Green supply chain visualization control platform based on industrial Internet of Things

Abstract

The present invention discloses a green supply chain visualization control platform based on the Industrial Internet of Things, which belongs to the field of green supply chain management. The platform includes the following steps: obtaining nodes throughout the entire supply chain process, conducting historical environmental impact assessments on the nodes throughout the process, and tracing the sub-process nodes based on key influencing factors; constructing a corresponding sub-process node impact system based on the influencing source corresponding to each sub-process node, and setting environmental optimization targets for each sub-process node based on the sub-process node impact system; collecting operating data of each sub-process node in real time and comparing it with the environmental optimization target; generating a control instruction set based on environmental optimization deviation information, and controlling the influencing source corresponding to each sub-process node based on a visual web interface. This application solves the technical problem in the prior art that the environmental impact of supply chain nodes cannot be monitored and controlled in real time, resulting in the inability to accurately implement environmental optimization and delayed response.

Description

Technical Field

[0001] The present invention relates to the field of green supply chain management, and in particular to a green supply chain visualization control platform based on the Industrial Internet of Things. Background Art

[0002] As global environmental issues become increasingly severe, businesses face increasing pressure to achieve green and sustainable development. Supply chains, as the core link in a company's production, transportation, and sales, have become a major concern for their environmental impact. The development of the Industrial Internet of Things (IIoT) provides technical support for enhanced visualization and intelligent management of supply chains. Through IoT devices, businesses can monitor data from every link in the supply chain in real time, promptly identify sources of environmental impact, and adjust and optimize production processes to minimize environmental impact and enhance green sustainability.

[0003] While most companies are gradually incorporating IoT technology for data collection in their supply chain management, they still lack systematic solutions for environmental impact assessment and control. Traditional supply chain environmental management methods rely heavily on manual monitoring and post-analysis, making real-time feedback and automated adjustments difficult. Furthermore, existing supply chain visualization management systems often focus on optimizing production efficiency while neglecting the real-time control of environmental factors. A technology is urgently needed to address the existing challenges of inability to monitor and control environmental impacts at supply chain nodes in real time, resulting in inaccurate environmental optimization and delayed response. Summary of the Invention

[0004] This application provides a green supply chain visualization and control platform based on the Industrial Internet of Things, aiming to solve the technical problems in the existing technology that the environmental impact of supply chain nodes cannot be monitored and controlled in real time, resulting in the inability to accurately implement environmental optimization and delayed response.

[0005] In view of the above problems, this application provides a green supply chain visualization and control platform based on the Industrial Internet of Things.

[0006] The present application provides a green supply chain visualization control method based on the industrial Internet of Things. The platform includes: a full-process node acquisition module: used to obtain the full-process nodes of the supply chain; an impact factor acquisition module: used to perform historical environmental impact assessment on the full-process nodes to obtain key impact factors; an impact source acquisition module: used to monitor each sub-process node in the full-process nodes of the current supply chain, and trace the sub-process nodes according to the key impact factors to obtain the impact sources corresponding to the sub-process nodes; an impact system construction module: used to construct a corresponding sub-process node impact system based on the impact sources corresponding to each sub-process node; an optimization target setting module: used to set environmental optimization targets for each sub-process node based on the sub-process node impact system; a deviation information acquisition module: used to collect the operating data of each sub-process node in real time, and compare it with the environmental optimization target to obtain environmental optimization deviation information; a control instruction set generation module: used to generate a control instruction set based on the environmental optimization deviation information, and control the impact sources corresponding to each sub-process node based on a visual Web interface.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By adopting a green supply chain visualization control platform based on the Industrial Internet of Things, the key influencing factors are first obtained by obtaining the nodes of the entire supply chain process and conducting a historical environmental impact assessment. Subsequently, each sub-process node is monitored in real time, and the source of influence is traced based on the key influencing factors to build a sub-process node impact system. Next, environmental optimization goals are set for each sub-process node, and operating data is collected in real time, environmental optimization deviation information is calculated, and a control instruction set is generated. The source of influence is controlled based on a visual web interface. This technical solution solves the technical problem in the existing technology that the environmental impact of supply chain nodes cannot be monitored and controlled in real time, resulting in the inability to accurately implement environmental optimization and delayed response. Through dynamic adjustment and optimization, the transparency and efficiency of environmental management are improved, and ultimately the technical effect of effectively reducing environmental pollution and resource waste is achieved.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of the structure of a green supply chain visualization control platform based on the Industrial Internet of Things is provided for the embodiment of this application;

[0011] Figure 2The present invention provides a flow chart of the influence source acquisition module in the green supply chain visualization control platform based on the industrial Internet of Things for the embodiment of the present application.

[0012] Explanation of the accompanying symbols: full-process node acquisition module M100, impact factor acquisition module M200, impact source acquisition module M300, impact system construction module M400, optimization target setting module M500, deviation information acquisition module M600, control instruction set generation module M700. DETAILED DESCRIPTION

[0013] The overall idea of the technical solution provided by this application is as follows:

[0014] The embodiments of the present application provide a green supply chain visualization and control platform based on the Industrial Internet of Things. First, the entire supply chain process nodes are obtained and a historical environmental impact assessment is performed to identify key influencing factors. Next, each sub-process node is monitored in real time, and the source of impact is traced to build an impact system. Environmental optimization goals are set based on the impact system, and real-time operating data is collected and compared with the optimization goals to generate a control instruction set. Finally, control is performed through a visual web interface to achieve dynamic management of environmental optimization.

[0015] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the drawings in the specification.

[0016] Examples, such as Figure 1 As shown, the embodiment of the present application provides a green supply chain visualization control platform based on the Industrial Internet of Things, which includes:

[0017] Full-process node acquisition module M100: used to obtain full-process nodes of the supply chain.

[0018] Specifically, supply chain nodes refer to all the links or steps involved in the supply chain, including every sub-process from raw material procurement, production, transportation, warehousing, sales, and recycling. These nodes can be physical equipment, departments, companies, or specific activities in the process.

[0019] Using Industrial Internet of Things (IIoT) technology, data from every link in the supply chain can be gradually collected and identified. First, the scope of the supply chain must be defined, including the sub-processes within each link, such as raw material suppliers, product production workshops, and logistics companies. Data can be collected at key nodes in these sub-processes using IoT sensors, RFID (radio frequency identification) technology, or barcode scanners. Each node uses specific sensors or data collection devices to upload operational information, such as temperature, inventory levels, and shipping status, to a cloud system or central database.

[0020] For example, in a clothing manufacturing supply chain, the entire process includes: raw material suppliers (purchasing cotton), production factories (processing and producing finished garments), logistics and transportation (distributing finished garments), and retailers (selling the final product). By installing sensors at these nodes, the operation of each node can be monitored in real time, capturing data such as inventory, production progress, and transportation temperature. For example, IoT sensors can monitor factory production equipment in real time and collect equipment operating status data; GPS devices can be used to monitor the location and travel time of logistics company transport vehicles. The data from these devices is aggregated to form a comprehensive understanding of each node in the entire supply chain.

[0021] Through this step, we can fully understand the operating status of each node, and then combine it with steps such as environmental impact assessment to further optimize each link of the supply chain.

[0022] Impact factor acquisition module M200: used to perform historical environmental impact assessment on the nodes of the entire process and obtain key impact factors.

[0023] Specifically, a historical environmental impact assessment involves analyzing and evaluating the environmental impacts of each supply chain node (such as carbon emissions, energy consumption, and pollution emissions) based on historical data. Impact factors refer to those factors with the greatest environmental impact within the supply chain, such as high-energy-consuming equipment and carbon emissions from long-distance transportation. Key impact factors are those with the greatest environmental impact among all influencing factors, determined through analysis and ranking.

[0024] First, historical operational data from each node in the supply chain is collected. This data can include information on energy consumption, waste generation, and pollutant emissions. This historical data can be obtained through IoT devices or existing enterprise data management systems (such as SCADA or ERP systems). Next, based on this data, an environmental impact assessment indicator system is constructed. Commonly used indicators include carbon footprint, water footprint, and energy efficiency. These indicators reflect the impact of each node on different dimensions of the environment.

[0025] Next, we use quantitative analysis methods to analyze the environmental impact data for each node. The goal of this step is to assess each node's contribution to the environment and automatically identify the most significant environmental impact factors from the data using machine learning algorithms. Key impact factors are typically those that have the greatest impact on the environment and require the most attention in the assessment.

[0026] For example, in a food production supply chain, a factory's energy use and greenhouse gas emissions are key factors. An energy management system (EMS) can analyze a factory's past energy consumption data to assess its environmental impact. Simultaneously, vehicle fuel consumption and carbon emissions generated during transportation can be assessed through historical data analysis using GPS systems and vehicle fuel consumption sensors. This historical data analysis can help companies identify nodes with the greatest negative environmental impact, providing a basis for subsequent optimization.

[0027] The influence source acquisition module M300 is used to monitor each sub-process node in the full process node of the current supply chain, trace the sub-process node according to the key influencing factors, and obtain the influence source corresponding to the sub-process node.

[0028] Specifically, a sub-process node refers to a specific sub-link within a full process node, such as a single piece of equipment in a production line, a single warehouse in a warehousing system, or a segment in a transportation network. Source tracing refers to tracing a problem back to its root cause by analyzing data from sub-process nodes to identify the primary source of environmental impact. The source of impact refers to the specific source or cause of a negative environmental impact, typically referring to a specific operation, equipment, or behavior within a sub-process node.

[0029] First, sensors or data acquisition devices need to be deployed at each sub-process node to monitor the node's operating parameters, such as energy consumption, emissions, temperature, etc. The real-time monitored data is transmitted to a central data system for storage and analysis.

[0030] Next, the data for each sub-process node is compared with key influencing factors previously identified through historical assessment. For example, if the key influencing factor in the production process is energy consumption, then the real-time power consumption of a specific device, such as a manufacturing machine, will be monitored within the sub-process node. If the energy consumption of a device exceeds the preset standard value, traceability analysis will be conducted.

[0031] The root cause of excessive energy consumption is identified through data analysis methods such as causal analysis or regression analysis. These can include equipment aging, inadequate maintenance, or improper operation. For example, monitoring equipment operating status revealed that the motor efficiency of a piece of production equipment had decreased due to prolonged lack of maintenance. In this case, the motor's low efficiency was the source of the problem.

[0032] For example, in a beverage production supply chain, a sub-process node involves the bottling line. IoT sensors monitoring the power consumption of this node revealed an abnormal increase in the line's energy consumption. Analysis and comparison with key influencing factors (energy consumption) identified the issue as related to the heating equipment on the line. Further tracing revealed that this equipment had low heating efficiency and high power consumption, making it the primary source of energy consumption for the bottling line.

[0033] Through this step, the specific impact source of each sub-process node on the environment can be identified, and corresponding optimization measures can be taken.

[0034] The impact system construction module M400 is used to construct the corresponding sub-process node impact system according to the impact source corresponding to each sub-process node.

[0035] Specifically, the sub-process node impact system is a comprehensive impact network or framework constructed by analyzing the direct and indirect relationships between the impact sources and the environment within a specific sub-process node in the supply chain. This system details how each sub-process node impacts the environment at different levels through its various impact sources (such as equipment, operations, and energy consumption), and clearly defines the impact path, intensity, and scope, forming a systematic structure.

[0036] After identifying the impact sources for each sub-process node, analyze their specific environmental impacts and present them graphically or in a structured manner. First, assess the direct and indirect environmental impacts of each impact source. Direct impacts are usually obvious, while indirect impacts are more complex.

[0037] Next, use data analysis tools or modeling software (such as MATLAB, Simulink, or a dedicated supply chain management system) to quantify and simulate the effects of these impact sources, assessing the extent and scope of each impact source's environmental impact. By creating an impact diagram, you can clearly identify the causal relationship between each impact source and the environmental impact, the impact path, and the ultimate impact outcome.

[0038] The construction of this sub-process node impact system can fully grasp the impact of each sub-process node on the environment at all levels, thereby providing a basis for setting the next step of environmental optimization goals.

[0039] Optimization target setting module M500: used to set an environmental optimization target for each sub-process node according to the sub-process node impact system.

[0040] Specifically, environmental optimization goals refer to specific goals set to reduce the negative impact of each sub-process node in the supply chain on the environment. The goals can be to reduce energy consumption, reduce emissions, and improve resource utilization efficiency.

[0041] The process of setting environmental optimization goals based on the sub-process node impact system involves analyzing the impact sources identified in each sub-process node, clarifying their environmental impact, and setting quantifiable and achievable optimization goals for them. First, based on the impact diagram drawn from the impact system, the main impact sources of each sub-process node can be identified, such as the energy consumption of a certain device or the emissions of a certain transportation link. Next, these impact sources are quantitatively analyzed to assess their current impact, such as the energy consumption and pollutant emissions of a certain device per hour.

[0042] After identifying the impact sources, the next step is to set environmental optimization targets for each sub-process node. These targets should be based on industry standards or best practices. Energy management systems (EMS) or environmental impact assessment software (such as SimaPro or GaBi) can often be used to help define reasonable optimization ranges. For example, a target might be to reduce energy consumption by 20% for a specific piece of production equipment or to reduce carbon emissions in transportation by 10%.

[0043] Through this step, the optimization target of each sub-process node is set based on its impact source, ensuring that the negative impact on the environment can be minimized while achieving sustainable supply chain management.

[0044] Deviation information acquisition module M600: used for collecting the operation data of each sub-process node in real time, and comparing it with the environmental optimization target to obtain environmental optimization deviation information.

[0045] Specifically, sub-process node operational data refers to the specific indicators generated during the operation of each sub-process node, such as production line energy consumption, transportation vehicle fuel consumption, and temperature data of temperature-control equipment. Environmental optimization deviation information refers to the difference between the actual operational data of a sub-process node and the set environmental optimization target, indicating the degree of deviation between actual operation and the ideal state.

[0046] First, to monitor the operating status of each sub-process node, IoT sensors and data acquisition devices are used to collect real-time data. These devices continuously monitor key parameters of the sub-process node, such as energy consumption, emissions, and temperature, and send this data to a central data system for storage and processing.

[0047] This real-time data is then compared with pre-set environmental optimization targets. For example, if the optimization target for a piece of production equipment is to reduce energy consumption to 500kWh per hour, but the actual energy consumption is 550kWh, this discrepancy is considered an environmental optimization deviation. By analyzing the discrepancy between the actual data and the optimization target, the environmental optimization deviation information is calculated, thereby determining whether the sub-process node has deviated from the predetermined environmental target.

[0048] To this end, commonly used data analysis tools, such as data management platforms (e.g., SQL databases), big data analysis software (e.g., Hadoop), or supply chain management systems, can automatically perform these data comparisons and calculate deviations. This helps managers promptly identify excessive energy consumption or emissions at sub-process nodes during operation, providing data support for subsequent adjustments and optimizations. For example, deviations discovered through comparison could indicate that a piece of equipment requires repair or upgrade to achieve established environmental goals. This real-time monitoring and comparison ensures that every link in the supply chain is continuously optimized to reduce environmental impact.

[0049] The control instruction set generation module M700 is used to generate a control instruction set according to the environmental optimization deviation information, and to control the influencing source corresponding to each sub-process node based on a visual web interface.

[0050] Specifically, the control instruction set refers to a set of instructions generated based on the environmental optimization deviation, used to adjust the operation of sub-process nodes to bring their performance within the range that meets the environmental optimization goals. The visual web interface displays data and instructions graphically through a web application, allowing users to monitor and adjust the operating status of sub-process nodes in real time through a browser.

[0051] The generation of control instruction sets is based on deviations from environmental optimization targets. For example, if deviation information indicates that a piece of production equipment is exceeding energy consumption standards, the system automatically generates instructions to adjust the equipment's operating status to reduce energy consumption. When generating control instructions, data analysis algorithms and rule engines (such as rule-based decision systems) are typically used to determine the specific action plan. In this way, the instruction set can include measures such as reducing equipment power, replacing more energy-efficient equipment components, and adjusting workloads.

[0052] These instructions are then presented to the user through a visual web interface. Users can view the current operating status of each sub-process node and the corresponding control instructions in a browser or console. The interface displays key operating parameters, deviation information, and the generated control plan for each sub-process node. Common visualization tools include D3.js and Grafana, which can display data in the form of charts and dashboards, making it easier for users to understand and operate.

[0053] Furthermore, control instructions are executed in real time. Through a system interface (API) or IoT controller, the system can automatically send instructions to the corresponding sub-process nodes. For example, if a deviation in equipment energy consumption is detected in a particular production process, the instructions may include reducing the equipment's power or increasing the production load during the nighttime period when electricity prices are low. The web interface displays the expected effects of these control measures, such as the percentage of energy consumption reduction or the reduction in carbon emissions.

[0054] For example, in an electronics manufacturing plant, if the power consumption of a welding machine exceeds a predetermined target, the system analyzes the deviation and generates a control instruction to reduce the machine's operating power. The system also displays the machine's real-time operating data, target power consumption, and adjusted expected energy consumption through a web interface. Users can view the execution of the instructions in real time through the interface and further adjust the machine's operating parameters based on the feedback.

[0055] Furthermore, the influencing factor acquisition module M200 is used to execute the following method: collect historical environmental data of the entire process nodes; define an indicator system for environmental impact assessment based on the historical environmental data; use a quantitative analysis method to perform environmental impact assessment on the entire process nodes; identify influencing factors through a machine learning algorithm; sort the influencing factors according to the degree of influence to obtain the key influencing factors.

[0056] Specifically, historical environmental data refers to the historical information on environmental impacts accumulated at each node throughout the entire process, including data related to energy consumption, exhaust emissions, water resource use, and other environmentally friendly data. Quantitative analysis: This method objectively assesses the environmental impact of each node throughout the entire process through statistical analysis. Commonly used quantitative analysis tools include regression analysis and principal component analysis.

[0057] First, historical environmental data is collected. This step involves using supply chain monitoring systems and data collection devices (such as energy consumption monitoring systems and pollution emission monitoring equipment) to obtain environmental data from each node over the past period of time. For example, energy consumption data for production equipment, fuel consumption for transportation vehicles, and exhaust emissions from emissions equipment all constitute historical environmental data.

[0058] Next, based on the collected historical data, define an environmental impact assessment indicator system to measure the environmental performance of each node. This indicator system can include carbon dioxide emissions, energy consumption, water resource utilization, etc. Using standardized indicators ensures the accuracy and comparability of the assessment. Common tools such as Excel and MATLAB can help establish these indicators and perform basic data processing.

[0059] Next, we use quantitative analysis to evaluate each node throughout the entire process. The purpose of quantitative analysis is to measure the environmental impact of each node through data analysis. For example, regression analysis can reveal the relationship between energy consumption and pollution emissions, or principal component analysis can identify which nodes have the greatest overall environmental impact. For example, in the production process, analysis revealed that high energy consumption of equipment A is one of the primary environmental impacts.

[0060] To further automate the identification of the most critical influencing factors, machine learning algorithms are used to process complex, multi-dimensional data. Common machine learning algorithms include decision trees or random forests. By training on historical data, they automatically identify which factors (such as equipment usage and energy consumption) at which nodes have the greatest environmental impact. For example, using a decision tree algorithm, it can be identified that vehicle fuel efficiency is the primary factor contributing to high emissions in the transportation sector.

[0061] Finally, all identified influencing factors are ranked according to their impact. This is done through weighted scoring or other prioritization methods. The ranking results can help managers prioritize those factors that have the most serious impact on the environment.

[0062] These key influencing factors can help companies identify areas that require priority optimization. For example, if analysis reveals that energy consumption of production equipment is the primary key influencing factor, subsequent optimization measures should focus on reducing equipment energy consumption.

[0063] Further, such as Figure 2 As shown, the influence source acquisition module M300 is used to execute the following method: set the monitoring parameters of the sub-process node, and obtain the historical monitoring parameter data of the sub-process node through the Internet of Things sensor; analyze the historical monitoring parameter data to identify the sub-process abnormality factors related to the key influencing factors; perform a causal chain analysis on the sub-process abnormality factors to determine the sub-process abnormality cause that causes the sub-process abnormality factor; trace back to the corresponding sub-process node based on the sub-process abnormality cause to determine its corresponding influence source.

[0064] Specifically, IoT sensors are devices used to collect real-time operational data from sub-process nodes, such as temperature sensors and energy consumption monitoring devices, and transmit this data over the network. Anomalies are factors or events that differ from normal operation, typically indicating an abnormality in the operation of a process node, such as equipment failure or excessive emissions.

[0065] First, appropriate monitoring parameters must be set for each sub-process node. These monitoring parameters should effectively reflect the node's environmental impact. For example, at a transportation node, the monitoring parameters are vehicle fuel consumption and carbon emissions, while at a production node, the monitoring parameters are equipment energy consumption and waste emissions. IoT sensors (such as temperature sensors and energy meters) are used to obtain real-time monitoring data from these sub-process nodes. Historical monitoring parameter data, covering the past few weeks, months, or even years, is also recorded and stored in the cloud or in a local database.

[0066] Next, this historical monitoring data is analyzed to identify anomalies related to the previously defined key influencing factors. Threshold-based anomaly detection or machine learning methods such as time series analysis are used to identify which nodes have abnormal fluctuations.

[0067] After identifying the abnormal factors, a causal chain analysis needs to be performed on them. Through causal chain analysis, the causes of these abnormal factors can be further traced. Specifically, a complex problem is decomposed into multiple related events or steps, and the real cause of the problem is found by analyzing the causal relationship between them. First, it is necessary to clarify the problem or phenomenon that needs to be analyzed. For example, in a production process, it is found that energy consumption exceeds the standard, which is a result or abnormal phenomenon that needs to be explained. Then analyze the direct cause of this result. For example, energy consumption exceeds the standard due to low equipment operation efficiency or untimely maintenance. Further analyze the upstream causes that lead to the direct cause. For example, the low equipment operation efficiency is due to equipment aging or improper maintenance strategy. By analyzing multiple levels of causes, a causal chain from phenomenon to root cause is gradually established. For example:

[0068] Excessive energy consumption → low equipment efficiency → aging equipment → untimely maintenance.

[0069] Verify the correlation between causes and consequences through data or experiments to ensure the rationality and correctness of the causal chain. Develop appropriate solutions based on the root causes found.

[0070] For example, suppose a factory's production line suddenly experiences a drop in efficiency. Through causal chain analysis, it is first determined that the drop in efficiency is due to the extended runtime of certain equipment. It is then discovered that the extended runtime is due to frequent equipment failures, which in turn are caused by the failure to perform scheduled maintenance on time. Ultimately, the root cause of the problem is determined to be the failure to effectively implement equipment maintenance procedures, which has led to increased equipment aging and reduced production efficiency. This causal chain is as follows:

[0071] Decreased production efficiency → Frequent equipment failures → Untimely maintenance → Imperfect management processes.

[0072] Finally, based on these analysis results, specific sub-process nodes are traced back and their corresponding impact sources are identified. This analysis helps companies determine which specific factors have adverse impacts on the environment, providing a basis for subsequent optimization and adjustment.

[0073] Furthermore, the impact system construction module M400 is used to execute the following methods: analyze the directness and indirectness of the impact of the impact source on the sub-process node environment; evaluate the degree and scope of the impact of the impact source on the sub-process node environment; draw the sub-process node impact diagram to clarify the impact source, impact path and impact result; integrate the impact diagrams of each sub-process node to obtain a comprehensive sub-process node impact system.

[0074] Specifically, directness refers to the direct environmental impact of the influencing source on the sub-process node, such as exhaust emissions during equipment operation; indirectness refers to the indirect impact on the environment through other factors, such as equipment failure leading to decreased production efficiency and increased energy consumption. Figure 1 A graphical tool used to show the relationship between influence sources and sub-process nodes, including influence sources, influence paths, and results. A comprehensive sub-process node influence system integrates the influence diagrams of all sub-process nodes into a comprehensive, systematic influence system diagram.

[0075] Based on the impact sources corresponding to each sub-process node, the first step is to analyze the direct and indirect environmental impacts of these impact sources on the sub-process node. This involves distinguishing between direct environmental impacts (e.g., increased exhaust emissions due to equipment failure) and indirect impacts (e.g., decreased production efficiency due to equipment failure, which indirectly increases energy consumption and emissions). Statistical analysis software (e.g., SPSS, MATLAB) and environmental modeling tools (e.g., Simulink) can be used to help quantify these impacts.

[0076] Next, each impact source is assessed, including the extent and scope of its environmental impact. Extent is determined through statistical analysis of historical data, for example, by measuring the volume of waste gas emissions and their specific impact on air quality. Scope determines the region or time period over which the impact extends, such as if the spread of waste gas covers multiple areas around a production facility. This is accomplished using data analysis tools (such as Excel's PivotTables) and geographic information systems (GIS), which can help assess the geographic distribution of environmental impacts.

[0077] After the analysis and assessment is complete, a sub-process node impact diagram is drawn, showing each sub-process node's impact source (e.g., equipment failure), impact path (e.g., from equipment failure to increased emissions), and final outcome (e.g., decreased environmental quality). This allows for an intuitive understanding of how each impact source affects the environment through different paths.

[0078] Finally, the impact diagrams for all sub-process nodes are integrated to form a comprehensive sub-process node impact system. This step combines the individual impact diagrams into a comprehensive system diagram that presents the environmental impact of the entire supply chain. This provides a holistic perspective and helps formulate a comprehensive environmental optimization strategy.

[0079] Furthermore, the optimization target setting module M500 is used to execute the following method: clarify the principles and standards of the environmental optimization target; based on the sub-process node impact system, analyze the degree of influence of the impact source on the environmental optimization target; set the environmental optimization target according to the degree of influence.

[0080] Specifically, the principles and standards of environmental optimization goals are the basic criteria and specific indicators used when setting environmental optimization goals, such as environmental protection laws and regulations, industry standards or internal company regulations.

[0081] First, establish the foundational principles and standards for setting environmental optimization goals. This typically involves consulting relevant laws and regulations, industry standards, and the company's internal environmental policies. For example, if a company wants to reduce waste gas emissions, the principles include complying with emission limits stipulated in national environmental protection laws and referencing industry best practices.

[0082] Utilize the established sub-process node impact system to analyze the specific impact of each influencing source on the set environmental optimization goals. Specifically, analyze how the influencing source of each sub-process node affects the achievement of the goal. For example, by analyzing the impact of exhaust gas emission sources on air quality, determine the contribution of exhaust gas reduction to achieving the goal.

[0083] After understanding the specific impact of each source on the environmental optimization goal, set the corresponding optimization goal. The goal should be specific and quantified, such as reducing the emission of a certain pollutant or lowering energy consumption, to ensure effective environmental improvement.

[0084] For example, a manufacturing plant plans to reduce exhaust emissions to improve air quality. First, based on relevant environmental regulations and the company's environmental policy, the principles and standards for environmental goals are clearly defined. For example, the goal is to reduce exhaust emissions to below the limit set by national regulations. Next, the sub-process node impact system is used to analyze the impact of exhaust emission sources on this goal. For example, it is found that the main exhaust emission source is old boilers, whose exhaust emissions have the greatest impact on overall air quality. Finally, specific environmental optimization goals are set. For example, a plan is to reduce boiler exhaust emissions by 30% within the next year, achieving this goal through equipment upgrades and process improvements.

[0085] Through this step, the environmental optimization goals can be clarified, ensuring that these goals are feasible and targeted in actual operations, thereby effectively improving environmental performance.

[0086] Furthermore, the deviation information acquisition module M600 is used to execute the following method: real-time collection of real-time operation data of the sub-process node; analysis of the real-time operation data to extract key indicator values related to the environmental optimization target; comparison of the key indicator values with the environmental optimization target to calculate the environmental optimization deviation information.

[0087] Specifically, sensors or data acquisition devices installed at sub-process nodes monitor and record various operational data in real time. For example, sensors can be installed on an exhaust gas treatment unit to measure exhaust gas concentration, flow rate, and temperature in real time.

[0088] Extract key indicators related to environmental optimization goals from collected real-time data. For example, use data analysis tools to identify whether exhaust emissions exceed set targets. This data analysis can be performed using statistical analysis tools, such as the Python pandas library, to process and analyze data. The extracted key indicator values are compared with the environmental optimization goals to calculate the deviation between the actual and target values.

[0089] Through this step, its operating status can be monitored in real time and adjusted in time to meet the set environmental optimization goals, thereby effectively controlling and reducing environmental impacts.

[0090] Furthermore, the control instruction set generation module M700 is used to execute the following method: formulate a targeted control strategy based on the environmental optimization deviation information; convert the control strategy into the control instruction, and integrate multiple control instructions to generate the control instruction set, wherein each control instruction corresponds to an influence source; display the control instruction set through a visual Web interface, and perform real-time control on the influence sources corresponding to each sub-process node.

[0091] Specifically, based on real-time information on environmental optimization deviations (e.g., exhaust gas emissions exceeding the standard by 5ppm), we analyze and formulate improvement measures. For example, if exhaust gas concentration exceeds the standard, the control strategy may include reducing the operating load of the equipment or increasing the exhaust gas treatment capacity.

[0092] The developed control strategies are concretely translated into operational instructions. These instructions clearly indicate how to adjust equipment or processes. For example, an instruction might be "Increase boiler exhaust fan speed by 10%" or "Reduce raw material input by 10%." Multiple control instructions are combined into a control instruction set, each targeting a different influencing source.

[0093] A visual web interface displays control instruction sets, including detailed information about each instruction and the target impact source. For example, the interface displays "Boiler Fan Speed Adjustment Instructions" and "Exhaust Emissions Monitoring." Users can view the instruction set in real time, execute instructions, and monitor their effects through the web interface. The interface provides graphical displays, such as dashboards and charts, to help users understand the current status and control effects.

[0094] Through this step, dynamic management of environmental optimization can be achieved and the environmental friendliness of the production process can be improved.

[0095] Furthermore, the control instruction set generation module M700 is also used to execute the following method: construct a visual display template of the control instruction set, the visual display template includes a control instruction list, the influence source identifier corresponding to the influence source, an environmental optimization target comparison chart and a control effect preview area; classify each control instruction in the control instruction set according to the corresponding influence source, and display it in the control instruction list, wherein each control instruction is accompanied by the influence source identifier; in the environmental optimization target comparison chart, the comparison results of the real-time operation data of each sub-process node and the environmental optimization target are displayed in the form of a chart, and the environmental optimization deviation information is marked; in the control effect preview area, according to the control strategy in the control instruction set, the expected effect after control is simulated and displayed, including the improvement of the environmental optimization target and the adjustment status of the influence source.

[0096] Specifically, the visual display template is an interface design template for graphically displaying information. It defines how to organize and display data, including charts, lists, and preview areas. The control instruction list displays a list of all generated control instructions. Each instruction is used for a specific control operation and is associated with its corresponding impact source. The impact source identifier is used to identify the graphic symbol or label of each impact source. For example, a certain impact source is represented by a specific color or icon to match the control instruction. The environmental optimization target comparison chart is a chart used to display the gap between actual operating data and the set environmental optimization target. For example, a bar chart or line chart shows the comparison between the actual value of exhaust gas emissions and the target value. The control effect preview area is an area that displays the expected effect of the control strategy, and displays the improvement after adjustment through simulation, such as the reduced pollutant emissions and the optimized system status.

[0097] Design a web interface template that includes multiple areas, such as a list of control instructions, an indicator of impact sources, a comparison chart of environmental optimization targets, and a preview area for control effects. This template can be designed using front-end development tools such as HTML5, CSS, and JavaScript, as well as data visualization libraries like D3.js or Chart.js.

[0098] All control commands are displayed on the web interface, categorized by impact source. For example, the "Increase fan speed" command is associated with the "Exhaust treatment device" impact source. Each command is accompanied by an icon or label to indicate its corresponding impact source.

[0099] Use charts to show real-time operating data compared to environmental optimization targets. For example, use a bar chart to show the comparison of real-time exhaust emissions for each sub-process node against the target value, highlighting areas of deviation.

[0100] Create a preview area to demonstrate the expected effects of control strategies. For example, simulate the reduction of exhaust emissions or the improvement of equipment operating efficiency after control, helping operators to foresee the effects of adjustments.

[0101] Through such a web interface, the operating status of sub-process nodes can be monitored and adjusted in real time to ensure that the production process meets the environmental optimization goals and intuitively view the control effects.

[0102] In summary, the green supply chain visualization control platform based on the Industrial Internet of Things provided by the embodiments of the present application has the following technical effects:

[0103] 1. Through real-time monitoring of nodes throughout the entire supply chain process and traceability analysis of key influencing factors, this method provides a comprehensive understanding of the environmental impact sources of each sub-process node and achieves environmental optimization goals. Its technical benefits include enhanced environmental visualization capabilities in the supply chain, reduced environmental impacts, and improved environmental management efficiency. Users can dynamically adjust operational strategies based on real-time data and historical assessment results, effectively reducing environmental pollution and resource waste.

[0104] 2. Conduct historical environmental impact assessments across all process nodes. By collecting historical data and defining an indicator system, key influencing factors can be systematically identified. This assessment method improves the accuracy and comprehensiveness of environmental impact identification. Utilizing quantitative analysis and machine learning algorithms, key influencing factors can be efficiently ranked and identified, providing a scientific basis for subsequent optimization efforts and enabling precise management of environmental impacts.

[0105] 3. By monitoring and tracing each sub-process node, the source of impact at each node can be accurately identified. This approach, combined with IoT sensors and causal chain analysis, effectively traces and identifies the root cause of impacts. This technical benefit improves monitoring accuracy and data traceability, enabling timely detection and resolution of anomalies in the production process, ensuring supply chain stability and environmental compliance.

[0106] 4. Build an impact system based on the impact sources of each sub-process node. By analyzing the directness and indirectness of the impact sources, you can systematically assess their environmental impact. This approach helps you fully understand the environmental impact of each sub-process node and create an impact diagram, enabling more effective environmental management and optimization. Ultimately, by integrating the impact diagrams, you can develop a comprehensive impact system to guide subsequent environmental improvement measures.

[0107] 5. Generate control instructions based on environmental optimization deviation information and implement them through a visual web interface, enabling dynamic management of environmental optimization. The transformation of control strategies and the integration of instructions improve the systematicity and efficiency of operations. Visual display helps users intuitively understand and operate control instructions, optimizing the environmental management process and increasing the real-time and effectiveness of adjustments.

[0108] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0109] Furthermore, the above technical solution only reflects the preferred technical solution of the technical solution of the embodiment of the present application. Some changes made by technical personnel in this technical field to certain parts thereof all reflect the novel principles of the embodiment of the present application. Obviously, technical personnel in this field can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A green supply chain visualization control platform based on the Industrial Internet of Things, characterized by: include: Full-process node acquisition module: used to obtain full-process nodes of the supply chain; An impact factor acquisition module is used to collect historical environmental data of the nodes in the entire process; define an indicator system for environmental impact assessment based on the historical environmental data; conduct environmental impact assessment on the nodes in the entire process using a quantitative analysis method; identify impact factors using a machine learning algorithm; and sort the impact factors according to their impact level to obtain key impact factors. Influence source acquisition module: used to set monitoring parameters for sub-process nodes and obtain historical monitoring parameter data of the sub-process nodes through IoT sensors; analyze the historical monitoring parameter data to identify sub-process abnormal factors related to key influencing factors; perform causal chain analysis on the sub-process abnormal factors to determine the sub-process abnormal cause that caused the sub-process abnormal factor; trace the sub-process abnormal cause back to the corresponding sub-process node based on the sub-process abnormal cause to determine its corresponding influence source; Impact system building module: used to analyze the directness and indirectness of the impact of the impact source on the environment of the sub-process node; evaluate the degree and scope of the impact of the impact source on the environment of the sub-process node; Draw the sub-process node impact diagram to clarify the impact source, impact path and impact result; integrate the impact diagrams of each sub-process node to obtain a comprehensive sub-process node impact system; Optimization target setting module: used to set environmental optimization targets for each sub-process node according to the sub-process node impact system; Deviation information acquisition module: used for collecting the operation data of each sub-process node in real time, and comparing it with the environmental optimization target to obtain environmental optimization deviation information; A control instruction set generation module is used to generate a control instruction set according to the environmental optimization deviation information, and to control the influencing sources corresponding to each sub-process node based on a visual web interface.

2. The green supply chain visualization control platform based on industrial Internet of Things according to claim 1 is characterized in that: According to the sub-process node impact system, set environmental optimization goals for each sub-process node, including: Clarify the principles and standards for environmental optimization goals; Based on the sub-process node impact system, analyze the impact of the impact source on the environmental optimization target; The environmental optimization target is set according to the degree of impact.

3. The green supply chain visualization control platform based on industrial Internet of Things according to claim 1 is characterized in that: The operation data of each sub-process node is collected in real time and compared with the environmental optimization target to obtain environmental optimization deviation information, including: Collecting real-time operation data of the sub-process nodes in real time; Analyze real-time operation data and extract key indicator values related to the environmental optimization goals; The key indicator value is compared with the environmental optimization target, and the environmental optimization deviation information is calculated.

4. The green supply chain visualization control platform based on industrial Internet of Things according to claim 1 is characterized in that: Generate a control instruction set based on the environmental optimization deviation information, and control the impact source corresponding to each sub-process node based on a visual web interface, including: Optimize the deviation information according to the environment and formulate targeted control strategies; Converting the control strategy into the control instruction, and integrating multiple control instructions to generate the control instruction set, wherein each control instruction corresponds to an influencing source; The control instruction set is displayed through a visual web interface, and the influencing sources corresponding to each sub-process node are controlled in real time.

5. The green supply chain visualization control platform based on industrial Internet of Things according to claim 4 is characterized in that: The control instruction set is displayed through a visual web interface, including: Constructing a visual display template for the control instruction set, wherein the visual display template includes a control instruction list, an influence source identifier corresponding to the influence source, an environmental optimization target comparison chart, and a control effect preview area; Classifying each control instruction in the control instruction set according to the corresponding impact source and displaying them in the control instruction list, wherein each control instruction is accompanied by the impact source identifier; In the environmental optimization target comparison chart, the comparison results of the real-time operation data of each sub-process node and the environmental optimization target are displayed in the form of a chart, and the environmental optimization deviation information is marked; In the control effect preview area, according to the control strategy in the control instruction set, the expected effect after the control is simulated and displayed, including the improvement of the environmental optimization target and the adjustment status of the influencing source.

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