Bulk commodity storage supervision method

Abstract

The invention provides a bulk commodity storage supervision method, which comprises the following steps: S1, multi-dimensional data acquisition: configuring acquisition equipment according to bulk commodity types, and acquiring commodity quantity data, environment data, safety protection data and circulation information; s2, data preprocessing: denoising, calibrating and splicing the collected original data; s3, data calculation and analysis: calculating the actual weight or volume of the commodity based on the preprocessed data; s4, real-time monitoring and early warning are carried out, and commodity inventory, environment and safety states are displayed on a monitoring terminal; and S5, control and information management are executed, and the automatic control equipment is docked to execute the regulation and control instruction. Through laser radar three-dimensional modeling and density dynamic correction, the method is superior to traditional manual estimation, solves the problem of inconsistency between accounts and reality, prevents and controls quality risks in real time, collects environmental data in real time, can effectively avoid losses such as grain mildew and dangerous chemical leakage, realizes multi-sensor linkage monitoring and automatic control, and realizes early warning and rapid disposal of risks such as fire and theft.

Description

Technical Field

[0001] This invention relates to the field of warehousing supervision technology, specifically a method for supervising bulk commodity warehousing. Background Technology

[0002] Currently, bulk commodity warehousing supervision still relies primarily on traditional manual methods, which have significant technical shortcomings: First, the accuracy of quantity measurement is low. For bulk commodities such as coal and grain, manual estimation of volume or weighing in multiple batches often results in errors exceeding 10%, and inventory data cannot be updated in real time, easily leading to discrepancies between records and actual inventory. Second, quality monitoring is lagging. The storage environment of bulk commodities directly affects quality, and the long intervals between manual inspections make it difficult to detect problems such as moldy grains and leaks of hazardous chemicals in a timely manner, causing economic losses. Third, safety control is weak. Manual personnel cannot cover the storage area 24 hours a day, resulting in delayed responses to safety risks such as fire prevention, theft prevention, and explosion prevention, which can easily lead to safety accidents. Fourth, data collaboration is poor. Warehousing data is only stored locally and cannot be shared with cargo owners, logistics companies, and financial institutions, forming data silos and hindering the efficiency of supply chain collaboration. Summary of the Invention

[0003] (a) Technical problems to be solved

[0004] To address the shortcomings of existing technologies, this invention provides a method for supervising bulk commodity warehousing, which solves problems such as discrepancies between records and actual inventory, slow response time, high incidence of safety accidents, and repetitive and complex system development.

[0005] (II) Technical Solution

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for supervising bulk commodity warehousing, comprising the following steps: S1: Multi-dimensional data collection. Data collection equipment is configured according to the type of bulk commodity to collect commodity quantity data, environmental data, safety protection data, and circulation information. S2: Data preprocessing. Noise reduction, calibration, and stitching are performed on the collected raw data. S3: Data calculation and analysis. The actual weight or volume of the commodity is calculated based on the preprocessed data. S4: Real-time monitoring and early warning. Commodity inventory, environmental, and safety status are displayed on the monitoring terminal. S5: Execution control and information management. Automatic control equipment is connected to execute control commands.

[0007] Preferably, for bulk commodities, lidar is used to collect point cloud data from at least two angles, and for canned goods, a level gauge combined with a pressure sensor is used to collect data.

[0008] Preferably, in step S2, a bilateral filtering algorithm is used to remove point cloud noise, and multi-angle point cloud data is stitched together by an iterative nearest point algorithm; mean filtering is used to calibrate the environmental sensor data.

[0009] Preferably, in step S3, the actual weight of the product is calculated by combining the volume of the three-dimensional model with the density correction coefficient. The density correction coefficient is retrieved from a preset coefficient table based on the product type, temperature and humidity, and stacking height. The quality change trend is predicted using an LSTM model, and the input parameters include historical environmental data and product characteristic parameters.

[0010] Preferably, in step S4, the warning threshold is set differently according to the commodity type. The humidity warning threshold for grain storage is 70-75%, and the gas concentration warning threshold for hazardous chemical storage is 0.05-0.1%. The alarm channels include audible and visual alarms, SMS push and APP notification.

[0011] Preferably, in step S5, the data can be manually triggered by the monitoring terminal or automatically triggered by the system; data sharing adopts hierarchical management of permissions, allowing cargo owners to query inventory data and regulatory agencies to query all data.

[0012] Preferably, the data acquisition module includes a lidar, weighing sensor, environmental sensor, RFID reader, and camera for collecting multi-dimensional data; the data processing module is used to perform data preprocessing, calculation, and analysis steps; the monitoring and early warning module includes a monitoring terminal and alarm unit for real-time display and anomaly alarms; the execution module connects to automatic control equipment for executing control commands; and the information management module is used for data storage, ledger generation, and multi-terminal sharing.

[0013] (III) Beneficial Effects

[0014] This invention provides a method for supervising the storage of bulk commodities. It has the following beneficial effects: 1. Through LiDAR 3D modeling and density dynamic correction, the measurement error of bulk commodity weight is ≤3%, which is better than traditional manual estimation and solves the problem of discrepancy between accounts and actual quantities; Real-time quality risk prevention and control: Real-time environmental data collection and quality anomaly response time are ≤5 seconds, which can effectively avoid losses such as grain mold and hazardous chemical leakage, and reduce the quality loss rate by ≥20%.

[0015] 2. Multi-sensor linkage monitoring and automatic control enable early warning and rapid response to risks such as fire and theft, reducing the incidence of safety accidents by ≥50%; breaking down information silos, all entities in the supply chain can share data in real time, improving warehouse supervision efficiency by more than 60%, and shortening the time for inbound and outbound verification to minutes; highly adaptable and versatile: customized supervision dimensions and thresholds for different bulk commodities, adaptable to multiple scenarios such as coal, grain, and hazardous chemicals, without the need for repeated system development, reducing application costs. Attached Figure Description

[0016] Figure 1 This is a flowchart of the steps of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example: like Figure 1 As shown in the figure, this embodiment of the invention provides a method for supervising bulk commodity warehousing, including the following steps: S1: Multi-dimensional data collection. Data collection equipment is configured according to the type of bulk commodity to collect commodity quantity data, environmental data, safety protection data, and circulation information. Quantity data includes LiDAR 3D point cloud data, weighing sensor weight data, or liquid level gauge liquid level data. Environmental data includes temperature, humidity, and gas concentration data. S2: Data preprocessing. The collected raw data is denoised, calibrated, and stitched together. Different types of data are associated with unique commodity identifiers and collection times to construct a structured dataset. All data is stored as a sequence of "grain warehouse number + grain variety - time". S3: Data calculation and analysis. Based on the preprocessed data, the actual weight or volume of the commodity is calculated. Algorithms are used to analyze the correlation between environmental data and commodity quality to predict quality change trends. S4: Real-time monitoring and early warning. The monitoring terminal displays commodity inventory, environmental, and safety status. When data exceeds preset thresholds, multi-channel alarms are triggered, and anomaly handling suggestions are generated. S5: Execution control and information management. The system connects to automatic control equipment to execute control commands, records all regulatory data, and generates ledgers. It supports data sharing among multiple entities. Cargo owners can query real-time inventory and historical data via an app without on-site verification.

[0019] For bulk commodities, lidar is used to collect point cloud data from at least two angles. For canned goods, a level gauge combined with a pressure sensor is used to collect data. The data collection frequency is 10-30 minutes per time.

[0020] In step S2, a bilateral filtering algorithm is used to remove point cloud noise, and multi-angle point cloud data is stitched together by an iterative nearest point algorithm; mean filtering is used to calibrate the environmental sensor data, with a calibration cycle of 24 hours / time.

[0021] In step S3, the actual weight of the goods is calculated by combining the volume of the three-dimensional model with the density correction coefficient. The density correction coefficient is retrieved from the preset coefficient table based on the type of goods, temperature and humidity, and stacking height. The trend of quality change is predicted using an LSTM model, and the input parameters include historical environmental data and goods characteristic parameters.

[0022] In step S4, the warning thresholds are set differently according to the commodity type. The humidity warning threshold for grain storage is 70-75%, and the gas concentration warning threshold for hazardous chemical storage is 0.05-0.1%. The alarm channels include audible and visual alarms, SMS push and APP notifications.

[0023] In step S5, it can be triggered manually through the monitoring terminal or automatically by the system; data sharing adopts hierarchical management of permissions, and cargo owners can query inventory data, while regulatory agencies can query the full data.

[0024] Data acquisition module: includes lidar, weighing sensor, environmental sensor, RFID reader and camera, used to collect multi-dimensional data; Data processing module: used to perform data preprocessing, calculation and analysis steps; Monitoring and early warning module: includes monitoring terminal and alarm unit, used for real-time display and abnormal alarm; Execution module: connects to automatic control equipment, used to execute control commands; Information management module: used for data storage, ledger generation and multi-terminal sharing.

[0025] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for supervising bulk commodity warehousing, characterized in that: Includes the following steps: S1: Multi-dimensional data collection, configuring collection equipment according to the type of bulk commodity to collect commodity quantity data, environmental data, safety protection data and circulation information; S2: Data preprocessing, which involves denoising, calibrating, and stitching the collected raw data; S3: Data calculation and analysis, calculating the actual weight or volume of goods based on preprocessed data; S4 Real-time monitoring and early warning, displaying product inventory, environmental and safety status on the monitoring terminal; S5: Execution control and information management, interfacing with automatic control equipment to execute control commands.

2. The bulk commodity warehousing supervision method according to claim 1, characterized in that: For bulk commodities, lidar is used to collect point cloud data from at least two angles, while for canned goods, a level gauge combined with a pressure sensor is used to collect data.

3. The bulk commodity warehousing supervision method according to claim 1, characterized in that: In step S2, a bilateral filtering algorithm is used to remove point cloud noise, and multi-angle point cloud data is stitched together by an iterative nearest point algorithm; mean filtering is used to calibrate the environmental sensor data.

4. The bulk commodity warehousing supervision method according to claim 1, characterized in that: In step S3, the actual weight of the goods is calculated by combining the volume of the three-dimensional model with the density correction coefficient. The density correction coefficient is retrieved from a preset coefficient table based on the type of goods, temperature and humidity, and stacking height. The trend of quality change is predicted using an LSTM model, with input parameters including historical environmental data and product characteristic parameters.

5. A method for supervising bulk commodity warehousing according to claim 1, characterized in that: In step S4, the warning thresholds are set differently according to the commodity type. The humidity warning threshold for grain storage is 70-75%, and the gas concentration warning threshold for hazardous chemical storage is 0.05-0.1%. The alarm channels include audible and visual alarms, SMS push and APP notifications.

6. The bulk commodity warehousing supervision method according to claim 1, characterized in that: In step S5, it can be triggered manually through the monitoring terminal or automatically by the system; data sharing adopts hierarchical management of permissions, and cargo owners can query inventory data, while regulatory agencies can query the full data.

7. A method for supervising bulk commodity warehousing according to claims 1-6, characterized in that: Data acquisition module: includes lidar, weighing sensor, environmental sensor, RFID reader and camera, used to collect multi-dimensional data; Data processing module: used to perform data preprocessing, calculation and analysis steps; Monitoring and early warning module: includes monitoring terminal and alarm unit, used for real-time display and abnormal alarm; Execution module: Connects to automatic control equipment and is used to execute control commands; Information management module: Used for data storage, ledger generation, and multi-terminal sharing.

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