Water treatment plant full life cycle data management and control method, device, equipment and medium

Abstract

The application provides a water treatment plant full life cycle data management and control method, device, equipment and medium, comprising: constructing a first data asset library of the water treatment plant and establishing a data standard specification system; in the construction and equipment installation stage of the management and control period, collecting various types of business data during the project construction period, and constructing a second data asset library based on the business data; constructing a mapping model based on the globally unique code; in the operation period, real-time collection of dynamic operation data of each asset object is performed, and the dynamic operation data is standardized to obtain standardized operation data; based on the mapping model, the standardized operation data and attribute data are associated and matched; and a full life cycle data model is constructed based on the matched data. The method can solve the technical problem that the data of the management and control period and the operation period of the water treatment plant are difficult to intercommunicate, and improve the continuity and traceability of the asset full life cycle data.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment information management and data processing technology, and in particular to a data management method, device, equipment and medium for the entire life cycle of a water treatment plant. Background Technology

[0002] With increasingly stringent environmental protection requirements and the deepening of smart city construction, wastewater treatment plants are gradually transforming and upgrading from traditional manual management models to digital and intelligent ones. As a crucial infrastructure for urban water circulation, wastewater treatment plants typically go through two key phases: the construction and management phase, which usually includes design, construction, equipment installation, and commissioning; and the production and operation phase, which includes daily operation and maintenance after formal commissioning.

[0003] However, in existing technologies, the control and operation phases of wastewater treatment plants are independent of each other, and their data are not interconnected. Specifically, engineering construction data such as design drawings, equipment ledgers, construction records, concealed works data, and equipment commissioning parameters generated during the control phase, and production data such as water quality monitoring data, equipment operating parameters, inspection records, and maintenance work orders generated during the operation phase, are often stored in different systems, lacking unified data standards and interoperability interfaces.

[0004] Secondly, in actual engineering operations, when a wastewater treatment plant is handed over to the operation team from the control phase, the change of personnel means that a large amount of valuable engineering construction information cannot be effectively transmitted to the operation system, resulting in an information gap for the operation personnel when taking over the new plant. For example, they may not be familiar with the underground pipeline routes, equipment installation parameters, or the construction details of concealed works, which severely restricts the work efficiency in the initial stage of operation. This also makes it difficult for maintenance personnel to trace back and retrieve key information such as the original installation parameters, factory test reports, and commissioning records of the equipment during the control phase when equipment failures or process abnormalities occur during the operation period, leading to low efficiency in troubleshooting.

[0005] Existing smart water management platforms mostly focus on the management and analysis of operational data, rarely incorporating control-period data into a unified data system. Even when attempts are made to link and integrate control-period data with operational data, the heterogeneous and complex nature of multi-source data during the control-period makes it difficult to achieve effective correlation and matching between the two types of data due to the lack of a unified benchmark. Consequently, it is difficult to achieve data connectivity throughout the entire lifecycle of assets.

[0006] Therefore, there is an urgent need for a method that can break down the data barriers between the control and operation phases of wastewater treatment plants, and achieve seamless integration of asset data from construction to operation. Summary of the Invention

[0007] The purpose of this invention is to provide a data management method, device, equipment, and medium for the entire lifecycle of a water treatment plant. By constructing a data standard and specification system in the early stage of management and assigning a globally unique code to each asset object, and establishing a link between each asset object during the management and operation periods based on the globally unique code, the technical problem of data interoperability between the management and operation periods of a water treatment plant is solved, thereby improving the continuity and traceability of asset lifecycle data.

[0008] To achieve the above objectives, the technical solution adopted by this invention is: a data management and control method for the entire life cycle of a water treatment plant, comprising the following steps: S1: Construct the first data asset database of the water treatment plant and establish a data standard specification system; the data standard specification system is constructed through the back-end server, including the attribute data specifications of each asset object and the preset operating period dynamic operation data specifications corresponding to the asset object, the preset operating period dynamic operation data specifications include the measurement point identifiers corresponding to the operating period dynamic operation data to be collected; In the initial stage of management and control, all asset objects are given a globally unique code, and the asset code information is stored in the first data asset database. S2: During the construction and equipment installation phase of the control period, collect various business data during the project construction period, and construct a second data asset library based on the business data; the second data asset library includes attribute data of each asset object; S3: Construct a mapping model on the server side based on the globally unique code, wherein the mapping model establishes an association between the attribute data of the asset object during the management period and the measurement point identifiers corresponding to the dynamic operation data to be collected during the operation period based on the globally unique code; S4: During the operation period, collect dynamic operation data of each asset object in real time, and perform standardized processing on the dynamic operation data to obtain standardized operation data; S5: Based on the mapping model, perform association matching between the standardized operational data and attribute data; and construct a full lifecycle data model based on the matched data.

[0009] This invention establishes a unified data coding and standard system covering the entire lifecycle of a water treatment plant during the initial management and control phase, synchronously collecting data during the management and control period to avoid the inability to link with operational data due to missing management and control data in the later stages. At the same time, it assigns a globally unique code to each asset object and pre-builds a mapping model based on the globally unique code to establish the correlation between attribute data during the management and control period and dynamic operation data during the operation period, solving the problem of the lack of a unified benchmark and difficulty in effective matching between the two types of data.

[0010] Furthermore, constructing a full lifecycle data model based on the matched data includes fusing the matched data of each asset object to obtain the fused data corresponding to each asset object; and then structurally reconstructing and uniformly storing the fused data of each asset object to obtain the full lifecycle data model. By integrating the static attribute data of the management and control period and the dynamic operation data of the operation period on an asset-by-asset basis, the two independent phases of data are incorporated into a unified structured carrier, opening up the data pathway between the management and control period and the operation period, and achieving deep integration of the two types of data.

[0011] Furthermore, S2 also includes constructing a BIM three-dimensional design base model based on various business data collected during the project construction period, and storing the BIM three-dimensional design base model in the second data asset library; The method further includes, after S5, mapping and overlaying the fused data corresponding to each asset object onto the asset object corresponding to the BIM 3D design base model to obtain a 3D digital delivery model. Compared with traditional BIM models that only store static data during the construction period, this method combines the fused data of the control and operation periods with the 3D model to achieve visualization of asset lifecycle information.

[0012] Furthermore, in S4, the dynamic operational data is standardized according to the aforementioned data standard specification system to obtain standardized operational data. Standardizing the dynamic operational data through a pre-established data standard specification system ensures that the dynamic operational data during the operation period and the asset attribute data during the control period adhere to unified data semantics and format specifications, eliminating heterogeneous differences between data from different sources. This allows the dynamic operational data to be correlated with the asset attribute data through a mapping model, providing a unified benchmark for the effective integration of the two types of data during the control and operation periods, and avoiding matching failures caused by inconsistent data specifications.

[0013] Furthermore, in S4, if the missing dynamic operation data is detected, breakpoint resume processing is performed; and the missing dynamic operation data is supplemented according to the asset operation characteristics to avoid the inability to complete the association and matching with the attribute data of the control period according to the mapping model due to data missing, thereby improving the data adaptability and anti-interference capability of the mapping model.

[0014] Furthermore, S4 also includes retrieving asset attribute data corresponding to the control period based on the mapping model and the dynamic operation data during the operation period. When abnormal operation data occurs in the equipment, the corresponding factory parameters, installation information and other control data can be quickly retrieved, which improves the information traceability capability of the entire asset life cycle.

[0015] Furthermore, both the first and second data asset repositories are configured with version management mechanisms; when the attribute information of an asset object changes, a data snapshot is automatically generated and a change log is retained. When an asset object is modified, the mapping model is updated synchronously. When an asset object is replaced, the globally unique code and the mapping model are updated synchronously.

[0016] This invention, while establishing a two-stage data association, also considers the subsequent changes to assets. Even after an asset is modified or replaced, it maintains an effective association between the attribute data during the control period and the dynamic operation data during the operation period, ensuring the long-term availability of the mapping model and solving the problem that traditional static association schemes cannot adapt to asset changes.

[0017] Based on the same concept, the present invention also provides a data management and control device for the entire life cycle of a water treatment plant, which is used to implement the above method, the device comprising: The data access module is used to construct the first data asset database of the water treatment plant and establish a data standard specification system; the data standard specification system includes the attribute data specifications of each asset object and the preset dynamic operation data specifications of the asset object for the corresponding operating period. In the initial stage of management and control, all asset objects are given a globally unique code, and the asset code information is stored in the first data asset database. The data cleaning and quality control module is used to collect various business data during the construction and equipment installation phases of the project construction period, and to build a second data asset library based on the business data; the second data asset library includes attribute data of each asset object; The data mapping and association module is used to construct a mapping model based on the globally unique code, wherein the mapping model establishes an association between the attribute data of the asset object during the management period and the dynamic operation data to be collected during the operation period based on the globally unique code; The operation period data acquisition module is used to collect dynamic operation data of each asset object in real time during the operation period, and to standardize the dynamic operation data to obtain standardized operation data. The full lifecycle data model construction module is used to perform association matching between the standardized operational data and attribute data based on the mapping model; and to construct a full lifecycle data model based on the matched data.

[0018] Based on the same concept, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0019] Based on the same concept, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0020] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention establishes a unified data coding and standard system in the early stages of water treatment plant management and control, and synchronously collects asset data during the management and control period, avoiding the inability to link with operational data due to missing management and control data in the later stages; at the same time, it assigns a globally unique code to each asset object, and pre-builds a mapping model based on the globally unique code to establish the relationship between attribute data during the management and control period and dynamic operation data during the operation period, solving the problem of the lack of a unified benchmark and difficulty in effective matching between the two types of data; in addition, it considers the scenario of dynamic changes throughout the asset life cycle, and provides a supporting data version management mechanism, which can synchronously maintain the mapping model and globally unique code when assets are modified or replaced, overcoming the problem that traditional static association schemes cannot adapt to asset changes; thus forming a complete asset life cycle data network. Attached Figure Description

[0021] To more clearly illustrate the technical method of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the data management method for the entire life cycle of a water treatment plant in this embodiment of the invention; Figure 2 This is a schematic diagram of the unified data encoding architecture according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the cross-stage data mapping model according to an embodiment of the present invention; Figure 4 This is an architecture diagram of the cross-stage data fusion platform according to an embodiment of the present invention. Detailed Implementation

[0023] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. For ease of description, the terms "upper," "lower," "left," and "right" used below only indicate that they correspond to the upper, lower, left, and right directions in the accompanying drawings and do not limit the structure.

[0024] The technical methods of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The technical methods of this application will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0026] Figure 1 A flowchart of a data management method for the entire lifecycle of a water treatment plant, as provided in an embodiment of the present invention, is shown. Figure 1 As shown, the method includes the following steps: S1: Construct the first data asset database of the water treatment plant and establish a data standard specification system; the data standard specification system is constructed through the back-end server, including the attribute data specifications of each asset object and the preset operating period dynamic operation data specifications corresponding to the asset object, the preset operating period dynamic operation data specifications include the measurement point identifiers corresponding to the operating period dynamic operation data to be collected; In the initial stage of management, all asset objects are given a globally unique code, and the asset code information is stored in the first data asset database.

[0027] At the outset of the wastewater treatment plant's management and control phase, a unified data coding and standard system covering the entire lifecycle of the plant is established. This includes uniquely coding all assets, such as process equipment, buildings, pipelines, valves, instruments, and electrical equipment. Dynamic operational data specifications, such as unified data definitions, transmission protocols, storage formats, and semantic tags, ensure that data from the management and control phase has a consistent data dictionary and semantic specifications with data from the operation phase. Specifically, during the entire management and control phase of construction and commissioning, static asset data and initial commissioning parameters are collected through project design, procurement, construction, and equipment commissioning and trial operation.

[0028] Figure 2A schematic diagram of the unified data coding architecture of an embodiment of the present invention is shown. In one embodiment, the unified data coding adopts a four-level hierarchical tree structure. The first level represents the plant area code: located at the root node of the tree structure, consisting of 2 to 4 uppercase letters or numbers, used to uniquely identify the wastewater treatment plant (e.g., "A" represents plant area A, "BJ" represents plant area XX). The second level represents the process unit code: a child node of the first level, consisting of 4 to 6 letters or numbers, following the rule of "English abbreviation + serial number" (e.g., "PR E01" represents pretreatment section 01 unit, "BIO02" represents biochemical treatment section 02 unit). This code cannot be repeated within the same plant area. The third level represents the asset category code: a child node of the second level, consisting of 3 to 5 uppercase letters, representing the type of equipment or structure (e.g., "PMP" represents a water pump, "MIX" represents a mixer, "SEN" represents a sensor, "VAL" represents a valve, "PLC" represents a controller). The fourth level represents the serial number: a child node of the third level, consisting of 3 to 4 digits, numbered sequentially according to installation order within the same asset category (e.g., "001", "012", "099"), forming a unique serial number for that type of asset. This four-level hierarchical tree-like coding system is not simply an asset numbering management rule; by embedding plant area, process unit, and equipment type information into the hierarchical coding, it facilitates the program's automatic parsing of the asset's process location and equipment type, providing a structured data foundation for subsequent mapping models to quickly match corresponding measurement point identifiers and retrieve full lifecycle data by region.

[0029] The data collection and pre-processing work during the control period in this embodiment of the invention is equipped with physical hardware devices, providing a reliable hardware implementation foundation. The water treatment plant asset system adapted in this invention covers five core asset categories: process equipment, buildings and structures, pipelines and valves, instruments and meters, and electrical equipment. Specifically, it includes typical plant equipment and facilities such as bar screens, aeration tanks and / or secondary sedimentation tanks, electric butterfly valves, dissolved oxygen sensors (DO meters), and frequency converter control cabinets. These assets can be physically and logically linked through cables, signal lines, and process pipelines. Under the unified scheduling of the automation system, a complete wastewater treatment process covering wastewater influent to effluent and from the water line to the sludge line is constructed, ensuring the plant operates compliantly and efficiently around the clock. From a full life cycle perspective, instrument sensors only perform data acquisition and monitoring functions during the operation period, while the main wastewater treatment equipment and facilities of the plant connect the entire process of the control and operation periods. They can serve as the hardware foundation to effectively support the pre-processing software data processing work such as asset data collection, data organization, unified coding, and model building during the control period. This enables the hardware entity system and the software data processing system to be synergistically adapted, providing solid hardware and data support for the cross-stage correlation and fusion of static attribute data during the control period and dynamic operation data during the operation period, and for the construction of a full life cycle data system.

[0030] In one embodiment, mobile QR codes and radio frequency identification (RFID) technologies are introduced. During the equipment installation phase, a unique QR code tag is generated for each device and affixed to the device itself, with the tag containing a globally unique code corresponding to the asset. During the operation phase, inspection personnel can quickly retrieve the fused data of static attributes and dynamic history of the device by scanning the QR code with a mobile terminal, and can also enter inspection information on-site, improving the efficiency of on-site operation and maintenance.

[0031] S2: During the construction and equipment installation phase of the control period, various business data from the project construction period are collected, and a second data asset library is constructed based on the business data. The second data asset library includes the attribute data of each asset object. During the construction and equipment installation of the wastewater treatment plant, structured data from the control period, such as engineering design documents, equipment procurement information, construction process records, concealed works acceptance records, equipment factory inspection reports, installation and commissioning parameters, and as-built drawings, are collected, reviewed, and stored through mobile terminals or data acquisition systems to construct the control period data asset library.

[0032] S3: Based on the globally unique code, a mapping model is constructed on the server side. This mapping model, based on the globally unique code, establishes a connection between the attribute data of the asset object during the management period and the corresponding measurement point identifiers of the dynamic operation data to be collected during the operational period. That is, a unified asset data model is constructed based on data cleaning and standardization, asset coding mapping, and master data management to achieve cross-stage data fusion and integration.

[0033] Using the unified data coding system as the core index, a two-way mapping relationship model is established between engineering data during the control period and operational data during the operation period. Figure 3 The diagram illustrates the structure of a cross-stage data mapping model, including a control-phase data table recording the static attributes of equipment (unified code, model, manufacturer, installation date); and an operation-phase data table recording the dynamic data of the same equipment (unified code, current, temperature, fault). The two data tables are linked through a "unified code" field, forming a fused view across the entire lifecycle. A unified data coding system is constructed using globally unique codes, which serve as the core index to establish a bidirectional mapping relationship model between control-phase engineering data and operation-phase operational data. It is important to note that the mapping model is a data index framework built upon globally unique codes and does not require prior acquisition of dynamic operational data. Once the dynamic operational data for the operation phase is collected, the same globally unique code is used to link the static attribute data from the control-phase to the dynamic operational data according to the mapping model, thereby completing data fusion.

[0034] It should be noted that the mapping model is not a static binding relationship pre-configured manually; the system extracts attribute tags such as equipment type, process location, and media attributes of asset objects, as well as equipment affiliation, process section, and monitored physical quantity tags corresponding to the measurement point identifiers. Through the tag similarity matching algorithm, the system automatically filters the measurement point identifiers belonging to the same asset object, autonomously establishes the mapping relationship between asset attribute data during the control period and dynamic measurement point data during the operation period, and automatically generates the mapping model.

[0035] For example, in the pre-defined dynamic operation data specifications for the initial management phase, measurement point identifiers such as current, temperature, and fault codes are defined, with each measurement point identifier corresponding to a type of dynamic operation data. During the operation phase, the system collects data such as "Current: 12.5A," with the data accompanied by a measurement point identifier. Based on the association between the globally unique code and the measurement point identifier in the mapping model, the asset object to which the dynamic data belongs is identified, and the dynamic operation data is added to the corresponding asset entry.

[0036] This mapping model associates and binds the attribute data (such as model specifications, installation location, factory serial number, warranty period, etc.) of each asset object recorded during the control period with the dynamic operation data (such as runtime, fault records, maintenance records, energy consumption data, etc.) generated by the asset object during the operation period.

[0037] S4: During the operation period, collect dynamic operational data of each asset in real time, and standardize the dynamic operational data to obtain standardized operational data. That is, continuously collect operational data such as real-time monitoring, production management, and equipment maintenance during the formal commercial operation phase of the plant.

[0038] After the wastewater treatment plant is put into operation, IoT sensors, PLC controllers and SCADA systems deployed in each process unit (including pretreatment section, biological treatment section, advanced treatment section and sludge treatment section) are used to collect operational data in real time, such as influent water quality, effluent water quality, equipment operating status and energy consumption parameters.

[0039] Wastewater treatment plants contain a variety of monitoring instruments and PLC control units of different brands and types. The data reporting cycles, output formats, and time bases of these devices are inconsistent, leading to problems such as sensor disconnections, numerical drift, and misaligned sampling sequences. The raw data is disorganized and cannot be directly matched with the static asset archives during the management and control period. After receiving the raw sensor data, the server performs automated preprocessing: First, it performs semantic parsing, label normalization, unit unification, and format conversion on the raw data. Then, it uniformly calibrates the timestamps of each acquisition terminal to achieve time sequence alignment. It automatically filters out abnormal values ​​outside the normal operating range of the equipment and performs interpolation compensation for missing sampling points caused by short-term communication interruptions, outputting standardized operational data with consistent time sequences.

[0040] In one embodiment, if missing dynamic operating data is detected, breakpoint resume acquisition processing is performed; and the missing dynamic operating data is supplemented according to the asset's operating characteristics. For example, the system continuously receives dynamic operating data uploaded by each asset object and verifies the data's temporal integrity in real time; when data packet loss or communication interruption causes missing dynamic operating data, the breakpoint resume acquisition mechanism is triggered, sending an instruction to the acquisition terminal to resume data acquisition from the point of data interruption, without needing to collect data for the entire time period from scratch. Simultaneously, the system retrieves the asset object's preset operating characteristic curves and process condition thresholds, and combines them with adjacent valid operating sequences to supplement the missing dynamic operating data for the missing period through interpolation, outputting temporally continuous dynamic operating data for subsequent standardized processing.

[0041] In one embodiment, based on the mapping model, asset attribute data corresponding to the control period is retrieved in reverse from the dynamic operation data during the operation period. For example, the mapping model stores the association between dynamic operation data streams and globally unique asset codes. When dynamic operation data or equipment anomaly alarm information is obtained during the operation period, the system extracts the identification information corresponding to the dynamic operation data, calls the mapping model for reverse retrieval, and matches the corresponding globally unique asset code. Based on the globally unique code, the system searches the database to retrieve asset attribute data stored during the control period, including static files such as equipment factory parameters, installation information, and as-built drawings, completing the reverse query from operation data to controlled asset data.

[0042] S5: Based on the mapping model, perform association matching between the standardized operational data and attribute data; and construct a full lifecycle data model based on the matched data. Specifically, constructing a full lifecycle data model based on the matched data includes fusing the matched data of each asset object to obtain the fused data corresponding to each asset object; and structurally reconstructing and uniformly storing the fused data of each asset object to obtain the full lifecycle data model.

[0043] Figure 4This diagram illustrates the architecture of a cross-stage data fusion platform according to an embodiment of the present invention. The platform adopts a five-layer top-down architecture: a data source layer, a data access and cleaning layer, a data mapping and storage layer, a service output layer, and an upper-layer application layer. Specifically: the data source layer aggregates various types of raw data, including control-period data composed of static asset information during the control period, operational-period data composed of dynamic operational data, and external data such as meteorological information industry standards; the data access and cleaning layer enables multi-protocol data access and completes abnormal data cleaning and data format standardization; the data mapping and storage layer implements cross-stage data mapping with globally unified coding as its core, achieving the fusion storage of static asset attribute data and dynamic time-series operational data; the service output layer provides standardized data services such as API interfaces, visual dashboards, and message pushes; and the upper-layer application layer, based on the data output by the platform, supports various business applications such as asset management, fault diagnosis, and predictive maintenance.

[0044] For example, during the control period, the attribute data of assets such as submersible pumps and electric butterfly valves are derived from the equipment nameplates and manuals: submersible pump rated power 45kW, insulation class F, bearing model SKF 6309, lubricating oil grade ISO VG 32; electric butterfly valve lifespan 10,000 cycles, actuator torque 100Nm. During the operation period, dynamic operating data of the equipment is collected and standardized. Based on the mapping model, dynamic and static data are correlated and matched. Data is fused and matched on a per-asset basis to establish a correlation benchmark between static design parameters and time-series operating data, rather than simple data splicing. After structured reconstruction, the data is sent to the fusion platform for storage. Based on the established full lifecycle data model, the original design parameters during the control period can be transformed into operational control rules: establish standardized maintenance work order templates, set the submersible pump bearing grease to be replaced every 2000 hours of operation, and conduct valve plate sealing checks after 3000 cumulative opening and closing cycles of electric butterfly valves; configure a cumulative running time counter to automatically generate maintenance tasks when maintenance thresholds are reached; at the same time, bind spare parts information such as bearing models with the globally unique asset code and synchronize them to the EAM system to realize spare parts association management, and complete the business transformation of basic parameters during the control period into maintenance cycles, spare parts management parameters, and early warning thresholds during the operational period.

[0045] In one embodiment, the present invention configures an adaptive maintenance mechanism for scenarios where information changes dynamically throughout the entire lifecycle of assets. Both the first data asset repository and the second data asset repository are configured with a version management mechanism. When the attribute information of an asset object changes, a data snapshot is automatically generated and a change log is retained. The change content, change time, and operation subject are fully recorded and the change log is persistently retained. When an asset object undergoes modification and its structure or process parameters are adjusted, the corresponding asset association information in the mapping model is updated synchronously. When old equipment is removed and new equipment is brought in, resulting in a complete replacement of asset objects, a brand-new globally unique code is assigned to the new asset, and the association mapping relationship in the mapping model is updated synchronously to ensure that the mapping model continues to adapt to the current status of the assets.

[0046] In one embodiment, step S2 further includes constructing a BIM 3D design base model based on various business data collected during the project construction period, and storing the BIM 3D design base model in a second data asset library. The method also includes, after step S5, mapping and overlaying the fused data corresponding to each asset object onto the asset object corresponding to the BIM 3D design base model to obtain a 3D digital delivery model. Specifically, based on the fused full lifecycle data and combined with BIM (Building Information Modeling) technology, a 3D digital delivery model of the wastewater treatment plant is generated. This model overlays the 3D design model during the control period with real-time operational data during the operation period, achieving virtual visualization and real-time status mapping of the assets.

[0047] The method of this invention can provide integrated services based on full lifecycle data to upper-layer applications such as operation management platforms, asset management systems, inspection systems, and fault diagnosis systems through data service interfaces, supporting cross-stage data query, traceability analysis, equipment health prediction, and intelligent decision-making.

[0048] In one embodiment, for a water group comprising multiple wastewater treatment plants, each plant employs the method of this invention to achieve cross-stage fusion of plant control and operation data. A unified data aggregation platform is deployed at the group level, employing a flexible organizational permission configuration mechanism to balance the data isolation requirements of plant operations with the ability to exchange comprehensive operational data across plants, thereby achieving integrated management. Group management personnel can perform full lifecycle data aggregation and analysis, as well as cross-plant operational benchmarking analysis, within the unified platform according to multiple dimensions such as plant area, process section, and asset category. This provides data support for the group's overall asset optimization and allocation, and the standardization of operation and maintenance management strategies.

[0049] In one embodiment, hardware sensing devices such as dissolved oxygen sensors, pressure transmitters, and equipment current acquisition terminals deployed at various process points on site continuously collect real-time operating data of the equipment and upload it to the backend server. The server first performs automated preprocessing operations on the multi-source heterogeneous sensor data, uniformly calibrates the timestamp reference of the data reported by different acquisition terminals to complete the time sequence alignment, automatically identifies and removes abnormal sudden values ​​that exceed the physical operating range of the equipment, and at the same time, uses linear interpolation of adjacent time sequence data to compensate for the missing sampling points caused by short-term communication interruption of sensors, thereby eliminating the defects of time sequence misalignment and signal distortion in the original acquired data. After data preprocessing, the system does not rely on manual input of each asset and measuring point binding table to build a mapping relationship. Instead, it uses the plant area, process unit, and equipment type hierarchical information inherent in the four-level hierarchical global code of the equipment, combined with the process affiliation and monitoring object metadata built into the measuring point identifier. The program automatically retrieves all measuring point identifiers belonging to the same asset object, autonomously establishes the association between static asset attribute data during the control period and dynamic measuring point operation data during the operation period, and automatically generates an initial mapping model. Subsequently, when equipment is modified in the plant area or old equipment is replaced with new assets, the system automatically retrieves and matches the corresponding measuring points according to the addition and change of the global code of the asset, adaptively iteratively updates the mapping relationship, and finally completes the fusion of static asset archives and standardized dynamic operating condition data based on the real-time updated mapping model, and builds a complete equipment life cycle data model.

[0050] The method of this invention effectively solves the problem of data fragmentation between the control and operation phases of wastewater treatment plants by constructing a unified data standard system and a two-stage data mapping model, achieving seamless integration of asset data from construction to operation. When the operation team takes over a new plant, they can use the system to query all original information of the plant's assets during the control phase with one click, significantly shortening the familiarization period and reducing operational risks caused by missing information. When equipment failure occurs during the operation phase, maintenance personnel can trace and query historical information such as factory testing, installation and commissioning, and concealed works of the equipment during the control phase, greatly improving the accuracy and efficiency of fault diagnosis. With unified data coding as the core, a data model combining static attributes and dynamic history is constructed, providing a solid data foundation for refined management, predictive maintenance, and performance evaluation of assets throughout their entire lifecycle. The knowledge graph integrating data from both phases provides more comprehensive information support for operational optimization, process parameter adjustment, and asset update decisions.

[0051] It should be noted that the concepts of control period, operation period, attribute data standard, business data, data asset library, and full lifecycle data model in this paper are proprietary concepts defined by this invention in the context of wastewater treatment plant asset management. Specifically, the control period corresponds to the construction and preparation stage before the wastewater treatment plant equipment is put into operation, and the operation period corresponds to the formal operation stage of the plant. The attribute data standard is used to unify the data standards for static asset information. Business data includes static data such as equipment completion documents, nameplates, and technical manuals. The data asset library is used to uniformly store standardized asset-related data. The full lifecycle data model is a data model formed by integrating static asset attribute data and time-series dynamic operation data.

[0052] Example 2 Based on the same concept, embodiments of the present invention also provide a data management and control device for the entire lifecycle of a water treatment plant, the device comprising: The data access module is used to construct the first data asset database of the water treatment plant and establish a data standard specification system; the data standard specification system includes the attribute data specifications of each asset object and the preset dynamic operation data specifications of the asset object for the corresponding operating period. In the initial stage of management, all asset objects are given a globally unique code, and the asset code information is stored in the first data asset database.

[0053] This module completes its initial configuration during the project initiation phase. Based on the process characteristics of the wastewater treatment plant, all plant assets are systematically categorized, including but not limited to: process equipment (such as bar screens, lift pumps, aerators, sludge scrapers, dewatering machines, etc.), buildings and structures (such as influent wells, grit chambers, biological treatment tanks, secondary sedimentation tanks, disinfection tanks, sludge thickening tanks, etc.), pipelines and valves, instruments and meters (such as pH meters, dissolved oxygen sensors, ammonia nitrogen analyzers, COD analyzers, flow meters, level gauges, etc.), and electrical control systems.

[0054] Each type of asset object generates a unique data code according to the hierarchical rule of "plant area code - process unit code - asset category code - serial number". At the same time, the data type, unit format, collection frequency and effective value range of each data item are uniformly specified.

[0055] The data cleaning and quality control module is used to collect various business data during the construction and equipment installation phases of the project construction period, and to build a second data asset library based on the business data; the second data asset library includes attribute data of each asset object.

[0056] During the construction and management phase of the wastewater treatment plant, the data collection module uses mobile terminals or PC applications as carriers, and project managers input structured data in stages during the construction and installation process.

[0057] Taking a submersible mixer in a biological treatment tank as an example, the data collected during the control period includes: Static attributes: equipment code (e.g., "Factory A-Biological Unit 01-Agitator-001"), equipment name, model specifications, manufacturer, serial number, date of manufacture, and warranty period; Installation information: installation location coordinates, installation elevation, installation date, installers, and tightening torque; Commissioning information: no-load test current value, load test current value, vibration value, noise value, and commissioning conclusion; Document attachments: equipment manual (PDF), factory inspection report, installation acceptance form, and as-built drawing number. After review, the above data is stored in the control period data asset database.

[0058] The data mapping and association module is used to construct a mapping model based on the globally unique code. This model, based on the globally unique code, establishes a link between attribute data of asset objects during the management and control period and dynamic operational data to be collected during the operation period. This module uses the unified data code as the core index key to establish a two-way association between static asset data in the management and control period data asset repository and dynamic operational data during the operation period.

[0059] The operation period data acquisition module is used to collect dynamic operation data of each asset object in real time during the operation period, and to standardize the dynamic operation data to obtain standardized operation data.

[0060] After the wastewater treatment plant enters the operational phase, the operational data acquisition module collects operational data in real time through sensing devices deployed in each process unit. Specifically, online monitoring instruments for COD, ammonia nitrogen, pH, and flow rate are installed at the inlet; dissolved oxygen sensors, sludge concentration meters, and oxidation-reduction potentiometers are installed in the biological treatment tank; level gauges and sludge interface meters are installed in the secondary sedimentation tank; online monitoring instruments for COD, ammonia nitrogen, total phosphorus, total nitrogen, and pH are installed at the effluent outlet; and operational status (start / stop, frequency, current, temperature, vibration, etc.) and energy consumption data are collected from each major piece of equipment via PLC. All collected data is transmitted to the data fusion platform via industrial Ethernet or 4G / 5G networks, and undergoes semantic parsing, tag normalization, and unit unification according to the specifications defined by the data standard management module.

[0061] After collecting dynamic operational data, the specific mapping logic is as follows: When the operational data acquisition module receives a new operational data point (e.g., the current value of equipment coded "Plant A-Biochemical Unit 01-Agitator-001"), this data point is automatically associated with all static attribute records of the same coded equipment in the control period data asset library. Operations personnel can view the current operating current value in the equipment's visual interface, and can also jump to view historical records such as factory inspection reports, installation and commissioning records, and warranty information during the control period with a single click. Conversely, starting from the control period asset list, one can also trace the entire operational history of the asset during the operational period (cumulative runtime, fault records, maintenance work orders, energy consumption statistics, etc.).

[0062] The full lifecycle data model construction module is used to perform association matching between the standardized operational data and attribute data based on the mapping model; and to construct a full lifecycle data model based on the matched data.

[0063] A cross-stage data fusion platform is constructed, comprising a data access module, a data cleaning and quality control module, a data mapping and association module, a data fusion storage module, and a data service output module. The data fusion storage module supports multi-protocol access for structured, semi-structured, and unstructured data, and is compatible with industrial IoT protocols such as Modbus, OPC UA, MQTT, and HTTP. Using the unified data encoding as the key, it associates and merges structured static asset data from the control phase with dynamic operational data from the operation phase, forming a data model that combines static attributes and dynamic history covering the entire asset lifecycle. The data cleaning and quality control module removes outliers, marks or imputes missing values, and performs graded assessments of data quality. The data mapping and association module invokes the association logic of the cross-stage mapping module to bind data from the control phase to the operation phase. The data fusion storage module employs a hybrid storage architecture using static attribute tables, dynamic time-series tables, and document index tables, with the unified data encoding as the primary key, to achieve the fusion storage of data from both phases. In one embodiment, the data service output module provides data query, data analysis, and event subscription services to upper-layer applications through a standard RESTful API interface. This unified processing platform effectively addresses pain points in traditional wastewater treatment plants, such as fragmented data sources, inconsistent data quality, and information silos across different stages, providing a solid and reliable data foundation for upper-layer intelligent analysis and applications.

[0064] In one embodiment, the system further includes an application service module, which includes, but is not limited to, the following sub-modules: building an asset lifecycle management system, using a joint data model of static attributes and dynamic history as the core to achieve full-process electronic file management of assets from procurement, installation, commissioning, operation, maintenance to disposal; constructing a visualized asset map, using GIS and BIM technologies to visualize the integrated data in three dimensions, allowing maintenance personnel to select target assets within the three-dimensional model and retrieve the corresponding asset's static attribute information and real-time operating status; deploying a cross-stage fault tracing system, automatically linking and pushing commissioning parameters from the asset management period, factory inspection data, historical maintenance records, and real-time operating parameters when equipment malfunctions, supporting root cause analysis of the fault; and building an intelligent maintenance decision-making system, establishing an equipment health prediction model based on the integrated lifecycle data, outputting predictive maintenance alarm information, and pushing appropriate maintenance strategies.

[0065] Example 3 Based on the same concept, embodiments of the present invention also provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in Embodiment 1 above.

[0066] Example 4 Based on the same concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in Embodiment 1 above.

[0067] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0068] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0069] The above embodiments should be understood as being used only to illustrate the present invention more clearly, and not to limit the scope of the present invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art fall within the scope defined by the appended claims.

Claims

1. A data management and control method for the entire lifecycle of a water treatment plant, characterized in that, Includes the following steps: S1: Construct the first data asset database of the water treatment plant and establish a data standard specification system; the data standard specification system is constructed through the back-end server, including the attribute data specifications of each asset object and the preset operating period dynamic operation data specifications corresponding to the asset object, the preset operating period dynamic operation data specifications include the measurement point identifiers corresponding to the operating period dynamic operation data to be collected; In the initial stage of management and control, all asset objects are given a globally unique code, and the asset code information is stored in the first data asset database. S2: During the construction and equipment installation phase of the control period, collect various business data during the project construction period, and construct a second data asset library based on the business data; the second data asset library includes attribute data of each asset object; S3: Construct a mapping model on the server side based on the globally unique code, wherein the mapping model establishes an association between the attribute data of the asset object during the management period and the measurement point identifiers corresponding to the dynamic operation data to be collected during the operation period based on the globally unique code; S4: During the operation period, collect dynamic operation data of each asset object in real time, and perform standardized processing on the dynamic operation data to obtain standardized operation data; S5: Based on the mapping model, perform association matching between the standardized operational data and attribute data; and construct a full lifecycle data model based on the matched data.

2. The data management and control method for the entire life cycle of a water treatment plant according to claim 1, characterized in that, The construction of a full lifecycle data model based on the matched data includes: merging the matched data of each asset object to obtain the fused data corresponding to each asset object; and reconstructing and storing the fused data of each asset object in a structured manner to obtain the full lifecycle data model.

3. The data management and control method for the entire life cycle of a water treatment plant according to claim 2, characterized in that, S2 also includes constructing a BIM 3D design base model based on various business data collected during the project construction period, and storing the BIM 3D design base model in the second data asset library; The method further includes, after S5, mapping and overlaying the fused data corresponding to each asset object onto the asset object corresponding to the BIM 3D design base model to obtain a 3D digital delivery model.

4. The data management and control method for the entire life cycle of a water treatment plant according to claim 1, characterized in that, In S4, the dynamic operation data is standardized according to the data standard specification system to obtain standardized operation data.

5. The data management and control method for the entire life cycle of a water treatment plant according to claim 1, characterized in that, In S4, if the missing dynamic running data is detected, breakpoint resume processing is performed; And based on the characteristics of asset operation, the missing dynamic operation data is supplemented.

6. The data management and control method for the entire life cycle of a water treatment plant according to claim 1, characterized in that, S4 also includes retrieving asset attribute data corresponding to the control period based on the dynamic operation data during the operation period, using the mapping model.

7. The data management and control method for the entire life cycle of a water treatment plant according to claim 1, characterized in that, Both the first and second data asset repositories are configured with version management mechanisms; when the attribute information of an asset object changes, a data snapshot is automatically generated and a change log is retained. When an asset object is modified, the mapping model is updated synchronously. When an asset object is replaced, the globally unique code and the mapping model are updated synchronously.

8. A data management and control device for the entire life cycle of a water treatment plant, used to implement the method according to any one of claims 1 to 7, characterized in that, The device includes: The data access module is used to construct the first data asset database of the water treatment plant and establish a data standard specification system; the data standard specification system includes the attribute data specifications of each asset object and the preset dynamic operation data specifications of the asset object for the corresponding operating period. In the initial stage of management and control, all asset objects are given a globally unique code, and the asset code information is stored in the first data asset database. The data cleaning and quality control module is used to collect various business data during the construction and equipment installation phases of the project construction period, and to build a second data asset library based on the business data; the second data asset library includes attribute data of each asset object; The data mapping and association module is used to construct a mapping model based on the globally unique code, wherein the mapping model establishes an association between the attribute data of the asset object during the management period and the dynamic operation data to be collected during the operation period based on the globally unique code; The operation period data acquisition module is used to collect dynamic operation data of each asset object in real time during the operation period, and to standardize the dynamic operation data to obtain standardized operation data. The full lifecycle data model construction module is used to perform association matching between the standardized operational data and attribute data based on the mapping model; and to construct a full lifecycle data model based on the matched data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

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