Operation method of unattended intelligent mobile emergency cabin

Abstract

The invention relates to an operation method of an unattended intelligent mobile emergency cabin. Comprising the steps of system initialization, material information configuration, authority verification, order generation, material warehouse-in and warehouse-out, inventory monitoring, intelligent prediction, decision support, data processing and synchronization and the like. Various parameters are obtained and configured through the system configuration module, material information is managed through the material detail directory tree module, accuracy of access authority is ensured through the face recognition and authority verification module, and automatic order generation and inventory updating are achieved through the automatic order generation and inventory updating module. A gravity sensing unit is used for monitoring warehouse-in and warehouse-out of materials, an inventory alarm and decision-making auxiliary module is used for monitoring an inventory state and sending an alarm signal, an intelligent prediction unit is used for predicting a material use trend and generating a replenishment plan, and finally data processing and synchronous management are realized through a data processing module. Manual operation is reduced, management efficiency is improved, timeliness and reliability of material supply are ensured, and the method is suitable for the fields of electric power engineering and the like.

Description

Technical Field

[0001] The present invention relates to an operation method of a mobile emergency warehouse, and in particular to an operation method of an unmanned intelligent mobile emergency warehouse. Background Art

[0002] In many areas of modern society, especially in power engineering, emergency rescue and medical equipment supply, efficient management and timely supply of materials are crucial. However, traditional warehouse management methods often have problems such as high labor costs, complex operations and low efficiency, which are difficult to meet the needs of modern and efficient management. With the development of technology, unmanned intelligent mobile emergency warehouse cabinets have emerged. Its purpose is to improve the efficiency of material management and supply through intelligent means, reduce manual intervention, and ensure that materials can be supplied in time in emergency situations.

[0003] Traditional warehouse management usually relies on manual operations, mainly including the warehousing, outbound, inventory, classification, storage and allocation of materials. Specifically, these links include: First, during the warehousing process, manual records of the material code, name, specification, quantity and other information are required, and the materials are classified and placed in designated locations. Secondly, when the materials are outbound, the materials are manually extracted according to demand, and the outbound information is recorded and the inventory data is updated. In addition, regular inventory counts need to check the actual inventory with the recorded inventory to find and correct inventory differences. The material classification and storage links classify the materials according to their attributes and arrange appropriate storage locations. In terms of allocation and supply, the materials need to be allocated and supplied according to the usage needs to ensure that the materials are in place in a timely manner.

[0004] Although these processes can achieve warehouse management to a certain extent, there are still obvious defects and problems. First, high labor costs are a major problem. A large amount of human resources are required for daily operations and management, which increases the operating costs of enterprises. Secondly, manual operations are inefficient. The speed and accuracy are affected by personal ability and working environment, and errors and delays are prone to occur. Third, data lag is another challenge. The process of manual recording and updating data is cumbersome, which can easily lead to information lag, thus affecting the timeliness of decision-making. Management complexity cannot be ignored. There are many types and quantities of materials, and the management of classification and storage is difficult, which is prone to confusion and errors. Finally, in an emergency, manual operations cannot respond quickly, and it is difficult to ensure the timely supply of materials, which further highlights the shortcomings of traditional warehousing management methods. Summary of the invention

[0005] In order to solve the above problems in the prior art, the present invention proposes an operation method of an unattended intelligent mobile emergency cabinet, which is characterized in that the operation method comprises the following steps:

[0006] a. System initialization: Obtain and configure material information, authority verification information, order generation and inventory update parameters, alarm thresholds and decision parameters, gravity sensing device configuration, and data processing and transmission strategies through the system configuration module;

[0007] b. Material information configuration: collect basic information of materials and generate a directory tree through the material detailed directory tree module;

[0008] c. Permission verification: Permission verification is performed through face recognition and permission verification modules;

[0009] d. Order generation: Generate orders and update inventory through automatic order generation and inventory update modules;

[0010] e. Material in and out of the warehouse: monitor the material in and out of the warehouse and update the inventory information through the gravity sensing unit;

[0011] f. Inventory monitoring: Monitor inventory status and issue alarm signals through inventory alarm and decision support modules;

[0012] g. Intelligent forecasting and decision support: Use the intelligent forecasting unit to predict material usage trends and generate replenishment plans;

[0013] h. Data processing and synchronization: Data is processed and transmitted through the data processing module to achieve comprehensive management of material management, authority verification, order generation, inventory update, alarm and decision support, gravity sensing and data processing.

[0014] The system initialization further includes dynamically adjusting the power distribution of each part according to the energy consumption data and the system operation status, the steps are as follows:

[0015] a-1. Collect real-time energy consumption data of each device;

[0016] a-2. Based on the current system load, give priority to ensuring the power supply of key equipment;

[0017] a-3. Limit the power or cut off the power supply to non-critical equipment to reduce overall energy consumption.

[0018] The material information configuration includes the following steps:

[0019] a. Material data collection steps: Obtain material information from suppliers, production systems or manual user input, including material codes, names, specifications, pictures and descriptions;

[0020] b. Directory tree generation step: Generate a directory tree based on the collected material information according to the classification rules;

[0021] c. Directory tree storage step: store the generated directory tree structure in the database for quick retrieval and update;

[0022] d. Intelligent material classification step: Based on the set classification rules, use machine learning algorithms to intelligently classify materials;

[0023] e. Classification result verification step: Regularly verify the accuracy of classification results and adjust classification rules and model parameters according to actual usage;

[0024] f. Partition storage optimization steps: Optimize the storage location and method of materials in the warehouse according to the frequency and characteristics of materials;

[0025] g. Storage strategy adjustment steps: dynamically adjust the storage location and method of materials based on real-time inventory data and material usage to ensure optimal use of warehouse space;

[0026] h. Result storage step: Store the classification and storage optimization results in the database for quick retrieval and update.

[0027] The authorization verification includes the following steps:

[0028] a. System initialization steps: start the system, and the camera and sensor enter standby mode;

[0029] b. Image capture step: When a person approaches the warehouse entrance, a high-resolution camera and infrared sensor are used to capture facial images;

[0030] c. Image preprocessing step: preprocess the captured original image, including image enhancement, grayscale conversion and noise removal;

[0031] d. Feature extraction step: extract facial feature points from the preprocessed image using a convolutional neural network (CNN);

[0032] e. Feature comparison step: compare the extracted feature points with the authorized personnel features in the database and calculate the distance between the feature points;

[0033] f. Permission verification step: Permission verification is performed based on the feature comparison results. If the feature comparison distance is less than the preset threshold, entry into the warehouse is allowed, otherwise entry is denied;

[0034] g. Result display step: The verification result is displayed on the display screen and voice prompt system. If the verification is passed, the controller opens the warehouse door;

[0035] h. Data update step: regularly update the facial feature database of authorized personnel;

[0036] i. Database management steps: Enter the facial images and identity information of authorized personnel through the background management system and back up the database regularly.

[0037] The order generation comprises the following steps:

[0038] a. User demand input step: The user enters the code and quantity of the required materials through the system interface;

[0039] b. Inventory query step: The system queries the current inventory data to determine the inventory status of the required materials;

[0040] c. Material selection step: select the materials that need to be shipped out or replenished based on user input and inventory status;

[0041] d. Order generation steps: Automatically generate an outbound order or replenishment order based on the material selection results, specifically i. When the material required by the user is in sufficient quantity in inventory, the system generates an outbound order, including material code, name, quantity, storage location, outbound time and other information; ii. When the material required by the user is in insufficient quantity in inventory, the system generates a replenishment order, including material code, name, quantity, supplier information, estimated arrival time and other information.

[0042] The materials storage and unloading comprises the following steps:

[0043] Outbound operation steps: According to the generated outbound order, the system controls the gravity sensing unit and shelf indicator lights to guide the operator to outbound materials, and monitors the weight changes of materials in real time. After outbound delivery, the system automatically updates the inventory data;

[0044] Warehouse entry operation steps: According to the generated replenishment order, after the materials arrive, the system controls the gravity sensing unit and shelf indicator light to guide the operator to enter the materials into the warehouse, and monitors the weight changes of the materials in real time. After entering the warehouse, the system automatically updates the inventory data;

[0045] Data synchronization steps: collect data on material outbound and inbound operations in real time, process and update inventory information in the system database, and synchronize the updated inventory data to other modules of the system.

[0046] The inventory monitoring comprises the following steps:

[0047] a. Alarm threshold setting steps: according to the importance and usage frequency of different materials, the alarm threshold of inventory quantity is set and managed through the alarm threshold setting unit;

[0048] b. Real-time inventory monitoring step: monitor the inventory status in real time through the gravity sensing unit and data processing module, collect inventory data, monitor and analyze the inventory data, and issue an alarm signal when the quantity of materials approaches or falls below the alarm threshold;

[0049] c. Alarm notification step: notify warehouse management personnel via SMS, email or system message to perform replenishment or adjustment operations.

[0050] The intelligent prediction and decision support further includes

[0051] a. Intelligent forecasting step: Based on historical data and current inventory status, the intelligent forecasting unit predicts material usage trends and demand changes;

[0052] b. Replenishment plan generation step: Based on the forecast results of the intelligent forecast unit and the current inventory status, the replenishment plan generation unit calculates the replenishment demand and automatically generates a replenishment plan and adjustment strategy, including material code, name, replenishment quantity, supplier information and estimated arrival time;

[0053] c. Data processing and analysis steps: Process and analyze inventory data through the data processing unit, generate data reports, optimize replenishment strategies and adjust inventory structure.

[0054] Beneficial effects: The operating method of the unattended intelligent mobile emergency cabinet proposed in the present invention realizes comprehensive intelligent and automated management through the steps of system configuration, material information management, authority verification, order generation, material entry and exit, inventory monitoring, intelligent prediction and data processing. The method can efficiently configure material information, accurately verify entry and exit permissions, automatically generate and update orders, monitor and update inventory status in real time, intelligently predict material demand and generate replenishment plans, effectively reduce manual operations, and improve management efficiency. In particular, through the intelligent prediction and decision support modules, it is possible to predict material usage trends in advance, optimize inventory structure, avoid excess or shortage of inventory, and ensure the timeliness and reliability of material supply, which is widely applicable to fields such as power engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application, but do not constitute an improper limitation of the present invention. In the drawings:

[0056] Figure 1 It is the overall design block diagram of the unattended intelligent mobile emergency storage cabinet of the present invention;

[0057] Figure 2 It is a connection relationship diagram between the modules of the unattended intelligent mobile emergency storage cabinet of the present invention;

[0058] Figure 3 The invention discloses an unattended intelligent mobile emergency storage cabinet operation method. DETAILED DESCRIPTION

[0059] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0060] Example 1: Overall design of unattended intelligent mobile emergency storage cabinet

[0061] An unattended intelligent mobile emergency storage cabinet, the overall design of which is as follows Figure 1 As shown, it includes system configuration module 1, material detailed directory tree module 2, face recognition and authority verification module 3, automatic order generation and inventory update module 4, inventory alarm and decision support module 5, gravity sensing unit 6 and data processing module 7. These modules work together through specific connection relationships to realize various functions of the warehouse, such as material management, authority verification, order generation, inventory update, alarm and decision support, gravity sensing and data processing, etc.

[0062] As attached Figure 2 As shown in the figure, the connection relationship between each module is specifically explained.

[0063] The system configuration module is connected to the material detail directory tree module to obtain and configure material information, and is connected to the face recognition and authority verification module to configure authority verification information. It is also connected to the automatic order generation and inventory update module, the inventory alarm and decision support module, the gravity sensing unit and the data processing module to configure order generation and inventory update parameters, alarm thresholds and decision parameters, gravity sensing equipment, and data processing and transmission strategies.

[0064] The material detail directory tree module is connected to the system configuration module to receive the configured material information, and is connected to the automatic order generation and inventory update module to provide material information to generate orders and update inventory. It is also connected to the data processing module to transmit and receive material information data.

[0065] The face recognition and permission verification module is connected to the system configuration module to receive and configure permission verification information, and is connected to the data processing module to transmit and receive permission verification data. The automatic order generation and inventory update module is connected to the system configuration module to receive order generation and inventory update parameters. It is also connected to the material detail directory tree module, inventory alarm and decision support module, gravity sensing unit and data processing module to obtain material information to generate orders and update inventory, receive alarms and decision information to generate replenishment plans, monitor weight changes of materials out of the warehouse and in the warehouse, and transmit and receive relevant data.

[0066] The inventory alarm and decision-making assistance module is connected to the system configuration module, receives and configures alarm thresholds and decision parameters, and is connected to the automatic order generation and inventory update module and the data processing module to provide alarm and decision information to generate replenishment plans, as well as transmit and receive alarm and decision data. The gravity sensing unit is connected to the system configuration module to configure and manage gravity sensing devices, and is connected to the automatic order generation and inventory update module and the data processing module to monitor the weight changes of materials entering and leaving the warehouse, and transmit and receive weight data.

[0067] As a key node, the data processing module is connected to the system configuration module, material detail directory tree module, face recognition and authority verification module, automatic order generation and inventory update module, inventory alarm and decision support module and gravity sensing unit. It is responsible for configuring data processing and transmission strategies, and transmitting and receiving various data.

[0068] Through the above connection relationship, each module works together to realize the overall functions of the unmanned intelligent mobile emergency cabinet. These functions include material management, authority verification, order generation, inventory update, alarm and decision support, gravity sensing and data processing, ensuring that the system can still operate efficiently and intelligently without human supervision.

[0069] Example 2: System Configuration Module

[0070] System configuration module 1 is used to obtain and configure material information, authority verification information, order generation and inventory update parameters, alarm thresholds and decision parameters, gravity sensing equipment configuration, and data processing and transmission strategies; in order to improve energy utilization efficiency, system configuration module 1 is equipped with an energy management unit that can intelligently allocate and manage power resources and reduce energy waste.

[0071] The system configuration module in the embodiment is used to obtain and configure material information, authority verification information, order generation and inventory update parameters, alarm thresholds and decision parameters, gravity sensing device configuration, and data processing and transmission strategies. The accuracy and efficiency of each configuration are ensured through the user interface and automated processes.

[0072] The system configuration module obtains material information from the material detail directory tree module and performs configuration. First, through the material information collection step, the basic information of the material is obtained from the material detail directory tree module, including the material code, name, specification, picture and description. Manual entry and batch import functions are provided to facilitate users to add and update material information. Subsequently, the material information configuration step sets the material classification rules, classifies the materials by category, purpose, material, etc., configures the material storage location, optimizes the storage location in the warehouse based on the frequency of use and characteristics of the materials, and configures the safety inventory and replenishment parameters of the materials to ensure the efficiency of material management.

[0073] In terms of permission verification information, the system configuration module is connected to the face recognition and permission verification module to configure permission verification information. The permission information collection step obtains and enters the basic information of the authorized personnel, including name, work number, department, and facial image, etc., and provides a batch import function to facilitate users to quickly add authorized personnel information. The permission information configuration step sets the permission level, assigns different permission levels according to the personnel's position and responsibilities, configures access control rules, and defines the areas and operation permissions that personnel with different permission levels can access.

[0074] The system configuration module is also connected to the automatic order generation and inventory update module to configure order generation and inventory update parameters. In terms of order generation parameter configuration, set the order generation rules, including the generation conditions and priorities of outbound orders and replenishment orders, configure the order format and content, and define the basic information fields and formats of the orders. The inventory update parameter configuration steps set the inventory update rules, including the trigger conditions and update frequency of inventory updates, configure the inventory warning threshold, define the upper and lower limits of inventory and the safety stock, and ensure the accuracy and timeliness of inventory management.

[0075] In addition, the system configuration module is connected to the inventory alarm and decision support modules to configure alarm thresholds and decision parameters. The alarm threshold configuration step sets the alarm threshold for material inventory, defines alarm trigger conditions and alarm levels, and configures alarm notification methods, including SMS, email, and system messages, to ensure that alarm information is communicated in a timely manner. The decision parameter configuration step sets the parameters for decision support, including material usage frequency analysis, demand forecasting, and replenishment plan generation, configures decision rules, and defines decision strategies and processing procedures under different circumstances.

[0076] In terms of gravity sensing device configuration, the system configuration module is connected to the gravity sensing unit to configure and manage the gravity sensing device. The device information collection step obtains the basic information of the gravity sensing device, including the device model, specifications, installation location and calibration parameters, and provides manual entry and batch import functions to facilitate users to add and update device information. The device configuration and management step sets the working mode and collection frequency of the gravity sensing device to ensure the efficient operation of the device, configures the calibration parameters and maintenance plan of the device, and calibrates the device regularly to ensure the accuracy of the measurement data.

[0077] The system configuration module is also connected to the data processing module to configure data processing and transmission strategies. The data processing configuration step sets data processing rules, including data collection, preprocessing, analysis and storage, configures data processing algorithms and models, and defines specific steps and methods for data processing. The data transmission configuration step sets data transmission rules, including the frequency, format and security strategy of data transmission, configures data transmission protocols and encryption methods, and ensures the reliability and security of data transmission.

[0078] The system configuration module provides a user-friendly interface to facilitate users to perform various configuration operations. The specific functions of the user interface include information entry and import, providing manual entry and batch import functions, which facilitate users to quickly add and update various types of information. The parameter setting and management interface facilitates users to configure and adjust various parameters. The alarm and notification interface facilitates users to view and process various alarm information. The data query and analysis function facilitates users to view and analyze various types of data.

[0079] The energy management unit is used to intelligently allocate and manage power resources and reduce energy waste. It includes the following parts:

[0080] Energy monitoring module: real-time monitoring of energy consumption data of various parts of the system, including gravity sensing unit 6, shelf indicator lights, electronic tags, etc.

[0081] Energy allocation algorithm: Dynamically adjust the power allocation of each part according to energy consumption data and system operation status. The algorithm steps are as follows:

[0082] 1. Collect real-time energy consumption data of each device.

[0083] 2. Based on the current system load, give priority to ensuring the power supply of key equipment.

[0084] 3. Limit power or power off non-critical equipment to reduce overall energy consumption.

[0085] Energy consumption optimization module: Based on historical energy consumption data and usage patterns, it provides energy consumption optimization suggestions, including equipment operation time adjustment and energy-efficient equipment replacement.

[0086] Example 3: Material Details Directory Tree Module

[0087] The material detailed directory tree module 2 is used to establish a directory tree containing material codes, material names, material specifications, material pictures and material descriptions. In order to improve the efficiency of material management, the material detailed directory tree module 2 adds intelligent material classification and partition storage functions, which can optimize the storage location and method in the warehouse according to the frequency and characteristics of material use, thereby improving the efficiency of picking and placing.

[0088] (I) Establishment of material catalog tree

[0089] The material detailed directory tree module 2 first establishes a directory tree including material codes, material names, material specifications, material pictures and material descriptions. The specific method for establishing the directory tree includes the following steps:

[0090] Material data collection: Obtain material information from suppliers, production systems or manual user entry, including material codes, names, specifications, pictures and descriptions.

[0091] Directory tree generation algorithm: Generate a directory tree based on the collected material information according to certain classification rules. The algorithm steps are as follows:

[0092] 1. Classify materials, such as by category, purpose, material, etc.

[0093] 2. Create sub-directories under each category to further subdivide the materials.

[0094] 3. Generate leaf nodes based on the specific information of the materials, including material code, name, specifications, pictures and descriptions.

[0095] Directory tree storage: Store the generated directory tree structure in the database for quick retrieval and update.

[0096] (II) Intelligent material classification

[0097] The material detail catalog tree module 2 adds intelligent material classification function, which is achieved through the following technical means:

[0098] Classification rule setting: Set the rules for material classification, including but not limited to material purpose, frequency, weight, volume, etc.

[0099] Classification algorithm: Based on the set classification rules, the machine learning algorithm is used to intelligently classify materials. The algorithm steps are as follows:

[0100] 1. Collect historical inbound and outbound data as training data sets.

[0101] 2. Select appropriate machine learning algorithms, such as decision trees, random forests, etc., to train classification models.

[0102] 3. Use the trained model to classify new materials and store the classification results in the directory tree.

[0103] Classification result verification: Regularly verify the accuracy of classification results and adjust classification rules and model parameters according to actual usage.

[0104] (III) Partition storage optimization

[0105] The material detailed directory tree module 2 optimizes the storage location and method of materials in the warehouse according to the frequency and characteristics of materials through the partition storage function. The specific implementation includes the following steps:

[0106] Frequency of use analysis: Based on historical in-and-out data, analyze the frequency of use of each material. The calculation formula is as follows:

[0107]

[0108] Storage location optimization: Based on the frequency and characteristics of materials, the following optimization strategies are adopted:

[0109] 1. High-frequency materials should be stored in locations close to entrances and exits to reduce the time for retrieval and placement.

[0110] 2.Heavy materials are stored on the bottom shelves for easy access.

[0111] 3. Store larger materials in areas with larger space to avoid taking up too much storage space.

[0112] Storage strategy adjustment: Dynamically adjust the storage location and method of materials based on real-time inventory data and material usage to ensure optimal use of warehouse space.

[0113] (IV) Specific implementation methods

[0114] The specific implementation steps of material detail directory tree module 2 are as follows:

[0115] 1. Initialize the material directory tree, collect material information and generate the directory tree structure.

[0116] 2. Set classification rules, train classification models, and intelligently classify new materials.

[0117] 3. Analyze the frequency of material use and optimize the location and method of material storage.

[0118] 4. Dynamically adjust storage strategies to ensure optimal utilization of warehouse space.

[0119] 5. Store the classification and storage optimization results in the database for quick retrieval and update.

[0120] Example 4: Face recognition and permission verification module

[0121] The face recognition and permission verification module 3 is used to scan faces to verify entry and exit permissions. This module uses a high-precision face recognition algorithm to ensure that only authorized personnel can enter the warehouse, ensuring the safety of warehouse materials.

[0122] (I) Hardware composition

[0123] The face recognition and permission verification module 3 includes the following hardware components:

[0124] High-resolution camera: used to capture facial images of people entering.

[0125] Infrared sensor: used for face recognition in low-light environments.

[0126] Image processing unit: used to process the captured facial image and compare it with the authorized person data in the database.

[0127] Display screen and voice prompt system: used to display verification results and provide operation prompts.

[0128] Controller: used to control the operation of hardware such as cameras, sensors, and displays.

[0129] (II) Software Composition

[0130] The software part of face recognition and permission verification module 3 includes the following main functional modules:

[0131] Image capture module: responsible for capturing the facial images of people entering in real time and performing preprocessing such as image enhancement and noise removal.

[0132] Feature extraction module: extracts feature points from captured facial images using deep learning algorithms, including convolutional neural networks (CNN).

[0133] Feature matching module: compares the extracted feature points with the authorized personnel features in the database. The matching algorithms include Euclidean distance, cosine similarity, etc.

[0134] Permission verification module: performs permission verification based on the comparison results to decide whether to allow access to the warehouse.

[0135] Data update module: Regularly update the facial feature database of authorized personnel to ensure the timeliness and accuracy of data.

[0136] 3. High-precision face recognition algorithm

[0137] The face recognition and authority verification module 3 adopts a high-precision face recognition algorithm. The specific algorithm steps are as follows:

[0138] 1. Image preprocessing:

[0139] I processed =f(I raw )

[0140] Among them, I processed is the preprocessed image, I raw is the original captured image, and f is the preprocessing function, including image enhancement, grayscale, and noise removal operations.

[0141] 2. Feature extraction: F = CNN (I processed )

[0142] Among them, F is the feature vector extracted from the preprocessed image, and CNN is the convolutional neural network model.

[0143] 3. Feature comparison:

[0144]

[0145] Among them, D is the Euclidean distance, F i is the captured feature point, T i is the feature point corresponding to the authorized person in the database, n is the number of feature points, and i is the i-th feature point.

[0146] 4. Permission verification:

[0147] If D <Threshold,then access granted;otherwise,access denied.

[0148] Among them, Threshold is the preset feature comparison threshold.

[0149] (IV) System workflow

[0150] The workflow of face recognition and permission verification module 3 is as follows:

[0151] 1. Initialization: Start the system, and the camera and sensor enter standby mode.

[0152] 2. Image capture: When a person approaches the warehouse entrance, the camera and infrared sensor capture facial images.

[0153] 3. Feature extraction: The image capture module passes the image to the feature extraction module to extract facial feature points.

[0154] 4. Feature comparison: The feature extraction module passes the feature points to the feature comparison module to compare them with the authorized personnel features in the database.

[0155] 5. Permission verification: The feature matching module passes the matching results to the permission verification module to decide whether to allow access to the warehouse.

[0156] 6. Result display: The display screen shows the verification result, and the voice prompt system provides corresponding prompts; if the verification passes, the controller opens the warehouse door.

[0157] (V) Database Management

[0158] The database management in the face recognition and authority verification module 3 includes the following parts:

[0159] Authorized personnel data entry: Enter the facial image and identity information of authorized personnel through the background management system.

[0160] Data update: Regularly update the data of authorized personnel to ensure the accuracy of the database.

[0161] Data backup and recovery: Back up the database regularly to prevent data loss and provide data recovery function.

[0162] Example 5: Automatically generate order and inventory update module

[0163] The automatic order generation and inventory update module 4 is used to support the selection of multiple materials and generate orders, and update inventory information at the same time. This module can automatically generate outbound orders and replenishment orders according to actual needs, and update inventory data in real time after the materials are outbound or inbound, to ensure the accuracy and timeliness of inventory information.

[0164] (I) Functional composition

[0165] The automatic order generation and inventory update module 4 includes the following main functional units:

[0166] Material selection unit: used to select materials that need to be shipped out or replenished.

[0167] Order generation unit: automatically generates outbound orders or replenishment orders based on material selection results.

[0168] Inventory update unit: Update inventory data in real time after materials are shipped out or received.

[0169] Data synchronization unit: ensures that all inventory data is synchronized between various modules of the system.

[0170] (II) Material Selection Unit

[0171] The material selection unit selects materials that need to be shipped out or replenished according to user needs and inventory status. Its specific implementation includes the following steps:

[0172] User input requirements: The user enters the code and quantity of the required materials through the system interface.

[0173] Inventory Query: The system queries the current inventory data to determine the inventory status of the required materials.

[0174] Material selection algorithm: Select materials for shipment or replenishment based on user input and inventory status.

[0175] (III) Order generation unit

[0176] The order generation unit automatically generates a delivery order or a replenishment order based on the material selection results. Its specific implementation includes the following steps:

[0177] Generation of outbound order: When there is sufficient quantity of the materials required by the user in stock, the system generates an outbound order. The steps for order generation are as follows:

[0178] 1. Get the material code and quantity entered by the user.

[0179] 2. Query the material information in the material detail directory tree module 2, including material name, specifications, storage location, etc.

[0180] 3. Generate an outbound order, including material code, name, quantity, storage location, outbound time and other information.

[0181] Replenishment order generation: When the quantity of materials required by the user is insufficient in inventory, the system generates a replenishment order.

[0182] The steps to generate an order are as follows:

[0183] 1. Get the material code and quantity entered by the user.

[0184] 2. Query supplier information and determine the source of replenishment.

[0185] 3. Generate replenishment orders, including material code, name, quantity, supplier information, estimated arrival time and other information.

[0186] (IV) Inventory Update Unit

[0187] The inventory update unit updates the inventory data in real time after the materials are shipped out or in. Its specific implementation includes the following steps:

[0188] Outbound operation: According to the generated outbound order, the system controls the gravity sensing unit 6 and the shelf indicator light to guide the operator to outbound the materials, and monitors the weight changes of the materials in real time to ensure the accuracy of the outbound operation. After outbound, the system automatically updates the inventory data.

[0189] Warehouse entry operation: According to the generated replenishment order, after the materials arrive, the system controls the gravity sensing unit 6 and the shelf indicator light to guide the operator to enter the materials, and monitors the weight changes of the materials in real time to ensure the accuracy of the warehouse entry operation. After entering the warehouse, the system automatically updates the inventory data.

[0190] (V) Data synchronization unit

[0191] The data synchronization unit ensures that all inventory data is synchronized between the modules of the system. Its specific implementation includes the following steps:

[0192] Data collection: Real-time collection of data on material outbound and inbound operations.

[0193] Data processing: Process the collected data and update the inventory information in the system database.

[0194] Data distribution: Synchronize the updated inventory data to other modules of the system, such as the material detail directory tree module 2, inventory alarm and decision support module 5, etc., to ensure data consistency.

[0195] (VI) Automatically generate order and inventory update module workflow

[0196] The workflow of the automatic order generation and inventory update module 4 is as follows:

[0197] 1. User demand input: The user enters the code and quantity of the required materials through the system interface.

[0198] 2. Inventory query: The system queries the current inventory data to determine the inventory status of the required materials.

[0199] 3. Material selection: Select materials for shipment or replenishment based on user input and inventory status.

[0200] 4. Order generation: The system automatically generates an outbound order or a replenishment order based on the material selection results.

[0201] 5. Outgoing / ingoing operation: The system controls the gravity sensing unit 6 and the shelf indicator light to guide the operator to carry out the outgoing or incoming materials, and monitors the weight changes of the materials in real time.

[0202] 6. Inventory update: After the outbound or inbound operation is completed, the system automatically updates the inventory data.

[0203] 7. Data synchronization: The system synchronizes the updated inventory data to other modules of the system to ensure data consistency.

[0204] Example 6: Inventory Alert and Decision Support Module

[0205] The inventory alarm and decision-making support module 5 is used to set the inventory quantity alarm threshold to remind the warehouse management personnel to operate. In order to improve the intelligent level of inventory management, the inventory alarm and decision-making support module 5 adds an intelligent prediction unit, which can predict the material usage trend and demand changes in advance based on historical data and current inventory status, automatically generate replenishment plans and adjustment strategies, and avoid sudden shortages or excess inventory.

[0206] (I) Functional composition

[0207] The inventory warning and decision support module 5 includes the following main functional units:

[0208] Alarm threshold setting unit: used to set and manage inventory quantity alarm thresholds.

[0209] Real-time inventory monitoring unit: used to monitor inventory status in real time.

[0210] Intelligent Forecasting Unit: used to predict material usage trends and demand changes based on historical data and current inventory status.

[0211] Replenishment plan generation unit: used to automatically generate replenishment plans and adjustment strategies.

[0212] Data processing unit: used to process and analyze inventory data and support decision-making assistance.

[0213] (II) Alarm threshold setting unit

[0214] The alarm threshold setting unit is used to set and manage the inventory quantity alarm threshold. Its specific implementation includes the following steps:

[0215] Alarm threshold setting: Set different alarm thresholds according to the importance and usage frequency of different materials. The alarm threshold setting formula is as follows:

[0216] Alarm threshold = baseline inventory × safety factor

[0217] Among them, the benchmark inventory level is the average inventory level calculated based on historical data, and the safety factor is the adjustment factor set according to the importance of the materials.

[0218] Threshold management: Provides a user interface for managers to adjust alarm thresholds and review and update them regularly.

[0219] (III) Real-time Inventory Monitoring Unit

[0220] The real-time inventory monitoring unit is used to monitor the inventory status in real time to ensure that an alarm is issued in time when the quantity of materials reaches the alarm threshold. Its specific implementation includes the following steps:

[0221] Data collection: Inventory data is collected in real time through the gravity sensing unit 6 and the data processing module 7.

[0222] Status monitoring: Monitor and analyze the collected inventory data. When the quantity of materials approaches or falls below the alarm threshold, the system will issue an alarm signal.

[0223] Alarm notification: Notify warehouse managers via SMS, email or system message to prompt them to replenish or adjust inventory.

[0224] (IV) Intelligent prediction unit

[0225] The intelligent prediction unit predicts material usage trends and demand changes based on historical data and current inventory status. Its specific implementation includes the following steps:

[0226] Data preprocessing: Preprocess historical inbound and outbound data, including data cleaning, denoising and standardization.

[0227] Forecasting model selection: Select an appropriate forecasting model, such as a time series model, regression analysis model, etc. The specific model selection is based on the material usage characteristics and data characteristics.

[0228] Prediction model training: Use historical data to train prediction models.

[0229] Generate forecast results: Use the trained model to predict future material usage trends and demand changes, and generate forecast results.

[0230] Forecast result verification: Regularly verify the accuracy of forecast results and adjust forecast model parameters based on actual usage.

[0231] (V) Replenishment plan generation unit

[0232] The replenishment plan generation unit automatically generates a replenishment plan and adjustment strategy based on the prediction results of the intelligent prediction unit and the current inventory status. Its specific implementation includes the following steps:

[0233] Replenishment demand calculation: Calculate the replenishment demand in the future based on the forecast results. The calculation formula is as follows:

[0234] Replenishment demand = forecast usage - current inventory

[0235] Replenishment plan generation: Generate a replenishment plan based on replenishment needs, including material code, name, replenishment quantity, supplier information, estimated arrival time, etc.

[0236] Adjustment strategy generation: Generate inventory adjustment strategies based on forecast results and actual inventory status, such as adjusting material storage locations, optimizing inventory structure, etc.

[0237] (VI) Data processing unit

[0238] The data processing unit is used to process and analyze inventory data to support decision-making. Its specific implementation includes the following steps:

[0239] Data summary: Summarize and organize the collected inventory data and generate data reports.

[0240] Data analysis: Use data analysis tools to analyze inventory data and identify problems and improvement opportunities in inventory management.

[0241] Decision support: Provide decision support suggestions based on data analysis results, such as optimizing replenishment strategies, adjusting inventory structure, etc.

[0242] (VII) Inventory Alert and Decision Support Module Workflow

[0243] The workflow of the inventory warning and decision support module 5 is as follows:

[0244] 1. Alarm threshold setting: Managers set and adjust inventory quantity alarm thresholds through the system interface.

[0245] 2. Real-time inventory monitoring: The system monitors the inventory status in real time through the gravity sensing unit 6 and the data processing module 7.

[0246] 3. Alarm notification: When the quantity of materials approaches or falls below the alarm threshold, the system sends an alarm signal and notifies the warehouse manager.

[0247] 4. Intelligent forecasting: The system predicts material usage trends and demand changes based on historical data and current inventory status.

[0248] 5. Replenishment plan generation: Automatically generate replenishment plans and adjustment strategies based on forecast results and current inventory status.

[0249] 6. Data processing and analysis: The system processes and analyzes inventory data to support decision-making.

[0250] Example 7: Gravity Sensing Unit

[0251] The gravity sensing unit 6 is used to sense the weight and position of the material. Through the high-precision gravity sensor, the gravity sensing unit 6 can monitor the weight change of the material on each shelf in real time, thereby judging the placement of the material and ensuring the accuracy of the inventory data.

[0252] The working process of the gravity sensing unit 6 is as follows:

[0253] 1. Data collection: The gravity sensor collects the weight data of materials on the shelf in real time and performs preliminary processing through the data collection module.

[0254] 2. Data transmission: The preliminarily processed data is transmitted to the system configuration module through the data transmission module.

[0255] 3. Data processing: After receiving the weight data, the system configuration module further processes and analyzes the data.

[0256] 4. Weight change detection: monitor the changes in weight data in real time and calculate the weight change.

[0257] 5. Judgment of the picking and placing situation: judge the picking and placing situation of materials based on the weight change.

[0258] 6. Inventory update: The data processing module updates the material quantity and weight information in the inventory database based on the pick-and-place situation and weight change.

[0259] 7. Data synchronization: Synchronize the updated inventory data to other modules of the system to ensure data consistency.

[0260] The gravity sensing unit 6 uses a high-precision gravity sensor to monitor the weight changes of materials on the shelf in real time, judge the placement of materials, and ensure the accuracy of inventory data. Through data collection, data transmission, data processing, weight change detection, placement judgment and inventory update, intelligent, automated and efficient inventory management is achieved.

[0261] Embodiment 8: Data processing module

[0262] As a key node, the data processing module is connected to the system configuration module, material detail directory tree module, face recognition and authority verification module, automatic order generation and inventory update module, inventory alarm and decision support module and gravity sensing unit. It is responsible for configuring data processing and transmission strategies, and transmitting and receiving various data.

[0263] Example 9: Operation method of unattended intelligent mobile emergency storage cabinet

[0264] As attached Figure 3 As shown, the operation method of the unattended intelligent mobile emergency cabinet of the present invention comprises the following steps:

[0265] a. System initialization steps: obtain and configure material information, authority verification information, order generation and inventory update parameters, alarm thresholds and decision parameters, gravity sensing device configuration, and data processing and transmission strategies through the system configuration module;

[0266] b. Material information configuration steps: collect basic information of materials through the material detailed directory tree module, generate a directory tree containing material codes, material names, material specifications, material pictures and material descriptions, and configure material classification rules and storage locations;

[0267] c. Permission verification step: The facial recognition and permission verification module collects and inputs the facial image and identity information of the authorized personnel, and uses a high-precision facial recognition algorithm to verify permissions to ensure that only authorized personnel can enter the warehouse;

[0268] d. Order generation step: According to user needs and inventory status, the automatic order generation and inventory update modules automatically generate outbound orders or replenishment orders, including material code, name, quantity, storage location, supplier information and estimated arrival time, etc.;

[0269] e. Material in and out steps: The gravity sensing unit is used to monitor the material out and in operations, collect the weight data of the materials on the shelf in real time, and update the inventory information through the data processing module to ensure the accuracy of the inventory data;

[0270] f. Inventory monitoring step: Monitor inventory status in real time through inventory alarm and decision support modules, set inventory quantity alarm thresholds, and issue an alarm signal and notify warehouse managers when the quantity of materials approaches or falls below the alarm threshold;

[0271] g. Intelligent forecasting and decision support steps: The intelligent forecasting unit predicts material usage trends and demand changes based on historical data and current inventory status, generates replenishment plans and adjustment strategies, and ensures efficient inventory management;

[0272] h. Data processing and synchronization steps: The data of each module is processed and transmitted through the data processing module to ensure the real-time and consistency of the data in the system, and to achieve comprehensive management of material management, authority verification, order generation, inventory update, alarm and decision support, gravity sensing and data processing.

[0273] The material information configuration includes the following steps:

[0274] a. Material data collection steps: Obtain material information from suppliers, production systems or manual user input, including material codes, names, specifications, pictures and descriptions;

[0275] b. Directory tree generation step: Generate a directory tree based on the collected material information according to the classification rules;

[0276] c. Directory tree storage step: store the generated directory tree structure in the database for quick retrieval and update;

[0277] d. Intelligent material classification step: Based on the set classification rules, use machine learning algorithms to intelligently classify materials;

[0278] e. Classification result verification step: Regularly verify the accuracy of classification results and adjust classification rules and model parameters according to actual usage;

[0279] f. Partition storage optimization steps: Optimize the storage location and method of materials in the warehouse according to the frequency and characteristics of materials;

[0280] g. Storage strategy adjustment steps: dynamically adjust the storage location and method of materials based on real-time inventory data and material usage to ensure optimal use of warehouse space;

[0281] h. Result storage step: Store the classification and storage optimization results in the database for quick retrieval and update.

[0282] The authorization verification includes the following steps:

[0283] a. System initialization steps: start the system, and the camera and sensor enter standby mode;

[0284] b. Image capture step: When a person approaches the warehouse entrance, a high-resolution camera and infrared sensor are used to capture facial images;

[0285] c. Image preprocessing step: preprocess the captured original image, including image enhancement, grayscale conversion and noise removal;

[0286] d. Feature extraction step: extract facial feature points from the preprocessed image using a convolutional neural network (CNN);

[0287] e. Feature comparison step: compare the extracted feature points with the authorized personnel features in the database and calculate the distance between the feature points;

[0288] f. Permission verification step: Permission verification is performed based on the feature comparison results. If the feature comparison distance is less than the preset threshold, entry into the warehouse is allowed, otherwise entry is denied;

[0289] g. Result display step: The verification result is displayed on the display screen and voice prompt system. If the verification is passed, the controller opens the warehouse door;

[0290] h. Data update step: regularly update the facial feature database of authorized personnel;

[0291] i. Database management steps: Enter the facial images and identity information of authorized personnel through the background management system and back up the database regularly.

[0292] The order generation comprises the following steps:

[0293] a. User demand input step: The user enters the code and quantity of the required materials through the system interface;

[0294] b. Inventory query step: The system queries the current inventory data to determine the inventory status of the required materials;

[0295] c. Material selection step: select the materials that need to be shipped out or replenished based on user input and inventory status;

[0296] d. Order generation steps: Automatically generate an outbound order or replenishment order based on the material selection results, specifically i. When the material required by the user is in sufficient quantity in inventory, the system generates an outbound order, including material code, name, quantity, storage location, outbound time and other information; ii. When the material required by the user is in insufficient quantity in inventory, the system generates a replenishment order, including material code, name, quantity, supplier information, estimated arrival time and other information.

[0297] The materials storage and unloading comprises the following steps:

[0298] Outbound operation steps: According to the generated outbound order, the system controls the gravity sensing unit and shelf indicator lights to guide the operator to outbound materials, and monitors the weight changes of materials in real time. After outbound delivery, the system automatically updates the inventory data;

[0299] Warehouse entry operation steps: According to the generated replenishment order, after the materials arrive, the system controls the gravity sensing unit and shelf indicator light to guide the operator to enter the materials into the warehouse, and monitors the weight changes of the materials in real time. After entering the warehouse, the system automatically updates the inventory data;

[0300] Data synchronization steps: collect data on material outbound and inbound operations in real time, process and update inventory information in the system database, and synchronize the updated inventory data to other modules of the system.

[0301] The inventory monitoring comprises the following steps:

[0302] a. Alarm threshold setting steps: according to the importance and usage frequency of different materials, the alarm threshold of inventory quantity is set and managed through the alarm threshold setting unit;

[0303] b. Real-time inventory monitoring step: monitor the inventory status in real time through the gravity sensing unit and data processing module, collect inventory data, monitor and analyze the inventory data, and issue an alarm signal when the quantity of materials approaches or falls below the alarm threshold;

[0304] c. Alarm notification step: notify warehouse management personnel via SMS, email or system message to perform replenishment or adjustment operations.

[0305] The intelligent prediction and decision support further includes

[0306] a. Intelligent forecasting step: Based on historical data and current inventory status, the intelligent forecasting unit predicts material usage trends and demand changes;

[0307] b. Replenishment plan generation step: Based on the forecast results of the intelligent forecast unit and the current inventory status, the replenishment plan generation unit calculates the replenishment demand and automatically generates a replenishment plan and adjustment strategy, including material code, name, replenishment quantity, supplier information and estimated arrival time;

[0308] c. Data processing and analysis steps: Process and analyze inventory data through the data processing unit, generate data reports, optimize replenishment strategies and adjust inventory structure

[0309] The above description is only a preferred embodiment of the present invention, so all equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the scope of the patent application of the present invention.

Claims

1. An operation method of an unmanned intelligent mobile emergency warehouse, characterized in that: The operation method comprises the following steps: a. System initialization: Obtain and configure material information, authority verification information, order generation and inventory update parameters, alarm thresholds and decision parameters, gravity sensing device configuration, and data processing and transmission strategies through the system configuration module; b. Material information configuration: collect basic information of materials and generate a directory tree through the material detailed directory tree module; c. Permission verification: Permission verification is performed through face recognition and permission verification modules; d. Order generation: Generate orders and update inventory through automatic order generation and inventory update modules; e. Material in and out of the warehouse: monitor the material in and out of the warehouse and update the inventory information through the gravity sensing unit; f. Inventory monitoring: Monitor inventory status and issue alarm signals through inventory alarm and decision support modules; g. Intelligent forecasting and decision support: Use the intelligent forecasting unit to predict material usage trends and generate replenishment plans; h. Data processing and synchronization: Data is processed and transmitted through the data processing module to achieve comprehensive management of material management, authority verification, order generation, inventory update, alarm and decision support, gravity sensing and data processing.

2. The method according to claim 1, characterized in that: The system initialization further includes dynamically adjusting the power distribution of each part according to the energy consumption data and the system operation status, the steps are as follows: a-1. Collect real-time energy consumption data of each device; a-2. Based on the current system load, give priority to ensuring the power supply of key equipment; a-3. Limit the power or cut off the power supply to non-critical equipment to reduce overall energy consumption.

3. The method according to claim 1, characterized in that: The material information configuration includes the following steps: a. Material data collection steps: Obtain material information from suppliers, production systems or manual user input, including material codes, names, specifications, pictures and descriptions; b. Directory tree generation step: Generate a directory tree based on the collected material information according to the classification rules; c. Directory tree storage step: store the generated directory tree structure in the database for quick retrieval and update; d. Intelligent material classification step: Based on the set classification rules, use machine learning algorithms to intelligently classify materials; e. Classification result verification step: Regularly verify the accuracy of classification results and adjust classification rules and model parameters according to actual usage; f. Partition storage optimization steps: Optimize the storage location and method of materials in the warehouse according to the frequency and characteristics of materials; g. Storage strategy adjustment steps: dynamically adjust the storage location and method of materials based on real-time inventory data and material usage to ensure optimal use of warehouse space; h. Result storage step: Store the classification and storage optimization results in the database for quick retrieval and update.

4. The method according to claim 1, characterized in that: The authorization verification includes the following steps: a. System initialization steps: start the system, and the camera and sensor enter standby mode; b. Image capture step: When a person approaches the warehouse entrance, a high-resolution camera and infrared sensor are used to capture facial images; c. Image preprocessing step: preprocess the captured original image, including image enhancement, grayscale conversion and noise removal; d. Feature extraction step: extract facial feature points from the preprocessed image using a convolutional neural network; e. Feature comparison step: compare the extracted feature points with the authorized personnel features in the database and calculate the distance between the feature points; f. Permission verification step: Permission verification is performed based on the feature comparison results. If the feature comparison distance is less than the preset threshold, entry into the warehouse is allowed, otherwise entry is denied; g. Result display step: The verification result is displayed on the display screen and voice prompt system. If the verification is passed, the controller opens the warehouse door; h. Data update step: regularly update the facial feature database of authorized personnel; i. Database management steps: Enter the facial images and identity information of authorized personnel through the background management system and back up the database regularly.

5. The method according to claim 1, characterized in that: The order generation comprises the following steps: a. User demand input step: The user enters the code and quantity of the required materials through the system interface; b. Inventory query step: The system queries the current inventory data to determine the inventory status of the required materials; c. Material selection step: select the materials that need to be shipped out or replenished based on user input and inventory status; d. Order generation steps: Automatically generate an outbound order or replenishment order based on the material selection results, specifically i. When the material required by the user is in sufficient quantity in inventory, the system generates an outbound order, including material code, name, quantity, storage location, outbound time and other information; ii. When the material required by the user is in insufficient quantity in inventory, the system generates a replenishment order, including material code, name, quantity, supplier information, estimated arrival time and other information.

6. The method according to claim 1 or 5, characterized in that: The materials storage and unloading comprises the following steps: Outbound operation steps: According to the generated outbound order, the system controls the gravity sensing unit and shelf indicator light to guide the operator to outbound materials, and monitors the weight changes of materials in real time. After outbound delivery, the system automatically updates the inventory data; Warehouse entry operation steps: According to the generated replenishment order, after the materials arrive, the system controls the gravity sensing unit and shelf indicator light to guide the operator to enter the materials into the warehouse, and monitors the weight changes of the materials in real time. After entering the warehouse, the system automatically updates the inventory data; Data synchronization steps: collect data on material outbound and inbound operations in real time, process and update inventory information in the system database, and synchronize the updated inventory data to other modules of the system.

7. The method according to claim 1, characterized in that: The inventory monitoring comprises the following steps: a. Alarm threshold setting steps: according to the importance and usage frequency of different materials, the alarm threshold of inventory quantity is set and managed through the alarm threshold setting unit; b. Real-time inventory monitoring step: monitor the inventory status in real time through the gravity sensing unit and data processing module, collect inventory data, monitor and analyze the inventory data, and issue an alarm signal when the quantity of materials approaches or falls below the alarm threshold; c. Alarm notification step: notify warehouse management personnel via SMS, email or system message to perform replenishment or adjustment operations.

8. The method according to claim 1, characterized in that: The intelligent prediction and decision support further includes: a. Intelligent forecasting step: Based on historical data and current inventory status, the intelligent forecasting unit is used to forecast material usage trends and demand changes; b. Replenishment plan generation step: Based on the forecast results of the intelligent forecast unit and the current inventory status, the replenishment plan generation unit calculates the replenishment demand and automatically generates a replenishment plan and adjustment strategy, including material code, name, replenishment quantity, supplier information and estimated arrival time; c. Data processing and analysis steps: Process and analyze inventory data through the data processing unit, generate data reports, optimize replenishment strategies and adjust inventory structure.

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