Electric energy metering management method for coking enterprise

Abstract

The invention relates to the technical field of energy management, in particular to a coking enterprise electric energy metering management method, which comprises the following steps of: forming a matrix metering network through an electric meter, a current sensor and a process control system deployed by a sensing layer; preprocessing the collected electric energy data through an edge computing gateway of the network layer and transmitting the electric energy data to the platform layer; the platform layer stores the electric energy data, performs correlation analysis in combination with the production data, and is used for completing dynamic calibration and peak-valley power consumption optimization; three-level electric energy metering management is completed in an application layer, and electric energy consumption, energy efficiency benchmarking and abnormal alarm information are displayed through a visual platform. The method has the advantages that step-by-step energy consumption monitoring and management from a whole plant to a workshop section to equipment are realized, links and equipment with high energy consumption can be accurately positioned, and a basis is provided for targeted energy-saving measures; and each level is clear in metering, and energy consumption indexes are decomposed layer by layer and are implemented to specific working sections and equipment, so that each level is clear to own energy consumption conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a method for electric energy metering management in a coking enterprise. Background Art

[0002] Traditional coking enterprises have multi-dimensional technical bottlenecks in power management:

[0003] 1. Data collection defects: only basic parameters such as voltage and current are monitored, and the sampling period is more than 15 minutes;

[0004] 2. System silo problem: Lack of correlation analysis between energy data and production data (such as equipment start and stop records, coke output);

[0005] 3. Extensive management: The minimum control unit is only at the workshop level, and it is impossible to track energy consumption at the process-equipment level.

[0006] The above problems lead to inefficient power management in high-energy-consuming production scenarios, abnormal response delays exceeding 30 minutes, and comprehensive power consumption higher than the industry's advanced level. Especially under the time-of-use electricity price policy, traditional systems are unable to optimize peak and valley power consumption, resulting in annual electricity waste of millions of yuan. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for electricity metering management in a coking enterprise, fill the gap in electricity management in coking enterprises, and achieve the goal of reducing energy consumption and improving energy efficiency; realize refined electricity management through real-time data collection, dynamic analysis and management models, and promote the transformation of enterprises from extensive energy management to data-driven refined mode, thereby reducing production costs and improving energy-saving benefits.

[0008] To achieve the above object, the present invention is implemented through the following technical solutions:

[0009] A method for electric energy metering management in a coking enterprise includes the following contents:

[0010] S1. A matrix metering network is formed by the electric meters, current sensors, and process control systems deployed in the perception layer. This network reads the electric energy data collected by the electric meters and current sensors, and the equipment operating status data collected by the process control system in real time.

[0011] S2. Pre-process the collected power data through the edge computing gateway of the network layer and transmit the pre-processed data to the platform layer through industrial Ethernet or wireless communication protocol;

[0012] S3. Use a time series database to store power data at the platform level and perform correlation analysis in combination with production data to complete dynamic calibration and peak-valley power optimization.

[0013] S4. Complete the three-level electricity metering management at the application layer, and display the electricity consumption, energy efficiency benchmarking and abnormal alarm information through the visualization platform.

[0014] In S1, the sensing layer includes multifunctional energy meters configured for each circuit of the high-voltage power system, multifunctional energy meters configured for each system and process unit power supply circuit of the low-voltage power system, and multifunctional energy meters configured for the power supply circuit of key energy-consuming equipment in each process unit. Communication interfaces are reserved for the multifunctional energy meters.

[0015] Electrical energy data includes voltage, current, power, and power factor.

[0016] In S2, the edge computing gateway of the network layer collects power data in real time to complete the conversion from the application layer protocol to the Modbus-TCP protocol, and performs differentiated processing on the data according to the data classification rules; preprocessing includes data classification, filtering, outlier elimination, and data compression.

[0017] Data classification rules include:

[0018] Fault signals are given a first-level priority and are transmitted immediately using raw data.

[0019] The process parameters are given a second-level priority and are transmitted within 5 seconds and edge feature extraction is performed;

[0020] Energy efficiency data is prioritized at level three, transmitted within 15 minutes, and aggregated and stored locally.

[0021] In S3, the dynamic calibration of the platform layer includes: comparing the logical consistency of the power data of adjacent nodes (for example, due to changes in the production plan, the output of a certain process decreases, resulting in a decrease in the power consumption of all power-consuming equipment. If the power consumption of a certain device increases, it means that the device has failed and there is a logical inconsistency, which requires maintenance), identifying equipment deviations (identifying equipment deviations means that after the equipment has logical inconsistencies, the expert database is used to give the cause of the equipment failure) and triggering the measurement and calibration process.

[0022] In S3, production data includes production plans, actual output, and equipment start and stop times.

[0023] In S3, the peak-valley electricity consumption optimization at the platform level includes increasing the operating time of non-continuous production equipment during periods of low electricity prices and optimizing the peak-valley electricity consumption ratio.

[0024] In S4, the three-level electricity metering management includes: energy-consuming unit level, secondary energy-consuming unit level, and basic energy-consuming unit level. The energy-consuming unit level is used to measure the electricity entering and leaving the coking enterprise, the secondary energy-consuming unit level is used to measure the electricity entering and leaving the energy accounting units directly under the energy-consuming unit, and the basic energy-consuming unit level is used to measure the electricity entering and leaving the production processes, sections, or equipment under the secondary energy-consuming unit.

[0025] In S4, the visualization platform of the application layer issues early warnings for abnormal conditions based on the equipment energy efficiency threshold, and generates the most cost-effective coal loading time plan through a dynamic programming algorithm.

[0026] It also includes identifying sensor drift or mechanical wear and predicting equipment life by comparing energy consumption data with adjacent equipment.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. A hierarchical structure is adopted to build a four-layer network architecture consisting of the perception layer, network layer, platform layer, and application layer. Coking enterprises are managed according to a three-level metering structure: energy-consuming unit level (whole plant), secondary energy-consuming unit level (work section), and basic energy-consuming unit level (equipment). This system has high security, flexible configuration, and anti-interference capabilities, and realizes step-by-step energy consumption monitoring and management from the whole plant to the work section and then to the equipment. It can accurately locate high-energy-consuming links and equipment, avoid the extensive management brought about by traditional "big pot" accounting, and provide a basis for targeted energy-saving measures. The clear metering at each level facilitates the decomposition and implementation of energy consumption indicators to specific work sections and equipment, so that each level has a clear understanding of its own energy consumption status, enhances energy-saving awareness, and promotes proactive energy-saving actions in each link. Management can formulate more targeted production plans and equipment maintenance plans based on energy consumption data at different levels. For example, the overall production strategy can be adjusted according to the energy consumption trend of the whole plant, the process flow can be optimized for energy consumption problems in specific work sections, and equipment-level energy consumption anomalies can be repaired or replaced in a timely manner.

[0029] 2. Adopt edge intelligent processing: Implement data classification (see Table 1), dynamic compression, and protocol conversion (such as supporting Modbus / Profibus protocols) at the gateway layer. Data classification can perform differentiated processing based on the importance and urgency of the data, ensuring the immediate transmission and priority processing of critical data (such as fault signals), avoiding data congestion, improving the system's response speed to abnormal situations, and ensuring the safety of the production process. Dynamic data compression can significantly reduce the amount of data and reduce the demand for communication network bandwidth. Especially in wireless communication environments, it can reduce communication costs and improve communication efficiency, enabling the system to transmit more valid data within limited bandwidth. Supporting conversion of multiple common industrial protocols can achieve interoperability between devices of different manufacturers and types, breaking down communication barriers between devices, building a unified power metering and management system, and facilitating enterprise equipment integration and management. Data is pre-processed at the edge gateway, such as filtering out redundant data and performing preliminary analysis, and only valuable data is transmitted to the server, reducing the data processing pressure on the cloud server, improving the operating efficiency of the entire system, and reducing the demand and cost of cloud computing resources.

[0030] 3. Use process coupling analysis: Associate electricity data with production time series data to establish core indicators such as electricity consumption per ton of coke and equipment energy efficiency ratio. By closely combining electricity data with production time series, the electricity consumption in the production process of each ton of coke and the energy efficiency ratio of various equipment can be accurately calculated, providing a scientific and accurate quantitative basis for evaluating the energy utilization efficiency of the production process and helping enterprises to clearly understand their own energy efficiency level. Based on process coupling analysis, high-energy-consuming production links and equipment can be quickly located, and the causes of abnormal energy consumption can be deeply analyzed to provide a clear direction for formulating optimization measures. For example, if the electricity consumption per ton of coke in a certain process is found to be too high, the process parameters and equipment operating status of the process can be adjusted and optimized in a targeted manner to reduce energy consumption. The core indicators can intuitively reflect the energy efficiency status of the production process. Operators can adjust production operations in real time based on these indicators, optimize production processes, achieve refined management of the production process, improve production efficiency, and reduce production costs.

[0031] 4. Deploy a matrix metering network to collect various electrical parameters in real time, pre-process them through the edge computing gateway, and then transmit the pre-processed data to the power metering management system server through industrial Ethernet or wireless communication protocols. The matrix metering network can widely cover various key nodes of the coking enterprise, including high-voltage circuits, low-voltage circuits and key energy-consuming equipment, etc., to achieve comprehensive and real-time collection of various electrical parameters, ensure the integrity and accuracy of the data, and provide a rich and reliable data foundation for power management and analysis; the edge computing gateway pre-processes the original collected data, such as removing noise, filtering, and eliminating outliers, to improve the quality and availability of the data and reduce the impact of erroneous data on subsequent analysis The impact of data loss and decision-making on energy efficiency and energy consumption can ensure the accuracy and reliability of energy metering. Industrial environments are often complex and changeable. The matrix metering network combined with edge computing has good adaptability. Even in the case of unstable communication conditions or network failures, the edge computing gateway can temporarily store data and retransmit it after communication is restored, ensuring data continuity and integrity and avoiding energy management gaps caused by data loss. The matrix metering network can flexibly deploy metering equipment according to the company's production layout and actual needs, facilitating rapid expansion and access to new equipment or production areas. At the same time, the edge computing gateway can also facilitate corresponding configuration adjustments based on the increase or decrease of collection points, making the system highly flexible and scalable.

[0032] 5. Utilize a time-series database to store electricity data and perform correlation analysis with production data such as production plans, actual output, and equipment start and stop times. Combining electricity data with production data allows for in-depth analysis of energy consumption trends from the perspective of time and production activities, comprehensive assessment of energy efficiency in the production process, identification of hidden energy efficiency issues, and strong support for developing more effective energy-saving strategies. By correlating this data with production plans, output, and other data, accurate energy efficiency benchmarking can be performed. Actual energy consumption can be compared with planned energy consumption and industry-leading energy consumption levels to identify gaps and deficiencies in energy efficiency and clarify energy-saving targets and improvement directions. Understanding the demand for and impact of production activities on electricity allows for coordinated optimization of production plans and energy supply. For example, energy allocation can be rationally adjusted based on equipment start and stop times and production task schedules to avoid energy waste, improve energy efficiency, and ensure efficient production operations. Correlation analysis can promptly detect abnormal changes in electricity and production data, such as sudden increases in energy consumption caused by abnormal equipment operating conditions, enabling early diagnosis and early warning of faults, helping companies take preemptive measures to avoid escalating equipment failures and reduce production losses and energy waste caused by downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a network diagram of electricity metering management at key nodes in a coking enterprise.

[0034] Figure 2 It is a logic diagram of electricity metering management in coking enterprises.

[0035] Figure 3 It is a three-level structure diagram of electric energy metering management in the gas purification section of a coking project. DETAILED DESCRIPTION

[0036] The present invention will be described in detail below with reference to the accompanying drawings, but it should be noted that the implementation of the present invention is not limited to the following embodiments.

[0037] The following examples are implemented under the premise of the technical solution of the present invention, and provide detailed implementation methods and specific operating processes, but the scope of protection of the present invention is not limited to the following examples. The methods used in the following examples are conventional methods unless otherwise specified.

[0038] Example 1

[0039] Given that the existing power management model of coking enterprises is single, production data and energy data are separated, and it is impossible to complete an overall unified power management system, a complete power management model for coking enterprises should be rebuilt:

[0040] Perception layer:

[0041] A matrix metering network is formed by deploying multifunctional energy meters, current sensors, and PLC control systems at key nodes in the coking enterprise, collecting real-time energy data, including voltage, current, power, power factor, and equipment operating status. Multifunctional energy meters are deployed on each circuit in the high-voltage power system, requiring multiple communication interfaces, such as Modbus-RTU and Profibus-DP, with one interface reserved for energy metering. For the low-voltage power system, multifunctional energy meters are deployed on the power supply circuits of each system and process unit in accordance with the coking enterprise's energy metering management requirements. One communication interface is reserved for energy metering. Multifunctional energy meters are deployed on the power supply circuits of key energy-consuming equipment in each process unit, such as coke oven vehicles, gas blowers, dust removal fans, dry coke quenching circulation fans, boiler feed water pumps, gas purification circulation water pumps, low-temperature water pumps, and circulating ammonia water pumps. One communication interface is reserved for energy metering.

[0042] Network layer:

[0043] The edge computing gateway pre-processes the collected raw data, including data classification, filtering, outlier removal, and data compression. The pre-processed data is then transmitted to the energy metering management system server via industrial Ethernet or wireless communication protocols. The edge computing gateway can collect energy data from on-site energy meters in real time and convert the application layer protocol to the Modbus-TCP protocol.

[0044] Platform layer:

[0045] A time-series database is used to store energy data, and correlation analysis is performed based on production data such as production plans, actual output, and equipment start and stop times. This correlation analysis also includes a dynamic calibration method for energy metering equipment: by comparing the logical consistency of energy data from adjacent nodes, equipment deviations are automatically identified and a calibration process is triggered. During periods of low electricity prices, the operating time of non-continuous production equipment is increased to optimize peak-to-valley power usage.

[0046] Application layer:

[0047] In accordance with the management needs of coking enterprises, the electricity metering management of coking enterprises can achieve: three-level electricity metering management of energy-consuming units, secondary energy-consuming units, and basic energy-consuming units, and display electricity consumption, energy efficiency benchmarking and abnormal alarm information in real time through a visualization platform; the energy-consuming unit level in the three-level electricity metering management of coking enterprises: refers to the electricity metering of entering and leaving the coking enterprise, marked by the red line range; the secondary energy-consuming unit level: refers to the electricity metering of energy accounting units directly under the energy-consuming units. The secondary energy-consuming units of the coking plant include: coking facilities, gas purification facilities, public facilities, storage and transportation facilities, etc.; the basic energy-consuming unit level: the electricity metering of basic production units such as production processes, sections, stations, etc. under the secondary energy-consuming units.

[0048] See Figure 1 ,See Figure 3 Taking the gas purification section of a coking project as an example, the three-level metering structure of power management achieves refined control through the following methods:

[0049] 1. Energy-consuming unit level:

[0050] At the coking plant level, the metering point is located at the main incoming power distribution cabinet of the gas purification section, which is used to count the total power consumption of the section, compare the energy efficiency ratio of all secondary energy-consuming units in the plant, and calculate the comprehensive electricity cost of the section.

[0051] 2. Secondary energy-consuming unit level:

[0052] Corresponding to the gas purification facility level, the metering point is located in the power supply circuit of each subsystem in the gas purification section, which is used to count the total power consumption of the section, compare the energy efficiency ratio of other secondary units such as coking, public utilities, etc. in the whole plant, and calculate the comprehensive electricity cost of the section.

[0053] 3. Basic energy unit level:

[0054] At the equipment / process level, the metering point is located in the power supply circuit of key equipment such as the desulfurization circulation pump, gas blower, and electrostatic tar precipitator, to monitor the real-time efficiency of a single device and identify abnormal operating conditions such as pump idling and motor overload.

[0055] The perception layer collects voltage, current, power and other data in real time through multi-communication interface electricity meters and current sensors deployed in high-voltage distribution cabinets, single-interface electricity meters and current sensors in low-voltage circuits and power supply circuits of key equipment such as coke oven vehicles, etc. At the same time, the DCS controller synchronously obtains the start and stop status and process parameters of the equipment; the network layer uses the edge computing gateway to aggregate electricity meter and DCS data through polling, completes the conversion of application layer protocol to Modbus-TCP protocol, and transmits it to the platform layer through industrial Ethernet fiber optic ring network or wireless LoRaWAN / 5G module; the platform layer uses time-series database servers and relational database servers to store electricity data and equipment metadata respectively; the application layer receives data through web servers, large-screen display terminals and mobile devices, drives visual dashboards and alarm signal output, and forms a closed-loop hardware link from sensor collection, gateway protocol conversion, server storage to terminal display.

[0056] Table 1 shows the data classification rules.

[0057]

[0058]

[0059] As shown in Table 1, the edge computing gateway categorizes collected data into three categories based on its importance: fault signals, process parameters, and energy efficiency data. Fault signals are given the highest priority and utilize lossless transmission and immediate response mechanisms to ensure critical alarms are not lost or delayed. Process parameters are given medium priority and utilize sliding window filtering and data compression to reduce transmission overhead while preserving detailed features. Energy efficiency data is given the lowest priority and is uploaded in batches and stored in a low-cost relational database. Data classification rules within the electricity metering management system utilize differentiated processing strategies to set priorities, processing methods, and storage requirements for different data types. This effectively optimizes resources and improves efficiency, ensuring optimal allocation of bandwidth and computing power, and focusing edge computing resources on critical tasks. Furthermore, the classification rules mark fault signals, such as equipment overload, as the highest priority, directly triggering the platform-level alarm module, bypassing the processing queue for lower-priority data, thereby enhancing system response speed and reliability. When wireless channels are unstable, real-time data upload is prioritized, while lower-frequency data can be temporarily stored in the gateway's local cache for subsequent transmission upon network recovery. In addition, the coking plant can flexibly adjust the classification strategy according to the production stage: increase the priority of equipment-level data during production peak periods to monitor the stable operation of various work sections; increase the priority of alarm data during maintenance periods to increase the maintenance rate, shield the transmission of statistical data, and avoid the generation of invalid data.

[0060] Example 2

[0061] In this embodiment, a method for managing power metering of a coking enterprise is the same as that in embodiment 1, and a specific data flow for power metering management of a coke oven car is added thereto. Figure 2 .

[0062] 1. Perception layer:

[0063] The electric energy meter of the coke oven trolley power supply circuit collects the motor drive power, voltage, and current, and the DCS reads the coke oven trolley position and operating status.

[0064] 2. Network layer:

[0065] The edge gateway polls the above devices, cleans, classifies, and compresses the collected data, and then integrates the power data with the vehicle location and start / stop status, and transmits it to the platform layer's time series database server and relational database server via industrial Ethernet optical fiber or 5G wireless module.

[0066] 3. Platform layer:

[0067] The time series database records the energy consumption of each coke oven vehicle during its operation cycle, integrates the electrical energy and location data, determines whether the coke oven vehicle is in the "moving", "loading coal" or "discharging coke" state, calculates the instantaneous power when the state switches, and calculates the power consumption of a single operation in conjunction with the coke production.

[0068] 4. Application layer:

[0069] The visualization platform displays the energy efficiency ranking of each coke oven vehicle based on its single-operation power consumption and no-load energy consumption ratio, and issues early warnings of abnormal coke oven vehicles such as "motor overload" or "abnormal track friction" based on the equipment energy efficiency threshold.

[0070] Example 3

[0071] In this embodiment, a method for electricity metering management in a coking enterprise is the same as that in Example 1. On the basis of Example 1 and / or Example 2, a non-periodic operation section such as coal loading in the coal tower is added, and an operation plan is formulated according to production data and time-of-use electricity prices.

[0072] First, the platform layer uses a dynamic programming algorithm to generate a cost-optimal coal loading schedule based on historical electricity consumption data such as belt conveyor power curves and feeder start and stop records in a time-series database. This data is combined with a time-of-use electricity price model constructed by dividing peak and valley periods and electricity price gradients. Combined with production data such as the coke oven coal demand plan, the real-time coal bunker level, and the rated capacity of the belt conveyor, the platform uses a dynamic programming algorithm to generate a cost-optimal coal loading schedule. For example, coal can be loaded and stored in the coal bunker during low-price periods to avoid high electricity prices during peak periods.

[0073] Subsequently, the application layer sends the planned instructions to the edge gateway of the network layer, controls the frequency of the vibrating feeder and the start and stop time of the belt conveyor through the PLC, and simultaneously monitors the actual energy consumption and the deviation from the plan; if there are sudden working conditions such as coal blockage alarms or a surge in demand for coal in the coke oven, the system will associate the power data in real time, dynamically adjust the coal loading plan or switch to backup equipment to ensure production continuity while avoiding peak electricity prices.

[0074] The three-level structure collaborates, enabling a step-by-step breakdown of power consumption, from the overall process section at the energy-consuming unit level to the individual equipment at the basic unit level. This allows for precise power consumption statistics, avoiding traditional "big pot" accounting, enabling refined metering and cost allocation, and clarifying the direction of process improvements. Furthermore, based on current and power data at the basic unit level and by comparing the energy consumption of adjacent equipment, sensor drift or mechanical wear can be automatically identified. If a sudden increase in power consumption is detected for a specific piece of equipment, the faulty device can be quickly located at the basic unit level. The collected data can be compared with the unit energy consumption of gas purification sections in similar coking plants to identify energy consumption shortcomings. Optimal energy efficiency curves can also be established using historical equipment-level data to guide variable frequency drive parameter settings and reduce ineffective energy consumption. Based on basic unit-level data trends, equipment lifespan can also be predicted, reducing unplanned downtime.

[0075] The present invention adopts a hierarchical structure to construct a four-layer network architecture of perception layer, network layer, platform layer and application layer, and manages coking enterprises according to the three-level metering structure of energy-consuming unit level (whole plant), secondary energy-consuming unit level (work section) and basic energy-consuming unit level (equipment). It has high security, flexible configuration and anti-interference ability, and realizes step-by-step energy consumption monitoring and management from the whole plant to the work section and then to the equipment. It can accurately locate the links and equipment with high energy consumption, avoid the extensive management brought about by the traditional "big pot" accounting, and provide a basis for targeted energy-saving measures; the metering at each level is clear, which facilitates the decomposition of energy consumption indicators to specific work sections and equipment, so that each level can clearly understand its own energy consumption status, enhance energy-saving awareness, and promote the initiative of each link. Energy-saving actions can be taken proactively. Management can develop more targeted production plans and equipment maintenance plans based on energy consumption data at different levels. For example, they can adjust the overall production strategy based on the energy consumption trends of the entire plant, optimize the process flow to address energy consumption issues in specific work sections, and promptly repair or replace equipment-level energy consumption anomalies. Edge intelligent processing is used: data classification (see Table 1), dynamic compression, and protocol conversion (such as support for Modbus / Profibus protocols) are implemented at the gateway layer. Data classification can be differentiated based on the importance and urgency of the data, ensuring the immediate transmission and priority processing of critical data (such as fault signals), avoiding data congestion, improving the system's response speed to abnormal situations, and ensuring the safety of the production process. Dynamically compressing data can significantly reduce the amount of data and the demand for communication network bandwidth. Especially in wireless communication environments, it can reduce communication costs, improve communication efficiency, and enable the system to transmit more effective data within limited bandwidth. It supports the conversion of multiple common industrial protocols, which can achieve interconnection between different manufacturers and different types of equipment, break down communication barriers between devices, build a unified power metering and management system, and facilitate enterprise equipment integration and management. It pre-processes data at the edge gateway, such as filtering out redundant data and performing preliminary analysis, and only transmits valuable data to the server, reducing the data processing pressure on the cloud server, improving the operating efficiency of the entire system, and reducing the demand and cost for cloud computing resources. Adopting process coupling analysis: This method links power data with production time series data to establish core indicators such as power consumption per ton of coke and equipment energy efficiency ratio. By closely integrating power data with production time series, the power consumption per ton of coke production and the energy efficiency ratio of various equipment can be accurately calculated. This provides a scientific and accurate quantitative basis for evaluating the energy utilization efficiency of the production process and helps companies clearly understand their own energy efficiency levels. Based on process coupling analysis, high-energy-consuming production links and equipment can be quickly located, and the causes of abnormal energy consumption can be deeply analyzed to provide clear directions for formulating optimization measures. For example, if the power consumption per ton of coke in a certain process is found to be too high, the process parameters and equipment operating status of that process can be adjusted and optimized to reduce energy consumption.The core indicators can intuitively reflect the energy efficiency status of the production process. Operators can adjust production operations in real time according to these indicators, optimize production processes, realize refined management of the production process, improve production efficiency, and reduce production costs; deploy a matrix metering network to collect various electrical parameters in real time, pre-process them through the edge computing gateway, and then transmit the pre-processed data to the power metering management system server through industrial Ethernet or wireless communication protocols. The matrix metering network can widely cover various key nodes of the coking enterprise, including high-voltage circuits, low-voltage circuits and key energy-consuming equipment, etc., to achieve comprehensive and real-time collection of various electrical parameters, ensure the integrity and accuracy of the data, and provide rich services for power management and analysis. , a reliable data foundation; the edge computing gateway pre-processes the original collected data, such as removing noise, filtering, and eliminating outliers, to improve the quality and availability of the data, reduce the impact of erroneous data on subsequent analysis and decision-making, and ensure the accuracy and reliability of electricity metering; the industrial environment is often complex and changeable, and the matrix metering network combined with edge computing has good adaptability. Even in the case of unstable communication conditions or network failures, the edge computing gateway can temporarily store data and retransmit it after communication is restored to ensure the continuity and integrity of the data and avoid gaps in electricity management due to data loss; the matrix metering network can flexibly deploy metering equipment according to the company's production layout and actual needs, It is convenient for rapid expansion and access to new equipment or production areas. At the same time, the edge computing gateway is also convenient for corresponding configuration adjustments according to the increase or decrease of collection points, making the system highly flexible and scalable. The time series database is used to store power data, and the production data such as production plan, actual output, equipment start and stop time are combined for correlation analysis. The power data is combined with the production data to deeply analyze the changing pattern of energy consumption from the perspective of time and production activities, comprehensively evaluate the energy utilization efficiency in the production process, discover hidden energy efficiency problems, and provide strong support for the formulation of more effective energy-saving strategies. Through the association with production plan, output and other data, energy efficiency benchmarking can be accurately carried out, and actual energy consumption can be compared with the planned energy consumption. Compare and analyze planned energy consumption with industry-leading energy consumption levels to identify gaps and deficiencies in energy efficiency and clarify energy-saving targets and improvement directions. Understand the demand for and impact of production activities on electricity and achieve coordinated optimization of production plans and energy supply. For example, based on equipment start-up and shutdown times and production task schedules, reasonably adjust energy allocation to avoid energy waste, improve energy utilization efficiency, and ensure efficient production operations. Correlation analysis can promptly detect abnormal changes in electricity and production data, such as sudden increases in energy consumption caused by abnormal equipment operating conditions. This enables early diagnosis and early warning of faults, helping companies take preemptive measures to prevent equipment failures from escalating and reduce production losses and energy waste caused by downtime.

Claims

1. A method for electric energy metering management in a coking enterprise, characterized in that: Includes the following: S1. A matrix metering network is formed by the electric meters, current sensors, and process control systems deployed in the perception layer. This network reads the electric energy data collected by the electric meters and current sensors, and the equipment operating status data collected by the process control system in real time. S2. Pre-process the collected power data through the edge computing gateway of the network layer and transmit the pre-processed data to the platform layer through industrial Ethernet or wireless communication protocol; S3. Use a time series database to store power data at the platform level and perform correlation analysis in combination with production data to complete dynamic calibration and peak-valley power optimization. S4. Complete the three-level electricity metering management at the application layer, and display the electricity consumption, energy efficiency benchmarking and abnormal alarm information through the visualization platform.

2. A method for electric energy metering management in a coking enterprise according to claim 1, characterized in that: In S1, the sensing layer includes multifunctional electric energy meters configured for each circuit of the high-voltage power system, multifunctional electric energy meters configured for each system and process unit power supply circuit of the low-voltage power system, and multifunctional electric energy meters configured for the power supply circuit of key energy-consuming equipment of each process unit. The multifunctional electric energy meters all have reserved communication interfaces. The electric energy data includes voltage, current, power and power factor.

3. A method for electric energy metering management in a coking enterprise according to claim 1, characterized in that: In S2, the edge computing gateway of the network layer collects electric energy data in real time, which is used to complete the conversion from the application layer protocol to the Modbus-TCP protocol, and performs differentiated processing on the data according to the data classification rules; the preprocessing includes data classification, filtering, outlier elimination, and data compression.

4. A method for electric energy metering management in a coking enterprise according to claim 3, characterized in that: The data classification rules include: Fault signals are given a first-level priority and are transmitted immediately using raw data. The process parameters are given a second-level priority and are transmitted within 5 seconds and edge feature extraction is performed; Energy efficiency data is prioritized at level three, transmitted within 15 minutes, and aggregated and stored locally.

5. The method for electric energy metering management of a coking enterprise according to claim 1, characterized in that: In S3, the dynamic calibration of the platform layer includes: identifying equipment deviations and triggering the measurement calibration process by comparing the logical consistency of the power data of adjacent nodes.

6. A method for electric energy metering management in a coking enterprise according to claim 1, characterized in that: In S3, the production data includes production plan, actual output, and equipment start and stop time.

7. A method for electric energy metering management in a coking enterprise according to claim 1, characterized in that: In S3, the peak-valley electricity consumption optimization at the platform level includes increasing the operating time of non-continuous production equipment during periods of low electricity prices and optimizing the peak-valley electricity consumption ratio.

8. The method for electric energy metering management of a coking enterprise according to claim 1, characterized in that: In S4, the three-level electricity metering management includes: energy-consuming unit level, secondary energy-consuming unit level, and basic energy-consuming unit level. The energy-consuming unit level is to measure the electricity entering and leaving the coking enterprise, the secondary energy-consuming unit level is to measure the electricity entering and leaving the energy accounting unit directly under the energy-consuming unit, and the basic energy-consuming unit level is to measure the electricity entering and leaving the production process, section or equipment under the secondary energy-consuming unit.

9. The method for electric energy metering management of a coking enterprise according to claim 1, characterized in that: In S4, the visualization platform of the application layer issues early warnings for abnormal conditions based on the equipment energy efficiency threshold, and generates the most cost-effective coal loading time plan through a dynamic programming algorithm.

10. The method for electric energy metering management of a coking enterprise according to claim 1, characterized in that: It also includes identifying sensor drift or mechanical wear and predicting equipment life by comparing energy consumption data with adjacent equipment.

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