A smart warehouse management system for bulk condiments
The design of the intelligent warehouse management system has solved the problems of chaotic classification and inefficient information management in the storage of bulk condiments, and has achieved accurate inventory monitoring and early anomaly identification, thereby improving storage quality and management efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FUQING DEHUI LIANCAI SUPPLY CHAIN CO LTD
- Filing Date
- 2026-03-02
- Publication Date
- 2026-06-02
AI Technical Summary
The existing bulk condiment warehousing management suffers from problems such as chaotic classification, large errors in inventory data, condiment deterioration due to abnormal storage environment, and inefficient information management, and lacks real-time monitoring and intelligent management.
The intelligent warehouse management system is adopted, which forms independent placement areas by separating shelves, side panels and partitions. Combined with weight sensors, gas detection sensors, temperature and humidity sensors and RFID readers, it realizes the classification and storage of condiments, real-time data collection and environmental monitoring, and uses weight change analysis module and threshold setting module for intelligent analysis and status determination.
It enables precise inventory management of condiments, improves the accuracy and real-time performance of storage quality monitoring, can identify abnormalities early and issue warnings, and enhances the intelligence and efficiency of warehouse management.
Smart Images
Figure CN122134250A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of storage equipment technology for bulk condiments, and more particularly to an intelligent storage management system for bulk condiments. Background Technology
[0002] Bulk condiments are widely used in food processing, catering services and other fields. The standardization, safety and efficiency of their warehousing management directly affect product quality, inventory control accuracy and ease of use. Intelligent warehousing management is a key link to ensure the storage quality and management efficiency of bulk condiments. The corresponding warehousing management system is indispensable in the storage scenario of bulk condiments. Existing bulk condiment storage facilities mostly use traditional shelving for simple classification and storage, relying on manual recording of goods information and manual inventory counting. They also lack real-time monitoring of the temperature and humidity of the storage environment and the gas state of the storage area. At the same time, the goods protection measures are relatively simple, which can easily lead to problems such as disordered classification, large errors in inventory data, abnormal storage environment leading to condiment deterioration, and inefficient goods information management. Therefore, improvements need to be made based on the above issues. Summary of the Invention
[0003] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent warehouse management system for bulk condiments.
[0004] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent warehouse management system for bulk condiments, including a shelf, with multiple side panels installed on both sides of the shelf and multiple equidistant partitions installed inside the shelf; the shelf is divided into multiple placement areas by the partitions and side panels, and detection components are installed in the placement areas; The management system is equipped with a weight change analysis module and a threshold setting module. The weight change analysis module preprocesses the acquired data, retrieves the initial and real-time weight data of bulk condiments, and calculates the cumulative weight change, weight change rate, and relative weight change rate. The threshold setting module divides storage levels based on experimental group scores, extracts temperature and humidity data from high-quality experimental groups, and calculates safe temperature and humidity ranges; it establishes a linear relationship between gas concentration and quality score to determine safe gas concentration thresholds; and it sets weight loss and weight gain thresholds by combining weight change data and experimental data, and constructs a quality status judgment model to determine the status of condiments.
[0005] Preferably, the data analysis steps of the weight change analysis module are as follows: M1: Arrange the collected data in chronological order, calculate the mean and standard deviation of the corresponding data collected at the same time, set the fluctuation range of each data item, mark the data that exceeds the fluctuation range as outliers, if the number of outliers exceeds 30% of the total number of data at that time, it is determined to be data anomaly and the re-examination process is started; otherwise, after removing outliers, the effective mean is calculated and used as the effective data at that time. M2: Based on the preprocessed effective weight data, retrieve the initial weight of the condiments upon arrival in the warehouse. and Real-time weight Calculate the cumulative weight change Rate of weight change and relative weight change rate This enables dynamic monitoring and quantitative evaluation of changes in the quality of condiments.
[0006] Preferably, the data analysis steps of the threshold setting module are as follows: N1: Establish a seasoning storage status scoring system based on experimental groups, extract temperature and humidity data sets corresponding to experimental groups with excellent storage status, calculate their mean and standard deviation, and set a safe temperature range in combination with the physical properties of seasonings. Within the safe humidity range Simultaneously, establish gas concentration... With quality rating linear relationship The coefficients were fitted using the least squares method. and And based on critical scores Calculate the safe gas concentration threshold using the safety redundancy factor. ; N2: Calculate the weight loss threshold based on weight change rate data under normal storage conditions and weight mutation information from deterioration experiments. Weight gain threshold A quality status judgment model is established, incorporating the influence coefficients of temperature, humidity, and gas concentration. By combining the real-time weight change rate with the preset threshold, the model can intelligently judge and warn whether the condiment is in a state of normal volatilization, slight moisture absorption, severe moisture absorption, or packaging leakage.
[0007] Preferably, the detection component includes multiple weight sensors installed inside the shelf, and two support plates are horizontally installed at the front and rear ends in the placement area, with the support plates mounted on the weight sensors.
[0008] Preferably, display panels are installed at the front and rear ends of the placement area, two display panels are installed at the far ends of two support plates, and a gas detection sensor is installed on the top surface of the placement area.
[0009] Preferably, the weight sensor and the gas detection sensor are electrically connected to the display panel.
[0010] Preferably, the placement area is movably hinged above the front and rear ends of a cargo door.
[0011] Preferably, RFID readers for reading condiment information are installed in the middle of both the front and rear ends of the shelf, and a temperature and humidity sensor is installed on the front side of one side of the top surface of the shelf.
[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. By combining shelves, side panels, and partitions, multiple independent placement areas are formed, enabling the categorized and organized storage of bulk condiments; furthermore, by combining weight sensors, support plates, and display panels, the weight data of condiments is collected in real time and presented intuitively, facilitating accurate inventory management for staff; and by combining gas detection sensors, temperature and humidity sensors, and RFID readers, the gas state, temperature, and humidity of the storage environment are monitored simultaneously, and the information of the goods is automatically read. 2. Through the weight change analysis module and threshold setting module, intelligent analysis and status determination of the condiment storage environment are achieved, effectively improving the accuracy and real-time performance of storage quality monitoring. The weight change analysis module, based on multi-sensor data preprocessing and dynamic weight calculation, can accurately reflect the real-time quality change trend of condiments. The threshold setting module, combined with experimental data, establishes a multi-parameter safety threshold and status determination model, which can intelligently identify abnormal situations such as normal volatilization, moisture absorption, and leakage, and achieve early warning and precise control. Attached Figure Description
[0013] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the overall three-dimensional structure proposed in this invention; Figure 2 This is a schematic diagram of the internal structure of the shelf proposed in this invention; Figure 3 This is a schematic diagram of the overall cross-sectional three-dimensional structure proposed in this invention; Figure 4 This is a schematic diagram of the three-dimensional structure of the detection component proposed in this invention; Figure 5 This is a flowchart of the system proposed in this invention.
[0014] The numbers in the diagram are: 1. Shelf; 2. Side panel; 3. Partition; 4. Door; 5. Weight sensor; 6. Support plate; 7. Display panel; 8. Gas detection sensor; 9. RFID reader; 10. Temperature and humidity sensor. Detailed Implementation
[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0016] Example 1: See Figures 1 to 4 This invention discloses an intelligent warehouse management system for bulk condiments, comprising a shelf 1 with multiple side panels 2 installed on both sides and multiple equidistant partitions 3 installed inside the shelf 1. The shelf 1 is divided into multiple placement areas by the partitions 3 and side panels 2, and a detection component is installed in each placement area. The shelf 1 is designed to support condiments, and the side panels 2 and partitions 3 are designed to separate different condiments. The detection component includes multiple weight sensors 5 installed inside the shelf 1, and two support plates 6 are horizontally installed at the front and rear ends of each placement area, with the support plates 6 mounted on the weight sensors 5. The weight sensors 5 and support plates 6 are designed to detect the weight information of the bulk condiments. The weight sensors 5 are of model SSH-100kg.
[0017] In this invention, display panels 7 are installed at the front and rear ends of the placement area, and two display panels 7 are installed at the far ends of two support plates 6. A gas detection sensor 8 is installed on the top surface of the placement area. The display panels 7 facilitate the intuitive display of the detection data of the two types of sensors. The gas detection sensor 8 facilitates the detection of the gas state in the placement area, ensuring the storage environment of bulk condiments. The gas detection sensor 8 is model O2-A3. The weight sensor 5 and the gas detection sensor 8 are electrically connected to the display panels 7 respectively. A cargo door 4 is movably hinged above the front and rear ends of the placement area. The cargo door 4 facilitates the control of the placement area. The inner condiment bottles serve a protective function, and the door 4 is made of transparent acrylic sheet. RFID readers 9 for reading condiment information are installed in the middle of both the front and rear sides of the shelf 1, and a temperature and humidity sensor 10 is installed on the front side of one side of the top surface of the shelf 1. The RFID readers 9 facilitate the reading of relevant information about the goods, enabling intelligent management of goods information. The model of the RFID reader 9 is HZ514M. The temperature and humidity sensor 10 facilitates the detection of temperature and humidity data in the storage environment, providing a basis for monitoring the condiment storage environment. The model of the temperature and humidity sensor 10 is TL-SEN102-TH.
[0018] Working principle: When using this invention, first connect all the electrical equipment on shelf 1 with wires and power it on. The electrical equipment includes the weight sensor 5 and gas detection sensor 8 in the detection components, as well as the display panel 7, RFID reader 9 and temperature and humidity sensor 10. After powering on, the staff opens the movable hinged door 4 at the front and rear ends of the shelf 1 (the door 4 is a transparent acrylic plate, which can protect the condiment bottles in the placement area). Then, the bulk condiments are placed in the placement area of shelf 1. Shelf 1 has multiple side panels 2 installed on both sides and multiple equidistant partitions 3 installed inside. Shelf 1 is divided into multiple placement areas by the side panels 2 and partitions 3. Two support plates 6 are horizontally installed at the front and rear ends of each placement area. The two support plates 6 are respectively installed on the corresponding weight sensors 5 in the shelf 1. After the bulk condiments are placed on the support plates 6, the weight sensors 5 detect the weight information of the bulk condiments in real time. Meanwhile, the gas detection sensor 8 installed on the top surface of the storage area continuously monitors the gas status within the storage area, ensuring a safe storage environment for bulk condiments. The temperature and humidity sensor 10 installed on one front side of the top surface of the shelf 1 simultaneously detects the temperature and humidity data of the entire storage environment, providing a reliable basis for monitoring the condiment storage environment. The weight sensor 5 and the gas detection sensor 8 are electrically connected to the display panels 7 installed at the front and rear ends of the storage area, respectively. The two display panels 7 are installed at opposite ends of the two support plates 6, which can intuitively display the weight data detected by the weight sensor 5 and the gas status data detected by the gas detection sensor 8, making it convenient for staff to view in real time. The RFID readers 9 installed in the middle of both the front and rear sides of the shelf 1 automatically read the relevant information of the condiments, realizing intelligent management of cargo information. During the storage of bulk condiments, staff can close the door 4, which provides dust protection and protection for the condiment bottles in the storage area through the transparent acrylic door 4. When it is necessary to retrieve or inventory condiments, staff can first open the cargo door 4 and view the weight and gas status data of the corresponding placement area through the display panel 7. At the same time, they can read and confirm the relevant information of the condiments through the RFID reader 9. Combined with the temperature and humidity data of the storage environment detected by the temperature and humidity sensor 10, the staff can complete the retrieval, inventory and other storage-related operations of the condiments. The whole process realizes intelligent monitoring and management of bulk condiment storage.
[0019] Example 2: See Figure 5 The RFID reader 9 has a built-in management system, which includes a weight change analysis module and a threshold setting module. The weight change analysis module preprocesses the acquired data, retrieves the initial and real-time weight data of bulk condiments, and calculates the cumulative weight change, weight change rate, and relative weight change rate. The threshold setting module divides storage levels based on experimental group scores, extracts temperature and humidity data from high-quality experimental groups, and calculates safe temperature and humidity ranges; it establishes a linear relationship between gas concentration and quality score to determine safe gas concentration thresholds; it combines weight change data and experimental data to set weight loss and weight gain thresholds, and constructs a quality status judgment model to determine whether the condiment is in a state of normal volatilization, moisture absorption, or packaging leakage. Data acquired by weight sensor 5, gas detection sensor 8 and temperature and humidity sensor 10 are retrieved and preprocessed. The preprocessed data is recorded as valid data. Preprocessing: The collected data is sorted according to the collection time, and corresponding items collected at the same time are processed. averaging the data and standard deviation The calculation, and the mean obtained from the calculation. and standard deviation Collect data fluctuation range for corresponding items The system is configured to compare the collected data for a given item with its fluctuation range, mark data outside the fluctuation range as outliers, and record the number of outliers. ,like If the collected data is abnormal, the data will be re-tested; if If outliers are removed, the mean of the remaining corresponding test data after outlier removal is calculated. The calculation, and the mean obtained from the calculation. As the corresponding data detected at the corresponding time; Weight data of bulk seasonings upon initial warehousing Perform the retrieval and retrieve the corresponding library. Real-time weight data at any given moment Then from the initial to Cumulative weight change at time step , A positive value indicates an increase in weight. A positive value indicates a decrease in weight; the rate of weight change , for Real-time weight data at any given moment; relative weight change rate ; An experimental group was established, and a ten-point scoring system was set up. The storage status of bulk condiments in the experimental group was divided into four levels: spoiled, warning, qualified, and high-quality, with scores of 3, 6, and 8 as the dividing lines. Extract all scores The temperature and humidity data of the experimental group were collected and summarized into the corresponding temperature data. Collection and humidity data Set, calculate the mean of the data in the corresponding dataset. , and standard deviation , Then the 95% confidence interval for the corresponding data is , The minimum safe temperature for seasonings. , This refers to the physical solidification point / crystallization temperature of this type of condiment, and its maximum safe temperature. , For safety redundancy coefficient, This refers to the critical temperature for oxidation / deterioration of this type of condiment. The data obtained from the above analysis were organized to determine the safe temperature range. ; at the minimum / highest safe temperature and Replace the minimum moisture retention humidity and critical moisture absorption humidity of this type of condiment with the mean and standard deviation of the temperature data, respectively, and replace the mean and standard deviation of the humidity data with the mean and standard deviation of the humidity data, respectively, to obtain the safe humidity range. ; The more severe the deterioration, the higher the gas concentration, and the lower the score; based on the above relationship, the gas concentration is determined. With quality rating They exhibit a linear negative correlation, that is The intercept is obtained by fitting using the least squares method. and slope Specific values; based on safety criticality scores Substituting into the equation, we obtain the critical gas concentration. Considering safety redundancy and risk level, the safety gas concentration threshold , Risk level coefficient; Based on the "weight change rate under normal storage conditions" and combined with the critical value of weight mutation in the deterioration experiment, two thresholds are established: "weight loss threshold" and "weight gain threshold"; normal weight loss baseline. , This represents the relative weight change rate for all control groups. The total number of samples, Sample number; normal weight gain benchmark , This represents the relative weight change rate of the weight gain control group; all [data / samples / etc.] were extracted. The mean relative weight change rate of the experimental group was calculated. Weight loss threshold Weight gain threshold Relative weight change threshold ; If the measured weight data continues to decrease, the rate is within the normal fluctuation range, and the ambient temperature, humidity, and gas concentration are all normal; quality status judgment value. Temperature influence coefficient , For statistical analysis of historical data The lower limit of the fluctuation range, for Temperature data at any given time; In such cases, it is determined that the cause is normal volatilization or evaporation; If the detected weight data continues to increase, the rate is within the normal fluctuation range, the humidity is high, and the gas concentration is normal; [Mass status judgment value] Humidity influence coefficient , For statistical analysis of historical data The upper limit of the fluctuation range, for Humidity data at any given time; At that time, it was determined to be slight moisture absorption; At that time, it was determined to be severely hygroscopic, and the condiments showed signs of clumping. If the measured weight decreases rapidly, exceeding the normal fluctuation range, and the gas concentration is abnormal, but the temperature and humidity are normal; the mass status judgment value... Gas influence coefficient , for Gas concentration data at any given time; If this occurs, it is determined to be due to packaging leakage or seal failure.
[0020] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. An intelligent warehouse management system for bulk condiments, comprising shelves (1), characterized in that: The shelf (1) has multiple side panels (2) installed on both sides, and multiple equidistant partitions (3) installed inside the shelf (1); the shelf (1) is divided into multiple placement areas by the partitions (3) and the side panels (2), and detection components are installed in the placement areas; The management system is equipped with a weight change analysis module and a threshold setting module. The weight change analysis module preprocesses the acquired data, retrieves the initial and real-time weight data of bulk condiments, and calculates the cumulative weight change, weight change rate, and relative weight change rate. The threshold setting module divides storage levels based on experimental group scores, extracts temperature and humidity data from high-quality experimental groups, and calculates safe temperature and humidity ranges; it establishes a linear relationship between gas concentration and quality score to determine safe gas concentration thresholds; and it sets weight loss and weight gain thresholds by combining weight change data and experimental data, and constructs a quality status judgment model to determine the status of condiments.
2. The intelligent warehouse management system for bulk condiments according to claim 1, characterized in that: The data analysis steps for the weight change analysis module are as follows: M1: Arrange the collected data in chronological order, calculate the mean and standard deviation of the corresponding data collected at the same time, set the fluctuation range of each data item, mark the data that exceeds the fluctuation range as outliers, if the number of outliers exceeds 30% of the total number of data at that time, it is determined to be data anomaly and the re-examination process is started; otherwise, after removing outliers, the effective mean is calculated and used as the effective data at that time. M2: Based on the preprocessed effective weight data, retrieve the initial weight of the condiments upon arrival in the warehouse. and Real-time weight Calculate the cumulative weight change Rate of weight change and relative weight change rate This enables dynamic monitoring and quantitative evaluation of changes in the quality of condiments.
3. The intelligent warehouse management system for bulk condiments according to claim 1, characterized in that: The data analysis steps for the threshold setting module are as follows: N1: Establish a seasoning storage status scoring system based on experimental groups, extract temperature and humidity data sets corresponding to experimental groups with excellent storage status, calculate their mean and standard deviation, and set a safe temperature range in combination with the physical properties of seasonings. Within the safe humidity range Simultaneously, establish gas concentration... With quality rating linear relationship The coefficients were fitted using the least squares method. and And based on critical scores Calculate the safe gas concentration threshold using the safety redundancy factor. ; N2: Calculate the weight loss threshold based on weight change rate data under normal storage conditions and weight mutation information from deterioration experiments. Weight gain threshold A quality status judgment model is established, incorporating the influence coefficients of temperature, humidity, and gas concentration. By combining the real-time weight change rate with the preset threshold, the model can intelligently judge and warn whether the condiment is in a state of normal volatilization, slight moisture absorption, severe moisture absorption, or packaging leakage.
4. The intelligent warehouse management system for bulk condiments according to claim 1, characterized in that: The detection assembly includes multiple weight sensors (5) installed in the shelf (1), and two support plates (6) are horizontally installed at the front and rear ends in the placement area, with the support plates (6) mounted on the weight sensors (5).
5. The intelligent warehouse management system for bulk condiments according to claim 4, characterized in that: Display panels (7) are installed at the front and rear ends of the placement area. Two display panels (7) are installed at the far ends of two support plates (6). A gas detection sensor (8) is installed on the top surface of the placement area.
6. The intelligent warehouse management system for bulk condiments according to claim 5, characterized in that: The weight sensor (5) and the gas detection sensor (8) are electrically connected to the display panel (7), respectively.
7. The intelligent warehouse management system for bulk condiments according to claim 1, characterized in that: The placement area is hinged at the front and rear ends with cargo doors (4).
8. The intelligent warehouse management system for bulk condiments according to claim 1, characterized in that: The shelf (1) is equipped with RFID readers (9) for reading condiment information in the middle of both the front and rear ends, and a temperature and humidity sensor (10) is installed on the front side of the top surface of the shelf (1).