A business hall equipment fault prediction and health management system and method thereof

Abstract

The application relates to the technical field of power business hall equipment operation and maintenance, in particular to a business hall equipment fault prediction and health management system and a method thereof, which comprises an equipment access authentication module, a multi-source data acquisition module, a health degree evaluation and prediction module, a panoramic life cycle management module and an intelligent alarm and disposal closed-loop module; safe access and binding are realized through equipment digital fingerprints, running state, business flow and network port data are collected in parallel, a weighted model fusing static attribute attenuation and dynamic operation indexes is used to calculate health degree scores and predict trends, visual health archives are generated by integrating account books, alarms and maintenance records, and when the health degree is lower than an adaptive threshold, diagnosis instructions are automatically dispatched to generate disposal work orders to realize closed-loop management; the application realizes equipment predictive maintenance, and improves the operation and maintenance automation level and decision scientificity.

Description

Technical Field

[0001] This invention relates to the field of power service hall equipment operation and maintenance technology, specifically to a service hall equipment fault prediction and health management system and method. Background Technology

[0002] As an important window for power companies to serve their customers, power supply business halls are widely equipped with dedicated equipment such as self-service terminals and business acceptance machines to support offline processing of services such as electricity bill payment and business application. The stable operation of these devices is the foundation for ensuring service quality and improving customer experience. Currently, the industry generally manages these devices by combining regular inspections with fault reporting.

[0003] The existing equipment operation and maintenance management model of power business halls lacks unified security authentication for equipment access, resulting in management loopholes; the monitoring of equipment status is passive and isolated, failing to effectively warn of potential faults such as CPU overheating and memory leaks; equipment operation and maintenance are disconnected from front-end business quality and efficiency management, making it difficult to assess the impact of equipment status on customer waiting time and business efficiency, resulting in a lack of data support for operation and maintenance decisions.

[0004] Therefore, in order to improve the intelligence level and operation and maintenance efficiency of equipment management in business halls, a fault prediction and health management system and its method for business hall equipment are proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a business hall equipment fault prediction and health management system and method, which solves the problems of extensive equipment management, delayed fault response, and inability to optimize operation and maintenance and business efficiency in a coordinated manner.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a business hall equipment fault prediction and health management system, the system being built on a power intranet and including the following modules connected in sequence:

[0007] M1, Device Access Authentication Module, is used to securely access and bind self-service payment terminals and business acceptance machines based on digital fingerprints generated by the hash of device model, MAC address and hardware serial number.

[0008] M2, Multi-source data acquisition module, is used to collect equipment operating status, business flow and network port data in parallel;

[0009] The M3 health assessment and prediction module uses a weighted model that integrates static attribute decay and dynamic operating indicators to calculate real-time health scores and predict trends.

[0010] M4, the panoramic lifecycle management module, integrates ledgers, alarms, maintenance records and health trends to generate a visual health record;

[0011] The M5 intelligent alarm and handling closed-loop module is used to automatically dispatch diagnostic instructions and generate handling work orders according to the device type when the health status is lower than the adaptive threshold, and track the status until the loop is closed.

[0012] Furthermore, the device access authentication module includes a device information verification unit and an interface access control unit:

[0013] The device information verification unit is used to match and verify the code, model, and MAC address of the access device with the pre-registered device list;

[0014] The interface access control unit is used to assign application programming interface access permissions according to the device type.

[0015] Furthermore, the weighted model used in the health assessment and prediction module is as follows:

[0016]

[0017] in, Indicates the device at time Health score, Based on the duration of equipment use The static attenuation factor, For the first Dynamic operating indicators The normalized value, For its weight, This is the adjustment coefficient.

[0018] Furthermore, the dynamic operating indicators This includes at least two of the following: average daily business processing volume of the device, average CPU utilization, and average network request response latency.

[0019] Furthermore, the system also includes a channel service monitoring module and a quality and efficiency analysis module:

[0020] The channel service monitoring module is used to monitor and diagnose anomalies in the business hall service interface in real time.

[0021] The quality and efficiency analysis module is used to calculate the operational quality and efficiency indicators of the business hall based on customer flow, business volume, and equipment status data.

[0022] Furthermore, the quality and efficiency analysis module uses the following formula to calculate the service load of the business hall. :

[0023]

[0024] in, For load factor, For the first Average processing time for each service For the number of this business, For the number of service windows, This is the maximum service duration.

[0025] This invention also includes a method for a business hall equipment failure prediction and health management system, the method comprising:

[0026] Securely access and bind equipment in the business hall based on device digital fingerprints;

[0027] Parallel acquisition of equipment operating status, service flow, and network port data;

[0028] A weighted model that integrates static attribute decay and dynamic operational indicators is used to calculate the real-time health score and predict its trend.

[0029] When the health score falls below the adaptive threshold, an alarm is automatically triggered and a work order is generated, forming a closed-loop process.

[0030] Integrate multidimensional data to generate and visualize the health records of devices.

[0031] Furthermore, the weighted model employs a multi-index weighted fusion approach, and dynamically adjusts its weights by combining historical equipment fault data with real-time operational data. .

[0032] Furthermore, the step of forming a closed-loop disposal process also includes:

[0033] Automatically assign personnel and diagnostic scripts based on device type and alarm level;

[0034] The processing steps are recorded and fed back to the evaluation model for adaptive threshold and weight optimization.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] 1. The device access authentication module securely connects and binds self-service payment terminals and business acceptance machines based on device digital fingerprints, realizing the unification and standardization of device access, ensuring the legality and uniqueness of accessed devices, and building a secure and reliable business hall device access environment from the source, effectively preventing the access and impersonation of illegal devices.

[0037] 2. The multi-source data acquisition module collects equipment operating status, business flow, and network port data in parallel, realizing real-time perception and aggregation of panoramic data on equipment operation. This provides a comprehensive and reliable multi-dimensional data foundation for subsequent accurate analysis and decision-making, solving the problems of single and isolated data sources.

[0038] 3. The health assessment and prediction module uses a weighted model that integrates static attribute decay and dynamic operating indicators to calculate real-time health scores and predict trends. This enables scientific and quantitative assessment of equipment health status and early prediction of failure risks, providing forward-looking data for operation and maintenance decisions and realizing a fundamental shift from passive fault repair to proactive predictive maintenance.

[0039] 4. The intelligent alarm and handling closed-loop module automatically dispatches diagnostic instructions and generates handling work orders when the health status is lower than the adaptive threshold, and tracks the status until the loop is closed. This realizes the automation and closed-loop management of the operation and maintenance process, improves the response speed and handling efficiency of fault handling, ensures the timely resolution of abnormal problems, and reduces business interruption time.

[0040] 5. The panoramic lifecycle management module integrates ledgers, alarms, maintenance records, and health trends to generate a visual health profile, enabling refined and visual management of the entire lifecycle of equipment from access to scrapping. It provides a complete and intuitive data view for equipment maintenance, allocation, and upgrade decisions, thereby improving asset operation efficiency. Attached Figure Description

[0041] Figure 1 This is a diagram of the overall system architecture of the present invention;

[0042] Figure 2 This is a flowchart of the health assessment process of the present invention;

[0043] Figure 3 This is a flowchart illustrating the closed-loop process of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figures 1 to 3 This invention provides a technical solution: a fault prediction and health management system for business hall equipment. The system is built on the power intranet and includes the following modules connected in sequence:

[0046] M1, Device Access Authentication Module, is used to securely access and bind self-service payment terminals and business acceptance machines based on digital fingerprints generated by the hash of device model, MAC address and hardware serial number.

[0047] M2, Multi-source data acquisition module, is used to collect equipment operating status, business flow and network port data in parallel;

[0048] The M3 health assessment and prediction module uses a weighted model that integrates static attribute decay and dynamic operating indicators to calculate real-time health scores and predict trends.

[0049] M4, the panoramic lifecycle management module, integrates ledgers, alarms, maintenance records and health trends to generate a visual health record;

[0050] M5, Intelligent Alarm and Handling Closed-Loop Module, is used to automatically dispatch diagnostic instructions and generate handling work orders according to the device type when the health status is lower than the adaptive threshold, and track the status until the loop is closed.

[0051] By constructing a complete technical solution that includes equipment authentication, data collection, health assessment, panoramic management, and intelligent disposal, intelligent management of the entire lifecycle of equipment in the business hall, from access to scrapping, has been realized, improving the systematicness and reliability of equipment management.

[0052] The device access authentication module includes a device information verification unit and an interface access control unit:

[0053] The device information verification unit is used to match and verify the code, model, and MAC address of the access device with the pre-registered device list;

[0054] The interface access control unit is used to assign application programming interface access permissions according to the device type.

[0055] The dual authentication mechanism of device information verification and interface access control ensures the security of device access and the accuracy of permission allocation, preventing unauthorized access and unauthorized operations.

[0056] The weighted model used in the health assessment and prediction module is:

[0057]

[0058] in, Indicates the device at time Health score, Based on the duration of equipment use The static attenuation factor, For the first Dynamic operating indicators The normalized value, For its weight, This is the adjustment coefficient;

[0059] By adopting a weighted evaluation model that integrates static attributes and dynamic indicators, a scientific quantitative assessment of equipment health is achieved, providing accurate data for predictive maintenance.

[0060] Dynamic operating indicators This includes at least two of the following: average daily business processing volume of the device, average CPU utilization, and average network request response latency.

[0061] By monitoring multi-dimensional operational indicators, a comprehensive understanding of the equipment's operating status is achieved, improving the accuracy of health assessments.

[0062] The system also includes a channel service monitoring module and a performance analysis module:

[0063] The channel service monitoring module is used for real-time monitoring and anomaly diagnosis of the service interface of the business hall;

[0064] The quality and efficiency analysis module is used to calculate the operational quality and efficiency indicators of the business hall based on customer flow, business volume, and equipment status data.

[0065] By adding channel service monitoring and performance analysis functions, the correlation analysis between equipment status and business efficiency was realized, providing data support for the operation and management of business halls.

[0066] The quality and efficiency analysis module uses the following formula to calculate the service load of the business hall. :

[0067]

[0068] in, For load factor, For the first Average processing time for each service For the number of this business, For the number of service windows, For maximum service duration;

[0069] The load quantification calculation model enables a scientific assessment of the service capacity of the business hall, providing a basis for decision-making on resource allocation and optimization.

[0070] This invention also includes a method for a business hall equipment failure prediction and health management system, the method comprising:

[0071] Securely access and bind equipment in the business hall based on device digital fingerprints;

[0072] Parallel acquisition of equipment operating status, service flow, and network port data;

[0073] A weighted model that integrates static attribute decay and dynamic operational indicators is used to calculate the real-time health score and predict its trend.

[0074] When the health score falls below the adaptive threshold, an alarm is automatically triggered and a work order is generated, forming a closed-loop process.

[0075] Integrate multidimensional data to generate and visualize the health records of devices;

[0076] Through a complete equipment management methodology, standardized management of the entire process from equipment access to health assessment and disposal has been achieved, thereby improving operation and maintenance efficiency.

[0077] The weighted model employs a multi-indicator weighted fusion approach, dynamically adjusting its weights by combining historical equipment fault data with real-time operational data. ;

[0078] By employing a dynamic weight adjustment mechanism, the health assessment model can adapt to changes in equipment operating characteristics, thereby improving the model's accuracy and applicability.

[0079] The steps to form a closed-loop disposal system also include:

[0080] Automatically assign personnel and diagnostic scripts based on device type and alarm level;

[0081] The processing steps are recorded and fed back to the evaluation model for adaptive threshold and weight optimization;

[0082] Through automatic task assignment and feedback optimization mechanisms, closed-loop management of the operation and maintenance process has been achieved, improving the efficiency and effectiveness of fault handling.

[0083] Example 1: Device Access Authentication Implementation Method

[0084] First, the device access authentication module is deployed in the core network area of ​​the power company's internal information network. When a new device needs to be connected in the business hall, the authorized maintenance personnel first log in to the system management platform through secure authentication and enter the device pre-registration management interface. In this interface, the maintenance personnel need to enter the standard identification information of the device, which usually includes the device model, MAC address and hardware serial number.

[0085] Next, the system calls an encrypted hash algorithm to process these device identification information and generate a unique digital fingerprint for the device. This digital fingerprint, along with the device's basic information, is stored in a protected pre-registered device list database and marked as pending activation.

[0086] Then, after the device is physically installed and powered on at the business hall, its embedded security agent program runs automatically; the agent program establishes a connection with the device access authentication module through a secure communication protocol and transmits the generated device digital fingerprint to the device information verification unit in encryption.

[0087] Secondly, after receiving the data, the device information verification unit decrypts it and queries the pre-registration list database for matching verification. Verification includes digital fingerprint consistency, device status, and validity period. Upon successful verification, the device status is updated to "activated."

[0088] Finally, the interface access control unit then assigns specific application programming interface permissions to the device based on the predefined security policy and the device type, while denying it access to unauthorized interfaces; all permission configuration information is sent to the device agent program through a secure channel, thus completing the entire process of secure device access and binding.

[0089] Example 2: Multi-source data acquisition implementation method

[0090] First, the multi-source data acquisition module starts data acquisition after the device completes access authentication; this module acquires heterogeneous data in parallel through multiple independent data channels to ensure the comprehensiveness and real-time nature of the data:

[0091] The operational status data acquisition channel collects operational metrics of the device hardware at set time intervals using a standard network management protocol. The collected metrics typically include CPU utilization, memory usage, storage space status, and network connectivity status. Data compression and encrypted transmission are employed during the acquisition process to ensure data security.

[0092] The business flow data acquisition channel periodically synchronizes transaction data from the business system through data middleware; the synchronized data includes information reflecting the business load of the equipment, such as the number of business transactions, transaction amount, number of documents printed, and business type distribution.

[0093] The network port data acquisition channel uses network probing technology to monitor the communication status of device ports in real time; the monitoring indicators include key parameters reflecting network quality such as network traffic, data transmission latency, and packet loss rate.

[0094] Next, all collected data is appended with a timestamp and a unique device identifier, and then transmitted via a message queue to the distributed file system of the data storage server for centralized storage and processing; the data storage adopts a columnar storage format to optimize the query efficiency of subsequent data analysis.

[0095] Example 3: Implementation of Health Assessment and Prediction

[0096] The health assessment and prediction module periodically initiates assessment tasks. The core of this module is a weighted assessment model that integrates the static attribute decay factor and dynamic operating indicators of the equipment.

[0097] First, the evaluation process begins by calculating the static attribute decay factor. The system queries the equipment management database to obtain the length of time the equipment has been in use, and then substitutes it into a decay model that takes into account the natural aging and technical depreciation of the equipment for calculation.

[0098] Next, dynamic operating indicators are processed. The system obtains multiple recent operating indicator data of the equipment from the data storage subsystem. These indicators are normalized to eliminate the influence of units and facilitate comprehensive calculation.

[0099] The system then invokes a weighted evaluation model to perform calculations. This model assigns corresponding weight coefficients to different types of operational indicators to reflect their impact on the equipment's health status. The weighted sum of the static decay factor and dynamic indicators is combined through adjustment coefficients to ultimately generate a quantitative health score.

[0100] The system determines the health level of the equipment based on the threshold range of the scoring results and automatically generates an assessment report to update the equipment health record; at the same time, the module can predict the future trend of equipment health based on time series analysis.

[0101] Example 4: Intelligent Alarm and Handling Closed-Loop Implementation Method

[0102] When the device's health score remains below a preset threshold, the intelligent alarm and handling closed-loop module automatically initiates the processing flow. The adaptive threshold is dynamically calculated and generated based on the device type's historical health data and failure rate statistics.

[0103] First, the system generates an alarm event. The alarm generation engine assembles the alarm details, which typically include key information such as device identifier, abnormal indicators, health score, and trigger time.

[0104] Next, the alarm information is pushed to different terminals such as the operation and maintenance management platform and mobile application through the integrated message distribution mechanism to ensure that relevant personnel can be aware of it in a timely manner.

[0105] Then, the system synchronously starts the standardized work order creation process. The work order management system will automatically select the corresponding work order template according to the alarm type and level, fill in the relevant equipment information, alarm details and processing time limit, and assign the work order to the corresponding responsible team or personnel.

[0106] Next, for common anomaly types, the system can automatically match and issue a preliminary set of diagnostic instructions. These instructions can be sent to the device through a remote execution channel to attempt self-repair or information collection.

[0107] Finally, after the maintenance personnel intervene, they need to update the work order status and processing result through the terminal. After the system receives the processing result, it will automatically close the corresponding alarm event and record the handling information in the case knowledge base for the purpose of optimizing future evaluation models and alarm thresholds.

[0108] Example 5: Implementation of Panoramic Lifecycle Management

[0109] The panoramic lifecycle management module builds a complete digital health record for each device, spanning the entire process from device access to decommissioning:

[0110] This file integrates basic equipment information, status data, event logs, and analysis reports. The basic information includes static attributes such as equipment model, serial number, supplier, and deployment location.

[0111] The lifecycle event dimension systematically records all major historical events of the device, including detailed logs of activities such as hardware upgrades, software version updates, planned maintenance, fault repairs, and performance optimizations;

[0112] Historical health data, presented in time series format, continuously records the health scores and trends of the equipment, providing a data foundation for assessing equipment performance degradation.

[0113] All alarm events and handling processes are also fully recorded, forming a traceable alarm history, including alarm cause, handling measures, time taken and results;

[0114] The system provides multi-dimensional query and statistical analysis functions through a visual analysis interface, displaying the evolution of the equipment's health status throughout its entire lifecycle, and providing data support for equipment maintenance, allocation, and upgrade decisions.

[0115] Example 6: Channel Service Monitoring Implementation Method

[0116] The channel service monitoring module regularly performs health checks on all service interfaces provided by the business hall to ensure service availability and performance;

[0117] First, the monitoring process is usually executed by distributed monitoring nodes. The monitoring nodes send a constructed test request to the target service interface and then verify the interface's response.

[0118] Next, the verification items include checking whether the service returns a success status code, whether the response time is within an acceptable range, and whether the returned data format and content meet the expected specifications;

[0119] For critical business interfaces, the monitoring process also simulates scenarios of concurrent access by multiple users to test the interface's processing capacity and stability under high load.

[0120] Then, the monitoring controller will summarize all the inspection results and use a comprehensive scoring algorithm to evaluate the health status of each service; when an interface response is abnormal or performance is degraded, the system will immediately generate a service anomaly alarm, notify technical support personnel, and automatically create a fault handling work order.

[0121] Finally, the system regularly generates service monitoring reports, statistically analyzes service availability, performance trends, and abnormal events, and provides a basis for service optimization decisions.

[0122] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0123] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A business hall equipment fault prediction and health management system, characterized in that, The system is built on the power grid and includes the following modules that are connected to the data network in sequence: M1, Device Access Authentication Module, is used to securely access and bind self-service payment terminals and business acceptance machines based on digital fingerprints generated by the hash of device model, MAC address and hardware serial number. M2, Multi-source data acquisition module, is used to collect equipment operating status, business flow and network port data in parallel; The M3 health assessment and prediction module uses a weighted model that integrates static attribute decay and dynamic operating indicators to calculate real-time health scores and predict trends. M4, the panoramic lifecycle management module, integrates ledgers, alarms, maintenance records and health trends to generate a visual health record; The M5 intelligent alarm and handling closed-loop module is used to automatically dispatch diagnostic instructions and generate handling work orders according to the device type when the health status is lower than the adaptive threshold, and track the status until the loop is closed.

2. The business hall equipment fault prediction and health management system according to claim 1, characterized in that, The device access authentication module includes a device information verification unit and an interface access control unit: The device information verification unit is used to match and verify the code, model, and MAC address of the access device with the pre-registered device list; The interface access control unit is used to assign application programming interface access permissions according to the device type.

3. The business hall equipment fault prediction and health management system according to claim 1, characterized in that, The weighted model used in the health assessment and prediction module is: in, Indicates the device at time Health score, Based on the duration of equipment use The static attenuation factor, For the first Dynamic operating indicators The normalized value, For its weight, This is the adjustment coefficient.

4. The business hall equipment fault prediction and health management system according to claim 3, characterized in that, The dynamic operating indicators This includes at least two of the following: average daily business processing volume of the device, average CPU utilization, and average network request response latency.

5. The business hall equipment fault prediction and health management system according to claim 1, characterized in that, The system also includes a channel service monitoring module and a performance analysis module. The channel service monitoring module is used to monitor and diagnose anomalies in the business hall service interface in real time. The quality and efficiency analysis module is used to calculate the operational quality and efficiency indicators of the business hall based on customer flow, business volume, and equipment status data.

6. The business hall equipment fault prediction and health management system according to claim 5, characterized in that, The performance analysis module uses the following formula to calculate the service load of the business hall. : in, For load factor, For the first Average processing time for each service For the number of this business, For the number of service windows, This is the maximum service duration.

7. The method for a business hall equipment fault prediction and health management system according to any one of claims 1-6, characterized in that, The method includes: Securely access and bind equipment in the business hall based on device digital fingerprints; Parallel acquisition of equipment operating status, service flow, and network port data; A weighted model that integrates static attribute decay and dynamic operational indicators is used to calculate the real-time health score and predict its trend. When the health score falls below the adaptive threshold, an alarm is automatically triggered and a work order is generated, forming a closed-loop process. Integrate multidimensional data to generate and visualize the health records of devices.

8. The method for predicting and managing equipment failures in a business hall according to claim 7, characterized in that, The weighted model employs a multi-index weighted fusion approach, dynamically adjusting its weights by combining historical equipment fault data with real-time operational data. .

9. The method for predicting and managing the equipment failure of a business hall according to claim 7, characterized in that, The step of forming a closed-loop disposal process also includes: Automatically assign personnel and diagnostic scripts based on device type and alarm level; The processing steps are recorded and fed back to the evaluation model for adaptive threshold and weight optimization.

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