Event data processing method based on Dccu

Abstract

The invention discloses a community event data processing method and system based on a double-layer Dccu architecture, and belongs to the technical field of smart communities and grassroots governance, and the method comprises the steps: a cell Dccu server deployed in a cell collects data, and generates a trusted event through the recognition of a built-in AI model; a community Dccu server deployed in a community receives the trusted event through a private network, carries out local statistics and non-cloud processing, generates a report form and a work order and issues the report form and the work order; and finally, receiving a disposal result to realize closed-loop management. The system comprises the cell Dccu server and the community Dccu server. According to the invention, through a double-layer cooperative processing architecture of a cell and a community, in combination with private network transmission and local AI processing, full-process closed-loop management of event data from identification, generation, transmission, disposal to feedback is realized, and the real-time performance of event processing, the data credibility and the grassroots governance efficiency are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart community and grassroots governance, and particularly relates to a community event data processing method and system based on a double-layer Dccu (Digital Community Core Unit) architecture. BACKGROUND

[0002] With the deepening of smart city construction, the intelligent management level of community as the basic unit of urban governance needs to be improved. In the prior art, the smart community system mostly adopts a centralized cloud platform architecture, and data collected by various Internet of Things devices in the community is directly uploaded to the cloud for processing. This architecture has the following defects: first, uploading massive data to the cloud causes high network transmission pressure and high delay, which is difficult to meet the requirements of real-time response in scenarios such as security and fire protection; second, data transmission in the public network has security risks, and centralized processing in the cloud cannot meet the compliance requirements of local data storage and processing; third, the existing system often forms a data island, and the data of management systems such as property, parking, and community economy are separated from the Internet of Things event data, which cannot be deeply fused and analyzed to support accurate decision-making; and finally, the event processing process is not closed-loop, and lacks an effective feedback mechanism, making it difficult to trace and evaluate the disposal effect.

[0003] Therefore, there is an urgent need for a new community event data processing method and system that can realize real-time, secure, closed-loop, and support data deep fusion. SUMMARY

[0004] In view of the technical defects and technical disadvantages in the prior art, the present application provides an event data processing method based on Dccu (Digital community core units, referred to as Dccu), which overcomes the above problems or at least partially solves the above problems, and the specific scheme is as follows:

[0005] As a first aspect of the present application, an event data processing method based on Dccu is provided, comprising the following steps:

[0006] S1: collecting data from Internet of Things devices and management systems by a community Dccu server deployed in a community, performing event recognition through an AI model built-in, and generating trusted events conforming to a standard data processing protocol, wherein the trusted events contain three characteristics of event subject uniqueness, occurrence location, and occurrence time;

[0007] S2: receiving the trusted events reported by the Dccu servers of each community in the jurisdiction through a private network by a community Dccu server deployed in the community, performing non-cloud processing of statistical analysis and priority evaluation on the trusted events based on a locally deployed AI model, automatically generating data statistical reports and event work orders, and issuing the event work orders to corresponding community or community staff.

[0008] S3: Receive the processing result of the event work order, associate the processing result with the original trusted event, and update the event status to form a closed-loop management.

[0009] In some embodiments, generating a trusted event in S1 specifically includes:

[0010] The DCCU server in the community preprocesses the collected raw data, including data cleaning and anomaly detection.

[0011] Spatial location of the event is determined using an integrated geographic information system;

[0012] The identified trusted events are matched and verified against the personnel, housing, and vehicle information databases pre-stored in the community DCCU server to confirm and bind a unique event subject identifier;

[0013] Trusted event data, including the event subject, location, and time, is encapsulated according to a standard protocol and stored and transmitted in groups to form the starting node of a trusted event chain.

[0014] In some embodiments, the statistical analysis in S2 specifically includes:

[0015] Data reception and aggregation: The community DCCU server receives trusted event chain data transmitted in groups through a private network, and performs multi-dimensional classification and storage according to event type, time interval and geographical region through the built-in data aggregation module;

[0016] Statistical analysis and report generation: Utilizing the statistical engine integrated with the community DCCU, the aggregated trusted events are analyzed from multiple dimensions to generate periodic statistical reports that include event type distribution, high-frequency occurrence locations, and processing timeliness indicators. These reports are then uploaded to the superior integrated management information platform via a dedicated network.

[0017] In some embodiments, generating an event work order in S2 specifically includes:

[0018] The AI ​​evaluation model built into the community DCCU automatically prioritizes the trusted events based on their type and severity.

[0019] Based on the assessment results, a detailed work order is automatically generated, including an event description, location, time, information on relevant personnel, and handling instructions.

[0020] The event work order is sent to the staff's handheld terminal via the public communication network, and alarm information is simultaneously pushed to the user terminals of the personnel related to the event.

[0021] In some embodiments, the processing result in S3 includes automatic feedback, which is specifically implemented as follows:

[0022] For events that can be automatically adjusted and handled through IoT devices, the cell DCCU server continuously receives and monitors the data streams from relevant sensors;

[0023] When monitoring data indicates that the alarm condition that triggered the trusted event has been eliminated, a new trusted event for the handling result is automatically generated. This event includes a confirmation signal that the condition has been eliminated, the elimination time, and key sensor data indicating that the sensor has returned to normal.

[0024] The trusted event of the processing result is fed back to the community DCCU server via the private network. The community DCCU server then adds it as a terminal node to the corresponding original trusted event chain and marks the entire event chain as closed.

[0025] In some embodiments, the processing result in S3 includes manual feedback, which is specifically implemented as follows:

[0026] After completing the on-site handling, the staff uploaded the handling results, including text descriptions, on-site photos or video evidence, to the community DCCU server via handheld terminals;

[0027] The cell DCCU server performs format verification and logical consistency check on the received processing result data;

[0028] Once verified, it is encapsulated as a new trusted event for the disposal result and forwarded to the community DCCU server;

[0029] The community DCCU server associates the trusted event of the manual handling result with the original trusted event of AI recognition, and adds it as a subsequent node to the same trusted event chain. The event chain status is updated to resolved or pending follow-up based on the specific content of the handling result.

[0030] In some embodiments, both the cell DCCU server and the community DCCU server are locally deployed non-cloud servers, and are securely connected via a physical private network or a virtual private network.

[0031] The IoT devices include fire protection equipment, security equipment, and feature-based devices; among them, the fire protection equipment includes water system detection equipment for monitoring fire hydrant water pressure, smoke sensors, and temperature sensors; the security equipment includes high-definition cameras for behavior recognition and access control systems; and the feature-based devices include charging piles and photovoltaic energy storage equipment.

[0032] In some embodiments, the method further includes data fusion, specifically including:

[0033] The community DCCU server is unidirectionally connected to the community's property management system and parking management system via a trusted data link or system gateway.

[0034] The community DCCU server is unidirectionally connected to the community fund system and community economic system on the community side through a trusted data chain or system gateway;

[0035] The community DCCU server integrates and analyzes the property management data and parking management data obtained from the community DCCU server and the community management data obtained from the community data management system with the trusted event data to generate a comprehensive governance report and upload it to the comprehensive management information platform.

[0036] In some embodiments, the community DCCU server integrates and analyzes property management data and parking management data obtained from the community DCCU server and community management data obtained from the community data management system with the trusted event data to generate a comprehensive governance report, specifically including:

[0037] S901: The community DCCU server establishes a unified data association model, and performs spatiotemporal association and subject mapping between the event location and related personnel information in the trusted event data, the repair records in the property management data, the vehicle parking information in the parking management data, and the community fund usage and health and wellness service needs in the community management data, forming a cross-business domain associated data set;

[0038] S902: Based on the aforementioned associated data set, calculate multidimensional indicators for comprehensive governance assessment, wherein the indicators include at least:

[0039] Security situation indicators: Based on the frequency and distribution of reliable security events, coupled analysis with property security patrol records and fire equipment status data, a community safety coefficient is generated;

[0040] Service efficiency indicators: Based on the service response time, number of complaints and suggestions and the corresponding event work order handling time in property management data, the property service efficiency is evaluated by comparison and analysis.

[0041] Resource supply and demand indicators: Based on the parking space turnover rate and peak-hour parking space shortage in parking management data, and the commercial pedestrian flow data in community economic data, a fitting analysis is conducted to identify resource allocation bottlenecks;

[0042] S903: Utilizing the report template engine built into the community DCCU server, the multidimensional indicators and analysis results are automatically populated into the preset comprehensive governance report template to generate a visualized comprehensive governance report containing data trend charts, heat maps, and early warning prompts. The report is then uploaded to the comprehensive management information platform of the street-level government or government commissions and bureaus via a dedicated network to provide data decision support for cross-departmental collaborative governance.

[0043] In some embodiments, prior to S1, system pre-configuration is further included, specifically including:

[0044] A cell-wide information database is pre-built and stored in the cell DCCU server, and the database includes at least:

[0045] A personnel information database, in which personnel are categorized into owners, family members, and tenants;

[0046] The housing information database categorizes properties into owner-occupied housing, rental housing, and commercial properties, and records their spatial location and associated owners or responsible persons.

[0047] A public facilities database records the location of facilities such as fire hydrants, fitness equipment, and charging stations, as well as information on their managers.

[0048] The database is used for AI models to quickly call and match during event identification, in order to identify and bind the event subject and its unique identifier of a credible event.

[0049] The present invention has the following beneficial effects:

[0050] This invention, by constructing a two-layer DCCU server architecture of "community-residential area" and a closed-loop process of "identification-processing-feedback," decentralizes the core capabilities of event handling to the grassroots edge. Specifically, by utilizing local AI processing and dedicated network transmission, it completely avoids the latency and network congestion risks of cloud computing, achieving minute-level ultra-fast response to community emergencies. At the same time, by attaching a tag with unique subject-specific and precise spatiotemporal information to each event and forming a closed-loop management system, this method constructs a trustworthy foundation for data from generation and transmission to disposal, ensuring the reliability of the entire process and its high evidentiary value in judicial evidence collection and dispute mediation. Attached Figure Description

[0051] Figure 1 A flowchart illustrating an event data processing method based on DCCU provided in an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the system framework of a digital community management system based on DCCU, provided for an embodiment of the present invention. Detailed Implementation

[0053] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0054] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0055] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0056] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0057] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0058] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0059] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides an event data processing method based on DCCU. Figure 1 A flowchart illustrating an event data processing method based on DCCU provided in this embodiment of the invention includes the following steps:

[0060] S1: The community DCCU server deployed in the community collects data from IoT devices and management systems, identifies events through the built-in AI model, and generates trusted events that conform to standard data processing protocols. The trusted events include three characteristics: the uniqueness of the event subject, the location of occurrence, and the time of occurrence.

[0061] S2: The community DCCU server deployed in the community receives the trusted events reported by the DCCU servers of each community in the jurisdiction through the private network, performs non-cloud processing on the trusted events based on the locally deployed AI model to perform statistical analysis and priority evaluation, automatically generates data statistical reports and event work orders, and sends the event work orders to the corresponding community or community staff.

[0062] S3: Receive the processing result of the event work order, associate the processing result with the original trusted event, and update the event status to form a closed-loop management.

[0063] Figure 2 A schematic diagram of a system framework for a DCCU-based digital community management system is provided as an embodiment of the present invention, as shown below. Figure 2 As shown in the figure, this embodiment of the invention provides a digital community management system based on DCCU.

[0064] In addition to the sub-district offices, the district-level integrated management information platform also includes, but is not limited to, various government commissions and bureaus, such as the relevant government departments. The DCCU server can upload data collected on-site, as well as business data such as early warnings and event handling based on intelligent identification, to the sub-district integrated information platform and the information platforms of the relevant government departments, and can also connect with the relevant government commissions and bureaus at the same time.

[0065] The community DCCU server is also used to connect to the community data management systems deployed in the corresponding community, obtain the corresponding community management data from each community data management system, and transmit the community management data to the community DCCU server through the private network; the community DCCU server is also used to connect to the community data management systems deployed in the corresponding community, and obtain the corresponding community management data from each community data management system.

[0066] The data collected on-site by the DCCU server in this invention (including corresponding data obtained from various IoT devices and third-party data) is trusted data. The DCCU server needs to process the trusted data into trusted event data, that is, from trusted data to trusted event data, where trusted event data is a part of the trusted data.

[0067] Event data is data generated around events that may occur or have already occurred, requiring attention or action. Event data is dynamic; it needs to describe a process, including event identification, status, handling, and results; event data appears in groups and can be stored and transmitted in groups to facilitate traceability. Event data described with trusted data (with time and space tags) is called trusted event data, and a group of trusted event data is called a trusted event (data) chain.

[0068] This invention constructs an efficient, reliable, and closed-loop paradigm for handling grassroots incidents, builds a data credibility foundation, enhances the judicial and evidentiary value of data, and realizes "grassroots closed-loop" and "real-time response" in incident handling, fundamentally improving the modernization level of community governance.

[0069] In this embodiment of the invention, both the community DCCU server and the neighborhood DCCU server adopt an intelligent micro-unit architecture. Their edge intelligent gateways support multi-protocol access (such as MQTT and CoAP) and have a built-in lightweight AI model. Taking a fire lane parking violation incident as an example, the community DCCU analyzes the video stream in real time at the edge, generating a trusted event chain containing the license plate number, location, and time. This chain is then transmitted to the neighborhood DCCU via a dedicated network for priority assessment and work order distribution. This architecture ensures low latency and data reliability in event processing.

[0070] Take the case of illegal parking in a fire lane in Community A as an example. In the traditional model, after the community's cameras capture the footage, the data needs to be uploaded to a remote cloud platform for analysis before instructions are issued—a lengthy process. However, after adopting the method described above:

[0071] The community's DCCU server uses a built-in AI model to analyze camera video streams in real time, immediately identifying illegal parking incidents and generating a reliable event containing the license plate number (the vehicle itself), the location of the fire lane (the location), and the time of discovery (the time of discovery). This reliable event is instantly reported to the community DCCU server via a dedicated network. The community DCCU performs a priority assessment based on its local rule base (blocking fire lanes is considered a high-risk event), immediately generates a work order, and distributes it via the public network to the handheld terminals of the nearest patrolling security guards. Simultaneously, a parking violation alert is pushed to the vehicle owner's mobile phone. Security guards respond to the situation, or the vehicle owner drives away; the result is fed back to the system through the terminal, and the event status is updated to "resolved."

[0072] The outcome of incident handling not only provides feedback to the community DCCU (District Office Control Center) to form a closed loop, but also links with the street-level information platform and the systems of public security, housing and construction, and other relevant departments through an API gateway. For example, information on illegally parked vehicles can be synchronized to the traffic police platform for violation recording, while governance reports generated by the community DCCU are uploaded to the street-level platform to support cross-departmental decision-making. This vertically integrated and horizontally coordinated mechanism improves the efficiency of grassroots governance.

[0073] The entire process has been shortened from minutes or even hours to less than minutes. Since data processing is carried out on a local private network, the risks of cloud transmission delays and network congestion are avoided. This achieves a rapid closed loop of "event discovery - instruction issuance - on-site handling - result feedback", meeting the stringent real-time requirements of emergency events.

[0074] As in the previous example, if the illegal parking incident causes delays in fire rescue and leads to disputes, requiring the tracing of responsibility, the "credible event" generated by this method and the resulting "credible event chain" effectively prevent data tampering during transmission and storage because it contains verified subjects, precise spatiotemporal information, and all data flows in a closed loop within a dedicated network. This complete, spatiotemporally tagged data chain can serve as highly credible electronic evidence, providing strong support for judicial evidence collection and liability determination. This directly reflects "spatiotemporal credibility" and "traceability."

[0075] In some embodiments, generating a trusted event in S1 specifically includes:

[0076] The DCCU server in the community preprocesses the collected raw data, including data cleaning and anomaly detection.

[0077] Utilize an integrated geographic information system to accurately locate the site of the event;

[0078] The identified trusted events are matched and verified against the personnel, housing, and vehicle information databases pre-stored in the community DCCU server to confirm and bind a unique event subject identifier;

[0079] Trusted event data, including the event subject, location, and time, is encapsulated according to a standard protocol and stored and transmitted in groups to form the starting node of a trusted event chain.

[0080] The above embodiments further define the "generating credible events" step, improving the accuracy, standardization, and machine readability of event data, and laying a solid foundation for upper-level intelligent analysis.

[0081] For example, Community A needs to implement intelligent management of "mobile stalls". The system needs to identify whether the stalls are in designated areas and whether the operation time has exceeded the limit.

[0082] After adopting the method described in the above embodiments: the community DCCU preprocesses the raw data (such as pedestrian counts and images) collected by the stall monitoring cameras, filtering out interference data caused by sudden changes in light or temporary obstruction, ensuring the quality of the data input to the AI ​​model, and reducing the false alarm rate from the source. Through the integrated Geographic Information System (GIS), when the AI ​​identifies a stall operating beyond its designated area, it can accurately locate it at a specific coordinate point on the map (such as "longitude XXX, latitude YYY"), rather than just "snack street", providing a basis for precise law enforcement.

[0083] The system matches identified stalls with a pre-stored "digital passport for mobile vendors" database, binding the stall owner's identity information to ensure the uniqueness and accuracy of the event subject. This realizes the vision of "locating (marking) and supervising mobile stalls, vendors, and services through QR codes and satellite positioning technology." All information is encapsulated into "trusted events" according to standard protocols, enabling the community DCCU server to unambiguously parse and process event data from different communities and different types of IoT devices, achieving data interconnection and standardization.

[0084] In some embodiments, the statistical analysis in S2 specifically includes:

[0085] Data reception and aggregation: The community DCCU server receives trusted event chain data transmitted in groups through a private network, and performs multi-dimensional classification and storage according to event type, time interval and geographical region through the built-in data aggregation module;

[0086] Statistical analysis and report generation: Utilizing the statistical engine integrated with the community DCCU, the aggregated trusted events are analyzed from multiple dimensions to generate periodic statistical reports that include event type distribution, high-frequency occurrence locations, and processing timeliness indicators. These reports are then uploaded to the superior integrated management information platform via a dedicated network.

[0087] In the above embodiments, scattered event data is transformed into systematic governance insights, helping management to upgrade from "passive response" to "proactive early warning," and realizing data-driven scientific decision-making and optimized resource allocation.

[0088] In some embodiments, this solution is further integrated with the concepts of complete communities and future communities. For example, the Community DCCU integrates data from elderly care, education, and other sources to generate a "community profile" and predict resource needs (such as the need to increase health and wellness services in densely populated elderly areas); simultaneously, it simulates future community scenarios (such as low-carbon travel planning) through a digital twin platform, enabling multi-service collaboration. This collaborative mechanism significantly improves the accuracy and sustainability of community services.

[0089] For example, community managers need to understand the overall security situation and public service bottlenecks in Community A.

[0090] After adopting the method of the above embodiment: the community DCCU server will classify and store all "trusted events" (such as illegal parking, objects thrown from high-rise buildings, gas alarms, noise complaints, etc.) from various communities within a week according to "event type" (security, fire protection, environment), "time interval" (weekday / weekend, day / night), and "geographical area" (dining area, long-term rental apartment area). This structured data organization method makes the analysis dimensions richer and more flexible.

[0091] The system automatically generates weekly reports showing that "fire lane obstruction incidents" occur three times more frequently during the evening restaurant peak hours than on weekdays, and are mainly concentrated at a specific intersection. Meanwhile, "noise complaint incidents" increase significantly on weekend nights. These quantifiable and visualized statistical results (such as "event type distribution" and "high-frequency occurrence locations") enable managers to clearly understand the patterns of the problems.

[0092] Based on the above reports, the community can make more informed decisions: increase patrols in high-risk periods and areas; optimize parking resource planning; and develop specific management plans for noise issues. This achieves the goal of "improving governance efficiency and collaborative problem-solving," shifting the governance model from "treating each incident as it occurs" to "prevention and intervention based on data prediction."

[0093] In some embodiments, generating an event work order in S2 specifically includes:

[0094] The AI ​​evaluation model built into the community DCCU automatically prioritizes the trusted events based on their type and severity.

[0095] Based on the assessment results, a detailed work order is automatically generated, including an event description, location, time, information on relevant personnel, and handling instructions.

[0096] The event work order is sent to the staff's handheld terminal via the public communication network, and alarm information is simultaneously pushed to the user terminals of the personnel related to the event.

[0097] The above embodiments disclose how to efficiently and accurately issue and simultaneously issue alarms after an event work order is generated, thus breaking down the "last mile" of information transmission and achieving cross-network collaboration and precise reach.

[0098] For example, in "Community A", the community DCCU server generates an emergency repair work order based on a reported credible event of "liquefied petroleum gas cylinder leakage". At this time, it is crucial to quickly notify the relevant personnel.

[0099] After adopting the method of the above embodiments: the work order is sent to the handheld terminal of the property engineering personnel through a public communication network (such as 4G / 5G), ensuring the reliability of reception in the mobile office scenario. At the same time, alarm information is pushed to the user terminals (such as mobile APP, SMS) of the event-related personnel (such as merchants and shop owners) through the same network.

[0100] Traditional methods may only notify staff in one direction, leaving related parties uninformed and potentially leading to poor on-site coordination. This solution synchronizes information between the responder and related parties, allowing shop owners to evacuate staff and open doors and windows in advance, creating safe working conditions for maintenance personnel. This maximizes the efficiency of cross-entity collaboration and embodies the concept of "grassroots collaboration."

[0101] In renovation projects of old residential communities, laying out a dedicated wireless network with full coverage is costly. This solution eliminates the need for building a new dedicated wireless network, utilizing the widely covered public communication network to issue commands. The solution cleverly combines a dedicated network (for reliable data transmission between DCCU servers) and a public network (for flexible delivery of final commands and alarms). This design ensures the security and controllability of core data transmission while reducing system deployment and maintenance costs by leveraging mature and inexpensive public networks. It is particularly suitable for cost-sensitive renovation projects like "Community A," enhancing the technology's versatility and applicability.

[0102] In some embodiments, the processing result in S3 includes automatic feedback, which is specifically implemented as follows:

[0103] For events that can be automatically adjusted and handled through IoT devices, the cell DCCU server continuously receives and monitors the data streams from relevant sensors;

[0104] When monitoring data indicates that the alarm condition that triggered the trusted event has been eliminated, a new trusted event for the handling result is automatically generated. This event includes a confirmation signal that the condition has been eliminated, the elimination time, and key sensor data indicating that the sensor has returned to normal.

[0105] The trusted event of the processing result is fed back to the community DCCU server via the private network. The community DCCU server then adds it as a terminal node to the corresponding original trusted event chain and marks the entire event chain as closed.

[0106] The above embodiments realize an intelligent closed loop of "unattended operation" for events that can be handled automatically, which greatly improves response speed and reduces labor costs.

[0107] Take the "abnormal pressure of fire hydrants in high-rise residential buildings" incident as an example. This is a continuous risk situation that requires continuous monitoring until it returns to normal.

[0108] After adopting the method of the above embodiment: the community DCCU server continuously receives pressure sensor data. Once the pressure value is lower than the threshold, a "pressure anomaly" credible event is generated and reported. After the community DCCU issues a work order, the system does not wait for manual feedback. Instead, the community DCCU continuously monitors the pressure data stream. When the pressure value returns to the normal range, the system automatically generates a new "pressure returned to normal" credible event. This event is added to the original "pressure anomaly" event chain as a termination node, forming a complete and reliable record of "anomaly occurrence - automatic handling / monitoring - return to normal".

[0109] If relying on manual feedback, maintenance personnel need to be on-site to inspect and then manually enter the results, which carries the risk of delays and omissions. This solution's automatic feedback enables 24 / 7 uninterrupted monitoring and instantaneous feedback, maximizing the timeliness of handling such infrastructure hazards and truly achieving "smart" management.

[0110] The "trusted event chain" generated by the above solution fully records the occurrence time, duration, recovery time, and key pressure data curves of each pressure anomaly. This automatically generated and tamper-proof data chain provides accurate data support for equipment condition assessment and preventative maintenance, far exceeding the reliability and value of traditional paper inspection records.

[0111] In addition to fire hydrant pressure monitoring, smart gas cylinders can automatically trigger leak alarms, automatically shut off valves, and restore the concentration to normal, forming a closed-loop event chain through an automatic feedback mechanism. This allows for the handling and recording of an emergency without human intervention.

[0112] In some embodiments, the processing result in S3 includes manual feedback, which is specifically implemented as follows:

[0113] After completing the on-site handling, the staff uploaded the handling results, including text descriptions, on-site photos or video evidence, to the community DCCU server via handheld terminals;

[0114] The cell DCCU server performs format verification and logical consistency check on the received processing result data;

[0115] Once verified, it is encapsulated as a new trusted event for the disposal result and forwarded to the community DCCU server;

[0116] The community DCCU server associates the trusted event of the manual handling result with the original trusted event of AI recognition, and adds it as a subsequent node to the same trusted event chain. The event chain status is updated to resolved or pending follow-up based on the specific content of the handling result.

[0117] The above embodiments provide a specific implementation of the "human feedback" mechanism, which standardizes and digitizes human on-site handling behavior, and ensures the authenticity and effectiveness of feedback data, forming a closed loop that integrates online and offline processes.

[0118] Taking incidents such as "illegally parked vehicles" or "mobile stalls occupying the road" as an example, after staff handle the situation on-site, they need to provide feedback on the results.

[0119] After adopting the method described in the above embodiments: when staff upload the handling results via handheld terminals, they not only input text, but more importantly, they take photos or videos of the scene as evidence (such as illegally parked vehicles being moved or street vendors being cleared). The community's DCCU server will verify the uploaded data, for example, checking whether the images contain GPS geographical location information, whether the timestamp is reasonable, and whether it matches the location of the original event. This effectively prevents false, erroneous, or malicious feedback, ensuring the quality of the feedback data.

[0120] Traditional methods of telephone or simple text feedback provide limited information and are difficult to verify. This solution, through multimedia evidence and system verification, ensures that every manual action is traceable, authentic, and reliable, providing a solid basis for performance evaluation and dispute resolution.

[0121] For example, community managers need to track the entire process of handling a complex neighborhood dispute.

[0122] After adopting the methods described above, the process can proceed from AI identification or resident reporting of a dispute, to the issuance of a work order to a grid worker, followed by on-site mediation by the grid worker, and the uploading of results such as photos of the mediation agreement and videos of both parties' signatures. After system verification, this content is linked as a new node to the original event chain. Managers can clearly see the complete lifecycle of the event on the platform, achieving complete transparency and traceability of the process, and greatly improving the refinement and credibility of grassroots governance.

[0123] In some embodiments, both the cell DCCU server and the community DCCU server are locally deployed non-cloud servers, and are securely connected via a physical private network or a virtual private network.

[0124] The IoT devices include fire protection equipment, security equipment, and feature-based devices; among them, the fire protection equipment includes water system detection equipment for monitoring fire hydrant water pressure, smoke sensors, and temperature sensors; the security equipment includes high-definition cameras for behavior recognition and access control systems; and the feature-based devices include charging piles and photovoltaic energy storage equipment.

[0125] The above embodiments disclose a "non-cloud server" and a "private network connection", which builds a secure, reliable and efficient physical base for the entire community event handling system.

[0126] For example, in the "Community A" project, the system needs to process a large amount of data involving personal privacy (such as facial information, vehicle information, and rental information) as well as community security sensitive data. If this data is uploaded to the public cloud, there are risks of privacy leakage and compliance.

[0127] This invention limits the DCCU servers in the community and residential area to non-cloud servers (i.e., locally deployed servers). All data collection, identification, processing and storage are completed within the community's internal network, physically isolating direct access from external networks and greatly reducing the risk of data being remotely attacked or stolen.

[0128] Servers are connected via a physical private network or virtual private network (VPN), establishing an encrypted and controllable "dedicated channel" for data transmission between residential areas and communities, effectively preventing data from being eavesdropped on or tampered with during transmission. Compared to solutions that rely entirely on public clouds, this architecture meets the requirements of regulations such as the Cybersecurity Law and the Personal Information Protection Law for the local storage and processing of sensitive data. It is particularly suitable for scenarios with high standards of data security, such as smart communities, laying the foundation for the legal and compliant operation of the system.

[0129] The handling of incidents such as community security and fire alarms requires extremely high real-time performance and 24 / 7 uninterrupted operation. Even if the connection between the community and the external Internet is temporarily interrupted, the DCCU server at the community level can still rely on the local network and AI model to perform event identification, processing and closed-loop operation normally, ensuring the high availability of core business.

[0130] In some embodiments, the method further includes a data fusion step:

[0131] The community DCCU server is unidirectionally connected to the community's property management system and parking management system via a trusted data link or system gateway.

[0132] The community DCCU server is unidirectionally connected to the community fund system and community economic system on the community side through a trusted data chain or system gateway;

[0133] The community DCCU server integrates and analyzes the property management data and parking management data obtained from the community DCCU server and the community management data obtained from the community data management system with the trusted event data to generate a comprehensive governance report and upload it to the comprehensive management information platform.

[0134] The above embodiments disclose a data fusion process that breaks down information silos, achieves deep integration of cross-business data, and supports macro-governance decision-making.

[0135] For example, if community managers want to understand the comprehensive impact of the "nighttime catering economy" on community safety, environment, and traffic, they can adopt the solution of this invention: the community DCCU not only connects to IoT devices, but also obtains repair records from the property management system and parking space status data from the parking management system through a one-way data chain; the community DCCU obtains business operation data from the community fund system and the community economic system.

[0136] The solution enables the community DCCU to integrate this data with "credible event" data such as security and fire protection. For example, the system can analyze and find that when the foot traffic in the food street (from camera counts) reaches its peak every Friday night, the parking management system shows that the saturation rate of surrounding parking spaces exceeds 95%, and at the same time, the number of complaints about garbage collection in the property management system will increase significantly, and the number of gatherings of people identified by security cameras will also rise accordingly.

[0137] Based on this, the reports generated by the system are no longer isolated lists of events, but comprehensive governance recommendations that include correlation analysis of "business vitality, traffic pressure, environmental load, and safety risks," such as "it is recommended to increase the number of garbage collection vehicles on Friday night, strengthen the deployment of traffic controllers, and activate the temporary security patrol plan."

[0138] In the traditional approach, these data belong to different departments and are difficult to link. This solution, through data fusion, elevates the governance perspective from addressing individual problems to systematically optimizing resource allocation, thereby achieving the deeper goal of "improving governance efficiency and collaboratively solving problems".

[0139] In some embodiments, the community DCCU server integrates and analyzes property management data and parking management data obtained from the community DCCU server and community management data obtained from the community data management system with the trusted event data to generate a comprehensive governance report, specifically including:

[0140] S901: The community DCCU server establishes a unified data association model, and performs spatiotemporal association and subject mapping between the event location and related personnel information in the trusted event data, the repair records in the property management data, the vehicle parking information in the parking management data, and the community fund usage and health and wellness service needs in the community management data, forming a cross-business domain associated data set;

[0141] S902: Based on the aforementioned associated data set, calculate multidimensional indicators for comprehensive governance assessment, wherein the indicators include at least:

[0142] Security situation indicators: Based on the frequency and distribution of credible security events (such as objects thrown from high-rise buildings and gatherings of people), coupled analysis is performed with property security patrol records and fire equipment status data to generate a community safety coefficient;

[0143] Service efficiency indicators: Based on the service response time, number of complaints and suggestions and the corresponding event work order handling time in property management data, the property service efficiency is evaluated by comparison and analysis.

[0144] Resource supply and demand indicators: Based on the parking space turnover rate and peak-hour parking space shortage in parking management data, and the commercial pedestrian flow data in community economic data, a fitting analysis is conducted to identify resource allocation bottlenecks;

[0145] S903: Utilizing the report template engine built into the community DCCU server, the multidimensional indicators and analysis results are automatically populated into the preset comprehensive governance report template to generate a visualized comprehensive governance report containing data trend charts, heat maps, and early warning prompts. The report is then uploaded to the comprehensive management information platform of the street-level government or government commissions and bureaus via a dedicated network to provide data decision support for cross-departmental collaborative governance.

[0146] The above embodiments disclose a deeper and more detailed approach to data fusion analysis. By establishing a multi-dimensional indicator calculation model, heterogeneous data is transformed into quantifiable governance insights, thereby achieving precise and predictive community governance.

[0147] For example, community managers need to assess the overall safety risks of the food street in "Community A" rather than focusing on a single incident of objects being thrown from a height or fire lanes being blocked.

[0148] Based on the solution of this invention: The community DCCU server couples and analyzes the frequency and distribution of reliable security events (such as 5 instances of objects being thrown from heights and 3 instances of abnormal gatherings of people within a week) with security patrol check-in records and fire equipment status data (such as the normal pressure rate of fire extinguishers) from property management data. The system calculates security situation indicators, such as a quantified "community security coefficient" (e.g., 85 points). This coefficient is not a simple event count, but a composite indicator that integrates the severity of the event, the timeliness of the response, and the completeness of human and physical security measures.

[0149] Traditionally, managers are faced with a fragmented list of events. This solution, however, generates a comprehensive safety and health score, allowing managers to grasp the overall safety situation at a glance. When the coefficient falls below a threshold, the system can issue an early warning, suggesting "increased security patrols in the dining area at night" or "centralized replacement of outdated fire-fighting equipment," thus shifting from passively responding to alarms to proactively assessing and optimizing the safety system.

[0150] For example, it is necessary to assess the service effectiveness of property management companies to provide an objective basis for property fee pricing and service supervision.

[0151] Based on the solution of this invention: The system compares and analyzes the "average response time for repair requests" (e.g., 2 hours) and "number of complaints and suggestions" (e.g., 10 per month) in property management data with the corresponding "event work order processing time" (e.g., 30 minutes) in trusted event data. Through calculation, service efficiency indicators for the property are generated. For example, it is found that although the repair response is relatively fast, the complaint rate regarding the cleaning of public areas is high, and the quality score of related work orders is low. This indicates that the property performs well in rapid response, but has shortcomings in the quality of daily maintenance.

[0152] Traditional assessments often rely on subjective impressions and sporadic complaints. This solution, through data integration and indexation, provides homeowners' associations and community streets with objective and quantifiable performance evaluation tools, promoting transparency and refinement in property service quality and achieving the deeper goal of "improving governance efficiency."

[0153] For example, it is necessary to optimize the allocation of parking and transportation resources in and around Community A during peak hours at the night market.

[0154] Based on the solution of this invention, the system performs fitting analysis on parking management data (parking space turnover rate, peak-hour parking space shortage), community economic data (nighttime consumer flow heat map), and related reliable event data (traffic congestion events caused by parking difficulties); it generates resource supply and demand indicators to accurately identify resource allocation bottlenecks. For example, the analysis shows that parking space demand on Friday nights is three times that of weekdays, and congestion events mainly occur at specific intersections. The report not only points out the problems but can also make suggestions based on historical data simulations: "It is recommended to negotiate with surrounding businesses to open 100 shared parking spaces at night" or "Convert the road on the east side of the community into a temporary one-way street during peak hours."

[0155] All the above analysis results, including trend charts, heat maps, and early warning alerts, are presented in the comprehensive management report through S903.

[0156] This solution goes beyond simple event statistics. Through in-depth mining of multi-source data, it provides urban managers (such as street offices, urban management departments, and transportation departments) with forward-looking and actionable decision support, maximizing resource utilization and refining public services, thus responding to the core objective of "building an AI-enabled digital foundation for community governance".

[0157] In some embodiments, a system pre-configuration step is included before S1:

[0158] A cell-wide information database is pre-built and stored in the cell DCCU server, and the database includes at least:

[0159] A personnel information database, in which personnel are categorized into owners, family members, and tenants;

[0160] The housing information database categorizes properties into owner-occupied housing, rental housing, and commercial properties, and records their spatial location and associated owners or responsible persons.

[0161] A public facilities database records the location of facilities such as fire hydrants, fitness equipment, and charging stations, as well as information on their managers.

[0162] The database is used for AI models to quickly call and match during event identification, in order to identify and bind the event subject and its unique identifier of a credible event.

[0163] The above embodiments disclose a system pre-configuration method that provides accurate contextual information for AI event recognition, ensuring the accuracy and authority of the association of "trustworthy events" with the subject.

[0164] For example, if the system's AI camera detects an incident of "objects being thrown from a height," it is necessary to quickly identify the responsible unit and notify relevant personnel.

[0165] The solution of this invention requires the pre-construction of a detailed database before the system is put into use. For example, in community A, the database will clearly record: a certain unit in a certain building is a rental property, the current tenant is Li (contact information XXX), and the landlord is Wang; the manager of the fitness equipment in the community square is Zhang from the property management company.

[0166] When the AI ​​identifies that the object was thrown from a window, the system can instantly match the location (window coordinates) with the house information (room number, attributes) through a pre-set database, thereby associating it with the responsible parties (tenant Li and landlord Wang), and immediately generating a "credible event" containing accurate subject information.

[0167] Without this pre-configured database, the system might only report "an object was thrown from a high-rise building," requiring manual secondary investigation to pinpoint the specific unit and responsible person, which is inefficient and prone to errors. This solution, through pre-configured and structured data preparation, ensures that the AI's identification results are actionable and accurate from the outset, achieving "uniqueness of the event subject." This provides the most crucial prerequisite for accurate work order issuance and efficient handling in the future.

[0168] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for processing event data based on Dccu, characterized in that, The method comprises the following steps: S1: Collecting data from the Internet of Things device and the management system by the cell Dccu server deployed in the cell, identifying events through the built-in AI model, and generating trusted events conforming to the standard data processing protocol, the trusted events containing three characteristics of event subject uniqueness, occurrence location and occurrence time; S2: Receiving the trusted events reported by the cell Dccu servers in the jurisdiction through the private network by the community Dccu server deployed in the community, performing statistical analysis and priority evaluation of the trusted events based on the locally deployed AI model, automatically generating data statistical report and event work order, and issuing the event work order to the corresponding cell or community staff; S3: Receiving the treatment result for the event work order, and associating the treatment result with the original trusted event, updating the event state to form a closed-loop management.

2. The method of claim 1, wherein, The trusted event generated in S1 specifically comprises: The cell Dccu server pre-processes the collected raw data, which includes data cleaning and anomaly detection; Using the integrated geographic information system to spatially locate the event occurrence location; Matching and verifying the identified trusted event with the pre-stored personnel, house, vehicle information library in the cell Dccu server to confirm and bind the unique event subject identifier; Encapsulating the trusted event data containing the event subject, location and time according to the standard protocol, and storing and transmitting in a grouped manner to form the starting node of the trusted event chain.

3. The method of claim 1, wherein, The statistical analysis in S2 specifically comprises: Data reception and aggregation: The community Dccu server receives the trusted event chain data transmitted in a grouped form through the private network, and stores it through the built-in data aggregation module according to the event type, time interval and geographical area; Statistical analysis and report generation: Using the statistical engine integrated by the community Dccu, the trusted events after aggregation are analyzed in multiple dimensions to generate periodic statistical reports containing event type distribution, high-frequency occurrence location and processing time efficiency indicators, and uploaded to the upper-level comprehensive management information platform through the private network.

4. The method according to claim 1 or 3, characterized in that, The event work order generated in S2 specifically comprises: Through the AI evaluation model built-in the community Dccu, automatically performing priority evaluation according to the type and severity of the trusted event; Based on the evaluation result, automatically generating detailed work order content containing event description, occurrence location, time, associated personnel information and disposal guide; Through the public communication network, the event work order is issued to the handheld terminal of the staff, and the alarm information is pushed to the user terminal of the event associated personnel at the same time.

5. The method of claim 1, wherein, The treatment result in S3 includes automatic feedback, and the specific implementation manner is: For events that can be automatically adjusted and disposed through the Internet of Things device, the cell Dccu server continuously receives and monitors the data stream of the related sensors; When the monitoring data shows that the alarm condition triggering the trusted event has been eliminated, a new disposal result trusted event is automatically generated, which contains the confirmation signal of condition elimination, elimination time and key sensor recovery normal data; The treatment result credible event is fed back to the community Dccu server through a private network, and is added to the corresponding original credible event chain as a terminal node by the community Dccu server, and the state of the entire event chain is marked as closed loop.

6. The method of claim 1, wherein, The treatment result in the S3 includes artificial feedback, and a specific implementation manner is as follows: After the staff completes the treatment on the scene, the treatment result including a text description, a scene picture or a video evidence is uploaded to the community Dccu server through a handheld terminal; The community Dccu server performs format verification and logical consistency checking on the received treatment result data; After verification, the treatment result is encapsulated as a new treatment result credible event, and is forwarded to the community Dccu server; The community Dccu server associates the artificial treatment result credible event with the original AI recognition credible event, and adds the artificial treatment result credible event to the same credible event chain as a subsequent node, and updates the state of the event chain to be solved or to be followed up according to the specific content of the treatment result.

7. The method of claim 1, wherein, The community Dccu server and the community Dccu server are both locally deployed non-cloud servers, and are securely connected through a physical private network or a virtual private network; The Internet of Things devices include fire-fighting devices, security devices and feature devices; wherein the fire-fighting devices include water system detection devices for monitoring fire hydrant water pressure, smoke sensors and temperature sensors; the security devices include high-definition cameras for behavior recognition and access control systems; and the feature devices include charging piles and photovoltaic energy storage devices.

8. The method of claim 1, wherein, The method further includes data fusion, specifically including: The community Dccu server is unidirectionally connected to the property management system and the parking management system on the community side through a trusted data chain or a system gateway; The community Dccu server is unidirectionally connected to the community fund system and the community economic system on the community side through a trusted data chain or a system gateway; The community Dccu server fuses and analyzes the property management data and the parking management data obtained from the community Dccu server and the community management data obtained from the community data management system with the credible event data, generates a comprehensive management report and uploads the comprehensive management report to the comprehensive management information platform.

9. The method of claim 8, wherein, The community Dccu server fuses and analyzes the property management data and the parking management data obtained from the community Dccu server and the community management data obtained from the community data management system with the credible event data, generates a comprehensive management report specifically including: S901: The community Dccu server establishes a unified data association model, associates the event occurrence place and the associated personnel information in the credible event data with the repair record in the property management data, the vehicle parking information in the parking management data, and the community fund use condition and the health care service demand in the community management data, and performs time and space association and subject mapping to form an associated data set across business domains; S902: Based on the associated data set, a multi-dimensional index for comprehensive management evaluation is calculated, and the index at least includes: Security situation index: based on the frequency, distribution of security events, and the coupling analysis of property security personnel records, fire equipment state data, the community security coefficient is generated; Service efficiency index: based on the service response time, complaint suggestion quantity in property management data and the corresponding event work order disposal time, the property service efficiency is evaluated; Resource supply and demand index: based on the parking management data of parking space turnover rate, peak period parking gap and community economic data of commercial passenger flow data fitting analysis, the resource configuration bottleneck is identified; S903: using the report template engine built-in the community Dccu server, the multi-dimensional index and analysis results are automatically filled into the preset comprehensive management report template to generate a visual comprehensive management report containing data trend chart, heat map and early warning prompt, and uploaded to the comprehensive management information platform of street level or government office through the private network, providing data decision support for cross-departmental collaborative governance.

10. The method of claim 1, wherein, Before the S1, system pre-configuration is further included, specifically including: A cell Dccu server is pre-built and stores a cell global information database, which at least includes: A personnel information library, in which the personnel identity is distinguished as owner, family member and tenant; A house information library, in which the house attribute is distinguished as self-owned house, rental house and business, and the spatial position and associated owner or responsible person are recorded; A public facility information library, recording the position of facilities such as fire hydrant, fitness equipment, charging pile and the management information thereof; The database is used for AI model to quickly call and associate match when event identification to determine and bind the event subject and its unique identifier of trusted event.

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