AI predictive maintenance system of self-service terminal equipment

Abstract

The invention discloses an AI predictive maintenance system for self-service terminal equipment, and the system comprises a data collection module, a data preprocessing module, an equipment self-diagnosis module, a feature extraction module, a data modeling module, a state evaluation module, an early warning module, a maintenance plan making module, an optimization module, and a remote monitoring module. The acquisition module acquires equipment operation data; the data acquisition module further comprises wireless sensor nodes, and a wireless sensor network is formed through a star-shaped or mesh topological structure, so that the flexibility and the coverage range of data acquisition are improved, and the operation data of each part of the equipment can be accurately acquired; according to the invention, real-time monitoring and fault prediction of the self-service terminal equipment can be realized through the equipment self-diagnosis and data modeling technology, faults are actively prevented, a traditional passive maintenance mode is changed, and the risk of service interruption caused by sudden equipment faults is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of self-service terminals, and in particular to an AI predictive maintenance system for self-service terminal equipment. Background Art

[0002] Current self-service terminal equipment maintenance often relies on regular inspections or post-failure repairs. This approach makes it difficult to detect potential faults in a timely manner, leading to service interruptions when equipment fails, impacting user experience. Furthermore, the allocation of maintenance resources is irrational, resulting in high costs. Therefore, a system is needed that can predict equipment failures in advance and effectively optimize the maintenance process. Summary of the Invention

[0003] The purpose of the present invention is to provide an AI predictive maintenance system for self-service terminal equipment in response to the above-mentioned problems in the prior art, thereby solving all or one of the above-mentioned problems in the prior art.

[0004] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: The present invention provides an AI predictive maintenance system for self-service terminal equipment, comprising: Data acquisition module, data preprocessing module, equipment self-diagnosis module, feature extraction module, data modeling module, status assessment module, early warning module, maintenance plan formulation module, optimization module and remote monitoring module; A data acquisition module for collecting equipment operating data; the data acquisition module also includes wireless sensor nodes, which form a wireless sensor network through a star or mesh topology to improve the flexibility and coverage of data acquisition and ensure that operating data from all parts of the equipment can be accurately collected; Data preprocessing module, used to process the collected data; Equipment self-diagnosis module, used for real-time diagnosis of equipment failures; Feature extraction module, used to extract key features; Data modeling module, used to build prediction models; Status assessment module, used to assess the health status of equipment; Early warning module, used to issue early warning information; Maintenance plan making module, used to make maintenance plans; Optimization module, used to optimize operation and maintenance strategies; Remote monitoring module, used to implement remote monitoring.

[0005] As an improved solution, the data preprocessing module adopts a denoising autoencoder and normalization algorithm based on deep learning to effectively remove noise and abnormal interference in the data, and at the same time normalizes the data so that data of different types and ranges can be analyzed in the same mathematical space, thereby improving data quality and the training effect of subsequent models.

[0006] As an improved solution, the equipment self-diagnosis module has a built-in self-diagnosis algorithm for making comprehensive judgments by combining multiple machine learning models, including: decision trees, support vector machines and neural networks. The algorithm is updated regularly and the model is continuously trained with newly collected historical data to improve the accuracy and adaptability of fault diagnosis. As an improved solution, the feature extraction module is also used to apply methods such as principal component analysis and empirical mode decomposition to efficiently extract feature information that is highly relevant to equipment failures from massive data, and quantify it into numerical features to form a feature library for use in subsequent models. As an improved solution, the prediction model of the data modeling module is also used to adopt a hybrid model that integrates multiple algorithms. By integrating the advantages of deep learning and traditional machine learning, it shows excellent performance when processing data with complex nonlinear relationships. As an improved solution, the status assessment module is also used to comprehensively consider the equipment's historical operating data, real-time data, and environmental factors when evaluating the equipment's health status, and adopts a fuzzy comprehensive evaluation method to integrate multiple factors, so that the health index assigned to the equipment has higher accuracy and reliability. As an improved solution, the warning information issued by the warning module is graded according to the type of fault and the degree of urgency. Different levels of warning information use different notification methods and notification objects, including: for serious emergency faults, email and text message reminders are sent to multiple levels of operation and maintenance personnel at the same time, and sound and light alarms are triggered. As an improved solution, the maintenance plan formulation module is also used to dynamically generate maintenance plans through intelligent algorithms based on the type, importance, failure risk level of the equipment, and the skills and workload of the operation and maintenance personnel, to ensure the rational allocation and efficient use of maintenance resources. As an improved solution, the optimization module is also used to use intelligent optimization algorithms such as genetic algorithms and simulated annealing algorithms to analyze and evaluate historical maintenance data and real-time operation and maintenance effects, and automatically generate the optimal operation and maintenance strategy improvement plan. As an improved solution, the remote monitoring module is also used to provide multi-user concurrent access and real-time video monitoring functions, supporting users with different permissions to access the system anytime and anywhere through different terminal devices.

[0007] The beneficial effects of the technical solution of the present invention are: this system can realize real-time monitoring and fault prediction of self-service terminal equipment through equipment self-diagnosis and data modeling technology, actively prevent the occurrence of faults, change the traditional passive maintenance mode, effectively reduce the risk of service interruption caused by sudden equipment failure, improve operation and maintenance efficiency, and reduce operation and maintenance costs and unnecessary downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0009] Figure 1 2 is a schematic diagram of the architecture of the AI ​​predictive maintenance system for the self-service terminal device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0010] The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more precise definition of the protection scope of the present invention.

[0011] In the description of the present invention, it should be noted that the embodiments described in the present invention are only part of the embodiments of the present invention, rather than all of the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of the present invention.

[0012] The terms "first," "second," and the like in the specification and claims herein and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in orders other than those illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or device. Example

[0013] This embodiment provides an AI predictive maintenance system for self-service terminal equipment, such as Figure 1 Shown, including: Data acquisition module, data preprocessing module, equipment self-diagnosis module, feature extraction module, data modeling module, status assessment module, early warning module, maintenance plan formulation module, optimization module and remote monitoring module; A data acquisition module for collecting equipment operating data; the data acquisition module also includes wireless sensor nodes, which form a wireless sensor network through a star or mesh topology to improve the flexibility and coverage of data acquisition and ensure that operating data from all parts of the equipment can be accurately collected; Data preprocessing module, used to process the collected data; Equipment self-diagnosis module, used for real-time diagnosis of equipment failures; Feature extraction module, used to extract key features; Data modeling module, used to build prediction models; Status assessment module, used to assess the health status of equipment; Early warning module, used to issue early warning information; Maintenance plan making module, used to make maintenance plans; Optimization module, used to optimize operation and maintenance strategies; Remote monitoring module, used to implement remote monitoring.

[0014] As an improved solution, the data preprocessing module adopts a denoising autoencoder and normalization algorithm based on deep learning to effectively remove noise and abnormal interference in the data, and at the same time normalizes the data so that data of different types and ranges can be analyzed in the same mathematical space, thereby improving data quality and the training effect of subsequent models.

[0015] As an improved solution, the equipment self-diagnosis module has a built-in self-diagnosis algorithm for making comprehensive judgments by combining multiple machine learning models, including: decision trees, support vector machines and neural networks. The algorithm is updated regularly and the model is continuously trained with newly collected historical data to improve the accuracy and adaptability of fault diagnosis. As an improved solution, the feature extraction module is also used to apply methods such as principal component analysis and empirical mode decomposition to efficiently extract feature information that is highly relevant to equipment failures from massive data, and quantify it into numerical features to form a feature library for use in subsequent models. As an improved solution, the prediction model of the data modeling module is also used to adopt a hybrid model that integrates multiple algorithms. By integrating the advantages of deep learning and traditional machine learning, it shows excellent performance when processing data with complex nonlinear relationships. As an improved solution, the status assessment module is also used to comprehensively consider the equipment's historical operating data, real-time data, and environmental factors when evaluating the equipment's health status, and adopts a fuzzy comprehensive evaluation method to integrate multiple factors, so that the health index assigned to the equipment has higher accuracy and reliability. As an improved solution, the warning information issued by the warning module is graded according to the type of fault and the degree of urgency. Different levels of warning information use different notification methods and notification objects, including: for serious emergency faults, email and text message reminders are sent to multiple levels of operation and maintenance personnel at the same time, and sound and light alarms are triggered. As an improved solution, the maintenance plan formulation module is also used to dynamically generate maintenance plans through intelligent algorithms based on the type, importance, failure risk level of the equipment, and the skills and workload of the operation and maintenance personnel, to ensure the rational allocation and efficient use of maintenance resources. As an improved solution, the optimization module is also used to use intelligent optimization algorithms such as genetic algorithms and simulated annealing algorithms to analyze and evaluate historical maintenance data and real-time operation and maintenance effects, and automatically generate the optimal operation and maintenance strategy improvement plan. As an improved solution, the remote monitoring module is also used to provide multi-user concurrent access and real-time video monitoring functions, supporting users with different permissions to access the system anytime and anywhere through different terminal devices.

[0016] It should be noted that the above examples are only for explaining the present invention and are not intended to limit the scope of protection of the present invention.

[0017] It should be understood that in the various embodiments of this document, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0018] It should also be understood that in the embodiments herein, the term "and / or" merely describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" could represent: A alone, A and B simultaneously, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0019] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this document.

[0020] Those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, the specific logical process of the method described above can refer to the corresponding working processes of the systems, devices and units in the aforementioned method embodiments, and will not be repeated here.

[0021] In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices, or units, or can be an electrical, mechanical, or other form of connection.

[0022] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments herein.

[0023] In addition, the functional units in the various embodiments herein may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0024] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this article is essentially or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this article. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0025] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An AI predictive maintenance system for self-service terminal equipment, characterized in that: include: Data acquisition module, data preprocessing module, equipment self-diagnosis module, feature extraction module, data modeling module, status assessment module, early warning module, maintenance plan formulation module, optimization module and remote monitoring module; A data acquisition module for collecting equipment operating data; the data acquisition module also includes wireless sensor nodes, which form a wireless sensor network through a star or mesh topology to improve the flexibility and coverage of data acquisition and ensure that operating data from all parts of the equipment can be accurately collected; Data preprocessing module, used to process the collected data; Equipment self-diagnosis module, used for real-time diagnosis of equipment failures; Feature extraction module, used to extract key features; Data modeling module, used to build prediction models; Status assessment module, used to assess the health status of equipment; Early warning module, used to issue early warning information; Maintenance plan making module, used to make maintenance plans; Optimization module, used to optimize operation and maintenance strategies; Remote monitoring module, used to implement remote monitoring.

2. The AI ​​predictive maintenance system for self-service terminal equipment according to claim 1, characterized in that: The data preprocessing module uses a denoising autoencoder and normalization algorithm based on deep learning to effectively remove noise and abnormal interference in the data. At the same time, it normalizes the data so that data of different types and ranges can be analyzed in the same mathematical space, thereby improving data quality and the training effect of subsequent models.

3. The AI ​​predictive maintenance system for self-service terminal equipment according to claim 1, characterized in that: The device self-diagnosis module has a built-in self-diagnosis algorithm for making comprehensive judgments by combining multiple machine learning models, including: decision trees, support vector machines and neural networks. The algorithm is updated regularly and the model is continuously trained with newly collected historical data to improve the accuracy and adaptability of fault diagnosis.

4. The AI ​​predictive maintenance system for self-service terminal equipment according to claim 1, characterized in that: The feature extraction module is also used to use methods such as principal component analysis and empirical mode decomposition to efficiently extract feature information that is highly relevant to equipment failures from massive data, and quantify it into numerical features to form a feature library for use in subsequent models.

5. The AI ​​predictive maintenance system for self-service terminal equipment according to claim 1, characterized in that: The prediction model of the data modeling module is also used to adopt a hybrid model that integrates multiple algorithms. By integrating the advantages of deep learning and traditional machine learning, it shows excellent performance when processing data with complex nonlinear relationships.

6. The AI ​​predictive maintenance system for self-service terminal equipment according to claim 1, characterized in that: The status assessment module is also used to comprehensively consider the equipment's historical operating data, real-time data, and environmental factors when assessing the equipment's health status, and adopt a fuzzy comprehensive evaluation method to integrate multiple factors, thereby assigning a health index to the equipment with higher accuracy and reliability.

7. The AI ​​predictive maintenance system for self-service terminal equipment according to claim 1, characterized in that: The warning information issued by the warning module is graded according to the fault type and urgency. Different levels of warning information use different notification methods and notification objects, including: for serious emergency faults, email and text message reminders are sent to multiple levels of operation and maintenance personnel at the same time, and sound and light alarms are triggered.

8. The AI ​​predictive maintenance system for self-service terminal equipment according to claim 1, characterized in that: The maintenance plan formulation module is also used to dynamically generate maintenance plans through intelligent algorithms based on the type, importance, failure risk level of the equipment and the skills and workload of the operation and maintenance personnel, ensuring the rational allocation and efficient use of maintenance resources.

9. The AI ​​predictive maintenance system for self-service terminal equipment according to claim 1, characterized in that: The optimization module is also used to use intelligent optimization algorithms such as genetic algorithms and simulated annealing algorithms to analyze and evaluate historical maintenance data and real-time operation and maintenance effects, and automatically generate the optimal operation and maintenance strategy improvement plan.

10. The AI ​​predictive maintenance system for self-service terminal equipment according to claim 1, characterized in that: The remote monitoring module is also used to provide multi-user concurrent access and real-time video monitoring functions, supporting users with different permissions to access the system anytime and anywhere through different terminal devices.

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