Intelligent material warehouse management system based on digital twinning

Abstract

The invention relates to the technical field of digital twinning, in particular to an intelligent material storage management system based on digital twinning, and the system comprises a sensing module which is used for collecting storage index parameters and risk index parameters of target storage materials in a historical period; the analysis module is used for analyzing the storage index characterization value and the risk index characterization value; the evaluation module is used for determining whether target material storage meets a standard or not based on a difference result between the storage index characterization value and a preset storage index characterization threshold value; the prediction module is used for determining whether the target material meets a risk assessment standard based on a comparison result of the risk index characterization value and a predetermined risk index characterization value; the regulation and control module is used for determining a processing strategy based on a difference result between the risk indicator characterization value and a predetermined risk indicator characterization threshold value. According to the method, the risk assessment decision-making precision and efficiency of the digital twin technology are improved through adaptive regulation and control.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a smart material storage management system based on digital twins. Background Technology

[0002] Current applications of digital twins for power plant material storage suffer from technical bottlenecks due to insufficient model and data depth. While various types of data have been collected, environmental, operational, and risk-related multidimensional data remain isolated within the twin model, hindering true unified integration and comprehensive analysis. Management functions often rely on simple rule engines and static thresholds for binary judgments, such as isolated alarms for over-temperature or overdue items, failing to quantify the cumulative effects of multiple coupled factors within the twin or dynamically map the gradual changes in material health. This fragmented digital twin results in a lack of foresight in analysis and decision-making, often providing only passive alerts after substantial material deterioration, leading to significant economic losses and operational risks. Although existing technologies have achieved preliminary digital mapping of storage, twin models generally lack in-depth analysis, predictive warning, and self-optimization capabilities. The various functional modules of the system are disconnected, failing to form an intelligent closed loop from status perception and comprehensive assessment to decision feedback.

[0003] Chinese Patent Publication No. CN115983651A discloses a public warehouse system and method based on intelligent warehousing and digital twin technology. The system includes modules for multi-source data access, intelligent warehouse visualization and dynamic display, monitoring video stream display, early warning and control, and chart-based decision support. It constructs a three-dimensional warehouse scene by reconstructing elements such as the warehouse's internal environment, personnel, equipment, goods, and storage locations in warehousing management. Dynamic simulation is then performed and the simulation is published across multiple terminals, including digital twin screens and computers. The system allows for adjustments at any angle, scene switching, and real-time monitoring, achieving visualized management of equipment, materials, and personnel. Based on a three-dimensional scene, with warehouse materials as the core and warehousing management as the link, this platform creates a digital twin space for public warehouses. Utilizing new technologies and intelligent methods such as the Internet of Things, cloud computing, artificial intelligence, virtual reality, and augmented reality, it empowers efficient management and intelligent operation of public warehouses, effectively promoting new models and transformations in government asset management and warehousing management.

[0004] Chinese Patent Publication No. CN120875750A discloses a warehouse management method for emergency supplies in smart facilities. This method obtains production date, shelf life, and initial environmental parameters by parsing electronic tags on emergency supplies. A dynamic suitable temperature range is generated based on a nonlinear mapping between shelf life and initial temperature value, and a dynamic suitable humidity range is generated based on a correlation model between production date and initial humidity value. The overlap between the remaining shelf life and the suitable temperature range is calculated as a first priority factor, combined with the real-time warehouse load rate to generate a second dynamic allocation factor. A priority fusion algorithm is used to integrate and generate a material allocation matrix. The method monitors the target warehouse environmental parameters in real time, triggering multi-level adjustment strategies when parameters exceed the suitable range. If verification fails, a parameter self-learning engine is activated, achieving intelligent control and optimization of the warehouse environment, improving the safety and management efficiency of emergency supplies storage. This invention can dynamically adapt to changes in material characteristics and optimize the precision of warehouse environment control.

[0005] Therefore, it is evident that the existing technology has the following problems: In existing digital twin applications for power plant material storage management, the isolation of multidimensional data makes it impossible to uniformly quantify, integrate, and analyze environmental, business dynamics, and historical risks, resulting in a decline in the accuracy and efficiency of risk assessment and decision-making in digital twin technology. Summary of the Invention

[0006] To address this, the present invention provides a smart material warehousing management system based on digital twins, which overcomes the problem in existing digital twin applications of power plant material warehousing management where the isolation of multidimensional data makes it impossible to uniformly quantify, integrate, and analyze environmental, business dynamics, and historical risks, leading to a decrease in the accuracy and efficiency of risk assessment decisions using digital twin technology.

[0007] To achieve the aforementioned objective, the present invention provides a smart material warehousing management system based on digital twins, comprising: The sensing module is used to collect storage index parameters and risk index parameters of the target stored materials over a historical period. Analysis module: It is connected to the perception module and is used to analyze the characteristic values ​​of warehousing indicators based on warehousing indicator parameters and to analyze the characteristic values ​​of risk indicators based on risk indicator parameters. Evaluation module: It is connected to the analysis module and is used to determine whether the storage of target materials meets the standards based on the difference between the storage index characterization value and the predetermined storage index characterization threshold. Prediction module: It is connected to the assessment module. In response to the target material storage meeting the standards, it is used to determine whether the target material meets the risk assessment standards based on the comparison results of the risk indicator characterization value and the predetermined risk indicator characterization value. Control module: It is connected to the prediction module. In response to the failure of the risk assessment of the target material to meet the standard, it determines the handling strategy based on the difference between the risk indicator characterization value and the predetermined risk indicator characterization threshold. The storage index parameters include temperature, hydrogen sulfide gas concentration, usage frequency, and storage age. The risk indicator parameters include scrap rate and loss rate.

[0008] Furthermore, the analysis module determines the warehousing indicator characterization value based on the sum of the first characteristic-limited characterization parameter, the second characteristic-limited characterization parameter, the third characteristic-limited characterization parameter, and the fourth characteristic-limited characterization parameter, wherein, The first feature defines the characterization parameter as the ratio of temperature to a predetermined temperature threshold. The second feature defines the characterization parameter as the ratio of hydrogen sulfide gas concentration to a predetermined hydrogen sulfide gas concentration threshold. The third feature defines the characterization parameter as the ratio of the usage frequency to a predetermined usage frequency threshold; The fourth feature defines the characterization parameter as the ratio of the storage age to a predetermined storage age threshold.

[0009] Furthermore, the evaluation module determines that the condition for the target material storage to meet the standard is that the difference between the storage indicator value and the predetermined storage indicator threshold is less than the predetermined difference threshold.

[0010] Furthermore, the evaluation module determines that the condition for non-compliance of the target material storage is that the difference between the storage indicator value and the predetermined storage indicator threshold is greater than or equal to the predetermined difference threshold.

[0011] Furthermore, the analysis module determines the risk indicator characterization value based on the sum of the first risk-limiting characterization parameter and the second risk-limiting characterization parameter, wherein, The first risk limitation parameter is the ratio of the scrap rate to a predetermined scrap rate threshold; The second risk limit characterization parameter is the ratio of the loss rate to a predetermined loss rate threshold.

[0012] Furthermore, the prediction module determines that the target material meets the risk assessment criteria when the risk indicator characterization value is less than the predetermined risk indicator characterization threshold.

[0013] Furthermore, the prediction module determines that the condition for the target material not to meet the risk assessment standard is that the risk indicator characterization value is greater than or equal to the predetermined risk indicator characterization threshold.

[0014] Furthermore, the processing strategy of the control module is to adjust the threshold range of the warehousing indicator.

[0015] Furthermore, the condition for the control module to adjust the threshold range of the warehousing indicator is that the difference between the risk indicator value and the predetermined risk indicator threshold is greater than the predetermined difference threshold.

[0016] Furthermore, the system does not operate the control module under the following conditions: the difference between the storage indicator characterization value and the predetermined storage indicator characterization threshold is less than the predetermined difference threshold, and the risk indicator characterization value is less than the predetermined risk indicator characterization threshold.

[0017] Compared with existing technologies, the beneficial effects of this invention are that it provides a smart material storage management system based on digital twins. By constructing a sensing module, analysis module, evaluation module, prediction module, and control module, a complete intelligent closed loop is formed, from physical sensing to virtual space simulation, and then driving physical execution. The sensing module, acting as a bridge between physical and digital spaces, automatically collects multi-dimensional data such as temperature, hydrogen sulfide concentration, usage frequency, and storage age, constructing a real-time data foundation for the digital twin and achieving a high-fidelity digital mapping of the physical storage environment. The analysis module, relying on the data fusion capabilities of the digital twin, transforms isolated parameters into representational values ​​with clear physical meaning, realizing an intelligent transformation from discrete data to quantifiable insights. The evaluation and prediction modules are based on simulations of the digital twin model. The simulation function has achieved a leap from static threshold judgment to dynamic health prediction, and can quantify and simulate the cumulative effect and gradual process under the coupling of multiple factors, significantly improving the foresight and accuracy of status assessment. The control module, through the virtual-real interaction mechanism of the digital twin, forms an intelligent closed loop of perception, simulation, decision-making and feedback, upgrading management decision-making from passive response to proactive intervention based on model simulation. Based on the complete technology chain of digital twin, it not only realizes precise control of the entire life cycle of materials, but also enables the system to have continuously evolving intelligent decision-making capabilities through continuous data accumulation and model self-learning, ultimately improving management accuracy, risk prevention and control and operational efficiency.

[0018] In particular, by using the analysis module to calculate the normalized ratios of key warehousing indicators such as temperature, hydrogen sulfide gas concentration, usage frequency, and storage age, as well as risk indicators such as scrap rate and loss rate, and synthesizing them into a single characterization value, the unified quantitative fusion and cross-dimensional correlation analysis of multi-source heterogeneous data are realized within the digital twin. This not only transforms isolated environmental, business, and historical data into characteristic parameters that can accurately reflect the comprehensive load status of materials, but also provides standardized and calculable input basis for the dynamic simulation and trend prediction of the digital twin. This significantly improves the accuracy of status assessment and the foresight of risk warning, realizing a fundamental shift from static data comparison to dynamic intelligent decision-making.

[0019] In particular, by introducing digital twin technology into the material storage management system, a precise and dynamic decision-making mechanism has been constructed. The evaluation module compares the difference between the comprehensively calculated storage index values ​​and predetermined thresholds, achieving a refined classification of the material storage status. This overcomes the limitations of traditional single-threshold judgments and makes the status evaluation results more hierarchical and operable. After the evaluation is passed, the prediction module makes a forward-looking judgment based on the risk index values ​​derived from the simulation of the digital twin model. By directly comparing the simulation results with the risk thresholds, it achieves clear classification and early identification of potential risks. Based on the judgment logic of quantitative index values ​​and dynamic thresholds, a continuous and closed-loop intelligent decision-making chain from current status evaluation to future risk prediction is formed in the digital twin, significantly improving the scientific nature and accuracy of management decisions and the automation level of system response.

[0020] In particular, a model-based adaptive control mechanism was implemented using digital twin technology. The control module dynamically determines the processing strategy based on the difference between the risk indicator representation value and the threshold, and directly affects the adjustment of the warehousing indicator representation threshold. A closed-loop control link of risk prediction, strategy generation, and parameter adjustment was established in the digital twin. When the risk deviation exceeds the predetermined threshold, the system actively intervenes in the material status by increasing the warehousing indicator representation value. When both warehousing and risk indicators meet the safety threshold, the system intelligently maintains its operating status. The precise control mechanism based on the digital twin model ensures the system's ability to respond quickly to risks and actively intervene, while avoiding unnecessary control operations. This achieves resource optimization and energy saving, ultimately constructing a smart warehousing management system that can both prevent problems before they occur and optimize operational efficiency. Attached Figure Description

[0021] Figure 1 This is an architecture diagram of a smart material warehousing management system based on digital twins, as described in an embodiment of the present invention. Figure 2 This is a flowchart illustrating the steps of intelligent material warehousing management based on digital twins in an embodiment of the present invention. Figure 3 This invention provides a logic diagram for determining whether the storage of target materials meets the standards. Figure 4 This invention provides a logic diagram for determining whether a target material meets risk assessment criteria. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] Please see Figure 1 As shown, it is an architecture diagram of a smart material warehousing management system based on digital twins according to an embodiment of the present invention, including: The sensing module is used to collect storage index parameters and risk index parameters of the target stored materials over a historical period. Analysis module: It is connected to the perception module and is used to analyze the characteristic values ​​of warehousing indicators based on warehousing indicator parameters and to analyze the characteristic values ​​of risk indicators based on risk indicator parameters. Evaluation module: It is connected to the analysis module and is used to determine whether the storage of target materials meets the standards based on the difference between the storage index characterization value and the predetermined storage index characterization threshold. Prediction module: It is connected to the assessment module. In response to the target material storage meeting the standards, it is used to determine whether the target material meets the risk assessment standards based on the comparison results of the risk indicator characterization value and the predetermined risk indicator characterization value. Control module: It is connected to the prediction module. In response to the target material risk assessment not meeting the standards, it determines the handling strategy based on the difference between the risk indicator characterization value and the predetermined risk indicator characterization threshold. The storage index parameters include temperature, hydrogen sulfide gas concentration, usage frequency, and storage age. The risk indicator parameters include scrap rate and loss rate.

[0025] In this embodiment, by constructing a perception module, an analysis module, an evaluation module, a prediction module, and a control module, a complete intelligent closed loop is formed, from physical space perception to virtual space simulation, and then driving physical space execution. The perception module, as the core data interface between the physical storage space and the digital twin, automatically collects multi-dimensional data such as temperature, hydrogen sulfide concentration, usage frequency, and storage age, constructing the real-time data foundation for the digital twin and achieving a high-fidelity digital mapping of the physical storage environment. The analysis module, relying on the data fusion capabilities of the digital twin, transforms isolated parameters into representative values ​​with clear physical meaning, realizing the intelligent transformation from discrete data to quantifiable insights. The evaluation and prediction modules are based on… The simulation and deduction function of the digital twin model has achieved a leap from static threshold judgment to dynamic health prediction. It can quantify and simulate the cumulative effect and gradual process under the coupling of multiple factors, significantly improving the foresight and accuracy of status assessment. The control module, through the virtual-real interaction mechanism of the digital twin, forms an intelligent closed loop of perception, simulation, decision-making, and feedback, upgrading management decision-making from passive response to proactive intervention based on model simulation. Based on the complete technology chain of digital twin, it not only realizes precise control of the entire life cycle of materials, but also enables the system to have continuously evolving intelligent decision-making capabilities through continuous data accumulation and model self-learning, ultimately improving management accuracy, risk prevention and control, and operational efficiency.

[0026] Please see Figure 2 The diagram shown is a flowchart illustrating the steps of intelligent material warehousing management based on digital twins according to an embodiment of the present invention, including: Step S1: Collect the storage index parameters of the target material within the historical period; Step S2: Analyze the characteristic values ​​of the warehousing indicators based on the warehousing indicator parameters; Step S3: Determine whether the storage of the target material meets the standard based on the difference between the storage index characterization value and the predetermined storage index characterization threshold. Step S4: In response to the target material warehousing meeting the standards, collect risk indicator parameters of the target material within the historical period; analyze the risk indicator characterization value based on the risk indicator parameters; Step S5: Determine whether the target material meets the risk assessment standard based on the comparison result between the risk indicator characterization value and the predetermined risk indicator characterization threshold. Step S6: In response to the target material risk assessment not meeting the standard, a corresponding processing strategy is determined based on the difference between the risk indicator characterization value and the predetermined risk indicator characterization threshold, and the adjustment range of the warehousing indicator characterization threshold is determined.

[0027] In this embodiment, a quantitative assessment system for warehouse status based on difference analysis is established. Then, a strict logical mechanism is adopted to initiate the risk analysis process only when the warehouse status assessment meets the standards, ensuring the optimal allocation of system resources. Finally, a control strategy is generated based on the quantification of risk deviation, and closed-loop control is achieved through the adjustment of the characterization value. The process architecture realizes full-link controllable management from status perception to decision execution through the causal relationship and condition constraints of each link, ensuring a significant improvement in the system's accuracy, efficiency and reliability.

[0028] Specifically, the analysis module determines the warehousing indicator characterization value based on the sum of the first characteristic-limited characterization parameter, the second characteristic-limited characterization parameter, the third characteristic-limited characterization parameter, and the fourth characteristic-limited characterization parameter, wherein, The first feature defines the characterization parameter as the ratio of temperature to a predetermined temperature threshold. The second feature defines the characterization parameter as the ratio of hydrogen sulfide gas concentration to a predetermined hydrogen sulfide gas concentration threshold. The third feature defines the characterization parameter as the ratio of the usage frequency to a predetermined usage frequency threshold; The fourth feature defines the characterization parameter as the ratio of the storage age to a predetermined storage age threshold.

[0029] In this embodiment, the predetermined temperature threshold, hydrogen sulfide gas concentration threshold, usage frequency threshold, and storage age threshold are all obtained in advance. The benchmark reference values ​​are derived based on the statistical analysis of historical operation and maintenance data, and the safety boundaries are set in combination with industry standard and specification requirements. The parameters are calibrated through expert experience evaluation.

[0030] In the embodiments, in the power plant material storage management based on digital twins, four core parameters—temperature, hydrogen sulfide gas concentration, usage frequency, and storage age—form the data foundation for achieving a dual improvement in the accuracy and efficiency of storage management. The digital twin system, through deep integration of multi-dimensional data, accurately depicts the impact of all factors in a virtual model, from environmental conditions and chemical corrosion risks to business dynamics and time decay. This upgrades management from static threshold judgment to dynamic prediction and quantitative assessment of the gradual change in material health status and the cumulative effects of multiple coupled factors, greatly improving the accuracy of status perception and risk warning. Based on deep analysis, the system constructs an intelligent closed loop of perception, simulation, decision-making, and feedback, automatically driving the execution of inventory optimization, layout adjustments, and proactive repair strategies. This achieves a fundamental shift from relying on human experience to automated process decision-making, ultimately improving operational efficiency in areas such as inventory turnover, risk avoidance, and resource allocation.

[0031] In this embodiment, a comprehensive evaluation model based on multi-dimensional quantitative ratios is established to achieve accurate characterization and unified measurement of the material storage status. The model calculates normalized ratios of four key parameters—temperature, hydrogen sulfide gas concentration, usage frequency, and storage age—to their predetermined thresholds, and synthesizes a unified storage index characterization value through weighted summation. This solves the standardization problem of multi-source heterogeneous data, enabling the quantitative fusion and comprehensive evaluation of environmental, business, and time parameters with different dimensions within a unified mathematical framework. This analytical method not only accurately characterizes the deviation of each parameter from the safety threshold but also provides comprehensive and calculable decision-making basis for subsequent evaluation modules through multi-dimensional feature fusion, laying a precise data analysis foundation for building a high-fidelity digital twin model.

[0032] Please see Figure 3 As shown, it is a logic diagram for determining whether the storage of target materials meets the standards in an embodiment of the present invention, including: Calculate the difference between the warehousing indicator value and the predetermined warehousing indicator threshold; If the difference between the storage indicator value and the predetermined storage indicator threshold is less than the predetermined difference threshold, then the storage of the target material is determined to meet the standard. If the difference between the storage indicator value and the predetermined storage indicator threshold is greater than or equal to the predetermined difference threshold, then the storage of the target material is determined to be non-compliant with the standard.

[0033] In this embodiment, the predetermined storage index characterization threshold is obtained in advance. All storage index characterization values ​​of the target material within 3 months of storage are calculated, and their average value is determined as the storage index characterization threshold. The predetermined storage index characterization threshold in this embodiment is selected within the range [4.05, 4.35]. Preferably, the predetermined storage index characterization threshold in this embodiment is 4.15. By setting the storage index characterization threshold and comparing and judging, an objective and unified anomaly judgment standard is established.

[0034] In this embodiment, the predetermined difference threshold is obtained in advance. The difference between the storage index characterization value of the target material within 3 months and the predetermined storage index characterization threshold is calculated, and the average value is calculated. In this embodiment, the predetermined difference threshold is selected within the range [0.05, 0.35]. Preferably, the predetermined difference threshold is 0.15.

[0035] In this embodiment, by introducing digital twin technology into the material storage management system, a precise and dynamic decision-making mechanism is constructed. The evaluation module compares the difference between the comprehensive calculated storage index characterization value and the predetermined threshold, thereby realizing the refined classification and judgment of the material storage status. This overcomes the limitations of traditional single threshold judgment and makes the status evaluation results more hierarchical and operable.

[0036] Specifically, the analysis module determines the risk indicator representation value based on the sum of a first risk-limiting representation parameter and a second risk-limiting representation parameter, wherein, The first risk limitation parameter is the ratio of the scrap rate to a predetermined scrap rate threshold; The second risk limit characterization parameter is the ratio of the loss rate to a predetermined loss rate threshold.

[0037] In this embodiment, the predetermined scrap rate threshold and loss rate threshold are both obtained in advance. The benchmark values ​​are determined based on the statistical analysis of historical material scrapping data and operational loss data, and a reasonable range is set with reference to industry standards and specifications. The benchmark values ​​are also comprehensively calibrated in combination with the requirements of operating procedures and expert experience.

[0038] In this embodiment, the formula for calculating the scrap rate is: Scrap rate = Quantity of scrapped materials during the period / Total inventory at the beginning of the period In this embodiment, the formula for calculating the wear rate is: Loss rate = Value loss of materials during the period / Total value of materials inventory at the beginning of the period In this embodiment, the analysis module calculates the normalized ratios of key warehousing indicators such as temperature, hydrogen sulfide gas concentration, usage frequency, and storage age, as well as risk indicators such as scrap rate and loss rate, with their respective predetermined thresholds and synthesizes them into a single characterization value. This achieves unified quantitative fusion and cross-dimensional correlation analysis of multi-source heterogeneous data within the digital twin. It not only transforms isolated environmental, business, and historical data into characteristic parameters that can accurately reflect the comprehensive load status of materials, but also provides standardized and calculable input basis for the dynamic simulation and trend prediction of the digital twin. This significantly improves the accuracy of status assessment and the foresight of risk warning, realizing a fundamental shift from static data comparison to dynamic intelligent decision-making.

[0039] Please see Figure 4 As shown, it is a logic diagram for determining whether a target material meets the risk assessment criteria in an embodiment of the present invention, including: Extract the comparison results between the risk indicator characterization values ​​and the predetermined risk indicator characterization thresholds; If the risk indicator value is less than the predetermined risk indicator threshold, then the target material is determined to meet the risk assessment standard. If the risk indicator value is greater than or equal to the predetermined risk indicator threshold, then the target material is determined to be non-compliant with the risk assessment standard.

[0040] In this embodiment, the predetermined risk indicator characterization threshold is obtained in advance. All risk indicator characterization values ​​within 3 months of the target material storage are calculated, and their average value is calculated as the risk indicator characterization threshold. The predetermined risk indicator characterization threshold in this embodiment is selected within the range [2.05, 2.35]. Preferably, the predetermined risk indicator characterization threshold in this embodiment is 2.15.

[0041] In this embodiment, after the evaluation is passed, the prediction module makes a forward-looking judgment based on the risk indicator characterization value derived from the simulation of the digital twin model. By directly comparing the simulation results with the risk threshold, it realizes the clear classification and early identification of potential risks. Based on the judgment logic of quantitative characterization value and dynamic threshold, a continuous and closed-loop intelligent decision-making chain from current state assessment to future risk prediction is formed in the digital twin, which significantly improves the scientific nature and accuracy of management decisions and the automation level of system response.

[0042] Specifically, the processing strategy of the control module is to adjust the threshold range of the storage index.

[0043] In this embodiment, a model-based adaptive control mechanism is implemented using digital twin technology. The control module dynamically determines the processing strategy based on the difference between the risk indicator representation value and the threshold, and directly affects the adjustment of the storage indicator representation value. A closed-loop control link of risk prediction, strategy generation, and parameter adjustment is established in the digital twin. When the risk deviation exceeds the predetermined threshold, the system actively intervenes in the material status by increasing the storage indicator representation value. When both storage and risk indicators meet the safety threshold, the system intelligently maintains its operating status. The precise control mechanism based on the digital twin model ensures the system's rapid risk response and proactive intervention capabilities while avoiding unnecessary control operations, achieving resource optimization and energy saving. Ultimately, it constructs a smart warehouse management system that can both prevent problems before they occur and optimize operational efficiency.

[0044] Specifically, the condition for the control module to adjust the threshold range of the storage indicator is that the difference between the risk indicator value and the predetermined risk indicator threshold is greater than the predetermined difference threshold.

[0045] In this embodiment, the predetermined difference threshold is obtained in advance. The difference between the risk indicator characterization value of the target material storage within 3 months and the predetermined risk indicator characterization threshold is calculated, and the average value is calculated as the predetermined difference threshold. In this embodiment, the predetermined difference threshold is selected within the range [0.05, 0.35]. Preferably, the predetermined difference threshold is 0.15.

[0046] In this embodiment, when the degree of risk deviation exceeds a predetermined threshold, the system proactively intervenes in the status of materials by increasing the value of the warehousing indicator. The precise control mechanism based on the digital twin model ensures the system's ability to respond quickly to risks and proactively intervene, while avoiding unnecessary control operations. This achieves resource optimization and energy conservation, ultimately constructing a smart warehousing management system that can both prevent problems before they occur and optimize operational efficiency.

[0047] Specifically, the system does not operate the control module when the difference between the storage index characterization value and the predetermined storage index characterization threshold is less than the predetermined difference threshold and the risk index characterization value is less than the predetermined risk index characterization threshold.

[0048] In this embodiment, the predetermined difference threshold is obtained in advance. The difference between the storage index characterization value of the target material within 3 months and the predetermined storage index characterization threshold is calculated, and the average value is calculated. In this embodiment, the predetermined difference threshold is selected within the range [0.05, 0.35]. Preferably, the predetermined difference threshold is 0.15.

[0049] In this embodiment, the predetermined risk indicator characterization threshold is obtained in advance. All risk indicator characterization values ​​within 3 months of the target material storage are calculated, and their average value is calculated as the risk indicator characterization threshold. The predetermined risk indicator characterization threshold in this embodiment is selected within the range [2.05, 2.35]. Preferably, the predetermined risk indicator characterization threshold in this embodiment is 2.15.

[0050] In this embodiment, when both the warehousing and risk indicators meet the safety threshold, the system intelligently maintains its operating status. The precise control mechanism based on the digital twin model ensures both rapid response and proactive intervention capabilities for system risks, while avoiding unnecessary control operations. This achieves resource optimization and energy conservation, ultimately constructing a smart warehouse management system that can both prevent problems before they occur and optimize operational efficiency.

[0051] The technical solution of the present invention has been described in conjunction with the embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to the specific implementation methods of the embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A smart material warehousing management system based on digital twins, characterized in that, include: The sensing module is used to collect storage index parameters and risk index parameters of the target stored materials over a historical period. Analysis module: It is connected to the perception module and is used to analyze the characteristic values ​​of warehousing indicators based on warehousing indicator parameters and to analyze the characteristic values ​​of risk indicators based on risk indicator parameters. Evaluation module: It is connected to the analysis module and is used to determine whether the storage of target materials meets the standards based on the difference between the storage index characterization value and the predetermined storage index characterization threshold. Prediction module: It is connected to the assessment module. In response to the target material storage meeting the standards, it is used to determine whether the target material meets the risk assessment standards based on the comparison results of the risk indicator characterization value and the predetermined risk indicator characterization value. Control module: It is connected to the prediction module. In response to the target material risk assessment not meeting the standards, it determines the corresponding handling strategy based on the difference between the risk indicator characterization value and the predetermined risk indicator characterization threshold. The storage index parameters include temperature, hydrogen sulfide gas concentration, usage frequency, and storage age. The risk indicator parameters include scrap rate and loss rate.

2. The intelligent material warehousing management system based on digital twins according to claim 1, characterized in that, The warehousing indicator values ​​analyzed by the analysis module are determined based on the sum of the first characteristic-limited characterization parameter, the second characteristic-limited characterization parameter, the third characteristic-limited characterization parameter, and the fourth characteristic-limited characterization parameter, wherein, The first feature defines the characterization parameter as the ratio of temperature to a predetermined temperature threshold. The second feature defines the characterization parameter as the ratio of hydrogen sulfide gas concentration to a predetermined hydrogen sulfide gas concentration threshold. The third feature defines the characterization parameter as the ratio of the usage frequency to a predetermined usage frequency threshold; The fourth feature defines the characterization parameter as the ratio of the storage age to a predetermined storage age threshold.

3. The intelligent material warehousing management system based on digital twins according to claim 2, characterized in that, The evaluation module determines that the target material storage meets the standard if the difference between the storage indicator value and the predetermined storage indicator threshold is less than the predetermined difference threshold.

4. The intelligent material warehousing management system based on digital twins according to claim 3, characterized in that, The evaluation module determines that the target material storage does not meet the standard when the difference between the storage indicator value and the predetermined storage indicator threshold is greater than or equal to the predetermined difference threshold.

5. The intelligent material warehousing management system based on digital twins according to claim 4, characterized in that, The analysis module determines the risk indicator characterization value based on the sum of the first risk-limiting characterization parameter and the second risk-limiting characterization parameter, wherein, The first risk limitation parameter is the ratio of the scrap rate to a predetermined scrap rate threshold; The second risk limit characterization parameter is the ratio of the loss rate to a predetermined loss rate threshold.

6. The intelligent material warehousing management system based on digital twins according to claim 1, characterized in that, The prediction module is used to determine whether a target material meets the risk assessment criteria if the risk indicator value is less than a predetermined risk indicator threshold.

7. The intelligent material warehousing management system based on digital twins according to claim 6, characterized in that, The prediction module is used to determine the condition that the target material does not meet the risk assessment standard based on the comparison result between the risk indicator characterization value and the predetermined risk indicator characterization value. The condition is that the risk indicator characterization value is greater than or equal to the predetermined risk indicator characterization threshold.

8. The intelligent material warehousing management system based on digital twins according to claim 1, characterized in that, The control module is used to determine the corresponding processing strategy, including determining the adjustment range of the threshold representing the storage index.

9. The intelligent material warehousing management system based on digital twins according to claim 8, characterized in that, The control module adjusts the threshold range of the warehousing indicator representation under the condition that the difference between the risk indicator representation value and the predetermined risk indicator representation threshold is greater than the predetermined difference threshold.

10. The intelligent material warehousing management system based on digital twins according to claim 9, characterized in that, The system does not operate the control module when the difference between the storage index characterization value and the predetermined storage index characterization threshold is less than the predetermined difference threshold and the risk index characterization value is less than the predetermined risk index characterization threshold.

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