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CN-121119884-B - Service data processing method, system and terminal based on digital twin

CN121119884BCN 121119884 BCN121119884 BCN 121119884BCN-121119884-B

Abstract

The application relates to a service data processing method, a system and a terminal based on digital twinning, belonging to the technical field of service data processing; synchronizing the multisource sensing data into a virtual refrigeration house model, calculating a comprehensive risk value, wherein the virtual refrigeration house model is a digital twin model constructed according to a physical refrigeration house, judging whether the comprehensive risk value is larger than a risk threshold, if so, activating root cause diagnosis, locating a problem root from a pre-constructed causal rule base, calling an optimization strategy according to the located problem root, simulating in the virtual refrigeration house model according to the optimization strategy to generate a simulation value, judging whether the simulation value is larger than the simulation threshold, and if so, regulating and controlling the physical refrigeration house according to the optimization strategy. The application has the beneficial effect of improving the root cause diagnosis accuracy.

Inventors

  • WANG XI

Assignees

  • 北京流金岁月科技有限公司

Dates

Publication Date
20260508
Application Date
20250826

Claims (6)

  1. 1. A business data processing method based on digital twinning, which is characterized by comprising the following steps: Collecting multisource sensing data of a physical refrigeration house; Synchronizing the multisource sensing data into a virtual refrigeration house model, and calculating a comprehensive risk value, wherein the virtual refrigeration house model is a digital twin model constructed according to the physical refrigeration house; Judging whether the comprehensive risk value is larger than a risk threshold value or not; If yes, root cause diagnosis is activated, and a problem root is positioned from a pre-constructed causal rule base; calling an optimization strategy according to the positioned problem root; Simulating in the virtual refrigeration house model according to the optimization strategy to generate a simulation value; judging whether the simulation value is larger than a simulation threshold value or not; If yes, regulating and controlling the physical refrigeration house according to the optimization strategy; If not, calling a standby optimization strategy under the root of the problem; Continuously simulating the virtual refrigeration house model according to a standby optimization strategy to generate a standby simulation value; judging whether the standby simulation value is smaller than the simulation threshold value or not; If not, regulating and controlling the physical refrigeration house according to the standby optimization strategy; if yes, the user requirements and the refrigerator setting requirements are called; Analyzing the user requirements and the refrigerator setting requirements, and generating optimization indexes by combining historical regulation and control data of the refrigerator; according to the optimization index, the optimization strategy is adjusted, and an adjustment optimization strategy is generated; Continuously simulating the virtual refrigeration house model according to the adjustment optimization strategy to generate an adjustment simulation value; judging whether the adjustment simulation value is within the simulation threshold fluctuation range or not; If yes, regulating and controlling the physical refrigeration house according to the regulation and optimization strategy; if not, outputting early warning information; according to the problem source of the positioning, the step of calling the optimization strategy comprises the following steps: Matching a basic optimization strategy from a causal rule base according to the type of the problem root; acquiring current operation parameters of a physical refrigeration house; Performing parameter adjustment on the basic optimization strategy based on the current operation parameters to generate a personalized optimization strategy; Calculating implementation cost of the personalized optimization strategy; and if the implementation cost is smaller than a cost threshold, determining to call the personalized optimization strategy.
  2. 2. The method for processing business data based on digital twinning according to claim 1, wherein the step of calculating the integrated risk value comprises: normalizing the multisource sensing data, and mapping the multisource sensing data to a [0,1] interval; distributing corresponding weight factors for the multi-source sensing data according to the business rules; And calculating a comprehensive risk value according to the multi-source sensing data and the corresponding weight factors.
  3. 3. The method for processing service data based on digital twinning according to claim 1, wherein the step of collecting the multisource sensing data of the physical refrigerator comprises: initial sensing data detected by each sensor arranged in a physical refrigeration house are collected; monitoring the data acquisition state of each sensor arranged in the physical refrigeration house in real time; calculating a corresponding data confidence based on the device health, the historical error rate and the signal stability; Triggering a data compensation mechanism of a neighboring space redundant sensor when the data confidence of a certain sensor is detected to be lower than a set threshold value; and if the redundant sensor does not exist, performing cross-dimension correction on the abnormal data of the certain sensor by adopting time sequence correlation analysis, so that the corrected sensing data replaces the initial sensing data.
  4. 4. The method for processing service data based on digital twinning according to claim 1, wherein the step of constructing the causal rule base includes: collecting historical fault data of a physical refrigeration house and corresponding multi-source sensing data; performing causal relation mining on the historical fault data and the multi-source sensing data by adopting a Bayesian network to generate a primary causal rule; correcting the preliminary causal rule through expert knowledge to form a candidate causal rule; Performing simulation verification on the candidate causal rules in a virtual refrigeration house model, and calculating rule accuracy; and if the rule accuracy is greater than a preset accuracy threshold, storing the candidate causal rule into a causal rule library.
  5. 5. A digital twin based service data processing system, wherein a digital twin based service data processing method according to any of claims 1-4 is performed, comprising: the data acquisition module is used for acquiring multi-source sensing data of the physical refrigeration house; The data synchronization module is used for synchronizing the multi-source sensing data into a virtual refrigeration house model, wherein the virtual refrigeration house model is a digital twin model constructed according to the physical refrigeration house; The data processing module is used for calculating a comprehensive risk value and judging whether the comprehensive risk value is larger than a risk threshold value or not; The problem positioning module is used for activating root cause diagnosis and positioning a problem root cause from a pre-constructed causal rule base when the comprehensive risk value is greater than a risk threshold; the digital twin module is internally provided with a virtual refrigeration house model and is used for calling an optimization strategy according to a positioning problem source, simulating according to the optimization strategy to generate a simulation value, judging whether the simulation value is larger than a simulation threshold value or not, and outputting a regulation and control instruction if the simulation value is larger than the simulation threshold value; and the regulation and control module responds to the regulation and control instruction and is used for regulating and controlling the physical refrigeration house according to the optimization strategy.
  6. 6. A terminal, comprising: a memory storing a service data processing program based on digital twinning; A processor for executing a program stored on said memory to implement the steps of the digital twin based business data processing method as claimed in any one of claims 1 to 4.

Description

Service data processing method, system and terminal based on digital twin Technical Field The present application relates to the technical field of service data processing, and in particular, to a service data processing method, system and terminal based on digital twin. Background With the rapid development of industries such as global fresh electricity suppliers, medicine cold chains and the like, a cold storage is used as a core infrastructure of low-temperature storage, and the operation efficiency, the energy consumption control and the cargo safety guarantee of the cold storage become key for enterprise operation. Traditional freezer management relies on manual inspection and experience judgment, has the problems of lag in data acquisition, untimely risk early warning and the like, and is difficult to meet the requirements of modern cold chains on high-precision environmental control and intelligent operation and maintenance. At present, technologies such as the Internet of things, big data and the like are gradually introduced in the industry to promote the management level of the refrigeration house. The main flow method comprises the steps of acquiring real-time data through deployment of temperature and humidity sensors, energy consumption monitoring equipment and the like, transmitting the real-time data to a cloud platform for storage and visual display, constructing a static risk assessment model based on historical data, periodically analyzing the running state of a refrigerator, and triggering a manual investigation or predefined alarm flow when abnormal data are monitored. Although the scheme realizes preliminary digitization of data, the method still has the obvious technical defects that the traditional data processing is in a multi-dependent batch processing mode, the processing capability of real-time stream data is insufficient, the monitoring requirement of a freezer on millisecond-level change of environmental parameters cannot be met, the risk assessment model is mainly static rules (such as fixed threshold judgment), and dynamic association analysis of multi-source data is lacked, so that the situation of misjudgment is often caused due to low diagnosis accuracy. Disclosure of Invention In order to improve the accuracy of root cause diagnosis, the application provides a business data processing method, a system and a terminal based on digital twinning. In a first aspect, the present application provides a service data processing method based on digital twin, which adopts the following technical scheme: a business data processing method based on digital twinning comprises the following steps: Collecting multisource sensing data of a physical refrigeration house; Synchronizing the multisource sensing data into a virtual refrigeration house model, and calculating a comprehensive risk value, wherein the virtual refrigeration house model is a digital twin model constructed according to the physical refrigeration house; Judging whether the comprehensive risk value is larger than a risk threshold value or not; If yes, root cause diagnosis is activated, and a problem root is positioned from a pre-constructed causal rule base; calling an optimization strategy according to the positioned problem root; Simulating in the virtual refrigeration house model according to the optimization strategy to generate a simulation value; judging whether the simulation value is larger than a simulation threshold value or not; If yes, regulating and controlling the physical refrigeration house according to the optimization strategy. By adopting the technical scheme, a digital twin technology is introduced, and the physical refrigeration house and the virtual refrigeration house model are subjected to real-time data synchronization, so that a closed-loop feedback mechanism is constructed. By collecting the multi-source sensor data, the comprehensive perception of the running state of the refrigeration house is realized, the system can automatically judge whether the potential risk exists in the current running or not by combining the risk assessment, the diagnosis and regulation flow can be started without manual intervention, and the intelligent decision making capability and the automation degree of the system are obviously improved. After the risk is identified, the system calls a pre-constructed cause and effect rule base to carry out root cause diagnosis instead of traditional experience judgment or one-by-one investigation, so that the problem source can be rapidly and accurately positioned, the fault processing time is greatly shortened, and the operation cost is reduced. Simulation verification is carried out on the optimization strategy in the virtual refrigeration house model to generate a simulation value, and the simulation value is compared with a simulation threshold value to judge whether the optimization scheme is effective or not, the secondary risk or resource waste possibly caused by the dire