Construction method and electronic equipment of digital twin intelligent pump station integrated application platform

Abstract

The present application provides a method for constructing an integrated application platform of a digital twin intelligent pump station and an electronic device, which relates to the field of intelligent water conservancy facilities, and solves the technical problems of the lack of cross-system data integration capabilities and low energy efficiency of related technical data. The method includes: constructing a three-dimensional visual digital base of a pump station based on BIM and GIS; collecting pump station data based on the three-dimensional visual digital base of the pump station; constructing an equipment health prediction model based on an improved LSTM algorithm, and the equipment health prediction model is used to dynamically weight the key features extracted from the pump station data according to the time series through the attention mechanism; performing data analysis on the pump station data according to the equipment health prediction model to obtain prediction results; dynamically rendering the operating status and fault location information of the pump station equipment based on the prediction results and the three-dimensional visual digital base of the pump station, and triggering an alarm operation based on the alarm threshold. The present application is used in the process of constructing a water conservancy facility platform.

Description

Technical Field

[0001] The present application relates to the field of intelligent water conservancy facilities, and in particular to a method for constructing an integrated application platform for digital twin intelligent pumping stations and electronic equipment. Background Art

[0002] Current pump station management systems generally utilize independent supervisory control and data acquisition (SCADA) modules. These modules deploy sensor networks to collect real-time data on key parameters such as water level, flow rate, and equipment status, and integrate them with programmable logic controllers to automate pump and valve control. However, various pump station subsystems (such as power monitoring, security systems, and hydrological monitoring) often utilize heterogeneous technology standards, creating data silos. Consequently, current technical solutions lack cross-system data integration capabilities, making it difficult to achieve multi-system data fusion and 3D visualization interaction. This results in high pump station operation and maintenance costs and low energy efficiency. Summary of the Invention

[0003] This application provides a method for constructing a digital twin intelligent pump station integrated application platform and electronic equipment, which solves the technical problems of the existing technology that related technical data lacks cross-system data integration capabilities and has low energy efficiency.

[0004] To achieve the above objectives, this application adopts the following technical solutions:

[0005] In the first aspect, a method for constructing an integrated application platform of a digital twin intelligent pump station is provided, comprising: constructing a three-dimensional visualization digital base of a pump station based on a building information model (BIM) and a geographic information system (GIS); the three-dimensional visualization digital base of a pump station is used to establish a spatial mapping relationship between the physical structure of the pump station and the geographical structure in which the pump station is located; the physical structure of the pump station includes at least one of a pump station building, pump station equipment, and a pump station pipeline; the geographical structure includes at least one of topography and the surrounding environment; collecting pump station data based on the three-dimensional visualization digital base of the pump station; the pump station data includes pump station monitoring data, pump station water condition data, and pump station pipeline data. At least one item of the management information data; constructing an equipment health prediction model based on an improved long short-term memory network LSTM algorithm, the equipment health prediction model is used to dynamically weight the key features extracted from the pump station data according to the time series through the attention mechanism; performing data analysis on the pump station data according to the equipment health prediction model to obtain a prediction result; dynamically rendering the operating status and fault location information of the pump station equipment based on the prediction result and the three-dimensional visualization digital base of the pump station, and triggering an alarm operation based on the alarm threshold; the alarm threshold includes at least one of the equipment vibration threshold, the temperature threshold and the flow range threshold.

[0006] Based on the above technical solution, in the embodiment of the present application, the electronic device can construct a three-dimensional visual digital base of the pump station based on the building information modeling (BIM) and the geographic information system (GIS), and collect pump station data such as pump station monitoring data, pump station water condition data, and pump station management information data based on the three-dimensional visual digital base of the pump station. At the same time, the electronic device can also construct an equipment health prediction model based on the improved long short-term memory network (LSTM) algorithm. The equipment health prediction model dynamically weights the key features extracted from the pump station data according to the time series through the attention mechanism. In this way, the electronic device can analyze the pump station data according to the equipment health prediction model to obtain a prediction result. Afterwards, based on the prediction result and the three-dimensional visual digital base of the pump station, the operating status and fault location information of the pump station equipment are dynamically rendered, and an alarm operation is triggered based on the alarm threshold. Among them, the three-dimensional visualization digital base of the pump station establishes a spatial mapping relationship between the physical structure of the pump station and the geographical structure where the pump station is located. Through this method, the embodiment of the present application realizes the comprehensive integration and three-dimensional visualization of pump station data. With the help of the improved LSTM model, the accuracy of equipment health prediction is improved, potential faults can be discovered in advance, and alarms and linkage measures can be triggered in time, which effectively improves the operation and maintenance management efficiency of the pump station, reduces the equipment failure rate, and improves the safe, efficient and stable operation rate of the pump station.

[0007] In combination with the above-mentioned first aspect, in one possible implementation, the device health prediction model includes an LSTM module and an attention mechanism module; the LSTM module is composed of a forget gate, an input gate, and an update memory unit; wherein the forget gate is used to adjust the retention amount of historical data features; the input gate is used to adjust the amount of new data added; the update memory unit is used to generate current data features based on the forget gate and the input gate; the attention mechanism module is used to weight the generated data features.

[0008] In combination with the first aspect above, in a possible implementation, the forget gate satisfies the following formula:

[0009]

[0010] in, Indicates the current time step t The retention amount, W f represents the weight matrix of the forget gate, represents the bias term of the forget gate, σ represents the activation function, ht 1 indicates the previous time step t A hidden state of -1, x t Indicates the current time step t Input data;

[0011] The input gate satisfies the following formula:

[0012]

[0013] in, i t Indicates the current time step t The amount of new data added when W i represents the weight matrix of the input gate, b i Represents the bias term of the input gate; is a candidate memory unit, used to represent the new data calculated at the current time step t; W C represents the weight matrix of the candidate memory unit, b C represents the bias term of the candidate memory unit, ht 1 indicates the previous time step t A hidden state of -1, x t Indicates the current time step t Input data;

[0014] The update memory unit satisfies the following formula:

[0015]

[0016] in, C t Indicates the current time step t Update memory unit when f t Indicates the current time step t The retention amount, C t-1 Represents the previous time step t -1 update memory unit, i t Indicates the current time step t The amount of new data added when is a candidate memory unit, ⊙ represents element-by-element multiplication;

[0017] The attention mechanism module satisfies the following formula:

[0018]

[0019] in, α t Indicates the current time step t The attention weight vector when ; Q represents the query matrix, which is used to retrieve the key features in the input sequence; K Represents the key matrix, which is used to calculate the correlation of each element of the input sequence; d k represents the dimension scaling factor, S t represents the weighted eigenvector; V Represents a value matrix used to store the feature vectors of the input sequence.

[0020] In combination with the first aspect above, in a possible implementation, the method includes: performing feature fusion on the vibration spectrum data and real-time monitoring data in the pump station data, and performing feature extraction through the feature extractor in the equipment health prediction model; performing equipment health prediction based on the extracted feature data to obtain a prediction result.

[0021] In combination with the first aspect above, in a possible implementation method, the feature extractor is constructed based on a cross-pumping station equipment failure knowledge base of transfer learning; the cross-pumping station equipment failure knowledge base includes pumping station data of the original pumping station and pumping station data of the pumping station to be trained.

[0022] In combination with the first aspect above, in a possible implementation, the platform includes a blade angle adjustment system, an excitation control system, and a fire linkage module; wherein the blade angle adjustment system is used to adjust the blade angle based on the operating parameters of the pump station; the excitation control system is used to perform service life analysis based on the parameters of different components of the excitation device, and perform demagnetization operations when an internal fault occurs in the motor; the fire linkage module is used to trigger a safe evacuation guidance announcement when receiving a fire alarm signal, and control the access control system to perform evacuation operations.

[0023] In combination with the first aspect above, in one possible implementation, the blade angle adjustment system adjusts the blade angle to satisfy the following formula:

[0024] η=α flow ²+b flow + H+ θ+

[0025] Among them, η represents the pump efficiency, flow represents flow rate, H represents head, θ represents blade angle, α, b, 、 、 are fitting coefficients.

[0026] In combination with the first aspect above, in a possible implementation, the platform also includes a three-dimensional simulation system, an equipment management system and a visual inspection system: wherein the three-dimensional simulation system is used to simulate the water flow state and structural stress distribution under different working conditions based on the imported BIM model data; the equipment management system is used to manage the basic information of each engineering equipment; the visual inspection system is used to perform first-person perspective and third-person perspective roaming operations, and perform automatic inspection operations in the platform according to the set inspection route.

[0027] In combination with the first aspect above, in a possible implementation, the platform also includes an engineering archive management system and a mobile management application system; wherein, the engineering archive management system is used to store and maintain the engineering archive data of the pump station, and the mobile management application system is used to remotely query monitoring data in real time.

[0028] In a second aspect, an electronic device is provided, comprising: a communication unit and a processing unit; the processing unit is used to construct a three-dimensional visualization digital base of a pump station based on a building information model (BIM) and a geographic information system (GIS); the three-dimensional visualization digital base of a pump station is used to establish a spatial mapping relationship between the physical structure of the pump station and the geographical structure in which the pump station is located; the physical structure of the pump station includes at least one of a pump station building, pump station equipment, and a pump station pipeline; the geographical structure includes at least one of topography and surrounding environment; the communication unit is used to collect pump station data based on the three-dimensional visualization digital base of the pump station; the pump station data includes pump station monitoring data, pump station water condition data, and pump station management information data At least one item; the processing unit is used to construct an equipment health prediction model based on an improved long short-term memory network LSTM algorithm, and the equipment health prediction model is used to dynamically weight the key features extracted from the pump station data according to the time series through an attention mechanism; the processing unit is used to perform data analysis on the pump station data according to the equipment health prediction model to obtain a prediction result; the processing unit is used to dynamically render the operating status and fault location information of the pump station equipment based on the prediction result and the three-dimensional visualization digital base of the pump station, and trigger an alarm operation based on the alarm threshold; the alarm threshold includes at least one of an equipment vibration threshold, a temperature threshold, and a flow range threshold.

[0029] In a third aspect, the present application provides an electronic device comprising: a processor and a storage medium; the storage medium comprising instructions, the processor configured to execute the instructions to implement the method described in the first aspect and any possible implementation of the first aspect. The electronic device may be an electronic device or a chip within the electronic device.

[0030] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed on an electronic device, the electronic device executes the method described in the first aspect and any possible implementation of the first aspect.

[0031] In a fifth aspect, the present application provides a computer program product comprising instructions, which, when executed on an electronic device, enables the electronic device to execute the method as described in the first aspect and any possible implementation manner of the first aspect.

[0032] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in this application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution or beneficial effect is included in at least one embodiment. Therefore, the description of a technical feature, technical solution or beneficial effect in this specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the present embodiment can also be combined in any appropriate manner. Those skilled in the art will understand that the embodiment can be implemented without one or more specific technical features, technical solutions or beneficial effects of a specific embodiment. In other embodiments, additional technical features and beneficial effects can also be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A system architecture diagram of a digital twin intelligent pump station integrated application platform system provided in an embodiment of the present application;

[0034] Figure 2 A logical distribution diagram of a digital twin intelligent pump station integrated application platform provided in an embodiment of the present application;

[0035] Figure 3 A flowchart of a method for constructing a digital twin intelligent pump station integrated application platform provided in an embodiment of the present application;

[0036] Figure 4 A flowchart of another method for constructing a digital twin intelligent pump station integrated application platform provided in an embodiment of the present application;

[0037] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0038] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In the description of this application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" means one or more, and "a plurality" means two or more. Words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not limit them to be different.

[0040] It should be noted that, in this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described in this application as "exemplary" or "for example" should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0041] Traditional pumping stations face numerous challenges in operation and management. For one thing, data silos exist between systems, making it difficult to communicate and share data generated by different devices and subsystems, leading to low overall operational efficiency. Furthermore, fault response is delayed, often requiring only after equipment failures occur. This prevents potential problems from being anticipated, increasing maintenance costs and downtime. Furthermore, the lack of effective predictive maintenance methods makes it difficult to conduct real-time assessments and trend analysis of equipment health, resulting in a lack of full lifecycle management for pumping station equipment.

[0042] Current pump station management systems generally utilize independent supervisory control and data acquisition (SCADA) modules. These modules deploy sensor networks to collect real-time data on key parameters such as water level, flow rate, and equipment status, and integrate them with programmable logic controllers to automate pump and valve control. However, various pump station subsystems (such as power monitoring, security systems, and hydrological monitoring) often utilize heterogeneous technology standards, creating data silos. Consequently, current technical solutions lack cross-system data integration capabilities, making it difficult to achieve multi-system data fusion and 3D visualization interaction. This results in high pump station operation and maintenance costs and low energy efficiency.

[0043] In view of this, the embodiment of the present application provides a method for constructing a digital twin intelligent pump station integrated application platform. The electronic device can construct a three-dimensional visualized digital base of the pump station based on BIM and GIS, and collect pump station data such as pump station monitoring data, pump station water condition data, and pump station management information data based on the three-dimensional visualized digital base of the pump station. At the same time, the electronic device can also construct an equipment health prediction model based on the improved LSTM algorithm. The equipment health prediction model dynamically weights the key features extracted from the pump station data according to the time series through the attention mechanism. In this way, the electronic device can perform data analysis on the pump station data according to the equipment health prediction model to obtain prediction results. Afterwards, based on the prediction results and the three-dimensional visualized digital base of the pump station, the operating status and fault location information of the pump station equipment are dynamically rendered, and the alarm operation is triggered based on the alarm threshold. Among them, the three-dimensional visualization digital base of the pump station establishes a spatial mapping relationship between the physical structure of the pump station and the geographical structure where the pump station is located. Through this method, the embodiment of the present application realizes the comprehensive integration and three-dimensional visualization of pump station data. With the help of the improved LSTM model, the accuracy of equipment health prediction is improved, potential faults can be discovered in advance, and alarms and linkage measures can be triggered in time, which effectively improves the operation and maintenance management efficiency of the pump station, reduces the equipment failure rate, and improves the safe, efficient and stable operation rate of the pump station.

[0044] The following will describe in detail the implementation of the embodiment of the present application in conjunction with the accompanying drawings.

[0045] The construction method of the digital twin intelligent pump station integrated application platform provided in the embodiment of the present application can be applied to Figure 1 In the digital twin intelligent pump station integrated application platform system shown in Figure 1 As shown, the system consists of three parts: data center (bottom layer), basic services (middle layer) and system applications (upper layer).

[0046] The data center is the data support foundation of the platform, responsible for the full life cycle management of data and ensuring data availability and reliability. The data center includes the following functional modules:

[0047] Data storage and access: Data storage and access are used to build data warehouses, supporting real-time databases (storing data updated in seconds, such as pump group vibration and flow), and historical databases (storing equipment operation and maintenance records, historical water conditions data, etc.), to meet different time-sensitive data requirements.

[0048] Cross-region data synchronization: Cross-region data synchronization is used to break the limitations of physical areas, such as achieving data interoperability between different pumping stations and different management areas, thereby realizing cross-regional pumping station data sharing.

[0049] Data backup and recovery: Data backup and recovery is used for regular backups (such as daily / weekly full backups). In the event of a failure (hard drive damage, accidental deletion), data can be restored to ensure business continuity.

[0050] Data standardization management: Data standardization management is used to classify and encode collected multi-source data (sensors, manual input, etc.) according to specifications (such as industry standards and custom formats), so that the data can be organized into a unified spectral feature format.

[0051] Model management: Model management is used to store various models required for the maintenance system, such as equipment health prediction models (such as improved LSTM models) and hydraulic simulation models. It supports the application of algorithms for upper-level functions, such as using models to predict water pump bearing failures.

[0052] Basic services are used to connect data centers and system applications, provide general technical capabilities, and make upper-level function development more efficient. This basic service includes the following functional modules:

[0053] Data acquisition and processing: Data acquisition and processing is used to collect raw data (vibration, temperature, video, etc.) from the perception layer (sensors, cameras, etc.), perform cleaning (noise removal, missing data filling), and conversion (such as converting vibration time domain data to the frequency domain) to provide clean and usable data for the upper layer.

[0054] Human-machine interface: The human-machine interface is used to design a visual operation interface (web page, mobile application), allowing operation and maintenance personnel to operate and view data intuitively, such as dragging a three-dimensional model on the interface to view the internal structure of the pump station.

[0055] Alarm service: The alarm service is used to set multi-level alarm rules (such as equipment vibration exceeding the threshold, abnormal water level). When the alarm is triggered, it can be linked to multiple methods (pop-up window, SMS, sound and light) for alarm. It can also be linked to emergency plans, such as automatically pushing a maintenance work order when the vibration exceeds the threshold.

[0056] Permission management: Permission management is used to assign permissions to different roles (administrators, operators, and visitors), control data access and modification permissions, and ensure data security. For example, visitors can only view the public pump station operation overview, while administrators can modify system configurations.

[0057] Network management: Network management is used to protect the platform network, monitor network status (bandwidth, latency), and achieve load balancing of network bandwidth. For example, when data from multiple pumping stations is uploaded concurrently, network resources can be reasonably allocated to avoid congestion.

[0058] Log management: Log management is used to record platform operation data, operation logs, and operation logs, thereby realizing fault tracing and auditing. For example, when checking for equipment misoperation, the log can be checked to see the operator and time.

[0059] Chart component: The chart component is used to provide rich visualization tools (line charts, bar charts, heat maps) to make data more intuitive. For example, a line chart can be used to show the trend of water pump efficiency changing with flow rate, assisting in optimizing operation.

[0060] Full life cycle management: Full life cycle management is used to cover the entire process of equipment from procurement, installation, operation and maintenance to scrapping, record equipment history, and support preventive maintenance. For example, based on the length of equipment use and the number of failures, it can predict when the entire equipment should be replaced.

[0061] 3D engine: The 3D engine is used to drive the digital twin 3D model, enabling model loading and interaction (rotation, scaling, and disassembly). It can also map device status in real time (such as using colors to mark device temperatures), allowing operation and maintenance personnel to inspect problems more intuitively.

[0062] External information release: External information release can output platform data (after desensitization) or results (such as pump station optimization reports) through official websites, API interfaces, etc. for supervision by relevant units and scientific research cooperation. For example, data interfaces can be opened to water conservancy departments to synchronize the real-time water level of pump stations.

[0063] GIS components: GIS components are used to integrate geographic information, locate pumping stations on maps, and display the surrounding environment (rivers, roads). They can also be combined with pumping station data (such as drainage range) for spatial analysis. For example, GIS can be used to determine whether there are gaps in the drainage coverage area of ​​each pumping station during heavy rain.

[0064] Workflow management: Workflow management is used to define maintenance processes (repair reporting - dispatching - inspection - acceptance), automatically transfer tasks, and track progress. For example, after a device alarm occurs, a repair report is automatically generated and dispatched to the corresponding operation and maintenance team.

[0065] The system applications mainly include intelligent monitoring platform, intelligent operation and management platform and intelligent monitoring platform. It is a collection of functions for users (operation and maintenance, management, decision makers, etc.), combined with pump station business scenarios to solve practical problems.

[0066] Among them, the intelligent monitoring platform includes the following systems:

[0067] Intelligent host monitoring system: The intelligent host monitoring system is used to monitor the core parameters (speed, current, vibration) of the pump station host (water pump, motor), and respond quickly to abnormalities, such as sudden changes in motor current, triggering shutdown protection.

[0068] Intelligent blade angle adjustment system: The intelligent blade angle adjustment system is used to dynamically adjust the blade angle according to the pump station operating parameters (flow rate, head) to optimize the water pump efficiency. For example, when the flow rate increases, the angle is automatically adjusted to avoid reduced efficiency.

[0069] Intelligent gate control system: The intelligent gate control system is used to remotely / intelligently control the opening and closing of the gate, and the opening degree, and coordinate with water conditions. For example, during flood season, the gate can be automatically adjusted to discharge flood water according to the water level.

[0070] Intelligent technology water supply system: The intelligent technology water supply system is used to ensure water supply (cooling, lubrication) within the pump station, monitor water supply pressure and water quality, and alarm when there is water shortage / poor water quality. For example, if the cooling water pipe is blocked, low pressure will trigger the water supply pipe to start.

[0071] Intelligent drainage control system: The intelligent drainage control system is used to ensure internal drainage control of the pumping station and ensure that the water level of the pumping station is at a reasonable level.

[0072] Intelligent sewage cleaning control system: The intelligent sewage cleaning control system is used to process the water inlet of the pump station (filter impurities and adjust water quality) to ensure equipment safety. For example, when the sand content of the inlet water is high, multi-stage filtration is automatically activated.

[0073] Intelligent power distribution control system: The intelligent power distribution control system is used to manage the power distribution of the pump station, monitor the status of the distribution cabinet and power consumption, and switch to the backup power supply in the event of an abnormal power outage to ensure uninterrupted operation.

[0074] Intelligent fire control system: The intelligent fire control system is used to monitor fire hazards (smoke, temperature) in real time, automatically link in case of fire (fire extinguishing device activated, access control opened), and trigger safe evacuation. For example, if the distribution cabinet smokes, the fire extinguishing gas will be automatically sprayed and the escape route will be opened at the same time.

[0075] Intelligent lighting control system: The intelligent lighting control system is used to control the pump station lighting on demand (zoning, timing, sensing), which is energy-saving and convenient. For example, during night inspections, the inspection route lights are automatically lit.

[0076] Intelligent ventilation control system: The intelligent ventilation control system is used to adjust the air quality and temperature in the pumping station. For example, when the temperature in the equipment area is high, the fan will be automatically turned on to ventilate and cool down.

[0077] Intelligent excitation control system: The intelligent excitation control system is used to perform service life analysis based on the parameters of different components of the excitation device and perform deexcitation operation when there is an internal fault in the motor.

[0078] Intelligent dehumidification control system: The intelligent dehumidification control system is used to monitor the humidity of the pump station. When the humidity is too high (such as condensation in the equipment area), the dehumidifier is automatically started to protect the electrical equipment.

[0079] The intelligent operation management platform includes the following systems:

[0080] Intelligent video surveillance and security system: The intelligent video surveillance and security system monitors the entire pump station through cameras, uses AI to identify anomalies (personnel intrusion, equipment displacement), and can also link access control and alarms. For example, if a stranger enters the equipment area, it will automatically capture the photo and trigger an audible and visual alarm.

[0081] Pumping station economic operation system: The intelligent video surveillance and security system is used to analyze the energy consumption and efficiency data of the pumping station, determine the optimal operation mode, reduce costs and increase efficiency. For example, the energy consumption of different pump combinations at different time periods can be compared to implement the optimal solution.

[0082] 3D simulation and training system: Based on the BIM model, the 3D simulation and training system simulates the operation of the pump station under different working conditions (rainstorm, low water) for employee training and emergency plan drills.

[0083] Smart workbench management system: The smart workbench management system integrates various data and tools to create a "one-stop" workbench for operation and maintenance. For example, in one interface, you can view equipment status, check operation and maintenance records, and issue work orders without switching systems back and forth.

[0084] Intelligent operation management system: The intelligent workbench management system is responsible for planning and tracking operation and maintenance operations (inspections and repairs), recording the operation process, and generating reports. For example, during inspections, employees use the app to punch in and fill in questions, and the background automatically summarizes them into an inspection report.

[0085] Intelligent equipment management system: The intelligent equipment management system is used to manage the entire life cycle of equipment, from procurement and filing to operation and maintenance records and scrap assessment. For example, when the equipment is about to reach the end of its service life, it will remind you to purchase a replacement in advance.

[0086] Intelligent comprehensive data analysis system: The intelligent comprehensive data analysis system is used to conduct in-depth analysis of collected data (equipment, water conditions, operation and maintenance), discover patterns (equipment failure cycles, water condition change trends), and support decision-making. For example, it can analyze flood season data from previous years, predict this year's drainage pressure, and add equipment in advance.

[0087] 3D visual retrieval system: The 3D visual retrieval system is used to quickly retrieve equipment and pipelines in a 3D model (for example, searching for "pump bearing No. 3" directly locates the model position), associate equipment files and real-time data, and facilitate operation and maintenance information retrieval.

[0088] The intelligent monitoring platform includes the following systems:

[0089] Main unit device efficiency real-time monitoring system: The main unit device efficiency real-time monitoring system is used to monitor the operating efficiency of the current pump station equipment in real time to ensure that the entire pump station can operate efficiently.

[0090] Host group full life cycle monitoring and health assessment system: The host group full life cycle monitoring and health assessment system is used to combine operating data and environmental factors to predict the host life and evaluate its health, such as counting the number of motor starts and stops and the load rate, predicting the remaining service life, and assisting in equipment replacement decisions.

[0091] Intelligent Engineering Safety Monitoring and Analysis System: The intelligent engineering safety monitoring and analysis system is used to monitor pump station buildings and structural safety (such as foundation settlement and pipeline stress) and analyze safety risks. For example, sensors are used to measure the displacement of the pump room foundation, and if the threshold is exceeded, an early warning of structural safety issues will be issued.

[0092] Intelligent water condition monitoring system: The intelligent water condition monitoring system is used to collect water condition data such as water level, flow, and water quality, provide real-time warnings (such as when the water level exceeds the warning level), and support scheduling decisions. For example, when the river water level rises, the pump station is linked to add drainage pumps.

[0093] Intelligent construction management system: The intelligent construction management system is used to manage new construction and renovation projects of pump stations, from planning and construction to acceptance, tracking progress and quality. For example, when building a new unit of a pump station, the construction progress and material usage can be monitored in real time to ensure that the project is completed on time and with high quality.

[0094] General office system: The general office system is used to provide daily office tools (process approval, document collaboration), making the pump station management process online. For example, leave and reimbursement processes can be submitted and approved in the system.

[0095] Engineering Archives Management System: The Engineering Archives Management System is used to digitally store pump station engineering archives (design drawings, completion data, operation and maintenance records), making it easy to query and access. For example, if you want to check the pump station renovation drawings from 5 years ago, you can directly search and download them in the system.

[0096] Human Resources Management System: The Human Resources Management System is used to manage pump station personnel information, attendance, training, and performance, such as scheduling training courses for operation and maintenance personnel (3D simulation system operation), tracking learning progress, and evaluating performance.

[0097] Mobile APP management system: The mobile APP management system is used to migrate platform functions to mobile devices, supporting mobile inspection, data viewing, and alarm reception. Operation and maintenance personnel can use their mobile phones to view equipment data and report problems on site.

[0098] Information release system: The information release system is used to issue notices and announcements (such as system upgrades and maintenance arrangements) internally (to pump station employees) and to release compliance information (such as whether the pump station's drainage meets standards) externally (to the public and regulatory authorities), making information delivery more timely.

[0099] As a possible embodiment, Figure 2 As shown, the digital twin intelligent pump station integrated application platform provided by the embodiment of the present application can be divided into the following three layers according to the data integration method: perception layer, transmission layer and application layer. Among them, the transmission layer and the perception layer are interconnected, and the application layer and the transmission layer are interconnected.

[0100] The perception layer is used to collect basic data through data collection devices. Examples include barcode readers (for rapid device identification by scanning device barcodes), sensor nodes (for measuring vibration, temperature, water level, etc.), radio frequency identification (RFID) readers / writers (for device identity / status), and cameras (for video surveillance). For example, IoT devices such as micro-electro-mechanical system (MEMS) triaxial vibration sensors (sampling frequency 50kHz) and infrared thermal imagers (resolution 3840×2160) can be deployed.

[0101] The transport layer is responsible for data transmission and network interoperability, connecting the perception layer and the application layer. For example, the transport layer can be composed of wired networks, wireless networks, satellite networks, or other heterogeneous networks. For example, the transport layer can utilize fifth-generation mobile communication technology (5G) multi-access edge computing (MEC) computing nodes and industrial Ethernet to achieve cross-security zone data transmission.

[0102] The application layer is used for data value mining and business applications, making decisions based on data from the transport layer. For example, the application layer can be used for data storage, data exchange and sharing, data analysis, and data management, thereby implementing a 3D visual operation and maintenance interface, a mobile inspection app, and an emergency plan management module.

[0103] In some embodiments, the present application may also include a platform layer, such as a data middle platform, an algorithm middle platform, etc. The data middle platform can be built by clustering a time series database (such as InfluxDB) and a relational database (such as MySQL), and the algorithm middle platform is used to integrate microservices such as equipment life prediction and energy efficiency optimization.

[0104] By building the above-mentioned system architecture, the embodiments of the present application can implement digital twin modeling, IoT-based data acquisition technology, and intelligent decision-making. Digital twin modeling is to build an interactive 3D model through BIM + 3DSMax, supporting real-time data-driven dynamic simulation. IoT-based data acquisition technology is to connect various low-level devices through I / O modules, connecting devices with upper-level services. Intelligent decision-making is to use algorithms to interpret model prediction results and generate maintenance recommendations and scheduling plans.

[0105] For example, in a large drainage pump station, a wireless vibration sensor is installed at the centrifugal pump bearing. When the monitored vibration acceleration value exceeds a threshold, the platform automatically triggers the following linkage. Simultaneously, the application can also call the nearest camera to capture a close-up of the device and mark the faulty area. The faulty component is then highlighted in the 3D model and a maintenance work order is pushed to the operator's mobile app, thus achieving integrated operation and maintenance management of the digital twin intelligent pump station.

[0106] It should be pointed out that the various embodiments of the present application can refer to each other, for example, the same or similar steps, method embodiments, system embodiments and device embodiments can refer to each other without limitation.

[0107] Figure 3 This is a flow chart of a method for constructing a digital twin intelligent pump station integrated application platform provided in the embodiment of this application. Figure 3 As shown, the method includes the following steps:

[0108] Step 301: Construct a three-dimensional visualized digital base of the pump station based on BIM and GIS.

[0109] The 3D visualization digital base of the pump station is used to establish a spatial mapping relationship between the pump station's physical structure and the geographic structure within which it is located. The physical structure of the pump station includes at least one of the pump station building, pump station equipment, and pump station pipelines. The geographic structure includes at least one of the topography and surrounding environment.

[0110] For example, the 3D visualization digital base of a pump station can include a 3D geometric model of the pump station's architectural structure and equipment layout, constructed using BIM technology. This model, created using 3D design software, includes the precise dimensions and spatial relationships of physical components such as the pump station building, pump units, and pipelines. Furthermore, the 3D visualization digital base integrates a 3D scene of GIS geographic information, incorporating data on the pump station's location, topography, and surrounding environment, to achieve a 3D mapping of the pump station's physical entities and geographic space.

[0111] Step 302: Collect pump station data based on the three-dimensional visualized digital base of the pump station.

[0112] The pump station data includes at least one of pump station monitoring data, pump station water condition data and pump station management information data.

[0113] For example, pump station monitoring data may include pump unit vibration frequency, temperature, and flow rate. Pump station water condition data may include water level, flow rate changes, and water pH. Pump station management information may include equipment maintenance records, inspection logs, and operator authority information. Pump station data can be standardized and transmitted across multiple sources and heterogeneous data using the object linking and embedding for process control (OPC) protocol.

[0114] Step 303: Build a device health prediction model based on the improved LSTM algorithm.

[0115] Among them, the equipment health prediction model is used to dynamically weight the key features extracted from the pump station data in time series through the attention mechanism.

[0116] For example, the device health prediction model can be based on the traditional LSTM and introduced an attention mechanism, and trained with historical operation and maintenance data. A loss function needs to be constructed, such as a joint loss function that combines a regression task (such as mean squared error (MSE)) with a classification task (cross entropy). The loss function is as follows:

[0117]

[0118] in, Represents the loss value, λ is the weight distribution coefficient, such as λ=0.7. N represents the number of samples, represents the true value, Represents the predicted value.

[0119] During model training, historical O&M data, including parameters such as equipment vibration, temperature, and flow rate, can be used for data preprocessing (e.g., normalization and denoising). The constructed loss function can be used to simultaneously optimize both regression and classification tasks. Furthermore, dynamic learning rate adjustment can be used during training, combined with early stopping to prevent overfitting.

[0120] Step 304: Analyze the pump station data according to the equipment health prediction model to obtain prediction results.

[0121] For example, the prediction results may include the current operating status of the pump station unit and fault location information.

[0122] Step 305: Dynamically render the operating status and fault location information of the pump station equipment based on the prediction results and the three-dimensional visualized digital base of the pump station, and trigger an alarm operation based on the alarm threshold.

[0123] The alarm threshold includes at least one of a device vibration threshold, a temperature threshold, and a flow range threshold.

[0124] For example, the device vibration threshold can be an acceleration value, such as 10 m / s². If the current device vibration acceleration is greater than 10 m / s², an alarm is triggered. Similarly, the temperature threshold can be a bearing temperature, such as 80°C.

[0125] Alarm operations can be linked with other equipment to achieve, such as automatically starting the backup pump group, sending SMS / APP alarms to operation and maintenance personnel, linking the video surveillance system to record the fault process, etc.

[0126] Based on the above technical solution, in the embodiment of the present application, the electronic device can construct a three-dimensional visual digital base of the pump station based on BIM and GIS, and collect pump station data such as pump station monitoring data, pump station water condition data, and pump station management information data based on the three-dimensional visual digital base of the pump station. At the same time, the electronic device can also construct an equipment health prediction model based on the improved LSTM algorithm. The equipment health prediction model dynamically weights the key features extracted from the pump station data according to the time series through the attention mechanism. In this way, the electronic device can perform data analysis on the pump station data according to the equipment health prediction model to obtain prediction results. Afterwards, based on the prediction results and the three-dimensional visual digital base of the pump station, the operating status and fault location information of the pump station equipment are dynamically rendered, and the alarm operation is triggered based on the alarm threshold. Among them, the three-dimensional visualization digital base of the pump station establishes a spatial mapping relationship between the physical structure of the pump station and the geographical structure where the pump station is located. Through this method, the embodiment of the present application realizes the comprehensive integration and three-dimensional visualization of pump station data. With the help of the improved LSTM model, the accuracy of equipment health prediction is improved, potential faults can be discovered in advance, and alarms and linkage measures can be triggered in time, which effectively improves the operation and maintenance management efficiency of the pump station, reduces the equipment failure rate, and improves the safe, efficient and stable operation rate of the pump station.

[0127] As a possible embodiment, the device health prediction model includes an LSTM module and an attention mechanism module. The LSTM module consists of a forget gate, an input gate, and an update memory unit.

[0128] The forget gate adjusts the amount of historical data features retained. The input gate adjusts the amount of new data added. The update memory unit generates current data features based on the forget gate and input gate. The attention mechanism module weights the generated data features. For example, this attention mechanism module can be a multi-head attention mechanism module.

[0129] For example, the forget gate satisfies the following formula:

[0130]

[0131] in, Indicates the current time step t The retention amount, W f represents the weight matrix of the forget gate, represents the bias term of the forget gate, σ represents the activation function, ht 1 indicates the previous time step t A hidden state of -1, x t Indicates the current time step t The input data.

[0132] The input gate satisfies the following formula:

[0133]

[0134] in, i t Indicates the current time step t The amount of new data added when W i represents the weight matrix of the input gate, b i Represents the bias term of the input gate; is a candidate memory unit, used to represent the new data calculated at the current time step t; W C represents the weight matrix of the candidate memory unit, b C represents the bias term of the candidate memory unit, ht 1 indicates the previous time step t A hidden state of -1, x t Indicates the current time step t The input data.

[0135] Update memory units to satisfy the following formula:

[0136]

[0137] in, C t Indicates the current time step t Update memory unit when f t Indicates the current time step t The retention amount, C t-1 Represents the previous time step t -1 update memory unit, i t Indicates the current time step tThe amount of new data added when is a candidate memory unit, and ⊙ represents element-by-element multiplication.

[0138] The attention mechanism module satisfies the following formula:

[0139]

[0140] in, α t Indicates the current time step t The attention weight vector when ; Q represents the query matrix, which is used to retrieve the key features in the input sequence; K Represents the key matrix, which is used to calculate the correlation of each element of the input sequence; d k represents the dimension scaling factor, S t represents the weighted eigenvector; V Represents a value matrix used to store the feature vectors of the input sequence.

[0141] As a possible embodiment, combining Figure 3 ,like Figure 4 As shown, the above step 304 can also be implemented through the following steps.

[0142] Step 401: Perform feature fusion on the vibration spectrum data and real-time monitoring data in the pump station data, and perform feature extraction through the feature extractor in the equipment health prediction model.

[0143] It should be noted that the data used in the relevant technology for analyzing fault information is usually real-time data collected by various sensors, that is, the operating parameters of various equipment in the time domain dimension. However, relying solely on a single dimension of data for analysis will result in some problems being unable to be identified. For example, when the vibration of the pumping station equipment suddenly becomes larger, it means that the rotating structure of the current pumping station equipment has a fault. However, the rotating structure is usually composed of multiple components. It is difficult to identify which specific component or components have problems by only analyzing the vibration data in the time domain. Therefore, the embodiment of the present application performs feature fusion on the data in the time domain dimension and the frequency domain dimension, so that the fault situation can be identified more accurately.

[0144] For example, the present application can record the vibration intensity once per second (for example, the vibration amplitude increases from 5 to 10), thereby obtaining data in the time domain. Simultaneously, a spectrum analyzer can be used to break down the vibration and identify the dominant frequency (for example, 80% of the vibration comes from a frequency of 50 beats per second), thereby obtaining data in the frequency domain.

[0145] Subsequently, embodiments of the present application can use dynamic weighting to enable electronic devices to intelligently determine when to prioritize vibration intensity (in the time domain) and frequency components (in the frequency domain). For example, when vibration suddenly becomes intense, the electronic device can prioritize time domain data (perhaps due to a sudden malfunction). When a frequency exhibits chronic abnormalities, the electronic device can prioritize frequency domain data (perhaps due to component wear).

[0146] Exemplarily, the feature extractor may be an artificial neural network, such as a neural network model structure used for recognition (similar to face recognition technology) to extract key features, such as a combination of "vibration intensity + high frequency anomaly".

[0147] In some embodiments, the feature extractor is constructed based on a cross-pumping station equipment fault knowledge base using transfer learning. The cross-pumping station equipment fault knowledge base includes pumping station data of the original pumping station and pumping station data of the pumping station to be trained. In this way, cross-pumping station knowledge transfer can be achieved.

[0148] It's important to note that a newly deployed pump station fault analysis system typically requires collecting thousands of data points for training before deployment, which takes months and significantly impacts deployment progress. Furthermore, existing pump stations often differ from newly deployed ones, making it difficult to adapt training data from existing stations to new ones.

[0149] Therefore, the embodiment of the present application establishes a knowledge base of equipment faults across pumping stations, so that some common data can be effectively utilized, thereby accelerating the deployment progress.

[0150] For example, the cross-pumping station equipment fault knowledge base can use the pre-trained ResNet50 model as a feature extractor, and achieve cross-pumping station knowledge transfer by fine-tuning the fully connected layer to adapt to the fault data of different pumping station equipment. The improved training method is as follows:

[0151] First, this application can establish a cross-pumping station equipment fault knowledge base as a general knowledge base, and then use a neural network model (such as ResNet50) as the basic model structure of the feature extractor, and train it through the data in the cross-pumping station equipment fault knowledge base (such as vibration spectrum diagram) to identify common fault information (such as bearing wear and blade breakage).

[0152] Afterwards, this application can freeze the model parameters of the first few layers of the model (equivalent to retaining the basic feature recognition ability of the model), and train the latter layers of the model through the data of the new pumping station, so as to achieve the effect of adapting to the new pumping station environment with a small amount of data.

[0153] For example, in the embodiment of the present application, the trained model can also be evaluated by a supervised model to verify the training effect. The training is performed using the data of the same new pumping station (taking 100 items as an example). The accuracy of the model obtained by the traditional model training method is only 76%, while the accuracy of the model obtained by the training method based on the cross-pumping station equipment fault knowledge base provided in the embodiment of the present application can reach 89%.

[0154] Step 402: Perform equipment health prediction based on the extracted feature data to obtain a prediction result.

[0155] In some embodiments, the input can be set as the fused data and the output can be set as the fault type (such as "bearing damage" or "blade crack") to train the subsequent neural network.

[0156] Based on the above technical solution, the electronic device in the embodiment of the present application can fuse the pump station data in the frequency domain and the time domain, and perform feature extraction, so as to make predictions based on the extracted features and obtain prediction results. Compared with the identification method in the related technology that only relies on single-dimensional data for fault analysis, it can further improve the level of detail and accuracy of fault identification.

[0157] As a possible embodiment, the platform includes a blade angle adjustment system, an excitation control system, and a fire linkage module.

[0158] The blade angle adjustment system is used to adjust the blade angle based on the pump station operating parameters. For example, the blade angle adjustment system adjusts the blade angle to meet the following formula:

[0159] η=α flow ²+b flow + H+ θ+

[0160] Where η represents the pump efficiency (%), flow represents flow rate (m³ / s), H represents head (m), θ represents blade angle (°), α, b, 、 、 are fitting coefficients.

[0161] In this way, the blade angle adjustment system can be optimized according to the operating conditions of the pump station, such as the head and flow required by the real-time working conditions, through the above formula.

[0162] The excitation control system is used to analyze the service life of various excitation device components based on their parameters and to perform demagnetization operations in the event of internal motor faults. For example, the excitation control system can analyze the excitation device's service life based on the usage patterns and fault characteristics of various excitation device components, using extensive engineering data and expert system analysis. In the event of an internal motor fault, demagnetization can be performed to minimize losses.

[0163] The fire linkage module is used to trigger safe evacuation guidance announcements upon receiving fire alarm signals and control the access control system to execute evacuation operations. For example, in the event of a fire, the platform uses the industrial television monitoring system, broadcast system, and information system to provide safe evacuation guidance to personnel, and links the access control system to perform corresponding operations to facilitate escape. It also receives alarm signals and fire mode signals from the fire alarm system and issues zone fire linkage mode instructions.

[0164] As a possible embodiment, the platform also includes a three-dimensional simulation system, an equipment management system, and a visual inspection system.

[0165] The 3D simulation system simulates water flow patterns and structural stress distribution under different working conditions based on imported BIM model data. The equipment management system manages basic information for each project's equipment. The visual inspection system allows for first-person and third-person perspective roaming, executing automated inspections within the platform based on pre-defined inspection routes.

[0166] As a possible embodiment, the platform also includes an engineering archive management system and a mobile management application system.

[0167] The engineering archive management system is used to store and maintain the pump station's engineering archive data, while the mobile management application system allows for remote, real-time querying of monitoring data. This fully digitalizes and intelligentizes the pump station's technical management and office operations, improving project management efficiency and reducing costs. The mobile management app system enables real-time querying of monitoring data, mobile consultations, and project inspections.

[0168] The above mainly introduces the scheme of the embodiment of the present application from the perspective of device implementation. It is understandable that each device, for example, an electronic device, in order to implement the above functions, includes at least one of the hardware structure and software modules corresponding to the execution of each function. Those skilled in the art should easily appreciate that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0169] The embodiment of the present application can divide the functional units of the electronic device according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0170] In the case of an integrated unit, Figure 5 A possible structural diagram of an electronic device (denoted as electronic device 50 ) involved in the above embodiment is shown. The electronic device 50 includes a processing unit 501 and a communication unit 502 , and may further include a storage unit 503 . Figure 5 The structural schematic diagram shown can be used to illustrate the structure of the electronic device involved in the above embodiments.

[0171] when Figure 5 The structural diagram shown is used to illustrate the structure of the electronic device involved in the above embodiment. The processing unit 501 is used to control and manage the actions of the electronic device, the communication unit 502 is used for the electronic device to communicate with other devices, and the storage unit 503 is used to store program codes and data of the electronic device.

[0172] For example, the processing unit 501 is configured to construct a three-dimensional visualized digital base of a pump station based on a building information model (BIM) and a geographic information system (GIS); the three-dimensional visualized digital base of the pump station is configured to establish a spatial mapping relationship between the physical structure of the pump station and the geographical structure in which the pump station is located; the physical structure of the pump station includes at least one of the pump station building, the pump station equipment, and the pump station pipeline; and the geographical structure includes at least one of the topography and the surrounding environment.

[0173] Communication unit 502, configured to collect pump station data based on the pump station three-dimensional visualization digital base; the pump station data includes at least one of pump station monitoring data, pump station water condition data, and pump station management information data;

[0174] Processing unit 501 is used to build an equipment health prediction model based on an improved long short-term memory network (LSTM) algorithm. The equipment health prediction model is used to dynamically weight key features extracted from pump station data in time series using an attention mechanism.

[0175] Processing unit 501 is used to analyze pump station data according to the equipment health prediction model to obtain prediction results;

[0176] Processing unit 501 is used to dynamically render the operating status and fault location information of the pump station equipment based on the prediction results and the three-dimensional visualization digital base of the pump station, and trigger an alarm operation based on the alarm threshold; the alarm threshold includes at least one of the equipment vibration threshold, temperature threshold and flow range threshold.

[0177] In one possible implementation, the device health prediction model includes an LSTM module and an attention mechanism module; the LSTM module consists of a forget gate, an input gate, and an update memory unit; the forget gate is used to adjust the retention amount of historical data features; the input gate is used to adjust the amount of new data added; the update memory unit is used to generate current data features based on the forget gate and the input gate; and the attention mechanism module is used to weight the generated data features.

[0178] In one possible implementation, the forget gate satisfies the following formula:

[0179]

[0180] in, Indicates the current time step t The retention amount, W f represents the weight matrix of the forget gate, represents the bias term of the forget gate, σ represents the activation function, ht 1 indicates the previous time step t A hidden state of -1, x t Indicates the current time step t Input data;

[0181] The input gate satisfies the following formula:

[0182]

[0183] in, it Indicates the current time step t The amount of new data added when W i represents the weight matrix of the input gate, b i Represents the bias term of the input gate; is a candidate memory unit, used to represent the new data calculated at the current time step t; W C represents the weight matrix of the candidate memory unit, b C represents the bias term of the candidate memory unit, ht 1 indicates the previous time step t A hidden state of -1, x t Indicates the current time step t Input data;

[0184] Update memory units to satisfy the following formula:

[0185]

[0186] in, C t Indicates the current time step t Update memory unit when f t Indicates the current time step t The retention amount, C t-1 Represents the previous time step t -1 update memory unit, i t Indicates the current time step t The amount of new data added when is a candidate memory unit, ⊙ represents element-by-element multiplication;

[0187] The attention mechanism module satisfies the following formula:

[0188]

[0189] in, α t Indicates the current time step t The attention weight vector when ; Q represents the query matrix, which is used to retrieve the key features in the input sequence; K Represents the key matrix, which is used to calculate the correlation of each element of the input sequence; d k represents the dimension scaling factor, S t represents the weighted eigenvector; VRepresents a value matrix used to store the feature vectors of the input sequence.

[0190] In one possible implementation, the processing unit 501 is used to: perform feature fusion on the vibration spectrum data and real-time monitoring data in the pump station data, and perform feature extraction through the feature extractor in the equipment health prediction model; perform equipment health prediction based on the extracted feature data to obtain a prediction result.

[0191] In one possible implementation, the feature extractor is constructed based on a cross-pumping station equipment failure knowledge base of transfer learning; the cross-pumping station equipment failure knowledge base includes pumping station data of the original pumping station and pumping station data of the pumping station to be trained.

[0192] In one possible implementation, the platform includes a blade angle adjustment system, an excitation control system, and a fire linkage module; wherein the blade angle adjustment system is used to adjust the blade angle based on the operating parameters of the pump station; the excitation control system is used to perform service life analysis based on the parameters of different components of the excitation device, and perform demagnetization operations when an internal fault occurs in the motor; the fire linkage module is used to trigger a safe evacuation guidance announcement when receiving a fire alarm signal, and control the access control system to perform evacuation operations.

[0193] In one possible implementation, the blade angle adjustment system adjusts the blade angle to satisfy the following formula:

[0194] η=α flow ²+b flow + H+ θ+

[0195] Among them, η represents the pump efficiency, flow represents flow rate, H represents head, θ represents blade angle, α, b, 、 、 are fitting coefficients.

[0196] In one possible implementation, the platform also includes a three-dimensional simulation system, an equipment management system, and a visual inspection system: the three-dimensional simulation system is used to simulate the water flow pattern and structural stress distribution under different working conditions based on the imported BIM model data; the equipment management system is used to manage the basic information of each engineering equipment; the visual inspection system is used to perform first-person and third-person perspective roaming operations, and perform automatic inspection operations on the platform according to the set inspection route.

[0197] In one possible implementation, the platform also includes an engineering archive management system and a mobile management application system; wherein, the engineering archive management system is used to store and maintain the engineering archive data of the pump station, and the mobile management application system is used to remotely query the monitoring data in real time.

[0198] The processing unit 501 may be a processor or a controller, and the communication unit 502 may be a communication interface, a transceiver, a transceiver, a transceiver circuit, a transceiver device, etc. The communication interface is a general term and may include one or more interfaces. The storage unit 503 may be a memory. When the electronic device 50 is a chip, the processing unit 501 may be a processor or a controller, and the communication unit 502 may be an input interface and / or output interface, a pin or a circuit, etc. The storage unit 503 may be a storage unit within the chip (for example, a register, a cache, etc.), or a storage unit located outside the chip (for example, a read-only memory (ROM), a random access memory (RAM), etc.).

[0199] Among them, the communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the electronic device 50 can be regarded as the communication unit 502 of the electronic device 50, and the processor with processing function can be regarded as the processing unit 501 of the electronic device 50. Optionally, the device used to implement the receiving function in the communication unit 502 can be regarded as the communication unit, and the communication unit is used to perform the receiving steps in the embodiment of the present application. The communication unit can be a receiver, a receiver, a receiving circuit, etc. The device used to implement the sending function in the communication unit 502 can be regarded as a sending unit, and the sending unit is used to perform the sending steps in the embodiment of the present application. The sending unit can be a transmitter, a transmitter, a sending circuit, etc.

[0200] Figure 5 If the integrated units are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The storage medium for storing computer software products includes various media that can store program codes, such as USB flash drives, mobile hard drives, read-only memories, random access memories, magnetic disks or optical disks.

[0201] Figure 5A unit in a can also be called a module, for example, a processing unit can be called a processing module.

[0202] The embodiment of the present application also provides a hardware structure diagram of an electronic device (denoted as electronic device 60), see Figure 6 The electronic device 60 includes a processor 601 and, optionally, a memory 602 connected to the processor 601 .

[0203] In the first possible implementation, see Figure 6 The electronic device 60 further includes a transceiver 603. The processor 601, the memory 602, and the transceiver 603 are connected via a bus. The transceiver 603 is used to communicate with other devices or a communication network. Optionally, the transceiver 603 may include a transmitter and a receiver. The device used to implement the receiving function in the transceiver 603 can be considered a receiver, and the receiver is used to perform the receiving steps in the embodiments of the present application. The device used to implement the transmitting function in the transceiver 603 can be considered a transmitter, and the transmitter is used to perform the transmitting steps in the embodiments of the present application.

[0204] Based on the first possible implementation, Figure 6 The structural schematic diagram shown can be used to illustrate the structure of the electronic device involved in the above embodiments.

[0205] in, Figure 6 It can also represent a system chip in an electronic device. In this case, the actions performed by the electronic device can be implemented by the system chip. The specific actions performed can be found above and will not be repeated here.

[0206] During implementation, each step of the method provided in this embodiment can be completed by hardware integrated logic circuits in a processor or by software instructions. The steps of the method disclosed in the embodiments of this application can be directly implemented as execution by a hardware processor, or as a combination of hardware and software modules in a processor.

[0207] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, among other types of computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform operations or processing. The processor may be a standalone semiconductor chip or integrated into a semiconductor chip with other circuits. For example, it may form a system-on-chip (SoC) with other circuits (such as a codec circuit, a hardware acceleration circuit, or various bus and interface circuits). Alternatively, it may be integrated into an ASIC as a built-in processor. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the core for executing software instructions to perform operations or processing, the processor may further include necessary hardware accelerators, such as a field programmable gate array (FPGA), a PLD (programmable logic device), or logic circuits that implement specialized logic operations.

[0208] The memory in the embodiments of the present application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, and electrically erasable programmable read-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0209] An embodiment of the present application also provides a computer-readable storage medium, comprising instructions, which, when executed on a computer, enables the computer to execute any of the above methods.

[0210] An embodiment of the present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute any of the above methods.

[0211] An embodiment of the present application also provides a chip, which includes a processor and an interface circuit, the interface circuit is coupled to the processor, the processor is used to run a computer program or instruction to implement the above method, and the interface circuit is used to communicate with other modules outside the chip.

[0212] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using a software program, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. Available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media (eg, solid state disks (SSDs)).

[0213] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. The fact that certain measures are recorded in different dependent claims does not mean that these measures cannot be combined to produce good results.

[0214] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely illustrative of the present application as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the claims of the present application and their equivalents.

Claims

1. A method for constructing a digital twin intelligent pump station integrated application platform, characterized in that: include: Construct a 3D visual digital base for the pump station based on the Building Information Model (BIM) and Geographic Information System (GIS); The three-dimensional visualization digital base of the pump station is used to establish a spatial mapping relationship between the physical structure of the pump station and the geographical structure in which the pump station is located; the physical structure of the pump station includes at least one of the pump station building, pump station equipment and pump station pipeline; the geographical structure includes at least one of the topography and the surrounding environment; Collecting pump station data based on the three-dimensional visualization digital base of the pump station; the pump station data includes at least one of pump station monitoring data, pump station water condition data and pump station management information data; An equipment health prediction model is constructed based on an improved long short-term memory network (LSTM) algorithm. The equipment health prediction model is used to dynamically weight key features extracted from the pump station data in time series through an attention mechanism. The equipment health prediction model includes an LSTM module and an attention mechanism module. The LSTM module consists of a forget gate, an input gate, and an update memory unit. The forget gate is used to adjust the amount of historical data features retained; the input gate is used to adjust the amount of new data added; the update memory unit is used to generate current data features based on the forget gate and the input gate; and the attention mechanism module is used to weight the generated data features. Performing data analysis on the pump station data according to the equipment health prediction model to obtain a prediction result; Dynamically rendering the operating status and fault location information of the pump station equipment based on the prediction results and the three-dimensional visualization digital base of the pump station, and triggering an alarm operation based on an alarm threshold; the alarm threshold includes at least one of an equipment vibration threshold, a temperature threshold, and a flow range threshold; The forget gate satisfies the following formula: ； in, Indicates the current time step t The retention amount, W f represents the weight matrix of the forget gate, represents the bias term of the forget gate, σ represents the activation function, ht 1 indicates the previous time step t A hidden state of -1, x t Indicates the current time step t Input data; The input gate satisfies the following formula: ； in, i t Indicates the current time step t The amount of new data added when W i represents the weight matrix of the input gate, b i Represents the bias term of the input gate; is a candidate memory unit, used to represent the new data calculated at the current time step t; W C represents the weight matrix of the candidate memory unit, b C represents the bias term of the candidate memory unit, ht 1 indicates the previous time step t A hidden state of -1, x t Indicates the current time step t The input data.

2. The construction method according to claim 1, characterized in that The update memory unit satisfies the following formula: ； in, C t Indicates the current time step t Update memory unit when f t Indicates the current time step t The retention amount, C t-1 Represents the previous time step t -1 update memory unit, i t Indicates the current time step t The amount of new data added when is a candidate memory unit, ⊙ represents element-by-element multiplication; The attention mechanism module satisfies the following formula: ； in, α t Indicates the current time step t The attention weight vector when ; Q represents the query matrix, which is used to retrieve the key features in the input sequence; K Represents the key matrix, which is used to calculate the correlation of each element of the input sequence; d k represents the dimension scaling factor, S t represents the weighted eigenvector; V Represents a value matrix used to store the feature vectors of the input sequence.

3. The construction method according to claim 1, characterized in that The performing of data analysis on the pump station data according to the equipment health prediction model to obtain a prediction result includes: Performing feature fusion on the vibration spectrum data and the real-time monitoring data in the pump station data, and performing feature extraction through the feature extractor in the equipment health prediction model; The equipment health is predicted based on the extracted feature data to obtain the prediction results.

4. The construction method according to claim 3, characterized in that The feature extractor is constructed based on a cross-pumping station equipment failure knowledge base of transfer learning; the cross-pumping station equipment failure knowledge base includes pumping station data of the original pumping station and pumping station data of the pumping station to be trained.

5. The construction method according to claim 1, characterized in that The platform includes a blade angle adjustment system, an excitation control system, and a fire linkage module; Among them, the blade angle adjustment system is used to adjust the blade angle based on the operating parameters of the pump station; the excitation control system is used to perform service life analysis based on the parameters of different components of the excitation device, and perform demagnetization operations when there is an internal fault in the motor; the fire linkage module is used to trigger a safe evacuation guidance notice when receiving a fire alarm signal, and control the access control system to perform evacuation operations.

6. The construction method according to claim 5, characterized in that: The blade angle adjustment system adjusts the blade angle to satisfy the following formula: n=a flow ²+b flow + H+ θ+ ; Among them, η represents the pump efficiency, flow represents flow rate, H represents head, θ represents blade angle, α, b, 、 、 are fitting coefficients.

7. The construction method according to claim 1, characterized in that The platform also includes a 3D simulation system, an equipment management system, and a visual inspection system: Among them, the three-dimensional simulation system is used to simulate the water flow state and structural stress distribution under different working conditions based on the imported BIM model data; the equipment management system is used to manage the basic information of each engineering equipment; the visual inspection system is used to perform first-person perspective and third-person perspective roaming operations, and perform automatic inspection operations in the platform according to the set inspection route.

8. The construction method according to claim 1, wherein: The platform also includes an engineering archive management system and a mobile management application system; Among them, the engineering archive management system is used to store and maintain the engineering archive data of the pump station, and the mobile management application system is used to remotely query monitoring data in real time.

9. An electronic device, characterized in that: include: A processor and a communication interface; the communication interface is coupled to the processor, and the processor is used to run a computer program or instruction to implement the construction method according to any one of claims 1 to 8.

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