Industrial park energy, electricity and carbon intelligent monitoring method and system

Abstract

The invention discloses an industrial park energy, electricity and carbon intelligent monitoring method and system. The method comprises the steps that energy, electricity and carbon basic data of an industrial park are collected and preprocessed; calling a preset calculation model to calculate main indexes; according to a transient response strategy, judging the operation state of the system, automatically triggering a preset regulation and control action, and performing automatic early warning and recording on abnormal data; and carrying out visualization processing on the calculation result and outputting an evaluation report. The system provided by the invention adopts a layered modular architecture, and is formed by sequentially communicating and connecting four subsystems of an acquisition layer, a processing layer, an operation layer and an application layer through predefined interfaces, so that closed-loop control is realized, dynamic interaction and full-link tracing are supported, and a monitoring strategy can be automatically adjusted according to the change of a system running state. According to the method and the system provided by the invention, static and passive modes of a traditional monitoring system are overcome, and the accuracy and the timeliness of monitoring data and the robustness of the system are greatly improved.

Description

Technical Field

[0001] This invention relates to the fields of energy consumption, electricity consumption, and carbon emission monitoring technology, specifically to a smart monitoring method and system for energy, electricity, and carbon emissions in industrial parks. Background Technology

[0002] With the intensification of global climate change and the advancement of the "dual carbon" goal, intelligent monitoring of energy consumption, electricity consumption, and carbon emissions (referred to as "energy, electricity, and carbon") in industrial parks has become a key measure to achieve green and low-carbon transformation.

[0003] Industrial parks typically deploy various sensors and information systems to collect data on energy consumption (such as electricity and gas), power load characteristics (such as active / reactive power and power quality), and carbon emission-related data (such as direct and indirect emission sources). These data sources include industrial sensors (such as electricity meters, gas analyzers, and flow meters), non-sensor data (such as equipment operation logs and raw material logistics records), and manually entered information. However, existing monitoring methods and systems face significant technical bottlenecks, limiting their monitoring accuracy and management efficiency.

[0004] First, integrating multi-source, heterogeneous data from industrial parks presents systemic challenges. This data exhibits high heterogeneity in format, frequency, and semantics, making it difficult for acquisition devices to achieve standardized output. Existing technologies typically rely on static preprocessing modules, which cannot dynamically adapt to different data sources. This results in high error rates in data verification, timestamp alignment, and unit conversion, thus affecting the reliability of subsequent analysis.

[0005] A deeper problem lies in the fact that traditional methods for calculating carbon emissions from energy consumption are generally static and passive, lacking the ability to dynamically adapt to changes in complex industrial environments. Existing accounting systems primarily rely on manual data entry, statistical ledgers, or simple system integration to acquire data. This approach not only suffers from untimely data collection and is prone to errors, but also makes it difficult to achieve full-process data traceability and verification, failing to meet the urgent national requirements for the authenticity, accuracy, and uniformity of carbon emission monitoring data. Furthermore, these systems are typically unidirectional and passive; they are merely recorders and displayers of data, unable to automatically adjust their operating modes based on changes in production conditions, equipment status, or the external environment, such as fluctuations in production load, equipment failures, or the integration of new production lines. This passivity results in a lack of robustness in the face of complex situations, requiring significant manual intervention to maintain effective operation, making it difficult to guarantee data quality and system stability.

[0006] Therefore, this invention aims to overcome the shortcomings of the prior art and, through innovative architecture design, provides an intelligent method and system that can achieve closed-loop control of "sensing-collection-computation-application", support dynamic interaction and full-link traceability, realize real-time, comprehensive and accurate monitoring of energy and carbon emissions in industrial parks, and automatically adjust monitoring strategies according to changes in system operating status, with adaptive and closed-loop control capabilities, so as to improve the efficiency of intelligent monitoring and management of energy and carbon emissions in industrial parks. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a smart monitoring method and system for energy, electricity, and carbon emissions in industrial parks. Its core innovation lies in the introduction of a transient response strategy, enabling the system to automatically trigger preset control actions based on changes in real-time operating status and dynamically modify its acquisition, calculation, and output parameters, thus achieving an adaptive closed-loop feedback mechanism. This method and system break away from the static and passive mode of traditional monitoring systems, significantly improving the accuracy, timeliness, and robustness of monitoring data.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: A smart monitoring method for energy, electricity, and carbon emissions in industrial parks includes the following steps: Step 1: Obtain the collection requirements, processing algorithms, transient response strategies, and output parameters for the basic energy, electricity, and carbon data of the industrial park stored in the storage medium; Step 2: According to the data collection requirements, match the monitoring sensor element or input interface, verify the effectiveness of the data collection service, collect the basic energy, electricity and carbon data of the industrial park, and preprocess the collected data. Step 3: Based on the processing algorithm, call the preset energy and electricity carbon emission intensity calculation model to calculate the preprocessed data; Step 4: Based on the transient response strategy, determine the system's operating status and execute preset automatic control actions according to the determination results. Modify the relevant settings stored in the storage medium, thereby adjusting the system's acquisition, calculation, and output behavior in real time in the next operating cycle. This enables automatic response to system changes and complex situations, maintaining robustness in complex application scenarios. Step 5: Based on the output method parameters, perform visualization post-processing on the calculation results, output the evaluation report in a graphical manner, and automatically trigger and record early warnings for abnormal data; In step 2, the basic energy, electricity, and carbon data of the industrial park include various energy consumption, electricity consumption, raw material consumption, auxiliary material consumption, energy carbon emission coefficient, raw material carbon emission coefficient, product output, and output value data.

[0009] In step 2, the acquisition requirements include the requirements and instructions for the monitoring sensing elements or input interfaces, ensuring that all data sources are in the desired working state during the acquisition process.

[0010] In step 3, the preset energy carbon emission intensity calculation model and output data in the processing algorithm are matched, and at least one of the following can be obtained through calculation: energy consumption per unit output, electricity consumption per unit output, real-time value of carbon emission per unit output, and time period comparison result. The energy consumption per unit of output value = total energy consumption during the period / total output value during the period; The power consumption per unit of output value = total power consumption during the period / total output value during the period; The carbon emission per unit output value = Σ(total energy consumption during the period * energy carbon emission coefficient + total raw material consumption during the period * raw material carbon emission coefficient) / total output value during the period; The time period comparison result = value of the current time period / value of the previous time period; In step 4, the transient response strategy can automatically modify at least one of the acquisition requirements, processing algorithm, and output method according to the changing operating conditions and preset conditions. The preset conditions include the system state that can be determined by the program, which includes at least one of variable value exceeding the limit, equipment error signal, and external input manual command.

[0011] This invention also discloses an intelligent monitoring system for energy, electricity, and carbon in industrial parks based on an intelligent monitoring method for energy, electricity, and carbon in industrial parks, comprising a data acquisition subsystem, a processing subsystem, a computing subsystem, and an application subsystem that are sequentially connected via predefined interfaces. The data acquisition subsystem is used to acquire data on various energy consumption, electricity consumption, raw material consumption, auxiliary material consumption, energy carbon emission coefficient, raw material carbon emission coefficient, product output and output value of the industrial park through at least one of the sensor interface, system interconnection interface and manual input interface according to the data acquisition requirements, and to dynamically adjust the data acquisition strategy and sensor operating parameters in response to the instructions of the processing subsystem. The processing layer subsystem is communicatively connected to the acquisition layer subsystem and the computing layer subsystem, and is used to perform cleaning, unit unification and formatting preprocessing on the data uploaded by the acquisition layer subsystem. The computing layer subsystem is communicatively connected to the processing layer subsystem and the application layer subsystem. It is used to call the energy carbon emission intensity calculation model to store, calculate and analyze the preprocessed data according to the processing algorithm and response strategy, and generate monitoring results and control instructions. The application layer subsystem is communicatively connected to the computing layer subsystem and is used to display monitoring results, execute early warnings and generate reports through a graphical interface according to the output method parameters, and provide user management, policy configuration and operation auditing functions.

[0012] Furthermore, the acquisition layer subsystem includes: Sensor interface unit: Supports multiple industrial buses and IoT protocols, used to connect to various industrial sensors and collect data on energy consumption, electricity consumption, raw material consumption, and auxiliary material consumption in the industrial park; System interconnection interface unit: used to obtain data from energy management system and power grid dispatch system, including carbon emission coefficients of specific energy categories and carbon emission coefficients of raw materials; Manual input interface unit: Supports input via file import or graphical interface, including product output and output value data; Acquisition and control unit: responds to instructions from the processing layer and dynamically adjusts the acquisition frequency, sensor operating parameters, signal type, and data source selection.

[0013] Furthermore, the processing layer subsystem includes: The data cleaning unit is configured to perform validity checks and outlier removal on production output, input logistics data, and calculated coefficient data. Unit unification: Configured to convert heterogeneous data into a defined standard unit; Data formatting unit: configured to reorganize data according to a predetermined data model for use by the computing layer; Processing and control unit: Synchronizes data acquisition settings and adjusts data cleaning thresholds, unit conversion rules, or communication protocol parameters each time it runs.

[0014] Furthermore, the computing layer subsystem includes: The calculation trigger management module is configured to automatically start calculation tasks according to timed rules or event-driven rules. Energy and electricity carbon emission intensity calculation model module: stores at least one of the calculation models for energy consumption per unit of output, electricity consumption per unit of output, carbon emission per unit of output in real time and time period comparison results; Response Management Module: Configured to monitor system status variables and activate the response strategy when preset conditions are met, automatically modifying the acquisition requirements, processing algorithm, or output method parameters; Analysis and execution engine module: used to call specified functions to execute computational tasks, generate intensity assessment results and anomaly indicators, and send control adjustment instructions to lower-level subsystems.

[0015] Furthermore, the application layer subsystem includes: Visualization module: Provides a real-time dashboard of energy and electricity carbon emission intensity, multi-dimensional trend charts, and comparative analysis views; Early warning and reporting module: Supports automatic triggering of early warning prompts, generation of monitoring reports, and export of results; User and Policy Management Module: Controls system access based on role-based access control and provides a configuration interface for response policies, allowing users to manually adjust system settings; Operation audit module: Records all user operations, policy changes and system response events, forming a traceable audit log.

[0016] Compared with existing technologies, the present invention provides a smart monitoring method and system for energy, electricity and carbon emissions in industrial parks, which has the following beneficial effects: (1) Excellent Adaptability and Robustness: The core innovation of this invention lies in its embedded dynamic feedback loop. This loop enables the system to automatically modify its acquisition, processing, and output strategies based on preset conditions such as variable values ​​exceeding limits and equipment error signals. This self-adjustment capability allows the system to maintain continuous and stable operation without manual intervention when facing complex operating conditions such as production load fluctuations, equipment failures, or the access of new production lines, fundamentally solving the problem of the lack of robustness in traditional systems.

[0017] (2) Precise Quantification and In-Depth Management: The intensity indicators such as energy consumption per unit output, electricity consumption per unit output, and carbon emissions per unit output provided by this invention are not simple calculation results, but rather in-depth tools serving the energy efficiency management of industrial parks. These indicators introduce the concept of macroeconomic indicators into the micro-level management of industrial parks, directly reflecting the intensity relationship between production efficiency and energy consumption, and indirectly reflecting the level of equipment technology and energy utilization efficiency. Through the calculation of "time period comparison results," park managers can quantify the effectiveness of energy-saving measures and conduct budget management and dynamic adjustment of energy consumption and carbon emissions.

[0018] (3) High degree of automation and real-time performance: This system significantly reduces the reliance on manual data entry and intervention through automated data collection and calculation, ensuring the real-time performance and accuracy of energy, electricity and carbon data, and overcoming the limitations of traditional accounting methods such as data lag and error-proneness.

[0019] (4) Intuitive Decision Support and Traceability: This system provides real-time dashboards, multi-dimensional trend charts, and comparative analysis views through a visualization module, and supports automatic early warning and assessment report generation. This provides park managers with intuitive and timely decision support. In addition, the operation audit module records all user operations, policy changes, and system response events, forming a traceable audit log, which is crucial for meeting carbon verification and regulatory compliance requirements. Attached Figure Description

[0020] Figure 1 This is a flowchart of a smart monitoring method for energy, electricity, and carbon in industrial parks according to the present invention.

[0021] Figure 2 This is a complete flowchart of the transient response strategy of the present invention.

[0022] Figure 3 This is a schematic diagram of the overall architecture of an intelligent monitoring system for energy, electricity, and carbon in industrial parks according to the present invention.

[0023] Figure 4 This is a schematic diagram of the functional modules of the data acquisition layer subsystem of the present invention.

[0024] Figure 5 This is a flowchart of the data preprocessing process of the processing layer subsystem of the present invention.

[0025] Figure 6 This is a schematic diagram of the functional modules of the computational layer subsystem of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1:

[0027] like Figure 1 As shown, this is an embodiment of the present invention providing a smart monitoring method for energy, electricity, and carbon in industrial parks, comprising the following steps: S1. Obtain the collection requirements, processing algorithms, transient response strategies, and output parameters for the basic energy, electricity, and carbon data of the industrial park stored in the storage medium. S2. According to the data collection requirements, match the monitoring sensor element or input interface, verify the effectiveness of the data collection service, collect the basic data of energy, electricity and carbon in the industrial park, and preprocess the collected data. S3. Based on the processing algorithm, call the preset energy and electricity carbon emission intensity calculation model to calculate the preprocessed data; S4. Based on the transient response strategy, determine the system operating status and execute preset automatic control actions according to the determination results. Modify the relevant settings stored in the storage medium, so as to adjust the system's acquisition, calculation and output behavior in real time in the next operating cycle, thereby automatically responding to system changes and complex situations and maintaining robustness in complex application scenarios. S5. Based on the output method parameters, perform visualization post-processing on the calculation results, output the evaluation report in a graphical manner, and automatically trigger and record early warnings for abnormal data.

[0028] Specifically, in S2, the basic energy, electricity, and carbon data of the industrial park includes various energy consumption, electricity consumption, raw material consumption, auxiliary material consumption, energy carbon emission coefficient, raw material carbon emission coefficient, product output, and output value data.

[0029] Specifically, in S2, the acquisition requirements include requirements and instructions for monitoring sensing elements or input interfaces, ensuring that all data sources are in the desired working state during the acquisition process.

[0030] Specifically, in S3, the preset energy carbon emission intensity calculation model and output data in the processing algorithm are matched, and at least one of the following can be obtained through calculation: energy consumption per unit output, electricity consumption per unit output, real-time value of carbon emission per unit output, and time period comparison result; The energy consumption per unit of output value = total energy consumption during the period / total output value during the period; The power consumption per unit of output value = total power consumption during the period / total output value during the period; The carbon emission per unit output value = Σ(total energy consumption during the period * energy carbon emission coefficient + total raw material consumption during the period * raw material carbon emission coefficient) / total output value during the period; The time period comparison result = value of the current time period / value of the previous time period; Specifically, in S4, the transient response strategy can automatically modify at least one of the acquisition requirements, processing algorithm, and output method according to the changing operating conditions and preset conditions. The preset conditions include the system state that can be determined by the program, which includes at least one of variable value exceeding the limit, device error signal, and external input manual command.

[0031] like Figure 2 The diagram shows the complete workflow of the transient response strategy provided in this embodiment, including the following steps: T1: The operator initiates an operation command from the application layer. The relevant command contains the specific execution subsystem, object and operation information, and is sent to the computing layer. T2: The computing layer automatically operates based on a preset decision-making strategy, taking into account any one of the triggering conditions, including application layer instructions, data exceeding limits, and device malfunctions. T3: The processing layer reads the latest working conditions each time it runs, so that the changes take effect immediately; T4: The data acquisition layer updates equipment operating parameters in a time-based poll to ensure that modifications take effect promptly; it also updates data acquisition requirements to on-site personnel through a manual online reporting system.

[0032] Specifically, in T2, the computing layer can simultaneously monitor multiple judgment conditions and perform corresponding operations, including automatically modifying at least one of the acquisition requirements, processing algorithms, and output methods according to preset conditions, as well as automatically triggering and recording early warnings. Example 2:

[0033] like Figure 3As shown, this embodiment of the invention also provides a park carbon emission management system, which includes a data acquisition layer subsystem, a processing layer subsystem, a computing layer subsystem, and an application layer subsystem that are sequentially connected through predefined interfaces. The data acquisition subsystem is used to acquire data on various energy consumption, electricity consumption, raw material consumption, auxiliary material consumption, energy carbon emission coefficient, raw material carbon emission coefficient, product output and output value of the industrial park through at least one of the sensor interface, system interconnection interface and manual input interface according to the data acquisition requirements, and to dynamically adjust the data acquisition strategy and sensor operating parameters in response to the instructions of the processing subsystem. The processing layer subsystem is communicatively connected to the acquisition layer subsystem and the computing layer subsystem, and is used to perform cleaning, unit unification and formatting preprocessing on the data uploaded by the acquisition layer subsystem. The computing layer subsystem is communicatively connected to the processing layer subsystem and the application layer subsystem. It is used to call the energy carbon emission intensity calculation model to store, calculate and analyze the preprocessed data according to the processing algorithm and response strategy, and generate monitoring results and control instructions. The application layer subsystem is communicatively connected to the computing layer subsystem and is used to display monitoring results, execute early warnings and generate reports through a graphical interface according to the output method parameters, and provide user management, policy configuration and operation auditing functions.

[0034] like Figure 4 As shown, the acquisition layer subsystem includes: Sensor interface unit: Supports multiple industrial buses and IoT protocols, used to connect to various industrial sensors and collect data on energy consumption, electricity consumption, raw material consumption, and auxiliary material consumption in the industrial park; System interconnection interface unit: used to obtain data from energy management system and power grid dispatch system, including carbon emission coefficients of specific energy categories and carbon emission coefficients of raw materials; Manual input interface unit: Supports input via file import or graphical interface, including product output and output value data; Acquisition and control unit: responds to instructions from the processing layer and dynamically adjusts the acquisition frequency, sensor operating parameters, signal type, and data source selection.

[0035] like Figure 5 As shown, the processing layer subsystem includes: The data cleaning unit is configured to perform validity checks and outlier removal on production output, input logistics data, and calculated coefficient data. Unit unification: Configured to convert heterogeneous data into a defined standard unit; Data formatting unit: configured to reorganize data according to a predetermined data model for use by the computing layer; Processing and control unit: Synchronizes data acquisition settings and adjusts data cleaning thresholds, unit conversion rules, or communication protocol parameters each time it runs.

[0036] like Figure 6 As shown, the computing layer subsystem includes: The calculation trigger management module is configured to automatically start calculation tasks according to timed rules or event-driven rules. Energy and electricity carbon emission intensity calculation model module: stores at least one of the calculation models for energy consumption per unit of output, electricity consumption per unit of output, carbon emission per unit of output in real time and time period comparison results; Response Management Module: Configured to monitor system status variables and activate the response strategy when preset conditions are met, automatically modifying the acquisition requirements, processing algorithm, or output method parameters; Analysis and execution engine module: used to call specified functions to execute computational tasks, generate intensity assessment results and anomaly indicators, and send control adjustment instructions to lower-level subsystems.

[0037] Specifically, the application layer subsystem includes: Visualization module: Provides a real-time dashboard of energy and electricity carbon emission intensity, multi-dimensional trend charts, and comparative analysis views; Early warning and reporting module: Supports automatic triggering of early warning prompts, generation of monitoring reports, and export of results; User and Policy Management Module: Controls system access based on role-based access control and provides a configuration interface for response policies, allowing users to manually adjust system settings; Operation audit module: Records all user operations, policy changes and system response events, forming a traceable audit log.

Claims

1. A smart monitoring method for energy, electricity, and carbon emissions in industrial parks, characterized in that, The method includes the following steps: Step 1: Obtain the collection requirements, processing algorithms, transient response strategies, and output parameters for the basic energy, electricity, and carbon data of the industrial park stored in the storage medium; Step 2: According to the data collection requirements, match the monitoring sensor element or input interface, verify the effectiveness of the data collection service, collect the basic energy, electricity and carbon data of the industrial park, and preprocess the collected data. Step 3: Based on the processing algorithm, call the preset energy and electricity carbon emission intensity calculation model to calculate the preprocessed data; Step 4: Based on the transient response strategy, determine the system's operating status and execute preset automatic control actions according to the determination results. Modify the relevant settings stored in the storage medium, thereby adjusting the system's acquisition, calculation, and output behavior in real time in the next operating cycle. This enables automatic response to system changes and complex situations, maintaining robustness in complex application scenarios. Step 5: Based on the output method parameters, perform visualization post-processing on the calculation results, output the evaluation report in a graphical manner, and automatically trigger and record early warnings for abnormal data.

2. The intelligent monitoring method for energy, electricity, and carbon emissions in industrial parks according to claim 1, characterized in that: In step 2, the basic energy, electricity, and carbon data of the industrial park include various energy consumption, electricity consumption, raw material consumption, auxiliary material consumption, energy carbon emission coefficient, raw material carbon emission coefficient, product output, and output value data.

3. The intelligent monitoring method for energy, electricity, and carbon emissions in industrial parks according to claim 1, characterized in that: In step 2, the acquisition requirements include the requirements and instructions for the monitoring sensing elements or input interfaces, ensuring that all data sources are in the desired working state during the acquisition process.

4. The intelligent monitoring method for energy, electricity, and carbon emissions in industrial parks according to claim 1, characterized in that: In step 3, the preset energy carbon emission intensity calculation model and output data in the processing algorithm are matched, and at least one of the following can be obtained through calculation: energy consumption per unit output, electricity consumption per unit output, real-time value of carbon emission per unit output, and time period comparison result. The energy consumption per unit of output value = total energy consumption during the period / total output value during the period; The power consumption per unit of output value = total power consumption during the period / total output value during the period; The carbon emission per unit output value = Σ(total energy consumption during the period * energy carbon emission coefficient + total raw material consumption during the period * raw material carbon emission coefficient) / total output value during the period; The comparison result of the time period = value of the current time period / value of the previous time period.

5. The intelligent monitoring method for energy, electricity, and carbon emissions in industrial parks according to claim 1, characterized in that: In step 4, the transient response strategy can automatically modify at least one of the acquisition requirements, processing algorithm, and output method according to the changing operating conditions and preset conditions. The preset conditions include the system state that can be determined by the program, which includes at least one of variable value exceeding the limit, equipment error signal, and external input manual command.

6. The intelligent monitoring system for energy, electricity, and carbon in industrial parks according to any one of claims 1-5, characterized in that, This includes the acquisition layer subsystem, processing layer subsystem, computing layer subsystem, and application layer subsystem, which are sequentially connected via predefined interfaces. The data acquisition subsystem is used to acquire data on various energy consumption, electricity consumption, raw material consumption, auxiliary material consumption, energy carbon emission coefficient, raw material carbon emission coefficient, product output and output value of the industrial park through at least one of the sensor interface, system interconnection interface and manual input interface according to the data acquisition requirements, and to dynamically adjust the data acquisition strategy and sensor operating parameters in response to the instructions of the processing subsystem. The processing layer subsystem is communicatively connected to the acquisition layer subsystem and the computing layer subsystem, and is used to perform cleaning, unit unification and formatting preprocessing on the data uploaded by the acquisition layer subsystem. The computing layer subsystem is communicatively connected to the processing layer subsystem and the application layer subsystem. It is used to call the energy carbon emission intensity calculation model to store, calculate and analyze the preprocessed data according to the processing algorithm and response strategy, and generate monitoring results and control instructions. The application layer subsystem is communicatively connected to the computing layer subsystem and is used to display monitoring results, execute early warnings and generate reports through a graphical interface according to the output method parameters, and provide user management, policy configuration and operation auditing functions.

7. The intelligent monitoring system for energy, electricity, and carbon emissions in industrial parks according to claim 6, characterized in that, The acquisition layer subsystem includes: Sensor interface unit: Supports multiple industrial buses and IoT protocols, used to connect to various industrial sensors and collect data on energy consumption, electricity consumption, raw material consumption, and auxiliary material consumption in the industrial park; System interconnection interface unit: used to obtain data from energy management system and power grid dispatch system, including carbon emission coefficients of specific energy categories and carbon emission coefficients of raw materials; Manual input interface unit: Supports input via file import or graphical interface, including product output and output value data; Acquisition and control unit: responds to instructions from the processing layer and dynamically adjusts the acquisition frequency, sensor operating parameters, signal type, and data source selection.

8. The intelligent monitoring system for energy, electricity, and carbon emissions in industrial parks according to claim 6, characterized in that, The processing layer subsystem includes: The data cleaning unit is configured to perform validity checks and outlier removal on production output, input logistics data, and calculated coefficient data. Unit unification: Configured to convert heterogeneous data into a defined standard unit; Data formatting unit: configured to reorganize data according to a predetermined data model for use by the computing layer; Processing and control unit: Synchronizes data acquisition settings and adjusts data cleaning thresholds, unit conversion rules, or communication protocol parameters each time it runs.

9. The intelligent monitoring system for energy, electricity, and carbon emissions in industrial parks according to claim 6, characterized in that, The computational layer subsystem includes: The calculation trigger management module is configured to automatically start calculation tasks according to timed rules or event-driven rules. Energy and electricity carbon emission intensity calculation model module: stores at least one of the calculation models for energy consumption per unit of output, electricity consumption per unit of output, carbon emission per unit of output in real time and time period comparison results; Response Management Module: Configured to monitor system status variables and activate the response strategy when preset conditions are met, automatically modifying the acquisition requirements, processing algorithm, or output method parameters; Analysis and execution engine module: used to call specified functions to execute computational tasks, generate intensity assessment results and anomaly indicators, and send control adjustment instructions to lower-level subsystems.

10. The intelligent monitoring system for energy, electricity, and carbon emissions in industrial parks according to claim 6, characterized in that, The application layer subsystem includes: Visualization module: Provides a real-time dashboard of energy and electricity carbon emission intensity, multi-dimensional trend charts, and comparative analysis views; Early warning and reporting module: Supports automatic triggering of early warning prompts, generation of monitoring reports, and export of results; User and Policy Management Module: Controls system access based on role-based access control and provides a configuration interface for response policies, allowing users to manually adjust system settings; Operation audit module: Records all user operations, policy changes and system response events, forming a traceable audit log.

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