Intelligent operation and maintenance management method and system for logistics storage equipment

Abstract

The invention provides an intelligent operation and maintenance management method and system for logistics storage equipment. According to the intelligent operation and maintenance management method and system for the logistics storage equipment, S1, multiple sensors are installed on the storage equipment and used for collecting operation data, including temperature, vibration, power and operation efficiency parameters, of the equipment in real time, S2, the collected data are sent to a central data processing center through encryption wireless transmission, and S3, the central data processing center is connected with the storage equipment through a wireless communication module. And the received data is subjected to initial processing by using an adaptive learning module. According to the intelligent operation and maintenance management method and system for the logistics storage equipment, multiple sensors are installed on the storage equipment, the comprehensiveness and accuracy of data are improved, a data processing center adopts an adaptive learning module to screen and clean the received data, and then a deep neural network is used for deep analysis.

Description

Technical Field

[0001] The present invention relates to the field of intelligent operation and maintenance management technology, and specifically to an intelligent operation and maintenance management method and system for logistics warehousing equipment. Background Art

[0002] This intelligent operation and maintenance management method is designed for automated equipment in logistics warehousing, such as conveyor belts, robotic arms, and sorting systems. The system integrates the functions of sensor data acquisition, real-time monitoring, and predictive maintenance. First, by installing a variety of sensors on key equipment, such as temperature, vibration, and power sensors, the system is able to collect equipment operating status data in real time. This data is then uploaded to the central processing system, and the health status of the equipment is evaluated through data analysis and machine learning models, thereby achieving fault warning and predictive maintenance. In addition, the system is equipped with a user-friendly interface that allows operators to easily monitor the status of all equipment in the warehouse and perform remote troubleshooting or scheduling maintenance when necessary.

[0003] Although this intelligent operation and maintenance management system has significant advantages, there are also some technical and practical application defects. First, the system's reliance on high-quality sensor data and advanced data processing capabilities may result in a higher initial investment in equipment and software. Second, due to the complexity and variability of the logistics environment, machine learning models need to be constantly adjusted and trained to adapt to new operating conditions and equipment aging processes, which increases the complexity and cost of maintenance. Finally, data security and privacy are also major concerns, because the system needs to process a large amount of equipment data and sensitive information, which may be at risk of leakage if not adequately protected. Summary of the invention

[0004] 1. Technical issues to be resolved In response to the shortcomings of the prior art, the present invention provides an intelligent operation and maintenance management method and system for logistics warehousing equipment, which solves the problem that the system relies on high-quality sensors and machine learning models that need to be constantly adjusted and trained to adapt to new operating conditions and equipment aging processes, which increases the complexity and cost of maintenance, and the risk of leakage.

[0005] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for intelligent operation and maintenance management of logistics warehousing equipment, comprising: S1. Install a variety of sensors on storage equipment to collect real-time operating data of the equipment, including temperature, vibration, power and operating efficiency parameters; S2, sending the collected data to the central data processing center via encrypted wireless transmission; S3. In the central data processing center, the received data is preliminarily screened and cleaned using the adaptive learning module; S4. Apply machine learning algorithms using deep neural network technology to conduct in-depth analysis of the cleaned data to evaluate the current status and performance of the equipment; S5. The machine learning algorithm self-adjusts its parameters based on the equipment operation data to adapt to the aging process and changing operating conditions of the equipment, and has a feedback link for adjusting the learning model based on historical data and maintenance records; S6. Based on the output of the machine learning algorithm, predict potential equipment failures and maintenance needs, and update the failure prediction model to integrate the latest equipment performance data and maintenance feedback; S7 automatically sends prediction results and maintenance recommendations to operation and maintenance managers and displays them through the user interface, allowing operation and maintenance managers to view device status, prediction results, and execute remote troubleshooting instructions; S8. Adopt data encryption and verification mechanisms, use advanced encryption standards to ensure the security and integrity of all encrypted wireless transmission and storage data, and implement data access rights verification through multi-factor authentication to prevent data leakage and unauthorized access; S9. Use fault-tolerance mechanisms, including local data caching and automatic data recovery, to ensure that the system can continue to operate and maintain the highest possible data integrity in the event of unstable network connections or partial data loss; S10, the data processing and analysis module is equipped with self-diagnosis to detect and report system internal errors and performance bottlenecks; S11. The user interface has voice control function to facilitate managers to operate in complex environments, and can display real-time data streams and historical analysis results, allowing users to customize alarm and notification settings.

[0006] Preferably, the sensors described in S2 include temperature sensors, vibration sensors, power sensors and efficiency sensors.

[0007] Preferably, the prediction result in S7 includes the type of equipment failure, the time when it may occur, and its potential impact on warehousing operations.

[0008] Preferably, the maintenance suggestion in S8 includes specific maintenance steps, a list of required maintenance tools and parts, and an estimated maintenance time.

[0009] Preferably, the machine learning algorithm can automatically identify and adapt to new or unknown equipment performance degradation patterns. The machine learning algorithm uses a logistic regression model to predict whether a device is about to fail. The formula of the logistic regression model can be expressed as:

[0010] Among them, p(y=1) is the probability of equipment failure, Xi is the input feature, and βi is the model parameter. For binary classification problems, such as judging whether the equipment is operating normally, the support vector machine model can be used:

[0011] Among them, xi and yi are training data and labels, αi is the parameter learned by the model, K is the kernel function, such as a linear kernel or a radial basis function kernel, and b is the bias term.

[0012] Preferably, the data encryption mechanism in S9 can encrypt all data and perform strict authority verification on data access, and the fault-tolerant mechanism also includes automatic backup of data to prevent data loss.

[0013] Preferably, the self-diagnosis function includes real-time monitoring of system operating status, automatic reporting of any abnormal conditions and suggesting possible solutions.

[0014] An intelligent operation and maintenance management system for logistics warehousing equipment, comprising: a data processing center, an encrypted wireless transmission module, a sensor module, a temperature sensor, a vibration sensor, a power sensor and an efficiency sensor, a fault prediction and maintenance suggestion module, a data security and access control module, a fault tolerance and data recovery module, and a system monitoring and self-diagnosis module; The data processing center includes a data receiving unit responsible for receiving data from the encrypted wireless transmission module, a data cleaning unit using an adaptive learning module to screen and clean the received data, a data analysis unit including a machine learning algorithm of deep neural network technology, which performs in-depth analysis on the cleaned data and evaluates the current status and performance of the equipment. The algorithm can automatically adjust parameters to adapt to equipment aging and changes in operating conditions. A fault prediction and maintenance suggestion module predicts potential equipment failures and maintenance requirements based on the output of the data analysis unit and generates maintenance suggestions. A communication interface module is used to automatically send the prediction results and maintenance suggestions to the operation and maintenance management personnel and allow access to this information through the user interface. A user interface module provides real-time data streams and historical analysis results display, and has a voice control function to facilitate management personnel to operate in complex environments, including customized alarm and notification settings. A data security and access control module includes a data encryption mechanism and a multi-factor authentication system to ensure the security and integrity of stored and accessed data. A fault tolerance and data recovery module includes a local data cache and an automatic data recovery function to ensure that the system can continue to operate and maintain data integrity in the event of unstable network connection or data loss. A system monitoring and self-diagnosis module is used to monitor the system operation status, automatically detect and report system internal errors and performance bottlenecks, and recommend possible solutions.

[0015] (III) Beneficial effects The present invention provides a method and system for intelligent operation and maintenance management of logistics warehousing equipment, which has the following beneficial effects: This intelligent operation and maintenance management method and system for logistics warehousing equipment improves the comprehensiveness and accuracy of data by installing multiple sensors on the warehousing equipment. The data processing center uses an adaptive learning module to screen and clean the received data, and then uses a deep neural network for in-depth analysis. This algorithm can self-adjust its parameters according to the equipment operation data, and automatically adapt to new operating conditions and equipment aging processes. The system uses advanced encryption standards to ensure the security and integrity of all data during wireless transmission and storage. The data access control module includes a multi-factor authentication system to strictly limit data access rights, effectively prevent the risk of data leakage and unauthorized access, and ensure that the system can continue to operate and maintain data integrity in the event of unstable network connection or data loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] like Figure 1 As shown, an embodiment of the present invention provides an intelligent operation and maintenance management method and system for logistics warehousing equipment, including: S1, installing multiple sensors on the warehousing equipment to collect real-time operating data of the equipment, including temperature, vibration, power and operating efficiency parameters.

[0019] S2. Send the collected data to the central data processing center via encrypted wireless transmission. The sensors include temperature sensors, vibration sensors, power sensors and efficiency sensors.

[0020] S3. In the central data processing center, the adaptive learning module is used to perform preliminary screening and cleaning of the received data.

[0021] S4. Apply machine learning algorithms based on deep neural network technology to conduct in-depth analysis of the cleaned data to evaluate the current status and performance of the equipment.

[0022] S5. The machine learning algorithm self-adjusts its parameters based on the equipment operation data to adapt to the aging process and changing operating conditions of the equipment, and has a feedback link for adjusting the learning model based on historical data and maintenance records.

[0023] S6. Based on the output of the machine learning algorithm, predict the potential failure and maintenance needs of the equipment, and update the failure prediction model to integrate the latest equipment performance data and maintenance feedback. The machine learning algorithm can automatically identify and adapt to new or unknown equipment performance degradation patterns. The machine learning algorithm uses a logistic regression model to predict whether the equipment is about to fail. The formula of the logistic regression model can be expressed as:

[0024] Among them, p(y=1) is the probability of equipment failure, Xi is the input feature, and βi is the model parameter. For binary classification problems, such as judging whether the equipment is operating normally, the support vector machine model can be used:

[0025] Among them, xi and yi are training data and labels, αi is the parameter learned by the model, K is the kernel function, such as a linear kernel or a radial basis function kernel, and b is the bias term.

[0026] S7 automatically sends prediction results and maintenance recommendations to operation and maintenance managers and displays them through a user interface, allowing operation and maintenance managers to view equipment status, prediction results and execute remote troubleshooting instructions. The prediction results include the type of equipment failure, when it may occur and its potential impact on warehouse operations.

[0027] S8. Adopt data encryption and verification mechanisms, use advanced encryption standards to ensure the security and integrity of all encrypted wirelessly transmitted and stored data, and implement data access permission verification through multi-factor authentication to prevent data leakage and unauthorized access. Maintenance recommendations include specific maintenance steps, required maintenance tools and parts list, and estimated maintenance time.

[0028] S9. Fault-tolerant mechanisms are adopted, including local data caching and automatic data recovery functions, to ensure that the system can continue to run and maintain the highest possible data integrity in the event of unstable network connection or partial data loss. The data encryption mechanism can encrypt all data and perform strict permission verification on data access. The fault-tolerant mechanism also includes automatic backup of data to prevent data loss.

[0029] S10, the data processing and analysis module is equipped with self-diagnosis to detect and report internal system errors and performance bottlenecks. The self-diagnosis function includes real-time monitoring of the system operation status, automatic reporting of any abnormal conditions and suggesting possible solutions.

[0030] S11. The user interface has voice control function to facilitate managers to operate in complex environments, and can display real-time data streams and historical analysis results, allowing users to customize alarm and notification settings.

[0031] An intelligent operation and maintenance management system for logistics warehousing equipment includes: a data processing center, an encrypted wireless transmission module, a sensor module, a temperature sensor, a vibration sensor, a power sensor and an efficiency sensor, a fault prediction and maintenance suggestion module, a data security and access control module, a fault tolerance and data recovery module, and a system monitoring and self-diagnosis module.

[0032] The data processing center includes a data receiving unit responsible for receiving data from the encrypted wireless transmission module, a data cleaning unit that uses an adaptive learning module to screen and clean the received data, a data analysis unit that contains a machine learning algorithm using deep neural network technology, which performs in-depth analysis on the cleaned data and evaluates the current status and performance of the equipment. The algorithm can automatically adjust parameters to adapt to equipment aging and changes in operating conditions. A fault prediction and maintenance suggestion module predicts potential equipment failures and maintenance requirements based on the output of the data analysis unit and generates maintenance suggestions. A communication interface module is used to automatically send prediction results and maintenance suggestions to operation and maintenance managers and allow access to this information through a user interface. A user interface module provides real-time data streaming and historical analysis results display, and has voice control functions to facilitate managers to operate in complex environments, including customized alarm and notification settings. A data security and access control module includes a data encryption mechanism and a multi-factor authentication system to ensure the security and integrity of stored and accessed data. A fault tolerance and data recovery module includes a local data cache and automatic data recovery function to ensure that the system can continue to operate and maintain data integrity in the event of unstable network connection or data loss. A system monitoring and self-diagnosis module is used to monitor the system operation status, automatically detect and report system internal errors and performance bottlenecks, and recommend possible solutions.

[0033] 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. A method for intelligent operation and maintenance management of logistics warehousing equipment, characterized in that: include: S1. Install a variety of sensors on storage equipment to collect real-time operating data of the equipment, including temperature, vibration, power and operating efficiency parameters; S2, sending the collected data to the central data processing center via encrypted wireless transmission; S3. In the central data processing center, the received data is preliminarily screened and cleaned using the adaptive learning module; S4. Apply machine learning algorithms using deep neural network technology to conduct in-depth analysis of the cleaned data to evaluate the current status and performance of the equipment; S5. The machine learning algorithm self-adjusts its parameters based on the equipment operation data to adapt to the aging process and changing operating conditions of the equipment, and has a feedback link for adjusting the learning model based on historical data and maintenance records; S6. Based on the output of the machine learning algorithm, predict potential equipment failures and maintenance needs, and update the failure prediction model to integrate the latest equipment performance data and maintenance feedback; S7 automatically sends prediction results and maintenance recommendations to operation and maintenance managers and displays them through the user interface, allowing operation and maintenance managers to view device status, prediction results, and execute remote troubleshooting instructions; S8. Adopt data encryption and verification mechanisms, use advanced encryption standards to ensure the security and integrity of all encrypted wireless transmission and storage data, and implement data access rights verification through multi-factor authentication to prevent data leakage and unauthorized access; S9. Use fault-tolerance mechanisms, including local data caching and automatic data recovery, to ensure that the system can continue to operate and maintain the highest possible data integrity in the event of unstable network connections or partial data loss; S10, the data processing and analysis module is equipped with self-diagnosis to detect and report system internal errors and performance bottlenecks; S11. The user interface has voice control function to facilitate managers to operate in complex environments, and can display real-time data streams and historical analysis results, allowing users to customize alarm and notification settings.

2. According to claim 1, a method for intelligent operation and maintenance management of logistics warehousing equipment is characterized by: The sensors described in S2 include temperature sensors, vibration sensors, power sensors, and efficiency sensors.

3. According to claim 1, the intelligent operation and maintenance management method for logistics warehousing equipment is characterized by: The prediction results described in S7 include the type of equipment failure, the time when it may occur, and its potential impact on warehouse operations.

4. The intelligent operation and maintenance management method for logistics warehousing equipment according to claim 1 is characterized by: The maintenance recommendations described in S8 include specific maintenance steps, a list of required maintenance tools and parts, and an estimated maintenance time.

5. The intelligent operation and maintenance management method for logistics warehousing equipment according to claim 1 is characterized by: The machine learning algorithm can automatically identify and adapt to new or unknown equipment performance degradation patterns. The machine learning algorithm uses a logistic regression model to predict whether a device is about to fail. The formula of the logistic regression model can be expressed as: Among them, p(y=1) is the probability of equipment failure, Xi is the input feature, and βi is the model parameter. For binary classification problems, such as judging whether the equipment is operating normally, the support vector machine model can be used: Among them, xi and yi are training data and labels, αi is the parameter learned by the model, K is the kernel function, such as a linear kernel or a radial basis function kernel, and b is the bias term.

6. The intelligent operation and maintenance management method for logistics warehousing equipment according to claim 1 is characterized by: The data encryption mechanism described in S9 can encrypt all data and perform strict authority verification on data access. The fault-tolerant mechanism also includes automatic backup of data to prevent data loss.

7. The intelligent operation and maintenance management method for logistics warehousing equipment according to claim 1 is characterized by: The self-diagnosis function includes real-time monitoring of system operating status, automatic reporting of any abnormal conditions and suggesting possible solutions.

8. An intelligent operation and maintenance management system for logistics warehousing equipment, characterized in that: include: Data processing center, encrypted wireless transmission module, sensor module, temperature sensor, vibration sensor, power sensor and efficiency sensor, fault prediction and maintenance suggestion module, data security and access control module, fault tolerance and data recovery module, system monitoring and self-diagnosis module; The data processing center includes a data receiving unit, a data cleaning unit, and a data analyzing unit.

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