Intelligent supply chain storage safety management system
By using data collection and early warning models in the warehouse safety management system, combined with decision trees and deep learning models, the problem of untimely discovery of safety issues in existing technologies is solved, and rapid identification of the warehouse environment and comprehensive safety management are achieved.
Patent Information
- Application Number
- CN202510800816.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-16
- Publication Date
- 2025-10-03
AI Technical Summary
In the existing technology, the warehouse safety management system is difficult to adapt to various situations, resulting in the delay in discovering safety issues.
The data acquisition module is used to obtain real-time monitoring data, which is processed using pre-trained early warning models, including decision trees and deep learning sub-models, to identify and classify risks. The data is combined in time windows to generate prediction results and issue alarm information.
It achieves rapid identification and adaptation to complex warehousing environments, improves the accuracy and timeliness of safety risk management, and ensures the comprehensiveness and perfection of safety management.
Smart Images
Figure CN120746445A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of warehousing safety technology, and in particular to an intelligent supply chain warehousing safety management system. Background Art
[0002] Intelligent supply chain refers to the comprehensive upgrade and optimization of traditional supply chains using modern information technology and data analysis to improve efficiency, flexibility, and visibility, enabling intelligent operations throughout the entire process. By collecting and analyzing real-time data, it enables supply-demand coordination and optimized logistics and distribution, promoting innovation and enhancing corporate competitiveness.
[0003] Smart supply chain applications involve the storage and management of goods in warehouses. Warehouse security, as a core component of warehouse management, plays a crucial role in the operation of the entire smart supply chain. However, existing solutions are mostly based on established regulations and monitoring of incoming and outgoing goods records. These fixed management areas are difficult to apply to the diverse aspects of current warehouse security, resulting in delayed detection of security issues. Summary of the Invention
[0004] In view of the technical deficiencies mentioned in the background technology, an embodiment of the present invention aims to provide an intelligent supply chain warehousing safety management system.
[0005] To achieve the above objectives, an embodiment of the present invention provides an intelligent supply chain warehousing safety management system, the system comprising:
[0006] A data acquisition module, configured to acquire real-time monitoring data, wherein the monitoring data includes multimedia data;
[0007] Processing module for:
[0008] The monitoring data is transmitted to a pre-trained early warning model for processing to obtain corresponding prediction results; the early warning model includes multiple sub-models;
[0009] Determining whether to issue an alarm message based on the prediction result;
[0010] The interactive module is used to receive and display the published alarm information.
[0011] As a specific implementation of the present application, the interactive module is also used to upload rectification records and push them to a remote monitoring center.
[0012] As a specific implementation of the present application, the multimedia data includes voice, image and video data;
[0013] When the data is voice data, the voice data is first converted into text data.
[0014] As a specific implementation of the present application, the sub-model includes a decision tree sub-model and a deep learning sub-model;
[0015] The decision tree sub-model is used to identify and classify security risks and provide a basis for risk management;
[0016] The deep learning sub-model is used to further improve the accuracy of risk assessment and optimize security risk management.
[0017] As a specific implementation of the present application, the processing module is further configured to:
[0018] Perform feature extraction, compare and check with preset safety hazard image features, and record the data that meets the preset features.
[0019] As a specific implementation method of the present application, the preset safety hazard image features include channel blockage image features, employee leaving their posts image features, shelf tilt image features, illegal operation image features and illegal intrusion image features.
[0020] As a specific implementation of the present application, the processing module is further configured to:
[0021] During training, new data similar to the training data is generated by rotating, translating, and scaling the training data, thereby expanding the dataset and improving the generalization ability of the model.
[0022] As a preferred implementation of the present application, when issuing alarm information, a response strategy is also displayed through the interactive module; wherein, a corresponding response strategy is pre-established for each type of alarm information, and each strategy has established response steps.
[0023] As a preferred implementation of the present application, the processing module further combines the monitoring data with a preset time window to serve as sequence data input into the early warning model.
[0024] The technical solution provided by the embodiment of the present invention, through the proposed intelligent supply chain warehouse safety management system, utilizes the real-time monitoring data to be transmitted to a pre-trained early warning model for processing to obtain corresponding prediction results; and the early warning model includes multiple sub-models, and determines whether to issue an alarm information based on the prediction results; the entire solution utilizes the rapid processing capabilities of each sub-model to enable complex warehouse environments to be identified, and is applicable to various aspects of current warehouse safety; thereby overcoming the defect of untimely discovery of safety issues in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] In order to more clearly illustrate the specific implementation of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the specific implementation or the description of the prior art.
[0026] Figure 1 This is a principle block diagram of the intelligent supply chain warehousing safety management system provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0027] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are intended to be illustrative only and are not intended to limit the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that these specific details are not necessarily required to practice the present invention.
[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0029] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples.
[0030] It should be noted that, unless otherwise specified, the technical terms in this embodiment have the common meanings understood in the relevant technical field.
[0031] Please refer to Figure 1 , an embodiment of the present invention provides an intelligent supply chain warehousing safety management system, the system comprising:
[0032] A data acquisition module, configured to acquire real-time monitoring data, wherein the monitoring data includes multimedia data;
[0033] Processing module for:
[0034] The monitoring data is transmitted to a pre-trained early warning model for processing to obtain corresponding prediction results; the early warning model includes multiple sub-models;
[0035] Determining whether to issue an alarm message based on the prediction result;
[0036] The interactive module is used to receive and display the published alarm information.
[0037] In this embodiment, the monitoring data is collected by various types of devices, such as cameras, microphones, etc. The monitoring data also includes sensor data collected by sensor devices, such as temperature sensors, smoke alarms, and induction switches.
[0038] The multimedia data includes voice, image and video data;
[0039] When the data is voice data, the voice data is first converted into text data.
[0040] Furthermore, in order to improve the comprehensiveness of management, the interactive module is also used to upload rectification records and push them to a remote monitoring center.
[0041] Specifically, the interactive module may adopt a smart terminal, so as to facilitate viewing by relevant personnel; at the same time, in order to facilitate interactive management, for the alarm information involved, a closed loop of management is formed by uploading rectification records.
[0042] Furthermore, in order to avoid misprocessing based on a certain data, the processing module also combines the monitoring data with a preset time window to serve as the sequence data input into the early warning model; thereby making predictions based on a section of data, which can more objectively reflect the changing patterns of the current situation and thus improve the accuracy of the prediction.
[0043] In this embodiment, in order to reduce the overhead of the model, the processing module is further configured to:
[0044] Perform feature extraction, compare and check with preset safety hazard image features, and record the data that meets the preset features.
[0045] During implementation, the preset safety hazard image features include channel blockage image features, employee leaving their posts image features, shelf tilt image features, illegal operation image features and illegal intrusion image features; thereby achieving comparative inspection of multiple aspects such as relevant personnel, sites and construction operations, thereby improving processing speed; and achieving identification of unsafe conditions of objects and unsafe conditions of the environment.
[0046] Furthermore, in order to find the intrinsic connections between data and predict possible risks in the future, the sub-model includes a decision tree sub-model and a deep learning sub-model;
[0047] The decision tree sub-model is used to identify and classify security risks and provide a basis for risk management;
[0048] The deep learning sub-model is used to further improve the accuracy of risk assessment and optimize security risk management.
[0049] It should be noted that the above is just an example of the specific algorithm used in the sub-model;
[0050] Algorithms such as decision trees are also widely used in the identification and classification of rail transit safety risks. They can extract key factors and provide a basis for risk management; deep learning algorithms can use networks such as deep neural networks.
[0051] In this embodiment, the processing module is further configured to:
[0052] During training, new data similar to the training data is generated by rotating, translating, and scaling the training data, thereby expanding the dataset and improving the generalization ability of the model.
[0053] This solution proposes an intelligent supply chain warehouse safety management system, which transmits real-time monitoring data to a pre-trained early warning model for processing to obtain corresponding prediction results. The early warning model includes multiple sub-models and determines whether to issue an alarm based on the prediction results. The entire solution utilizes the rapid processing capabilities of each sub-model to identify complex warehouse environments and is applicable to various aspects of current warehouse safety. It overcomes the defect of untimely discovery of safety issues in the existing technology.
[0054] Furthermore, in another embodiment, based on the above technical solution, when issuing alarm information, the response strategy is also displayed through the interactive module; wherein, a corresponding response strategy is pre-established for each type of alarm information, and each strategy has established response steps.
[0055] This ensures that employees can properly respond to possible accidents, effectively protects personnel safety during safety education, and makes safety management more complete and comprehensive.
[0056] In the embodiments provided in this application, it should be understood that the disclosed system can also be implemented in other ways. The embodiments described above are merely illustrative. It should also be noted that in some alternative implementations, the functions marked in the blocks can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flow chart, and the combination of blocks in the block diagram and / or flow chart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.
[0057] In addition, the functional modules in each embodiment of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.
[0058] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.
Claims
1. An intelligent supply chain warehousing safety management system, characterized by: The system comprises: A data acquisition module, configured to acquire real-time monitoring data, wherein the monitoring data includes multimedia data; Processing module for: The monitoring data is transmitted to a pre-trained early warning model for processing to obtain corresponding prediction results; the early warning model includes multiple sub-models; Determining whether to issue an alarm message based on the prediction result; The interactive module is used to receive and display the published alarm information.
2. The intelligent supply chain warehousing safety management system according to claim 1, characterized in that: The interactive module is also used to upload rectification records and push them to a remote monitoring center.
3. The intelligent supply chain warehousing safety management system according to claim 2, characterized in that: The multimedia data includes voice, image and video data; When the data is voice data, the voice data is first converted into text data.
4. The intelligent supply chain warehousing safety management system according to claim 2, characterized in that: The sub-models include a decision tree sub-model and a deep learning sub-model; The decision tree sub-model is used to identify and classify security risks and provide a basis for risk management; The deep learning sub-model is used to further improve the accuracy of risk assessment and optimize security risk management.
5. The intelligent supply chain warehousing safety management system according to claim 4, characterized in that: The processing module is further configured to: Perform feature extraction, compare and check with preset safety hazard image features, and record the data that meets the preset features.
6. The intelligent supply chain warehousing safety management system according to claim 5, characterized in that: The preset safety hazard image features include aisle blockage image features, employee leaving post image features, shelf tilt image features, illegal operation image features and illegal intrusion image features.
7. The intelligent supply chain warehousing safety management system according to claim 6, characterized in that: The processing module is further configured to: During training, new data similar to the training data is generated by rotating, translating, and scaling the training data, thereby expanding the dataset and improving the generalization ability of the model.
8. The intelligent supply chain warehousing safety management system according to any one of claims 1 to 7, characterized in that: When the alarm information is released, the response strategy is also displayed through the interactive module; wherein, for each type of alarm information, a corresponding response strategy is pre-established, and each strategy has established response steps.
9. The intelligent supply chain warehousing safety management system according to claim 8, characterized in that: The processing module further combines the monitoring data with a preset time window to serve as sequence data input into the early warning model.