Online monitoring system and method for comprehensive energy flow direction based on industrial park

Abstract

The invention relates to an industrial park-based comprehensive energy flow direction on-line monitoring system and method, and the system comprises an energy modeling module which is used for representing instrument information, carrying out the modeling of collection labels and collection frequencies of different types of instruments, constructing nodes of different energy layered structures, carrying out the modeling of a directed flow direction relation between the nodes, and carrying out the modeling of the directed flow direction relation between the nodes; binding the logic relationship between the node and the instrument; the data acquisition module is used for performing data acquisition through an instrument, performing edge processing and encrypted transmission through communication equipment, and storing the data in a database; the energy monitoring module is used for monitoring and counting the state of the instrument and monitoring the energy flow direction of the enterprise in real time according to the directed flow direction of the node; and the statistical analysis module is used for providing energy balance analysis for different links of the enterprise energy in the conversion and use process according to the nodes, evaluating the energy consumption loss degrees of the different links, visualizing the nodes with abnormal loss and giving an alarm. Through the real-time and accurate calculation of the energy loss among the nodes at all levels, the backtracking of the nodes with abnormal loss and the timely pushing of the alarm information, the enterprise can timely discover the problems occurring in the energy supply, and can quickly find out the corresponding solution, reduce the energy consumption loss and guarantee the robustness of the energy flow network.

Description

Technical Field

[0001] This invention relates to the field of real-time energy consumption statistics technology in industrial parks, specifically to an online monitoring system and method for the comprehensive energy flow direction in industrial parks. Background Technology

[0002] With the rapid development of enterprises, the continuous growth of energy load, the emergence of various new types of loads, and the demand for energy conservation and emission reduction, traditional energy metering and statistics, due to their extensive management methods and lack of precise monitoring and effective classification of energy consumption, cannot meet the needs, resulting in uneven and unclear energy consumption among enterprises.

[0003] Traditional energy metering and statistics often lack precise information on energy supply across different regions of an enterprise, making it difficult to understand energy consumption in each area and potentially leading to irrational energy allocation. For example, critical production areas may face risks of energy shortages and reduced production efficiency, while non-production areas may experience energy waste. Furthermore, the lack of real-time and accurate energy data collection and corresponding data analysis algorithms makes it impossible to accurately calculate various energy losses and monitor abnormal data in real time, resulting in increased energy costs and, in severe cases, impacting the normal operation of production equipment and production safety. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes an online monitoring system and method for the integrated energy flow direction in industrial parks.

[0005] This invention adopts the following technical solution: an online monitoring system for the integrated energy flow direction in an industrial park, comprising:

[0006] The energy modeling module is used to characterize instrument information, model the acquisition tags and acquisition frequencies of different types of instruments, construct nodes with different energy hierarchical structures, model the directed flow relationships between nodes, and bind the logical relationships between nodes and instruments.

[0007] The data acquisition module is used to acquire data through instruments, perform edge processing and encrypted transmission through communication equipment, and save the data to the database.

[0008] The energy monitoring module is used to monitor and statistically analyze the status of instruments, and to monitor the enterprise's energy flow in real time based on the directional flow of nodes.

[0009] The statistical analysis module is used to provide energy balance analysis for enterprises at different stages of energy conversion and use based on each node, assess the degree of energy loss at different stages, visualize nodes with abnormal losses, and issue alarms.

[0010] The energy modeling module includes:

[0011] Energy meter modeling is used to characterize meter information and to model the acquisition tags and acquisition frequencies of different types of meters; the acquisition tags are used to represent the meter type and the acquired parameter data.

[0012] Energy basic node modeling is used to divide the park into regions and construct nodes with a hierarchical energy structure. The energy division structure includes the whole plant level, workshop level, process level and energy-consuming equipment level. For each level, energy flow nodes are constructed to represent the node's name, level, energy type and consumption type.

[0013] Node directed flow relationship modeling is used to model the directed flow relationship between nodes based on the actual energy process after the node modeling is completed.

[0014] Node meter logical binding is used to logically bind a node to at least one corresponding meter in order to calculate the node's energy consumption.

[0015] The data acquisition module includes:

[0016] The IoT access module is used to collect data through instruments and perform edge processing and encrypted transmission through communication devices.

[0017] The data upload module is used to upload the edge-processed instrument data to the EMQX cluster via network protocol and store it in the time-series database.

[0018] The energy monitoring module includes:

[0019] The energy acquisition and monitoring module is used to monitor the online status of instruments and perform historical online statistics, thereby enabling online status perception of instruments.

[0020] The energy flow monitoring module is used to monitor the enterprise's energy flow in real time based on the directional flow of nodes, so as to track the flow of energy from the initial stage to the consumption stage.

[0021] The statistical analysis module includes:

[0022] The energy loss calculation and analysis module is used to collect parameter data from the instruments bound to the nodes and provide energy balance analysis for different stages of the enterprise's energy conversion and use process. By separately statistically analyzing the energy supply and energy consumption of a specified energy-consuming stage and calculating the difference between the two, the degree of energy loss in different stages can be evaluated.

[0023] The abnormal alarm module is used to automatically calculate the abnormal alarm threshold based on a comprehensive analysis of the energy loss of each node; after an alarm occurs, the node with abnormal loss is traced back, the level and location of abnormal loss are determined according to the node attributes and visualized, and alarm information is pushed.

[0024] An online monitoring method for integrated energy flow in industrial parks includes the following steps:

[0025] The energy modeling module represents instrument information, models the acquisition tags and acquisition frequencies of different types of instruments, constructs nodes with different energy hierarchical structures, models the directed flow relationships between nodes, and binds the logical relationships between nodes and instruments.

[0026] The data acquisition module collects data through instruments, performs edge processing and encrypted transmission through communication equipment, and saves the data to the database.

[0027] The energy monitoring module monitors and statistically analyzes the status of instruments, and monitors the enterprise's energy flow in real time based on the directional flow of nodes;

[0028] The statistical analysis module provides energy balance analysis for enterprises at different stages of energy conversion and use based on each node, assesses the degree of energy loss at different stages, visualizes nodes with abnormal losses, and issues alarms.

[0029] The energy modeling module performs the following steps:

[0030] Energy meter modeling: Characterizing meter information and modeling the acquisition tags and acquisition frequencies of different types of meters; the acquisition tags are used to represent the meter type and the acquired parameter data;

[0031] Energy basic node modeling: Based on the regional division of the park, construct nodes with a hierarchical energy structure. The energy structure includes the whole plant level, workshop level, process level, and energy-consuming equipment level. For each level, construct energy flow nodes to represent the node's name, level, energy type, and consumption type.

[0032] Node-directed flow relationship modeling: After the node modeling is completed, the directed flow relationship between each node is modeled according to the actual energy process;

[0033] Node meter logical binding: Logically bind a node to at least one corresponding meter to calculate the node's energy consumption.

[0034] The data acquisition module performs the following steps:

[0035] The IoT access module collects data through instruments and performs edge processing and encrypted transmission through communication devices.

[0036] The data upload module uploads the edge-processed instrument data to the EMQX cluster via network protocol and stores it in the time-series database.

[0037] The energy monitoring module performs the following steps:

[0038] The energy acquisition and monitoring module performs online status monitoring of instruments and historical online statistics to achieve online status perception of instruments;

[0039] The energy flow monitoring module enables real-time monitoring of enterprise energy flow based on the directional flow of nodes, in order to track the flow of energy from the initial stage to the consumption stage.

[0040] The statistical analysis module performs the following steps:

[0041] The energy loss calculation and analysis module collects parameter data from the instruments bound to the nodes, and provides energy balance analysis for different stages of the enterprise's energy conversion and use process. By separately statistically analyzing the energy supply and energy consumption of a specified energy-consuming stage and calculating the difference between the two, it assesses the degree of energy loss in different stages.

[0042] The abnormal alarm module automatically calculates the abnormal alarm threshold based on a comprehensive analysis of the energy loss of each node. After an alarm occurs, it traces back the node with abnormal loss, determines the level and location of the abnormal loss based on the node attributes, visualizes it, and pushes alarm information.

[0043] The present invention has the following beneficial effects and advantages:

[0044] 1. Improve Energy Management Efficiency: By dividing the enterprise into regions and constructing a hierarchical energy structure, the enterprise's energy distribution can be displayed more clearly and intuitively. This provides a comprehensive foundation of information for refined energy management. Data collection for regions at each energy level and the establishment of a comprehensive energy flow model allow for a holistic understanding of the enterprise's energy consumption and flow patterns, further enhancing the level of refined energy management and improving energy efficiency.

[0045] 2. Reduce energy loss: Through real-time and accurate calculation of energy loss between nodes at all levels, backtracking of nodes with abnormal losses, and timely push of alarm information, enterprises can promptly detect problems in energy supply and quickly find corresponding solutions to reduce energy loss and ensure the robustness of energy flow to the network. Attached Figure Description

[0046] Figure 1 The present invention provides an overall flowchart of an online monitoring system and method for integrated energy flow in industrial parks.

[0047] Figure 2This invention provides an energy modeling diagram for an online monitoring system and method for integrated energy flow in industrial parks.

[0048] Figure 3 This invention provides a schematic diagram of the directed relationship of energy nodes in an online monitoring system and method for integrated energy flow in industrial parks.

[0049] Figure 4 This invention provides an IoT access diagram for an online monitoring system and method for integrated energy flow in industrial parks.

[0050] Figure 5 This invention provides a schematic diagram of the abnormal alarm process for an online monitoring system and method for integrated energy flow in industrial parks. Detailed Implementation

[0051] To better understand the purpose, technical solution, and advantages of this invention, the following detailed description of the invention is provided in conjunction with the accompanying drawings and specific embodiments.

[0052] An online monitoring system and method for integrated energy flow in industrial parks includes an energy modeling module: a fundamental module that models energy flow in a visual manner, characterized by its flexibility and configurability. It covers energy meter modeling, basic energy node modeling, directed flow relationship modeling between nodes, and logical binding modeling of nodes and meters, providing basic data support for online monitoring of energy flow. A data acquisition module: to achieve automatic collection of energy consumption data during on-site production, it establishes a highly scalable and applicable IoT access solution, transmitting data via wired or wireless networks to achieve energy data collection, aggregation, and uploading functions. An energy monitoring module: based on energy modeling and IoT meter data collection, it achieves comprehensive energy monitoring, from enterprise energy collection monitoring to enterprise energy flow monitoring, to improve the stability of energy collection, the security of energy flow networks, and the level of energy consumption control. An energy consumption statistical analysis module: including energy loss calculation and analysis and anomaly alarms, helps enterprises promptly identify energy consumption problems, reduce energy losses, and ensure the robustness of the energy flow network.

[0053] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0054] 1. Energy Modeling Module

[0055] (1) Energy meter modeling: The content covers meter data acquisition attributes, including meter name, meter code, meter acquisition method, etc. Supports third-party system integration.

[0056] (2) Energy basic node modeling: Based on the park design map, the area is divided and an energy hierarchical structure is constructed, covering four levels of energy metering depth, including the whole plant level, workshop level, process level and energy-consuming equipment level.

[0057] (3) Node directed flow relationship modeling: After the modeling of each node is completed, the directed flow relationship between each node needs to be modeled. Taking a certain node as an example, the inflow node (source node) and outflow node (target node) of the node need to be recorded.

[0058] (4) Logical binding of nodes and meters: Energy consumption needs to be calculated using data collected by meters. Therefore, to calculate the energy consumption of each node, the node information and meter information need to be logically bound together.

[0059] 2. Data Acquisition Module

[0060] (1) IoT Access: Through devices such as fiber optic converters, switches, serial servers, LoRa nodes, carrier modules, Bluetooth modules, and infrared modules, instruments, equipment, and various heterogeneous systems are efficiently networked and data is converted. Data acquisition, edge processing, and encrypted transmission are performed through centralized and distributed deployment of different types of gateways.

[0061] (2) Data upload: After edge processing of the collected meter data, the gateway uploads the meter data to the EMQX cluster via MQTT and HTTP protocols. The EMQX cluster performs access authentication, ACL authorization, and rule engine flow processing on the energy data uploaded by the enterprise, and finally stores the energy data in the time series database.

[0062] 3. Energy monitoring module

[0063] (1) Energy acquisition and monitoring: online status monitoring and historical online statistics of energy meters and energy IoT acquisition devices, to achieve accurate perception of the online status of energy meters and IoT acquisition devices, improve the management capabilities of energy meters and energy IoT devices, and ensure the accuracy of energy acquisition data.

[0064] (2) Energy Flow Monitoring: Real-time monitoring of enterprise energy flow, tracking the flow of energy from the initial stage to the consumption stage. Displaying energy supply, conversion, loss, and usage in a visual and quantitative manner. Supports custom configuration of energy flow maps.

[0065] 4. Statistical Analysis Module

[0066] (1) Energy loss calculation and analysis: mainly provides energy balance analysis for enterprises in the process of conversion and use of energy such as electricity, water and gas. By separately calculating the energy supply and energy consumption of important energy-consuming links and calculating the difference between the two, the degree of energy loss in each link is evaluated.

[0067] (2) Anomaly Alarm: Based on a comprehensive analysis of energy loss at each node, the anomaly alarm threshold is automatically calculated. After an alarm occurs, the node with abnormal loss is traced back, and the level and location of the abnormal loss are determined based on the node attributes in the first step of modeling. This information is displayed in the system's energy flow diagram, and the alarm information is pushed to the user.

[0068] like Figure 1 As shown, an online monitoring system and method for integrated energy flow in an industrial park includes an energy modeling module, a data acquisition module, an energy monitoring module, and a statistical analysis module, wherein:

[0069] Energy Modeling Module: This is the foundational module of the system. Taking a meat processing industrial park as an example, this invention collects 2D CAD construction drawings of the park and, combined with the park's facility layout, divides the park into three areas: a slaughterhouse, a high-temperature workshop, and an office and living area.

[0070] Energy instrument modeling: such as Figure 2 As shown, the energy meters within the park are modeled for ledger and data collection. The energy modeling in this invention can cover four levels of energy metering depth.

[0071] (1) Instrument Ledger Modeling: This involves creating basic instrument information, including instrument name, type, code, installation location, and data acquisition method. Specific basic information for each instrument is as follows:

[0072] Park main electricity meter D1;

[0073] Park's main water meter S1;

[0074] Park's total steam meter Z1;

[0075] Park's main gas meter T1;

[0076] D2-1, a shunt meter for the slaughterhouse;

[0077] High-temperature workshop shunt meter D2-2;

[0078] High-temperature workshop shunt meter D2-3;

[0079] Office and residential area separate current meter D2-4;

[0080] S2-1, a water meter for the slaughterhouse;

[0081] High-temperature workshop diversion water meter S2-2;

[0082] Office and living area separation flow chart S2-3;

[0083] Slaughterhouse steam diversion meter Z2-1;

[0084] High-temperature workshop steam diversion meter Z2-2;

[0085] Office and living area separate steam meter Z2-3;

[0086] Slaughterhouse gas diversion meter T2-1;

[0087] High-temperature workshop gas diversion meter T2-2;

[0088] Gas meter T2-3 for office and residential areas;

[0089] Electricity meter D3-1-1 in the waiting area of ​​the slaughterhouse;

[0090] Electricity meter D3-1-2 in the slaughterhouse cutting area;

[0091] Electricity meter D3-1-3 in the cold storage area of ​​the slaughterhouse;

[0092] Electricity meter D3-2-1, No. 1, in the sterilization area of ​​the high-temperature workshop;

[0093] Electricity meter D3-2-2 in the sterilization area of ​​the high-temperature workshop;

[0094] Electricity meter D3-2-3 in the high-temperature workshop filling area;

[0095] Electricity meter D3-2-4 in the packaging area of ​​the high-temperature workshop;

[0096] Office and living area lighting meter D3-4-1;

[0097] Office and living area air conditioner electricity meter D3-4-2;

[0098] Steam meter Z3-2-1 in the sterilization area of ​​the high-temperature workshop;

[0099] Steam meter Z3-2-2 in the packaging area of ​​the high-temperature workshop;

[0100] (2) Instrument data acquisition modeling: Model the data acquisition tags and acquisition frequencies of various types of instruments.

[0101] Collection Tags:

[0102] Electricity meter: Voltage, active power, reactive power, maximum demand, power factor

[0103] Water meters: pressure, cumulative flow, instantaneous flow

[0104] Gas meter: pressure, instantaneous flow rate, cumulative flow rate

[0105] Steam gauges: pressure, temperature, instantaneous flow rate, cumulative flow rate

[0106] Data collection frequency: Electricity meters, steam meters, and gas meters are collected every minute; water meters are collected once every 4 hours.

[0107] Basic energy node modeling: Modeling attributes such as name, level, energy type, and consumption type of energy flow nodes. This includes four plant-level nodes (plant-level electricity, plant-level water, plant-level steam, and plant-level gas), workshop-level nodes (electricity, water, steam, and gas consumption in the slaughterhouse, high-temperature workshop, and office / living area respectively), and process-level nodes (electricity consumption in the slaughterhouse waiting area, cutting area, and cold storage area; electricity consumption in the high-temperature workshop sterilization area, filling area, and packaging area; electricity consumption for lighting and air conditioning in the office / living area; and steam consumption in the high-temperature workshop sterilization area and packaging area).

[0108] Node-directed flow relationship modeling: Utilizing the energy flow node model constructed in the previous step, and combining it with the actual energy flow direction provided by the park, the input and output nodes of each energy flow node are identified. Taking the park's electricity consumption as an example, the input node for the plant-wide electricity consumption node is empty, and the output nodes are the slaughterhouse electricity consumption node, the high-temperature workshop electricity consumption node, and the office and living area electricity consumption node; the input node for the slaughterhouse electricity consumption node is the plant-wide electricity consumption node, and the output nodes are the slaughtering area electricity consumption node, the cutting area electricity consumption node, and the cold storage area electricity consumption node; the input node for the high-temperature workshop electricity consumption node is the plant-wide electricity consumption node, and the output nodes are the high-temperature workshop sterilization area electricity consumption node, the filling area electricity consumption node, and the packaging area electricity consumption node; the input node for the office and living area electricity consumption node is the plant-wide electricity consumption node, and the output nodes are the office and living area lighting electricity consumption node and the air conditioning electricity consumption node. A specific energy flow diagram is shown below. Figure 3 As shown.

[0109] Meter logic binding: Energy flow nodes are bound to energy meters. The relationship between energy flow nodes and energy meters is one-to-many, and the relationship between energy meters and energy flow nodes is one-to-one. Four plant-wide nodes correspond to the park's main electricity meter, main water meter, main steam meter, and main gas meter. Twelve workshop-level nodes correspond to the electricity, water, steam, and gas meters on the main pipelines of the slaughterhouse, high-temperature workshop, and office / living area. For example, the electricity node in the slaughterhouse corresponds to meter D2-1; the electricity node in the high-temperature workshop corresponds to meters D2-2 and D2-3; and the electricity node in the office / living area corresponds to D2-4. Each process-level node corresponds to the electricity, water, steam, and gas meters on different process sections within each workshop. For example, in the high-temperature workshop, the sterilization area node corresponds to meters D3-2-1 and D3-2-2; the filling area node corresponds to meter D3-2-3; and the packaging area node corresponds to meter D3-2-4.

[0110] Data acquisition module: such as Figure 4 As shown, based on the data flow, the data acquisition module can be divided into three layers: the first layer is the instrument and equipment layer, which is responsible for collecting data from various energy instruments and equipment; the second layer is the network conversion device layer, which is responsible for converting the RS485 interface protocol into the RJ45 Ethernet interface; and the third layer is the data acquisition gateway layer, which is responsible for receiving the data uploaded by the instruments and uploading the data to the time series database.

[0111] (1) Instrumentation Layer: In this example, the instrumentation layer refers to various energy metering instruments, including electricity meters, steam meters, water meters, natural gas meters, etc. All energy metering instruments uniformly adopt the RS485 hardware communication interface. Based on the RS485 hardware interface, different devices can be connected together in series via RS485 communication lines (shielded twisted pair) to form a serial instrumentation chain. The number of devices included in each instrumentation chain varies slightly depending on the site conditions, and RS485 repeaters may also be required depending on the number and distance of the connected instruments. An RS485 repeater is a signal isolation enhancement device that can effectively extend the RS485 transmission distance. Multiple instrumentation chains need to be formed depending on the number of each type of meter. The configuration principle of the instrumentation chains is to place similar instruments in the same instrumentation chain as much as possible.

[0112] (2) Network conversion device layer: This refers to devices such as fiber optic converters, switches, serial servers, LoRa nodes, carrier modules, Bluetooth modules, and infrared modules. A network conversion device is a hardware protocol conversion device that can convert the RS485 hardware interface protocol into an RJ45 Ethernet hardware interface. At the same time, the network conversion device acts as the master station of the device instrument chain, and can access each device instrument through its address to communicate and read data.

[0113] (3) Data Acquisition Gateway Layer: After edge processing of the collected meter data, the meter data is uploaded to the EMQX cluster via MQTT and HTTP protocols. The edge gateway supports data caching for a maximum of 10 days to ensure that data is not lost when the gateway loses connection with the cloud. The gateway uploads data in JSON format, uploading the collected data of a group of meters at the same time. The EMQX cluster processes the energy data uploaded by the enterprise sequentially through access authentication, ACL authorization, and rule engine flow, and finally stores the energy data in the time-series database. The time-series database is built using the TDengine cluster and adopts the modeling concept of one data table per meter. It supports persistent storage of energy data on a scale of tens of billions of records and also provides enterprises with diversified data query interfaces based on the time-series database.

[0114] Energy monitoring module:

[0115] (1) Energy Acquisition Monitoring: To ensure the accuracy of energy acquisition data and the stability of the energy data acquisition network, the online status of energy meters and energy IoT acquisition devices is monitored. For energy meters, based on the acquisition cycle of different meters, if a meter fails to report data within multiple acquisition cycles, it is considered offline. For energy IoT acquisition devices, offline status is determined based on the acquired device status tags. If an energy meter or energy IoT acquisition device goes offline, maintenance personnel should quickly go to the site to confirm the situation, investigate the cause of the fault, and resolve the problem promptly.

[0116] (2) Energy Flow Monitoring: Real-time monitoring of the enterprise's energy flow status ensures the stability of energy flow and the accuracy of energy loss calculation, which can further improve the enterprise's energy utilization efficiency and contribute to the enterprise's refined energy management. For energy flow nodes, if the meter bound to the node is offline, the energy flow node will show a red and flashing indicator on the energy flow diagram. Staff should quickly identify the problem with the energy flow node and contact maintenance personnel to confirm the meter's offline status.

[0117] Statistical Analysis Module:

[0118] (1) Energy Loss Calculation and Analysis: The specific calculation method for energy loss is as follows: Calculate the energy consumption change curves of each level node (time granularity is every fifteen minutes), compare the change curves of the total energy consumption of two adjacent level nodes, calculate the difference in the total energy consumption of the two adjacent level nodes within this time interval, and plot the total energy loss curve of the two adjacent level nodes with time as the X-axis and the calculated difference as the Y-axis. Calculate the ratio of the total energy loss of the two adjacent level nodes to the total energy consumption of the higher-level node within this time interval, plot the total energy loss rate curve of the two adjacent level nodes with time as the X-axis and the calculated ratio as the Y-axis. Figure 3 Taking the example of the energy flow diagram for electricity consumption as an example, the first step is to calculate the energy consumption curves of all power consumption nodes in the plant and the combined energy consumption curves of the slaughterhouse, high-temperature workshop, and work and living area power consumption nodes, relative to the energy consumption curve of meter D1 and the combined energy consumption curves of four meters: D2-1, D2-2, D2-3, and D2-4. The second step is to calculate the difference between D1 and the sum of D2-1, D2-2, D2-3, and D2-4, which is the energy loss. The third step is to compare the energy loss obtained in the previous step with the energy consumption of D1 to obtain the energy loss rate. With time as the X-axis and the energy loss rate as the Y-axis, the energy flow for this electricity consumption is obtained. Figure 1 Energy loss rate curves between secondary nodes.

[0119] (2) Abnormal alarm: The specific abnormal alarm process is as follows Figure 5As shown, the system automatically calculates the average energy loss based on the energy loss rate data of two adjacent nodes over the past month and defines it as the standard value of the energy loss rate for that adjacent node level. An energy loss alarm threshold is then determined based on this standard value. After obtaining the threshold, the online real-time monitored energy loss rate data is compared with the energy loss alarm threshold. When the loss rate exceeds the alarm threshold, it is determined to be abnormal loss. The abnormal energy loss information is then displayed visually in the system's energy flow diagram, and alarm information is pushed to the user.

Claims

1. An online monitoring system for integrated energy flow in an industrial park, characterized in that, The energy modeling module is used for representing meter information, modeling collection tags and collection frequencies of different types of meters, and constructing nodes of different energy hierarchical structures, and modeling directed flow relationships between nodes, and binding logical relationships between nodes and meters. The data collection module is used for collecting data through meters, performing edge processing and encrypted transmission through a communication device, and saving the data into a database. The energy monitoring module is used for monitoring and counting meter states, and monitoring enterprise energy flow in real time according to the directed flow of nodes. The statistical analysis module is used for providing energy balance analysis for different links in the conversion and use of enterprise energy according to nodes, evaluating energy consumption degrees of different links, visualizing and alarming abnormal nodes. The energy modeling module comprises:

2. The online monitoring system of integrated energy flow direction based on industrial park according to claim 1, characterized in that, Energy meter modeling is used for representing meter information and modeling collection tags and collection frequencies of different types of meters; the collection tag is used for indicating meter types and collection parameter data; Energy basic node modeling is used for dividing regions according to a park, constructing nodes of energy hierarchical structures, and the energy hierarchical structure comprises a whole plant level, a workshop level, a process level, and an energy-using equipment level; for each level, an energy flow node is constructed to represent the name, level, energy type, and consumption type of the node; Node directed flow relationship modeling is used for modeling directed flow relationships between nodes according to actual energy processes after node modeling is completed; Node meter logical binding is used for logically binding a node to at least one corresponding meter to calculate the energy consumption of the node. The data collection module comprises:

3. The online monitoring system for integrated energy flow direction based on industrial park according to claim 1, characterized in that, The Internet access module is used for collecting data through meters and performing edge processing and encrypted transmission through a communication device; The data upload module is used for uploading meter data after edge processing to an EMQX cluster through a network protocol and storing the data into a time series database. The energy monitoring module comprises:

4. The online monitoring system for integrated energy flow direction based on industrial park according to claim 1, characterized in that, The energy collection monitoring module is used for monitoring online states of meters and counting historical online states, and realizing online state sensing of meters; The energy flow monitoring module is used for realizing real-time monitoring of enterprise energy flow according to the directed flow of nodes to track the flow of energy from an initial stage to a consumption stage. The statistical analysis module comprises:

5. The online monitoring system for integrated energy flow direction based on industrial park according to claim 1, characterized in that, The energy consumption calculation and analysis module is used for providing energy balance analysis for different links in the conversion and use of enterprise energy according to node-bound meter collection parameter data, evaluating energy consumption degrees of different links by respectively counting energy supply and energy consumption of a specified energy-using link and calculating a difference loss between the two, and evaluating energy consumption degrees of different links; The abnormal alarm module is used for automatically calculating an abnormal alarm threshold according to comprehensive analysis of energy consumption of each node, backtracking abnormal nodes after an alarm occurs, visualizing and determining the level and position of abnormal energy consumption according to node attributes, and pushing alarm information. The method comprises the following steps:

6. An online monitoring method of integrated energy flow in an industrial park, characterized in that, ​ The energy modeling module characterizes instrument information, models collection tags and collection frequencies of different types of instruments, and constructs nodes of different energy hierarchical structures, and models directed flow relationships between nodes, and binds logical relationships between nodes and instruments; The data collection module collects data through instruments, performs edge processing and encrypted transmission through a communication device, and saves the data to a database; The energy monitoring module monitors and counts instrument states, and monitors enterprise energy flow in real time according to the directed flow of nodes; The statistical analysis module provides energy balance analysis for different links in the conversion and use of enterprise energy according to nodes, evaluates the energy consumption degree of different links, visualizes and alarms abnormal nodes.

7. The online monitoring method of integrated energy flow direction based on industrial park according to claim 6, characterized in that, The energy modeling module performs the following steps: Energy instrument modeling: characterizing instrument information and modeling collection tags and collection frequencies of different types of instruments; The collection tag is used to represent the instrument type and collection parameter data; Energy basic node modeling: according to the regional division of the park, the nodes of the energy hierarchical structure are constructed, and the energy hierarchical structure includes the whole plant level, the workshop level, the process level and the energy-using equipment level; for each level, an energy flow node is constructed to represent the name, level, energy type and consumption type of the node; Node directed flow relationship modeling: after node modeling, the directed flow relationship between nodes is modeled according to the actual energy process; Node instrument logic binding: the node and the corresponding at least one instrument are logically bound to calculate the energy consumption of the node.

8. The online monitoring method of integrated energy flow direction based on industrial park according to claim 6, characterized in that, The data collection module performs the following steps: The Internet access module collects data through instruments and performs edge processing and encrypted transmission through a communication device; The data upload module uploads the instrument data after edge processing to the EMQX cluster through a network protocol and stores it in a time series database.

9. The online monitoring method of integrated energy flow direction based on industrial park according to claim 6, characterized in that, The energy monitoring module performs the following steps: The energy collection monitoring module monitors the online state of the instrument and counts the historical online state, and realizes the online state perception of the instrument; The energy flow monitoring module realizes real-time monitoring of enterprise energy flow according to the directed flow of nodes to track the flow of energy from the initial stage to the consumption stage.

10. The online monitoring method of integrated energy flow direction based on industrial park according to claim 6, characterized in that, The statistical analysis module performs the following steps: The energy consumption calculation and analysis module provides energy balance analysis for different links in the conversion and use of enterprise energy according to node-bound instrument collection parameter data, evaluates the energy consumption degree of different links by respectively counting the energy supply and energy consumption of a specified energy-using link and calculating the difference between the two, and calculates the abnormal alarm threshold according to the comprehensive analysis of the energy consumption of each node; after the alarm, the nodes with abnormal consumption are traced back, the level and position of the abnormal consumption are determined according to the node attributes, and the abnormal consumption is visualized and the alarm information is pushed. The abnormal alarm module automatically calculates the abnormal alarm threshold according to the comprehensive analysis of the energy consumption of each node; after the alarm, the nodes with abnormal consumption are traced back, the level and position of the abnormal consumption are determined according to the node attributes, and the abnormal consumption is visualized and the alarm information is pushed.

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