Intelligent warehouse-in and warehouse-out recording system for monitoring equipment

Abstract

The invention relates to the technical field of warehouse-in recording systems, and discloses an intelligent warehouse-in and warehouse-out recording system for monitoring equipment, and the system comprises a monitoring equipment module which is used for recognizing the in-out condition of an object; the data acquisition and processing module is used for processing and storing the collected data; the Internet of Things module is used for connecting the monitoring equipment to the Internet and carrying out remote monitoring and management; the data analysis module is used for analyzing the access record, generating a report and providing visual display; the predictive analysis module is used for deeply analyzing the access records and predicting access conditions in a specific time period; according to the invention, predictive analysis is carried out on the data, the time sequence data is continuously updated and the moving average value is recalculated, so that the prediction of the number of the articles in and out in the future time period is provided in real time, the inventory management is optimized, and the unreasonable use of various spare part inventory of the underground water in the warehouse caused by excess or insufficient inventory is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of storage entry and exit recording systems, and in particular to an intelligent storage entry and exit recording system for monitoring equipment. Background Art

[0002] With the development of Internet of Things technology, the intelligent in-and-out storage record system has begun to introduce more sensors and connected devices to achieve real-time monitoring and tracking of various spare parts and in-and-out processes of warehouse groundwater. Through the Internet of Things platform, the intelligent management of various spare parts and in-and-out processes of warehouse groundwater can be achieved;

[0003] Traditional systems lack analysis and management capabilities for large-scale data, making it difficult to provide managers with in-depth data analysis and decision support. They lack analysis and management capabilities for large-scale data, as well as predictive equipment management.

[0004] After searching, the patent with announcement number CN109087038B discloses a prefabricated component in-and-out warehouse management method and system based on RFID technology. It has the function of completing the sorting and storage of information by reading the RFID chip, associating the in-warehouse information with the production information, so that the production of prefabricated components is not repeated, and providing a convenient, standardized and efficient prefabricated component in-and-out warehouse management method and system; at the same time, the management information is traceable throughout the process, the management method is paperless, and the management information is shared;

[0005] However, the above patents are difficult to provide high-quality data for predictive analysis modules, lack the ability to integrate with other system data, and are difficult to quickly analyze data and generate intelligent alarm functions, making it difficult to detect potential problems or risks in a timely manner. Therefore, an intelligent in-and-out storage record system for monitoring equipment is proposed. Summary of the invention

[0006] The present invention is to solve the problem in existing patents that the system lacks the ability to integrate with other system data, making it difficult to quickly analyze data and generate intelligent alarm functions, and proposes an intelligent storage and in-and-out recording system for monitoring equipment.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] Monitoring equipment module, used to identify the entry and exit of items;

[0009] Data collection and processing module, used to process and store the collected data;

[0010] IoT module, used to connect monitoring equipment to the Internet for remote monitoring and management;

[0011] Data analysis module, used to analyze input and output records, generate reports and provide visual presentation;

[0012] Predictive analysis module, which is used to conduct in-depth analysis of access records and predict access in a specific time period;

[0013] Intelligent alarm module to identify abnormal situations and notify relevant personnel in time;

[0014] The monitoring equipment module collects warehouse environment data and transmits it to the data collection and processing module. The data collection and processing module transmits the processed data to the Internet of Things module and the data analysis module. The data analysis module performs data analysis and mining on the processed data. The predictive analysis module performs time series analysis and prediction based on the analysis and mining of the data analysis module.

[0015] The above technical solution further includes:

[0016] The monitoring equipment module uses sensors to collect warehouse environment data and transmits the data to the system for analysis and processing. It monitors and manages the status of the warehouse environment or equipment by identifying patterns, trends and abnormal situations.

[0017] The data collection and processing module detects the device and reads its unique identification when the device passes through the warehouse entrance. The camera captures images or videos of the device entering and exiting. The collected data is transmitted to the central processing unit through a wireless network or a wired connection, and duplicate records are removed, missing values ​​and abnormal data are processed, raw data is parsed into structured data, and the processed data is stored in a database. The entry and exit records of the device are analyzed in real time to generate statistical reports.

[0018] The central processing unit is connected to various data sources, stores and manages the collected data, monitors and processes the data in real time, transmits and communicates data between different devices, cleans and preprocesses the collected data, including processing missing values ​​and standardizing data, and integrates and converts the processed data into data sets.

[0019] The security management module sets different permission levels for different users, automatically and regularly backs up data to restore it to its original state, records all user operations in the system, and regularly analyzes and reviews logs;

[0020] The IoT module needs to support the connection and communication of various devices, collect data from devices and sensors, remotely monitor and control connected devices, including remote switching, adjustment and fault diagnosis functions, and store data for integration with the cloud platform.

[0021] The data analysis module, by integrating machine learning and predictive modeling functions, predicts, classifies, clusters and recognizes patterns in data, processes data in real time, and analyzes and mines data for an in-depth understanding of the data.

[0022] The predictive analysis module predicts the purchase and spare quantity of equipment in the future by analyzing the usage of items, and uses time series analysis to predict the entry and exit of items in a certain period of time in the future. The moving average method is used as one of the algorithms for time series analysis. The moving average method is used to calculate the average value of time series data in a specific window to predict future trends:

[0023] MA_t=(X_{t-1}+X_{t-2}+...+X_{tn}) / n

[0024] Among them, MA_t represents the moving average at time point t, X_{t-1}, X_{t-2},..., X_{tn} are the data of several time points in the past, n is the window size, and the number of items in and out in each time period collected by the monitoring equipment is used as time series data, and the moving average method is used to predict the number of items in and out in a certain time period in the future.

[0025] The intelligent alarm module uses an anomaly detection algorithm to identify abnormal access behaviors and trigger corresponding intelligent alarms. The LOF algorithm is a commonly used anomaly detection algorithm that identifies abnormal points in a data set based on the concept of density. The degree of abnormality of a data point is determined by calculating the density ratio between each data point and its neighboring points. The LOF algorithm calculates the local outlier factor:

[0026]

[0027] Among them, p represents the data point to be judged, N represents the set of neighboring points of p, reach_dist(o,p) represents the reachable distance from o to p, which represents the density contribution of data point o to data point p, |N| represents the number of neighboring points, and takes the employee entry and exit records as the data set. The LOF algorithm is used to identify abnormal entry and exit behaviors, calculate their LOF values, and trigger intelligent alarms when the LOF values ​​exceed the preset threshold.

[0028] The present invention has the following beneficial effects:

[0029] 1. In the present invention, predictive analysis is performed on the data. By continuously updating the time series data and recalculating the moving average, a real-time forecast of the number of items entering and leaving in the future time period is provided to optimize inventory management and avoid unreasonable use of various spare parts inventory in the warehouse groundwater due to excess or insufficient inventory.

[0030] 2. In the present invention, data is analyzed through intelligent algorithms, and alarms are issued in time to deal with potential abnormal situations or problems. Real-time alarms are generated by analyzing large amounts of data to help managers make correct decisions quickly and reduce the impact of human factors on alarm processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is an overall system block diagram of an intelligent storage-in and storage-out recording system for monitoring equipment proposed by the present invention. DETAILED DESCRIPTION

[0032] Embodiment 1

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] like Figure 1 As shown, the present invention proposes an intelligent in-and-out storage record system for monitoring equipment, including: a monitoring equipment module, used to identify the in-and-out situation of items; a data acquisition and processing module, used to process and store the collected data; an Internet of Things module, used to connect the monitoring equipment to the Internet for remote monitoring and management; a data analysis module, used to analyze the in-and-out records, generate reports and provide visual displays; a predictive analysis module, used to perform in-depth analysis of the in-and-out records, and predict the in-and-out situation in a specific time period; an intelligent alarm module, used to identify abnormal situations and notify relevant personnel in a timely manner; the monitoring equipment module collects warehouse environment data and transmits it to the data acquisition and processing module, the data collected and processed by the data acquisition and processing module is transmitted to the Internet of Things module and the data analysis module, the data analysis module performs data analysis and mining on the processed data, and the predictive analysis module performs time series analysis and prediction based on the analysis and mining of the data analysis module;

[0035] The monitoring equipment module uses sensors to collect warehouse environment data and transmits the data to the system for analysis and processing. It monitors and manages the status of the warehouse environment or equipment by identifying patterns, trends and abnormal situations;

[0036] Data collection and processing module: When a device passes through the warehouse entrance, the RFID reader, barcode scanner or sensor detects the device and reads its unique identification. The camera captures images or videos of the device entering and exiting. The collected data is transmitted to the central processing unit through a wireless network or wired connection. Duplicate records are removed, missing values ​​and abnormal data are processed, raw data is parsed into structured data, and processed data is stored in the database. The entry and exit records of the device are analyzed in real time to generate statistical reports.

[0037] The central processing unit connects to various data sources, stores and manages the collected data, monitors and processes the data in real time, and transmits and communicates data between different devices. It cleans and preprocesses the collected data, including processing missing values ​​and standardizing data, and integrates and converts the processed data into data sets;

[0038] The predictive analysis module uses time series analysis to predict the entry and exit of items within a certain period of time in the future. The moving average method is used as one of the algorithms for time series analysis. The moving average method is used to calculate the average value of time series data within a specific window to predict future trends:

[0039] MA_t=(X_{t-1}+X_{t-2}+...+X_{tn}) / n

[0040] Among them, MA_t represents the moving average at time point t, X_{t-1}, X_{t-2},..., X_{tn} are the data of several time points in the past, n is the window size, and the number of items in and out in each time period collected by the monitoring equipment is used as time series data, and the moving average method is used to predict the number of items in and out in a certain time period in the future.

[0041] In this embodiment, the warehouse environment data is collected by the monitoring device module and the sensor, and the data is transmitted to the system for analysis and processing to sense various parameters and indicators in the environment, and the environmental data collected by the sensor is processed and integrated for real-time analysis, and the data is transmitted to the data collection and processing module through the monitoring device module, and various data sources are connected, the collected data is stored and managed, and the collected data is cleaned and preprocessed, including processing missing values ​​and standardized data, and the processed data is integrated and converted into a data set, and the data of the Internet of Things module is obtained to support the connection and communication of various devices;

[0042] Collect data from devices and sensors, remotely monitor and control connected devices, and transmit the data to the data analysis module through the data acquisition and processing module to predict, classify, cluster and pattern recognize the data, and process the data in real time.

[0043] Embodiment 2

[0044] like Figure 1 As shown, in the embodiments of the present invention, the Internet of Things module needs to support the connection and communication of various devices, collect data from devices and sensors, remotely monitor and control the connected devices, including remote switching, adjustment and fault diagnosis functions, and store the data and integrate it with the cloud platform; the data analysis module predicts, classifies, clusters and recognizes patterns of data by integrating machine learning and predictive modeling functions, and analyzes the real-time data, and mines the data for in-depth understanding;

[0045] The intelligent alarm module uses anomaly detection algorithms to identify abnormal in-and-out behaviors and trigger corresponding intelligent alarms. The LOF algorithm is a commonly used anomaly detection algorithm that identifies abnormal points in a data set based on the concept of density. The degree of abnormality of a data point is determined by calculating the density ratio between each data point and its neighboring points. The LOF algorithm calculates the local outlier factor:

[0046]

[0047] Among them, p represents the data point to be judged, N represents the set of neighboring points of p, reach_dist(o,p) represents the reachable distance from o to p, which represents the density contribution of data point o to data point p, |N| represents the number of neighboring points, and takes the employee entry and exit records as the data set. The LOF algorithm is used to identify abnormal entry and exit behaviors, calculate their LOF values, and trigger intelligent alarms when the LOF values ​​exceed the preset threshold.

[0048] In this embodiment, the data analysis module obtains data from the predictive analysis module, uses time series analysis to predict the entry and exit of items in a certain time period in the future, uses the moving average method as one of the algorithms for time series analysis, and uses the moving average method to collect the number of entry and exit of items in each time period collected by the monitoring equipment as time series data, and uses the moving average method to predict the entry and exit of items in a certain time period in the future. The data is transmitted to the intelligent alarm module through the data analysis module to use the anomaly detection algorithm to identify abnormal entry and exit behavior, and trigger the corresponding intelligent alarm. The employee entry and exit records are used as a data set, and the LOF algorithm is used to identify abnormal entry and exit behavior, calculate its LOF value, and trigger the intelligent alarm when the LOF value exceeds the preset threshold.

[0049] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent storage and in-and-out recording system for monitoring equipment, characterized in that: include: The monitoring equipment module uses sensors to collect warehouse environment data to identify the entry and exit of items; Data collection and processing module, used to process and store the collected data; IoT module, used to connect monitoring equipment to the Internet for remote monitoring and management; Data analysis module, used to analyze input and output records, generate reports and provide visual presentation; Predictive analysis module, which is used to conduct in-depth analysis of access records and predict access in a specific time period; Intelligent alarm module to identify abnormal situations and notify relevant personnel; The monitoring equipment module collects warehouse environment data and transmits it to the data collection and processing module. The data collection and processing module transmits the processed data to the Internet of Things module and the data analysis module. The data analysis module performs data analysis and mining on the processed data. The predictive analysis module performs time series analysis and prediction based on the analysis and mining of the data analysis module.

2. According to claim 1, the intelligent storage and in-and-out recording system for monitoring equipment is characterized in that: The monitoring equipment module transmits data to the system for analysis and processing, monitors and manages the status of the warehouse environment or equipment by identifying patterns, trends and abnormal situations, and the monitoring equipment module includes RFID readers, barcode scanners, sensors and cameras.

3. According to claim 1, the intelligent storage and in-and-out recording system for monitoring equipment is characterized in that: When the equipment passes through the warehouse entrance, the monitoring device module detects the equipment and reads its unique identification. The monitoring device module captures images or videos of the equipment when it enters and exits, and transmits the collected data to the data acquisition and processing module through a wireless network or a wired connection. The data acquisition and processing module includes a central processing unit. The central processing unit removes duplicate records, processes missing values ​​and abnormal data, parses the unique identification, image or video into structured data, stores the processed data in a database, analyzes the equipment's entry and exit records in real time, and generates statistical reports.

4. The intelligent storage and in-and-out recording system for monitoring equipment according to claim 3 is characterized in that: The central processing unit stores and manages the collected data, monitors and processes the data in real time, transmits and communicates data between different devices, cleans and preprocesses the collected data, including processing missing values ​​and standardizing data, and integrates and converts the processed data into data sets.

5. According to claim 1, the intelligent storage and in-and-out recording system for monitoring equipment is characterized in that: The IoT module needs to support the connection and communication of various devices, collect data from devices and sensors, remotely monitor and control connected devices, including remote switching, adjustment and fault diagnosis functions, and store data for integration with the cloud platform.

6. The intelligent storage and in-and-out recording system for monitoring equipment according to claim 1 is characterized in that: The data analysis module predicts, classifies, clusters and recognizes patterns of data by integrating machine learning and predictive modeling functions, and processes data in real time, analyzes data collected in real time, and mines data to gain an in-depth understanding of the data.

7. The intelligent storage and in-and-out recording system for monitoring equipment according to claim 1 is characterized in that: The predictive analysis module uses time series analysis to predict the entry and exit of items within a certain period of time in the future. The moving average method is used as one of the algorithms for time series analysis. The moving average method is used to calculate the average value of time series data within a specific window to predict future trends: MA_t=(X_{t-1}+X_{t-2}+...+X_{tn}) / n Among them, MA_t represents the moving average at time point t, X_{t-1}, X_{t-2},..., X_{tn} are the data of several time points in the past, n is the window size, and the number of items in and out in each time period collected by the monitoring equipment is used as time series data, and the moving average method is used to predict the number of items in and out in a certain time period in the future.

8. The intelligent storage and in-and-out recording system for monitoring equipment according to claim 1 is characterized in that: The intelligent alarm module uses an anomaly detection algorithm to identify abnormal access behaviors and trigger corresponding intelligent alarms. The LOF algorithm is a commonly used anomaly detection algorithm that identifies abnormal points in a data set based on the concept of density. The degree of abnormality of a data point is determined by calculating the density ratio between each data point and its neighboring points. The LOF algorithm calculates the local outlier factor: Among them, p represents the data point to be judged, N represents the set of neighboring points of p, reach_dist(o,p) represents the reachable distance from o to p, which represents the density contribution of data point o to data point p, |N| represents the number of neighboring points, and takes the employee entry and exit records as the data set. The LOF algorithm is used to identify abnormal entry and exit behaviors, calculate their LOF values, and trigger intelligent alarms when the LOF values ​​exceed the preset threshold.

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