CN-121998543-A - Intelligent material warehouse management system based on digital twinning

CN121998543ACN 121998543 ACN121998543 ACN 121998543ACN-121998543-A

Abstract

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

Inventors

CAO RONGRONG
SUN MINGMING
NI SIYU
YANG KEKE
ZHAI ZHIMIN
DONG GUOQIANG
Wu Youpin
KAI XIANG

Assignees

国能神皖安庆发电有限责任公司

Dates

Publication Date: 20260508
Application Date: 20251203

Claims (10)

1. An intelligent material warehouse management system based on digital twinning is characterized by comprising: The sensing module is used for respectively collecting storage index parameters and risk index parameters in the history period of the target storage materials; The analysis module is connected with the perception module and used for analyzing the storage index characterization value based on the storage index parameter and analyzing the risk index characterization value based on the risk index parameter; The evaluation module is connected with the analysis module and is used for determining whether the target material warehouse meets the standard or not based on the difference result of the warehouse index characterization value and the preset warehouse index characterization threshold value; the prediction module is connected with the assessment module, responds to the condition that the target material warehouse meets the standard, and is used for determining whether the target material meets the risk assessment standard or not based on the comparison result of the risk index characterization value and the preset risk index characterization value; the regulation and control module is connected with the prediction module, and determines a corresponding processing strategy based on a difference result between the risk index representation value and a preset risk index representation threshold value in response to the target material risk assessment not meeting the standard; wherein, the storage index parameters comprise temperature, hydrogen sulfide gas concentration, use frequency and storage age; the risk index parameters include a rejection rate and a loss rate.
2. The digital twinning-based intelligent material warehouse management system of claim 1, wherein the warehouse index characterization value analyzed by the analysis module is determined based on a sum of a first feature definition characterization parameter, a second feature definition characterization parameter, a third feature definition characterization parameter, and a fourth feature definition characterization parameter, wherein, The first characteristic defining a characterization parameter as a ratio of temperature to a predetermined temperature threshold; The second characteristic defining characterization parameter is the ratio of the hydrogen sulfide gas concentration to a predetermined hydrogen sulfide gas concentration threshold; The third characteristic limiting characterization parameter is the ratio of the frequency of use to a predetermined frequency of use threshold; the fourth characteristic defining characterization parameter is a ratio of a library age to a predetermined library age threshold.
3. The digital twinning-based intelligent material storage management system of claim 2, wherein the evaluation module determines that the target material storage meets the criteria as the storage index characterization value differs from the predetermined storage index characterization threshold by less than a predetermined difference threshold.
4. The digital twinning-based intelligent material warehouse management system of claim 3, wherein the evaluation module determines that the target material warehouse does not meet the criteria is that a difference between the warehouse index characterization value and a predetermined warehouse index characterization threshold is greater than or equal to a predetermined difference threshold.
5. The digital twinning-based intelligent material warehouse management system of claim 4, wherein the analysis module analyzes that the risk indicator characterization value is determined based on a sum of a first risk defining characterization parameter and a second risk defining characterization parameter, wherein, The first risk limiting characterization parameter is the ratio of the rejection rate to a preset rejection rate threshold; The second risk definition characterization parameter is a ratio of a loss rate to a predetermined loss rate threshold.
6. The digital twinning-based intelligent material warehouse management system of claim 1, wherein the prediction module is configured to determine that the target material meets the risk assessment criteria if the risk indicator characterization value is less than a predetermined risk indicator characterization threshold.
7. The digital twinning-based intelligent material warehouse management system of claim 6, wherein the prediction module is configured to determine that the target material does not meet the risk assessment criterion based on a comparison of the risk indicator characterization value and a predetermined risk indicator characterization value is that the risk indicator characterization value is greater than or equal to a predetermined risk indicator characterization threshold.
8. The digital twinning-based intelligent material warehouse management system of claim 1, wherein the regulation module is configured to determine a corresponding processing strategy, including determining a regulation magnitude of a warehouse index characterization threshold.
9. The digital twinning-based intelligent material warehouse management system of claim 8, wherein the condition for adjusting the warehouse index characterization threshold amplitude is that a difference between the risk index characterization value and a predetermined risk index characterization threshold is greater than a predetermined difference threshold.
10. The digital twinning-based intelligent material warehouse management system of claim 9, wherein the system does not operate the regulation module under conditions that a difference between the warehouse index characterization value and a predetermined warehouse index characterization threshold is less than a predetermined difference threshold and the risk index characterization value is less than a predetermined risk index characterization threshold.

Description

Intelligent material warehouse management system based on digital twinning Technical Field The invention relates to the technical field of digital twinning, in particular to a digital twinning-based intelligent material warehouse management system. Background In the current digital twin application of the material warehouse of the power plant, the technical bottlenecks of insufficient model and data depth are commonly existed, various data are acquired, but environment, business and risk multidimensional data are still in an isolated state in the twin model, so that real unified fusion and comprehensive analysis are difficult to realize, the management function is mainly dependent on a simple rule engine to carry out binary judgment with a static threshold value, for example, only an overtemperature and an overtime are isolated and alarmed, the accumulation effect under the coupling effect of simulation multifactor cannot be quantized in the twin body, the gradual change process of the health of the material cannot be dynamically mapped, the analysis decision of the single-sided digital twin body is lack of prospective, and the passive alarm is often provided only after the material is substantially deteriorated, so that huge economic loss and operation and maintenance risk are caused. In the prior art, although the digital mapping of storage is preliminarily realized, the twin model generally lacks the capabilities of deep analysis, predictive early warning and self optimization. The functional modules of the system are disjoint from each other and cannot form an intelligent closed loop from state sensing, comprehensive evaluation to decision feedback. Chinese patent publication No. CN115983651A discloses a public warehouse system and method for intelligent storage and digital twinning, which comprises a multi-source data access module, an intelligent storage visual dynamic display module, a monitoring video stream display module, an early warning management and control module and a chart auxiliary decision-making module, wherein three-dimensional reconstruction is carried out on elements such as live-action, personnel, equipment, goods and warehouse positions in the storage Guan Lizhong warehouse, a warehouse three-dimensional scene is built for dynamic simulation, and the adjustment, scene switching and real-time monitoring of any angle can be carried out through multi-terminal release such as digital twinning large screens and computers, so that equipment visualization, material visualization and personnel visualization management are realized. The platform relies on a three-dimensional scene, storage materials are used as cores, storage management is used as a tie, a digital twin space is created for the public storage, and the public storage is enabled to be managed efficiently and operated intelligently by means of new technologies and intelligent means such as Internet of things, cloud computing, artificial intelligence, virtual reality and augmented reality, so that government asset management, storage management new modes and new changes are effectively promoted. Chinese patent publication No. CN120875750A discloses a warehouse management method for intelligent station emergency materials, which comprises the steps of obtaining production date, quality guarantee period and initial environment parameters by analyzing an emergency material electronic tag, generating a dynamic temperature suitable range based on nonlinear mapping of the quality guarantee period and an initial temperature value, generating a dynamic humidity suitable range based on a correlation model of the production date and the initial humidity value, calculating the coincidence degree of the residual date of the quality guarantee period and the temperature suitable range as a first priority factor, generating a second dynamic allocation factor in combination with the real-time load rate of a warehouse, integrating the second dynamic allocation factor through a priority fusion algorithm to generate a material allocation matrix, monitoring the environment parameters of a target warehouse in real time, triggering a multistage regulation strategy when the environment parameters exceed the suitable range, starting a parameter self-learning engine when verification fails, realizing intelligent regulation and optimization of the warehouse environment, and improving the storage safety and management efficiency of the emergency materials. The invention can dynamically adapt to the characteristic change of materials and optimize the control precision of the warehouse environment. It follows that the prior art has the following problems: In the digital twin application of the material warehouse management of the power plant in the prior art, the environment, the business dynamics and the history risks cannot be unified, quantized, fused and analyzed due to mutual isolation of multidimensional data, so that the problems of reduced decision a