A park energy-saving control system and energy-saving control method

Abstract

The present invention provides a park energy-saving management and control system and an energy-saving management and control method. A park energy-saving management and control system includes a data acquisition module for collecting data of park hardware equipment; an edge computing module for controlling and adjusting park equipment; a basic platform module for improving basic support; a simulation optimization module for building an energy consumption analysis model and performing visual configuration; and a data visualization module for visually presenting the operating conditions of the system and equipment. The beneficial effects of the present invention are: by establishing a multi-scenario, multi-device, configurable energy consumption dynamic simulation platform, integrating equipment mechanisms and mathematical analysis models, establishing an energy consumption analysis model library for high-energy consumption equipment, and setting boundary conditions and adjusting model parameters through visual configuration tools, the energy consumption simulation optimization of the park, system, and equipment levels is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving management and control, and in particular to a park energy-saving management and control system and an energy-saving management and control method. Background Art

[0002] With the rapid development of my country's economy, industrial parks, buildings, commercial streets, office buildings and public buildings have experienced long-term vigorous development. Along with the construction of these parks and buildings, carbon emissions and high energy consumption have been brought about. Building energy consumption involves multiple stages such as building materials production, construction, and building operation. It is not only closely related to carbon emissions, but also has a significant impact on the effective use of energy, especially for large public buildings with high energy consumption. According to statistics from the National Science and Technology Data Center, there are more than 15,000 industrial parks in the country, and the carbon dioxide emissions of the parks account for 31% of the total national emissions. The energy production and distribution management models of most domestic parks are not advanced enough, and there is huge room for energy-saving optimization and reduction of energy carbon intensity. With the acceleration of my country's smart city construction and the maturity of information technology, the demand for smart park construction will continue to increase, and there is broad room for future development.

[0003] The current park management has the following problems:

[0004] (1) The energy management method is extensive, and the electricity is managed only from the perspective of ensuring the normal operation of equipment, without lean management of electricity from the perspectives of terminal environment, usage efficiency, production cost and equipment life;

[0005] (2) It is difficult to accumulate management and control experience. Relying on manpower to control equipment requires a high level of experience and responsibility from professionals, resulting in high human cost investment. In addition, the mobility of grassroots property management personnel is high, making it difficult to accumulate energy consumption management experience.

[0006] (3) It is difficult to optimize energy consumption management. There are many types and brands of smart terminal equipment in the park. Some equipment and power supply lines are outdated, and the production process is backward. The efficiency is low, the power consumption is high, and the waste is very serious. However, due to the lack of scientific and effective power utilization and power quality management methods, it is difficult to conduct overall thinking and macro-control through manpower;

[0007] (4) The proportion of green electricity access is relatively low. From the perspective of the park's energy structure, due to the limitations of my country's current electricity production status, the power supply of the park that uses grid-powered power still mainly relies on fossil energy. Summary of the invention

[0008] In order to overcome the above problems existing in the prior art, the present invention provides a park energy-saving management and control system and method.

[0009] The present invention discloses a park energy-saving management and control system, comprising a data acquisition module for collecting data of park hardware equipment; an edge computing module for controlling and adjusting park equipment; a basic platform module for improving basic support; a simulation optimization module for building an energy consumption analysis model and performing visual configuration; and a data visualization module for visually presenting the operating conditions of the system and equipment.

[0010] On this basis, the park energy-saving management and control system also includes a data processing module, which is used to access, analyze, and store data collected by the data acquisition and edge computing modules.

[0011] On this basis, the basic platform includes a front-end module, a microservice module and a persistence layer. The front-end module is used to display the calculation and analysis results of the corresponding application; the microservice module is used to perform calculation and analysis management according to the needs of different business scenarios; the persistence layer is used to provide data support for the basic platform front-end and microservices, simulation optimization modules and data visualization.

[0012] On this basis, the front-end module includes data display, equipment detection, abnormal monitoring, operation assessment, alarm analysis, statistical reports and system management; the microservice module includes scheduled tasks, real-time data services, user services, statistical report services, monitoring alarm services and data management services; the persistence layer uses MySQL and the industrial time series database SIPHD.

[0013] On this basis, the data processing module includes data access, data storage, data analysis, AI and CDH cluster. The data access, data storage, data analysis and AI adopt cloud proxy cluster and Kafka cluster. The CDH cluster includes real-time analysis, batch processing and distributed storage system HBase.

[0014] On this basis, the simulation optimization module includes energy consumption analysis model library construction and visualization configuration, and the simulation optimization module is connected to the data visualization module.

[0015] On this basis, the data visualization module adopts GIS, BIM and WEBGL.

[0016] A park energy-saving control method comprises the following steps:

[0017] S1. Obtaining park equipment scene information;

[0018] S2. Based on the scene information of the park equipment, the simulation optimization model and model parameter adjustment are constructed through the simulation optimization module;

[0019] S3, the simulation optimization model is deployed to the edge computing module;

[0020] S4, based on data collection, obtain the real-time operation data of the park equipment system, and perform real-time calculation through the simulation optimization model;

[0021] S5. Based on energy-saving control indicators, control and adjust park equipment through edge computing;

[0022] S6, the simulation optimization module calculates data in real time and transmits it to the big data cluster;

[0023] S7. Based on the scene information of the park equipment, a 3D visualization scene is constructed through a 3D visualization engine and a development framework;

[0024] S8. Display the three-dimensional visualization scene through the data visualization module.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] (1) The present invention establishes a multi-scenario, multi-device, configurable energy consumption dynamic simulation platform, integrates equipment mechanism and mathematical analysis model, establishes an energy consumption analysis model library for high-energy consumption equipment, and uses visual configuration tools to set boundary conditions and adjust model parameters to achieve energy consumption simulation optimization at the park, system and device levels.

[0027] (2) The present invention, under the premise of ensuring building comfort and production energy demand, establishes a park energy-saving control strategy based on a dynamic energy consumption simulation model of equipment and systems, and reduces the energy consumption of the park through real-time automatic control.

[0028] (3) The present invention provides multi-protocol IoT data collection technology for complex scenarios, and a GIS+BIM+simulation cross-platform and cross-scenario fusion rendering and data visualization engine. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the structure of the energy-saving control system for big data application scenarios of the present invention;

[0030] Figure 2 It is a schematic diagram of the structure of the energy-saving control system for non-big data application scenarios of the present invention;

[0031] Figure 3 It is a flow chart of the steps of the energy-saving control method of the present invention. DETAILED DESCRIPTION

[0032] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0033] The present invention discloses a park energy-saving management and control system, including a data acquisition module for collecting data of park hardware equipment; an edge computing module for controlling and adjusting park equipment; a basic platform module for providing basic support; a simulation optimization module for building an energy consumption analysis model and performing visual configuration; and a data visualization module for visually presenting the operating status of the system and equipment.

[0034] Embodiment 1

[0035] Based on the scenario of big data application,

[0036] The basic platform module includes the front-end, microservices and persistence layer. The front-end is used to display the calculation and analysis results, the microservices are used for calculation and analysis management, and the persistence layer is used to provide data support for the basic platform front-end and microservices, simulation optimization module and data visualization module. The front-end is a front-end application developed according to business display needs, and the microservices are the back-end basic applications developed by the basic platform according to the business.

[0037] The front end includes data display, equipment detection, abnormal monitoring, operation assessment, alarm analysis, statistical reports and system management; the microservice module includes scheduled tasks, real-time data services, user services, statistical report services, monitoring alarm services, and data management services; the persistence layer uses MySQL and the industrial time series database SIPHD. MySQL is used to store relational data of the basic platform microservices and simulation optimization modules, and SIPHD is used to store the time series data of equipment operation.

[0038] The energy-saving management and control system also includes a data processing module for accessing, analyzing and storing data collected by the edge computing module; the data processing module includes data access, data storage, data analysis, AI and CDH cluster. Data access, data storage, data analysis and AI use cloud proxy clusters and Kafka clusters. The CDH cluster includes real-time analysis, batch processing and distributed storage system HBase. The data storage in the data processing module is used for data storage in big data scenarios. HBase is a distributed and scalable big data storage that relies on Hadoop. The Hadoop Distributed File System (HDFS) is designed to be a distributed file system suitable for running on commodity hardware. Based on data collection under the big data platform, the equipment operation data, logs and other data sources and data structures are transmitted through Kafka. Kafka is an open source stream processing platform developed by the Apache Software Foundation, written in Scala and Java, and stored in HDFS, MongoDB and Redis. HDFS is a distributed file system. MapReduce is a programming model for parallel computing of large-scale data sets (greater than 1TB). Hive is a data warehouse tool based on Hadoop. It can map structured data files to a database table and provide complete SQL query functions. It can convert SQL statements into MapReduce tasks for execution. Apache Spark is a fast and general computing engine designed for large-scale data processing. Data on HDFS is read and calculated through computing tasks such as MapReduce, Hive, and Spark, and the results are written to HDFS, MongoDB, and Redis. MongoDB is a database based on distributed file storage. Redis is an open source log-type, Key-Value database written in ANSI C language, supporting networks, and can be based on memory or persistence. It also provides APIs in multiple languages. Simulation optimization modules, microservices, and data visualization modules can be obtained from MongoDB and redis for analysis, calculation, and display.

[0039] The simulation optimization module includes the construction and visualization configuration of the energy consumption analysis model library. The construction of the energy consumption analysis model library is the process of building an energy consumption analysis model based on the physical components, mathematical components, connector components, data components, machine learning components, and solver components of the simulation optimization module. The energy consumption analysis model is combined by dragging the model visualization connections under various components in the web interface. After the model is built, the model system is initialized, and the real-time or historical operation data of the equipment is obtained by connecting to the data service to start the model calculation. The time range required for simulation can also be selected for recalculation. After the simulation is completed, the indicators, original measurement points and comparison charts can be displayed through trend charts, or trend charts that only display some required parameters can be displayed. The simulation data can also be exported.

[0040] The simulation optimization module is connected to the data visualization module. The data visualization module adopts GIS, BIM and WEBGL. By building a lightweight, GIS and BIM fusion engine based on WebGL technology, it can realize 3D visualization of park information and energy-saving data in different dimensions. It uses BIM technology to establish a building model based on various buildings in the park, optimizes the produced model through post-processing software, obtains high-definition three-dimensional model data, and then combines the data of related GIS maps and data services to realize the display of park scenes, equipment systems, operation data, energy-saving parameters and other scenes or key information in a three-dimensional visualization form.

[0041] Each system of the park's hardware equipment is connected to the switch through network cables, optical fibers or WiFi, and connected to the collection server. The data collection and edge computing modules collect data from the park equipment through the Modbus protocol, and transmit the data to the data processing module in the cloud. The field end is connected to the cloud through the 5G network, and a firewall is deployed to ensure network security; data storage modules, basic platform modules, simulation optimization modules, and data visualization modules are deployed in the cloud.

[0042] The data service uses a big data platform. The data collection of the big data platform transmits the data from different data sources and data structures such as equipment operation data and logs through Kafka and stores them in HDFS, MongoDB, and Redis. Computing tasks such as MapReduce, Hive, and Spark read the data on HDFS for calculation, and then write the results to HDFS, MongoDB, and Redis. The data service runs the data of the simulation optimization module and optimizes the user model parameters.

[0043] After completing the model establishment and model optimization process, the simulation optimization module generates a simulation model for a specific scenario. The established simulation model is sent to the edge computing module, which combines the real-time operation data and the model to complete the calculation and equipment control. The simulation optimization module combines the real-time operation data and the model of the big data platform to perform optimization calculations, writes the calculation results to the big data platform database or the database of the basic platform module, and completes the control optimization of the equipment based on the optimization results.

[0044] Data services provide data for applications on the basic platform. The basic platform adopts microservices and a front-end and back-end separated architecture. The application services of the basic platform are continuously integrated according to business needs. The calculation and analysis result data of the application services can be stored in the big data platform database or persistence layer. Data services provide display data for data visualization, and display the data of equipment and systems through GIS, BIM, WEBGL and other technologies. The data of data visualization services may also come from the persistence layer database of the basic platform.

[0045] Embodiment 2

[0046] Based on the scenario of non-big data application, the difference from the first embodiment is that there is no data processing module.

[0047] The operation data of each system of the park's hardware equipment is collected and transmitted to the database of the persistence layer. The persistence layer provides data for the simulation optimization module to complete the model parameter optimization work. The application and control process of the created simulation model are the same as those in the big data scenario. On the one hand, the persistence layer provides data for the data visualization module to display the data of equipment and systems through GIS, BIM, and WEBGL technologies. On the other hand, it provides data for the application services and front-end of the basic platform and maintains the analysis and calculation results of the application.

[0048] Embodiment 3

[0049] An energy-saving control method comprises the following steps:

[0050] S1. Obtaining park equipment scene information;

[0051] S2. Based on the equipment scenario information of the park, an energy consumption analysis model is constructed through the simulation optimization module, and the simulation optimization model and model parameter adjustment are performed;

[0052] S3, the simulation optimization model is deployed to the edge computing module;

[0053] S4, based on data collection, obtain the real-time operation data of the park equipment system, and perform real-time calculation through the simulation optimization model in step S3;

[0054] S5. Based on energy-saving control indicators, control and adjust park equipment through edge computing;

[0055] S6, the simulation optimization module calculates data in real time and transmits it to the big data cluster;

[0056] S7. Based on the scene information of the park equipment, a 3D visualization scene is constructed through a 3D visualization engine and a development framework;

[0057] S8. Display the three-dimensional visualization scene through the data visualization module.

[0058] Park equipment scene information includes working equipment system information and expected energy-saving effects. Working equipment systems include lighting systems, such as office lighting LED-related equipment; HVAC systems, such as air conditioners and heat pumps; and air compressor station systems, such as air compressor stations. Equipment system information includes equipment system composition, location distribution, spatial scope, and equipment environment and working condition information. The expected energy-saving effect can be set to 10% to 15% based on seasonal changes and different equipment working conditions.

[0059] According to the equipment system information and expected energy-saving effects, on the visual management interface, the energy consumption analysis model of the equipment system is formed by visually connecting and combining the models under the physical components, mathematical components, connector components, data components, machine learning components, and solver components based on the simulation optimization module, thus completing the construction of the simulation optimization system model.

[0060] The historical working data and equipment environment condition information of the equipment system are collected by the data acquisition module after connecting with the park hardware equipment through the communication protocol, and the data is transmitted to the database through the network to realize the transparent interaction of the horizontal and vertical data of the system. The big data module realizes the storage and analysis of the equipment system data information, studies the big data cluster and data analysis algorithm, and supports the massive data scale and fast real-time data flow. The energy consumption analysis and optimization system model for high-energy consumption equipment established in the simulation optimization module is calculated by obtaining the real-time and / or historical data in the big data module to complete the parameter adjustment and function iteration improvement of the simulation optimization system model;

[0061] By deploying the iteratively improved simulation optimization model to the edge computing module, the data acquisition module connects to the park hardware equipment through the communication protocol, collects data, and transmits the data to the edge computing module through the network for real-time computing. Based on edge computing, lightweight computing and real-time analysis of data in the park are achieved to achieve low-latency control, fast and intelligent adjustment of the equipment system, and achieve the expected energy-saving effect of the equipment system;

[0062] The equipment simulation optimization model deployed in the edge computing end transmits the real-time calculation data to the data processing module for storage. The equipment simulation optimization model created in the simulation optimization module obtains real-time and / or historical data from the data module, continuously performs model calculations to realize real-time iterative upgrades and improvements of the model to achieve the desired control optimization effect, and transmits the calculation data and model parameters to the data module for storage. The equipment optimization model can also be synchronously updated to the edge computing end to upgrade the adjustment and optimization of the equipment system when necessary;

[0063] Based on various buildings in the park, BIM technology is used to establish a building model. The produced model is optimized through post-processing software to obtain high-definition three-dimensional model data. Combined with the data of related GIS maps and data services, a lightweight GIS+BIM fusion engine based on WebGL technology is built to achieve three-dimensional visualization. Based on the real-time and / or historical data stored in the data processing module and the early warning, monitoring and management function data contained in the basic platform module, the park information and energy-saving data of different dimensions are viewed through the visualization module, and the park scene, equipment system, operation data, energy-saving parameters and other scenes or key information are displayed in a three-dimensional visualization form.

[0064] In the description of the present invention, it is necessary to understand that the terms "coaxial", "bottom", "one end", "top", "middle", "the other end", "upper", "one side", "top", "inside", "front", "center", "both ends" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0065] In the present invention, unless otherwise clearly stipulated and limited, the terms such as "installation", "setting", "connection", "fixation", "screwing", "pad installation" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. Unless otherwise clearly defined, ordinary technicians in this field can understand the specific meanings of the above terms in the present invention according to the specific circumstances.

[0066] The above description shows and describes the preferred embodiments of the present invention. As mentioned above, it should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the invention concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art do not depart from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A park energy-saving management and control system, characterized in that: include: Data collection module, used to collect data from park hardware equipment; Edge computing module, used to control and adjust campus equipment; Basic platform module, used to improve foundation support; Simulation optimization module, used to build energy consumption analysis models and perform visual configuration; Data visualization module, used to visualize the operation status of systems and equipment; Data processing module, used to access, analyze, and store data collected by data acquisition and edge computing modules; The basic platform module includes a front-end module, a microservice module and a persistence layer; The front-end module is used to display the calculation and analysis results of the corresponding application; the microservice module is used to perform calculation and analysis management according to the needs of different business scenarios; the persistence layer is used to provide data support for the front-end module and microservice module, simulation optimization module and data visualization of the basic platform module; The data acquisition module and edge computing module collect data from the park equipment and transmit the data to the data processing module in the cloud. The field end is connected to the cloud through the network, and a firewall is deployed to ensure network security. Data storage module, basic platform module, simulation optimization module, and data visualization module are deployed in the cloud; After completing the model building and model optimization process, the simulation optimization module generates a simulation model for a specific scenario. The established simulation model is sent to the edge computing module, which combines real-time operation data and models to complete calculations and equipment control. In the simulation optimization module, the real-time operation data and model of the big data platform are combined to perform optimization calculations, the calculation results are written into the big data platform database or the database of the basic platform module, and the control optimization of the equipment is completed according to the optimization results; The front-end module includes data display, equipment detection, abnormal monitoring, operation assessment, alarm analysis, statistical reports and system management. The microservice module includes scheduled tasks, real-time data services, user services, statistical report services, monitoring alarm services, and data management services. The persistence layer uses MySQL and the industrial time series database SIPHD; The data processing module includes data access, data storage, data analysis, AI and CDH cluster. The data access, data storage, data analysis and AI use cloud proxy cluster and Kafka cluster. The CDH cluster includes real-time analysis, batch processing and distributed storage system HBase. The simulation optimization module includes energy consumption analysis model library construction and visualization configuration, and the simulation optimization module is connected to the data visualization module; The data visualization module adopts GIS, BIM and WEBGL.

2. A park energy-saving control method, which is executed by the park energy-saving control system according to claim 1, characterized in that: The following steps are involved: S1, the data acquisition module and the edge computing module collect data from the park equipment, obtain the scene information of the park equipment, and transmit the data to the data processing module in the cloud; S2. Based on the scene information of the park equipment, the simulation optimization model and model parameter adjustment are constructed through the simulation optimization module; S3, the simulation optimization model is deployed to the edge computing module; S4, based on data collection, obtain the real-time operation data of the park equipment system, and perform real-time calculation through the simulation optimization model; S5. Based on energy-saving control indicators, control and adjust park equipment through edge computing; S6, the simulation optimization module calculates data in real time and transmits it to the big data cluster; S7. Based on the scene information of the park equipment, a 3D visualization scene is constructed through a 3D visualization engine and a development framework; S8. Display the three-dimensional visualization scene through the data visualization module.

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