Warehouse safety management monitoring system

Abstract

The invention discloses a warehouse safety management monitoring system, and the system achieves the comprehensive and real-time monitoring and intelligent management of the activities of materials and personnel in a warehouse through the introduction of a video monitoring module and an intelligent analysis module. The video monitoring module can automatically analyze real-time video streams through a high-definition camera and a computer vision technology, accurately recognize the types and moving paths of the materials, compare the actual state with records of the materials entering and exiting the automatic monitoring module, and find and mark abnormal conditions in time. According to the mode, errors and delay caused by manual operation are effectively reduced, and the accuracy and safety of warehouse material management are ensured. And the intelligent analysis module is used for predicting warehouse material demand change by utilizing an artificial intelligence algorithm through integrating data of each module, identifying potential safety hazards and generating optimization suggestions and decision support.

Description

Technical Field

[0001] The invention belongs to the field of warehouse monitoring, and in particular, relates to a warehouse safety management monitoring system. Background Art

[0002] In modern warehouse management, efficient monitoring and safety management of materials are crucial. However, traditional warehouse management systems often rely on manual records and decentralized monitoring methods, which are not only inefficient, but also prone to data inconsistencies and security loopholes. With the expansion of warehouse scale and the increase in logistics demand, the types, quantities and flow frequencies of materials in warehouses are also increasing, and the limitations of manual management are becoming more and more obvious. Relying solely on manual management, it is difficult to track the entry and exit of materials and inventory status in a timely and accurate manner, monitor personnel activities in the warehouse, and identify potential safety hazards and abnormal situations. Since manual operations are prone to errors, delays and errors in information entry will also greatly reduce the real-time and accuracy of data. In addition, traditional systems lack intelligent data analysis and prediction capabilities, and cannot effectively use warehouse data for in-depth analysis, making it difficult for managers to grasp changes in material demand and potential safety risks in a timely manner. Such a situation may lead to excess or shortage of inventory, increased operating costs, inefficiency, and even serious safety accidents, causing immeasurable losses. Therefore, warehouse management urgently needs a more efficient and intelligent solution to meet the increasingly complex management challenges.

[0003] In view of this, the present invention is proposed. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a warehouse safety management and monitoring system to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0006] A warehouse safety management and monitoring system includes: a material in and out automatic monitoring module for real-time monitoring and management of the in and out of the warehouse materials;

[0007] Video monitoring module: The video monitoring module is linked with the material entry and exit automatic monitoring module. It obtains real-time video streams through high-definition cameras deployed inside the warehouse, monitors the materials and personnel activities in the warehouse, and uses computer vision technology to automatically analyze video images, identify the types and movement paths of materials. Finally, based on the records of the material entry and exit automatic monitoring module, it verifies whether the actual status of the materials is consistent with the records, and marks relevant video clips when abnormalities are found;

[0008] The abnormality detection and alarm module monitors abnormal conditions in the warehouse in real time based on the data provided by the material entry and exit automatic monitoring module and the video monitoring module;

[0009] Intelligent analysis module: The intelligent analysis module is used to comprehensively analyze the data of the material in and out automatic monitoring module and the video monitoring module, and use artificial intelligence algorithms to predict changes in warehouse material demand and identify potential safety hazards. This module can generate optimization suggestions for warehouse operations and provide decision support for inventory management, personnel scheduling and safety strategies;

[0010] Remote monitoring and control module, which is interconnected with the video monitoring module and the anomaly detection and alarm module, allowing warehouse managers to view the surveillance video, material status and alarm information in the warehouse in real time through the network interface or mobile application;

[0011] Data management and storage module: The data management and storage module is used to centrally store data generated by material entry and exit, video surveillance, abnormal alarms and intelligent analysis.

[0012] Optionally, when real-time monitoring and management of the entry and exit of materials in the warehouse is performed, first, a barcode scanning device is used to automatically scan the barcode on the material when the material passes through the warehouse entrance or exit to obtain the material's unique identification information. Next, based on the material information obtained by the scan, the system determines the status of the material, which includes but is not limited to waiting to be entered into the warehouse, waiting to be exited from the warehouse, already entered into the warehouse, and already exited from the warehouse. When the status of the material is determined, the system will automatically record the relevant information of the material, including the entry time, exit time, material number, material name, quantity, operator, material category and storage location. Subsequently, the recorded material information is uploaded to the central database. Finally, the uploaded information is compared with the warehouse's inventory data to verify whether the entry and exit information of the material is accurate. If there is any abnormality, the system will automatically generate a prompt to notify the management personnel to check.

[0013] Optionally, high-definition cameras are deployed at the entrance, exit and important storage areas of the warehouse. The cameras capture images and video data inside the warehouse in real time and generate continuous real-time video streams. Then, the real-time video streams obtained by the cameras are preprocessed and the types and specific features of materials are automatically detected and identified in the preprocessed video streams using deep learning image recognition algorithms. After identifying the materials, the movement paths of the materials are monitored in real time through object detection and tracking algorithms, and the flow direction and storage location of the materials in the warehouse are analyzed. Then, the material status detected in real time is compared with the material status recorded by the material entry and exit automatic monitoring module. If it is found that the status of the materials in the video is inconsistent with the records in the database, the system will mark the material or the corresponding video clip as abnormal.

[0014] When it is detected that the material movement path is abnormal, the material is not stored according to the prescribed route, or it is inconsistent with the material entry and exit records, the system automatically marks the corresponding video clips and generates an abnormal event log; finally, all detection results and marked abnormal information are synchronized to the central database, and the warehouse managers are notified to view the abnormal video clips and corresponding log records. After the abnormal mark appears, according to the preset security policy, decide whether to trigger the sound and light alarm, send an alarm notification to the manager's mobile device, or perform other emergency response operations.

[0015] Optionally, the preset security policies include area access control policy, material movement monitoring policy, entry and exit verification policy, personnel behavior monitoring policy, emergency response policy, data consistency check policy, and abnormal frequency monitoring policy.

[0016] Optionally, based on the data provided by the material in and out automatic monitoring module and the video monitoring module, the steps of detecting abnormal conditions in the warehouse in real time are:

[0017] The material in and out automatic monitoring module and video monitoring module can obtain the material in and out data, material status, personnel in and out records and video image data in real time in the warehouse, and build a full picture of the internal operation of the warehouse based on these data;

[0018] After completing data collection, the current status of the materials is analyzed based on the data provided by the automatic material entry and exit monitoring module, and it is verified whether the actual material movement path conforms to the predetermined material entry and exit process. Subsequently, the image data captured by the video surveillance module is used to confirm whether the personnel entering the warehouse have the corresponding access rights through face recognition or other identity verification technologies. By combining the material status and personnel identity information, the system further uses computer vision technology to analyze the video data, identify the type and location of the materials, and thus determine whether the materials are stored in accordance with regulations and detect whether there is any unauthorized material movement.

[0019] Optionally, in the process of detecting abnormal events, when the movement trajectory of materials deviates from the predetermined path or unauthorized area, it is determined to be unauthorized material movement, and the quantity and type information of the materials entering and leaving are checked to see if they match the system records. If a mismatch is detected, it is determined to be abnormal material entry and exit, and then combined with the personnel entry permission check, if unauthorized personnel are found to enter, it is marked as unauthorized personnel entering. At the same time, the system monitors the environmental conditions, detects fire, smoke, leakage hazards, or detects abnormal behavior in the monitoring image, and determines it as other security threats. Finally, all detected abnormal events are recorded and marked, and a corresponding event log is generated;

[0020] When an abnormal event is detected, the system automatically triggers the sound and light alarm equipment in the warehouse, issuing an alarm sound and flashing light signal to alert the personnel on site and send the detailed information of the abnormal event via text message to the mobile terminal of the preset warehouse manager.

[0021] Optionally, the data of the material in and out automatic monitoring module and the video monitoring module are comprehensively analyzed, and the artificial intelligence algorithm is used to predict the changes in the demand for warehouse materials and identify potential safety hazards. The module can generate optimization suggestions for warehouse operations and provide decision support for inventory management, personnel scheduling and safety strategies. The steps are as follows:

[0022] The material in and out automatic monitoring module obtains material in and out records, current inventory status, and material movement information. At the same time, the video monitoring module collects relevant image and video data to capture the material stacking, personnel activities, and environmental conditions in the warehouse.

[0023] Through data analysis, the key features received in the automatic monitoring module for materials in and out are extracted, and then a prediction model is established using a regression analysis algorithm to analyze the demand trend of materials and identify potential safety hazards in warehouse operations;

[0024] Combine data analysis and model prediction results to predict material demand in the warehouse, identify future inventory shortages or surpluses, and use anomaly detection algorithms to analyze patterns of personnel activities and material movement to identify potential safety hazards;

[0025] Based on the results of risk assessment and predictive analysis, the system automatically generates optimization suggestions for warehouse operations. Finally, the generated optimization suggestions and analysis results are summarized into easy-to-understand reports, and visual charts are provided to help managers intuitively understand the operating status and potential problems of the warehouse.

[0026] Optionally, the steps of establishing a prediction model using a regression analysis algorithm are:

[0027] Collect historical data of warehouse materials and data of external influencing factors into a data set and divide the data set into a training set and a test set. According to the data characteristics and analysis objectives, select a linear regression algorithm to establish a prediction model. Then, use the training data set to input into the selected regression algorithm and predict the model parameters through the least squares method so that the model can best fit the training data. Then, apply the trained model to the test set, calculate the error between the predicted value and the actual value, and use the evaluation index to evaluate the prediction performance and fit of the model.

[0028] According to the results of model evaluation, the model's hyperparameters are adjusted, and finally, the final optimized model is deployed to the warehouse management system for real-time prediction and decision support.

[0029] Optionally, the remote monitoring and control module allows managers to perform operations remotely, including remote locking in emergency situations, material reallocation, and system parameter adjustments.

[0030] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0031] The warehouse safety management and monitoring system of the present invention realizes comprehensive, real-time monitoring and intelligent management of materials and personnel activities in the warehouse by introducing video monitoring modules and intelligent analysis modules. The video monitoring module can automatically analyze the real-time video stream through high-definition cameras and computer vision technology, accurately identify the types and movement paths of materials, and compare the actual status with the records of the automatic monitoring module for materials entering and exiting, so as to promptly discover and mark abnormal situations. This method effectively reduces the errors and delays caused by manual operations and ensures the accuracy and safety of warehouse material management. The intelligent analysis module integrates the data of each module, uses artificial intelligence algorithms to predict changes in warehouse material demand, identifies potential safety hazards, and generates optimization suggestions and decision support.

[0032] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0034] In the figure:

[0035] Figure 1 This is a block diagram of the warehouse safety management and monitoring system.

[0036] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0037] The present invention will now be described in further detail with reference to the accompanying drawings.

[0038] See also Figure 1 As shown, in this embodiment, a warehouse safety management and monitoring system is provided, including an automatic monitoring module for materials entering and exiting the warehouse, which is used to monitor and manage the entry and exit of materials in the warehouse in real time; the automatic monitoring module for materials entering and exiting monitors the entry and exit of materials in real time to ensure accurate management of warehouse materials.

[0039] The video surveillance module is linked with the automatic monitoring module for materials entering and exiting. It obtains real-time video streams through high-definition cameras deployed inside the warehouse, monitors the activities of materials and personnel in the warehouse, and uses computer vision technology to automatically analyze video images, identify the types and movement paths of materials. Finally, based on the records of the automatic monitoring module for materials entering and exiting, it verifies whether the actual status of the materials is consistent with the records, and marks relevant video clips when abnormalities are found. The video surveillance module monitors the activities of materials and personnel in the warehouse through high-definition cameras and computer vision technology, and compares them with the records of materials entering and exiting to ensure data consistency.

[0040] The anomaly detection and alarm module detects abnormal situations in the warehouse in real time based on the data provided by the material entry and exit automatic monitoring module and the video monitoring module; the anomaly detection and alarm module detects and marks abnormal situations in real time based on material and video data to ensure safety.

[0041] Intelligent analysis module: The intelligent analysis module is used to comprehensively analyze the data from the automatic material entry and exit monitoring module and the video monitoring module, and use artificial intelligence algorithms to predict changes in warehouse material demand and identify potential safety hazards. This module can generate optimization suggestions for warehouse operations and provide decision support for inventory management, personnel scheduling and security strategies; the intelligent analysis module uses artificial intelligence algorithms to analyze multi-source data, predict material demand and potential risks, and generate optimization suggestions to improve operational efficiency.

[0042] Remote monitoring and control module: The remote monitoring and control module is interconnected with the video monitoring module and the anomaly detection and alarm module, allowing warehouse managers to view the monitoring video, material status and alarm information in the warehouse in real time through the network interface or mobile application; the remote monitoring and control module provides managers with real-time remote monitoring and control functions to enhance management flexibility.

[0043] Data management and storage module: The data management and storage module is used to centrally store data generated by material entry and exit, video surveillance, abnormal alarms, and intelligent analysis. The data management and storage module centrally stores and manages all operation data to ensure the integrity and traceability of information. These modules together build an intelligent warehouse management system, which significantly improves the safety, efficiency, and management effect of the warehouse.

[0044] In this embodiment, when real-time monitoring and management of the entry and exit of materials in the warehouse is performed, first, a barcode scanning device is used. When the materials pass through the warehouse entrance or exit, the barcode on the materials is automatically scanned to obtain the unique identification information of the materials. Then, based on the material information obtained by scanning, the system determines the status of the materials, which includes but is not limited to waiting to be stored, waiting to be shipped out, already stored, and already shipped out. When the status of the materials is determined, the system will automatically record the relevant information of the materials, including the storage time, the shipping time, the material number, the material name, the quantity, the operator, the material category and the storage location. Subsequently, the recorded material information is uploaded to the central database. Finally, the uploaded information is compared with the warehouse inventory data to verify whether the entry and exit information of the materials is accurate. If there is any abnormality, the system will automatically generate a prompt to notify the management personnel to check. Through the automated barcode scanning technology, the system can instantly capture the unique identification information of each material, avoiding human operation errors and significantly improving the operation efficiency. In addition, the system determines the status of materials in real time and automatically records detailed in-and-out information, including time, operator, material category, and storage location. These data are uploaded to the central database in a timely manner to ensure the timeliness and consistency of the information. In the data verification phase, the system compares the real-time uploaded information with the actual inventory data, automatically detects and marks abnormal situations, and quickly generates prompts to remind managers to check and correct them. This series of automated and data-driven processes not only significantly improves the efficiency of warehouse operations and reduces the need for human intervention, but also greatly enhances the safety and reliability of warehouse management, ensuring that all operations are within the controllable and traceable range.

[0045] In this embodiment, high-definition cameras are deployed at the entrance, exit and important storage areas of the warehouse. The cameras capture the images and video data inside the warehouse in real time and generate continuous real-time video streams. Then, the real-time video streams obtained by the cameras are preprocessed and the types and specific features of the materials are automatically detected and identified in the preprocessed video streams using the deep learning image recognition algorithm. After identifying the materials, the moving paths of the materials are monitored in real time through the object detection and tracking algorithm, and the flow direction and storage position of the materials in the warehouse are analyzed; then the material status detected in real time is compared with the material status recorded by the material entry and exit automatic monitoring module. If it is found that the status of the materials in the video is inconsistent with the record in the database, the system will mark the material or the corresponding video clip as abnormal; high-definition cameras are deployed at key locations in the warehouse, which can capture and generate continuous high-quality video streams in real time, providing a reliable data source for comprehensive monitoring. Then, the video streams are preprocessed using the deep learning image recognition algorithm, and the types and features of the materials are automatically detected and identified, which effectively improves the recognition accuracy and efficiency. Through the object detection and tracking algorithm, the system can monitor the moving paths of the materials in real time and accurately analyze the flow direction and storage position of the materials in the warehouse, which is crucial for optimizing the warehouse layout and improving operational efficiency. The system then compares the real-time monitored material status with the records of the material in and out automatic monitoring module, automatically detects potential abnormalities, and immediately marks abnormal materials or video clips if any inconsistencies are found, ensuring the safety of warehouse operations and the accuracy of data. This improves the automation and intelligence level of warehouse management, enhances data verification and anomaly detection capabilities, and greatly improves the overall efficiency and safety of warehouse operations.

[0046] When the material movement path is detected to be abnormal, the material is not stored according to the prescribed route, or it is inconsistent with the material entry and exit records, the system automatically marks the corresponding video clips and generates an abnormal event log; finally, all the detection results and marked abnormal information are synchronized to the central database, and the warehouse manager is notified to view the abnormal video clips and corresponding log records. After the abnormal mark appears, according to the preset security policy, it is decided whether to trigger the sound and light alarm, send an alarm notification to the manager's mobile device, or perform other emergency response operations. The system can automatically identify and mark the abnormal material movement path, the material is not stored according to the prescribed route, or it is inconsistent with the material entry and exit records, and generate the corresponding abnormal event log, which ensures that any abnormality can be recorded and tracked in time. Then, all the detection results and marked abnormal information will be synchronized to the central database immediately, which not only ensures the real-time and integrity of the data, but also provides reliable decision-making support for warehouse management. In addition, the system will notify the warehouse manager to view the abnormal video clips and log records, which is convenient for rapid review and disposal, and improves the efficiency and response speed of management. Finally, after the abnormal mark appears, the system automatically determines the subsequent emergency response measures according to the preset security policy, such as triggering sound and light alarms, sending notifications to the manager's mobile device, etc. This preset automated emergency response mechanism significantly improves the warehouse's safety and ability to respond to emergencies, ensuring that any potential safety risks can be quickly handled and controlled.

[0047] In this embodiment, the preset security policies include regional access control policies, material movement monitoring policies, in-and-out warehouse verification policies, personnel behavior monitoring policies, emergency response policies, data consistency check policies, and abnormal frequency monitoring policies. The regional access control policies are designed to prevent unauthorized personnel from entering sensitive areas. Personnel entering specific areas of the warehouse are monitored through personnel identification technologies (such as face recognition and employee identity verification). If unauthorized entry is detected, an alarm is immediately sounded and security personnel are notified.

[0048] The material movement monitoring strategy is to ensure that materials are moved and stored according to the predetermined path and process. By tracking the movement path of materials and comparing it with the predetermined path in real time. If the materials deviate from the predetermined movement route or are placed in the wrong location, the system will mark the abnormality and trigger an audible and visual alarm or send a notification to the warehouse manager.

[0049] The in-and-out verification strategy is to verify the legality and accuracy of material in-and-out operations. When materials enter or leave the warehouse, the system automatically checks the records in the material in-and-out automatic monitoring module. If the in-and-out material information (such as quantity, type, time, etc.) is detected to be inconsistent with the records, the system will mark the event and notify the relevant management personnel for verification.

[0050] The personnel behavior monitoring strategy is to analyze personnel behavior through video surveillance to identify potential safety violations, such as improper operation, not wearing safety equipment, etc. If any abnormal behavior is detected, the system will record the event and may trigger an alarm or notify management to intervene. In order to prevent abnormal personnel behavior in the warehouse, such as unauthorized operation or safety violations.

[0051] The emergency response strategy is to quickly respond to and handle emergency situations such as fire, leaks or other dangerous events. When an emergency event (such as smoke, flames, chemical leaks, etc.) is detected, the system will automatically trigger an emergency alarm and initiate an emergency plan, including closing area access control, activating the fire extinguishing system, notifying the fire brigade and management personnel, etc.

[0052] The data consistency check strategy is to ensure the consistency and integrity of all monitoring data. The consistency between material entry and exit records and video surveillance data is checked regularly. If any data inconsistency or loss is found, the system will generate an exception report and notify the warehouse management team for investigation.

[0053] The abnormal frequency monitoring strategy is to monitor and analyze the frequency of abnormal events to prevent potential systemic problems or safety hazards. By recording and analyzing the frequency of all abnormal events, if a certain type of abnormal event occurs multiple times in a short period of time, the system will generate an alarm and recommend that the management team inspect and maintain the relevant processes or equipment.

[0054] In this embodiment, based on the data provided by the material in and out automatic monitoring module and the video monitoring module, the steps of detecting abnormal conditions in the warehouse in real time are as follows:

[0055] The material in and out automatic monitoring module and video monitoring module can obtain the material in and out data, material status, personnel in and out records and video image data in real time in the warehouse, and build a full picture of the internal operation of the warehouse based on these data;

[0056] After completing data collection, the current status of the materials is analyzed according to the data provided by the automatic monitoring module for materials in and out, and the actual material movement path is checked to see if it complies with the predetermined material in and out process. Subsequently, the image data captured by the video monitoring module is used to confirm whether the personnel entering the warehouse have the corresponding access rights through face recognition or other identity verification technologies. By combining the material status and personnel identity information, the system further uses computer vision technology to analyze the video data, identify the type and location of the materials, and thus determine whether the materials are stored in accordance with regulations and detect whether there is unauthorized material movement. By acquiring material in and out data, material status, personnel in and out records, and video image data in real time, the system can comprehensively build a complete view of the internal operations of the warehouse, laying the foundation for precise management. Next, the system analyzes the current status of the materials based on these data, verifies whether the material movement path complies with the predetermined process, and ensures the compliance and accuracy of the operation. Then, using video monitoring data, through face recognition or other identity verification technologies, the system confirms whether the personnel entering the warehouse have the corresponding permissions, further enhancing security. Finally, combined with the material status and personnel identity information, the system uses computer vision technology to deeply analyze the video data, identify the type and location of materials, determine whether they are stored correctly, and effectively detect unauthorized material movement. This invention not only realizes comprehensive monitoring of materials and personnel in the warehouse, but also improves the intelligence level and response speed of security management.

[0057] In this embodiment, when detecting abnormal events, when the movement trajectory of materials deviates from the predetermined path or unauthorized area, it is determined to be unauthorized material movement, and the quantity and type information of the materials entering and leaving are checked to see if they match the system records. If a mismatch is detected, it is determined to be abnormal material entry and exit, and then combined with the personnel entry permission check, if unauthorized personnel are found to enter, it is marked as unauthorized personnel entering. At the same time, the system monitors the environmental conditions, and when fire, smoke, leakage dangers are detected, or abnormal behavior is found in the monitoring image, it is determined to be other security threats. Finally, all detected abnormal events are recorded and marked, and corresponding event logs are generated;

[0058] When an abnormal event is detected, the system automatically triggers the sound and light alarm equipment in the warehouse, issuing an alarm sound and flashing light signal to alert the personnel on site and send the detailed information of the abnormal event via text message to the mobile terminal of the preset warehouse manager.

[0059] In this embodiment, the steps of comprehensively analyzing the data of the material in and out automatic monitoring module and the video monitoring module, using artificial intelligence algorithms to predict changes in warehouse material demand, and identifying potential safety hazards are as follows:

[0060] The material in and out automatic monitoring module obtains material in and out records, current inventory status, and material movement information. At the same time, the video monitoring module collects relevant image and video data to capture the material stacking, personnel activities, and environmental conditions in the warehouse.

[0061] Through data analysis, the key features received in the automatic monitoring module for materials in and out are extracted, and then a prediction model is established using a regression analysis algorithm to analyze the demand trend of materials and identify potential safety hazards in warehouse operations;

[0062] Combine data analysis and model prediction results to predict material demand in the warehouse, identify future inventory shortages or surpluses, and use anomaly detection algorithms to analyze patterns of personnel activities and material movement to identify potential safety hazards;

[0063] Based on the risk assessment and forecast analysis results, the system automatically generates optimization suggestions for warehouse operation, and finally summarizes the generated optimization suggestions and analysis results into an easy-to-understand report, and provides visual charts to help managers intuitively understand the operation status and potential problems of the warehouse. Through multi-level data analysis and forecasting models, it provides comprehensive and intelligent support for warehouse management. First, the system can obtain data such as material entry and exit records, inventory status, material movement information, and personnel activities and environmental conditions in the warehouse in real time from the material entry and exit automatic monitoring module and the video monitoring module. The system can build a comprehensive view of warehouse operations and provide a reliable data basis for further analysis. Secondly, by analyzing these data, extracting key features, and using regression analysis algorithms to establish a forecasting model, it can accurately predict the trend of material demand changes, identify potential safety hazards in warehouse operations in advance, and help warehouse managers prevent possible problems. Subsequently, the system combines the results of data analysis and forecasting models to make detailed forecasts of material demand in the warehouse, promptly identify the risks of inventory shortages or excesses, and ensure that inventory management is more accurate and efficient. Using anomaly detection algorithms, the system can also analyze the patterns of personnel activities and material movement, identify possible safety hazards, and further ensure the safe operation of the warehouse. Finally, based on these analyses and predictions, the system automatically generates optimization suggestions for warehouse operations and summarizes these suggestions and analysis results into easy-to-understand reports and visual charts, allowing managers to intuitively grasp the operating status and potential problems of the warehouse.

[0064] In this embodiment, the steps of establishing a prediction model using a regression analysis algorithm are as follows:

[0065] Collect historical data of warehouse materials and data of external influencing factors into a data set and divide the data set into a training set and a test set. According to the data characteristics and analysis objectives, select a linear regression algorithm to establish a prediction model. Then, use the training data set to input into the selected regression algorithm and predict the model parameters through the least squares method so that the model can best fit the training data. Then, apply the trained model to the test set, calculate the error between the predicted value and the actual value, and use the evaluation index to evaluate the prediction performance and fit of the model.

[0066] According to the results of model evaluation, the hyperparameters of the model are adjusted. Finally, the final optimized model is deployed to the warehouse management system for real-time prediction and decision support. By collecting historical data and external influencing factors of warehouse materials, the data is integrated into a comprehensive data set, which ensures that the input data of the model is rich and representative, laying the foundation for accurate prediction. Then, the data set is divided into a training set and a test set, so that the training and verification of the model can be carried out separately, ensuring the reliability and generalization ability of the model. According to the data characteristics and analysis objectives, the linear regression algorithm is selected to establish a prediction model, which helps to intuitively understand the relationship between the data and has good interpretability. Then, by using the training data set and the least squares method to optimize the model parameters, the model can accurately fit the training data, improving the performance of the model on known data. Subsequently, the trained model is applied to the test set, the error between the predicted value and the actual value is calculated, and multiple evaluation indicators are used to evaluate the prediction performance and fit of the model. This process helps to accurately measure the actual effect of the model and ensure its effectiveness on new data. Based on the model evaluation results, the model is further optimized by adjusting the hyperparameters of the model, which improves the prediction accuracy and robustness of the model. Finally, the optimized model is deployed to the warehouse management system for real-time prediction and decision support, making warehouse management more dynamic and intelligent.

[0067] In this embodiment, the remote monitoring and control module enables the administrator to remotely perform operations, including remote locking in an emergency, reallocation of materials, and adjustment of system parameters.

[0068] The present invention is not limited to the above-mentioned embodiments. Anyone should be aware that any structural changes made under the enlightenment of the present invention, and any technical solutions that are the same or similar to the present invention, fall within the protection scope of the present invention. The technology, shape, and structural parts not described in detail in the present invention are all well-known technologies.

Claims

1. A warehouse safety management and monitoring system, characterized in that: include: The material in and out automatic monitoring module is used to monitor and manage the in and out of the warehouse in real time; Video monitoring module: The video monitoring module is linked with the material entry and exit automatic monitoring module. It obtains real-time video streams through high-definition cameras deployed inside the warehouse, monitors the materials and personnel activities in the warehouse, and uses computer vision technology to automatically analyze video images, identify the types and movement paths of materials. Finally, based on the records of the material entry and exit automatic monitoring module, it verifies whether the actual status of the materials is consistent with the records, and marks relevant video clips when abnormalities are found; The abnormality detection and alarm module monitors abnormal conditions in the warehouse in real time based on the data provided by the material entry and exit automatic monitoring module and the video monitoring module; Intelligent analysis module: The intelligent analysis module is used to comprehensively analyze the data of the material in and out automatic monitoring module and the video monitoring module, and use artificial intelligence algorithms to predict changes in warehouse material demand and identify potential safety hazards. This module can generate optimization suggestions for warehouse operations and provide decision support for inventory management, personnel scheduling and safety strategies; Remote monitoring and control module, which is interconnected with the video monitoring module and the anomaly detection and alarm module, allowing warehouse managers to view the surveillance video, material status and alarm information in the warehouse in real time through the network interface or mobile application; Data management and storage module: The data management and storage module is used to centrally store data generated by material entry and exit, video surveillance, abnormal alarms and intelligent analysis.

2. A warehouse safety management and monitoring system according to claim 1, characterized in that: When real-time monitoring and management of the entry and exit of materials in the warehouse is carried out, first, a barcode scanning device is used. When the materials pass through the warehouse entrance or exit, the barcodes on the materials are automatically scanned to obtain the unique identification information of the materials. Then, based on the material information obtained by the scan, the system determines the status of the materials, which includes but is not limited to waiting to be entered into the warehouse, waiting to be exited from the warehouse, already entered into the warehouse, and already exited from the warehouse. When the status of the materials is determined, the system will automatically record the relevant information of the materials, including the entry time, exit time, material number, material name, quantity, operator, material category and storage location. Subsequently, the recorded material information is uploaded to the central database. Finally, the uploaded information is compared with the warehouse's inventory data to verify whether the entry and exit information of the materials is accurate. If there is any abnormality, the system will automatically generate a prompt to notify the management personnel to check.

3. A warehouse safety management and monitoring system according to claim 1, characterized in that: High-definition cameras are deployed at the entrance, exit and important storage areas of the warehouse. The cameras capture images and video data inside the warehouse in real time and generate continuous real-time video streams. Then, the real-time video streams obtained by the cameras are preprocessed and the types and specific features of materials are automatically detected and identified in the preprocessed video streams using deep learning image recognition algorithms. After identifying the materials, the movement paths of the materials are monitored in real time through object detection and tracking algorithms, and the flow direction and storage location of the materials in the warehouse are analyzed. The material status detected in real time is then compared with the material status recorded by the material entry and exit automatic monitoring module. If the status of the materials in the video is found to be inconsistent with the records in the database, the system will mark the material or the corresponding video clip as abnormal. When it is detected that the material movement path is abnormal, the material is not stored according to the prescribed route, or it is inconsistent with the material entry and exit records, the system automatically marks the corresponding video clips and generates an abnormal event log; finally, all detection results and marked abnormal information are synchronized to the central database, and the warehouse managers are notified to view the abnormal video clips and corresponding log records. After the abnormal mark appears, according to the preset security policy, decide whether to trigger the sound and light alarm, send an alarm notification to the manager's mobile device, or perform other emergency response operations.

4. A warehouse safety management and monitoring system according to claim 1, characterized in that: The preset security policies include area access control policy, material movement monitoring policy, entry and exit verification policy, personnel behavior monitoring policy, emergency response policy, data consistency check policy, and abnormal frequency monitoring policy.

5. A warehouse safety management and monitoring system according to claim 1, characterized in that: Based on the data provided by the material in and out automatic monitoring module and the video monitoring module, the steps for real-time detection of abnormal conditions in the warehouse are as follows: The material in and out automatic monitoring module and video monitoring module can obtain the material in and out data, material status, personnel in and out records and video image data in real time in the warehouse, and build a full picture of the internal operation of the warehouse based on these data; After completing data collection, the current status of the materials is analyzed based on the data provided by the automatic material entry and exit monitoring module, and it is verified whether the actual material movement path conforms to the predetermined material entry and exit process. Subsequently, the image data captured by the video surveillance module is used to confirm whether the personnel entering the warehouse have the corresponding access rights through face recognition or other identity verification technologies. By combining the material status and personnel identity information, the system further uses computer vision technology to analyze the video data, identify the type and location of the materials, and thus determine whether the materials are stored in accordance with regulations and detect whether there is any unauthorized material movement.

6. A warehouse safety management and monitoring system according to claim 1, characterized in that: In the process of detecting abnormal events, when the movement trajectory of materials deviates from the predetermined path or unauthorized area, it is determined to be unauthorized material movement, and the quantity and type information of materials entering and leaving are checked to see if they match the system records. If a mismatch is detected, it is determined to be abnormal material entry and exit, and then combined with the personnel entry permission check, if unauthorized personnel are found to enter, it is marked as unauthorized personnel entering. At the same time, the system monitors the environmental conditions and detects fire, smoke, leakage hazards, or abnormal behavior in the monitoring image, and determines it as other security threats. Finally, all detected abnormal events are recorded and marked, and corresponding event logs are generated; When an abnormal event is detected, the system automatically triggers the sound and light alarm equipment in the warehouse, issuing an alarm sound and flashing light signal to alert the personnel on site and send the detailed information of the abnormal event via text message to the mobile terminal of the preset warehouse manager.

7. A warehouse safety management and monitoring system according to claim 1, characterized in that: Comprehensively analyze the data of the material in and out automatic monitoring module and the video monitoring module, use artificial intelligence algorithms to predict changes in warehouse material demand, identify potential safety hazards, and generate optimization suggestions for warehouse operations. The module also provides decision support for inventory management, personnel scheduling, and safety strategies. The steps are as follows: The material in and out automatic monitoring module obtains material in and out records, current inventory status, and material movement information. At the same time, the video monitoring module collects relevant image and video data to capture the material stacking, personnel activities, and environmental conditions in the warehouse. Through data analysis, the key features received in the automatic monitoring module for materials in and out are extracted, and then a prediction model is established using a regression analysis algorithm to analyze the demand trend of materials and identify potential safety hazards in warehouse operations; Combine data analysis and model prediction results to predict material demand in the warehouse, identify future inventory shortages or surpluses, and use anomaly detection algorithms to analyze patterns of personnel activities and material movement to identify potential safety hazards; Based on the results of risk assessment and predictive analysis, the system automatically generates optimization suggestions for warehouse operations. Finally, the generated optimization suggestions and analysis results are summarized into easy-to-understand reports, and visual charts are provided to help managers intuitively understand the operating status and potential problems of the warehouse.

8. A warehouse safety management and monitoring system according to claim 1, characterized in that: The steps to build a prediction model using regression analysis algorithm are: Collect historical data of warehouse materials and data of external influencing factors into a data set and divide the data set into a training set and a test set. According to the data characteristics and analysis objectives, select a linear regression algorithm to establish a prediction model. Then, use the training data set to input into the selected regression algorithm and predict the model parameters through the least squares method so that the model can best fit the training data. Then, apply the trained model to the test set, calculate the error between the predicted value and the actual value, and use the evaluation index to evaluate the prediction performance and fit of the model. According to the results of model evaluation, the model's hyperparameters are adjusted, and finally, the final optimized model is deployed to the warehouse management system for real-time prediction and decision support.

9. A warehouse safety management and monitoring system according to claim 1, characterized in that: The remote monitoring and control module enables managers to perform operations remotely, including remote locking in emergency situations, reallocation of supplies, and adjustment of system parameters.

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