Intelligent power plant digital engineering platform

Abstract

The invention discloses an intelligent power plant digital engineering platform, and relates to the technical field of data engineering.The intelligent power plant digital engineering platform is characterized in that data from different sources are processed through a data integration module, and the data are managed and integrated in a centralized mode; by constructing a data classification type storage structure, classification storage, indexing and management of data of different sources are achieved, and convenient query and retrieval functions are provided; the data analysis module performs deep calculation and analysis processing on the collected data to generate a power plant operation index Dcyzs, and evaluates the operation state of the power plant through the index; the system integration module calculates a performance index Xnzs by extracting performance related data of each functional module, and evaluates and optimizes the system performance according to the performance index Xnzs; through comprehensive system testing and optimization and establishment and continuous adjustment of a user feedback mechanism, continuous improvement of system functions and performance is ensured. Not only is the problem of insufficient data processing capability solved, but also the overall operation efficiency and management level of the power plant are improved.

Description

Technical Field

[0001] The present invention relates to the field of digital engineering technology, and in particular to a digital engineering platform for an intelligent power plant. Background Art

[0002] Modern power plants are gradually developing in the direction of intelligence and digitization, using advanced technologies such as big data, the Internet of Things, and cloud computing to achieve comprehensive digitization of power plant operation, maintenance, and management. Traditional power plant systems mainly rely on manual operation and experience judgment, which is inefficient and prone to errors. The existing digital engineering platform integrates various data acquisition and monitoring equipment to monitor and analyze the operating status of the power plant in real time, thereby improving operating efficiency and reducing operating costs. However, the existing technology still has some shortcomings;

[0003] 1. The existing digital engineering platform lacks effective data sharing and integration among subsystems, resulting in information silos and making it difficult to achieve global optimization and collaborative management;

[0004] 2. With the explosive growth of power plant data, the existing platform's data processing and analysis capabilities are limited, making it difficult to meet real-time and high-efficiency requirements, affecting the timeliness and accuracy of decision-making. Summary of the invention

[0005] Technical issues solved

[0006] In view of the deficiencies in the prior art, the present invention provides a smart power plant digital engineering platform, which solves the data island problem and the problem of insufficient data processing capability mentioned in the above background technology.

[0007] Technical Solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a smart power plant digital engineering platform, including a data integration module, a data management module, a data analysis module, a data display module, a system integration module and a platform deployment optimization module;

[0009] The data integration module is used to collect and integrate power plant data; by deploying sensors to collect data within the power plant, including equipment operating status and environmental parameters; then pre-processing and standardizing data from different sources, and finally centrally managing and integrating them;

[0010] The data management module is used to establish a unified data storage system to classify, store, index and manage data from different sources, and provide data query and retrieval functions;

[0011] The data analysis module is used to calculate and analyze the collected data using computing and big data analysis technology, including obtaining the power plant operation index Dcyzs, and evaluating the power plant operation status through the power plant operation index Dcyzs, and finally generating a power plant operation decision;

[0012] The data display module is used to develop and integrate various business applications for power plant management and operation, including providing users with visualization services through an interactive interface for the analyzed data;

[0013] The system integration module is used to integrate and test various functional modules to maintain the overall performance and stability of the system; by extracting the performance-related data of each functional module, obtaining the performance index Xnzs by calculation, and evaluating it, and finally making corresponding adjustments to the functional modules based on the evaluation;

[0014] The platform deployment optimization module is used to complete the system online deployment and conduct comprehensive system testing and optimization; secondly, a user feedback mechanism is established to collect user opinions and suggestions and continuously adjust system functions and performance.

[0015] Preferably, the data integration module includes a sensor deployment unit, a data acquisition processing unit and a data integration unit; the sensor deployment unit is used to deploy sensors at various locations in the power plant, including equipment operation sites and in-plant environment monitoring points, and the deployed sensors include temperature sensors, humidity sensors, vibration sensors and current sensors, so as to collect equipment operation-related parameters and in-plant environment-related parameters in real time;

[0016] The data acquisition and processing unit receives the collected equipment operation related parameters and factory environment related parameters by wireless means; then cleans, pre-processes and dimensionlessly processes the collected raw data and then integrates them;

[0017] The data integration unit is used to integrate the processed raw data and construct them into a device operation data set and an environment data set.

[0018] Preferably, the data management module classifies, stores, indexes and manages data from different sources by constructing a data classification storage structure; first, the collected data is classified and stored according to source, type and time; secondly, through the indexing mechanism, a unique index tag is generated for each piece of data based on the environment, equipment, manpower and production capacity of the power plant; finally, the required data is retrieved in real time through the query algorithm and indexing mechanism, and data analysis is performed to evaluate the operating status of the power plant.

[0019] Preferably, the data analysis module includes an operation evaluation unit and a decision generation unit; the operation evaluation unit is used to evaluate the operation status of the power plant, firstly by extracting relevant data of the equipment data set and the environmental data set, and calculating and obtaining the power plant operation index Dcyzs, the specific formula is as follows:

[0020]

[0021] In the formula, Sbx represents the number of equipment maintenance, Fdx represents the power generation efficiency, Sby represents the equipment utilization rate, Sdc represents the average daily indoor humidity difference, Pnd represents the emission concentration, and Fpl represents the wastewater discharge;

[0022] Secondly, by comparing the preset operating threshold Q and the power plant operating index Dcyzs, the following evaluation results are generated:

[0023] If the power plant operation index Dcyzs>operation threshold Q, the first evaluation result is generated, which means that the power plant is operating normally, including normal operation of the equipment in the plant, normal production capacity and environmental compliance;

[0024] If the power plant operation index Dcyzs is less than the operation threshold Q, a second evaluation result is generated, indicating that the operation in the power plant is abnormal, including abnormal operation of the equipment in the plant, abnormal production capacity and non-compliance with the environment.

[0025] Preferably, the decision generation unit further analyzes and generates a power plant operation decision based on the first evaluation result and the second evaluation result, and the specific contents are as follows:

[0026] If the power plant operation index Dcyzs is the first evaluation result, the existing management and operation strategy should be maintained, and the original equipment inspection and maintenance frequency should be maintained;

[0027] If the power plant operation index Dcyzs is the second evaluation result, the equipment maintenance times Sbx, power generation efficiency Fdx, equipment utilization rate Sby, indoor daily average humidity difference Sdc, emission concentration Pnd and wastewater discharge volume Fpl are ranked high and low, the primary cause of the operation abnormality is analyzed, and measures are taken, including equipment fault troubleshooting, adjustment of production processes, treatment of emissions and wastewater that exceed environmental protection standards, and cost reduction and efficiency improvement.

[0028] Preferably, the data display module includes a business integration unit and a data visualization unit; the business integration unit collects all parameters and evaluation results of the power plant operation index Dcyzs, including equipment maintenance times Sbx, power generation efficiency Fdx, equipment utilization rate Sby, indoor daily average humidity difference Sdc, emission concentration Pnd and wastewater discharge Fpl;

[0029] The data visualization unit is used to help users obtain the latest operation information in real time, and integrates early warning and notification functions. When the power plant operation index Dcyzs exceeds the preset threshold, the system automatically sends a notification to remind the user to take measures.

[0030] Preferably, the system integration module includes an integration unit, a performance evaluation unit and an adjustment unit; the integration unit is used to use the service bus architecture to interactively connect all modules after the user understands the operation information or takes measures, and at the same time perform performance testing on the integrated system, and monitor and extract performance-related data of each functional module in real time, including the total module response time Jmk, page loading time Jym, query response time Jxy, throughput Lt, CPU utilization Lyu and error rate Lcw. After all data are dimensionlessly processed, a performance data set is constructed.

[0031] Preferably, the performance evaluation unit extracts the performance data set for calculation to obtain the performance index Xnzs, and the specific formula is as follows:

[0032]

[0033] Then, by setting the performance threshold W 1 and performance threshold W 2 , the performance index Xnzs is evaluated, and the performance threshold W 1 <Performance threshold W 2 , generating the following evaluation results:

[0034] If the performance index Xnzs ≤ performance threshold W 1 , at this time, the first performance result is generated, which means that the system performance is abnormal and needs to be optimized;

[0035] If the performance threshold W 1 <Performance index Xnzs<Performance threshold W 2 , at this time, the second performance result is generated, which means that the system performance is normal, but there is room for improvement;

[0036] If the performance index Xnzs ≤ performance threshold W 2 , at this time, a third evaluation result is generated, indicating that the system performance is normal and the current system performance does not need to be improved.

[0037] Preferably, the adjustment unit is used to make the following adjustments based on the first performance result and the second performance result:

[0038] If the result is the first performance result, make comprehensive adjustments to the system, including adjusting bottleneck codes, database queries, resource allocation, load balancing, and error mechanism processing. Finally, recalculate the performance index Xnzs of the adjusted system to verify the optimization effect.

[0039] If the result is the second performance result, it includes refining performance adjustment measures, adjusting resource utilization strategies, and reducing page loading time and query response time. Finally, the evaluation performance index Xnzs is calculated again to verify the optimization effect.

[0040] Preferably, the platform deployment optimization module is used to complete the system online deployment and conduct comprehensive system testing and optimization; its deployment process includes environment preparation, formulation and implementation of detailed deployment plan, function verification and security configuration; system testing covers function testing, performance testing and security testing, aiming to discover and fix problems; secondly, a user feedback mechanism is established to collect user opinions and suggestions through multiple channels, conduct feedback analysis and processing, and continuously improve system functions; finally, combined with regular evaluation and performance monitoring, the system is continuously and dynamically adjusted.

[0041] Beneficial Effects

[0042] The present invention provides a communication network fault early warning method and system based on artificial intelligence, which has the following beneficial effects:

[0043] (1) This intelligent power plant digital engineering platform deploys sensors through the data integration module to collect the operating status and environmental parameters of power plant equipment in real time, and pre-processes and standardizes data from different sources, ultimately realizing centralized management and integration of data; the data management module further realizes the classified storage, indexing and management of data from different sources by constructing a data classification storage structure, and provides convenient query and retrieval functions; various types of data can be uniformly stored and managed, solving the problem of data islands and providing a solid foundation for subsequent analysis and decision-making.

[0044] (2) This is a smart power plant digital engineering platform. The data analysis module uses advanced computing and big data analysis technologies to perform in-depth calculations and analysis on the collected data to generate the power plant operation index Dcyzs, and uses this index to evaluate the power plant's operating status and ultimately generate intelligent operation decisions; the system integration module extracts performance-related data from each functional module, calculates the performance index Xnzs, and evaluates and optimizes system performance; after the system is deployed online, the platform deployment optimization module ensures continuous improvement of system functions and performance through comprehensive system testing and optimization, the establishment of a user feedback mechanism, and continuous adjustments; it not only solves the problem of insufficient data processing capabilities, but also improves the overall operating efficiency and management level of the power plant through scientific decision support. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The figure is a schematic diagram of the framework structure of a digital engineering platform for an intelligent power plant according to the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Example 1

[0048] A digital engineering platform for smart power plants, see Figure 1 , including data integration module, data management module, data analysis module, data display module, system integration module and platform deployment optimization module;

[0049] The data integration module is used to collect and integrate power plant data; by deploying sensors to collect data within the power plant, including equipment operating status and environmental parameters; then pre-processing and standardizing data from different sources, and finally centrally managing and integrating them;

[0050] The data management module is used to establish a unified data storage system to classify, store, index and manage data from different sources, and provide data query and retrieval functions;

[0051] The data analysis module is used to calculate and analyze the collected data using computing and big data analysis technology, including obtaining the power plant operation index Dcyzs, and evaluating the power plant operation status through the power plant operation index Dcyzs, and finally generating a power plant operation decision;

[0052] The data display module is used to develop and integrate various business applications for power plant management and operation, including providing users with visualization services through an interactive interface for the analyzed data;

[0053] The system integration module is used to integrate and test various functional modules to maintain the overall performance and stability of the system; by extracting the performance-related data of each functional module, obtaining the performance index Xnzs by calculation, and evaluating it, and finally making corresponding adjustments to the functional modules based on the evaluation;

[0054] The platform deployment optimization module is used to complete the system online deployment and conduct comprehensive system testing and optimization; secondly, a user feedback mechanism is established to collect user opinions and suggestions and continuously adjust system functions and performance.

[0055] In this embodiment, the data integration module is used to centrally manage and integrate data from different sources; the data management module classifies, stores, indexes and manages data from different sources, and provides data query and retrieval functions; the data analysis module calculates and analyzes the collected data, and finally generates power plant operation decisions; the data display module is used to provide users with visualization services for the analyzed data through an interactive interface; the system integration module extracts performance-related data of each functional module, calculates and evaluates to obtain the performance index Xnzs, and finally adjusts each functional module according to the evaluation results; the platform deployment optimization module is used to establish a user feedback mechanism, collect user opinions and suggestions, and continuously adjust system functions and performance.

[0056] Example 2

[0057] See also Figure 1 The data integration module includes a sensor deployment unit, a data acquisition and processing unit, and a data integration unit; the sensor deployment unit is used to deploy sensors at various locations in the power plant, including equipment operation sites and in-plant environment monitoring points, and the deployed sensors include temperature sensors, humidity sensors, vibration sensors, and current sensors to collect equipment operation-related parameters and in-plant environment-related parameters in real time;

[0058] The data acquisition and processing unit receives the collected equipment operation related parameters and factory environment related parameters by wireless means; then cleans, pre-processes and dimensionlessly processes the collected raw data and then integrates them;

[0059] The data integration unit is used to integrate the processed raw data and construct them into a device operation data set and an environment data set.

[0060] In this embodiment, sensors are arranged at various key locations in the power plant through the sensor deployment unit to achieve real-time monitoring of equipment operating status and environmental parameters, ensuring the comprehensiveness and accuracy of the data; the data acquisition and processing unit receives data wirelessly, and performs cleaning preprocessing and dimensionless processing to ensure the consistency and standardization of the data; the data integration unit integrates the processed data into equipment operation data sets and environmental data sets, effectively solving the data island problem, improving data utilization efficiency and processing capabilities, and providing a reliable data basis for subsequent data analysis and decision-making.

[0061] Example 3

[0062] The data management module classifies, stores, indexes and manages data from different sources by constructing a data classification storage structure; first, the collected data is classified and stored according to source, type and time; secondly, through the indexing mechanism, a unique index tag is generated for each data based on the power plant's environment, equipment, manpower and production capacity; finally, the required data is retrieved in real time through the query algorithm and indexing mechanism, and data analysis is performed to evaluate the operating status of the power plant.

[0063] In this embodiment, by constructing a data classification storage structure, data from different sources are classified, stored, indexed and managed to ensure the orderliness and traceability of the data; the index mechanism generates a unique index tag for each piece of data to facilitate quick search and retrieval; the combination of query algorithm and index mechanism realizes real-time retrieval and efficient analysis of data, improves data management efficiency and response speed, and provides accurate and timely data support for the evaluation of the operating status of the power plant.

[0064] Example 4

[0065] See also Figure 1 The data analysis module includes an operation evaluation unit and a decision generation unit; the operation evaluation unit is used to evaluate the operation status of the power plant. First, the relevant data of the equipment data set and the environmental data set are extracted to calculate the power plant operation index Dcyzs. The specific formula is as follows:

[0066]

[0067] In the formula, Sbx represents the number of equipment maintenance, Fdx represents the power generation efficiency, Sby represents the equipment utilization rate, Sdc represents the average daily indoor humidity difference, Pnd represents the emission concentration, and Fpl represents the wastewater discharge;

[0068] Secondly, by comparing the preset operating threshold Q and the power plant operating index Dcyzs, the following evaluation results are generated:

[0069] If the power plant operation index Dcyzs>operation threshold Q, the first evaluation result is generated, which means that the power plant is operating normally, including normal operation of the equipment in the plant, normal production capacity and environmental compliance;

[0070] If the power plant operation index Dcyzs is less than the operation threshold Q, a second evaluation result is generated, indicating that the operation in the power plant is abnormal, including abnormal operation of the equipment in the plant, abnormal production capacity and non-compliance with the environment.

[0071] The decision generation unit further analyzes and generates a power plant operation decision based on the first evaluation result and the second evaluation result. The specific contents are as follows:

[0072] If the power plant operation index Dcyzs is the first evaluation result, the existing management and operation strategy should be maintained, and the original equipment inspection and maintenance frequency should be maintained;

[0073] If the power plant operation index Dcyzs is the second evaluation result, the equipment maintenance times Sbx, power generation efficiency Fdx, equipment utilization rate Sby, indoor daily average humidity difference Sdc, emission concentration Pnd and wastewater discharge volume Fpl are ranked high and low, the primary cause of the operation abnormality is analyzed, and measures are taken, including equipment fault troubleshooting, adjustment of production processes, treatment of emissions and wastewater that exceed environmental protection standards, and cost reduction and efficiency improvement.

[0074] In this embodiment, the number of equipment maintenance times Sbx collected reflects the failure frequency and reliability of the equipment. Frequent maintenance may indicate that the equipment is aging or not properly maintained, affecting the stable operation and production efficiency of the power plant. The power generation efficiency Fdx measures the energy conversion efficiency of the power plant, that is, the efficiency of fuel or other energy being converted into electrical energy. Higher power generation efficiency means more efficient energy utilization and lower operating costs. The equipment utilization rate Sby represents the actual use of the equipment. A higher equipment utilization rate indicates efficient use and management of the equipment, while a low utilization rate may mean waste of resources or poor management. The indoor daily average humidity difference Sdc reflects the stability of the internal environment of the power plant. Excessive humidity fluctuations may have an adverse effect on the normal operation of the equipment, resulting in increased failures or decreased performance. The emission concentration Pnd measures the pollution level of the power plant's emissions, including waste gas and wastewater. A lower emission concentration means that the power plant's environmental protection measures are effective. The wastewater discharge volume Fpl represents the treatment and discharge of wastewater in the power plant. A lower wastewater discharge volume indicates that the power plant has a high water resource utilization efficiency and the wastewater treatment facilities are operating well, which helps to reduce environmental pollution.

[0075] Example 5

[0076] See also Figure 1 , the data display module includes a business integration unit and a data visualization unit; the business integration unit collects all parameters and evaluation results of the power plant operation index Dcyzs, including equipment maintenance times Sbx, power generation efficiency Fdx, equipment utilization rate Sby, indoor daily average humidity difference Sdc, emission concentration Pnd and wastewater discharge Fpl;

[0077] The data visualization unit is used to help users obtain the latest operation information in real time, and integrates early warning and notification functions. When the power plant operation index Dcyzs exceeds the preset threshold, the system automatically sends a notification to remind the user to take measures.

[0078] In this embodiment, the business integration unit and data visualization unit of the data display module play an important role in the smart power plant digital engineering platform; the business integration unit integrates the key parameters and evaluation results of the power plant operation index Dcyzs to provide users with comprehensive operation analysis; the data visualization unit displays operation information in real time through an interactive interface, helping users to quickly understand the operation status of the power plant, and respond to operation changes in a timely manner through early warning and notification functions; these functions effectively improve the transparency and real-time nature of management decisions, and promote the continuous optimization of power plant operation efficiency and the accuracy of management.

[0079] Example 6

[0080] See also Figure 1 The system integration module includes an integration unit, a performance evaluation unit and an adjustment unit; the integration unit is used to interactively connect all modules using the service bus architecture after the user understands the operation information or takes measures, and at the same time perform performance testing on the integrated system, and monitor and extract performance-related data of each functional module in real time, including the total module response time Jmk, page loading time Jym, query response time Jxy, throughput Lt, CPU utilization Lyu and error rate Lcw. After all data are dimensionlessly processed, a performance data set is constructed.

[0081] The performance evaluation unit extracts the performance data set for calculation to obtain the performance index Xnzs. The specific formula is as follows:

[0082]

[0083] Then, by setting the performance threshold W 1 and performance threshold W 2 , the performance index Xnzs is evaluated, and the performance threshold W 1 <Performance threshold W 2 , generating the following evaluation results:

[0084] If the performance index Xnzs ≤ performance threshold W 1 , at this time, the first performance result is generated, which means that the system performance is abnormal and needs to be optimized;

[0085] If the performance threshold W 1 <Performance index Xnzs<Performance threshold W 2 , at this time, the second performance result is generated, which means that the system performance is normal, but there is room for improvement;

[0086] If the performance index Xnzs ≤ performance threshold W 2 , at this time, a third evaluation result is generated, indicating that the system performance is normal and the current system performance does not need to be improved.

[0087] The adjustment unit is used to make the following adjustments based on the first performance result and the second performance result;

[0088] If the result is the first performance result, make comprehensive adjustments to the system, including adjusting bottleneck codes, database queries, resource allocation, load balancing, and error mechanism processing. Finally, recalculate the performance index Xnzs of the adjusted system to verify the optimization effect.

[0089] If the result is the second performance result, it includes refining performance adjustment measures, adjusting resource utilization strategies, and reducing page loading time and query response time. Finally, the evaluation performance index Xnzs is calculated again to verify the optimization effect.

[0090] In this embodiment, the total response time Jmk, page loading time Jym and query response time Jxy of the acquisition module directly affect the user experience, including the system response speed and page loading efficiency; shorter response and loading time means that users can obtain information and perform operations more quickly, which improves user satisfaction and usage efficiency;

[0091] Throughput Lt is used to evaluate the system's ability to handle concurrent requests. A higher value means that the system can handle more requests and support large-scale user access and data processing requirements, thereby enhancing the system's scalability and performance.

[0092] The CPU utilization rate Lyu reflects the resource utilization efficiency and load of the system. A reasonable CPU utilization rate indicates that the system resources are well allocated and can effectively support the operation and response requirements of the system.

[0093] The error rate Lcw measures the anomalies and errors that occur during system operation; a low value indicates that the system is highly stable and can maintain good performance during long-term operation.

[0094] Example 7

[0095] The platform deployment optimization module is used to complete the system online deployment and conduct comprehensive system testing and optimization; its deployment process includes environment preparation, formulation and implementation of detailed deployment plan, function verification and security configuration; system testing covers function testing, performance testing and security testing, aiming to discover and fix problems; secondly, a user feedback mechanism is established to collect user opinions and suggestions through multiple channels, conduct feedback analysis and processing, and continuously improve system functions; finally, combined with regular evaluation and performance monitoring, the system is continuously and dynamically adjusted.

[0096] In this embodiment, the key to the platform deployment optimization module is that it can ensure the smooth launch of the system and optimize its performance and security; through environmental preparation and detailed deployment plan, the smooth progress of the system deployment process is ensured; functional verification and security configuration ensure that the system can operate as expected and maintain security; system testing includes comprehensive inspections of functions, performance and security to effectively discover and solve potential problems; establishing a user feedback mechanism to timely collect and analyze user opinions, and continuously improve the system's functions and user experience; regular evaluation and performance monitoring help the system to continuously optimize and dynamically adjust, ensuring the long-term stable operation of the system and meeting user needs.

[0097] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital engineering platform for smart power plants, characterized by: It includes data integration module, data management module, data analysis module, data display module, system integration module and platform deployment optimization module; The data integration module is used to collect and integrate power plant data; by deploying sensors to collect data within the power plant, including equipment operating status and environmental parameters; then pre-processing and standardizing data from different sources, and finally centrally managing and integrating them; The data management module is used to establish a unified data storage system to classify, store, index and manage data from different sources, and provide data query and retrieval functions; The data analysis module is used to calculate and analyze the collected data using computing and big data analysis technology, including obtaining the power plant operation index Dcyzs, and evaluating the power plant operation status through the power plant operation index Dcyzs, and finally generating a power plant operation decision; The data display module is used to develop and integrate various business applications for power plant management and operation, including providing users with visualization services through an interactive interface for the analyzed data; The system integration module is used to integrate and test various functional modules to maintain the overall performance and stability of the system; By extracting the performance-related data of each functional module, the performance index Xnzs is obtained by calculation and evaluated, and finally the functional modules are adjusted accordingly according to the evaluation; The platform deployment optimization module is used to complete the system online deployment and conduct comprehensive system testing and optimization; secondly, a user feedback mechanism is established to collect user opinions and suggestions and continuously adjust system functions and performance.

2. According to claim 1, a smart power plant digital engineering platform is characterized by: The data integration module includes a sensor deployment unit, a data acquisition processing unit and a data integration unit; the sensor deployment unit is used to deploy sensors at various locations in the power plant, including equipment operation sites and in-plant environment monitoring points, and the deployed sensors include temperature sensors, humidity sensors, vibration sensors and current sensors to collect equipment operation-related parameters and in-plant environment-related parameters in real time; The data acquisition and processing unit receives the collected equipment operation related parameters and factory environment related parameters by wireless means; then cleans, pre-processes and dimensionlessly processes the collected raw data and then integrates them; The data integration unit is used to integrate the processed raw data and construct them into a device operation data set and an environment data set.

3. The digital engineering platform for smart power plants according to claim 2, characterized in that: The data management module classifies, stores, indexes and manages data from different sources by constructing a data classification storage structure. First, the collected data is classified and stored according to source, type and time. Secondly, through the indexing mechanism, a unique index label is generated for each piece of data based on the power plant's environment, equipment, manpower and production capacity; finally, the required data is retrieved in real time through the query algorithm and indexing mechanism, and data analysis is performed to evaluate the operating status of the power plant.

4. The intelligent power plant digital engineering platform according to claim 3 is characterized by: The data analysis module includes an operation evaluation unit and a decision generation unit; the operation evaluation unit is used to evaluate the operation status of the power plant. First, the relevant data of the equipment data set and the environmental data set are extracted to calculate the power plant operation index Dcyzs. The specific formula is as follows: In the formula, Sbx represents the number of equipment maintenance, Fdx represents the power generation efficiency, Sby represents the equipment utilization rate, Sdc represents the average daily indoor humidity difference, Pnd represents the emission concentration, and Fpl represents the wastewater discharge; Secondly, by comparing the preset operating threshold Q and the power plant operating index Dcyzs, the following evaluation results are generated: If the power plant operation index Dcyzs>operation threshold Q, the first evaluation result is generated, which means that the power plant is operating normally, including normal operation of the equipment in the plant, normal production capacity and environmental compliance; If the power plant operation index Dcyzs is less than the operation threshold Q, a second evaluation result is generated, indicating that the operation in the power plant is abnormal, including abnormal operation of the equipment in the plant, abnormal production capacity and non-compliance with the environment.

5. The digital engineering platform for smart power plants according to claim 4 is characterized by: The decision generation unit further analyzes and generates a power plant operation decision based on the first evaluation result and the second evaluation result. The specific contents are as follows: If the power plant operation index Dcyzs is the first evaluation result, the existing management and operation strategy should be maintained, and the original equipment inspection and maintenance frequency should be maintained; If the power plant operation index Dcyzs is the second evaluation result, the equipment maintenance times Sbx, power generation efficiency Fdx, equipment utilization rate Sby, indoor daily average humidity difference Sdc, emission concentration Pnd and wastewater discharge volume Fpl are ranked high and low, the primary cause of the operation abnormality is analyzed, and measures are taken, including equipment fault troubleshooting, adjustment of production processes, treatment of emissions and wastewater that exceed environmental protection standards, and cost reduction and efficiency improvement.

6. The intelligent power plant digital engineering platform according to claim 5, characterized in that: The data display module includes a business integration unit and a data visualization unit; the business integration unit collects all parameters and evaluation results of the power plant operation index Dcyzs, including equipment maintenance times Sbx, power generation efficiency Fdx, equipment utilization rate Sby, indoor daily average humidity difference Sdc, emission concentration Pnd and wastewater discharge Fpl; The data visualization unit is used to help users obtain the latest operation information in real time, and integrates early warning and notification functions. When the power plant operation index Dcyzs exceeds the preset threshold, the system automatically sends a notification to remind the user to take measures.

7. The intelligent power plant digital engineering platform according to claim 6, characterized in that: The system integration module includes an integration unit, a performance evaluation unit and an adjustment unit; the integration unit is used to interactively connect all modules using a service bus architecture after the user understands the operation information or takes measures, and at the same time perform performance testing on the integrated system, and monitor and extract performance-related data of each functional module in real time, including the total module response time Jmk, page loading time Jym, query response time Jxy, throughput Lt, CPU utilization Lyu and error rate Lcw. After all data are dimensionlessly processed, a performance data set is constructed.

8. The intelligent power plant digital engineering platform according to claim 7, characterized in that: The performance evaluation unit extracts the performance data set for calculation to obtain the performance index Xnzs. The specific formula is as follows: Then, by setting the performance threshold W 1 and performance threshold W 2 , the performance index Xnzs is evaluated, and the performance threshold W 1 <Performance threshold W 2 , generating the following evaluation results: If the performance index Xnzs ≤ performance threshold W 1 , at this time, the first performance result is generated, which means that the system performance is abnormal and needs to be optimized; If the performance threshold W 1 <Performance index Xnzs<Performance threshold W 2 , at this time, the second performance result is generated, which means that the system performance is normal, but there is room for improvement; If the performance index Xnzs ≤ performance threshold W 2 , at this time, a third evaluation result is generated, indicating that the system performance is normal and the current system performance does not need to be improved.

9. The intelligent power plant digital engineering platform according to claim 8, characterized in that: The adjustment unit is used to make the following adjustments based on the first performance result and the second performance result; If the result is the first performance result, make comprehensive adjustments to the system, including adjusting bottleneck codes, database queries, resource allocation, load balancing, and error mechanism processing. Finally, recalculate the performance index Xnzs of the adjusted system to verify the optimization effect. If the result is the second performance result, it includes refining performance adjustment measures, adjusting resource utilization strategies, and reducing page loading time and query response time. Finally, the evaluation performance index Xnzs is calculated again to verify the optimization effect.

10. The intelligent power plant digital engineering platform according to claim 9, characterized in that: The platform deployment optimization module is used to complete the system online deployment and conduct comprehensive system testing and optimization; Its deployment process includes environment preparation, formulation and implementation of detailed deployment plan, functional verification and security configuration; System testing covers functional testing, performance testing and security testing, aiming to discover and fix problems; secondly, a user feedback mechanism is established to collect user opinions and suggestions through multiple channels, conduct feedback analysis and processing, and continuously improve system functions; finally, combined with regular evaluation and performance monitoring, the system is continuously and dynamically adjusted.

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