Digital intelligent material full-period management platform

Abstract

The invention relates to the technical field of informatization management, in particular to a digital intelligent material full-period management platform which comprises a material circulation partition module, a resource optimization configuration module, a carbon footprint accounting module, an operation state mapping module and a storage distribution adjustment module. According to the invention, by optimizing the spatial distribution and resource configuration of material circulation, improving the material management efficiency and precision, dividing the material circulation area and screening the node distribution density, the resource configuration is more accurate, high-frequency configuration is ensured to be preferentially applied to key nodes, invalid configuration is reduced, and inter-node carbon emission monitoring and analysis are enhanced. A fine response mechanism is provided for abnormal points of carbon emission fluctuation, and by setting region configuration priorities, the dynamic adjustment capability and flexibility of a material management system are improved, invalid fluctuation of carbon emission is reduced, layout and distribution of storage regions are readjusted, the storage space utilization rate is increased, and the material management efficiency is improved. And finally, more efficient, environment-friendly and intelligent full-period management is realized.

Description

Technical Field

[0001] This invention relates to the field of information management technology, and in particular to an index-based intelligent full-cycle management platform for materials. Background Technology

[0002] The field of information management technology involves the integrated management and collaboration of enterprise resources, supply chains, materials, and information flows. Its core aspects include material procurement, inventory control, flow tracking, supplier collaboration, data collection and analysis, cross-process collaborative scheduling, and decision support. It leverages the Internet of Things (IoT) and 5G-A networks to achieve real-time interconnection and online data interaction among multiple nodes, ensuring the visibility and traceability of materials throughout their entire lifecycle. This technology as a whole achieves systematic management and information-based control of material status and flow by collecting data online, storing it centrally, sharing and transmitting it, and processing it comprehensively during the material flow process. Traditional intelligent material lifecycle management platforms refer to management systems oriented towards the entire material lifecycle. They deploy IoT sensing terminals to collect material location and status information, upload the collected raw data to a central database via network transmission channels, and aggregate the data using pre-set material coding rules and classification standards. Based on established business processes, they perform online registration and flow control for material procurement, warehousing, requisition, allocation, and return. A visual interface displays material flow records and current status to managers, achieving digital management of the entire material lifecycle.

[0003] Existing technologies in material flow management rely on fixed processes and standardized data collection methods, lacking flexible response mechanisms to regional differences and changes in node characteristics during the material flow process. In complex environments, it is difficult to achieve real-time and accurate dynamic adjustments to the material flow status, resulting in frequent resource allocation in some areas and resource waste in others. Existing technologies for tracking and monitoring carbon emissions fail to effectively combine changes in node resource allocation, making it impossible to detect carbon emission anomalies in a timely manner, affecting the accuracy of environmental impact assessments and the optimization of decision-making. Existing technologies have shortcomings in high-frequency resource allocation and carbon emission control, which can easily lead to over- or under-allocation of resources, affecting overall efficiency and sustainability. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a digital and intelligent full-lifecycle material management platform. The technical solution is as follows: On the one hand, it provides a digital and intelligent full-lifecycle management platform for materials, which includes: The material circulation zoning module collects material status information, identifies the circulation characteristics and change range between nodes, determines the circulation area to which the node belongs, divides the area into three categories: primary circulation, secondary circulation, and tertiary circulation, calculates the spatial distribution density of nodes and filters them, and obtains the material circulation zoning results. Based on the material circulation zoning results, the resource optimization and allocation module sets the resource allocation priority, adjusts the node resource allocation frequency, sets the first-level circulation area as high-frequency configuration, adjusts the configuration interval of the second-level circulation area, and only configures the key time node resources of the third-level circulation area to obtain the node resource allocation frequency dataset. The carbon footprint accounting module calls the node resource configuration frequency dataset, extracts the carbon emission characteristic parameters of the nodes, classifies them according to their carbon emission characteristics, filters nodes within the same carbon emission characteristic unit, and obtains the node association comparison relationship. Based on the node association relationship, the operation status mapping module analyzes the synchronous changes of node carbon emission fluctuations and anomaly trends, judges the correlation of node data, sets a data correlation threshold, filters associated nodes, and obtains the optimization results of the full life cycle management of materials.

[0005] As a further aspect of the present invention, the material circulation zoning results include primary circulation areas, secondary circulation areas, and tertiary circulation areas; the node resource configuration frequency dataset includes high-frequency configuration areas, adjustment configuration frequency areas, and key time node configuration areas; the node correlation comparison relationship includes node units with similar carbon emission amplitudes, node units with consistent carbon fluctuation cycles, and node units with similar carbon change rates; and the material full-cycle management optimization results include node carbon emission fluctuation trends, node anomaly trends, data correlation thresholds, and associated nodes.

[0006] As a further aspect of the present invention, the material flow zoning module includes: The circulation characteristic calculation submodule collects material status information, measures and records the material status value of each node, analyzes the circulation characteristics and change range between nodes, analyzes the circulation gradient based on the circulation characteristics and change range, and obtains the node circulation gradient value. The gradient classification submodule calls the node flow gradient value, identifies the gradient based on the trend of changes in the flow gradient of adjacent nodes, determines the gradient range to which the node belongs, sets the gradient classification criteria, divides the nodes into three categories of flow areas: primary flow, secondary flow, and tertiary flow, analyzes the distribution of nodes within the area, and generates material flow zoning results. The density filtering submodule calls the material circulation zoning results, calculates the spatial density feature values ​​of the gradient region nodes, filters the density regions, and obtains the material circulation zoning results.

[0007] As a further aspect of the present invention, the resource optimization configuration module includes: The configuration priority setting submodule calls the material flow partitioning results, sets the configuration priority according to the node flow area, sorts them according to the degree of flow change, and obtains the node configuration priority data; The configuration frequency adjustment submodule calls the node configuration priority data, adjusts the configuration frequency according to the priority, sets high-frequency configuration for the first-level circulation area, adjusts the configuration interval for the second-level circulation area, and filters configuration for key time nodes for the third-level circulation area. It analyzes the changes in configuration frequency, adjusts the size of the configuration dataset, and obtains the node resource configuration frequency dataset.

[0008] As a further aspect of the present invention, the node flow area is configured with a priority, which means that when the degree of flow change is higher than a preset threshold, the configuration priority of the corresponding node is raised to the optimal level, and the node configuration priority data is arranged in order according to the degree of flow change. The priority adjustment configuration frequency refers to fixing the configuration frequency of the corresponding first-level circulation area to a high-frequency configuration interval when the node configuration priority data is set to the optimal level. The configuration frequency change refers to the process where, when the configuration frequency changes exceed a threshold three times consecutively, an adjustment operation on the size of the configuration dataset is triggered, and the adjusted node resource configuration frequency dataset is updated and stored.

[0009] As a further aspect of the present invention, the carbon footprint calculation module includes: The carbon parameter extraction submodule analyzes the stability of the frequency data based on the node resource configuration frequency dataset, identifies and removes abnormal data, calculates the carbon emission amplitude, fluctuation period, and rate of change for each node, and obtains the node carbon parameter set. The node classification submodule calls the node carbon parameter set, analyzes the differences in carbon characteristics based on carbon emission amplitude, fluctuation period, and change rate, filters nodes with similar characteristic values, determines the category to which the nodes belong, divides the same carbon feature units, optimizes the classification boundary, calculates the node carbon feature difference value, and obtains carbon feature unit distribution data. The correlation comparison generation submodule calls the carbon feature unit distribution data, identifies the correlation between nodes within the unit, establishes a node correlation model, identifies the connection between nodes through correlation analysis, and obtains node correlation comparison relationships.

[0010] As a further aspect of the present invention, the running state mapping module includes: The node trend analysis submodule calls the node correlation and comparison relationship to analyze the node carbon emission fluctuations and anomaly trends, extracts time series data, and obtains the node trend change characteristics; The data correlation judgment submodule calls the node trend change characteristics to analyze the synchronicity between node carbon emission fluctuations and anomaly changes, calculates node data correlation, sets a data correlation threshold, filters node data correlation, removes nodes with low correlation, optimizes data matching relationships, and obtains node correlation filtering results. The operation status trend filtering submodule calls the node association filtering results, filters the trend change data of the associated nodes, identifies the magnitude of changes in the operation status of materials and equipment, and obtains the optimization results of the full life cycle management of materials.

[0011] As a further aspect of the present invention, the node data correlation degree refers to the correlation coefficient when the synchronicity between the node carbon emission fluctuation and the anomaly point change exceeds the threshold in three consecutive time windows, and the node is judged as a node with high correlation degree. The set data correlation threshold means that when the correlation of node data is lower than the threshold, the corresponding node will be removed.

[0012] As a further aspect of the present invention, the platform also includes a storage distribution adjustment module: Based on the optimization results of the full life cycle management of materials, the storage distribution adjustment module extracts the spatial distribution information of nodes, identifies the corresponding positions between nodes, divides storage areas according to distribution characteristics, adjusts the storage partition layout, and obtains a digital and intelligent material security storage solution. The digital and intelligent material security storage solution includes storage area division, storage partition layout adjustment, node spatial distribution information extraction, and node corresponding location identification.

[0013] As a further aspect of the present invention, the storage distribution adjustment module includes: The spatial distribution extraction submodule calls the optimization results of the full life cycle management of materials, extracts the spatial distribution data of nodes, identifies the corresponding positions of nodes, and obtains the spatial distribution information of nodes; The storage area partitioning submodule calls the node spatial distribution information, identifies distribution characteristics based on the corresponding node locations, filters dense node areas, partitions storage areas, sets differentiated storage area boundaries, adjusts the node distribution balance, and obtains the storage area partitioning results. The partition layout adjustment submodule calls the storage area division results, adjusts the storage partition layout, optimizes the node storage data mapping, and obtains a digital and intelligent material security storage solution.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By optimizing the spatial distribution and resource allocation of material flow, the efficiency and accuracy of material management can be effectively improved. By dividing material flow areas and filtering node distribution density, the frequency of resource allocation can be adjusted more precisely, ensuring that high-frequency allocations are prioritized for key nodes, reducing ineffective allocations, and strengthening carbon emission monitoring and analysis between nodes, providing a more refined response mechanism for abnormal carbon emission fluctuations. In terms of resource allocation, setting different allocation priorities for different regions effectively enhances the dynamic adjustment capability and flexibility of the material management system, reduces ineffective fluctuations in carbon emissions, and effectively improves storage space utilization by readjusting the layout and allocation of storage areas, ultimately achieving more efficient, environmentally friendly, and intelligent full-cycle management. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the digital and intelligent full-cycle material management platform provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the platform framework of the present invention; Figure 3 This is a flowchart of the material flow zoning module in this invention; Figure 4 This is a flowchart of the resource optimization and allocation module in this invention; Figure 5 This is a flowchart of the carbon footprint accounting module in this invention; Figure 6 This is a flowchart of the running state mapping module in this invention; Figure 7 This is a flowchart of the storage distribution adjustment module in this invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a digital and intelligent full-lifecycle material management platform, such as... Figure 1-2 The diagram shown illustrates a digitalized, full-lifecycle materials management platform, which includes: The material circulation zoning module collects material status information, identifies the circulation characteristics and change range between nodes, determines the circulation area to which the node belongs, divides the area into three categories: primary circulation, secondary circulation, and tertiary circulation, calculates the spatial distribution density of nodes and filters them, and obtains the material circulation zoning results. Based on the results of material circulation zoning, the resource optimization and allocation module sets the priority of resource allocation, adjusts the frequency of node resource allocation, sets the first-level circulation area as high-frequency allocation, adjusts the allocation interval of the second-level circulation area, and only allocates key time node resources of the third-level circulation area to obtain the node resource allocation frequency dataset. The carbon footprint accounting module calls the node resource configuration frequency dataset, extracts the carbon emission characteristic parameters of the nodes, classifies them according to their carbon emission characteristics, filters nodes within the same carbon emission characteristic unit, and obtains the node association comparison relationship. The operation status mapping module analyzes the synchronous changes of node carbon emission fluctuations and anomaly trends based on node association and comparison relationships, judges the correlation degree of node data, sets data correlation degree thresholds, filters associated nodes, and obtains the optimization results of the full life cycle management of materials. Based on the optimization results of the full life cycle management of materials, the storage distribution adjustment module extracts the spatial distribution information of nodes, identifies the corresponding positions between nodes, divides storage areas according to distribution characteristics, adjusts the storage partition layout, and obtains a digital and intelligent material security storage solution.

[0023] The material circulation zoning results include primary circulation areas, secondary circulation areas, and tertiary circulation areas. The node resource configuration frequency dataset includes high-frequency configuration areas, adjustment configuration frequency areas, and key time node configuration areas. The node correlation and comparison relationships include node units with similar carbon emission amplitudes, node units with consistent carbon fluctuation cycles, and node units with similar carbon change rates. The material full-cycle management optimization results include node carbon emission fluctuation trends, node anomaly trends, data correlation thresholds, and associated nodes. The digital and intelligent material security storage solution includes storage area division, storage partition layout adjustment, node spatial distribution information extraction, and node corresponding location identification.

[0024] Specifically, such as Figure 2 , 3 As shown, the material flow zoning module includes: The circulation characteristic calculation submodule collects material status information, measures and records the material status value of each node, analyzes the circulation characteristics and change range between nodes, analyzes the circulation gradient based on the circulation characteristics and change range, and obtains the node circulation gradient value. By collecting material status information from each warehouse node, the inventory quantity, hourly inbound frequency, hourly outbound frequency, and average processing time per item are measured and recorded every 10 seconds. For example, node N1 currently has an inventory of 1500 items, an inbound frequency of 5 times / hour, an outbound frequency of 8 times / hour, and an average processing time of 10 minutes per item; node N2 currently has an inventory of 800 items, an inbound frequency of 3 times / hour, an outbound frequency of 2 times / hour, and an average processing time of 15 minutes per item. The material status values ​​are stored in the node material status database. Subsequently, the flow characteristics and variation range between nodes are analyzed. The flow characteristic value is defined as the average material flow efficiency per unit time, calculated by the formula (inbound frequency + outbound frequency) / average processing time. The flow characteristic value for node N1 is calculated as 13 times / hour / (10 minutes / item × 1 hour / 60 minutes / item) = 78 items / hour; the flow characteristic value for node N2 is calculated as 5 times / hour / (15 minutes / item × 1 hour / 60 minutes / item) = 78 items / hour. (1 hour / unit × 1 hour / 60 minutes / unit) = 20 units / hour. The difference between the maximum and minimum values ​​of the circulation characteristic value of each node in the past 24 hours is determined as the range of change. The range of change for node N1 is 15 units / hour, and for node N2 it is 7 units / hour. The circulation gradient is analyzed based on the circulation characteristic value and the range of change. The circulation gradient score is defined as circulation characteristic value + (range of change × weight coefficient). The weight coefficient is set to 0.5, which is determined based on regression analysis of historical circulation data. By analyzing the correlation between the circulation characteristic value and the range of change of a large number of nodes and their impact on business, it was found that for every unit increase in the range of change, the impact on the circulation gradient is approximately 0.5 times that of the circulation characteristic value. The circulation gradient score of node N1 is calculated as 78 + (15 × 0.5) = 85.5, and the circulation gradient score of node N2 is calculated as 20 + (7 × 0.5) = 23.5. The calculation results are used as the node circulation gradient values ​​and stored in the material circulation gradient database.

[0025] The gradient classification submodule calls the node flow gradient value, identifies the gradient based on the trend of changes in the flow gradient of adjacent nodes, determines the gradient range to which the node belongs, sets the gradient classification criteria, divides the nodes into three categories of flow areas: primary flow, secondary flow, and tertiary flow, analyzes the distribution of nodes within the area, and generates material flow zoning results. The system retrieves node flow gradient values ​​stored in the material flow gradient database, such as the N1 node's flow gradient value of 85.5 and the N2 node's flow gradient value of 23.5. Gradients are identified based on the trend of flow gradient changes between nodes. The gradient difference percentage is calculated as (|Node A flow gradient value - Node B flow gradient value| / Node A flow gradient value) × 100%. The gradient change trend threshold is set at 20%, based on the analysis of historical material flow data. When the gradient change percentage of adjacent nodes exceeds 20%, it corresponds to a business difference. For example, if nodes N1 and N3 (flow gradient value 110) are adjacent, the gradient difference percentage is (|110 - 85.5| / 85.5) × 100% ≈ 28.65%. Since the gradient difference percentage exceeds this threshold, N1 and N3 are determined to belong to different gradient ranges. The node's gradient range is determined by comparing its flow gradient score with the classification standard range. The classification standard is set as follows: a flow gradient score greater than 90 indicates a first-level flow area, and 30 to... A score of 90 indicates a Level 2 circulation area, and a score less than 30 indicates a Level 3 circulation area. The range values ​​are adjusted based on the industry's definition of material circulation efficiency levels and the company's historical operational data. Node N1 has a circulation gradient score of 85.5, classifying it as a Level 2 circulation area; node N2 has a circulation gradient score of 23.5, classifying it as a Level 3 circulation area. Nodes are categorized into Level 1, Level 2, and Level 3 circulation areas. N1 and N5 (circulation gradient score 45) are classified as Level 2 circulation areas, and N3 (circulation gradient score 110) is classified as a Level 3 circulation area. The system divides the warehouse into three levels: N1 and N2 (with a gradient score of 20). N2 and N4 are divided into three levels. The distribution of nodes within each level is analyzed, and the number of nodes and spatial clustering in each level are counted. For example, the level 2 level circulation area contains two nodes, N1 and N5, with N1 located on the east side of the warehouse and N5 located on the west side. The level 1 level circulation area contains N3, which is located in the center of the warehouse. The level 3 level circulation area contains N2 and N4, with N2 located on the north side of the warehouse and N4 located on the south side. This generates the results of the material circulation zoning.

[0026] The density filtering submodule calls the material circulation zoning results, calculates the spatial density feature values ​​of the gradient region nodes, filters the density regions, and obtains the material circulation zoning results. The system retrieves the zoning results of material circulation to obtain the category and distribution overview of the circulation area to which each node belongs. It identifies N1 and N5 as secondary circulation areas, N3 as a primary circulation area, and N2 and N4 as tertiary circulation areas. The spatial density characteristic values ​​of nodes in the gradient regions are calculated. First, the geographical coordinates of each node are determined, for example, N1 node coordinates (10, 20), N2 node (50, 30), N3 node (25, 25), N4 node (60, 10), and N5 node (15, 40). Within a 10-meter radius of each node, the number of other nodes is calculated. The number is divided by the area of ​​the detection range to obtain the local density. For example, the local density of node N1 is calculated to be 0.0032 nodes / square meter. The average local density of all nodes in each gradient region is... The mean value is determined as the spatial density characteristic value of nodes in the gradient region. For example, the spatial density characteristic value of nodes in the secondary circulation region is (N1 local density + N5 local density) / 2. Density regions are filtered, and the density threshold is set to 0.005 nodes / square meter. This threshold is set with reference to industry practices for warehouse space utilization. If the value is lower than this value, the node distribution in the region is sparse. Gradient regions with a node spatial density characteristic value higher than or equal to 0.005 nodes / square meter are determined to be density regions. For example, the average node density of the secondary circulation region is 0.004 nodes / square meter, which does not reach the density threshold. The density of the tertiary circulation region is 0.006 nodes / square meter, and the density of the primary circulation region is 0.008 nodes / square meter. The tertiary circulation region and the primary circulation region are selected as density regions to obtain the material circulation zoning results.

[0027] Specifically, such as Figure 2 , 4 As shown, the resource optimization and configuration module includes: The configuration priority setting submodule calls the material circulation partitioning results, sets the configuration priority according to the circulation area of ​​the node, sorts them according to the degree of circulation change, and obtains the node configuration priority data; The node flow area is configured with priority, which means that when the degree of flow change is higher than a preset threshold, the configuration priority of the corresponding node will be raised to the optimal level, and the node configuration priority data will be arranged in order according to the degree of flow change. The system retrieves information such as primary circulation zones (including N3) and tertiary circulation zones (including N2 and N4) from the resource circulation zoning results. Based on the circulation zone of each node, a configuration priority is set. When the degree of circulation change exceeds a preset threshold, the corresponding node's configuration priority is increased to the optimal level. The preset threshold is set as the average value of the circulation characteristic change range (e.g., 10 units / hour). If a node's circulation characteristic change range exceeds 10 units / hour in the past 24 hours, the node is considered to have a high degree of circulation change, and its configuration priority is set to the optimal level. This threshold setting is based on historical operational data analysis. A change range exceeding the average indicates large fluctuations in material demand, requiring flexible resource allocation. For N3 nodes, the change range is 20 nodes / hour, exceeding 10 nodes / hour, so the configuration priority is set to the optimal level. For N2 nodes, the change range is 7 nodes / hour, and for N4 nodes, the change range is 5 nodes / hour, below 10 nodes / hour, so the default level is maintained. Node configuration priority data is arranged in order of the degree of change in flow, with the optimal level node placed at the top of the list, and the remaining nodes arranged in descending order according to their flow characteristic values. For example, the arrangement order is N3 (optimal level), N2, N4. Obtain the node configuration priority data.

[0028] The configuration frequency adjustment submodule calls the node configuration priority data and adjusts the configuration frequency according to the priority. The first-level circulation area sets high-frequency configuration, the second-level circulation area adjusts the configuration interval, and the third-level circulation area filters the configuration of key time nodes, analyzes the configuration frequency changes, adjusts the configuration dataset size, and obtains the node resource configuration frequency dataset. Priority adjustment configuration frequency refers to fixing the configuration frequency of the corresponding first-level circulation area to a high-frequency configuration interval when the node configuration priority data is set to the optimal level. Configuration frequency change refers to the process where, when the configuration frequency changes exceed the threshold three times consecutively, an adjustment operation is triggered on the size of the configuration dataset, and the adjusted node resource configuration frequency dataset is updated and stored. The system retrieves node configuration priority data, e.g., N3 is the highest priority, N2 is next, and N4 is the lowest. Based on these priorities, the configuration frequency is adjusted. In the first-level circulation area, high-frequency configuration is set at an interval of once every hour. In the second-level circulation area, the configuration interval is once every four hours. In the third-level circulation area, key time nodes are selected for configuration. For example, nodes (N2, N4) in the third-level circulation area are only configured at 9:00 AM and 3:00 PM daily. When the node configuration priority data is set to the highest level, such as N3, its corresponding configuration frequency in the first-level circulation area is fixed at a high-frequency interval, i.e., once every hour. The system analyzes changes in configuration frequency, monitors the actual configuration frequency of each node, and calculates the change in configuration frequency within three consecutive time windows. One time window is set to 7 days. For example, the configuration frequency of node N3 within one month (4 time windows) is as follows: Week 1: 24 Week 2: 24 times, Week 3: 25 times. The first change is |24-24|=0, the second change is |25-24|=1, and the third change is |24-25|=1. If the configuration frequency changes exceed the threshold three times consecutively, the configuration dataset size adjustment operation is triggered. The threshold is set when the weekly configuration frequency change exceeds 10% or the absolute value exceeds 2 times. If the N3 node changes from 24 times to 21 times (3 changes) within a certain time window, the configuration dataset size adjustment operation is triggered. The adjustment operation increases or decreases the amount of historical data in the configuration decision dataset according to the configuration frequency trend. If the configuration frequency continues to decline, the data from the past month is reduced as a reference, and the proportion of data from the past three months is increased to capture the long-term trend. If the configuration frequency fluctuation intensifies, the weight of real-time data and data from the past week is increased, and the adjusted node resource configuration frequency dataset is updated and stored.

[0029] Specifically, such as Figure 2 , 5 As shown, the carbon footprint accounting module includes: The carbon parameter extraction submodule analyzes the stability of frequency data based on the node resource configuration frequency dataset, identifies and removes abnormal data, calculates the carbon emission amplitude, fluctuation period, and change rate of each node, and obtains the node carbon parameter set. Based on the node resource configuration frequency dataset, the configuration frequency of each node and its corresponding historical data scale are obtained. For example, the N3 node is configured once per hour, and its dataset includes high-weighted data from the most recent three weeks and low-weighted historical data from the past three months. The stability of the frequency data is analyzed, and the standard deviation of each node's configuration frequency over the past 24 hours is calculated to measure stability. For example, if the N3 node is configured 24 times in the past 24 hours, and the time interval between each configuration is strictly 1 hour, then the standard deviation is 0. Outliers are identified and removed. The threshold for outliers is set when the standard deviation of the configuration frequency exceeds 0.2 hours. Frequency data with a deviation exceeding 0.2 hours is considered abnormal and is removed. This threshold is empirically set based on the maximum allowable deviation of the configuration interval under stable operating conditions. A deviation exceeding this value indicates a scheduling anomaly. If the standard deviation of the configured frequency for node N3 on a certain day is 0.3 hours, then the frequency data for node N3 on that day is removed. The carbon emission amplitude, fluctuation period, and rate of change are calculated for each node. The carbon emission amplitude is defined as the total amount of carbon dioxide equivalent (CO2e) generated by the node in one material flow cycle. For example, if node N3 generates 0.05 kg CO2e for processing one material, and processes 50 materials in one cycle, the carbon emission amplitude is... The emission amplitude is 50 units × 0.05 kg / unit = 2.5 kg CO2e. This emission factor of 0.05 kg CO2e / unit is calculated based on the carbon emission coefficients of the average transportation distance, transportation mode, and energy consumption of the material. For example, assuming an average transportation distance of 100 meters, an electric forklift with an energy consumption of 0.01 kWh, corresponding to an electricity emission factor of 0.5 kg CO2e / kWh, and a small truck with an energy consumption of 0.005 L of diesel, corresponding to a diesel emission factor of 2.6 kg CO2e / L, then 0.01 × 0.5 + 0.005 × 2.6 = 0.018 kg CO2e, plus the internal operating... The process involves production and packaging, calculating 0.05 kg CO2e per item. The fluctuation period is defined as the time interval required for two consecutive peak values ​​of node carbon emissions. For example, if the carbon emission amplitude of node N3 reaches a peak of 2.5 kg CO2e on Monday and another peak of 2.8 kg CO2e on Thursday, the fluctuation period is 3 days. The rate of change is defined as the ratio of the change in amplitude between two adjacent peak values ​​of carbon emissions to the time interval. The rate of change of node N3 in 3 days is (2.8 kg CO2e - 2.5 kg CO2e) / 3 days = 0.1 kg CO2e / day. The node carbon parameter set is then obtained.

[0030] The node classification submodule calls the node carbon parameter set, analyzes the differences in carbon characteristics based on carbon emission amplitude, fluctuation period, and change rate, filters nodes with similar characteristic values, determines the node's category, divides the same carbon feature units, optimizes the classification boundary, calculates the node carbon feature difference value, and obtains carbon feature unit distribution data. The characteristic difference value of nodal carbon is calculated using the following formula: ; in, Represents the difference in carbon characteristics at the nodes. Representing the The carbon emission magnitude of the node, Representing the The carbon emission magnitude of the node, Representing the The fluctuation cycle of the node Representing the The rate of change of the node Representing the The rate of change of the node Represents the total number of nodes; The node carbon parameter set is invoked to obtain information such as the carbon emission amplitude, fluctuation period, and rate of change for each node. For example, the carbon emission amplitude of node N3 is 2.5 kg CO2e, the fluctuation period is 3 days, and the rate of change is 0.1 kg CO2e / day. Based on the carbon emission amplitude, fluctuation period, and rate of change, the differences in carbon characteristics are analyzed. This analysis process calculates the differences in node carbon characteristics. To quantify this, the formula is: ,in, This represents the difference in carbon characteristics between nodes, used to measure the degree of similarity or dissimilarity in carbon emission characteristics between nodes. A larger value indicates a greater difference. Representing the The carbon emission magnitude of a node, i.e., the node The total amount of carbon dioxide equivalent generated during one material circulation cycle, expressed in kilograms of carbon dioxide equivalent (kgCO2e). Representing the The carbon emission magnitude of a node, i.e., the node The total amount of carbon dioxide equivalent generated during one material circulation cycle, expressed in kilograms of carbon dioxide equivalent (kgCO2e). Representing the The fluctuation cycle of a node, i.e., the node The time interval required for carbon emissions to reach their peak values ​​twice consecutively, measured in days. Representing the The rate of change of a node is the ratio of the change in the magnitude of two consecutive peak carbon emission values ​​to the time interval, expressed in kilograms of carbon dioxide equivalent per day (kgCO2e / day). Representing the The rate of change of a node is the ratio of the change in the magnitude of two consecutive peak carbon emission values ​​to the time interval, expressed in kilograms of carbon dioxide equivalent per day (kgCO2e / day). Represents the total number of nodes; The calculation logic of this formula is as follows: First, calculate the nodes. With nodes The absolute value of the difference in carbon emission magnitude, divided by the node The fluctuation cycle, this item reflects the period at the node During the carbon emission fluctuation cycle, the average difference in carbon emission amplitude between the two nodes is then used to determine the nodes. rate of change divided by node The rate of change of , which reflects the node Relative to node The degree of drastic change in carbon emissions is taken into account. Finally, these two factors are multiplied together and summed across all node pairs to comprehensively consider the differences in carbon emission characteristics between nodes. The advantage of this formula is that by introducing a combination of amplitude differences, fluctuation periods, and change rates, it can comprehensively measure the differences in carbon emission scale, stability, and change trends among different nodes, thereby achieving a more refined node classification. Assume there are three nodes: N1, N2, and N3, and their carbon parameter sets are shown in the table below. Table 1: Example Table of Nodal Carbon Parameter Sets ; Here , and To represent different nodes, for example, calculate the difference between N1 and N2, the difference between N1 and N3, the difference between N2 and N1, the difference between N2 and N3, the difference between N3 and N1, and the difference between N3 and N2, and sum them up, for the sake of a simplified example; Compute node pairs Differences: ; Compute node pairs Differences: ; Calculate and sum the differences for all node pairs: for ; for ; for ; for ; Summing all terms yields ; Next, nodes with similar characteristic values ​​are screened. This screening process sets a carbon characteristic difference threshold of 0.6. This threshold is set based on empirical analysis and industry requirements for similarity judgment. Nodes with carbon characteristics below this value are considered sufficiently similar. For example, if the calculated D value is 1.76305, which is much higher than 0.6, it indicates a large difference in carbon characteristics among the three nodes (N1, N2, N3). The node classification process is then based on the commonalities of similar nodes in specific carbon characteristic dimensions (e.g., amplitude, fluctuation frequency). For example, if N1 and N3 have high carbon emission amplitudes, while N2 has a low amplitude, N1 and N3 are initially classified into one category, and N2 into another. Then, units with the same carbon characteristics are further divided. The partitioning process clusters all nodes with similar carbon characteristics into a carbon feature unit. For example, N1 and N3 are classified as "high emission fluctuation units," and N2 as "low emission stable units." The classification boundary is then optimized by iteratively adjusting the classification threshold or clustering algorithm parameters until the feature differences between nodes within a unit are minimized and the feature differences between nodes between units are maximized. For example, by adjusting the amplitude threshold and fluctuation period threshold, the dispersion of carbon parameters within each unit is minimized. The node carbon feature difference value is calculated and recalculated after unit optimization to ensure classification accuracy. Finally, carbon feature unit distribution data is obtained, which describes the nodes contained in each carbon feature unit and their optimized carbon feature difference values.

[0031] The correlation comparison generation submodule calls carbon feature unit distribution data, identifies the correlation between nodes within the unit, establishes a node correlation model, identifies the connection between nodes through correlation analysis, and obtains node correlation comparison relationships. The carbon feature unit distribution data is retrieved to obtain the nodes contained in each carbon feature unit and their optimized carbon feature difference values. For example, a "high emission fluctuation unit" contains N1 and N3 nodes, and its intra-unit carbon feature difference value is optimized to 0.2. A "low emission stable unit" contains N2 node, and its intra-unit carbon feature difference value is 0. The correlation between nodes within the unit is identified, and the Pearson correlation coefficient between each node within the unit is calculated to measure the degree of synchronous change of the three parameters: carbon emission amplitude, fluctuation period, and rate of change. For example, the carbon emission amplitudes of N1 and N3 show a highly consistent fluctuation trend within one month, and the calculated correlation coefficient is 0.85. The correlation coefficient threshold is set at 0.7. This threshold is based on the generally accepted strong correlation in statistics; values ​​higher than this are considered... There are connections between nodes. The correlation coefficient between N1 and N3 is 0.85, which is higher than 0.7, indicating a strong correlation. A node correlation model is established, and a directed graph is constructed to represent the strength and direction of the correlation between nodes. Nodes represent material flow nodes, edges represent correlation relationships, and the weight of the edges represents the correlation strength. For example, an edge with a weight of 0.85 is established between N1 and N3, indicating that N1's carbon emission behavior has a strong correlation with N3. The connections between nodes are identified through correlation analysis. Using the correlation model, node groups with high degree of influence or common changes are identified. For example, N1 and N3 are highly synchronized in terms of carbon emissions. Due to the handling of similar types of materials or the influence of common external factors (such as batch delivery or seasonal demand), the node correlation comparison relationship is obtained.

[0032] Specifically, such as Figure 2 , 6 As shown, the running status mapping module includes: The node trend analysis submodule calls the node correlation and comparison relationship to analyze the fluctuation of node carbon emissions and the trend of anomalies, extracts time series data, and obtains the characteristics of node trend changes; By calling the node association and comparison relationship, information such as N1 and N3 being a highly correlated group with an association strength of 0.85 is obtained. The carbon emission fluctuations and outlier trends of the nodes are analyzed, and the time series data of carbon emission amplitude for each node over the past three months are extracted. For example, the daily carbon emission amplitude data of node N1 constitutes a time series. Time series analysis methods are applied to remove the seasonality and trend of the time series to obtain the fluctuation component. For example, the daily carbon emission fluctuation series of node N1 after detrending is obtained. Outliers in the series are identified. The threshold for outliers is set as points where the fluctuation component exceeds three times its standard deviation. This threshold is based on the statistical 3σ principle. If the carbon emission fluctuation value of node N1 on a certain day suddenly exceeds three times its historical fluctuation standard deviation, it is marked as an outlier, and the node trend change characteristics are obtained.

[0033] The data correlation judgment submodule calls the node trend change characteristics, analyzes the synchronicity between node carbon emission fluctuations and anomaly changes, calculates node data correlation, sets data correlation thresholds, filters node data correlation, removes nodes with low correlation, optimizes data matching relationships, and obtains node correlation filtering results. Node data correlation refers to the correlation coefficient that exceeds the threshold in three consecutive time windows when the carbon emission fluctuations of a node and the changes of anomalies are identified as highly correlated nodes. Set a data correlation threshold. Once the correlation of node data is below the threshold, the corresponding node will be removed. By utilizing node trend change characteristics, such as the detrended daily carbon emission fluctuation sequence and outlier information for node N1, the synchronicity between node carbon emission fluctuations and outlier changes is analyzed. The cross-correlation coefficient between the carbon emission fluctuation sequences of each node in the highly correlated group is calculated. For example, for N1 and N3, the cross-correlation coefficient of their daily carbon emission fluctuation sequences with a lag of 0 days is calculated. If the synchronicity between carbon emission fluctuations and outlier changes exceeds the correlation coefficient threshold for three consecutive time windows, the node is identified as a highly correlated node. The correlation coefficient threshold is set to 0.75, based on business practices requiring high-precision synchronization. A data correlation threshold is set to 0.6, based on the fact that low... This threshold indicates insufficient inter-node connectivity, making them unsuitable for collaborative management. When the correlation of node data is below the threshold, the corresponding node is removed. For example, if the correlation between nodes N4 and N5 is calculated to be 0.5, which is below 0.6, then N4 and N5 are removed. The node data correlation is then filtered, retaining node pairs with a correlation higher than or equal to the data correlation threshold and removing node pairs with a correlation lower than the threshold. For example, N1 and N3 are retained, while N4 and N5 are removed. The data matching relationship is optimized, and the data consistency and integrity between the remaining highly correlated node pairs are reassessed. For example, the availability of carbon emission data for N1 and N3 is checked at all time points to obtain the node correlation filtering results.

[0034] The operation status trend filtering submodule calls the node association filtering results, filters the trend change data of the associated nodes, identifies the magnitude of changes in the operation status of materials and equipment, and obtains the optimization results of the full life cycle management of materials. The system retrieves node association filtering results, identifying, for example, node pairs N1 and N3 as having high correlation. It then filters trend change data for associated nodes, extracting trend data on carbon emission amplitude, fluctuation period, and rate of change for nodes N1 and N3 over the past three months. For instance, the carbon emission amplitude of node N1 gradually increased from 2.0 kg CO2e / day at the beginning of the month to 2.3 kg CO2e / day at the end, the fluctuation period changed from 2 days to 2.5 days, and the rate of change changed from 0.2 kg CO2e / day to 0.25 kg CO2e / day. This process identifies changes in the operating status of materials and equipment, and compares the changes in carbon emission parameters with preset operating conditions. The thresholds for state change were compared. The thresholds were set as follows: the monthly change in carbon emission amplitude exceeded 0.1 kg CO2e, the monthly change in fluctuation period exceeded 0.2 days, or the monthly change in change rate exceeded 0.02 kg CO2e / day. The thresholds were set based on the allowable fluctuation range of equipment operating status. Exceeding this value indicates that there is a change in the operating status of the equipment. The monthly change in carbon emission amplitude of node N1 was 0.3 kg CO2e, which exceeded the threshold of 0.1 kg CO2e, and the change in the operating status of node N1 was identified. The monthly change in carbon emission amplitude of node N3 was 0.08 kg CO2e, which did not exceed the threshold, and the optimization results of the full life cycle management of materials were obtained.

[0035] Specifically, such as Figure 2 , 7 As shown, the storage distribution adjustment module includes: The spatial distribution extraction submodule calls the optimization results of the full life cycle management of materials, extracts the spatial distribution data of nodes, identifies the corresponding locations of nodes, and obtains the spatial distribution information of nodes; The system retrieves the optimization results of the full lifecycle management of materials, obtains information such as changes in the operating status of materials and equipment at node N1 that require optimization, extracts the spatial distribution data of nodes, obtains the physical storage location coordinates of node N1 from the warehouse management system (e.g., node N1 is located in the shelving area at (10, 20), identifies the corresponding location of the node, maps the extracted physical coordinates onto the warehouse floor plan, determines the location of node N1 in the warehouse (e.g., node N1 is located on the second floor of the third row of shelves on the east side of the warehouse), and obtains the spatial distribution information of nodes.

[0036] The storage area partitioning submodule calls the node spatial distribution information, identifies distribution characteristics based on the corresponding node locations, filters dense node areas, partitions storage areas, sets differentiated storage area boundaries, adjusts the node distribution balance, and obtains the storage area partitioning results. The system retrieves node spatial distribution information, such as node N1 being located on the second shelf of the third row on the east side of the warehouse. Based on node location, it identifies distribution characteristics, calculates distances between nodes, and assesses the degree of spatial clustering or dispersion. For example, if the Euclidean distance between nodes N1 and N5 (located at (12, 22)) is less than 5 meters, it is identified as clustered; if the distance between nodes N1 and N2 (located at (50, 30)) is greater than 5 meters, it is identified as dispersed. The system then filters densely populated areas, setting a node density threshold of at least 3 nodes within a 10-meter radius. This threshold is based on the principle of maximizing warehouse efficiency; a density lower than this value results in longer picking paths. For instance, if, within a 10-meter radius of N1, there are two other nodes besides N1, such as N5 and N6, then the area containing N1 is filtered as a densely populated area. The system divides storage areas into independent storage regions using a density-based clustering algorithm. For example, the region containing N1, N5, and N6 is designated as "Storage Region A". Differentiated storage region boundaries are set, and the physical boundaries of each storage region are adjusted based on the material flow characteristics and carbon emission features. For instance, for materials with fast flow and low carbon emissions, the storage region boundaries are set to be compact, while for materials with slow flow and high carbon emissions, the boundaries are appropriately widened. The balance of node distribution is also adjusted by migrating some nodes to adjacent storage regions or readjusting the existing node layout to ensure a balance in the number and spatial density of nodes in each storage region. For example, if "Storage Region A" is dense, node N6 is migrated to the adjacent "Storage Region B". The storage region division results are then obtained.

[0037] The partition layout adjustment submodule calls the storage area division results, adjusts the storage partition layout, optimizes node storage data mapping, and obtains a digital and intelligent material security storage solution. The storage area partitioning results are retrieved to obtain information such as "Storage Area A" containing N1 and N5 with boundaries from (10, 20) to (15, 25), and "Storage Area B" containing N6 with boundaries from (18, 28) to (23, 33). The storage partition layout is then adjusted, and specific storage locations within the storage areas are reassigned, taking into account factors such as material size, weight, access frequency, and carbon footprint. For example, high-turnover, low-carbon-emission materials like N1 and N5 are placed in storage locations close to the outlet and at a suitable height to reduce handling time and energy consumption. For low-turnover, high-carbon-emission materials like N6, place them in higher or more remote storage locations. Optimize node storage data mapping, update the warehouse management system database, and associate the storage location data of each material with its status, turnover characteristics, carbon parameters, and storage area to ensure data consistency and traceability. For example, N1 material storage data is mapped to storage location (10.5, 20.2) in "Storage Area A" and associated with its turnover gradient score of 85.5 and carbon emission amplitude of 2.0 kg CO2e to obtain a digital and intelligent material safe storage solution.

[0038] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A digital and intelligent full-lifecycle material management platform, characterized in that, The platform includes: The material circulation zoning module collects material status information, identifies the circulation characteristics and change range between nodes, determines the circulation area to which the node belongs, divides the area into three categories: primary circulation, secondary circulation, and tertiary circulation, calculates the spatial distribution density of nodes and filters them, and obtains the material circulation zoning results. Based on the material circulation zoning results, the resource optimization and allocation module sets the resource allocation priority, adjusts the node resource allocation frequency, sets the first-level circulation area as high-frequency configuration, adjusts the configuration interval of the second-level circulation area, and only configures the key time node resources of the third-level circulation area to obtain the node resource allocation frequency dataset. The carbon footprint accounting module calls the node resource configuration frequency dataset, extracts the carbon emission characteristic parameters of the nodes, classifies them according to their carbon emission characteristics, filters nodes within the same carbon emission characteristic unit, and obtains the node association comparison relationship. Based on the node association relationship, the operation status mapping module analyzes the synchronous changes of node carbon emission fluctuations and anomaly trends, judges the correlation of node data, sets a data correlation threshold, filters associated nodes, and obtains the optimization results of the full life cycle management of materials.

2. The intelligent material lifecycle management platform according to claim 1, characterized in that: The material circulation zoning results include primary circulation areas, secondary circulation areas, and tertiary circulation areas. The node resource configuration frequency dataset includes high-frequency configuration areas, adjustment configuration frequency areas, and key time node configuration areas. The node correlation comparison relationship includes node units with similar carbon emission amplitudes, node units with consistent carbon fluctuation cycles, and node units with similar carbon change rates. The material full-cycle management optimization results include node carbon emission fluctuation trends, node anomaly trends, data correlation thresholds, and associated nodes.

3. The intelligent material lifecycle management platform according to claim 1, characterized in that: The material flow zoning module includes: The circulation characteristic calculation submodule collects material status information, measures and records the material status value of each node, analyzes the circulation characteristics and change range between nodes, analyzes the circulation gradient based on the circulation characteristics and change range, and obtains the node circulation gradient value. The gradient classification submodule calls the node flow gradient value, identifies the gradient based on the trend of changes in the flow gradient of adjacent nodes, determines the gradient range to which the node belongs, sets the gradient classification criteria, divides the nodes into three categories of flow areas: primary flow, secondary flow, and tertiary flow, analyzes the distribution of nodes within the area, and generates material flow zoning results. The density filtering submodule calls the material circulation zoning results, calculates the spatial density feature values ​​of the gradient region nodes, filters the density regions, and obtains the material circulation zoning results.

4. The intelligent material lifecycle management platform according to claim 3, characterized in that: The resource optimization and allocation module includes: The configuration priority setting submodule calls the material flow partitioning results, sets the configuration priority according to the node flow area, sorts them according to the degree of flow change, and obtains the node configuration priority data; The configuration frequency adjustment submodule calls the node configuration priority data, adjusts the configuration frequency according to the priority, sets high-frequency configuration for the first-level circulation area, adjusts the configuration interval for the second-level circulation area, and filters configuration for key time nodes for the third-level circulation area. It analyzes the changes in configuration frequency, adjusts the size of the configuration dataset, and obtains the node resource configuration frequency dataset.

5. The intelligent material lifecycle management platform according to claim 4, characterized in that: The node flow area is configured with a priority, which means that when the degree of flow change is higher than a preset threshold, the configuration priority of the corresponding node is raised to the optimal level, and the node configuration priority data is arranged in order according to the degree of flow change. The priority adjustment configuration frequency refers to fixing the configuration frequency of the corresponding first-level circulation area to a high-frequency configuration interval when the node configuration priority data is set to the optimal level. The configuration frequency change refers to the process where, when the configuration frequency changes exceed a threshold three times consecutively, an adjustment operation on the size of the configuration dataset is triggered, and the adjusted node resource configuration frequency dataset is updated and stored.

6. The intelligent material lifecycle management platform according to claim 4, characterized in that: The carbon footprint calculation module includes: The carbon parameter extraction submodule analyzes the stability of the frequency data based on the node resource configuration frequency dataset, identifies and removes abnormal data, calculates the carbon emission amplitude, fluctuation period, and rate of change for each node, and obtains the node carbon parameter set. The node classification submodule calls the node carbon parameter set, analyzes the differences in carbon characteristics based on carbon emission amplitude, fluctuation period, and change rate, filters nodes with similar characteristic values, determines the category to which the nodes belong, divides the same carbon feature units, optimizes the classification boundary, calculates the node carbon feature difference value, and obtains carbon feature unit distribution data. The correlation comparison generation submodule calls the carbon feature unit distribution data, identifies the correlation between nodes within the unit, establishes a node correlation model, identifies the connection between nodes through correlation analysis, and obtains node correlation comparison relationships.

7. The intelligent material lifecycle management platform according to claim 6, characterized in that: The running status mapping module includes: The node trend analysis submodule calls the node correlation and comparison relationship to analyze the node carbon emission fluctuations and anomaly trends, extracts time series data, and obtains the node trend change characteristics; The data correlation judgment submodule calls the node trend change characteristics to analyze the synchronicity between node carbon emission fluctuations and anomaly changes, calculates node data correlation, sets a data correlation threshold, filters node data correlation, removes nodes with low correlation, optimizes data matching relationships, and obtains node correlation filtering results. The operation status trend filtering submodule calls the node association filtering results, filters the trend change data of the associated nodes, identifies the magnitude of changes in the operation status of materials and equipment, and obtains the optimization results of the full life cycle management of materials.

8. The intelligent material lifecycle management platform according to claim 7, characterized in that: The node data correlation degree refers to the correlation coefficient when the synchronicity between the node's carbon emission fluctuation and the changes of the anomaly point exceeds the threshold in three consecutive time windows, and the node is judged as a node with high correlation degree. The set data correlation threshold means that when the correlation of node data is lower than the threshold, the corresponding node will be removed.

9. The intelligent material lifecycle management platform according to claim 1, characterized in that: The platform also includes a storage distribution adjustment module: Based on the optimization results of the full life cycle management of materials, the storage distribution adjustment module extracts the spatial distribution information of nodes, identifies the corresponding positions between nodes, divides storage areas according to distribution characteristics, adjusts the storage partition layout, and obtains a digital and intelligent material security storage solution. The digital and intelligent material security storage solution includes storage area division, storage partition layout adjustment, node spatial distribution information extraction, and node corresponding location identification.

10. The intelligent material lifecycle management platform according to claim 9, characterized in that: The storage distribution adjustment module includes: The spatial distribution extraction submodule calls the optimization results of the full life cycle management of materials, extracts the spatial distribution data of nodes, identifies the corresponding positions of nodes, and obtains the spatial distribution information of nodes; The storage area partitioning submodule calls the node spatial distribution information, identifies distribution characteristics based on the corresponding node locations, filters dense node areas, partitions storage areas, sets differentiated storage area boundaries, adjusts the node distribution balance, and obtains the storage area partitioning results. The partition layout adjustment submodule calls the storage area division results, adjusts the storage partition layout, optimizes the node storage data mapping, and obtains a digital and intelligent material security storage solution.

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