Method for monitoring and controlling integrated energy system and operation system

Abstract

The invention relates to the technical field of integrated energy system control, in particular to an integrated energy system monitoring control method and an operation system, data is collected in real time, data preprocessing is carried out, a distributed computing architecture is utilized, data collection tasks are dispersed to a plurality of computing nodes, and a memory carries out classified storage on the data; the database server analyzes and processes the data, and establishes and optimizes an economic operation mode optimization model SOM and a safe operation mode optimization model EOM; and the optimized control scheme model parameters are fed back to a control end in real time, the scheme parameters are analyzed and calibrated and fed back to a processor, and the processor issues a detailed operation parameter command to the energy consumption equipment, so that the energy consumption equipment adjusts the operation parameters to achieve an optimized operation state. And the data processing capability, the communication reliability and the data storage and management efficiency of the system are improved, so that the efficient, stable and optimized operation of the comprehensive energy system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system control, and in particular to an integrated energy system monitoring and control method and an operating system. Background Art

[0002] An integrated energy system is an integrated energy production, supply and consumption system that is formed by organically coordinating and optimizing the generation, transmission and distribution, conversion, storage, consumption and trading of various energy sources during the planning, construction and operation process. The integrated energy system involves the conversion, storage, distribution and consumption of various energy forms (such as electricity, gas, water, heat, etc.). Its complexity and dynamic nature require effective monitoring and control. Through monitoring and control, the operating status of the system can be understood in real time, potential problems can be discovered and handled in a timely manner, and the efficient, safe and stable operation of the system can be ensured. The fixed operating mode of the current integrated energy system lacks the necessary flexibility and cannot switch the operating mode according to the actual system operation needs.

[0003] The existing publication number CN115514098A discloses a coordinated control method and device for an integrated energy system, including an integrated energy system control terminal, an energy consumption monitoring data center and a database server, the integrated energy system control terminal is communicatively connected to the energy consumption monitoring data center and the database server, the integrated energy system control terminal includes a processor, a memory, a data acquisition gateway and an energy consumption device interface, and a control circuit board is installed inside the integrated energy system control terminal, and the control circuit board is integrated with a processor, a memory, a data acquisition gateway and an energy consumption device interface. It can monitor, collect, classify, sort, store and analyze the operating status and energy consumption data of energy consumption equipment, formulate relatively optimized control strategies, adjust the operating parameters of the equipment in a timely manner, and switch the operating mode of the equipment in a timely and accurate manner, with high flexibility, while ensuring the system operation safety, equipment safety and system economy.

[0004] However, as the scale of the system expands and the number of energy-consuming devices increases, the amount of data that needs to be processed will also increase dramatically. If the processor and memory performance of the integrated energy system control end is insufficient, it may not be able to process all the data in a timely and accurate manner, which will affect the accuracy of the control strategy and the operating efficiency of the equipment. Summary of the invention

[0005] The purpose of the present invention is to provide a method and operating system for monitoring and controlling an integrated energy system, which solves the problem that the processor and memory performance of the existing integrated energy system control end are insufficient and may not be able to process all data in a timely and accurate manner, thereby affecting the accuracy of the control strategy and the operating efficiency of the equipment.

[0006] To achieve the above object, the present invention provides a method for monitoring and controlling an integrated energy system, comprising the following steps:

[0007] Collect the operation data and energy consumption data of energy-consuming equipment in real time and transmit them to the control end, where data preprocessing is performed. The control end uses a distributed computing architecture to distribute data collection tasks to multiple computing nodes, and transmits the preprocessed data to the storage device, which stores the data in categories.

[0008] The data in the memory is sent to the energy consumption monitoring data center and database server, wherein the communication network adopts a redundant design and at the same time, the key data is transmitted and processed with priority;

[0009] After receiving the data, the database server analyzes and processes the data, and based on the analysis results, builds and optimizes the economic operation mode optimization model SOM and the safety operation mode optimization model EOM, and solves the expected operation state data parameter model under the coordinated and optimized operation modes;

[0010] The database server feeds back the optimized control scheme model parameters to the control end in real time. The control end memory analyzes and calibrates the scheme parameters and feeds them back to the processor. The processor issues detailed operating parameter commands to the energy-consuming equipment, so that the energy-consuming equipment adjusts the operating parameters to achieve an optimized operating state.

[0011] The operation data and energy consumption data of the energy-consuming equipment are collected in real time and transmitted to the control end, and the data is pre-processed at the control end. The steps also include:

[0012] Clean the collected data to remove noise, redundancy and erroneous data;

[0013] Convert the cleaned data into a unified format or structure;

[0014] Verify the converted data.

[0015] The control end utilizes a distributed computing architecture to distribute data collection tasks to multiple computing nodes, and transmits the pre-processed data to a memory, which stores the data in categories. The steps also include:

[0016] The control end decomposes the overall task into multiple subtasks according to the needs of the data collection task, and assigns the decomposed subtasks to multiple computing nodes in the distributed computing architecture. Each computing node is connected to the corresponding data source through the network to obtain the operation data and energy consumption data of the corresponding energy-consuming equipment, and pre-processes them;

[0017] Pack the preprocessed data and transfer it to the storage;

[0018] After receiving the data, the storage device performs metadata management and classifies the data according to the set factors of the data;

[0019] The classified data is stored in the memory and indexes are created for the stored data.

[0020] The data in the memory is sent to the energy consumption monitoring data center and the database server, wherein the communication network adopts a redundant design, and at the same time, the key data is transmitted and processed preferentially, and the steps also include:

[0021] Encapsulate the data in the memory according to a predetermined format;

[0022] During the data encapsulation process, key data is prioritized;

[0023] The multi-thread or multi-process technology is used to send data to the energy consumption monitoring data center and the database server at the same time.

[0024] After receiving the data, the database server analyzes and processes the data, and based on the analysis results, builds and optimizes the economic operation mode optimization model SOM and the safety operation mode optimization model EOM, and solves the expected operation state data parameter model under the coordination and optimization operation modes. The steps also include:

[0025] Extract key features from the data set according to the needs of economic operation mode and safety operation mode;

[0026] Based on the extracted economic operation characteristics, the economic operation mode optimization model SOM is constructed, and based on the extracted safety operation characteristics, the safety operation mode optimization model EOM is constructed;

[0027] Use historical data or simulated data to verify the model and evaluate the model's prediction accuracy and generalization ability indicators;

[0028] The economic operation mode optimization model SOM and the safety operation mode optimization model EOM are collaboratively optimized to obtain the expected operation state data parameter model under the coordinated and optimized operation modes.

[0029] A comprehensive energy system monitoring and control operation system is operated by using the comprehensive energy system monitoring and control method.

[0030] A method and operation system for monitoring and controlling an integrated energy system of the present invention collects operation data and energy consumption data of energy-consuming equipment in real time and transmits the data to a control terminal. Data preprocessing techniques such as data cleaning and data compression are used inside the control terminal to reduce the impact of invalid data on the processing system. The integrated energy system control terminal utilizes a distributed computing architecture to disperse data acquisition tasks to multiple computing nodes, realize parallel processing of data, and improve overall processing efficiency. The processor issues instructions to pass the processed data to a memory, which classifies and stores the data, stores important data and commonly used data on a high-performance storage device, and stores non-important data and historical data on a low-cost storage device. The data in the memory is sent to an energy consumption monitoring data center and a database server. The communication network adopts a redundant design to ensure that the system can still maintain smooth communication when some communication links fail. At the same time, key data is transmitted and processed with priority to ensure timely transmission of important information. The database server receives data. After the data is collected, advanced data processing algorithms such as machine learning and deep learning are used to quickly analyze and process massive data; based on the analysis results, the economic operation mode optimization model SOM and the safe operation mode optimization model EOM are built and optimized, and the expected operating state data parameter model under the coordinated and optimized operation modes is solved; the database server feeds back the optimized control scheme model parameters to the integrated energy system control end in real time; the control end memory parses and calibrates the scheme parameters and feeds back to the processor; the processor issues detailed operating parameter commands to the energy-consuming equipment, so that the energy-consuming equipment adjusts the operating parameters to achieve the optimized operating state; by introducing distributed computing architecture, advanced data processing algorithms, data preprocessing technology, redundant communication design, real-time operating system and database, data classification storage and compression, and advanced control algorithms, the system's data processing capability, communication reliability, data storage and management efficiency are comprehensively improved, thereby ensuring the efficient, stable and optimized operation of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0032] Figure 1 It is a flowchart of the steps of the method for monitoring and controlling an integrated energy system according to the first embodiment of the present invention.

[0033] Figure 2 It is a flowchart of the steps of collecting the operation data and energy consumption data of energy-consuming equipment in real time in the first embodiment of the present invention, transmitting the data to the control end, and performing data preprocessing at the control end, wherein the control end uses a distributed computing architecture to disperse the data collection tasks to multiple computing nodes, and passes the preprocessed data to a memory, and the memory classifies and stores the data.

[0034] Figure 3 This is a flowchart of the steps of sending data in a memory to an energy consumption monitoring data center and a database server in the first embodiment of the present invention, wherein a communication network adopts a redundant design and, at the same time, key data is transmitted and processed preferentially. DETAILED DESCRIPTION

[0035] Embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0036] The first embodiment of the present application is:

[0037] See also Figures 1 to 3 ,in, Figure 1 It is a flowchart of the steps of the method for monitoring and controlling an integrated energy system according to the first embodiment of the present invention. Figure 2 It is a flowchart of the steps of collecting the operation data and energy consumption data of energy-consuming equipment in real time in the first embodiment of the present invention, transmitting the data to the control end, and performing data preprocessing at the control end, wherein the control end uses a distributed computing architecture to disperse the data collection tasks to multiple computing nodes, and passes the preprocessed data to a memory, and the memory classifies and stores the data. Figure 3 The present invention is a flowchart of the steps of sending data in a memory to an energy consumption monitoring data center and a database server in the first embodiment of the present invention, wherein the communication network adopts a redundant design and at the same time, the key data is transmitted and processed preferentially. The present invention provides a method for monitoring and controlling an integrated energy system, comprising the following steps:

[0038] S100: collecting operation data and energy consumption data of energy-consuming equipment in real time, and transmitting them to the control end, and performing data preprocessing at the control end, wherein the control end uses a distributed computing architecture to distribute data collection tasks to multiple computing nodes, and transmits the preprocessed data to a memory, and the memory stores the data in a classified manner;

[0039] S101: The control end decomposes the overall task into multiple subtasks according to the requirements of the data collection task, and assigns the decomposed subtasks to multiple computing nodes in the distributed computing architecture. Each computing node is connected to a corresponding data source through a network, obtains the operation data and energy consumption data of the corresponding energy-consuming equipment, and pre-processes the data;

[0040] S102: Packing the preprocessed data and transmitting it to a storage device;

[0041] S103: After receiving the data, the storage device performs metadata management and classifies the data according to the set factors of the data;

[0042] S104: Store the classified data in a memory and create an index for the stored data.

[0043] Specifically, sensors such as current sensors, voltage sensors, temperature sensors, flow sensors, etc. are installed at key parts of energy-consuming equipment to monitor the operating status and energy consumption of the equipment. The data sampling frequency is set according to actual needs to ensure that resources are not over-occupied and the operating status and energy consumption changes of the equipment can be accurately reflected. The collected data is uploaded to the control end for preprocessing, wherein the control end intelligently decomposes the overall task into multiple subtasks according to the complexity and scale of data collection, and each subtask is assigned to a specific computing node in the distributed computing architecture. These nodes are connected to the corresponding data source through the network. Each computing node directly obtains data from the data source, such as the energy-consuming equipment, and performs preliminary cleaning to remove duplicate, erroneous, incomplete or irrelevant data to ensure the accuracy and consistency of the data. The cleaned data is integrated according to a specific data model or format to form a unified data set that can be used for subsequent analysis and processing. The converted data is verified to ensure the integrity and accuracy of the data. The verification is performed by comparison, checksum, etc. The preprocessed data is packaged for transmission over the network. After the storage receives the data, metadata management is first performed. Metadata is data about data, including information such as description, attributes, and location of the data. Effective metadata management can ensure accurate location and fast access to data. Data is classified according to factors such as data type, source, and business requirements. Classification can be based on different dimensions, such as time, region, and business type. The classified data is stored in the memory. The memory uses storage technologies such as distributed file systems and NoSQL databases to achieve high availability and scalability. The storage process involves data redundancy and fault-tolerant mechanisms to ensure data reliability and consistency. Indexes are created for stored data to improve query efficiency. At the same time, queries are optimized to reduce query time and resource consumption.

[0044] S200: sending the data in the memory to the energy consumption monitoring data center and the database server, wherein the communication network adopts a redundant design and at the same time, the key data is transmitted and processed preferentially;

[0045] S201: Encapsulating the data in the memory according to a predetermined format;

[0046] S202: During the data encapsulation process, priority marking is performed on key data;

[0047] S203: Using multi-threading or multi-process technology, simultaneously sending data to the energy consumption monitoring data center and the database server.

[0048] Specifically, define a data format suitable for transmission, such as JSON, XML or a custom binary format. This format should contain all necessary information of the data, such as timestamp, device ID, energy consumption value, operating status, etc. According to the defined format, the data in the memory is packaged into data packets. Each data packet should contain a complete data unit and necessary metadata, such as the sequence number and checksum of the data packet. According to business needs and the importance of data, identify which data is critical and needs to be transmitted and processed first, and set a priority tag in the header or a specific position of the data packet. This tag is a simple number or enumeration value used to indicate the priority of the data packet. Create sending threads or processes for the energy consumption monitoring data center and database server respectively. These threads / processes will be responsible for reading data packets from the memory and sending them to the corresponding targets through the communication network. Ensure that the communication network adopts a redundant design, that is, there are multiple paths to choose from to prevent data transmission interruption caused by single point failure. Thread / process A: Responsible for reading data packets and sorting them according to the priority tag. Then, send the data packets to the energy consumption monitoring data center through the communication network. Thread / process B: Similar to thread / process A, but the target is the database server. Similarly, the data packets are processed according to the priority tag and sent through the communication network. During the sending process, if there is a network failure or the target server is unresponsive, an error handling and retry mechanism should be implemented. For example, the number of retries and the retry interval can be set to ensure that the data can be successfully sent. After the data is successfully sent to the target, the target server can be requested to send a confirmation message. In this way, the sender can confirm that the data has been successfully received and processed. The data in the storage is sent to the energy consumption monitoring data center and database server efficiently and reliably, while ensuring that critical data is processed first.

[0049] S300: After receiving the data, the database server analyzes and processes the data, and based on the analysis results, builds and optimizes the economic operation mode optimization model SOM and the safety operation mode optimization model EOM, and solves the expected operation state data parameter model under the coordinated and optimized operation modes;

[0050] Specifically, statistical analysis and data visualization tools are used to conduct preliminary exploration of the data set to understand the distribution, trend, correlation and other characteristics of the data. According to the needs of the economic operation mode and the safe operation mode, key features such as load changes, resource utilization, and safety risk factors are extracted from the data set. Based on the extracted economic operation features, the economic operation mode optimization model SOM is constructed. The model should be able to reflect key indicators such as the cost-effectiveness and resource utilization efficiency of economic operation, and have an optimization algorithm to solve the optimal economic operation state. EOM construction: Based on the extracted safe operation features, the safe operation mode optimization model EOM is constructed. The model should be able to evaluate the safety and stability of system operation and have the ability to respond to potential security threats. The SOM and EOM are fine-tuned by adjusting model parameters and optimizing algorithms to improve the accuracy and robustness of the model. The model is verified using historical data or simulation data to evaluate the model's performance indicators such as prediction accuracy and generalization ability. If necessary, multiple rounds of iterative optimization are performed until the requirements are met. The SOM and EOM are collaboratively optimized, considering the dual needs of economic operation and safe operation, and the expected operation state data parameter model under the coordinated and optimized operation mode is solved. Conduct in-depth analysis on the expected operating status data parameters obtained to understand their performance and application value in different scenarios, providing strong support for the stable and efficient operation of the system.

[0051] S400: The database server feeds back the optimized control scheme model parameters to the control end in real time. The control end memory analyzes and calibrates the scheme parameters and feeds back to the processor. The processor issues detailed operating parameter commands to the energy-consuming device, so that the energy-consuming device adjusts the operating parameters to achieve an optimized operating state.

[0052] Specifically, the database server feeds back the parameters to the control end in real time. After the control end receives the optimization parameters sent by the database server, the memory is first responsible for receiving and storing these data. The software or firmware in the memory will parse these optimization parameters and convert them into a format that can be recognized by the processor of the control end. At the same time, according to the actual situation and performance limitations of the device, the parameters are calibrated and adjusted as necessary to ensure their feasibility and effectiveness in practical applications. After the parameter parsing and calibration are completed, the processor of the control end will generate detailed operating parameter commands based on these optimized parameters. These commands will specifically instruct the energy-consuming device on how to adjust its operating parameters to achieve the most optimized operating state. The processor transmits the generated operating parameter commands to the energy-consuming device. During the transmission process, the accuracy and real-time nature of the commands are ensured. After the energy-consuming device receives the operating parameter command from the control end, it first parses and verifies the command. According to the requirements of the command, the energy-consuming device will adjust its operating parameters, such as power, speed, temperature, etc. These adjustments are made based on the optimized control scheme model parameters to ensure that the device achieves the most optimized energy consumption and performance while meeting the operating requirements. After the parameter adjustment is completed, the energy-consuming equipment can feed back its new operating status to the control end for further monitoring and analysis.

[0053] By introducing distributed computing architecture, advanced data processing algorithms, data preprocessing technology, redundant communication design, real-time operating system and database, data classification storage and compression, and advanced control algorithms, the system's data processing capabilities, communication reliability, data storage and management efficiency have been comprehensively improved, thereby ensuring the efficient, stable and optimized operation of the integrated energy system.

[0054] The second embodiment of the present application is:

[0055] On the basis of the first embodiment, an operation system for monitoring and controlling an integrated energy system in this embodiment operates by adopting the method for monitoring and controlling an integrated energy system.

[0056] What is disclosed above is only one or more preferred embodiments of the present application, and cannot be used to limit the scope of rights of the present application. Ordinary technicians in this field can understand that all or part of the processes of implementing the above embodiments and equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A method for monitoring and controlling an integrated energy system, characterized in that: The following steps are involved: Collect the operation data and energy consumption data of energy-consuming equipment in real time and transmit them to the control end, where data preprocessing is performed. The control end uses a distributed computing architecture to distribute data collection tasks to multiple computing nodes, and transmits the preprocessed data to the storage device, which stores the data in categories. The data in the memory is sent to the energy consumption monitoring data center and database server, wherein the communication network adopts a redundant design and at the same time, the key data is transmitted and processed with priority; After receiving the data, the database server analyzes and processes the data, and based on the analysis results, builds and optimizes the economic operation mode optimization model SOM and the safety operation mode optimization model EOM, and solves the expected operation state data parameter model under the coordinated and optimized operation modes; The database server feeds back the optimized control scheme model parameters to the control end in real time. The control end memory analyzes and calibrates the scheme parameters and feeds them back to the processor. The processor issues detailed operating parameter commands to the energy-consuming equipment, so that the energy-consuming equipment adjusts the operating parameters to achieve an optimized operating state.

2. The method for monitoring and controlling an integrated energy system according to claim 1, characterized in that: The operation data and energy consumption data of the energy-consuming equipment are collected in real time and transmitted to the control end, and the data is pre-processed at the control end. The steps also include: Clean the collected data to remove noise, redundancy and erroneous data; Convert the cleaned data into a unified format or structure; Verify the converted data.

3. The method for monitoring and controlling an integrated energy system according to claim 2, characterized in that: The control end uses a distributed computing architecture to distribute data collection tasks to multiple computing nodes, and transmits the pre-processed data to the storage device, which classifies and stores the data. The steps also include: The control end decomposes the overall task into multiple subtasks according to the needs of the data collection task, and assigns the decomposed subtasks to multiple computing nodes in the distributed computing architecture. Each computing node is connected to the corresponding data source through the network to obtain the operation data and energy consumption data of the corresponding energy-consuming equipment, and pre-processes them; Pack the preprocessed data and transfer it to the storage; After receiving the data, the storage device performs metadata management and classifies the data according to the set factors of the data; The classified data is stored in the memory and indexes are created for the stored data.

4. The method for monitoring and controlling an integrated energy system according to claim 3, characterized in that: The data in the memory is sent to the energy consumption monitoring data center and the database server, wherein the communication network adopts a redundant design and at the same time, the key data is transmitted and processed preferentially. The steps also include: Encapsulate the data in the memory according to a predetermined format; During the data encapsulation process, key data is prioritized; The multi-thread or multi-process technology is used to send data to the energy consumption monitoring data center and the database server at the same time.

5. The method for monitoring and controlling an integrated energy system according to claim 4, characterized in that: After receiving the data, the database server analyzes and processes the data, and based on the analysis results, builds and optimizes the economic operation mode optimization model SOM and the safety operation mode optimization model EOM, and solves the expected operation state data parameter model under the coordination and optimization operation modes. The steps also include: Extract key features from the data set according to the needs of economic operation mode and safety operation mode; Based on the extracted economic operation characteristics, the economic operation mode optimization model SOM is constructed, and based on the extracted safety operation characteristics, the safety operation mode optimization model EOM is constructed; Use historical data or simulated data to verify the model and evaluate the model's prediction accuracy and generalization ability indicators; The economic operation mode optimization model SOM and the safety operation mode optimization model EOM are collaboratively optimized to obtain the expected operation state data parameter model under the coordinated and optimized operation modes.

6. An operating system for monitoring and controlling an integrated energy system, which operates using the method for monitoring and controlling an integrated energy system as claimed in claim 1.

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