Intelligent operation and maintenance method of sewage treatment equipment for modular data acquisition and analysis

Abstract

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of sewage treatment equipment, and in particular to an intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis. Background Art

[0002] Driven by my country's "Digital Environmental Protection" and "Smart Water" policies, sewage treatment equipment is gradually evolving towards digitalization, networking, and intelligence. Traditional equipment operation and maintenance methods, primarily based on manual inspections and post-repair, are no longer able to meet the water industry's urgent demands for stable operation, efficient management, and cost control. This is particularly true in distributed sewage treatment scenarios, where equipment is numerous and widespread. The lack of a unified and efficient operation and maintenance approach has become a significant constraint on operational management effectiveness. Although some water treatment companies have introduced technologies such as automated control systems and local remote monitoring terminals in recent years, overall, the operation and maintenance of current sewage treatment equipment still faces the following problems: Fragmented data collection methods and low system integration: Current equipment is often provided by different manufacturers, with inconsistent interface protocols, widely varying sensor types and layouts, and a lack of unified data collection and access standards. Traditional data collection methods mostly rely on a single Modbus or local PLC, which can only meet control logic feedback or report a small number of operating parameters, making it difficult to achieve multi-dimensional, continuous, and cross-site data integration. The acquisition system lacks modular design, making expansion and maintenance difficult: Existing data acquisition devices are mostly customized, with fixed hardware structures and high functional coupling. When adding or replacing equipment on site, rewiring or replacement of the main control is often required, resulting in long deployment cycles and high costs, which greatly restricts the scalability and versatility of the system. Lack of intelligent analysis capabilities based on high-frequency data: Most systems only provide status display and simple alarms, and are unable to support intelligent perception and proactive intervention of equipment operating trends, load changes, or abnormal signs. This is especially true for key components such as fans, pumps, and dosing systems, which are unable to provide operational health scores, lifespan predictions, or detailed maintenance recommendations. Insufficient stability and real-time performance of remote communication mechanisms: Some current solutions use traditional protocols such as HTTP / HTTPS to upload device data, which have problems such as large communication delays, poor data real-time performance, and easy interruption due to network fluctuations. They are not suitable for on-site sewage treatment applications in low-bandwidth, high-latency environments.

[0003] Lack of data linkage between the equipment layer and management systems: Although many companies have deployed OA or ERP systems for business management, they lack real-time data access to underlying equipment. Information such as equipment operating status, energy consumption data, and fault records cannot be effectively synchronized with management systems, creating "information silos" and hindering the digitalization of equipment lifecycle management and O&M decision-making.

[0004] While some currently available technical solutions have achieved remote data collection, alarm linkage, or platform visualization, most remain limited to specific device types or single stations, lacking systematic, standardized design concepts. In published patent literature, some technologies attempt to alleviate data collection bottlenecks through edge gateway collection and local caching strategies, while others employ methods like LoRa and NB-IoT for long-distance communication. However, these technologies still fail to address core issues such as rapid access to multiple source devices, weak cross-system data integration, and limited in-depth analysis capabilities. Therefore, in response to the current problems in the intelligent operation and maintenance of sewage treatment equipment, such as inconsistent interfaces, non-scalable data, unstable communication mechanisms, insufficient intelligent analysis capabilities, and management linkage faults, it is urgent to propose a new method system with modular access, high-frequency acquisition, remote stable communication and multi-system integration capabilities. It can improve the scalability and stability of the system while realizing intelligent analysis, predictive diagnosis and closed-loop management of sewage treatment equipment operation data. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis, so as to solve the problems existing in the operation and maintenance management of sewage treatment equipment in the prior art, such as low data collection integration, lack of modular design, insufficient intelligent analysis capabilities, unstable communication and management linkage faults, and realize efficient collection, stable transmission, intelligent analysis and full-process closed-loop management of equipment operation data.

[0006] A modular data collection and analysis method for intelligent operation and maintenance of sewage treatment equipment includes the following steps: S1. Modular data acquisition unit design According to the type of sewage treatment equipment on site, such as pumps, fans, dosing devices, liquid level meters, online water quality meters, etc., corresponding acquisition modules are configured. The acquisition module is provided with a standardized input interface, including at least one of a digital interface, an analog interface, an RS485 interface, a 4G interface or an NB-IoT interface, and is encapsulated through a unified data access protocol to achieve rapid deployment and replacement. Through standardized interface design, it can be adapted to equipment of different manufacturers and different types, solving the problem of inconsistent device interface protocols in the prior art and greatly improving the scalability and versatility of the system. When on-site equipment is increased, decreased or changed, it is only necessary to replace or add the corresponding acquisition module without redeploying hardware or modifying communication programs, which significantly reduces maintenance costs. Specifically, the acquisition module utilizes a standardized hardware architecture, integrating signal conditioning circuits, an A / D converter module, a microprocessor, and a communications module. Taking water pump equipment data collection as an example, the pump's operating status (start / stop signals) can be connected to the acquisition module via a digital interface. Analog signals such as the pump's flow rate and pressure are input through an analog interface. After filtering and amplification by the signal conditioning circuit, they are converted into digital signals by the A / D converter module. The microprocessor then performs preliminary data processing and protocol encapsulation. The unified data access protocol uses JSON format for data encoding, including metadata such as device identification, data type, and acquisition time. This ensures that the data output by different acquisition modules is in a uniform format, facilitating subsequent data processing and analysis. S2. Unified access and protocol conversion for edge devices All acquisition modules are connected through the edge computing gateway, and the edge computing gateway is used to perform pre-processing operations such as protocol standardization, lightweight compression, and timestamp completion on the field data. The edge computing gateway supports at least one of the ModbusRTU / TCP, OPCUA, or MQTT protocols to achieve adaptation to different industrial communication protocols. As a unified hub for data access, the edge computing gateway can perform protocol conversion and standardization on heterogeneous data from different acquisition modules, solving the problems of fragmented data acquisition methods and low system integration in the existing technology. Through lightweight compression and timestamp completion, the efficiency and accuracy of data transmission are improved, laying a good foundation for subsequent data processing and analysis. The edge computing gateway utilizes a high-performance multi-core processor and embedded operating system, and possesses powerful data processing and protocol conversion capabilities. Regarding protocol standardization, when the acquisition module transmits data using the Modbus RTU protocol, the edge computing gateway converts the data into a unified internal data format via the built-in Modbus RTU parser. For data using the OPC UA protocol, the gateway communicates with the device via the OPC UA client and, after acquiring the data, also converts it into an internal format. For lightweight compression processing, the LZ77 compression algorithm is used to compress data, with an average compression ratio of up to 3:1, effectively reducing the amount of data transmitted. The timestamp completion function uses a built-in high-precision clock module and time synchronization algorithm to complete incomplete timestamp data reported by the acquisition module, ensuring the temporal continuity and accuracy of the data. Furthermore, the edge computing gateway supports data caching. In the event of a network failure, data can be temporarily stored on a local storage device and automatically uploaded after the network is restored, preventing data loss. S3. Data reporting mechanism based on MQTT The data processed by the edge gateway is pushed to the cloud data center in real time through the MQTT protocol. The data in step S3 is pushed to the cloud data center in real time through the MQTT protocol. The protocol adopts a publish / subscribe model, supports will messages and session persistence functions, and can set QoS levels. The MQTT protocol has the characteristics of lightweight, stable, low latency and high adaptability to ensure real-time transmission of data in low-bandwidth, high-latency environments. It is suitable for sewage treatment sites with complex network environments and unstable bandwidth. Compared with the traditional HTTP / HTTPS protocol, the MQTT protocol has better stability and recovery capabilities when the network fluctuates, and can effectively reduce data delays, interruptions or refill failures, ensure the real-time and reliability of data, and solve the problem of unstable remote communication mechanisms in the existing technology. The MQTT protocol uses a publish / subscribe model. The edge computing gateway acts as a publisher, publishing processed data to the MQTT server according to predefined topics. The cloud data center acts as a subscriber, obtaining data by subscribing to the corresponding topic. In terms of connection management, the MQTT protocol supports Will Message and Session Persistence functions. When the connection between the edge computing gateway and the MQTT server is unexpectedly interrupted, a Will Message can promptly notify the subscriber of abnormal device status. The Session Persistence function ensures that unfinished message transmission can be continued after the network is restored, ensuring data integrity. In addition, the MQTT protocol also supports QoS (Quality of Service) level settings. For important device operating data, QoS1 or QoS2 can be set to ensure that data is transmitted at least once or only once without duplication, further improving the reliability of data transmission. S4, Time Series Database Storage and Multi-Source Data Fusion A time series database is used to store the collected data according to the time dimension. The time series database includes at least one of TDengine, InfluxDB or OpenTSDB, and supports data aggregation, segmented storage and long-term compression archiving according to equipment, process segment or project dimensions. At the same time, the collected data is integrated with the equipment ledger, maintenance records and energy consumption data in the enterprise OA / ERP system across sources to form a complete data association view. Through the efficient storage and management of the time series database, massive equipment operation data can be quickly retrieved and analyzed. Multi-source data fusion breaks the "information island" between the equipment layer and the management system, realizes the real-time sharing of equipment operation data and business management data, and provides strong support for the digitalization of equipment life cycle management and operation and maintenance decision-making. Taking TDengine as an example, it uses column-based storage and tagging mechanisms to efficiently store and query time series data. In terms of storage structure design, each device corresponds to a supertable, with the device's different parameters serving as columns in the table. Devices are categorized and identified using tags, such as the site to which the device belongs and the device type. When querying data, relevant data can be quickly retrieved based on conditions such as time range and device tags. Regarding multi-source data integration, ETL (Extract, Transform, Load) tools are used to extract data such as equipment ledgers and maintenance records from OA / ERP systems and correlate them with collected equipment operation data. For example, the equipment failure time is matched with the repair time in the maintenance record to analyze the relationship between equipment failure and maintenance; the equipment's energy consumption data is combined with its operating status data to evaluate the equipment's energy efficiency level. By establishing a data association model, deep integration and comprehensive analysis of multi-source data are achieved. S5. Remote diagnosis and closed-loop operation and maintenance management The remote fault analysis interface allows operation and maintenance personnel to remotely access site equipment, view equipment status, analyze historical data, and execute diagnostic commands. The remote fault analysis interface supports Web and UniApp mobile operations, enabling operation and maintenance personnel to remotely monitor and diagnose equipment in real time. At the same time, the platform's linked workflow engine feeds back diagnostic results, maintenance records, and processing opinions to the ERP / OA system, achieving closed-loop management of the entire process. The remote diagnosis function allows operation and maintenance personnel to troubleshoot and maintain equipment without having to go to the site, greatly improving operation and maintenance efficiency and reducing operation and maintenance costs. Full-process closed-loop management ensures that the entire process of equipment failure, from discovery, diagnosis to processing, is traceable, improving the scientific nature and standardization of equipment management. The remote fault analysis interface is built based on web technologies and mobile development frameworks, adopting a front-end and back-end separated architecture. The front-end uses frameworks such as Vue.js or React to implement interactive user interface display, while the back-end communicates with the cloud data center via a RESTful API. After logging in to the remote fault analysis interface, operations personnel can select devices to monitor from the device list and view real-time information such as operating parameters and status indicators. Historical data queries can be filtered by time range, parameter type, and other criteria, with data trends displayed in charts. To execute diagnostic commands, the system provides a series of standardized diagnostic scripts, such as device self-tests and parameter calibration. Operations personnel can trigger corresponding diagnostic commands through interface operations, with command execution results provided in real-time. The workflow engine utilizes open-source workflow engines such as Activiti or Camunda. When the system detects a device fault, it automatically generates a troubleshooting ticket and sends it to the appropriate operations personnel according to a pre-defined process. Upon completion, the personnel enter the diagnostic results, handling process, and replacement parts into the system. After the ticket process is complete, the relevant information is automatically synchronized to the ERP / OA system, achieving a closed-loop operation and maintenance management process. S6. Custom rule engine and early warning mechanism Equipment operation rules, upper and lower limit alarm strategies and multi-level linkage response measures are set in the platform. When the monitoring indicators exceed the threshold, the operations of alarm push, SMS, WeChat notification and automatic generation of work orders are triggered. The early warning mechanism includes a multi-level linkage response. When the monitoring indicators are abnormal, the closed-loop process of alarm push, notification issuance and work order generation is triggered simultaneously. Through the custom rule engine, users can flexibly set the operating parameters and alarm strategies of the equipment according to actual needs to achieve real-time monitoring and early warning of the equipment operation status. The multi-level linkage response mechanism ensures that relevant personnel can be notified in time when the equipment is abnormal, and maintenance work orders are automatically generated, which improves the response speed and fault handling efficiency of the system and solves the problem of lack of intelligent analysis capabilities and proactive intervention in existing technologies. The custom rule engine is built on the Drools or EasyRules rule engine frameworks and utilizes the principles of a rule-based expert system. Users can define device operation rules graphically or scripted through a visual rule editing interface, such as "When the operating current of a water pump exceeds 120% of the rated current for more than 5 minutes, trigger a level 1 alarm." The rule engine monitors collected device data in real time and immediately triggers the corresponding alarm action when the data meets the rule conditions. For alert push, the system integrates multiple communication methods, including SMS gateways and WeChat official account interfaces, to accurately push alert information to relevant personnel based on user-defined priorities and recipient lists. The automatic work order generation function selects a corresponding work order template from a library based on the alarm level and device type, automatically populates the template with device information, fault description, and other content, and then sends the work order to the operation and maintenance personnel's work platform. A multi-level linkage response mechanism supports different response levels. For example, a level 1 alarm notifies only the operation and maintenance personnel, a level 2 alarm simultaneously notifies the operation and maintenance supervisor and relevant leaders, and a level 3 alarm triggers the emergency plan, ensuring that appropriate response measures are taken for faults of varying severity. S7. Data visualization and operation and maintenance analysis dashboard By integrating configurable dashboard components, it displays equipment operating trends, site health indexes, alarm statistics, and maintenance cycle status, assisting managers in making multi-dimensional decisions. Data visualization presents complex equipment operating data in intuitive charts, enabling managers to quickly understand equipment operating conditions and potential issues. The operation and maintenance analysis dashboard provides managers with comprehensive equipment operation and maintenance data support, helping them make informed decisions and improving the efficiency and quality of equipment management. Data visualization utilizes visualization libraries such as ECharts and Highcharts, supporting a variety of chart types, including line charts, bar charts, pie charts, and dashboards. Equipment operation trends are displayed using line charts, showing real-time trends in key equipment parameters over time, allowing managers to observe equipment stability and abnormal fluctuations. The site health index, presented as a dashboard, integrates multiple metrics, including equipment operating status, number of failures, and maintenance records. An algorithm calculates a site health score, displaying the health level using different colors and status indicators. Alarm statistics display the distribution of alarms by type and site using bar charts or pie charts, helping managers quickly locate high-incidence areas and types. Maintenance cycle status displays equipment maintenance plans and actual execution using Gantt charts, helping managers monitor maintenance progress and effectively allocate maintenance resources. The configurable dashboard component allows users to customize the layout, display content, and refresh rate. Managers can flexibly configure personalized operation and maintenance analysis dashboards based on their needs, enabling comprehensive, multi-faceted display and analysis of equipment operation and maintenance data. Preferably, a multi-source access adapter module is also included. This module has a built-in protocol adaptation layer and supports Modbus RTU / TCP, OPCDA / UA, MQTT, HTTP / HTTPS, or WebSocket protocols to achieve unified access and formatted encapsulation of data from multiple brands and models of devices. The multi-source access adapter module further enhances the system's compatibility with different types of devices and protocols, ensuring efficient access and integration of multi-source data. The multi-source access adapter module adopts a plug-in architecture design, and each protocol adapter plug-in corresponds to a communication protocol. Taking the ModbusRTU protocol adapter plug-in as an example, it internally implements the parsing and encapsulation functions of the ModbusRTU protocol, communicates with the device through the serial port, obtains the device data, encapsulates it according to a unified data format, and sends it to the edge computing gateway. During the protocol conversion process, the adapter module also supports a data mapping function, which can map the data fields in the device's private protocol to a unified data model to achieve standardization of data from different devices. In addition, the multi-source access adapter module supports hot-swappable functions. When a new device type or protocol needs to be connected, the corresponding protocol adapter plug-in can be inserted without affecting system operation, achieving rapid expansion and compatibility. Preferably, a data tag management mechanism is also included. By establishing a multi-dimensional tag system that includes site identifiers, equipment identifiers, parameter identifiers, and time dimensions, collected data is structured and stored to support cross-dimensional data linkage analysis. This data tag management mechanism makes data more searchable, aggregated, and traceable, facilitating multi-dimensional data analysis and mining, and providing strong support for equipment operation optimization and fault prediction. The data tag management mechanism uses a tag tree structure for tag organization and management. The root node is the site identifier, and the child nodes are the device identifier, parameter identifier, and time dimension, respectively. During data storage, corresponding tag information is added to each piece of collected data. During data retrieval and analysis, users can quickly locate relevant data using tag filtering criteria. Cross-dimensional data linkage analysis performs correlation queries and statistical analysis on data from different tag dimensions. For example, this can analyze energy consumption differences for the same type of equipment at different sites, or analyze failure patterns for the same equipment over different time periods, thereby providing data support for equipment operation optimization and fault prediction. Preferably, the system also includes a unified time series model construction step, converting data from different sources and frequencies into a standard time series data model. After storage in a high-performance time series database, this creates a comprehensive operational database covering real-time device values, hourly averages, daily statistics, and abnormal segments. This unified time series model construction enables standardized management of data from different sources and frequencies, providing a unified data foundation for subsequent advanced applications such as visualization, early warning analysis, health assessment, and strategy simulation, thereby improving data utilization and overall system performance. The unified time series model construction begins with the definition of a standard data structure, including fields such as timestamp, device ID, parameter name, parameter value, and quality stamp. Data from various sources is converted to a standard time series data format through data cleansing and transformation. For example, high-frequency real-time sensor data is aggregated and calculated according to time windows to generate hourly averages and daily statistics. Low-frequency business data from OA / ERP systems is aligned with device operational data through time alignment and interpolation algorithms.

[0007] In summary, the technical solution of the present invention has the following beneficial effects in practical application: This invention utilizes a modular data acquisition unit design, configuring corresponding acquisition modules based on the type of sewage treatment equipment on-site. Standardized input interfaces and unified data access protocols enable rapid system integration for equipment from different manufacturers and types. When adding or replacing equipment, only the corresponding acquisition module needs to be replaced, eliminating the need for large-scale hardware and program changes. This significantly shortens deployment cycles, reduces system expansion costs and technical difficulties, and addresses the poor scalability of traditional data acquisition systems. This invention leverages an edge computing gateway to perform protocol standardization, lightweight compression, and timestamp completion on data collected from collection modules, enabling unified access to heterogeneous data. The MQTT protocol is used for data reporting. Leveraging its lightweight, stable, low-latency, and highly adaptable nature, it effectively reduces data transmission delays and interruptions compared to traditional protocols in the complex network environments of sewage treatment sites, ensuring real-time and reliable data upload to the cloud, providing high-quality data support for subsequent analysis. This invention uses a time-series database to store collected data by time and integrates it with equipment records, maintenance records, and energy consumption data in the enterprise's OA / ERP system across sources to form a data association view. This enables real-time sharing of equipment operation data and business management data. For example, equipment failures can be correlated with maintenance records to analyze maintenance effectiveness, and energy consumption and operating status can be combined to evaluate equipment energy efficiency. This provides comprehensive data support for equipment lifecycle management and helps enterprises achieve refined management. This invention uses a remote fault analysis interface that supports multi-terminal operation. Operations and maintenance personnel can remotely access site equipment to view status in real time, analyze historical data, and execute diagnostic commands, reducing the need for on-site inspections and lowering maintenance labor costs. Furthermore, the platform integrates a workflow engine to feed diagnostic results, maintenance records, and action suggestions back to the ERP / OA system, achieving closed-loop management from fault discovery to resolution, and improving the standardization and transparency of equipment management. This invention utilizes a custom rule engine to define equipment operating rules, alarm strategies, and multi-level coordinated response measures. When monitoring indicators exceed thresholds, relevant personnel are promptly notified through various means and a work order is automatically generated. This multi-level coordinated response ensures that faults of varying severity are properly addressed, transforming the traditional passive operation and maintenance model to enable intelligent monitoring of equipment operating status and proactive early warning, improving equipment operational stability and reliability. This invention integrates a configurable dashboard component that uses intuitive charts to display key information such as equipment operating trends, site health index, alarm statistics, and maintenance cycle status. Managers can quickly understand the overall operating status of equipment, locate problem areas and potential risks, and combine configurable design to meet diverse management needs. This provides powerful data support for optimizing operation and maintenance strategies and rationally allocating resources, thereby enhancing the scientific and effective nature of enterprise management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a method step diagram of this program. DETAILED DESCRIPTION

[0009] The present invention provides a technical solution, a modular data collection and analysis method for intelligent operation and maintenance of sewage treatment equipment. The following will describe the various steps of the method in detail in combination with specific application scenarios.

[0010] S1. Modular data acquisition unit design In actual wastewater treatment plant operations, a wide variety of equipment is present, including pumps for conveying wastewater, fans for providing oxygen, dosing devices for adding chemicals, and online water quality meters for real-time water quality monitoring. These devices often come from different manufacturers, with varying interface protocols and data output formats.

[0011] To address this issue, the present invention employs a modular data acquisition unit design. Taking a water pump as an example, during operation, the pump generates data such as operating status (start / stop signals), flow rate, and pressure. For digital signals such as operating status, an acquisition module with a digital interface is configured, through which the pump's start / stop signals are connected to the acquisition module. For analog signals such as flow rate and pressure, an acquisition module with an analog interface is used. The analog signal first enters the signal conditioning circuit within the acquisition module, which filters the signal to remove noise interference and amplifies the signal to a voltage range suitable for processing by the A / D conversion module. The conditioned analog signal is then converted into a digital signal by the A / D conversion module, which is then transmitted to the microprocessor.

[0012] The microprocessor plays a core control and data processing role in the acquisition module. It performs preliminary processing on received data, such as verifying it to ensure accuracy. It also encapsulates the data according to the unified data access protocol. In this embodiment, the unified data access protocol uses the JSON format for data encoding. The encoded data includes metadata such as the device identifier (which uniquely identifies the pump device), the data type (specifying flow rate, pressure, or operating status data), and the acquisition time. This ensures that regardless of the type of device connected to the acquisition module, the output data format remains uniform, facilitating subsequent data transmission and processing.

[0013] When a sewage treatment plant needs to add a new device or replace an existing one, it simply replaces or adds the corresponding acquisition module based on the new device's interface type and data output characteristics. This eliminates the need for major changes to the entire system's hardware architecture or rewriting complex communication programs, significantly shortening device integration time and reducing the cost and technical difficulty of system expansion. For example, if a new water quality monitoring instrument with an RS485 interface is needed, simply connect the corresponding acquisition module with the RS485 interface to the system and perform simple configuration to enable data collection and integration for the new device.

[0014] S2. Unified access and protocol conversion for edge devices After the acquisition module completes data collection and initial packaging, the data needs to be transmitted to higher-level layers for processing and analysis. This is where the edge computing gateway plays a key role. Deployed at the sewage treatment site, the edge computing gateway serves as a unified data access hub, responsible for accessing and processing data from all acquisition modules.

[0015] Suppose there are multiple acquisition modules on site, each collecting data from different devices, and these acquisition modules use different communication protocols. For example, some acquisition modules use the Modbus RTU protocol to transmit data, while others use the OPC UA protocol. When an acquisition module using the Modbus RTU protocol sends data, the edge computing gateway parses the data using the built-in Modbus RTU parser and converts it into a unified internal data format. For acquisition modules using the OPC UA protocol, the edge computing gateway communicates with the device through the OPC UA client and converts the acquired data into the internal format.

[0016] In terms of data processing, the edge computing gateway performs lightweight compression on the data. This embodiment uses the LZ77 compression algorithm, which can effectively reduce the size of the data without losing data accuracy. After actual testing, for common sewage treatment equipment operating data, the average compression ratio can reach 3:1. For example, after compression, the original 100KB of data can be reduced to approximately 33KB, greatly reducing the bandwidth occupied during data transmission.

[0017] Furthermore, due to the complex operating environments of field equipment, inaccurate time synchronization may occur, resulting in incomplete or inaccurate timestamps for collected data. The edge computing gateway uses its built-in high-precision clock module and time synchronization algorithm to complete incomplete timestamp data reported by the acquisition module. It synchronizes with the network time server to obtain accurate time information. It then corrects missing or erroneous timestamps based on the order and time interval of data collection, ensuring data temporal continuity and accuracy.

[0018] To prevent data loss in the event of a network failure, the edge computing gateway supports data caching. It has a built-in local storage device, such as a large-capacity SD card or solid-state drive. When a network interruption is detected, the edge computing gateway temporarily stores any data that cannot be uploaded to the local storage device. Once the network is restored, the cached data is automatically uploaded to the cloud data center, ensuring data integrity.

[0019] S3. Data reporting mechanism based on MQTT The data processed by the edge computing gateway needs to be transmitted to the cloud data center for further storage and analysis. Considering the complex network environment and unstable bandwidth of sewage treatment sites, the present invention adopts the MQTT protocol for data reporting.

[0020] The MQTT protocol uses the Publish / Subscribe model.

[0021] In this implementation scenario, the edge computing gateway acts as a publisher and the cloud data center acts as a subscriber. The edge computing gateway publishes the processed data to the MQTT server according to a pre-defined topic.

[0022] For example, for water pump equipment data, define the topic "sewageTreatment / pump / data," where "sewageTreatment" represents the sewage treatment scenario, "pump" represents the water pump equipment, and "data" represents the data. By subscribing to this topic, the cloud data center can obtain relevant data about the water pump equipment.

[0023] In terms of connection management, the MQTT protocol supports WillMessage and SessionPersistence. When the connection between an edge computing gateway and an MQTT server is unexpectedly lost, a WillMessage promptly notifies subscribers of the abnormal device status. For example, before the connection between the edge computing gateway and the MQTT server is lost, a WillMessage message can be pre-set with the content "Edge computing gateway connection lost, device data transmission suspended." When the connection is lost, the MQTT server sends this message to subscribers, allowing the cloud data center to be notified and take appropriate measures.

[0024] The session persistence feature ensures that unfinished message transmissions can be continued after network restoration, ensuring data integrity. If the edge computing gateway has unfinished data transmission during a network outage, it will resume data transmission from the last interruption point after network restoration based on the session persistence information, preventing data loss or duplication.

[0025] In addition, the MQTT protocol supports QoS (Quality of Service) level settings. Important device operational data, such as fault alarms and key parameter data, can be set to QoS1 or QoS2. QoS1 ensures that data is transmitted at least once; QoS2 ensures that data is transmitted only once and without duplication, further improving the reliability of important data transmission.

[0026] S4, Time Series Database Storage and Multi-Source Data Fusion After data is transmitted to the cloud data center, it needs to be stored and managed. This invention uses a time series database to store the collected data. In this embodiment, TDengine is selected as the time series database. TDengine uses columnar storage and a tag mechanism, making it ideal for storing and querying time series data.

[0027] In the storage structure design, a supertable is assigned to each sewage treatment device. For example, for a pump numbered P-001, a supertable named "pump_P-001" is created. This table contains multiple columns corresponding to different pump parameters, such as operating current, flow rate, pressure, and speed. Devices are also categorized and identified using tags. For example, the tag "site=SiteA" indicates that the pump is located at Site A, and "type=CentrifugalPump" indicates that the pump is a centrifugal pump.

[0028] When querying data, relevant data can be quickly retrieved based on conditions such as time range and device tags. For example, to query the operating current data of all centrifugal pumps at Site A on October 1, 2024, simply execute the corresponding query statement in TDengine. The database will quickly locate the relevant data and return the results, greatly improving the efficiency of data query.

[0029] To achieve multi-source data integration, ETL (Extract, Transform, Load) tools are used to extract equipment ledgers, maintenance records, and energy consumption data from the enterprise OA / ERP system. For example, equipment ledger information, including equipment model, purchase date, and supplier, is extracted from the ERP system; maintenance records, including maintenance time, maintenance personnel, and maintenance content, are extracted from the OA system; and energy consumption data, such as equipment power and water usage, is extracted from the energy management system.

[0030] The extracted data is correlated with the collected equipment operation data. Taking equipment failure analysis as an example, the equipment failure time is matched with the repair time in the maintenance record. If the equipment fails again within a short period of time after repair, it can be analyzed whether it is a repair quality issue or a potential hidden danger in the equipment itself. The equipment's energy consumption data is combined with the operating status data. By calculating the ratio of energy consumption per unit time to the amount of wastewater treated, the energy efficiency level of the equipment is evaluated, providing data support for enterprises to optimize equipment operation strategies and reduce energy consumption costs.

[0031] S5. Remote diagnosis and closed-loop operation and maintenance management To enable remote monitoring and maintenance of sewage treatment equipment, the present invention has developed a remote fault analysis interface that supports both web and UniApp mobile operations. The remote fault analysis interface is built based on web technology and a mobile development framework, using a front-end and back-end separation architecture.

[0032] On the front end, the Vue.js framework is used to implement the interactive display of the user interface. When the operation and maintenance personnel log in to the remote fault analysis interface on the Web side, they first enter the device list page, which displays all monitorable sewage treatment equipment in the form of a list, including information such as the equipment name, equipment type, location, and current operating status. The operation and maintenance personnel can quickly locate the equipment that needs to be monitored through the equipment list, and click on the equipment name to enter the equipment details page. On the equipment details page, the operating parameters of the equipment are displayed in real time in the form of charts, dashboards, etc., such as the operating current, flow rate, and pressure curve of the water pump, the speed and vibration value of the fan, etc.; at the same time, the status indicator information of the equipment is also displayed to intuitively reflect whether the equipment is in normal operation.

[0033] For historical data queries, filtering by time range, parameter type, and other criteria is supported. For example, if an operator wants to query the flow rate trends of a particular pump over the past week, they simply need to set the time range to the past week in the query interface and select the flow rate parameter. The system will then display the flow rate changes for that pump over that period in the form of a line graph, making it easier for operators to analyze the equipment's operational stability and abnormal fluctuations.

[0034] To execute diagnostic commands, the system provides a series of standardized diagnostic scripts, such as device self-test scripts and parameter calibration scripts. When operations personnel discover anomalies in device operating parameters, they can trigger corresponding diagnostic commands through user interface operations. For example, clicking the "Device Self-Test" button sends a self-test command to the device. Upon receiving the command, the device executes the self-test procedure and provides real-time feedback on the self-test results to the remote fault analysis interface. Based on these results, operations personnel can determine whether the device is faulty and what the fault type is.

[0035] On the mobile side, the UniApp framework is used for development, delivering a functional experience similar to the web side. Operations and maintenance personnel can check device status and execute diagnostic commands anytime, anywhere via their mobile phones, even when they are out on patrol or away from the office, greatly improving the convenience and timeliness of operations and maintenance.

[0036] At the same time, the platform links the workflow engine to achieve a closed-loop operation and maintenance management process. When the system detects a device failure, the workflow engine automatically generates a fault handling work order. The work order content includes information such as the device name, the time of the failure, and a description of the failure phenomenon (generated based on the abnormal parameters reported by the device). The workflow engine sends the work order to the corresponding operation and maintenance personnel according to the preset process, such as the operation and maintenance personnel responsible for the site where the equipment is located. After receiving the work order, the operation and maintenance personnel go to the site to troubleshoot and handle the fault. After the processing is completed, the diagnosis results, processing process, replacement parts and other information are entered into the system. After the work order process is completed, the relevant information is automatically synchronized to the ERP / OA system, making the entire process from fault discovery, diagnosis to processing traceable, and improving the scientificity and standardization of equipment management.

[0037] S6. Custom rule engine and early warning mechanism In order to achieve real-time monitoring and proactive early warning of the equipment's operating status, the present invention integrates a custom rule engine into the platform. The custom rule engine is built on the Drools framework and adopts the principle of rule-based expert system.

[0038] The platform's management interface provides a visual rule editing interface, allowing users to flexibly configure device operating rules and alarm policies based on actual needs. For example, for a water pump, users can use the graphical interface to set a rule such as: "When the pump's operating current exceeds 120% of the rated current for more than 5 minutes, trigger a level 1 alarm." The rule engine monitors collected device data in real time and, when the data meets the rule conditions, immediately triggers the corresponding alarm action.

[0039] For alert push, the system integrates multiple communication methods, including SMS gateways and WeChat official account interfaces. Users can set the list of personnel and priority levels for receiving alert information within the system. For example, for a Level 1 alert, only the O&M personnel at the device's site are notified, with alert information sent via SMS and WeChat official account messages. The alert information includes the device name, fault type, and time of occurrence. For a Level 2 alert, the O&M supervisor and relevant leaders are notified simultaneously. In addition to SMS and WeChat official account messages, a detailed fault report is also sent via email. For a Level 3 alert, the emergency plan is activated, not only notifying all relevant personnel but also automatically dialing a pre-set emergency contact number.

[0040] The automatic work order generation function selects the appropriate work order template from the work order template library based on the alarm level and device type. For example, for a Level 1 alarm, a simple troubleshooting work order template is selected, which automatically fills in the device information, fault description, and other content, and sends the work order to the operation and maintenance personnel's work platform. For Level 2 and Level 3 alarms, a detailed troubleshooting work order template is selected, which includes more comprehensive troubleshooting procedures and requirements. This ensures that appropriate countermeasures can be taken in the event of faults of varying severity, improving system response speed and troubleshooting efficiency.

[0041] S7. Data visualization and operation and maintenance analysis dashboard To help managers intuitively understand equipment operating conditions and make informed decisions, this invention integrates configurable dashboard components to achieve data visualization. Data visualization uses the ECharts visualization library, which supports a variety of chart types.

[0042] To display equipment operation trends, a line chart is used to display pump flow data. The horizontal axis represents time, and the vertical axis represents flow value. By updating the line chart in real time, managers can clearly observe the changing trend of pump flow over time, assessing whether equipment operation is stable and experiencing any abnormal fluctuations. For example, if a sudden drop in flow is detected, managers can promptly monitor the change and conduct further analysis.

[0043] The site health index is displayed in a dashboard format. This score is calculated using a specific algorithm, taking into account multiple metrics, including equipment operating status (percentage of uptime, number of failures, etc.), maintenance records (maintenance timeliness and quality), and energy consumption (energy consumption per unit of processing capacity). The dashboard displays the site's health level using different colors and status indicators. For example, green indicates good health, yellow indicates risks requiring attention, and red indicates serious issues requiring immediate attention.

[0044] Alarm statistics use bar charts to display the distribution of different alarm types. For example, they show the number of pump failure alarms, fan failure alarms, and water quality exceeding standard alarms over the past month. Managers can quickly locate high-incidence areas and types of alarms and perform targeted equipment maintenance and management.

[0045] The maintenance cycle status is displayed using a Gantt chart to show equipment maintenance plans and actual execution. The Gantt chart displays time on the horizontal axis and equipment name on the vertical axis. Different colored bars represent different maintenance tasks, including planned and actual maintenance times. This allows managers to intuitively monitor maintenance progress, determine whether maintenance plans are being completed on time, and optimally allocate maintenance resources.

[0046] Configurable dashboard components allow users to customize the layout, display content, and refresh rate. Based on their management needs, managers can add chart components of interest to the dashboard, adjust their size and position, select the data range and indicators to be displayed, and set the chart refresh rate, such as once every minute or every five minutes. This enables comprehensive, multi-faceted display and analysis of equipment operation and maintenance data, providing strong support for optimizing operation and maintenance strategies and improving management efficiency.

[0047] Through the above detailed specific implementation methods, the modular data collection and analysis intelligent operation and maintenance method of sewage treatment equipment of the present invention can realize efficient data collection, stable data transmission, intelligent data analysis and scientific operation and maintenance management in the intelligent operation and maintenance of sewage treatment equipment, effectively solve the problems existing in the existing technology, and have good practical application value and promotion prospects.

[0048] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0049] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A modular data collection and analysis method for intelligent operation and maintenance of sewage treatment equipment, characterized in that: The following steps are involved: S1. Modular data acquisition unit design: According to the type of sewage treatment equipment on site, the corresponding acquisition module is configured. The acquisition module is provided with a standardized input interface and encapsulated through a unified data access protocol; S2. Unified access and protocol conversion of edge devices: All acquisition modules are connected through the edge computing gateway, and the edge computing gateway is used to perform pre-processing operations such as protocol standardization, lightweight compression, and timestamp completion on the field data; S3, MQTT-based data reporting mechanism: Data processed by the edge gateway is pushed to the cloud data center in real time via the MQTT protocol; S4. Time-series database storage and multi-source data fusion: Use a time-series database to store collected data by time dimension, and integrate it with equipment ledgers, maintenance records, and energy consumption data in the enterprise OA / ERP system across sources to form a data association view; S5. Remote diagnosis and closed-loop operation and maintenance management: The remote fault analysis interface allows operation and maintenance personnel to remotely access site equipment, view equipment status, analyze historical data, and execute diagnostic commands. The system also provides feedback on diagnostic results, maintenance records, and handling opinions to the ERP / OA system, achieving closed-loop management of the entire process. S6. Custom rule engine and early warning mechanism: Set equipment operation rules, upper and lower limit alarm strategies and multi-level linkage response measures in the platform. When monitoring indicators exceed the threshold, relevant actions are triggered; S7. Data visualization and operation and maintenance analysis dashboard: By integrating configurable dashboard components, it displays equipment status and assists managers in making multi-dimensional decisions.

2. The intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis according to claim 1 is characterized in that: The standardized input interface in step S1 includes at least one of a digital interface, an analog interface, an RS485 interface, a 4G interface or an NB-IoT interface.

3. The intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis according to claim 1 is characterized in that: In step S2, the edge computing gateway supports at least one of Modbus RTU / TCP, OPC UA or MQTT protocols to achieve adaptation to different industrial communication protocols.

4. The intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis according to claim 1 is characterized in that: The data in step S3 is pushed to the cloud data center in real time through the MQTT protocol. The MQTT protocol adopts a publish / subscribe model, supports will messages and session persistence functions, and sets QoS levels.

5. The intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis according to claim 1 is characterized in that: The time series database in step S4 includes at least one of TDengine, InfluxDB or OpenTSDB, and supports aggregation, segmented storage and long-term compression archiving of data by equipment, process segment or project dimensions.

6. The intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis according to claim 1 is characterized in that: The remote fault analysis interface in step S5 supports Web and UniApp mobile terminal operations, enabling operation and maintenance personnel to remotely monitor and diagnose the equipment in real time.

7. The intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis according to claim 1 is characterized in that: The early warning mechanism in step S6 includes a multi-level linkage response. When the monitoring indicators are abnormal, a closed-loop process of alarm push, notification issuance and work order generation is triggered simultaneously.

8. The intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis according to claim 1 is characterized in that: It also includes a multi-source access adapter module with a built-in protocol adaptation layer that supports Modbus RTU / TCP, OPCDA / UA, MQTT, HTTP / HTTPS or WebSocket protocols to achieve unified access and formatted encapsulation of data from multiple brands and models of devices.

9. The intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis according to claim 1 is characterized in that: It also includes a data tag management mechanism, which establishes a multi-dimensional tag system including site identification, equipment identification, parameter identification and time dimensions to perform structured storage of collected data to support cross-dimensional data linkage analysis.

10. The intelligent operation and maintenance method for sewage treatment equipment with modular data collection and analysis according to claim 1, characterized in that: It also includes the steps of building a unified time series model, converting data from different sources and different frequencies into a standard time series data model. After storing it in a high-performance time series database, it forms an operation panoramic database covering the real-time values ​​of equipment, hourly averages, daily statistics and abnormal sections.

Images