A sewer network third party fine maintenance whole-process intelligent supervision system and a supervision method

Abstract

The application discloses a kind of sewer network third party fine maintenance whole process intelligent supervision system and supervision method, belong to municipal pipe network digital supervision field.The application sets up government-enterprise hierarchical data isolation architecture, prevents maintenance unit tampering with supervision data;Fusion multi-source inventory pipe network system constructs a well a code electronic archives, builds pipe network health automatic evaluation model batch generation maintenance list;Work order module is configured with limited space start lockout mechanism, avoids the risk of illegal operation in well;Carry self-defined weight automatic examination module, output quantitative settlement report;Support globally start-stop side station review sub-module, construction audio and video encryption storage evidence;Whole set of architecture is compatible with private, cloud SaaS Dual Deployment Mode simultaneously.The application solves the pain points such as traditional pipe network supervision data island, artificial examination subjective bias, underground operation control loose, greatly reduces the workload of artificial statistics and on-site judgment, reduces maintenance cost, meets the requirements of third-party supervision neutral audit throughout the process.

Description

Technical Field

[0001] This invention belongs to the field of intelligent operation and maintenance digital supervision technology for municipal drainage pipe networks, and is applicable to the dual-end management and control scenario of government and enterprise procurement of third-party digital supervision services. Background Technology

[0002] Currently, most municipal drainage information systems in China are developed and built in-house by water utilities, serving only the internal scheduling, leakage monitoring, and project management of the pipeline network operators. Existing publicly available patents related to drainage pipeline networks (including CN120297544A, CN116485606A, CN111798007A, CN115564068A, CN102915474A, CN119338140A, CN117540897A, CN120258780A, CN119624148A, CN119067466A, etc.) lack an independent data architecture that separates the owner's monitoring end from the maintenance operation end, exhibiting several significant shortcomings: 1. In confined space underground operations, paper-based approvals or even no approval process are often used, and there is no online work start-up lockout mechanism. Work can start without an approved work order, and safety management is seriously lagging behind. 2. The maintenance assessment relies on manual scoring, which is highly subjective and the assessment results cannot be used as a quantitative reference for the settlement of maintenance service fees; 3. The existing drainage GIS and operation and maintenance ledger systems in various regions have heterogeneous and incompatible data, which can only support one-way data reading and cannot update the pipeline asset files in both directions. 4. Lacks the ability to flexibly start and stop key processes for on-site digital verification, and cannot simultaneously adapt to both private deployment and lightweight cloud SaaS deployment scenarios; 5. Without a neutral third-party oversight of the underlying data isolation architecture and standardized electronic ledgers, maintenance units can arbitrarily tamper with operation and maintenance data, and the fairness of regulatory audits cannot be guaranteed; 6. Without an automated quantitative assessment system for pipeline health, maintenance locations and types rely entirely on manual on-site judgment, and maintenance lists are manually compiled, which easily leads to problems such as missed maintenance, duplicate maintenance, and confusion in maintenance priorities. Summary of the Invention

[0003] This invention provides a third-party refined maintenance intelligent supervision system for the entire process of drainage pipe network maintenance, including a government supervision terminal, a maintenance operation terminal, a core platform for the third-party refined maintenance supervision system, and IoT sensing devices. The IoT sensing devices include downhole gas sensors, level gauges, and GPS law enforcement recorders, used to upload real-time data on pipe network environment, operation location, and construction video monitoring to the core platform. The core platform for the third-party refined maintenance supervision system includes a layered data isolation unit, a multi-source pipe network data fusion unit, a pipe network health assessment and maintenance list generation unit, a work order closed-loop management unit, a weighted assessment calculation unit, and a start / stop on-site verification submodule. 1. The layered data isolation unit is configured with independent database partitions for both the government supervision terminal and the maintenance operation terminal, dividing independent government and enterprise data storage spaces, and setting one-way data flow permissions, allowing only the maintenance operation terminal to push operation and maintenance data to the supervision terminal; existing pipe network asset files, approval records, and assessment results can only be modified by the supervision terminal account, preventing maintenance units from tampering with supervision data from the bottom layer and ensuring the neutrality and fairness of third-party supervision. The unit establishes a two-way data synchronization interface, allowing the pipeline asset archives generated by system updates to be transmitted back to the owner's existing drainage platform. The synchronization process fully preserves the original data of the existing platform and does not overwrite existing ledger information. 2. The multi-source pipeline data fusion unit connects to multiple existing drainage information systems within the region, standardizing fields for pipelines, manholes, and pump stations, and automatically removing abnormal data such as duplicate pipelines, topology errors, and suspended pipelines. A unique QR code is assigned to each pipeline node and manhole, binding pipe material, pipe age, and maintenance records to construct a unified electronic archive for pipeline assets across the entire region. The fused structured data is then synchronously transmitted to the pipeline health assessment and maintenance list generation unit and the weighted assessment calculation unit. 3. The pipeline health assessment and maintenance checklist generation unit sets up four categories of assessment indicators: basic pipeline attributes (pipe material, pipe age, pipe diameter, burial depth), historical damage records (cracks, siltation, frequency of misconnections), real-time operational monitoring data (liquid level, fill level, downhole gas concentration), and past maintenance records. Managers can customize the original weights of each health indicator in the backend, and the system automatically performs weight normalization calculations. The normalization calculation formula is as follows: In the formula: Allow users to customize the original weights; These are the effective weights after normalization; This represents the total number of indicators at the same level. Normalized calculation eliminates the problem of inconsistent weight values ​​across different areas, enabling horizontal comparison of pipeline health status across different areas. The system calculates the comprehensive health score for single pipe sections and manholes based on normalized weights and monitoring values ​​of each indicator. It presets four health thresholds, dividing the pipeline network into four levels: healthy, minor defects, moderate defects, and severe defects. Different levels are automatically matched with corresponding maintenance operations: minor defects are matched with routine inspections; moderate defects with CCTV inspections and localized dredging; and severe defects with trenchless repairs and underground remediation. The system batch summarizes defect locations of the same level and automatically exports a standardized maintenance list. The list includes the location number, defect type, recommended maintenance process, estimated maintenance time, and safety requirements. The list data is synchronously pushed to the work order closed-loop management unit. 4. The work order closed-loop management unit uniformly manages four types of maintenance work orders: planned maintenance, temporary inspection, pipeline emergency repair, and pipeline leak detection. It distinguishes between regulatory review accounts and maintenance operation accounts; maintenance accounts only have work order submission permissions, not approval permissions. When a work order is initiated, the system automatically verifies the validity period of special operation personnel's certificates and the geographic number of the declared location. If the information is incomplete, the work order is directly rejected and resubmitted. High-risk, long-duration downhole operations initiate multi-level review and processing logic. The unit accesses downhole gas and liquid level sensor monitoring data in real time. When monitoring parameters exceed standards, it pushes warning information bidirectionally to the regulatory backend and the maintenance operation mobile terminal. A mandatory start-up lockout logic is set; for work orders that have not completed online hierarchical approval, the maintenance mobile terminal cannot record on-site maintenance, prohibiting on-site downhole repair operations. The entire system forms a complete business closed loop: work order dispatch → on-site data feedback → online review → automatic archiving of ledger data. 5. The weighted assessment calculation unit is structured hierarchically, comprising four primary assessment indicators: inspection execution, hazard rectification, safety compliance, and construction quality. Each primary indicator has multiple secondary detailed indicators. The system automatically collects four types of raw data sources monthly and quarterly: work order receipts, hazard rectification records, confined space approval documents, and on-site video footage, eliminating the need for manual data entry. The system uses a weighted summation method to calculate the overall monthly and quarterly maintenance assessment score for the area. The total score calculation formula is as follows: In the formula: The overall assessment score for the regional pipeline network; For the first Standardized score of each individual indicator; These are the normalized indicator weights; This represents the total number of primary indicators. After the assessment, the system automatically outputs an electronic maintenance ledger report and generates a detailed reference for maintenance service cost calculation based on the maintenance workload, maintenance unit price, and monthly fixed operation and maintenance basic costs, providing a quantitative basis for government procurement of third-party maintenance settlement. 6. The on-site verification submodule has a built-in global enable / disable control switch, and the switch status is stored in the system configuration library. Internally, it includes an enforcement recorder acquisition unit, a temporary data cache module, a hash encryption and spatiotemporal tag binding unit, a regulatory online verification interface, and an encrypted archive storage library. When the switch is on, the enforcement recorder acquisition unit simultaneously acquires high-definition construction video, GPS positioning, and downhole gas monitoring data. After the data is stored in the temporary cache module, it is processed by hash encryption and spatiotemporal tag binding, and then pushed to the regulatory online verification interface for online signature verification by management personnel. At the same time, it is stored in the encrypted archive storage library for long-term retention. All on-site acquisition files support retrieval and traceability by region and operation time. When the switch is off, the on-site submodule stops acquisition, verification, and archiving work, without affecting the independent operation of all other business units on the platform. 7. The entire core platform supports two deployment architectures: local private server deployment and cloud-based SaaS multi-tenant deployment. In the private deployment mode, the on-site verification submodule is fully enabled, connecting to existing local GIS devices and offline law enforcement recorder hardware. In the cloud-based SaaS multi-tenant deployment mode, the on-site verification submodule is disabled by default. All core business logic, including government and enterprise data isolation, work order management, weighted assessment, and multi-source data fusion, is fully reused with the private deployment version. II. Regulatory Methods and Technical Solutions

[0004] A third-party refined maintenance intelligent supervision method for drainage pipe networks based on the above system includes the following steps: Step S1: Build a hierarchical isolated database, connect to the existing drainage system to complete the multi-source pipe network data cleaning and standardization processing, assign a unique QR code to all pipe network nodes and inspection wells, and generate a one-well-one-code full-domain pipe network asset file; automatically calculate the pipe network health score based on the pipe network health assessment model and weight normalization formula, classify the disease level and generate a standardized maintenance list in batches, and push the work order module simultaneously; Step S2: The maintenance operation terminal initiates a regular maintenance work order or a confined space underground operation work order application; the system automatically verifies the special operation qualifications of the operators, the declared location, and the real-time monitoring data underground; if the verification fails, the work order is rejected and resubmitted; verification After the data is deemed qualified, it is transferred to the regulatory end for tiered online approval; Step S3: The system automatically determines whether the current work order process is a critical underground process; if so, it automatically activates the on-site verification submodule, collecting on-site audio and video, and encrypting and caching GPS positioning data; if not, it directly allows maintenance personnel to enter the site to carry out operations; Step S4: After the on-site maintenance work is completed, the construction and maintenance data is automatically transmitted back to the platform to complete full collection; the weighted assessment module retrieves the collected data, automatically calculates the monthly comprehensive maintenance assessment score through weight normalization and weighted summation formulas, and generates a reference report for third-party maintenance service fee settlement; Step S5: The system uniformly encrypts and archives all data, including work order approval records, on-site video files, and monthly assessment reports, supporting subsequent regulatory audit retrieval. III. Beneficial Effects

[0005] 1. Layered data isolation units divide independent government and enterprise data storage spaces, with one-way data flow, preventing maintenance units from tampering with regulatory files and assessment records from the bottom layer, and meeting the fair and compliant requirements of third-party supervision in government procurement; 2. A dual control mechanism of online hierarchical approval and work start-up lockout is set up for confined space operations. On-site construction is prohibited without an approved work order. Combined with real-time early warning from downhole sensors, the risk of safety accidents in downhole operations is greatly reduced. 3. The weighted assessment calculation unit automatically collects all operation and maintenance data, and with the standardized normalization and weighted scoring mathematical model, it completely replaces manual subjective scoring, reduces the workload of manual statistics by more than 90%, and the assessment results can be directly used as the quantitative basis for settlement of maintenance costs. 4. The multi-source pipeline network data fusion unit realizes bidirectional data synchronization of the existing drainage system, establishes a unified electronic asset archive of the entire pipeline network with one code for each well, breaks down data silos of multiple systems, and improves the efficiency of pipeline network ledger updates by more than 80%. 5. The on-site verification submodule is equipped with a global start / stop switch, which is suitable for both private and cloud SaaS project deployment scenarios. Hardware procurement and project implementation costs are flexible and controllable, construction images are encrypted and archived, and the entire process is traceable. 6. The same underlying business logic is compatible with both private server and cloud SaaS multi-tenant deployment modes, with high R&D reusability, and can be adapted and deployed in small towns and large urban areas; 7. The automatic quantitative assessment model for pipeline health automatically classifies pipeline defects and generates standardized maintenance lists in batches, enabling a shift from reactive emergency repairs to proactive preventative smart maintenance. This reduces the workload of manual assessment of maintenance plans by 85%, balances the allocation of maintenance resources, reduces redundant construction and missed maintenance issues, and compresses the overall annual maintenance investment. IV. Detailed Implementation

[0006] Taking the third-party outsourcing of maintenance of municipal drainage pipe networks in urban areas as an example: A layered and isolated database is built, which is connected to the existing GIS and operation and maintenance ledger system in the area. Abnormal pipe network data is cleaned, and a unique QR code is assigned to all inspection wells and pipe sections to establish a one-well-one-code asset file; four types of primary evaluation indicators are selected: basic pipe network attributes, historical defects, real-time monitoring, and maintenance records. The management personnel enter the original weights of each indicator in the background, and the system unifies the weight range through a normalization formula to eliminate differences in the magnitude of indicator values; the health score of each pipe network point is calculated by weighting the real-time monitoring data, and it is divided into four levels of defects. The monthly maintenance list of the area is automatically generated and the work order module is pushed. When maintenance units initiate work orders for underground dredging and pipeline repair, the system automatically verifies personnel credentials and underground gas monitoring data. High-risk work orders trigger multi-level regulatory approval. After approval, the on-site monitoring module automatically activates for key underground processes, collecting video from law enforcement recorders and encrypting and caching location data. After construction is completed, the system automatically collects all work orders, rectification, and on-site monitoring data for the entire month, substitutes them into a weighted summation formula to calculate the monthly maintenance assessment score for the area, and simultaneously generates a maintenance cost accounting report. When the project is deployed on a private platform, the on-site monitoring module is enabled throughout the process; for small-scale township projects using cloud-based SaaS deployment, the on-site monitoring module is disabled by default, while the core functions of data isolation, work order approval, and automatic assessment are fully retained. All work orders, images, and assessment reports are uniformly encrypted and archived to meet audit retrieval requirements. V. Description of the attached drawings

[0007] Figure 1 The overall system module connection diagram shows the complete data interaction link between the government supervision end, the maintenance operation end, the core platform units, and the IoT sensing devices; Figure 2 The overall process flowchart of the regulatory method fully demonstrates the entire business process of pipeline network filing, maintenance list generation, work order approval, on-site data collection, automatic assessment, and encrypted archiving. Figure 3 A schematic diagram of the internal data flow of the on-site review submodule, showing the complete chain of law enforcement recorder data collection, caching, encryption, online review, and archiving storage; Figure 4 A bar chart comparing traditional manual statistics with the monthly manual time consumption automatically assessed by this system; Figure 5A diagram comparing existing one-way data reading technology with the two-way data synchronization solution of this method; Figure 6 Line chart comparing the number of monthly violations of traditional paper-based approval processes with the system's work commencement lock-in mechanism; Figure 7 Bar chart comparing annual maintenance costs between traditional manual maintenance and the intelligent maintenance model of this system.

Claims

1. A third-party refined maintenance intelligent monitoring system for the entire process of drainage pipe network, characterized in that, It includes government regulatory end, maintenance operation end, third-party refined maintenance supervision system core platform, and Internet of Things sensing devices; the third-party refined maintenance supervision system core platform includes a layered data isolation unit, a multi-source pipeline network data fusion unit, a pipeline network health assessment and maintenance list generation unit, a work order closed-loop management unit, a weighted assessment calculation unit, and a start-stop on-site verification submodule; The hierarchical data isolation unit divides the government and enterprise into independent data storage spaces, allowing only the maintenance operation terminal to push operation and maintenance data unidirectionally, and prohibiting the maintenance terminal from modifying the files of the supervision terminal; the multi-source pipeline network data fusion unit connects with the existing drainage system to achieve bidirectional data synchronization and builds a one-well-one-code electronic file for the pipeline network; the pipeline network health assessment and maintenance list generation unit calculates the pipeline network health level based on multi-dimensional indicators, automatically generates a standardized maintenance list and pushes it to the work order closed-loop management unit; the work order closed-loop management unit is configured with limited space start-up lockout logic, and work orders without approval are prohibited from being dispatched. The weighted assessment calculation unit automatically collects all operation and maintenance data and outputs monthly / quarterly assessment scores through a normalized weighting algorithm; the start-stop on-site verification submodule has a global control switch for collecting audio and video data and location data from law enforcement recorders and encrypting and archiving it; the system supports private server deployment and cloud SaaS multi-tenant dual deployment architecture, with both deployments reusing all core unit logic; IoT sensing devices include downhole gas sensors, level gauges, and GPS law enforcement recorders, used to upload real-time monitoring data to the core platform.

2. A monitoring method based on the intelligent monitoring system for the entire process of third-party refined maintenance of drainage pipe networks as described in claim 1, characterized in that, The process includes the following steps: Step S1: Establish a hierarchical isolated database, connect with the existing drainage system to complete data cleaning and standardization, generate a one-well-one-code full-domain pipeline asset file, automatically classify diseases through the pipeline health assessment model and generate a maintenance list; Step S2: The maintenance operation terminal initiates a maintenance or confined space operation work order application. The system automatically verifies the personnel qualifications and point monitoring data. If unqualified, the application is rejected and resubmitted; if the data is qualified, it is approved by the supervisory terminal at different levels; Step S3: Determine whether the current process is a critical underground process. If so, activate the on-site verification submodule to collect audio, video and positioning data; if not, proceed directly to on-site operation; Step S4: After the on-site operation is completed, the operation and maintenance data is automatically transmitted back and collected. The weighted assessment module automatically calculates the monthly maintenance assessment score and generates a cost settlement report; Step S5: Work orders, on-site images, and all assessment data are encrypted, archived and stored.

3. The system according to claim 1, characterized in that, The hierarchical data isolation unit is configured with dual-end independent database partitions, and only the regulatory end has the right to modify existing pipeline archives, approval records, and assessment results.

4. The system according to claim 1, characterized in that, The evaluation indicators of the pipeline health assessment and maintenance list generation unit include four dimensions: basic pipeline attributes, historical defects, real-time monitoring data, and maintenance records. Managers can customize the weight of each indicator. After the system performs weight normalization calculation, it divides the pipeline into four levels: healthy, minor defects, moderate defects, and severe defects, and matches the corresponding maintenance operation type.

5. The system according to claim 1, characterized in that, The work order closed-loop management unit is designed for confined space operations and uses downhole sensors to send bidirectional warnings to the monitoring terminal and the mobile work terminal when the monitored parameters exceed the standard.

6. The system according to claim 1, characterized in that, The total score of the weighted assessment calculation unit is calculated as follows: the total score is equal to the sum of the scores of each individual indicator multiplied by the corresponding normalized indicator weight; where the normalized indicator weight is the processed indicator weight, the individual indicator score is the single-dimensional assessment score, and the number of first-level indicators is the total number of assessment dimensions participating in the calculation.

7. The system according to claim 1, characterized in that, The startable and stopable on-site verification submodule includes a law enforcement recorder acquisition unit, a temporary data caching module, a hash encryption and spatiotemporal tag binding unit, a regulatory verification interface, and an encrypted archive repository.

8. The system according to claim 1, characterized in that, In the private deployment mode, the on-site verification submodule is fully enabled; in the cloud SaaS multi-tenant deployment mode, the on-site verification submodule is disabled by default, while the functions of other units are fully reused.

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