system automatically generates a yellow warning signal to indicate the risk of expiration; when the remaining time is less than zero, indicating an overdue period, the system immediately generates a red warning signal. Regarding the monitoring of approval content, the system uses natural language processing technology to analyze the approval text at the current node. Deep semantic analysis is performed. In the specific implementation, the system calls a pre-trained language model (such as the BERT model) to process the approval opinion text. and a library of preset valid opinion templates Standard example in Convert them into high-dimensional semantic vectors respectively and Subsequently, the system calculates... and The cosine similarity is calculated using the following formula:

[0068] The system iterates through all standard examples in the template library and takes the maximum value among all similarity calculation results. If the maximum value is less than the preset similarity threshold... (In this embodiment, it can be set to 0.6), then it is determined that the content of the approval opinion is irrelevant to the current business scenario, triggering a red light warning. In addition, the system also performs text length and keyword detection, and counts the approval opinion text. character length ,like Less than the preset minimum effective length If the comment is too brief (e.g., 20 characters), a red light warning will be triggered. The system will also build a high-frequency, perfunctory word database. This includes common perfunctory phrases such as "agreed," "read," and "the situation is true," and the approval document... Belongs to the high-frequency perfunctory word library And its character length Less than or equal to the maximum allowed length of perfunctory words (A 10-character limit is recommended). If the comment is deemed perfunctory, a red light warning will be triggered. Regarding abnormal operation monitoring, the system automatically compares the machine review results with the manual approval results. When a conflict arises (e.g., the machine review result is "disagree" while the manual approval result is changed to "agree"), the system automatically marks the operation as high-risk and generates an abnormal operation warning signal. Finally, the system integrates the aforementioned time-limited warning signals, content warning signals, and abnormal operation warning signals to form a comprehensive warning message that includes specific risk types and risk levels.

[0069] After generating an early warning, this embodiment executes a closed-loop management process of "early warning - supervision - feedback - completion". The system automatically matches the corresponding responsible entity based on the risk level and business process type of the early warning information, typically including the specific handler and their department head. Through an integrated SMS notification interface, the system sends a notification message to the responsible entity containing near-expiration reminders, overdue alerts, or supervision instructions. The message carries a unique identifier for the early warning item and a system redirect link, allowing the responsible person to quickly locate the problem. Higher-level regulatory departments can view the early warning list through the management terminal and initiate supervision instructions for specific early warning items. Upon receiving the supervision instruction, the handler must fill in a detailed processing description in the system and upload supporting materials (such as on-site photos, supplementary supporting documents, etc.). The higher-level regulatory department then reviews and verifies the submitted feedback description and supporting materials, including a compliance review of the feedback text and verification of the completeness and authenticity of the supporting documents. Once the review is approved, the system generates an approval command, automatically turns off the warning light for the corresponding warning information, and updates the status of the entry to "completed," thus forming a complete regulatory loop and ensuring that every warning has a substantial processing result.

[0070] This embodiment also constructs an information overview and a penetrating regulatory view to achieve data visualization and in-depth drill-down. The system provides a dual-view switching function for "pre-rental management" and "post-rental management" to adapt to the regulatory needs at different stages. In the early warning data dashboard, the system displays in real time the total number of early warnings, the number of individual early warnings (categorized by time limit, content, and anomaly), and the distribution trend of the early warning structure by link, type, and region. To support penetrating supervision, all statistical indicators are designed with a layer-by-layer drill-down interactive logic. When a user clicks on the summary data, the system sequentially displays detailed regional data, then drills down to the individual household details, ultimately tracing back to the original business documents and responsible person information. This penetrating query mechanism, from macro-level summary data to micro-level specific documents, enables regulatory personnel to quickly locate the root cause of problems, achieving data traceability, process traceability, and accountability. At the same time, the system performs multi-dimensional statistical analysis, constructing a dual-track reporting system that includes early warning reports and business reports. Early warning reports include a general early warning report, a detailed early warning report, a feedback rate statistics report, and an annual review early warning report, used to quantitatively assess regulatory effectiveness. Business reports include a housing distribution report, a family structure report, a contract signing rate statistics report, a home visit record report, and a report on the status of various groups' support, used to assist in policy formulation and resource allocation. All statistical indicators in all reports support drill-down to original business documents or resident files, ensuring the transparency and credibility of data analysis.

[0071] In terms of post-rental dynamic monitoring, this embodiment achieves dynamic perception of the actual occupancy status of a property by integrating data on the property's usage status collected by IoT devices with business approval data. The system obtains entry and exit records from smart access control devices and usage curves from smart water and electricity meters through standardized data interfaces, and performs spatiotemporal correlation matching between this IoT data and tenant information in the business approval data. Specifically, the system analyzes the frequency of access control entry and exit and water and electricity usage within a preset time period, setting multiple anomaly judgment logics: if no access control records are detected for more than a preset number of days (e.g., 30 days), or water and electricity usage is below a preset threshold for several consecutive months, or non-tenants frequently enter and exit the property, the system determines that the actual occupancy status of the property is abnormal and generates an abnormal occupancy status indicator. For identified abnormal occupancy statuses, the system generates tiered handling instructions based on the risk level. For high-risk suspected vacancy or subletting situations, the system can generate remote door locking instructions to directly control the smart door lock to perform the locking operation; for medium- and low-risk situations, the system sends manual intervention verification tasks to inspection personnel via mobile terminals, requiring on-site verification. During this process, the system synchronously records the entire operation log from IoT data collection, anomaly identification and judgment, generation of handling instructions to final execution result confirmation.

[0072] To ensure the transparency and auditability of power operation, this embodiment implements a full-process logging and auditing mechanism. The system records all key operations in an immutable manner, covering user behavior trajectories, approval opinion modification history, warning trigger times and reasons, supervisory instruction issuance records, feedback submission times, and review results. This log data is indexed and stored according to multiple dimensions such as time, personnel, and business type, supporting flexible retrospective queries by disciplinary inspection and auditing departments. For example, auditors can retrieve all approval operations of a specific person within a time period, or trace the entire processing of a certain type of warning by business type. This full-process logging mechanism not only meets compliance requirements but also provides a solid data foundation for subsequent accountability and process optimization.

[0073] In summary, this embodiment achieves a shift from passive post-event accountability to proactive pre-event and in-event early warning by embedding intelligent monitoring rules into the entire business process; it effectively solves the problem of early warning information becoming merely a formality by constructing a closed-loop mechanism of "early warning-supervision-feedback-completion"; it breaks through the information silos of post-rental supervision by integrating IoT data and business data, and realizes dynamic monitoring of the actual usage status of housing; and it greatly improves the transparency, fairness, and efficiency of public rental housing management by combining full-process log recording and penetrating supervision view, providing strong technical support for building a transparent and standardized housing security system.

[0074] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0075] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0076] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for the transparent operation and management of public rental housing rights, characterized in that, Includes the following steps: The system acquires approval behavior data throughout the entire public rental housing business process, and intelligently analyzes processing time limits, approval opinions, and manual intervention operations based on preset monitoring rules to generate early warning information that includes risk types and risk levels. The warning information is pushed to the preset responsible entities, and the supervisory instructions initiated by the superior regulatory authorities in response to the warning information are received, so as to trigger the responsible entities to perform feedback processing operations. Obtain feedback and supporting materials submitted by the handling entity, review and verify the feedback and supporting materials, and eliminate the corresponding warning information after the review is approved to form a regulatory closed loop; By integrating housing usage status data collected by IoT devices with business approval data, the actual occupancy status of houses is dynamically monitored. When an abnormal occupancy status is detected, a handling instruction is generated and a full-process operation log is recorded.

2. The method as described in claim 1, characterized in that, The intelligent analysis of processing time limits, approval opinions, and manual intervention operations based on preset monitoring rules includes: Real-time monitoring of the remaining processing time for each business step; when the remaining time is less than a preset threshold, a time limit warning signal is generated. The approval opinion text of the current node is obtained. Natural language processing technology is used to calculate the semantic similarity between the approval opinion text and the standard examples in the preset valid opinion template library. The length of the approval opinion text and whether it contains high-frequency perfunctory words are detected. When the similarity is lower than the preset threshold, or the text length is insufficient, or it contains high-frequency perfunctory words, a content warning signal is generated. The system compares the machine review results with the manual approval results, and generates an abnormal operation warning signal when the two conflict. The time limit warning signal, content warning signal and abnormal operation warning signal are integrated into a warning information that includes risk type and risk level.

3. The method as described in claim 2, characterized in that, The step of obtaining the approval opinion text of the current node and calculating the semantic similarity between the approval opinion text and standard examples in the preset valid opinion template library using natural language processing technology includes: Use a pre-trained language model to process the approval opinion text. and a library of preset valid opinion templates Standard example in Convert to semantic vector and ; calculate and The cosine similarity is calculated using the following formula: Take the maximum value among all similarity calculation results If the maximum value is less than the preset similarity threshold If the content of the approval opinion is deemed irrelevant to the business scenario, a red light warning will be triggered.

4. The method as described in claim 2, characterized in that, The detection of the length of the approval opinion text and whether it contains high-frequency perfunctory words includes: Statistical approval document character length ,like Less than the preset minimum effective length If the judgment is too simplistic, a red light warning will be triggered. Building a high-frequency perfunctory word library If the approval document Belongs to the high-frequency perfunctory word library And its character length Less than or equal to the maximum allowed length of perfunctory words If so, it will be judged as a perfunctory opinion and trigger a red light warning.

5. The method as described in claim 1, characterized in that, The step of pushing the early warning information to the preset responsible parties includes: Based on the risk level and business process type of the early warning information, the corresponding person in charge and department head will be automatically matched as the responsible party. The system sends a notification message containing a near-expiration reminder, an overdue alarm, or a supervision instruction to the responsible entity via an SMS notification interface. The notification message carries a unique identifier for the warning item and a redirect link to trigger the responsible entity to fill in the processing instructions and upload supporting materials in the system to perform feedback processing.

6. The method as described in claim 1, characterized in that, The review and verification of the feedback and supporting materials includes: The higher-level regulatory authorities retrieve the feedback explanation text and supporting documents associated with the early warning information through the management terminal; The feedback description text was reviewed for compliance, and the completeness and authenticity of the supporting documents were verified. After the review is approved, an approval instruction is generated. In response to the approval instruction, the system automatically turns off the warning light corresponding to the warning information, thus forming a regulatory closed loop of warning-supervision-feedback-completion.

7. The method as described in claim 1, characterized in that, The data on building usage status and business approvals collected by the integrated IoT devices include: Data on entry and exit records from smart access control devices and usage curves from smart water and electricity meters are obtained through standardized data interfaces. The entry and exit record data and usage curve data are matched with the lessee information in the business approval data in a spatiotemporal correlation. The system analyzes the frequency of access control and water and electricity consumption within a preset time period. When it detects that there are no access control records for more than a preset number of days, or that water and electricity consumption is lower than the preset threshold for several consecutive months, or that non-tenants frequently enter and exit the house, it determines that the actual occupancy status of the house is abnormal and generates an abnormal occupancy status indicator.

8. The method as described in claim 7, characterized in that, The process of generating a handling instruction and recording a full-process operation log when an abnormal living status is detected includes: Based on the risk level of the abnormal living status indicator, a tiered handling instruction is generated, which includes a remote door locking operation instruction or a manual intervention verification instruction. Execute the remote door locking command or send a manual intervention verification task to the patrol personnel via a mobile terminal; The system synchronously records the entire process of operation logs, from IoT data collection, anomaly identification and judgment, generation of handling instructions to confirmation of final execution results. The entire process operation logs include user behavior, approval opinions, early warning triggers, supervision instructions and feedback content, and support multi-dimensional retrospective auditing by time, personnel and business type.

9. The method as described in claim 1, characterized in that, The method further includes: Construct an information overview and transparent regulatory view, and provide a dual-view switching function for pre-rental management and post-rental management; The early warning data dashboard displays the total number of early warnings, the number of individual early warnings, and the trend changes in the early warning structure across the entire jurisdiction. In response to user drill-down operations on statistical data, detailed regional data and individual household data are displayed sequentially, enabling a layer-by-layer penetrating query from macro-level summary data to specific business documents and responsible persons.

10. The method as described in claim 1, characterized in that, The method further includes: Multi-dimensional statistical analysis is performed to construct a dual-track reporting system that includes early warning reports and business reports. The early warning reports include a general early warning report, a detailed early warning report, a feedback rate statistics report, and an annual review early warning report, used to evaluate regulatory effectiveness. The business reports include a housing distribution report, a guaranteed family structure report, a contract signing rate statistics report, a home visit inspection record report, and a report on the guarantee status of various groups, used to assist decision-making. All statistical indicators support drill-down to original business documents or resident files to achieve data searchability and process traceability.

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