Industrial energy intelligent control passage system

Abstract

The invention discloses an industrial energy intelligent control passage system, and aims to solve the problems of low data acquisition efficiency, incomplete analysis, insufficient energy consumption optimization and the like in traditional energy management. The system comprises a data acquisition module, a data analysis module, an intelligent control module, an energy optimization scheduling module, a cost management and assessment module, a safety guarantee module and a man-machine interaction module. Energy data of water, electricity, gas and the like are collected in real time through a high-precision sensor, and energy consumption monitoring, optimal control, cost management and energy-saving effect evaluation are achieved in combination with big data analysis and an intelligent algorithm. The system can provide multi-dimensional energy consumption analysis reports, energy-saving optimization suggestions and energy consumption prediction, and provides support for enterprises to formulate scientific energy plans and optimization strategies. Besides, the system has a powerful early warning function and information safety guarantee, the safety and stability of equipment operation are effectively improved, meanwhile, the energy cost and the influence on the environment are reduced, and enterprises are assisted in achieving green production and sustainable development.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial energy management, and in particular to an industrial energy intelligent control channel system. Background Art

[0002] In the context of rapid industrial development, energy management faces multiple challenges:

[0003] Limitations of traditional methods: Traditional manual transcription and management methods are inefficient, data acquisition is untimely and inaccurate, and cannot meet the needs of modern industry for real-time energy monitoring and optimization, resulting in energy waste and frequent abnormal equipment operation.

[0004] Corporate costs and environmental pressures: As global energy prices continue to rise and environmental regulations become increasingly stringent, companies not only need to face energy cost pressures, but also need to fulfill their social responsibility for green development.

[0005] Urgency of technology needs: Modern enterprises are in urgent need of efficient and intelligent energy management solutions to improve energy utilization efficiency, reduce energy costs, and ensure the safety and stability of equipment operation through real-time collection, accurate analysis, and optimized control of energy data.

[0006] Driven by technological development: In recent years, the rapid development of sensor technology, big data analysis, intelligent control and Internet of Things technology has provided strong technical support for energy management. The integrated application of these technologies makes it possible to collect energy data in real time, accurately process it and make intelligent decisions, which has promoted industrial energy management towards high efficiency and intelligence.

[0007] Against this technological background, the industrial energy intelligent control system came into being, integrating advanced sensing, big data and intelligent control technologies to provide enterprises with comprehensive, efficient and intelligent energy management solutions, and help the green transformation and sustainable development of the industry. Summary of the invention

[0008] 1. Technical Problems Solved

[0009] Data collection and monitoring issues

[0010] The traditional manual energy data recording method is extremely inefficient and cannot obtain data in real time. For example, manual meter data recording may only be done once a day, and changes in energy usage during this period cannot be known in a timely manner, resulting in the company's inability to adjust its energy usage strategy in a timely manner.

[0011] Manual data transcription is prone to human errors, such as reading errors, recording errors, etc., which greatly reduce the accuracy of the data, affect the company's correct judgment of energy usage, and thus fail to effectively manage energy.

[0012] Energy analysis and optimization issues

[0013] Enterprises lack professional and effective data analysis tools, making it difficult to process large amounts of energy data in depth. For example, it is impossible to find out the energy consumption patterns and potential problems in each production link from complex energy consumption data.

[0014] Due to the lack of analytical means, enterprises cannot fully understand the energy usage situation, cannot determine the specific links and causes of energy waste, and find it difficult to formulate targeted energy-saving measures, resulting in energy utilization efficiency being at a low level for a long time.

[0015] Energy consumption forecasting and planning issues

[0016] Enterprises do not have a scientific energy consumption forecasting method and cannot accurately estimate energy demand in the future. For example, when formulating production plans, they are not clear about the amount of energy required for different production tasks, which can easily lead to insufficient or excessive energy supply.

[0017] Due to the inability to accurately predict energy consumption trends, companies lack a basis for formulating energy procurement and reserve plans, which may result in excessive energy procurement increasing costs, or too little procurement affecting production continuity.

[0018] Energy cost management and assessment issues

[0019] It is difficult for enterprises to accurately convert energy consumption into actual costs. They are not clear about the energy cost composition of various production activities or equipment and cannot effectively control energy costs. For example, if they do not know the proportion of energy costs in a production workshop in the total cost, it is difficult to conduct cost accounting and cost optimization.

[0020] Lacking objective energy consumption assessment standards and energy-saving effect evaluation methods, enterprises are unable to measure the effectiveness of energy-saving work, cannot motivate employees to actively participate in energy conservation, and find it difficult to promote continuous improvement of energy-saving measures.

[0021] Based on the above objectives, the present invention provides an industrial energy intelligent control system

[0022] Technical points of the present invention

[0023] Real-time data collection and transmission technology

[0024] High-precision sensor network

[0025] Various types of high-precision sensors are used, such as power sensors (including current transformers, voltage transformers, etc.), water flow sensors (such as electromagnetic flowmeters, turbine flowmeters, etc.), and gas sensors (such as gas turbine flowmeters, diaphragm gas meters, etc.), which are distributed in various key energy usage nodes of industrial enterprises, such as distribution rooms, pump rooms, gas pressure regulating stations, etc., to collect real-time usage data of various types of energy to ensure that the data can truly and accurately reflect the energy consumption of the enterprise.

[0026] The sensor has high sensitivity and stability, can adapt to complex industrial environments such as high temperature, high humidity, strong electromagnetic interference, etc., ensure long-term stable operation, reduce maintenance costs, and can accurately measure tiny energy changes.

[0027] Stable and reliable data transmission network

[0028] Build a data transmission network architecture that combines wired and wireless. The wired network uses industrial Ethernet technology and transmission media such as network cables or optical fibers to ensure high stability and high bandwidth of data transmission. It is suitable for the transmission needs of fixed equipment and large amounts of data within the enterprise. For example, equipment on the production line, large energy-consuming equipment, etc. are connected to the central processing system via Ethernet.

[0029] Wireless networks use 4G / 5G communication technology to provide data transmission solutions for mobile devices or areas where wiring is inconvenient. 4G / 5G communication modules are installed on the sensor end to transmit the collected data to the central processing system in real time. For example, sensors deployed in the company's outdoor warehouses, temporary production areas, etc. can transmit data through wireless networks. At the same time, reliable data transmission protocols such as TCP / IP protocols are used during the data transmission process to ensure the integrity and accuracy of the data and prevent data loss or damage.

[0030] Big data analysis and mining technology

[0031] Powerful data analysis platform

[0032] Build a big data analysis platform based on cloud computing technology, which has the ability to store massive data and uses a distributed file system (such as Hadoop Distributed File System, HDFS) to store energy data. It can accommodate a large amount of historical energy usage data accumulated over a long period of time and data collected in real time.

[0033] The platform has efficient data processing capabilities and uses distributed computing frameworks (such as Apache Spark) to clean, convert and analyze data. The data cleaning process can remove noise data, outliers and duplicate data to ensure data quality; data conversion can unify data in different formats and units into a standard format for subsequent analysis. For example, the power data collected by different models of meters can be uniformly converted into kilowatt-hours (kWh).

[0034] Data mining technology application

[0035] Use data mining algorithms, such as association rule mining (Apriori algorithm, etc.), cluster analysis (K-Means algorithm, etc.), classification algorithms (decision tree algorithm, etc.), etc., to conduct in-depth mining of energy data. Through association rule mining, the correlation between different energy consumption parameters can be discovered, such as the relationship between power consumption and equipment running time and output; cluster analysis can classify equipment or production links with similar energy usage patterns to facilitate the identification of groups with abnormal energy consumption; classification algorithms can classify and predict energy usage, such as predicting whether the equipment is in a normal or abnormal energy consumption state.

[0036] Intelligent prediction and decision support technology

[0037] Energy consumption prediction model

[0038] Based on historical energy consumption data and real-time collected data, an energy consumption forecasting model is established using intelligent algorithms (such as time series analysis algorithms, neural network algorithms, etc.). Time series analysis algorithms can predict future energy consumption based on the trends and periodicity of historical data; neural network algorithms can learn complex energy consumption patterns and improve the accuracy of forecasts. For example, for power consumption forecasting, a multivariate forecasting model is established taking into account factors such as the seasonality of corporate production and the differences between working days and non-working days.

[0039] The model can predict energy consumption according to different time scales (such as short-term: hours, days; medium-term: weeks, months; long-term: quarters, years), providing a basis for enterprises to formulate energy plans at different stages. At the same time, the model has self-learning and adaptive capabilities, and can continuously adjust and optimize the prediction results according to new data to improve the reliability of the prediction.

[0040] Decision support functions

[0041] The system provides intelligent decision support functions based on the actual needs and constraints of the enterprise's production plan, energy supply, energy cost, etc. For example, based on the output target and equipment operation arrangement in the production plan, as well as the current energy market price and the enterprise's energy reserve situation, it provides the enterprise with decision suggestions such as the best energy procurement time, procurement volume and energy allocation plan.

[0042] The system can simulate energy consumption and cost situations under different decision-making schemes to help enterprises evaluate the risks and benefits of decision-making. For example, it can simulate the changes in energy consumption and costs after adopting different energy-saving measures (such as equipment upgrades, production process improvements, etc.) to provide a reference for enterprises to choose the best energy-saving scheme.

[0043] Energy cost management and assessment technology

[0044] Energy cost management system

[0045] Establish a sound energy cost management system, link energy consumption data with energy price information in real time, and accurately calculate energy costs. Energy price information is obtained in real time through the interface with energy suppliers to ensure the timeliness and accuracy of cost calculation. For example, according to the real-time power market price fluctuations, timely adjust the power cost calculation.

[0046] Conduct multi-dimensional analysis of energy costs, including cost segmentation by energy type (electricity, water, gas, etc.), by production department or workshop, by equipment, etc., to identify the key links of cost control. For example, if the analysis shows that the energy cost of a certain high-energy-consuming equipment accounts for a large proportion, it provides a basis for targeted cost control.

[0047] Energy consumption assessment and incentive mechanism

[0048] Set reasonable energy consumption benchmarks and energy-saving targets. The energy consumption benchmark is determined based on the company's historical energy consumption data, industry averages, equipment operation standards, and other factors. For example, the average unit product energy consumption of similar companies in the same industry is used as a reference, and the energy consumption benchmark is formulated in combination with the actual situation of the company. The energy-saving target clearly stipulates the energy consumption reduction ratio or cost savings amount that the company or each department needs to achieve within a certain period of time (such as an annual period).

[0049] Energy consumption assessments are conducted regularly. The assessment cycle can be set monthly, quarterly or annually according to the actual situation of the enterprise. By comparing the gap between actual energy consumption and energy consumption benchmarks and energy-saving targets, the energy efficiency of each department or equipment is evaluated. An effective incentive mechanism is established based on the assessment results. Rewards (such as bonuses, honorary titles, etc.) are given to departments or individuals with significant energy-saving effects. Analyze and guide those that fail to meet the standards, propose improvement measures, and promote continuous improvement of the overall energy-saving work of the enterprise.

[0050] Safety and reliability assurance technology

[0051] Information Security Technology

[0052] Firewall technology is used to establish a security barrier at the system network boundary to prevent external illegal network access. The firewall sets strict access rules, allowing only authorized IP addresses and ports for data transmission to prevent external network attacks (such as hacker attacks, malware intrusions, etc.). For example, external unauthorized IP addresses are prohibited from accessing the database server of the enterprise energy management system.

[0053] Implement user identity authentication and authorization management, assign unique usernames and passwords to system users, and use multi-factor authentication methods (such as password + dynamic verification code, password + fingerprint recognition, etc.) to enhance authentication security. Assign different system operation permissions according to user roles and responsibilities to ensure that users can only access data and functions within their authorized scope to prevent data leakage and illegal operations. For example, ordinary operators can only view energy data reports, while system administrators can perform operations such as system configuration and data maintenance.

[0054] The sensitive data in the system is encrypted and stored, and advanced encryption algorithms (such as AES encryption algorithm) are used to encrypt the data to ensure the security of the data during storage. At the same time, during the data transmission process, encryption protocols such as SSL / TLS are used to encrypt the data to prevent the data from being stolen or tampered with during the transmission process. For example, the energy consumption data and cost data of the enterprise are encrypted during storage and transmission.

[0055] System reliability design

[0056] Redundancy design is implemented for key equipment and modules, such as using dual-machine hot standby or cluster technology for servers. During normal operation, the main server undertakes the main computing and data processing tasks of the system, and the backup server synchronizes the data and status of the main server in real time. When the main server fails, the backup server can quickly take over the work to ensure the uninterrupted operation of the system and the continuity of the enterprise's energy management. For example, the enterprise's energy data center adopts a dual-machine hot standby server architecture. When the main server hardware fails or the software crashes, the backup server can start and take over the work of the main server in a very short time (usually within a few seconds to a few minutes), avoiding data loss and system downtime.

[0057] A complete fault recovery mechanism is established, and the system can automatically detect equipment failures, network anomalies, etc. Once a fault is detected, the system immediately starts the fault diagnosis program to determine the fault location and cause, and automatically recovers according to the preset fault recovery strategy. For example, in the case of network communication failure, the system automatically switches to the backup network link; in the case of sensor failure, the system issues an alarm and uses the data of other related sensors for estimation and compensation, and notifies maintenance personnel to replace it. In addition, the system data is backed up regularly, and the backup data is stored in an off-site disaster recovery center to prevent local data from being lost due to natural disasters, hardware failures, etc., to ensure the integrity and availability of system data.

[0058] Beneficial effects of the present invention:

[0059] Efficient and accurate data collection and monitoring: Realize real-time automatic collection of energy data, detect anomalies in time, and improve equipment operation stability.

[0060] Intelligent energy analysis and optimization: Provide energy consumption analysis and energy-saving suggestions through big data technology to improve energy utilization efficiency.

[0061] Scientific energy consumption forecasting and planning: Supports energy consumption trend forecasting and optimizes resource allocation to ensure production continuity.

[0062] Transparent energy cost management: Convert energy consumption into actual cost to support refined management and energy-saving assessment.

[0063] Visual energy-saving effect evaluation: Provides before-and-after comparisons of energy saving to support enterprises in optimizing energy-saving strategies.

[0064] Safety and environmental protection guarantee: It has early warning function, improves the safety of energy use, and promotes green production.

[0065] Wide applicability and integration: covers the management of multiple energy types, suitable for industrial and new energy fields, and the system is stable and reliable.

[0066] The system effectively improves the company's energy management efficiency, reduces operating costs and promotes sustainable development. DETAILED DESCRIPTION

[0067] The present invention is described in detail below in conjunction with specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies.

[0068] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0069] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0070] The industrial energy intelligent control system of the present invention is intended to solve many problems in energy management of industrial enterprises. The specific implementation methods are as follows:

[0071] 1. Data collection and transmission technology

[0072] 1. High-precision sensor network

[0073] High-precision sensors are deployed at key nodes of industrial enterprises, such as power distribution rooms, pump rooms, gas stations, etc. These sensors can accurately collect usage data of various energy sources such as electricity (including parameters such as voltage, current, power factor, etc.), water (flow, pressure, etc.), and gas (flow, pressure, etc.). For example, power sensors use high-precision current transformers and voltage transformers to ensure accurate measurement of power parameters; water flow sensors use advanced electromagnetic induction principles, are sensitive to changes in water flow, and can obtain water flow data in real time.

[0074] The sensors are highly sensitive and can detect small changes in energy usage. At the same time, regular calibration and maintenance are required to ensure their measurement accuracy, thus providing the system with true and reliable energy usage data.

[0075] 2. Stable and reliable data transmission network

[0076] The network is built by combining wired and wireless data transmission. Within the enterprise, industrial Ethernet is used as the main wired transmission method. It has the characteristics of high stability and high bandwidth, and is suitable for the stable transmission of large amounts of data. For example, Ethernet cables are laid in factory workshops, office buildings and other areas to connect various sensors to the central processing system.

[0077] For some areas or mobile devices that are not convenient for wiring, such as outdoor energy monitoring equipment and temporary production equipment, 4G / 5G communication technology is used for wireless transmission. By installing a 4G / 5G communication module on the sensor end, the collected data is sent to the central processing system in real time. For example, in some open-air warehouses in large factory areas, 4G / 5G is used to wirelessly transmit energy usage data of power and lighting equipment.

[0078] During data transmission, data encryption technology, such as the Advanced Encryption Standard (AES) encryption algorithm, is used to encrypt the transmitted data to prevent the data from being stolen or tampered with during transmission, thereby ensuring the security and integrity of data transmission.

[0079] 2. Data Analysis and Processing Technology

[0080] 1. Big Data Processing Platform

[0081] A big data processing platform based on cloud computing architecture is built with powerful computing and storage capabilities. The platform can efficiently store the collected massive energy data, and the storage medium uses a combination of high-performance solid-state drives (SSDs) and large-capacity mechanical hard drives (HDDs) to meet the speed and capacity requirements of data storage.

[0082] Clean the data to remove noise data, outliers and duplicate data to ensure data accuracy and availability. For example, by setting a reasonable data threshold range, obviously erroneous energy data can be filtered out.

[0083] Data conversion is performed to convert data in different formats and units into a standard format that the system can process for subsequent analysis. For example, the unit of water flow is converted into cubic meters per hour, and the unit of electric power is converted into kilowatts.

[0084] Use data mining techniques, such as association rule mining, cluster analysis and other algorithms, to conduct in-depth mining of energy data. For example, through association rule mining, the relationship between power consumption and production equipment operation time can be discovered, providing a basis for energy-saving optimization for enterprises.

[0085] (II) Application of Intelligent Algorithms

[0086] Use machine learning algorithms, such as support vector machines (SVMs) and decision trees, to predict and analyze energy data. Build an energy consumption prediction model based on historical energy consumption data and related production data (such as output, equipment operating status, etc.). For example, based on the power consumption data of the past year and the corresponding production output data, train a prediction model to predict power demand under different future production plans.

[0087] Deep learning algorithms, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), are used for anomaly detection. By learning the normal energy usage pattern, the system can automatically identify abnormal situations in the energy usage process, such as sudden power peaks, abnormal water flow fluctuations, etc. For example, when the production equipment is operating normally, the power consumption shows a certain pattern. Once the power consumption is significantly different from the normal pattern, the system will immediately issue an abnormal alarm.

[0088] 3. Intelligent control and optimization technology

[0089] (I) Automatic control system

[0090] An automated control system is constructed with PLC (Programmable Logic Controller) and DCS (Distributed Control System) as the core control devices. PLC is responsible for the control of a single device or a small group of devices, such as precise control of motors, valves and other equipment on a production line. DCS focuses on the coordinated control of multiple devices and systems throughout the production process, achieving an organic combination of centralized management and decentralized control.

[0091] Based on the real-time collected energy data and the analysis results of the intelligent algorithm, the system automatically generates control instructions and automatically adjusts the energy equipment. For example, when it detects that the power load in a certain area is too high, the system automatically adjusts the operating status of some non-critical equipment in the area, such as reducing the cooling power of air-conditioning equipment, suspending the operation of some standby equipment, etc., to achieve optimal configuration and efficient use of energy.

[0092] 2. Energy Optimization Scheduling Strategy

[0093] Formulate energy optimization scheduling strategies based on the company's production plan and energy needs. Through intelligent scheduling algorithms, various energy resources (such as electricity, steam, gas, etc.) within the company are rationally allocated and scheduled by comprehensively considering factors such as energy costs, equipment operating efficiency, and production task priorities. For example, during peak production periods, priority is given to ensuring the energy supply of key production equipment, while reasonably adjusting the energy use time of other equipment to avoid bottlenecks and waste in energy supply.

[0094] Establish an energy use priority model to determine the energy use priority of different equipment and production links based on factors such as the importance of the equipment and the urgency of the production task. When energy supply is tight, allocate energy in order of priority to ensure the continuity and stability of enterprise production. For example, key equipment that directly affects product quality and production progress is given the highest energy use priority.

[0095] IV. Energy cost management and assessment technical means

[0096] 1. Energy cost management system

[0097] Establish a complete energy cost management system, combine energy consumption data with energy price information, and calculate energy costs in real time. Energy price information is obtained through the interface with energy suppliers to ensure the accuracy of cost calculation. For example, based on the real-time unit price of electricity and the company's electricity consumption data, accurately calculate the hourly, daily or monthly electricity cost.

[0098] Energy costs are managed by categories, and costs are segmented according to different energy types (such as electricity, water, gas, etc.), different production departments or workshops, different equipment groups, etc. Through cost segmentation, enterprises can clearly understand the composition and proportion of each part of energy costs, providing a detailed basis for cost control. For example, the analysis shows that the electricity cost of a production workshop accounts for too high a proportion of the total energy cost, and then the electricity usage of the workshop is optimized in a targeted manner.

[0099] Set energy consumption benchmarks and energy conservation targets. According to the company's historical energy consumption data and the industry average, formulate a reasonable energy consumption benchmark. For example, based on the company's average unit product energy consumption in the past three years, combined with the industry's advanced level, determine the energy consumption benchmark value for each production link. At the same time, formulate clear energy conservation targets, such as reducing unit product energy consumption by a certain percentage in the next year.

[0100] (II) Energy-saving effect evaluation and assessment

[0101] Regularly evaluate and assess the energy conservation work of the enterprise. The evaluation cycle can be set to monthly, quarterly or annually. By comparing the energy consumption data, energy cost data and other indicators before and after the implementation of energy conservation measures, the actual effect of energy conservation measures can be measured. For example, compare the power consumption of a certain equipment before and after the energy conservation transformation to calculate the energy conservation rate.

[0102] The energy-saving effect evaluation index system is used to comprehensively evaluate the benefits of energy-saving projects, including indicators such as energy saving amount, energy saving rate, cost reduction, and investment payback period. For example, for a lighting system energy-saving transformation project, the annual electricity savings, electricity bill savings, and investment payback period of the project are calculated to determine the economic feasibility and energy-saving effect of the project.

[0103] Based on the evaluation and assessment results, we will reward departments or individuals with outstanding energy-saving performance to encourage employees to actively participate in energy-saving work. At the same time, we will analyze and guide departments or individuals that have not achieved energy-saving targets, formulate improvement measures, and promote continuous improvement of energy-saving work.

[0104] V. Technical means to ensure safety and reliability

[0105] (I) Information security protection

[0106] Firewall technology is used to establish a security barrier between the system and the external network to prevent illegal external network access. The firewall sets strict access rules, allowing only authorized IP addresses and ports for data interaction to prevent external network attacks and malware intrusions. For example, external unknown IP addresses are prohibited from accessing the system database.

[0107] Implement user authentication and authorization management, assign unique usernames and passwords to system users, and use multi-factor authentication methods, such as password + dynamic verification code, password + fingerprint recognition, etc., to enhance the security of user authentication. According to the user's role and responsibilities, assign corresponding system operation permissions to ensure that users can only access functions and data within their authorized scope. For example, ordinary operators can only view energy data reports, while system administrators can perform system configuration and parameter adjustments.

[0108] The sensitive data in the system is encrypted and stored, and key information such as user passwords are encrypted using hash algorithms (such as MD5, SHA-256, etc.) to ensure the security of data during storage. At the same time, during data transmission, the SSL / TLS protocol is used to encrypt data transmission to prevent data from being stolen or tampered with. For example, when a user logs into the system, the password is encrypted during transmission, and the password stored in the database is also in encrypted ciphertext form.

[0109] (II) System redundancy design and fault recovery

[0110] Redundancy design is implemented for key equipment and modules, such as using dual-machine hot standby or cluster technology for servers. During normal operation, the main server undertakes the main computing and data processing tasks of the system, and the backup server synchronizes the data and status of the main server in real time. Once the main server fails, the backup server can immediately take over the work to ensure the uninterrupted operation of the system. For example, in the energy data center of an enterprise, two high-performance servers are configured, one as the main server and the other as the backup server. When the main server has a hardware failure or software crash, the backup server starts and takes over the work of the main server in a very short time (usually within a few seconds to a few minutes).

[0111] A complete fault recovery mechanism is established, and the system can automatically detect equipment failures and network anomalies. When a fault is detected, the system immediately starts the fault diagnosis program to determine the fault location and cause. At the same time, according to the preset fault recovery strategy, appropriate measures are taken to recover. For example, in the case of network communication failure, the system automatically switches to the backup network link to ensure the continuity of data transmission; in the case of sensor failure, the system issues an alarm and notifies maintenance personnel to replace it, and uses the data of other related sensors for estimation and compensation to maintain the normal operation of the system. In addition, the system data is backed up regularly, and the backup data is stored in a disaster recovery center in a different location to prevent local data loss. In the event of data corruption caused by a major failure, the data can be restored from the backup center to ensure the integrity and availability of the system data.

[0112] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0113] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An industrial energy intelligent control channel system, characterized in that: The system includes the following modules: Data acquisition module: A high-precision sensor network deployed at industrial sites to collect real-time data on energy sources such as water, electricity, gas, cooling and heat, and transmit the data to a central processing system via a wired or wireless transmission network; Data analysis module: Based on the big data processing platform and intelligent algorithms, it stores, cleans, converts, mines and performs multi-dimensional statistical analysis on energy data, identifies anomalies and waste in energy use, and generates energy-saving optimization suggestions; Intelligent control module: including core control components such as PLC (programmable logic controller) and DCS (distributed control system), which can realize remote monitoring and automatic control of energy equipment according to data analysis results; Energy optimization and scheduling module: Combines historical data, real-time data and production plans to dynamically adjust the operation mode of energy equipment to achieve optimal allocation and scheduling of energy resources; Cost management and assessment module: convert energy consumption into actual cost, set energy consumption benchmarks, and conduct energy consumption assessment and energy-saving effect evaluation; Security module: Use information security technology and system redundancy design to ensure the security and stability of data and equipment; Human-computer interaction module: Displays energy usage reports through smart terminals or software platforms, and provides real-time monitoring and adjustment suggestions.

2. According to the industrial energy intelligent control system according to claim 1, the data acquisition module combines RS485 communication protocol and 4G / 5G wireless communication technology to achieve efficient and stable data transmission.

3. The industrial energy intelligent control system according to claim 1, wherein the data analysis module adopts a machine learning algorithm to support energy usage trend prediction, anomaly detection and energy saving potential mining.

4. According to the industrial energy intelligent control system of claim 1, the intelligent control module automatically adjusts the operating parameters of the energy equipment through a feedback control mechanism to ensure efficient use of energy and stable operation of the equipment.

5. The industrial energy intelligent control system according to claim 1, wherein the energy optimization scheduling module formulates and implements a dynamic energy allocation strategy through an intelligent scheduling algorithm combined with the actual needs and constraints of the enterprise.

6. According to the industrial energy intelligent control system of claim 1, the cost management and assessment module can generate energy consumption analysis reports in real time and provide comparative data on the energy-saving effects of the enterprise.

7. The industrial energy intelligent control system according to claim 1, wherein the security module prevents data leakage through encrypted transmission and access control technology, and improves system reliability through redundant design and fault recovery mechanism.