A Map-Based Life Cycle Assessment System Based on a Dynamic Network Collaboration Model

Abstract

The present invention discloses a map-based life cycle assessment system based on a dynamic network collaborative model, which relates to the field of information processing technology. By integrating near-real-time data, geographic spatial positioning and multi-dimensional data processing, it not only improves the data accuracy, completeness and availability of life cycle assessment, but also enhances the dynamic monitoring and assessment capabilities of various stages of the product life cycle and spatiotemporal change characteristics. It solves the problem of how to effectively integrate and utilize the near-real-time data and geographic spatial positioning data of products in the prior art to achieve dynamic monitoring and assessment of the product life cycle, especially to build a dynamic network reflecting the full stage of the product life cycle in a multi-enterprise collaborative environment.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a map-based life cycle assessment system based on a dynamic network collaboration model. Background Art

[0002] With the rapid development of globalization and information technology, product lifecycle management (PLM) has become the core of improving corporate competitiveness. Traditional PLM methods mainly focus on the design, manufacturing and maintenance stages of products, but often ignore the spatiotemporal changes of products in their life cycle and their environmental impact. In addition, most existing PLM systems are built based on static data, which is incapable of coping with the rapid changes in market demand and complex supply chain networks. In this context, it is particularly important to obtain and effectively apply product data and its geographic spatial positioning information in real time, which can not only improve the efficiency of PLM, but also promote collaboration among enterprises.

[0003] However, how to effectively integrate these data and use them to achieve dynamic monitoring and evaluation of the product life cycle has become a technical challenge that needs to be solved urgently. Especially in a cross-enterprise collaborative environment, building a dynamic network system that can comprehensively reflect all stages of the product life cycle and accurately analyze and predict the spatiotemporal change characteristics of products is the key to improving the accuracy and efficiency of life cycle assessment. At present, existing life cycle assessment technologies often lack consideration of dynamics and collaboration, and are difficult to cope with the challenges of complex and changing market environments and supply chain networks. Therefore, there is an urgent need for a new PLM solution that can reflect the status of the product life cycle in real time, support cross-enterprise collaboration, and dynamically monitor and evaluate according to the spatiotemporal change characteristics of products. Summary of the invention

[0004] The purpose of the present invention is to provide a map-based life cycle assessment system based on a dynamic network collaborative model to solve the problem of how to effectively integrate and utilize the near-real-time data and geographic spatial positioning data of products in the prior art to achieve dynamic monitoring and evaluation of the product life cycle, especially to build a dynamic network reflecting the entire product life cycle in a multi-enterprise collaborative environment.

[0005] The embodiment of the present invention proposes a map-based life cycle assessment system based on a dynamic network collaboration model, including:

[0006] Data layer: The data layer is used to obtain product activity data through near-real-time data collection methods and geospatial positioning methods, and uses the multidimensional data including product logistics information and spatiotemporal change influencing factors stored in the spatiotemporal database / data warehouse. It uses data fusion methods and data quality management methods to integrate heterogeneous data sources to improve data accuracy, completeness and availability, and provide data support for life cycle assessment;

[0007] Model layer: The model layer is used to receive input from the data layer, form a dynamic collaborative network by building a shadow connection network and a physical flow network, and use the spatiotemporal data model to associate the data into a complete spatiotemporal map. It uses dynamic optimization models, matching learning mapping models, multi-directional feedback optimization models, and scenario simulation evaluation models to achieve dynamic evaluation, tracking, optimization, and prediction of product life cycles, and feeds back the model calculation results to the data layer to form a virtuous cycle of data-driven models and models promoting data optimization, and continuously improve the accuracy and real-time performance of life cycle evaluation;

[0008] The expression layer is used to realize the superposition of multiple layers including shadow connection network, physical flow network, and dynamic equation evolution results based on the map API, and use the shadow perspective design to realize the mapping of shadow nodes to physical nodes. It dynamically presents the spatiotemporal evolution trajectory of resource consumption, emissions, and product quality state variables through animation and color changes, displays the optimization and update process of the network topology structure, and provides an interactive interface to allow users to customize layer combinations and analysis granularity;

[0009] The application layer is used to develop personalized carbon emission accounting, comparison and decision-making support tools for user groups including consumers, enterprises and decision-makers. It is used for consumers to make green purchases by comparing the environmental impact of products throughout their life cycle, for enterprises to use dynamic trajectories to predict the carbon emission performance of different supply chain solutions and optimize resource allocation, and for decision-makers to formulate differentiated emission reduction strategies based on regional carbon emission heat maps.

[0010] Preferably, the data layer includes:

[0011] Near real-time data collection module, used to collect near real-time data and coordinate information of products through the Internet of Things combined with sensor networks;

[0012] The geospatial location processing module is used to process and map the collected near-real-time data and coordinate information, and associate the near-real-time data of each stage of the product with the coordinate information;

[0013] The spatiotemporal database module is used to store near-real-time data and coordinate information that are combined with map information after association, so as to store product logistics information and factors affecting spatiotemporal changes, and configure dynamic update and query mechanisms;

[0014] The data fusion module is used to integrate static data and streaming data of different rates, formats and granularities in the spatiotemporal database module to build a unified and associated big data warehouse to efficiently process and store multi-source heterogeneous data;

[0015] The data quality management module is used to clean, calibrate and supplement the data in the spatiotemporal database module to ensure the accuracy and completeness of the data.

[0016] Preferably, the near real-time data includes detailed activity data of each stage of the product life cycle, as a key component of the dynamic database, to support the spatiotemporal database module to timely reflect and evaluate the spatiotemporal change characteristics of the product life cycle.

[0017] Preferably, the spatiotemporal database module is also used to construct a spatiotemporal data model connecting activity data and map information based on the geographic location system, and to store multidimensional data including product logistics information and factors affecting spatiotemporal changes by mapping key events and activities in the product life cycle.

[0018] Preferably, the model layer includes:

[0019] Shadow connection network and physical flow network, the shadow connection network and the physical flow network work together to form a dynamic collaborative network for life cycle assessment; among them, the shadow connection network is used to achieve data update and collaboration between enterprise nodes through virtual dependencies without exposing real supply chain information; the physical flow network is used to establish a group of dynamic equations describing the time evolution of node state quantities based on the topological structure of actual material flow between enterprises;

[0020] The spatiotemporal data model, dynamic optimization model, matching learning mapping model, multi-directional feedback optimization model and scenario simulation evaluation model continuously update, optimize and iterate the dynamic collaborative network of life cycle assessment by providing corresponding data processing and optimization algorithms; among them, the spatiotemporal data model is used to associate product logistics information and the spatiotemporal changes of influencing factors to provide data for dynamic evaluation; the dynamic optimization model is used to continuously optimize the evaluation by connecting to the external spatiotemporal database using dynamic planning, and feed the results back to the database to form a process-based interaction; the matching learning mapping model is used to integrate multi-source data through data fusion, establish a dynamic mapping between shadow nodes and physical nodes, and introduce incremental learning for real-time updates; the multi-directional feedback optimization model is used to build a closed-loop update mechanism to continuously improve data quality and reduce timeliness differences; the scenario simulation evaluation model is used to use historical data to build and evaluate a variety of simulation scenarios to provide a basis for decision-making.

[0021] Preferably, the shadow communication network uses a chain update mechanism, and when the upstream enterprise data is updated, it automatically sends an update notification to the downstream node for effective information diffusion without leaking the enterprise's real business data and dependencies.

[0022] Preferably, the physical flow network simulates the state change trajectory by solving a group of dynamic equations and feeds back to the enterprise nodes so that upstream and downstream enterprises can adjust their decision plans according to the state changes.

[0023] Preferably, the expression layer is also used to use shadow perspectives and dynamic trajectories to display in detail the virtual edge information of the shadow connection network and the actual flow of the physical logistics path, wherein the shadow connection network expression layer adopts private cloud deployment and data encryption, and is accessed by internal personnel of specific enterprises for simulation and risk assessment, and the physical flow network expression layer uses public cloud deployment and is open to users, providing logistics path information display based on third-party public data.

[0024] Preferably, the expression layer is used to display and analyze the shadow contact network and physical flow network content of the enterprise through a multi-layer map interface, combining collaborative optimization between enterprises and dynamic monitoring results of the entire product life cycle, including:

[0025] A multi-layer map interface is constructed based on the shadow connection network and the physical logistics network, in which each layer displays supply chain information of different dimensions; through shadow perspective design, the shadow connection network is displayed with virtual edge information, and the spatiotemporal evolution of resource consumption, emissions and product quality state variables is displayed with dynamic trajectories; multiple layers are used to display the spatial distribution of each stage of the product life cycle; through interactive controls and scenario simulation, the feedback mechanism is used to optimize the topological connection of the shadow connection network according to the results of dynamic evolution.

[0026] Preferably, the application layer includes:

[0027] A personalized interactive query module for consumers, which allows users to select specific products and time ranges, and displays detailed environmental indicator values ​​of the selected products at each stage of their life cycle, so that consumers can make environmentally friendly purchasing decisions based on the comparison results of environmental indicator values;

[0028] The dynamic trajectory analysis module for enterprise users is used to achieve multi-dimensional time granularity comparison of carbon emissions through timeline controls, supporting enterprises to conduct comparative analysis of emission changes in daily, weekly and monthly dimensions. Through interactive tools for carbon accounting and early warning, it helps enterprises optimize supply chain management and implement low-carbon decisions.

[0029] The GIS spatial analysis and big data reporting module for decision-making users uses heat maps to visualize the spatial distribution of carbon emissions. The depth of color represents the level of carbon emissions in different regions, so as to intuitively display the differences in carbon intensity between regions and provide decision-making support for decision-makers to formulate targeted differentiated emission reduction strategies.

[0030] Beneficial effects: The map-based life cycle assessment system based on a dynamic network collaborative model proposed in the embodiment of the present invention not only improves the data accuracy, completeness and availability of life cycle assessment by integrating near real-time data, geographic spatial positioning and multi-dimensional data processing, but also enhances the dynamic monitoring and assessment capabilities of various stages of the product life cycle and spatiotemporal change characteristics. Including: through the close data collection and quality management of the data layer, the dynamic collaborative network construction and spatiotemporal data model application of the model layer, the high accuracy and real-time update of the life cycle assessment are ensured, so that enterprises can quickly respond to market changes and supply chain adjustment needs; the system makes information sharing and collaboration between different enterprises easier and more effective by building shadow contact networks and physical circulation networks, which helps to reduce resource waste and improve the overall efficiency and environmental performance of the supply chain; the personalized carbon emission accounting, comparison and decision support tools provided by the application layer can not only help consumers make more environmentally friendly purchasing decisions, but also assist enterprises in optimizing supply chain design and resource allocation, and help policymakers formulate more accurate environmental policies and measures based on actual carbon emission data; by comprehensively monitoring resource consumption and emissions in the product life cycle, and optimizing product design, production, logistics and other links, the system helps to promote enterprises to develop in a more environmentally friendly and sustainable direction and reduce the environmental footprint throughout the life cycle. This system not only improves the effectiveness of PLM at the technical level, but also provides strong support for enterprises at the strategic and decision-making levels, further promoting enterprises, consumers and policymakers to move towards environmental sustainability goals. This solves the problem of how to effectively integrate and utilize near-real-time data and geospatial positioning data of products to achieve dynamic monitoring and evaluation of the product life cycle, especially to build a dynamic network reflecting the entire product life cycle in a multi-enterprise collaborative environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0032] Figure 1 A map-based life cycle assessment method architecture based on a dynamic network collaboration model proposed in an embodiment of the present invention;

[0033] Figure 2 A flow chart of a dynamic collaborative network proposed in an embodiment of the present invention;

[0034] Figure 3 The physical flow network and shadow connection network proposed in the embodiment of the present invention;

[0035] Figure 4The dynamic collaborative network LCADCN proposed in the embodiment of the present invention;

[0036] Figure 5 This is a diagram of the expression layer architecture proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the invention, but does not constitute a limitation of the invention.

[0038] For the first aspect, see Figure 1 , is a map-based life cycle assessment method architecture based on a dynamic network collaborative model proposed in an embodiment of the present invention. A map-based life cycle assessment system based on a dynamic network collaborative model proposed in an embodiment of the present invention includes:

[0039] Data layer: The data layer is used to obtain product activity data through near-real-time data collection methods and geospatial positioning methods, and uses the multidimensional data including product logistics information and spatiotemporal change influencing factors stored in the spatiotemporal database / data warehouse. It uses data fusion methods and data quality management methods to integrate heterogeneous data sources to improve data accuracy, completeness and availability, and provide data support for life cycle assessment;

[0040] Model layer: The model layer is used to receive input from the data layer, form a dynamic collaborative network by building a shadow connection network and a physical flow network, and use the spatiotemporal data model to associate the data into a complete spatiotemporal map. It uses dynamic optimization models, matching learning mapping models, multi-directional feedback optimization models, and scenario simulation evaluation models to achieve dynamic evaluation, tracking, optimization, and prediction of product life cycles, and feeds back the model calculation results to the data layer to form a virtuous cycle of data-driven models and models promoting data optimization, and continuously improve the accuracy and real-time performance of life cycle evaluation;

[0041] The expression layer is used to realize the superposition of multiple layers including shadow connection network, physical flow network, and dynamic equation evolution results based on the map API, and use the shadow perspective design to realize the mapping of shadow nodes to physical nodes. It dynamically presents the spatiotemporal evolution trajectory of resource consumption, emissions, and product quality state variables through animation and color changes, displays the optimization and update process of the network topology structure, and provides an interactive interface to allow users to customize layer combinations and analysis granularity;

[0042] The application layer is used to develop personalized carbon emission accounting, comparison and decision-making support tools for user groups including consumers, enterprises and decision-makers. It is used for consumers to make green purchases by comparing the environmental impact of products throughout their life cycle, for enterprises to use dynamic trajectories to predict the carbon emission performance of different supply chain solutions and optimize resource allocation, and for decision-makers to formulate differentiated emission reduction strategies based on regional carbon emission heat maps.

[0043] Preferably, the data layer includes:

[0044] Near real-time data collection module, used to collect near real-time data and coordinate information of products through the Internet of Things combined with sensor networks;

[0045] The geospatial location processing module is used to process and map the collected near-real-time data and coordinate information, and associate the near-real-time data of each stage of the product with the coordinate information;

[0046] The spatiotemporal database module is used to store near-real-time data and coordinate information that are combined with map information after association, so as to store product logistics information and factors affecting spatiotemporal changes, and configure dynamic update and query mechanisms;

[0047] The data fusion module is used to integrate static data and streaming data of different rates, formats and granularities in the spatiotemporal database module to build a unified and associated big data warehouse to efficiently process and store multi-source heterogeneous data;

[0048] The data quality management module is used to clean, calibrate and supplement the data in the spatiotemporal database module to ensure the accuracy and completeness of the data.

[0049] The data layer aims to realize more dynamic data collection and modeling in order to support the evaluation of the spatiotemporal characteristics of the product life cycle. The following methods can be used for implementation:

[0050] The near real-time data collection module is used to collect near real-time data and coordinate information of products through the Internet of Things combined with sensor networks; near real-time data collection technology is used to introduce frequently collected near real-time prospect data as a key component of the dynamic database; for example, advanced Internet of Things (IoT) and sensor network technologies are used to realize the real-time collection of detailed activity data at each stage of the product, extract key geographic coordinate information, and ensure accurate modeling of the geographic location correlation of the product life cycle; among them, near real-time data includes detailed activity data at each stage of the product life cycle, as a key component of the dynamic database, to support the spatiotemporal database module to reflect and evaluate the spatiotemporal change characteristics of the product life cycle in a timely manner.

[0051] The geospatial location processing module is used to process and map the collected near-real-time data and coordinate information, and associate the near-real-time data and coordinate information at each stage of the product; using geospatial location technology, using geographic information system (GIS) and other technologies, the collected geographic coordinate information is accurately processed and mapped to achieve the geographic location association of each stage of the product, and provide spatial dimension support for the establishment of spatiotemporal data models. Through geospatial location technology, it is ensured that the geographic location association of the product life cycle is accurately modeled.

[0052] The spatiotemporal database module is used to store near-real-time data and coordinate information that are combined with map information after association, so as to store product logistics information and factors affecting spatiotemporal changes, and configure dynamic update and query mechanisms; the spatiotemporal database / data warehouse is adopted, and the spatiotemporal engine technology is applied. Based on the geographic location system, a spatiotemporal data model that connects activity data and map information is constructed. Among them, the spatiotemporal database stores multidimensional data such as product logistics information and factors affecting spatiotemporal changes, providing comprehensive data support for life cycle assessment. The spatiotemporal database ensures that the spatiotemporal change characteristics of the product life cycle are reflected in a timely manner through dynamic updates and queries; preferably, the spatiotemporal database module is also used to construct a spatiotemporal data model that connects activity data and map information based on the geographic location system, and stores multidimensional data including product logistics information and factors affecting spatiotemporal changes by mapping key events and activities in the product life cycle.

[0053] The data fusion module is used to integrate static data and streaming data of different rates, formats and granularities in the spatiotemporal database module, and build a unified and associated big data warehouse to efficiently process and store multi-source heterogeneous data; the application of data fusion technology enhances the compatibility with multiple data types and provides more comprehensive data foundation support.

[0054] The data quality management module is used to clean, calibrate and supplement the data in the spatiotemporal database module to ensure the accuracy and completeness of the data; the data quality management technology is used to effectively identify and deal with data quality problems such as poor timeliness and geographical restrictions in the background database; data cleaning, calibration and supplementation measures are implemented to ensure the accuracy and completeness of the data in the database; the use of data quality management technology improves the credibility of the data and provides a more reliable data basis for life cycle assessment. By properly integrating the above technical means, a life cycle assessment system with richer data and more dynamic models can be established to support the analysis of the spatiotemporal change characteristics of products.

[0055] Preferably, the model layer includes:

[0056] Shadow connection network and physical flow network, the shadow connection network and the physical flow network work together to form a dynamic collaborative network for life cycle assessment; among them, the shadow connection network is used to achieve data update and collaboration between enterprise nodes through virtual dependencies without exposing real supply chain information; the physical flow network is used to establish a group of dynamic equations describing the time evolution of node state quantities based on the topological structure of actual material flow between enterprises;

[0057] The spatiotemporal data model, dynamic optimization model, matching learning mapping model, multi-directional feedback optimization model and scenario simulation evaluation model continuously update, optimize and iterate the dynamic collaborative network of life cycle assessment by providing corresponding data processing and optimization algorithms; among them, the spatiotemporal data model is used to associate product logistics information and the spatiotemporal changes of influencing factors to provide data for dynamic evaluation; the dynamic optimization model is used to continuously optimize the evaluation by connecting to the external spatiotemporal database using dynamic planning, and feed the results back to the database to form a process-based interaction; the matching learning mapping model is used to integrate multi-source data through data fusion, establish a dynamic mapping between shadow nodes and physical nodes, and introduce incremental learning for real-time updates; the multi-directional feedback optimization model is used to build a closed-loop update mechanism to continuously improve data quality and reduce timeliness differences; the scenario simulation evaluation model is used to use historical data to build and evaluate a variety of simulation scenarios to provide a basis for decision-making.

[0058] At the model level, two key networks are constructed: the shadow contact network and the physical flow network. These two networks work together to establish a dynamic collaborative network Life Cycle Assessment Dynamic Collaborative Network (LCADCN). The physical flow network is based on the actual inter-enterprise material flow topology and establishes a set of dynamic equations that describe the time evolution of node state quantities. Through dynamic simulation, it is possible to deeply analyze the material flow laws in the network and realize dynamic monitoring of each stage of the life cycle. The shadow contact network is implemented on the basis of the enterprise entity information table and the shadow contact relationship table, so that each enterprise node can achieve chain update and collaboration. This contact network is an abstraction of the real supply chain. Through the chain update mechanism, the changes of upstream and downstream enterprises are effectively transmitted to each other, realizing the collaborative optimization of the whole network. Among them, the shadow contact is a virtual connection form based on the network topology. It establishes a virtual dependency relationship between enterprise nodes within the model, realizing the protection of the real supply chain details while enabling enterprises to perform chain data update and collaboration.

[0059] It is clear that the shadow contact network is an abstract network topology that reflects the dependencies between enterprise nodes, but does not correspond to the real logistics or information flow direction. These contact information is not visible to the outside. The shadow contact network uses a chain update mechanism. When the upstream enterprise data is updated, it automatically sends an update notification to the downstream node for effective information diffusion without leaking the real business data and dependencies of the enterprise. When the upstream enterprise data is updated, by finding its downstream nodes in the shadow contact network, an update notification is sent to these nodes, and the effective diffusion of changes is achieved while protecting the real supply chain. After receiving the notification, the downstream node will obtain the latest data input from the upstream node by calling the database interface, and use these new data to recalculate and update itself. The entire chain update diffusion process will not leak the real business data and dependencies of the enterprise, effectively realizing data security and enterprise privacy protection. Shadow contact provides enterprises with a low-cost shortcut to obtain dependent data updates, without the need to re-establish communication channels, and avoids the risk of excessive information exposure, achieving network collaboration while protecting the interests of the enterprise itself.

[0060] Preferably, the physical flow network simulates the state change trajectory by solving a group of dynamic equations and feeds back to the enterprise nodes so that upstream and downstream enterprises can adjust their decision plans according to the state changes.

[0061] The whole process, such as Figure 2 , containing the following:

[0062] 1. The upstream and downstream enterprise nodes in the shadow network form shadow dependencies; 2. The physical network establishes a group of dynamic equations to describe state evolution; 3. Solve the group of equations to simulate the trajectory of state changes; 4. Feedback the state changes to the enterprise nodes; 5. Upstream and downstream enterprises use the shadow network to propagate the impact of state changes; 6. Enterprises adjust their decision plans based on state changes; 7. Changes in decision plans are fed back to the group of equations; 8. Promote the re-solution of the group of equations to generate new trajectories; 9. Continue to feed back the new trajectory to the enterprise; 10. Form a closed loop to achieve network collaborative optimization.

[0063] Specifically, the following uses the life cycle of a smartphone as an example and explains it in terms of physical circulation and enterprise logic as follows: At the physical circulation level, raw materials flow from suppliers to manufacturing companies, reflecting the physical input and output of procurement and production; semi-finished products circulate within the workshop, reflecting the physical transfer between different processing processes; finished products flow from factories to warehouses and sales terminals, reflecting the physical state changes of products during transportation and storage; sold mobile phones flow from sales networks to consumers, initiating the physical circulation of the use stage. At the enterprise logic level, manufacturing companies rely on upstream suppliers, and the two establish logical linkages through purchase orders and feedback; sales companies rely on manufacturing companies' product supply, and the two establish logical linkages through production plans and inventory data; users' after-sales feedback will promote the service response of manufacturing companies and maintenance companies, and the three constitute the logical linkage of the aftermarket.

[0064] In practice, this smartphone life cycle assessment can be modeled symbolically:

[0065] 1. Define the participating entities, as shown in Table 1:

[0066] S: supplier node; M: manufacturing enterprise node; D: sales enterprise node; U: user node; R: recycling enterprise node.

[0067] Table 1: Business Entity Information Table

[0068] enterprise type coordinate S supplier (x1, y1) M Manufacturer (x2, y2) D Distributor (x3, y3) U user (x4, y4) R Recyclers (x5, y5)

[0069] 2. Define the shadow contact association, as shown in Table 2:

[0070] S→M: purchase order and feedback; M→D: production plan and inventory data; U→M: after-sales feedback; M→R: recycling information; Table 2: Shadow contact association table.

[0071] Table 2: Shadow contact association table

[0072] starting point end relation S M Purchase Orders and Feedback M D Production planning and inventory data U M After-sales feedback M R Recycling Information

[0073] III. Defining Physical Flow

[0074] raw→factory: raw materials flow to the factory; semi→factory: semi-finished products circulate within the factory; product→sale: finished products flow to sales companies; product→user: products flow to users; waste→recycle: waste flows to recycling companies.

[0075] 4. Build Figure 3 The physical circulation network and shadow contact network shown;

[0076] 5. LCA dynamic collaborative network model LCADCN-Life Cycle Assessment DynamicCollaborative Network, such as Figure 4 shown.

[0077] 6. Dynamic simulation state trajectory, as shown in the following example:

[0078] 1. On the physical flow network, first determine the nodes representing different life cycle stages, including raw material acquisition, product manufacturing, product transportation, product use and product recycling.

[0079] 2. Then, based on the actual flow direction of products and resources between the various stages of the life cycle, determine the directed edges between nodes. For example, raw material nodes flow to manufacturing nodes, manufacturing nodes flow to transportation nodes, etc.

[0080] 3. Establish the resource consumption matrix A and emission matrix B between nodes, combine the spatial information provided by GIS, such as the geographic information between nodes, and adjust the transfer coefficient to consider spatial influencing factors such as distance attenuation and directional distribution, so as to more accurately describe the impact of different geographical locations on resource consumption and emissions.

[0081] 4. For LCA, the state variables that need to be quantified should include:

[0082] Resource consumption at each stage (raw materials, water, energy, etc.)

[0083] x(t)=[x1(t), x2(t),...,xm(t)]

[0084] Emissions at each stage (waste gas, wastewater, solid waste, etc.)

[0085] y(t)=[y1(t), y2(t),...,yn(t)]

[0086] ·Product quality loss or utility change at each stage

[0087] z(t)=[z1(t),z2(t),...,zk(t)]

[0088] 5. Combine the network diagram and the spatial evolution path to establish a set of dynamic equations to describe the evolution of state variables over time:

[0089] dx / dt=f(x,y,z,A,B,u)

[0090] dy / dt=g(x,y,z,A,B,u)

[0091] dz / dt=h(x,y,z,A,B,u)

[0092] 6. Solve the system of equations to obtain the dynamic trajectory of each state variable.

[0093] The purpose of dynamic trajectories of three types of state variables: resource consumption, emissions, and product quality / utility:

[0094] Resource consumption trajectory: Identify the peak periods and key causes of resource consumption during the life cycle, analyze the resource consumption intensity curves at different stages, find the links with low efficiency, and provide a basis for formulating resource optimization strategies, such as improving technology, redesigning, etc., evaluate the resource savings under different solutions, and select the best solution;

[0095] Emissions trajectory: locate the hotspots and regions of environmental impact during the life cycle, analyze the pollutant generation rate curves at each stage, determine the focus of governance, evaluate the emission reduction effects of different pollution control technologies, and provide support for the formulation of environmental protection investment plans.

[0096] Product quality / utility trajectory: Observe how product quality or utility changes over the life cycle, analyze key causes such as design defects, transportation damage, years of use, etc., evaluate the quality improvement effects of different improvement plans, provide a basis for product optimization, and improve satisfaction.

[0097] VII. Mechanism of feedback of dynamic evolution results to shadow contact network:

[0098] The dynamic equations are applied to describe the actual state evolution of products in the logistics network. By solving them, the parameter change trajectories of different nodes in the time domain can be obtained. The state data of different nodes in the key period can be extracted from these trajectories, and then the format conversion is performed to match the input data interface requirements of the enterprise node. Among them, the spatiotemporal data model and the dynamic optimization model play a key role in this process; the spatiotemporal data model describes in detail the spatiotemporal changes of product logistics information and various related influencing factors, providing the necessary basic data for dynamic evolution; the dynamic optimization model continuously optimizes the product life cycle assessment by connecting to an external spatiotemporal database or data warehouse; this process forms an interactive link to ensure the accuracy and timeliness of the assessment.

[0099] Next, relying on the connection between enterprise nodes in the shadow connection network, the data changes of the source node are sent to all adjacent downstream nodes in the form of update notifications. The update notification will contain important meta-information, such as time range identifiers, data field representations, etc., to help the notification recipient parse and import new data. Among them, this involves matching learning mapping models, integrating multi-source data by calling data fusion technology, and establishing a dynamic correspondence between shadow nodes and physical entity nodes; this mechanism not only supports real-time data updates, but also ensures data security.

[0100] After receiving the update notification, the downstream enterprise node will call the pre-defined interface, parse the notification content, and initiate access to the database of the source node to obtain real-time change information of the upstream enterprise. After obtaining the new input data, the downstream enterprise will re-run its internal decision-making model, re-calculate and plan to adjust its own production batch, inventory, procurement volume and other operation plans. Among them, the multi-directional feedback optimization model is used to improve data quality and reduce timeliness differences.

[0101] 8. The two networks form a closed-loop collaboration:

[0102] These updated plans will be fed back into the dynamic equations of the logistics network, changing the parameter settings and promoting the recalculation and solution of the network state evolution trajectory. The output of the new round of solutions is fed back to the enterprise nodes again to form a closed loop. In this way, the two networks can achieve coordination and consistency through closed-loop feedback and flexible adjustment of state data, thereby achieving an overall optimization effect. Among them, a multi-directional feedback model is used to achieve closed-loop feedback.

[0103] To provide a basis for decision-making, it is necessary to build and evaluate a variety of simulation scenarios, covering normal and abnormal situations. For each scenario, it is necessary to simulate the response of the network under multi-step evolution and evaluate the impact of different parameters on the state. Finally, it is necessary to comprehensively analyze the changes in network stability under various scenarios and evaluate the potential impact of linkage on the overall network stability. (The scenario simulation evaluation model plays an important role in this process. It uses the rich historical data in the spatiotemporal database to realize the construction and evaluation of a large number of simulation scenarios, as well as the function of multi-step evolution simulation of the network.) In this way, the simulation evaluation provides decision makers with important risk prediction and reference basis, so that the entire system evolves to a more stable and optimized state.

[0104] In terms of specific technical implementation, a total of 5 models are designed at the model layer, each of which undertakes different functions such as connecting data, dynamic tracking, matching learning, multi-directional interaction, and scenario evaluation: Among them,

[0105] Spatiotemporal data model: In the spatiotemporal data model, near real-time data collection technology and geospatial location technology are closely coordinated to obtain and correlate spatiotemporal activity data, and to describe in detail the spatiotemporal changes of product logistics information and various related influencing factors. This model is designed to build a comprehensive and accurate spatiotemporal data map of the product life cycle, providing a rich foundation for subsequent dynamic evaluation.

[0106] Dynamic Optimization Model: The dynamic optimization model uses advanced dynamic programming algorithms to achieve dynamic tracking and evaluation of the entire life cycle of the product. The model continuously optimizes the product life cycle assessment by connecting to an external spatiotemporal database or data warehouse and calling the massive data stored in it. The optimized results will be fed back and saved in the database, becoming the key input to drive the subsequent new round of evaluation. A process-based interactive mechanism is formed between the model evaluation and the database. The database provides input, the model completes the evaluation optimization, and the optimization results are written to the database again, and so on. This mechanism realizes the continuous data drive of the evaluation process and ensures the accuracy and timeliness of the evaluation results.

[0107] Matching learning mapping model: The matching learning mapping model integrates multi-source heterogeneous data of the product life cycle by calling data fusion technology. Under the premise of fully protecting data confidentiality, the model successfully applies the concept of shadow contact network and establishes a dynamic correspondence between shadow nodes and physical entity nodes. Specifically, the model adopts a rule-based fuzzy matching algorithm, dynamically calculates the similarity between nodes through a predefined node feature matching rule library, and outputs the matching mapping results. At the same time, an incremental learning mechanism is introduced to continuously optimize the matching rules so that the mapping results are updated in real time with the influx of new data, keeping pace with changes in the network topology. Finally, it supports the construction and evolution of the relationship between shadow nodes and physical nodes under the premise of protecting the privacy of commercial information.

[0108] Multi-directional feedback optimization model: The multi-directional feedback optimization model works with data quality management technology to achieve continuous improvement of data quality and reduce timeliness differences by forming a closed-loop process. The model constructs a closed-loop update mechanism between the shadow node state configuration and the physical node state equation solution. Among them, the model includes a forward calculation module that predicts the evolution of the physical state according to the shadow node state change, and a backward optimization module that reverses the optimal shadow parameters according to the physical state trajectory. Through multiple rounds of trial, evaluation, feedback and re-decision, the entire network is driven to achieve a better global state in information flow and collaboration. Such a design strikes a balance between confidentiality requirements and performance improvement. An agent-based multi-stack reinforcement learning framework is introduced, in which the upper-level agent is responsible for decision-making to change the shadow node configuration, and the lower-level agent completes the state equation regression. By updating the reward mechanism, the information transmission strategy in the agent chain is continuously optimized, thereby achieving efficient collaboration and optimization of the entire system.

[0109] Scenario simulation evaluation model: The scenario simulation evaluation model can use the historical data of the spatiotemporal database to construct and evaluate a variety of simulation scenarios to provide a basis for decision-making. The model uses the Monte Carlo random sampling method to sample a large number of discrete scenarios in the shadow and physical network structures, covering various extreme situations and abnormal states. For each sampled scenario, the dynamic equations and the spatiotemporal engine are called to simulate the multi-step evolution of the network to obtain massive simulation trajectories. Then, variance sensitivity analysis is used to evaluate the impact of network topology and key parameters on state anomalies, identify the weak links of the network, and predict disturbance risks. Finally, a comprehensive analysis of the changes in network stability under different scenarios is conducted to evaluate the potential threat of linkage to the overall network stability, and provide advice and strategies for decision makers. This model emphasizes the simulation and evaluation of the potential disturbance and impact of shadow network states or linkage on the physical logistics network.

[0110] presentation layer, see Figure 5 , which is a diagram of the expression layer architecture proposed in an embodiment of the present invention.

[0111] Preferably, the expression layer is used to display and analyze the shadow contact network and physical flow network content of the enterprise through a multi-layer map interface, combining collaborative optimization between enterprises and dynamic monitoring results of the entire product life cycle, including:

[0112] A multi-layer map interface is constructed based on the shadow connection network and the physical logistics network, in which each layer displays supply chain information of different dimensions; through shadow perspective design, the shadow connection network is displayed with virtual edge information, and the spatiotemporal evolution of variables including resource consumption, emissions and product quality state is displayed with dynamic trajectories; additional layers such as GIS spatial analysis and heat maps are used to display the spatial distribution of each stage of the product life cycle; through interactive controls and scenario simulation, the set feedback mechanism is used to optimize the topological connection of the shadow connection network according to the results of dynamic evolution.

[0113] The expression layer includes an innovative map interface with multiple layers and shadow perspectives to show the linkage between the shadow connection network and the physical flow network, the results of the dynamic equation evolution, and the GIS spatial analysis method. The following are the key points of the design:

[0114] Multi-layer map interface: Use the map API to achieve the overlay of multiple layers, each layer represents a specific information or network. The layers include physical flow networks, shadow connection networks, state variable trajectories of dynamic equations, etc. The bottom layer shows the spatial flow of the product throughout its life cycle, and key nodes serve as physical markers on the map. The first layer overlays the shadow connection network, and the nodes and key entities are spatially mapped. The second layer overlays the results of the dynamic simulation of state transitions, reflecting the spatiotemporal evolution of resources and emissions through color changes. Using GIS spatial analysis, additional forms such as heat maps and spatial distribution maps are added to display the spatial distribution of each stage of the product life cycle, forming a comprehensive presentation of geographic information and enriching the content of multi-layer maps.

[0115] Shadow perspective design: Using the shadow perspective, the shadow connection network is presented as a transparent, abstract connection line, which does not directly reflect the real supply chain structure, but retains virtual edge information to support subsequent updates and diffusion. The nodes of the shadow connection network are projected and overlaid on the physical logistics path nodes in a visual way. Users can select specific enterprises at the shadow nodes on the map, and the matching key physical enterprises will be returned to establish a shadow-to-physical mapping.

[0116] Dynamic trajectory presentation: The dynamic trajectory of state variables such as resource consumption, emissions, and product quality / utility obtained by the evolution of kinetic equations is displayed in the form of animation or real-time update. Users can specify shadow nodes and activate the state space evolution monitoring of the node. Each node and edge can change over time to form a dynamic network state. When users change the parameter configuration or dependency of the shadow enterprise, the state space evolution of the entity node can be updated accordingly to reflect the latest life cycle assessment.

[0117] Feedback mechanism display: Emphasize how the results of dynamic evolution are fed back to the shadow connection network, and optimize the topological connection of the network to achieve two-way binding. The update and diffusion process of state variables can be intuitively displayed through flashing, color changes, etc.

[0118] Scenario simulation visualization: Provides a scenario selection panel, allowing users to select different simulation scenarios for visualization. Provides network stability assessment reports for different scenarios and displays them in the form of trend charts.

[0119] Interactive interface: Provides user interactive controls that allow users to freely switch layers, adjust time ranges, select enterprise nodes of interest, etc., so as to customize the information presentation of their interest.

[0120] Preferably, the expression layer is also used to use shadow perspectives and dynamic trajectories to display in detail the virtual edge information of the shadow connection network and the actual flow of the physical logistics path, wherein the shadow connection network expression layer adopts private cloud deployment and data encryption, and is accessed by internal personnel of specific enterprises for simulation and risk assessment, and the physical flow network expression layer uses public cloud deployment and is open to users, providing logistics path information display based on third-party public data.

[0121] From the perspective of technical implementation, the difference between the shadow contact network expression layer and the physical flow network expression layer is explained as follows: the shadow contact network expression layer uses private cloud deployment, data encryption, and requires internal enterprise authorization to access, in order to protect sensitive supply chain relationship data. The physical flow network expression layer uses the public cloud, is open to all users, and is based on third-party public data, in order to present actual logistics path information. The former is aimed at internal personnel of a specific enterprise and provides simulation and risk assessment functions. The latter is aimed at the public and is used to intuitively display product logistics dynamics.

[0122] Application layer: Based on the rich multi-layer map interface and shadow perspective, the application layer has developed customized applications for different user groups, which improves the practical value of the system. For consumer users, the application uses the expression layer map to intuitively compare the environmental impact of different products, helping users to choose more environmentally friendly purchasing decisions. For corporate users, the application predicts and compares carbon emissions based on dynamic trajectories, supports enterprises to optimize supply chain management and make the best low-carbon decisions. For decision-making unit users, the application combines GIS spatial analysis with big data reports to identify regional carbon emission hotspots and provide decision support for the formulation of differentiated low-carbon strategies.

[0123] Preferably, the application layer includes:

[0124] A personalized interactive query module for consumers, which allows users to select specific products and time ranges, and displays detailed environmental indicator values ​​of the selected products at each stage of their life cycle, so that consumers can make environmentally friendly purchasing decisions based on the comparison results of environmental indicator values;

[0125] The dynamic trajectory analysis module for enterprise users is used to achieve multi-dimensional time granularity comparison of carbon emissions through timeline controls, supporting enterprises to conduct comparative analysis of emission changes in daily, weekly and monthly dimensions. Through interactive tools for carbon accounting and early warning, it helps enterprises optimize supply chain management and implement low-carbon decisions.

[0126] The GIS spatial analysis and big data reporting module for decision-making users uses heat maps to visualize the spatial distribution of carbon emissions. The depth of color represents the level of carbon emissions in different regions, so as to intuitively display the differences in carbon intensity between regions and provide decision-making support for decision-makers to formulate targeted differentiated emission reduction strategies.

[0127] In summary, the embodiment of the present invention introduces a dynamic collaborative network, and establishes a dynamic network model through the synergy between the shadow contact network and the physical flow network. This helps to more accurately capture the state changes and mutual relationships of each node in the supply chain. Five models including spatiotemporal data model, dynamic optimization model, matching learning mapping model, multi-directional feedback optimization model, and scenario simulation evaluation model are proposed to form a multi-level model structure, providing a more comprehensive modeling and evaluation method. The shadow contact network is presented as a transparent, abstract connection line using a shadow perspective, retaining virtual edge information. The state variables such as resource consumption, emissions, and product quality / utility of the evolution of the dynamic equation are presented through dynamic trajectories, allowing users to monitor the evolution of the network state in real time. Expression layer designs such as multi-layer maps, interactive control, and feedback mechanisms are provided, allowing users to freely switch layers, adjust time ranges, and select enterprise nodes of interest to achieve real-time monitoring and adjustment of the state. An integrated intelligent decision support system from data collection to model building to multi-form expression and interactive application is constructed. A general method framework is provided that can be extended to the life cycle management of various products. By building a dynamic synergy model of shadow connection network and physical flow network, combined with life cycle assessment theory, the resource consumption and environmental impact of the product's entire life cycle are simulated. With the help of multi-layer map and shadow perspective expression technology, dynamic visualization analysis and interactive control of product life cycle assessment and optimization decision-making process are realized. Its technical innovation lies in the deep integration of dynamic network theory, life cycle assessment and geographic information system multidisciplinary advantages, and the construction of a new intelligent optimization method for product life cycle decision-making, which can effectively support the scientific decision-making of policy makers, entrepreneurs and individual consumers.

[0128] The second embodiment of the present invention provides a computer-readable storage medium storing instructions of the map-based life cycle assessment system based on a dynamic network collaborative model of the first embodiment, that is, the computer-readable storage medium stores instructions, and when the instructions are run on a computer, the map-based life cycle assessment system based on a dynamic network collaborative model of the first aspect is executed. The computer-readable storage medium refers to a carrier for storing data, which may include but is not limited to a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive and / or a memory stick, etc. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0129] The working process, working details and technical effects of the computer-readable storage medium proposed in the second embodiment of the present invention can be found in the first embodiment and will not be described in detail here.

[0130] A third aspect of this embodiment provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to execute a map-based life cycle assessment system based on a dynamic network collaborative model as in the first embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0131] The multiple embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0132] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware system, and of course, by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for enabling a warehouse code merging device to execute the methods of each embodiment or some parts of the embodiments.

[0133] Finally, it should be noted that the above are only preferred embodiments of the invention and are not intended to limit the scope of protection of the invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the invention should be included in the scope of protection of the invention.

Claims

1. A map-based life cycle assessment system based on a dynamic network collaboration model, characterized in that: include: The data layer is used to obtain product activity data through near-real-time data collection methods and geospatial positioning methods, and uses the multi-dimensional data including product logistics information and spatiotemporal change influencing factors stored in the spatiotemporal database / data warehouse, and uses data fusion methods and data quality management methods to integrate heterogeneous data sources to improve the accuracy, completeness and availability of data, and provide data support for life cycle assessment; the data layer includes: a near-real-time data collection module, which is used to collect near-real-time data and coordinate information of products through the Internet of Things combined with sensor networks; a geospatial location processing module, which is used to process and map the collected near-real-time data and coordinate information, and associate the near-real-time data of each stage of the product with the coordinate information; a spatiotemporal database module, which is used The near-real-time data and coordinate information combined with the map information after storage association are used to store product logistics information and factors affecting spatiotemporal changes, and configure dynamic update and query mechanisms; a data fusion module is used to integrate static data and streaming data of different rates, formats and granularities in the spatiotemporal database module, and build a unified and associated big data warehouse to efficiently process and store multi-source heterogeneous data; a data quality management module is used to clean, calibrate and supplement the data in the spatiotemporal database module to ensure the accuracy and completeness of the data; the near-real-time data includes detailed activity data at each stage of the product life cycle, as a key component of the dynamic database, to support the spatiotemporal database module to timely reflect and evaluate the spatiotemporal change characteristics of the product life cycle; The model layer is used to receive input from the data layer, form a dynamic collaborative network by constructing a shadow connection network and a physical flow network, use a spatiotemporal data model to associate data into a complete spatiotemporal graph, and use dynamic optimization models, matching learning mapping models, multi-directional feedback optimization models, and scenario simulation evaluation models to achieve dynamic evaluation, tracking, optimization, and prediction of product life cycles, and feed back model calculation results to the data layer to form a virtuous cycle of data-driven models and models promoting data optimization, and continuously improve the accuracy and real-time performance of life cycle assessment; the model layer includes: a shadow connection network and a physical flow network, and the shadow connection network and the physical flow network work together to form a dynamic collaborative network for life cycle assessment; wherein, the shadow connection network is used to achieve data update and collaboration between enterprise nodes through virtual dependencies without exposing real supply chain information; the physical flow network is used to establish a description of nodes based on the topological structure of actual material flow between enterprises. The dynamic equations for the time evolution of point state quantities; the spatiotemporal data model, dynamic optimization model, matching learning mapping model, multi-directional feedback optimization model and scenario simulation evaluation model continuously update, optimize and iterate the dynamic collaborative network for life cycle evaluation by providing corresponding data processing and optimization algorithms; the spatiotemporal data model is used to associate product logistics information and the spatiotemporal changes of influencing factors to provide data for dynamic evaluation; the dynamic optimization model is used to continuously optimize the evaluation by connecting to an external spatiotemporal database using dynamic programming, and feed the results back to the database to form a process-based interaction; the matching learning mapping model is used to integrate multi-source data through data fusion, establish a dynamic mapping between shadow nodes and physical nodes, and introduce incremental learning for real-time update; the multi-directional feedback optimization model is used to build a closed-loop update mechanism to continuously improve data quality and reduce timeliness differences; the scenario simulation evaluation model is used to use historical data to build and evaluate a variety of simulation scenarios to provide a basis for decision-making; The expression layer is used to realize the superposition of multiple layers including shadow connection network, physical flow network and dynamic equation evolution results based on the map API, realize the mapping of shadow nodes to physical nodes by using shadow perspective design, dynamically present the spatiotemporal evolution trajectory of resource consumption, emission and product quality state variables by means of animation and color change, display the optimization and update process of network topology structure, and provide an interactive interface to allow users to customize layer combination and analysis granularity; The application layer is used to develop personalized carbon emission accounting, comparison and decision-making support tools for user groups including consumers, enterprises and decision-makers; it is used for consumers to achieve green shopping by comparing the environmental impact of products throughout their life cycle, for enterprises to use dynamic trajectories to predict the carbon emission performance of different supply chain solutions and optimize resource allocation, and for decision-makers to formulate differentiated emission reduction strategies based on regional carbon emission heat maps.

2. The map-based life cycle assessment system based on a dynamic network collaborative model according to claim 1 is characterized in that: The spatiotemporal database module is also used to construct a spatiotemporal data model that connects activity data and map information based on a geographic location system, and to store multidimensional data including product logistics information and factors affecting spatiotemporal changes by mapping key events and activities in the product life cycle.

3. The map-based life cycle assessment system based on a dynamic network collaborative model according to claim 1 is characterized in that: The shadow contact network uses a chain update mechanism. When the upstream enterprise data is updated, it automatically sends update notifications to downstream nodes for effective information diffusion without leaking the enterprise's real business data and dependencies.

4. The map-based life cycle assessment system based on a dynamic network collaborative model according to claim 3 is characterized in that: The physical flow network simulates the state change trajectory by solving the dynamic equations and feeds back to the enterprise nodes so that upstream and downstream enterprises can adjust their decision plans according to the state changes.

5. The map-based life cycle assessment system based on a dynamic network collaborative model according to claim 1 is characterized in that: The expression layer is also used to use shadow perspectives and dynamic trajectories to display in detail the virtual edge information of the shadow connection network and the actual flow of the physical logistics path. The shadow connection network expression layer adopts private cloud deployment and data encryption, which is used for access by internal personnel of specific enterprises for simulation and risk assessment. The physical flow network expression layer uses public cloud deployment and is open to users, providing logistics path information display based on third-party public data.

6. The map-based life cycle assessment system based on a dynamic network collaborative model according to claim 1 is characterized in that: The expression layer is used to display and analyze the shadow contact network and physical flow network content of enterprises through a multi-layer map interface, combining collaborative optimization between enterprises and dynamic monitoring results of the entire product life cycle, including: A multi-layer map interface is constructed based on the shadow connection network and the physical logistics network, in which each layer displays supply chain information of different dimensions; through shadow perspective design, the shadow connection network is displayed with virtual edge information, and the spatiotemporal evolution of resource consumption, emissions and product quality state variables is displayed with dynamic trajectories; multiple layers are used to display the spatial distribution of each stage of the product life cycle; through interactive controls and scenario simulation, the feedback mechanism is used to optimize the topological connection of the shadow connection network according to the results of dynamic evolution.

7. The map-based life cycle assessment system based on a dynamic network collaborative model according to claim 1 is characterized in that: The application layer includes: A personalized interactive query module for consumers, which allows users to select specific products and time ranges, and displays detailed environmental indicator values ​​of the selected products at each stage of their life cycle, so that consumers can make environmentally friendly purchasing decisions based on the comparison results of environmental indicator values; The dynamic trajectory analysis module for enterprise users is used to achieve multi-dimensional time granularity comparison of carbon emissions through timeline controls, supporting enterprises to conduct comparative analysis of emission changes in daily, weekly and monthly dimensions. Through interactive tools for carbon accounting and early warning, it helps enterprises optimize supply chain management and implement low-carbon decisions. The GIS spatial analysis and big data reporting module for decision-making users uses heat maps to visualize the spatial distribution of carbon emissions. The depth of color represents the level of carbon emissions in different regions, so as to intuitively display the differences in carbon intensity between regions and provide decision-making support for decision-makers to formulate targeted differentiated emission reduction strategies.

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