Remote monitoring fault early warning system based on intelligent storage door

Abstract

The invention relates to the technical field of intelligent monitoring, and discloses a remote monitoring fault early warning system based on an intelligent storage door. According to the system, key indexes such as the opening and closing state, environment temperature and humidity, current and voltage and vibration of the storage door are monitored in real time through multiple sensors of the data acquisition unit, management personnel can comprehensively know the operation condition of the storage door, potential problems can be found in time, potential safety hazards and cargo loss caused by human factors are avoided, and the management efficiency is improved. The intelligent processing capability of the data processing and analysis unit enables the system to efficiently denoise and fill missing values, ensures the accuracy and reliability of data, calculates a fault early warning index based on an analysis result, gives out abnormal early warning in time, greatly improves the fault response speed and accuracy, and further improves the safety of the system. And the data display unit provides visual operation state display through an intelligent monitoring large screen, so that a manager can master the operation condition of the storage door in the first time, and the working efficiency is further improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to a remote monitoring fault early warning system based on intelligent storage doors. Background Art

[0002] With the continuous development of modern logistics and supply chain management, intelligent warehousing systems have gradually become an important means to improve warehousing efficiency and management level. In this series of intelligent equipment, intelligent warehouse doors, as a key component, play a vital role.

[0003] Smart warehouse doors are intelligent access control systems that integrate advanced sensor technology, the Internet of Things (IoT), and automatic control technology. Their main function is to intelligently manage access to and from storage areas, enabling automatic door opening and closing, remote monitoring, real-time recording of access information, and environmental monitoring. By seamlessly integrating with warehouse management systems, smart warehouse doors can improve warehouse operational efficiency while ensuring safety.

[0004] The application scenarios of smart warehouse doors are very wide. First, in large logistics centers and warehouses, smart warehouse doors can effectively control the entry and exit of people and goods, avoid unauthorized access, and ensure warehouse safety. Secondly, with the help of real-time monitoring and data analysis, managers can grasp the operating status of the warehouse in real time and promptly discover potential safety hazards and equipment failures. In addition, smart warehouse doors can also be linked with other smart devices (such as automatic forklifts, cargo tracking systems, etc.) to achieve comprehensive intelligent warehouse management.

[0005] Traditional warehouse door management methods often rely on manual monitoring and regular inspections, and are unable to grasp the operating status and environmental changes of warehouse doors in real time. This method not only reduces work efficiency but may also lead to potential safety hazards, especially in cases of door lock failure or environmental abnormalities, which can easily cause cargo loss or safety accidents. Summary of the Invention

[0006] (1) Technical problems solved

[0007] In response to the shortcomings of the existing technology, the present invention provides a remote monitoring fault warning system based on intelligent warehouse doors. Through the multiple sensors of the data acquisition unit, key indicators such as the switch status of the warehouse door, ambient temperature and humidity, current, voltage and vibration are monitored in real time. Managers can fully understand the operating status of the warehouse door, discover potential problems in time, and avoid safety hazards and cargo losses caused by human factors. The intelligent processing capabilities of the data processing and analysis unit enable the system to efficiently denoise and fill missing values, ensure the accuracy and reliability of the data, and calculate the fault warning index based on the analysis results, and issue abnormal warnings in time, greatly improving the speed and accuracy of fault response. In addition, the data display unit provides an intuitive operating status display through the intelligent monitoring screen, so that managers can grasp the operating status of the warehouse door at the first time, further improving work efficiency.

[0008] (2) Technical solution

[0009] To achieve the above-mentioned object, the present invention provides the following technical solutions: a remote monitoring fault warning system based on intelligent storage doors, comprising a data acquisition unit, a data processing unit, a data analysis unit, a fault monitoring and warning unit, and a data display unit;

[0010] The data acquisition unit collects data related to the storage door through sensors installed on the intelligent storage door and the surrounding environment. The data related to the storage door includes switch status data, ambient temperature data, ambient humidity data, current data, voltage data, vibration data and door lock status data;

[0011] The data processing unit receives the storage door related data acquired by the data acquisition unit and performs data preprocessing, wherein the data preprocessing process includes data denoising and missing value filling;

[0012] The data analysis unit analyzes the pre-processed warehouse door related data to calculate the door lock power consumption, vibration amplitude, average temperature of the warehouse door environment, average humidity of the warehouse door environment, and door lock device failure rate, and calculates the warehouse door failure warning index based on the warehouse door related data and sends it to the fault monitoring and warning unit;

[0013] The fault monitoring and early warning unit performs analysis based on the warehouse door fault early warning index. When the warehouse door fault early warning index exceeds the safe operation threshold of the warehouse door set by the system, an abnormal operation warning of the warehouse door is issued;

[0014] The data display unit is linked to a large intelligent warehouse door monitoring screen for remotely monitoring the warehouse door and displaying detailed information on abnormal operation of the warehouse door.

[0015] Preferably, the formula for denoising the warehouse door related data is as follows:

[0016]

[0017] In the formula, D n represents the denoised value of the nth data point, X i represents the value of the original data at time point i, k represents the number of historical data points calculated and averaged, and i represents the index subscript.

[0018] Preferably, the formula for filling missing values in the warehouse door related data is as follows:

[0019]

[0020] In the formula, X misssing Indicates the missing values to be filled, X i-1 represents the valid data point before the missing value, X i+1 Indicates valid data points following missing values.

[0021] Preferably, the formula for calculating the door lock power consumption is as follows:

[0022] P=V*I

[0023] In the formula, P represents the power consumption of the door lock, V represents the voltage, and I represents the current.

[0024] Preferably, the calculation formula of the vibration amplitude is as follows:

[0025]

[0026] In the formula, A represents the vibration amplitude, K i represents the i-th vibration data point, represents the average value of the vibration data, n represents the total number of data points, and i represents the index subscript.

[0027] Preferably, the calculation formula for the average temperature of the storage door environment is as follows:

[0028]

[0029] In the formula, T avg represents the average temperature of the storage door environment, m represents the total number of temperature data points, T i Represents the temperature data at the i-th time point, where i represents the index subscript.

[0030] Preferably, the calculation formula for the average humidity of the storage door environment is as follows:

[0031]

[0032] In the formula, H avg Represents the average humidity of the storage door environment, N represents the total number of humidity data points, Hi Represents the humidity data at the i-th time point, where i represents the index subscript.

[0033] Preferably, the calculation formula for the door lock device failure rate is as follows:

[0034]

[0035] In the formula, F rate Indicates the failure rate of door lock equipment, N f It represents the number of faults in the time period Tc, where Tc represents the total time the door lock is in operation.

[0036] Preferably, the calculation formula of the storage door failure warning index is as follows:

[0037] E=w1*P+w2*A+w3*T avg +w4*H avg +w5*F rate

[0038] In the formula, E represents the warehouse door failure warning index, w1, w2, w3, w4, and w5 represent the weights of each indicator, which range from 0 to 1 and sum to 1, and are automatically assigned by the system.

[0039] Preferably, the indicators need to be standardized in order to calculate the storage door fault warning index with unified dimensions. Therefore, the following formula is used to unify the dimensions of the indicators:

[0040]

[0041] In the formula, P norn represents the index value after standardization, P represents the index value to be standardized, and P min Represents the minimum value in the indicator parameter data set to be standardized, P max Indicates the maximum value in the indicator parameter data set to be standardized.

[0042] Compared with the existing technology, the present invention provides a remote monitoring fault warning system based on intelligent storage doors, which has the following beneficial effects:

[0043] The present invention uses multiple sensors of the data acquisition unit to monitor key indicators such as the opening and closing status of the warehouse door, ambient temperature and humidity, current, voltage, and vibration in real time. Managers can fully understand the operating status of the warehouse door, discover potential problems in a timely manner, and avoid safety hazards and cargo losses caused by human factors. The intelligent processing capabilities of the data processing and analysis unit enable the system to efficiently denoise and fill missing values, ensure the accuracy and reliability of the data, and calculate the fault warning index based on the analysis results, and issue abnormal warnings in a timely manner, greatly improving the speed and accuracy of fault response. In addition, the data display unit provides an intuitive operating status display through the intelligent monitoring screen, allowing managers to grasp the operating status of the warehouse door at the first time, further improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0046] Traditional warehouse door management methods often rely on manual monitoring and regular inspections, which cannot grasp the operating status and environmental changes of warehouse doors in real time. This method not only reduces work efficiency but also may lead to potential safety hazards. Therefore, a remote monitoring and fault warning system based on intelligent warehouse doors is proposed. Figure 1 ,The system includes a data acquisition unit, a data processing unit, a data analysis unit, a fault monitoring and early warning unit, and a data display unit;

[0047] The data acquisition unit uses advanced sensor technology. By installing a series of high-precision sensors at key locations on the smart warehouse door and its surrounding environment, it continuously and in real time collects data related to the warehouse door. These sensors include: proximity sensors and displacement sensors for detecting the open and close status of the warehouse door to monitor the opening and closing of the warehouse door, ensure the normal operation of the door and prevent safety hazards caused by accidental opening or closing. Ambient temperature and humidity sensors are responsible for real-time monitoring of climatic factors in the warehouse environment to assess the potential impact of temperature and humidity on stored items and take necessary measures to ensure the quality of the items and the stability of the storage environment. The combination of current sensors and voltage sensors can accurately monitor the power consumption of the door lock drive system, ensuring the efficiency and safety of equipment operation, and promptly detecting abnormal power supply conditions and taking corresponding measures. Vibration sensors regularly collect vibration data of the door body. By analyzing these signals, early signs of mechanical wear or failure can be identified, thereby achieving preventive maintenance. Door lock status sensors are used to monitor the security status of the door lock to prevent unauthorized opening, thereby ensuring the safety of the storage area.

[0048] By comprehensively collecting these key data, the system can not only comprehensively monitor the operating status of warehouse doors and provide real-time warnings, but also use subsequent data analysis and processing to optimize warehouse management decision support. This sensor-based big data collection technology not only improves the safety and reliability of smart warehouse doors, but also greatly enhances the efficiency of overall warehouse management, enabling them to play a greater role in modern logistics and warehouse management, and laying a solid data foundation for improving economic benefits and operational efficiency.

[0049] The data processing unit is responsible for receiving various warehouse door-related data obtained from the data acquisition unit and performing comprehensive data preprocessing to ensure the accuracy and reliability of subsequent analysis. First, the first step in data preprocessing is data denoising. This process aims to effectively filter out environmental noise and unnecessary interference signals from sensors. The commonly used method is the moving average method, and its formula is:

[0050]

[0051] In this formula, D n represents the nth denoised data point, and X i is the i-th data point in the original signal. By setting a reasonable window size k, the moving average method can smooth the signal and eliminate the impact of sudden interference by calculating the average value of adjacent data points in the context, thereby improving data quality. The benefit of data denoising is that it ensures that the basic data relied upon for subsequent data analysis and decision-making is accurate and reliable, thereby improving the sensitivity and accuracy of fault detection.

[0052] Secondly, the missing value filling step in the preprocessing process is indispensable. Missing data will lead to deviation or error in the analysis results. In this process, the commonly used processing method is linear interpolation, and its formula is:

[0053]

[0054] In this formula, X misssing Indicates the missing values that need to be filled, X i-1 and X i+1 The missing values are represented by valid data points before and after the missing value. Through linear interpolation, the system can infer the true value of the missing data to a certain extent, thereby maintaining the integrity and continuity of the data set. The significance of this technical approach is that it ensures that there will be no systematic deviations caused by missing data during the data analysis phase, thereby improving the accuracy and timeliness of the comprehensive fault warning index, allowing managers to better understand the equipment status and formulate effective maintenance strategies.

[0055] By precisely executing preprocessing steps like data denoising and missing value filling, the data processing unit provides a high-quality data foundation for the intelligent warehouse door monitoring system, making subsequent data analysis and decision-making more scientific and accurate. This is not only significant in improving operational efficiency, but also plays a vital role in ensuring equipment safety and extending its service life.

[0056] After receiving the pre-processed warehouse door data, the data analysis unit uses data analysis technology to conduct a comprehensive analysis and calculation of this data to ensure that the health status of the equipment operation can be accurately identified. First, the system calculates the power consumption of the door lock using the following formula:

[0057] P=V*I

[0058] In this formula, P represents the power consumption of the door lock, while V and I represent the measured voltage and current, respectively. By monitoring power consumption, the system can evaluate energy efficiency and identify potential power anomalies such as overloads or current fluctuations, allowing necessary protective measures to be taken in a timely manner to avoid equipment damage.

[0059] Second, the analysis unit will use the vibration sensor data to calculate the vibration amplitude using the formula:

[0060]

[0061] Here, A represents the vibration amplitude, K i is the vibration data of the i-th measurement point, and It is the average value of all vibration data. By calculating the vibration amplitude, the system can identify mechanical wear or imbalance problems of the equipment, which is of great significance for preventing potential failures and optimizing maintenance cycles.

[0062] Monitoring of ambient temperature and humidity is another key aspect of warehouse management. The data analysis unit calculates the average ambient temperature and average ambient humidity respectively. The formula is:

[0063]

[0064] Among them, T avg and H avg Representing the average temperature and humidity of the environment, respectively. Monitoring changes in temperature and humidity can help managers adjust the storage environment in a timely manner, ensuring that stored items are always in optimal conditions, thereby reducing the risk of loss.

[0065] In addition, the analysis unit will also calculate the door lock device failure rate, the formula is:

[0066]

[0067] Here, F rate Represents the equipment failure rate (unit: failure / time), N f is the number of failures that occur within a specific time period, while Tc is the total time during the evaluation period. By continuously monitoring the failure rate, the system can evaluate the long-term operational stability and reliability of the equipment, providing strong data support for formulating maintenance and replacement strategies;

[0068] Finally, based on the above analysis results, the data analysis unit combines all warehouse door related indicators to calculate the warehouse door failure warning index:

[0069] E=w1*P+w2*A+w3*T avg +w4*H avg +w5*F rate

[0070] In this formula, weights w1, w2, w3, w4, and w5 represent the importance of each indicator, and all parameters need to be standardized to ensure effective weighting. The standardized formula is as follows:

[0071]

[0072] In the formula, P norn represents the index value after standardization, P represents the index value to be standardized, and P min Represents the minimum value in the indicator parameter data set to be standardized, P max Represents the maximum value in the indicator parameter data set to be standardized. Through standardization, parameters of different dimensions can be involved in the calculation of the fault warning index, thereby obtaining an effective warning index. This approach can reasonably reflect the comprehensive evaluation of each parameter on the storage door failure;

[0073] The calculated warning index will be sent to the fault monitoring and warning unit. If the index exceeds the set threshold, an alarm will be automatically issued to prompt management personnel to conduct further inspections.

[0074] Through this series of data analysis and calculation processes, the data analysis unit can not only provide real-time equipment status assessments, but also provide a solid data foundation for decision support, thereby ensuring the safe and reliable operation of intelligent warehouse doors and improving the efficiency and safety of overall warehouse management. This refined data analysis method will inject new vitality into modern warehouse management and help companies maintain their competitive advantage.

[0075] The fault monitoring and early warning unit dynamically monitors and evaluates the status of the intelligent warehouse door by analyzing the warehouse door fault early warning index in real time. Within the early warning unit, the system sets multiple key thresholds to ensure rapid and accurate identification of potential fault conditions. For example, the system sets a safe operation threshold of 75, which means that when E>75, the system will immediately determine that the warehouse door is in an abnormal state and trigger the early warning mechanism. In addition, to enhance the sensitivity of the early warning system and ensure equipment safety, different types of thresholds are set, such as:

[0076] High power consumption threshold (80W): If the door lock power consumption exceeds this value, it may indicate that the device is at risk of overload;

[0077] Vibration amplitude threshold (0.5m): If the collected vibration amplitude exceeds this value, it may indicate a mechanical failure;

[0078] Ambient temperature threshold (over 35°C): In high temperature conditions, the system will issue an alarm when storage conditions are unfavorable;

[0079] When the fault warning index of the storage door or related monitoring data exceeds the set threshold, the fault monitoring and warning unit will immediately notify the management personnel through the predefined alarm mechanism, which may be through sound alarm, SMS or email, so that the management personnel can respond quickly and conduct on-site inspection and maintenance to prevent accidents;

[0080] At the same time, the data display unit is effectively connected to the fault monitoring and early warning unit through a high-resolution intelligent monitoring screen, supporting remote monitoring of the real-time status of the storage door. This monitoring screen can display various key indicators, such as door lock power consumption, vibration amplitude, ambient temperature and humidity, fault early warning index, etc., and provides trend charts and historical data playback functions to facilitate technical personnel to conduct comprehensive analysis of the operating status of the storage door. Through intuitive data display, staff can quickly identify abnormal conditions and their causes, and thus formulate corresponding countermeasures and maintenance plans. This integrated system not only improves the timeliness of fault detection, but also significantly enhances the intelligent level of warehouse management, ensuring the safety and efficiency of warehousing work.

[0081] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A remote monitoring fault warning system based on intelligent warehouse doors, characterized by: It includes data acquisition unit, data processing unit, data analysis unit, fault monitoring and early warning unit and data display unit; The data acquisition unit collects data related to the storage door through sensors installed on the intelligent storage door and the surrounding environment. The data related to the storage door includes switch status data, ambient temperature data, ambient humidity data, current data, voltage data, vibration data and door lock status data; The data processing unit receives the storage door related data acquired by the data acquisition unit and performs data preprocessing, wherein the data preprocessing process includes data denoising and missing value filling; The data analysis unit analyzes the pre-processed warehouse door related data to calculate the door lock power consumption, vibration amplitude, average temperature of the warehouse door environment, average humidity of the warehouse door environment, and door lock device failure rate, and calculates the warehouse door failure warning index based on the warehouse door related data and sends it to the fault monitoring and warning unit; The fault monitoring and early warning unit performs analysis based on the warehouse door fault early warning index. When the warehouse door fault early warning index exceeds the safe operation threshold of the warehouse door set by the system, an abnormal operation warning of the warehouse door is issued; The data display unit is linked to a large intelligent warehouse door monitoring screen for remotely monitoring the warehouse door and displaying detailed information on abnormal operation of the warehouse door.

2. The remote monitoring fault warning system based on intelligent warehouse doors according to claim 1 is characterized by: The formula for denoising the warehouse door related data is as follows: In the formula, D n represents the denoised value of the nth data point, X i represents the value of the original data at time point i, k represents the number of historical data points calculated and averaged, and i represents the index subscript.

3. The remote monitoring fault warning system based on intelligent storage door according to claim 2 is characterized by: The formula for filling missing values in the warehouse door related data is as follows: In the formula, X misssing Indicates the missing values to be filled, X i-1 represents the valid data point before the missing value, X i+1 Indicates valid data points following missing values.

4. The remote monitoring fault warning system based on intelligent storage doors according to claim 3 is characterized by: The formula for calculating the door lock power consumption is as follows: P=V*I In the formula, P represents the power consumption of the door lock, V represents the voltage, and I represents the current.

5. The remote monitoring fault warning system based on intelligent storage door according to claim 4 is characterized in that: The calculation formula of the vibration amplitude is as follows: In the formula, A represents the vibration amplitude, K i represents the i-th vibration data point, represents the average value of the vibration data, n represents the total number of data points, and i represents the index subscript.

6. The remote monitoring fault warning system based on intelligent storage door according to claim 5 is characterized by: The calculation formula for the average temperature of the storage door environment is as follows: In the formula, T avg represents the average temperature of the storage door environment, m represents the total number of temperature data points, T i Represents the temperature data at the i-th time point, where i represents the index subscript.

7. The remote monitoring fault warning system based on intelligent storage door according to claim 6 is characterized in that: The calculation formula for the average humidity of the storage door environment is as follows: In the formula, H avg Represents the average humidity of the storage door environment, N represents the total number of humidity data points, H i Represents the humidity data at the i-th time point, where i represents the index subscript.

8. The remote monitoring fault warning system based on intelligent storage doors according to claim 7 is characterized in that: The calculation formula for the door lock device failure rate is as follows: In the formula, F rate Indicates the failure rate of door lock equipment, N f It represents the number of faults in the time period Tc, where Tc represents the total time the door lock is in operation.

9. The remote monitoring fault warning system based on intelligent storage door according to claim 8, characterized in that: The calculation formula of the warehouse door failure warning index is as follows: E=w1*P+w2*A+w3*T avg +w4*H avg +w5*F rate In the formula, E represents the warehouse door failure warning index, w1, w2, w3, w4, and w5 represent the weights of each indicator, which range from 0 to 1 and sum to 1, and are automatically assigned by the system.

10. The remote monitoring fault warning system based on intelligent storage door according to claim 9, characterized in that: The indicators need to be standardized to calculate the warehouse door fault warning index with unified dimensions. Therefore, the following formula is used to unify the dimensions of the indicators: In the formula, P norn represents the index value after standardization, P represents the index value to be standardized, and P min Represents the minimum value in the indicator parameter data set to be standardized, P max Indicates the maximum value in the indicator parameter data set to be standardized.

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