Operation and maintenance management method and device of wireless access equipment, equipment and medium

Abstract

The invention relates to the technical field of big data, can be applied to the field of medical and financial equipment management, and discloses an operation and maintenance management method and device of wireless access equipment, equipment and a medium. The attribute information and the identifier of the device user are associated and stored in a configuration management database, and price information corresponding to the wireless access device is pre-configured in the configuration management database; acquiring usage information of the wireless access device of the device user and / or terminal information of a terminal of the wireless access device of the access device user according to a preset acquisition rule; and according to at least one of the usage information, the terminal information and the price information, automatically generating a standardized report of the device user according to a preset standardized report generation rule. According to the invention, integrated management of the operation and maintenance data and automatic report generation are realized, and the operation and maintenance management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of big data technology and can be applied to the fields of medical and financial equipment management, and in particular to an operation and maintenance management method, apparatus, equipment and medium for wireless access equipment. Background Art

[0002] Wireless access points (APs), as the core infrastructure of wireless networks, perform critical functions such as terminal device access, data forwarding, and radio frequency signal coverage. They are widely used in network deployments across industries such as healthcare, finance, and manufacturing. Current AP operations and maintenance management often face difficulties integrating multi-source data. Because different brands of APs utilize heterogeneous management systems and lack standardized interfaces with systems like configuration management databases (CMDBs) and alarm platforms, operations and maintenance personnel must manually extract device status, topology relationships, and alarm logs across multiple platforms. This leads to a series of problems: data fragmentation, response delays, and insufficient decision-making. For example, a tertiary hospital deployed multi-brand APs covering operating rooms, ICUs, and mobile diagnostic carts. When an operating room AP suddenly went offline, operations and maintenance personnel had to log into the AC management platform to verify the radio frequency status, verify the device's departmental affiliation using the CMDB, and then access historical alarm records on the alarm platform. This manual correlation analysis took tens of minutes, significantly delaying network restoration in emergency situations. In the insurance industry, the APs deployed by a certain group's national branches need to collect monthly statistics on equipment utilization to calculate premiums. However, because the data is scattered across multiple independent systems, the financial department needs to invest a lot of manpower to match data across systems, which is not only labor-intensive but also carries the risk of billing errors. The technical essence of the above problem lies in the fact that the data island effect between multi-source heterogeneous systems causes a serious disconnect between AP operating data, business attributes, and the operation and maintenance decision-making chain. There is a lack of unified real-time data fusion and intelligent analysis capabilities, forming data islands, resulting in low efficiency in operation and maintenance data collection and integration faults, making it difficult to support quantitative evaluation and efficient management of network operation status. The present invention automatically collects equipment usage information and terminal information from multi-source heterogeneous systems by associating and storing the attribute information, equipment user identification, and price information of wireless access devices in the configuration management database of the operation and maintenance management system, and generates standardized reports based on this information. This breaks the data islands between different brands of equipment and heterogeneous systems, realizes the integrated management of operation and maintenance data, and automated report generation, thereby improving the efficiency of operation and maintenance management. Summary of the Invention

[0003] The present invention provides a method, apparatus, computer equipment and medium for operation and maintenance management of wireless access equipment, so as to solve the technical problems of low efficiency of operation and maintenance data collection and faulty multi-source data integration caused by the formation of data islands in the operation and maintenance management of existing wireless access equipment due to the use of heterogeneous management systems.

[0004] In a first aspect, a method for operation and maintenance management of a wireless access device is provided, which is applied to an operation and maintenance management system, wherein the operation and maintenance management system is provided with a configuration management database, the method comprising:

[0005] receiving the entered attribute information of the wireless access device and the identifier of the device user, and associating the attribute information with the identifier of the device user and storing them in the configuration management database, wherein the configuration management database is pre-configured with price information corresponding to the wireless access device;

[0006] Collecting usage information of the wireless access device of the device user and / or terminal information of a terminal accessing the wireless access device of the device user according to a preset collection rule;

[0007] A standardized report for the device user is automatically generated according to at least one of the usage information, the terminal information, and the price information in accordance with preset standardized report generation rules.

[0008] In a second aspect, an operation and maintenance management apparatus for a wireless access device is provided, comprising a unit for executing the above method.

[0009] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0010] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0011] The present invention provides an operation and maintenance management method, apparatus, computer equipment, and medium for wireless access equipment. The method comprises: receiving input attribute information of a wireless access equipment and an identifier of a device user, and associating the attribute information and the identifier of the device user and storing them in a configuration management database, wherein the configuration management database is pre-configured with price information corresponding to the wireless access equipment; collecting usage information of the wireless access equipment of the device user and / or terminal information of a terminal accessing the wireless access equipment of the device user according to preset collection rules; and automatically generating a standardized report for the device user according to preset standardized report generation rules based on at least one of the usage information, the terminal information, and the price information. The present invention solves the problem of low operation and maintenance efficiency caused by data silos by automatically associating and storing the attribute information of the wireless access equipment, the identifier of the device user, and the usage information. It also generates standardized reports in real time based on multi-dimensional data, thereby achieving accurate monitoring and efficient management of cross-dimensional device operation and maintenance status. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only 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 labor.

[0013] Figure 1 This is a flow chart of an operation and maintenance management method for a wireless access device according to an embodiment of the present invention;

[0014] Figure 2 yes Figure 1 Schematic diagram of the flow of sub-steps of step S130;

[0015] Figure 3 yes Figure 1 A schematic flow chart of another sub-step of step S130;

[0016] Figure 4 yes Figure 1 A schematic flow chart of another sub-step of step S130;

[0017] Figure 5 yes Figure 4 A schematic flow chart of another sub-step of step S130;

[0018] Figure 6 1 is a flow chart of an operation and maintenance management method for a wireless access device according to another embodiment of the present invention;

[0019] Figure 7 1 is a flow chart of an operation and maintenance management method for a wireless access device according to another embodiment of the present invention;

[0020] Figure 8 This is a schematic block diagram of an operation and maintenance management device for a wireless access device according to an embodiment of the present invention;

[0021] Figure 9 is a structural diagram of a computer device according to an embodiment of the present invention;

[0022] Figure 10 FIG. 2 is another structural diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] 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 them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] The wireless access device operation and maintenance management method provided in embodiments of the present invention can be applied to either a client or a server. The client can include, but is not limited to, various personal computers, laptops, smartphones, tablet computers, and portable wearable devices. The server can be implemented as a standalone server or a server cluster consisting of multiple servers. The present invention is described in detail below using specific embodiments.

[0025] See also Figure 1 As shown, Figure 1 A flowchart of an operation and maintenance management method for a wireless access device provided by an embodiment of the present invention includes the following steps: S110-S130.

[0026] S110: Receive input attribute information of a wireless access device and an identifier of a device user, and associate the attribute information with the identifier of the device user and store them in the configuration management database, wherein the configuration management database is pre-configured with price information corresponding to the wireless access device;

[0027] In this embodiment, the wireless access device (AP) refers to a hardware device that provides wireless network access services to terminal devices, such as an Access Point, AP, enterprise-level wireless router, etc.; the attribute information includes physical and technical parameters such as the manufacturer, model, hardware version, maximum number of access terminals, and deployment location of the device; the device user refers to an organizational unit that uses the wireless access device, such as a professional company (BU) such as the R&D department or marketing department within an enterprise; the device user identifier is a code or name used to uniquely identify the device user, such as "BU001_R&D Department"; the configuration management database (CMDB) is the core database in the operation and maintenance management system for storing device assets and associated relationships; the operation and maintenance management system is a software system running on the server side, with data collection, storage, analysis and report generation functions; the price information is the pre-configured unit usage cost of the wireless access device, such as the monthly usage unit price of a certain model of AP is 100 yuan. In practice, the system administrator enters wireless access device attribute information through the O&M management system's web interface. For example, in the "Device Asset Entry" module, they select the manufacturer as "HuaX," the model as "AP4050DN," the hardware version as "V200R019C10," and the deployment location as "Area A, 3rd Floor, R&D Building." Furthermore, in the "Organizational Structure Management" module, they enter device user information, such as adding the "R&D Department" user and generating a unique identifier as "BU001." After completing this entry, the system triggers the data processing flow by clicking the "Associate and Store" button: the device attribute information is associated with the device user identifier using a database foreign key, creating a "Device-User" mapping table. For example, the "Owner_BU_ID" field is added to the CMDB's "Device Asset Table" and filled with "BU001." Furthermore, the device model is matched to the corresponding unit price of "100 yuan / month" in the "Pricing Information Table." Finally, all data is encrypted and stored in the CMDB's distributed database cluster. The purpose of this step is to establish a relationship between wireless access devices and business units, forming a standardized asset profile and providing basic data support for subsequent data collection and report generation. This step achieves structured integration of multi-source heterogeneous device information, breaking the traditional separation between device data and business attributes in operations and laying the foundation for data association to resolve data silos.

[0028] For example, in a medical scenario, the information department administrator of a tertiary hospital enters the wireless access device information of each hospital area through this step, associates the HuaxAP4050DN in the inpatient department with the "Inpatient Nursing Unit" (BU003), and configures a unit price of 150 yuan / month, so that the equipment usage and cost can be counted by department dimension in the future; in a financial scenario, such as an insurance scenario, the IT operation and maintenance personnel of a provincial branch of an insurance company associate and store the H3CAP devices of the 15 municipal branches in the province with the corresponding "municipal branches" (such as "BU012_Quanzhou Branch"). Subsequently, the equipment usage of each branch can be grasped in real time through usage reports, avoiding information gaps when cross-departmental data statistics are collected.

[0029] S120: Collect usage information of the wireless access device of the device user and / or terminal information of the terminal accessing the wireless access device of the device user according to a preset collection rule;

[0030] In this embodiment, the preset collection rules refer to scheduled task rules preconfigured in the operation and maintenance management system, including collection frequency, collection indicator range, and data filtering conditions. Usage information refers to the operating status data of wireless access devices, such as online duration, online / offline status data, and the number of access terminals. Terminal information refers to data related to terminals connected to the wireless access devices, including terminal MAC addresses, IP addresses, connection duration, signal strength, and so on. In specific implementation, the system administrator first configures collection rules for different device users in the "Data Collection Configuration" module of the operation and maintenance management system. For example, for the "R&D Department" (BU001), the system configures the collection of online status of all its subordinate APs every 5 minutes and terminal connection information every 15 minutes. The system automatically selects the appropriate data collection protocol based on the type of wireless access device. For "Huax" brand APs that support the SNMP protocol, the system uses the SNMPv2c protocol to obtain node data such as sysUpTime (online duration) and ifOperStatus (interface status) from the device MIB. For "Ruix" brand APs that support the RESTful API, the system calls its management interface to obtain a real-time list of access terminals. After a scheduled task is triggered, the system uses multiple threads to send data requests to each device in parallel. For example, it simultaneously sends SNMP GET requests to 10 APs in the R&D department to obtain the number of online terminals. The collected raw data then undergoes ETL processing, such as converting the OID values ​​returned by SNMP into readable status descriptions (e.g., "1" to "online") and filtering out outliers (e.g., data with a negative number of online terminals). Finally, the cleaned data is associated with the device user identifier in the CMDB. For example, the number of online terminals for the AP in Area A, Floor 3, R&D Building (BU_ID = BU001) is stored in the "Device Usage Details Table." The MAC addresses and connection times of terminals connected to this AP are also stored in the "Terminal Connection Record Table," with a timestamp added to mark the collection time. This step aims to obtain real-time operational status data for wireless access devices and terminals, providing data support for subsequent report generation. This step enables the automated collection and standardized storage of data from multiple heterogeneous devices, addressing the inefficient and untimely manual data collection issues of traditional O&M practices and providing a real-time, accurate data foundation for O&M decision-making.

[0031] For example, in a healthcare scenario, the system collects the online status of the access points (APs) of the inpatient nursing unit (BU003) every 5 minutes according to preset rules. It also collects connection information for medical devices connected to these APs (such as mobile nursing workstations) to analyze the frequency and distribution of medical device usage. In an insurance scenario, the system collects the number of terminals connected to the APs of the Quanzhou branch (BU012) every 15 minutes. Using the IP address segments in the terminal information, the system distinguishes between employee and customer terminals, providing data support for branch network planning.

[0032] S130. Automatically generate a standardized report for the device user according to preset standardized report generation rules based on at least one of the usage information, the terminal information, and the price information.

[0033] In this embodiment, the preset standardized report generation rules refer to the report templates, data aggregation logic and format specifications pre-configured in the operation and maintenance management system, including the generation rules of usage information reports, terminal information reports, billing reports and asset operation reports; the standardized reports refer to structured data reports generated in accordance with a unified format and dimension, including usage information reports, terminal information reports, billing reports and asset operation reports. In the specific process, the system extracts corresponding data from the CMDB and the database according to the report type. For example, when generating a usage information report, the AP online / offline status data of the specified equipment user (such as BU001 R&D Department) is obtained from the "Equipment Usage Details Table"; the data is aggregated and calculated according to the preset time dimension (such as day / month), for example, the average daily AP online time of the R&D Department is taken, and the maximum and minimum values ​​of the number of online devices in the month are counted; the usage information, terminal information and price information are cross-table associated, for example, when generating a billing report, the equipment user identification is used to identify the relevant data. Link the "Equipment Usage Details Table" and the "Price Information Table" to calculate the total price for the month (number of online devices × unit price × number of days); format the data according to the preset report template (such as Excel template or PDF template), for example, map the online rate indicator to the "Equipment Health" chart area in the template, and map the number of terminal connections to the "Access Trend" table area; when receiving the user's report export request (such as selecting "R&D Department Usage Report in May 2025"), the system retrieves the data for the corresponding time range in real time, re-executes statistical analysis and generates an exportable report file. The purpose of performing this step is to generate standardized operation and maintenance reports through an automated process, solving the time-consuming and error-prone problem of manual merging of multi-platform data in traditional operation and maintenance. Through this step, the integrated analysis and visual presentation of multi-source heterogeneous data are realized, and the equipment technical indicators, terminal behavior data and business cost data are converted into structured reports, providing a quantitative basis for operation and maintenance decisions, and further breaking the constraints of data silos on operation and maintenance efficiency.

[0034] For example, in the medical scenario, the system automatically generates a monthly billing report based on the usage and price information of the inpatient nursing unit (BU003), showing the online time, unit price and total price of the 15 APs used by the unit in May 2025, helping the hospital's finance department to accurately calculate the department's network costs; in the insurance scenario, the system generates a terminal details table based on the terminal information of the Quanzhou branch (BU012), showing the connection time and IP address of the customer terminal at each business window that month, providing data support for network bandwidth expansion decisions and avoiding resource planning deviations caused by data fragmentation.

[0035] In one embodiment, if Figure 2 As shown, the step S130 includes: S131-S133.

[0036] S131. Associating the acquired online / offline status data of the wireless access device with the identifier of the device user to which the wireless access device belongs, and performing statistics on the online duration, number of online devices, and online rate indicators of the device user's wireless access device based on the online / offline status data according to a preset time dimension;

[0037] S132: Format the online duration, number of online devices, online rate index, and attribute information according to a preset usage report template to generate a usage information report for the device user;

[0038] S133. If a report export request including a time range filter condition is received from a user, the online duration, number of online devices, and online rate indicator are counted according to the time range filter condition, and a usage information report including the device user, the attribute information, the online duration, number of online devices, and online rate indicator is generated accordingly.

[0039] In this embodiment, the online / offline status data refers to the connectivity status value of the wireless access device at a specific moment; the preset time dimension includes statistical periods such as day, week, and month; the online duration is the cumulative time that the device is online; the number of online devices is the total number of devices that are online at a specific moment or time period; the online rate indicator is the ratio of the online duration to the total duration of the statistical period; the preset usage report template is a predefined Excel or PDF format file, which contains a fixed data display area and chart style. During specific execution, the operation and maintenance management system first obtains the online / offline status data of all APs under the R&D department (BU001) through database association query, such as executing the SQL statement "SELECT device_id, online_status, record_time FROM device_usage eWHEREbu_id='BU001'ANDrecord_timeBETWEEN'2025-05-0100:00:00'AND'2025-05-3123:59:59'", then group by device ID, use time series analysis algorithm to calculate the online time of each AP (such as by accumulating the time difference between state change points), count the maximum / minimum / average number of online devices per day, and calculate the online rate indicator using the formula "online rate = online time / total duration of statistical period"; then, the system will calculate the online time and number of online devices. The usage and online rate indicators are mapped to the device attribute information stored in the CMDB (such as manufacturer, model, and deployment location). For example, the online rate of "HuaX's AP4050DN" is filled in the "Online Rate" cell corresponding to the "Device Model" column in the template, and a trend chart of device usage is generated at the same time. When receiving the "Export R&D Department Usage Report from May 1 to May 15, 2025" request initiated by the user through the web interface, the system parses the time range parameters, re-executes data filtering and statistical analysis, dynamically generates usage information reports that meet user needs, and provides download options in Excel, PDF and other formats. The purpose of performing this step is to convert the usage data of wireless access devices into intuitively understandable statistical reports, solving the problem of scattered data and difficulty in quickly obtaining key indicators in traditional operation and maintenance. Through this step, the transformation from device status monitoring to business indicator visualization is realized, providing operation and maintenance personnel with a quantitative assessment tool for equipment health, and further breaking the constraints of data silos on operation and maintenance decisions.

[0040] For example, in a medical scenario, the system generates a usage report for the inpatient nursing unit (BU003) through this step, showing that the average online rate of the unit's 20 APs was 99.8% in May. Among them, the three APs deployed in the intensive care unit were online for 720 hours (with no offline hours throughout the month), helping the hospital information department evaluate the reliability of the medical dedicated network; in an insurance scenario, the Quanzhou branch (BU012) exported a usage report from April 1 to 15 and found that the number of online AP devices at a certain business outlet peaked at 3 pm on weekdays (reaching 215 devices), exceeding 80% of the equipment's maximum carrying capacity. The customer diversion strategy was adjusted in a timely manner to prevent network congestion from affecting business processing.

[0041] In one embodiment, if Figure 3 As shown, the step S130 includes: S134-S136.

[0042] S134. Associating the acquired connection data of the terminal connected to the wireless access device with the identifier of the device user to which the wireless access device belongs, and counting the number of connections and connection duration of the terminal based on the connection data according to a preset time dimension;

[0043] S135: Format the number of connections, the connection duration, and the attribute information according to a preset terminal report template to generate a terminal information report for the device user;

[0044] S136. If a report export request including a time range filter condition is received from a user, the number of connections and the connection duration are counted according to the time range filter condition, and a terminal information report including the device user, the attribute information, the number of connections and the connection duration is generated accordingly.

[0045] In this embodiment, the terminal connection data refers to the timestamps and related parameters of the establishment and disconnection of the terminal device and the wireless access device; the preset time dimension includes statistical periods such as day, week, and month; the number of connections is the total number of terminals connected to a wireless access device within a specific time period; the connection duration is the duration from connection to disconnection of a single terminal; the preset terminal report template is a predefined Excel or PDF format file, which contains a fixed display area for the terminal connection trend chart. During the specific execution process, the operation and maintenance management system first obtains the terminal connection records of all APs under the R&D department (BU001) through database association query, for example, executing the SQL statement "SELECT ap_id, terminal_mac, connect_time, disconnect_time FROM terminal_connection WHERE ap_id IN (SELECT device_id FROM device_asset WHERE bu_id = 'BU001') AND connect_time The system then groups the data by AP ID, counts the number of connected terminals for each device, and calculates the total connection duration for each AP by adding the difference between the connection and disconnection times. The system then correlates the statistical results with device attribute information stored in the CMDB (e.g., "Area A, 3rd Floor, R&D Building"). For example, the system populates the terminal connection data for the APs in that area into the corresponding table in the template and generates a terminal connection trend chart by hour. Upon receiving a request from a user via the web interface to "Export R&D Department Terminal Report from May 1 to May 7, 2025," the system parses the time range parameters, re-performs data filtering and statistical analysis, and dynamically generates a terminal information report that meets the user's needs. The report also provides download options in formats such as CSV and PDF. This step aims to optimize network resource allocation by analyzing terminal connection behavior, addressing the inability of traditional O&M to accurately understand terminal usage patterns. Through this step, the operation and maintenance perspective is transformed from the device dimension to the terminal dimension, providing data support for decisions such as network expansion and load balancing, and further breaking the constraints of data silos on the refinement of operation and maintenance.

[0046] In the medical scenario, the system generates a terminal report for the inpatient nursing unit (BU003) through this step, showing that the average daily connection time of the unit's mobile nursing workstation was 6.8 hours in May, with the peak connection period being 9:00 a.m. to 11:00 a.m., helping the hospital information department to rationally arrange equipment charging plans. In the insurance scenario, the Quanzhou branch (BU012) exported terminal reports during the shopping festival promotion and found that the number of customer mobile terminal connections surged by 300% within 30 minutes of the start of the event, while the connection time of some business outlets' APs was disconnected in less than 15 minutes. This timely optimized the signal coverage in hot spots and improved the customer experience.

[0047] In one embodiment, if Figure 3 As shown, the step S130 includes: S137-S139.

[0048] S137. Associating the acquired online / offline status data of the wireless access device with the identifier of the device user to which the wireless access device belongs, and counting the online duration and the number of online devices of the device user's wireless access device based on the online / offline status data according to a first preset time dimension;

[0049] S138. Perform statistical aggregation on the online duration and the number of online devices according to a second preset time dimension, and calculate total price information of the wireless access device used by the device user based on the price information, the online duration, and the number of online devices, wherein the second preset time dimension is greater than the first preset time dimension;

[0050] S139: Format the price information, the online duration, the number of online devices, the total price information, and the attribute information according to a preset bill report template to generate a bill report for the device user.

[0051] In this embodiment, the first preset time dimension is a smaller statistical period, such as hours or days; the second preset time dimension is a larger statistical period, such as months or quarters; the online duration is the cumulative time that the device is online; the number of online devices is the total number of devices that are online at a specific time or time period; the price information is the pre-configured unit usage cost of the wireless access device, such as "yuan / hour" or "yuan / day"; the total price information is the total usage fee calculated based on the online duration, the number of online devices and the price information; the preset bill report template is a predefined Excel or PDF format file, which contains fixed display areas such as a cost details table and a trend chart.

[0052] In the specific execution process, the operation and maintenance management system first obtains the online / offline status data of all APs under the R&D department (BU001) through database association query, such as executing the SQL statement "SELEC The system then calculates the total price of the AP used by the R&D department that month using the formula "total price = number of online devices × unit price × number of days used" (for example, if the unit price is 10 yuan / day and an AP is online for 30 days in a month, the total price of the device is 300 yuan). Finally, the system combines the price, online time, number of online devices, and total price with the device attribute information stored in the CMDB (for example, if the model is "Hua x AP4050DN) and generates pre-set billing reports based on pre-set billing report templates, including detailed expense breakdowns by device user and monthly expense trend charts. This step aims to convert technical indicators into financial metrics, addressing the issue of extensive network cost accounting in traditional O&M. This step transforms device O&M data into financial data, providing an accurate basis for decision-making such as departmental cost allocation and budgeting, further breaking down data silos between IT O&M and financial management.

[0053] For example, in a medical scenario, the system generates a May billing report for the inpatient nursing unit (BU003) through this step, showing that the total cost of the unit's 15 APs is 2,250 yuan, of which the cost of three intensive care unit-specific APs accounts for 40%, helping the hospital's finance department accurately calculate the network costs of each department; in an insurance scenario, the Quanzhou branch (BU012) discovered a 30% year-on-year increase in AP usage costs at a certain business outlet by exporting the second-quarter billing report. Analysis revealed that this was due to the addition of new equipment, and the equipment procurement plan was adjusted in a timely manner to avoid waste of resources.

[0054] In one embodiment, if Figure 4 As shown, the step S130 also includes: S1310-S1312.

[0055] S1310: Obtain the usage information report and the billing report of the device user, and calculate the basic asset operation data of the device user according to a preset time dimension based on the device online / offline status data in the usage information report and the price information in the billing report;

[0056] S1311. Formatting the asset operation basic data according to a preset asset operation report template to generate an asset operation report for the equipment user;

[0057] S1312: If a report export request including a time range filter condition is received from a user, the asset operation basic data is counted according to the time range filter condition, and the corresponding asset operation report is generated.

[0058] In this embodiment, the asset operation basic data refers to a set of quantitative indicators used to evaluate the efficiency and cost-effectiveness of wireless access equipment, including equipment utilization (online time / total time), single terminal cost (total price / total number of connected terminals), equipment load rate (average number of online terminals / maximum number of connected terminals), etc.; the asset operation report refers to a standardized report that comprehensively displays the asset operation status of wireless access equipment, including equipment health analysis, cost-effectiveness assessment, resource optimization suggestions, etc. During the specific operation process, the operation and maintenance management system first obtains the usage information report and billing report of the R&D department (BU001) through database query. For example, the SQL statement "SELECT a.device_id, a.online_hours, b.price_per_day, b.total_price FROM usage_reporta JOIN billing_reportb ON a.device_id = b.device_id WHERE a.bu_id = 'BU001' AND a.report_date = '2025-05-31'" is executed. Then, the basic asset operation data is calculated according to the preset time dimension (such as month): device utilization rate = online time / (number of statistical days × 24 hours), single terminal cost = total price / total number of connected terminals, device load rate = average number of online terminals / maximum number of connected devices; then, the system compares the calculated basic asset operation data with the CMD The system integrates the device attribute information stored in B (such as the maximum number of connections and deployment location) and generates a visual report based on a preset asset operation report template, including a heat map of device utilization, a cost-benefit analysis table, and a load rate trend chart. Upon receiving a user request to "Export the 2025 Q2 R&D Department Asset Operation Report" via the web interface, the system parses the time range parameters, re-performs data filtering and statistical analysis, and dynamically generates an asset operation report that meets the user's needs, providing download options in formats such as PPT and Excel. This step enables the transition from device status monitoring to asset value management, providing a decision-making basis for optimizing network resource allocation and device lifecycle management, and further breaking down data silos between technical and business departments.

[0059] For example, in the medical scenario, the system generates the Q2 asset operation report for the inpatient nursing unit (BU003) through this step, showing that the average equipment utilization rate of the unit's AP is 87%, but the unit terminal cost of the AP in the intensive care unit is 2.3 times that of the general ward, and it is recommended to optimize the performance of high-cost equipment; in the insurance scenario, the Quanzhou branch (BU012) exported the asset operation report for the first half of 2025 and found that the load rate of the AP of a certain business outlet had exceeded 90% for a long time. Two APs were added in time, which reduced the unit terminal cost in the area by 18% and increased the equipment utilization rate to 85%.

[0060] In one embodiment, if Figure 5 As shown, the operation and maintenance management method of the access device also includes steps: S141-S144.

[0061] S141. Constructing a multidimensional feature vector including a device load rate, a terminal roaming frequency, and a signal interference strength based on the online rate indicator and the connection data, wherein the terminal roaming frequency is the number of times a single terminal device switches to connect to different wireless access devices within a unit time, the device load rate is the ratio of the number of terminals connected to the wireless device to the maximum number of terminals connected, and the signal interference strength is determined based on the received signal strength of the wireless access device;

[0062] S142. Input the multidimensional feature vector as a node feature and a pre-constructed topology map into a graph neural network model, wherein the topology map uses the addresses of wireless access devices as nodes and the signal coverage overlap coefficient between devices as edge weights, wherein the signal coverage overlap coefficient between devices is the degree of overlap of the radio frequency signal coverage areas of adjacent wireless access devices in physical space;

[0063] S143. Predicting the health score of the wireless access device of each device user within a preset future time period through the graph neural network, and identifying potential fault propagation paths;

[0064] S144. When the predicted health score is lower than a preset threshold, automatically generate an early warning report including an optimization strategy and a resource pre-allocation strategy for the wireless access device, and push the early warning report to an alarm platform.

[0065] In this embodiment, the online rate indicator refers to the ratio of the online time of a wireless access device to the total time during a statistical period, and is used to measure device availability. The multidimensional feature vector is a vector composed of multiple dimensional indicators such as device load rate, terminal roaming frequency, and signal interference strength, and is used to comprehensively characterize the operating status of the device. The node is a basic element in the topology graph, representing a wireless access device, and is usually identified by the device's network address (such as an IP address or MAC address). The topology graph is a graph structure composed of nodes and edges, where the nodes are wireless access devices and the edge weights are the signal coverage overlap coefficients between devices, which are used to model the spatial relationship between devices. The graph neural network model is a deep learning model specifically designed to process graph-structured data and is capable of capturing complex relationships between nodes. The edge weight represents the degree of overlap in the signal coverage areas of adjacent devices and is determined by calculating the ratio of the intersection to the union of the radio frequency signal coverage areas of the two devices. The fault propagation path refers to the conduction path that may cause abnormalities in other devices after a device in the network fails. The optimization strategy is an adjustment plan proposed for device health issues, such as parameter configuration optimization and physical location adjustment. The resource pre-allocation strategy refers to a plan for pre-allocating network resources to deal with potential failures, such as activating backup devices and pre-allocating bandwidth.

[0066] During implementation, the system obtains device load rate, terminal roaming frequency, signal interference strength, and constructs a multidimensional feature vector using the following methods: The number of terminals connected to each AP at the current moment is extracted from the terminal connection record table and divided by the maximum number of connected devices recorded in the CMDB (for example, the maximum number of connected devices for the HuaxAP4050DN is 256) to obtain the device load rate. The system also counts the number of times each terminal's MAC address switches between different APs using a sliding time window (for example, one hour). For example, a mobile phone terminal switches from AP1 to AP2 and then to AP3 between 9:00 and 10:00 AM, with a roaming frequency of 2 times per hour. The system also calculates signal interference strength using the RSSI value of wireless access devices. Specifically, for each AP, the RSSI values ​​of all adjacent APs are collected, the number of adjacent APs with RSSI values ​​above -75dBm is calculated, and the weighted sum is calculated based on signal strength (for example, a device with an RSSI of -70dBm has a weight of 1, and a device with an RSSI of -60dBm has a weight of 2). This results in a normalized interference strength value. The device load rate, terminal roaming frequency, signal interference intensity, and online rate indicators calculated above are Z-score normalized to construct a four-dimensional feature vector of the form [online rate 0.95, load rate 0.78, roaming frequency 2.3 times / hour, interference intensity 0.65].

[0067] The topology map is pre-built. The process of building the topology map is as follows: the physical location coordinates (x, y, z) and transmission power parameters of all APs are obtained from the CMDB. The signal coverage range between any two APs is calculated based on the FreeSpacePathLoss model. For example, if the coverage radius of AP1 is 30 meters and the coverage radius of AP2 is 25 meters, and the distance between the two AP centers is 20 meters, the overlapping area is calculated to be 150 square meters through the geometric algorithm, and the overlap coefficient is 150 / (π×30 2 +π×25 2 -150)≈0.09; a weighted undirected graph is constructed using the AP's IP address as the node and the calculated overlap coefficient as the edge weight. The graph neural network model uses the GCN (graph convolutional network) architecture, with the input being the constructed topology graph (adjacency matrix) and node feature vectors (multidimensional feature vectors). The output is the health score (0-100 points) and fault propagation probability matrix for each AP for the next 72 hours. The model training process is as follows: historical operation and maintenance data from the past six months is collected, including hourly device status feature vectors and corresponding health labels (determined by manual annotation or historical fault records). The data is divided into training, validation, and test sets in an 8:1:1 ratio. The stochastic gradient descent algorithm is used to optimize the model parameters by minimizing the mean squared error loss function between the predicted health and the actual health. Dropout technology is used during training to prevent overfitting. The final model's prediction accuracy on the test set meets the predetermined target.

[0068] When the real-time feature vector and topology graph are input into the trained graph neural network model, the model first aggregates node features through a graph convolution layer to capture spatial dependencies between devices. For example, changes in the load of adjacent APs can affect the health of the target AP. Then, a temporal convolution layer processes time series features to extract dynamic trends in device status. Finally, a fully connected layer outputs each AP's health score and fault propagation probability matrix for the next 72 hours. The process for identifying potential fault paths involves: applying a threshold to the fault propagation probability matrix (for example, retaining edges with a probability greater than 0.3), calculating the importance score of each node using the PageRank algorithm, and assigning a higher score to a node that is more likely to be a key hub for fault propagation. Starting from APs with predicted health scores below the threshold, a breadth-first search algorithm is used to identify all possible fault propagation paths. For example, AP1→AP2→AP5 indicates that a fault in AP1 could be transmitted to AP5 via AP2.

[0069] When the health score falls below the preset health threshold, an alert report is generated for optimization and resource pre-allocation strategies. The optimization strategy generation logic is as follows: When an AP's health score is predicted to be below 70, the system first analyzes the primary factors contributing to the low score (such as excessive load or high interference intensity). It then invokes the corresponding rule base to generate optimization recommendations. For example, if the primary cause is excessive load (>85%), recommendations such as "switching the AP channel from 2.4GHz to 5GHz" or "adjusting the power allocation of adjacent APs" are generated. Resource pre-allocation strategies are generated based on fault propagation path analysis. For example, if an AP1 failure is predicted to affect AP2 and AP5, AP2's backup device is pre-activated and an additional 10% of bandwidth is pre-allocated to AP5. These strategies are ultimately integrated into an early warning report, which is pushed to the alarm platform via a message queue. A visual fault impact analysis diagram is also generated on the O&M management system interface. This step aims to proactively identify potential network risks and provide response strategies through data-driven intelligent analysis, addressing the passive response to faults and reliance on manual judgment in traditional O&M. This step aims to shift network operations from post-processing to pre-emptive prevention. By exploring the relationships and operational patterns between devices, it provides operators with a quantitative basis for decision-making. Therefore, this step comprehensively characterizes device status through multidimensional feature vectors, addressing the limitations of single-metric assessments and improving the accuracy of device health assessments. It also leverages graph neural networks to capture implicit relationships between devices, breaking through the bottlenecks of traditional rule-based analysis and improving the lead time for fault prediction. Automatically generated optimization strategies and resource pre-allocation policies accelerate operations response and effectively reduce network outages.

[0070] In one embodiment, if Figure 6 As shown, the operation and maintenance management method of the access device also includes steps: S151-S152.

[0071] S151. Collect performance indicators of the wireless access device of the device user according to preset indicator collection rules;

[0072] S152: Monitor the performance indicator according to a preset alarm threshold, generate an alarm message when the performance indicator exceeds the preset threshold, and send the alarm message to an alarm platform.

[0073] In this embodiment, the preset indicator collection rules refer to pre-configured scheduled task rules, including collection frequency, collection indicator range and data filtering conditions; the performance indicators refer to operating status parameters of wireless access devices, including AC (access controller) latency, packet loss rate, ping response time, etc.; the preset alarm thresholds refer to critical values ​​set for different performance indicators, which are used to determine whether the device is in an abnormal state; the alarm information refers to a notification generated when a performance indicator exceeds the threshold, including information such as device identification, abnormal indicator, trigger time, etc. During the implementation process, the system sends OID query requests to Huax's AC6605 device via SNMP, collecting basic data such as SysUpTime (system uptime), ifInOctets (inbound bytes), and ifOutOctets (outbound bytes). It also sends ICMPEcho request packets to the AC to calculate the round-trip latency (e.g., average latency is 28ms). It also calculates the packet loss rate by counting the reception of 100 consecutive data packets (e.g., if 3 packets are lost, the packet loss rate is 3%). It also sends ping requests to the AC's management IP address every 5 minutes, recording the response time (e.g., average response time is 15ms). The system stores the collected raw data, recording, for example, the latency of 28ms, packet loss of 3%, and ping response time of 15ms for AC1 (IP: 192.168.1.1) at 2025-05-25 10:00:00. Next, the system invokes predefined alarm rules to monitor performance metrics in real time. For example, thresholds are set for latency at 30ms, packet loss at 2%, and ping response time at 20ms. If the packet loss rate of AC1 reaches 3% (exceeding the 2% threshold), an alarm message is immediately generated containing the device ID (AC1), the abnormal indicator (packet loss rate), the current value (3%), the threshold (2%), and the trigger time (2025-05-25 10:05:00). This alarm message is pushed to the operator's mobile app and monitoring screen via the Kafka message queue, and the device is marked as "abnormal" in the operation and maintenance management system. This step aims to monitor the operating status of wireless access devices in real time, promptly identifying and warning of potential network failures, and addressing the inefficiency and delayed fault detection inherent in traditional manual inspections. This step establishes an automated device performance monitoring system that quickly identifies abnormal conditions using pre-set thresholds and provides timely fault warnings to operators. By regularly collecting performance indicators, real-time monitoring of network equipment is achieved, shortening the fault discovery time compared to traditional manual inspections; the automatic alarm mechanism based on preset thresholds reduces the workload of manual monitoring and improves the fault response speed.

[0074] This invention uses a configuration management database to associate and store wireless access device attributes, user identification, and pricing information, integrating multi-source data such as usage information and terminal information to build an automated, standardized report generation system. It also introduces a graph neural network model to predict health and identify fault paths based on device topology and multidimensional feature vectors. It also incorporates a dynamic threshold mechanism to provide real-time alerts for performance indicators. This enables the integrated management of multi-source heterogeneous data, breaking down data silos and resolving the issues of low report generation efficiency and passive fault response in traditional operations and maintenance. It also enhances the automation and intelligence of operations and maintenance management, enabling operators to predict potential equipment failures in advance and optimize resource allocation, thereby improving network reliability and efficiency.

[0075] It should be understood that the size of the serial numbers of the steps in the above embodiments 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 the present invention.

[0076] The embodiment of the present invention further provides an operation and maintenance management device 200 for a wireless access device, which corresponds to the operation and maintenance management method for the wireless access device in the above embodiment. Figure 8 As shown, the operation and maintenance management device 200 of the wireless access device includes: an association unit 201, a collection unit 202 and a generation unit 203. The functional units are described in detail as follows:

[0077] an associating unit 201 configured to receive input attribute information of a wireless access device and an identifier of a device user, and associate the attribute information and the identifier of the device user and store them in the configuration management database, wherein the configuration management database is pre-configured with price information corresponding to the wireless access device;

[0078] A collecting unit 202 is configured to collect usage information of the wireless access device of the device user and / or terminal information of a terminal accessing the wireless access device of the device user according to a preset collection rule;

[0079] The generating unit 203 is configured to automatically generate a standardized report of the device user according to at least one of the usage information, the terminal information and the price information in accordance with preset standardized report generation rules.

[0080] In one embodiment, the generation unit 203 is further used to: associate the acquired online / offline status data of the wireless access device with the identifier of the device user to which the wireless access device belongs, and count the online duration, number of online devices, and online rate index of the wireless access device of the device user according to the online / offline status data according to a preset time dimension; format the online duration, number of online devices, online rate index, and the attribute information according to a preset usage report template to generate a usage information report of the device user; if a report export request initiated by a user containing a time range filtering condition is received, count the online duration, number of online devices, and online rate index according to the time range filtering condition, and correspondingly generate a usage information report containing the device user, the attribute information, the online duration, number of online devices, and online rate index.

[0081] In one embodiment, the generation unit 203 is further used to: associate the acquired connection data of the terminal accessing the wireless access device with the identifier of the device user to which the wireless access device belongs, and count the number of connections and the connection duration of the terminal based on the connection data according to a preset time dimension; format the number of connections, the connection duration, and the attribute information according to a preset terminal report template to generate a terminal information report of the device user; if a report export request initiated by a user containing a time range filtering condition is received, count the number of connections and the connection duration according to the time range filtering condition, and correspondingly generate a terminal information report containing the device user, the attribute information, the number of connections, and the connection duration.

[0082] In one embodiment, the generation unit 203 is further configured to: associate the acquired online / offline status data of the wireless access device with an identifier of the device user to which the wireless access device belongs, and count the online duration and the number of online devices of the device user's wireless access device based on the online / offline status data according to a first preset time dimension; perform statistical aggregation on the online duration and the number of online devices according to a second preset time dimension, and calculate the total price information of the wireless access device used by the device user based on the price information, the online duration, and the number of online devices, wherein the second preset time dimension is greater than the first preset time dimension; and format the price information, the online duration, the number of online devices, the total price information, and the attribute information according to a preset bill report template to generate a bill report for the device user.

[0083] In one embodiment, the generation unit 203 is further used to: obtain the usage information report and the billing report of the device user, and count the asset operation basic data of the device user according to a preset time dimension based on the device online / offline status data in the usage information report and the price information in the billing report; format the asset operation basic data according to a preset asset operation report template to generate an asset operation report of the device user; if a report export request initiated by a user containing a time range filtering condition is received, count the asset operation basic data according to the time range filtering condition, and generate the corresponding asset operation report.

[0084] In one embodiment, the operation and maintenance management device 200 of the wireless access device further includes a prediction unit, which is used to: construct a multidimensional feature vector including a device load rate, a terminal roaming frequency, and a signal interference strength based on the online rate index and the connection data, wherein the terminal roaming frequency is the number of times a single terminal device switches to connect to different wireless access devices within a unit time, the device load rate is the ratio of the number of terminals accessed by the wireless device to the maximum number of access terminals, and the signal interference strength is determined according to the received signal strength of the wireless access device; the multidimensional feature vector is used as a node feature and input into the graph together with the pre-constructed topology graph. A neural network model, wherein the topology graph uses the addresses of wireless access devices as nodes and the signal coverage overlap coefficient between devices as edge weights, wherein the signal coverage overlap coefficient between devices is the degree of overlap of the radio frequency signal coverage areas of adjacent wireless access devices in physical space; the graph neural network is used to predict the health score of the wireless access device of each device user within a preset time period in the future, and to identify potential fault propagation paths; when the predicted health score is lower than a preset health threshold, an early warning report containing the optimization strategy and resource pre-allocation strategy of the wireless access device is automatically generated, and the early warning report is pushed to an alarm platform.

[0085] In one embodiment, the operation and maintenance management device 200 of the wireless access device also includes an alarm unit, which is used to collect performance indicators of the wireless access device of the device user according to preset indicator collection rules; monitor the performance indicators according to preset alarm thresholds, generate alarm information when the performance indicators exceed the preset thresholds, and send the alarm information to the alarm platform.

[0086] The specific definitions of the wireless access device operation and maintenance management apparatus can be found in the definitions of the wireless access device operation and maintenance management method described above and are not further elaborated here. Each module in the wireless access device operation and maintenance management apparatus described above may be implemented in whole or in part via software, hardware, or a combination thereof. Each of these modules may be embedded in or independent of a processor in a computer device in hardware form, or may be stored in a computer device memory in software form, so that the processor can call and execute operations corresponding to each of these modules.

[0087] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 9 As shown. The computer device includes a processor, memory, network interface and database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it implements the functions or steps on the service side of a method for operation and maintenance management of a wireless access device.

[0088] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 10 As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the client side of a method for operation and maintenance management of a wireless access device.

[0089] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned operation and maintenance management method for wireless access devices are implemented.

[0090] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned operation and maintenance management method of the wireless access device are implemented.

[0091] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can be found in the relevant descriptions of the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0092] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0093] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0094] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for operation and maintenance management of wireless access equipment, characterized in that: Applied to an operation and maintenance management system, wherein the operation and maintenance management system is provided with a configuration management database, the method comprises: receiving the entered attribute information of the wireless access device and the identifier of the device user, and associating the attribute information with the identifier of the device user and storing them in the configuration management database, wherein the configuration management database is pre-configured with price information corresponding to the wireless access device; Collecting usage information of the wireless access device of the device user and / or terminal information of a terminal accessing the wireless access device of the device user according to a preset collection rule; A standardized report for the device user is automatically generated according to at least one of the usage information, the terminal information, and the price information in accordance with preset standardized report generation rules.

2. The method according to claim 1, wherein The step of automatically generating a standardized report for the device user according to the usage information and preset standardized report generation rules includes: Associating the acquired online / offline status data of the wireless access device with the identifier of the device user to whom the wireless access device belongs, and counting the online duration, number of online devices, and online rate indicators of the device user's wireless access device according to the online / offline status data in a preset time dimension; Formatting the online duration, number of online devices, online rate index, and attribute information according to a preset usage report template to generate a usage information report for the device user; If a report export request initiated by a user containing a time range filtering condition is received, the online duration, number of online devices, and online rate indicators are counted according to the time range filtering condition, and a usage information report containing the device user, the attribute information, the online duration, the number of online devices, and the online rate indicator is generated accordingly.

3. The method according to claim 1, wherein The step of automatically generating the standardized report of the device user according to the terminal information and preset standardized report generation rules includes: Associating the acquired connection data of the terminal accessing the wireless access device with the identifier of the device user to which the wireless access device belongs, and counting the number of connections and the connection duration of the terminal based on the connection data according to a preset time dimension; Formatting the number of connections, the connection duration, and the attribute information according to a preset terminal report template to generate a terminal information report for the device user; If a report export request is received from a user that includes a time range filtering condition, the number of connections and the connection duration are counted according to the time range filtering condition, and a terminal information report is generated accordingly that includes the device user, the attribute information, the number of connections and the connection duration.

4. The method according to claim 1, wherein The step of automatically generating the standardized report of the equipment user according to the usage information and the price information in accordance with preset standardized report generation rules includes: Associating the acquired online / offline status data of the wireless access device with an identifier of the device user to which the wireless access device belongs, and counting the online duration and the number of online devices of the device user's wireless access device based on the online / offline status data according to a first preset time dimension; performing statistical aggregation on the online duration and the number of online devices according to a second preset time dimension, and calculating total price information of the wireless access device used by the device user based on the price information, the online duration, and the number of online devices, wherein the second preset time dimension is greater than the first preset time dimension; The price information, the online duration, the number of online devices, the total price information and the attribute information are formatted according to a preset bill report template to generate a bill report for the device user.

5. The method according to claim 1, wherein The method further comprises: Obtaining a usage information report and a billing report of the device user, and calculating basic asset operation data of the device user according to a preset time dimension based on the device online / offline status data in the usage information report and the price information in the billing report; Formatting the asset operation basic data according to a preset asset operation report template to generate an asset operation report for the equipment user; If a report export request including a time range filter condition is received from a user, the asset operation basic data is counted according to the time range filter condition, and the corresponding asset operation report is generated.

6. The method according to claim 1, wherein The method further comprises: Constructing a multidimensional feature vector based on the online rate indicator and connection data, including device load rate, terminal roaming frequency, and signal interference strength, wherein the terminal roaming frequency is the number of times a single terminal device switches to connect to different wireless access devices within a unit time, the device load rate is the ratio of the number of terminals connected to the wireless device to the maximum number of terminals connected, and the signal interference strength is determined based on the received signal strength of the wireless access device; The multidimensional feature vector is used as a node feature and is input into a graph neural network model together with a pre-constructed topology map, wherein the topology map uses the addresses of wireless access devices as nodes and the signal coverage overlap coefficient between devices as edge weights, wherein the signal coverage overlap coefficient between devices is the degree of overlap of the radio frequency signal coverage areas of adjacent wireless access devices in physical space; Predicting the health score of the wireless access device of each device user within a preset future time period through the graph neural network, and identifying potential fault propagation paths; When the predicted health score is lower than a preset health threshold, an early warning report including an optimization strategy and a resource pre-allocation strategy of the wireless access device is automatically generated, and the early warning report is pushed to an alarm platform.

7. The method according to any one of claims 1 to 6, wherein: The method comprises: Collecting performance indicators of the wireless access device of the device user according to preset indicator collection rules; The performance indicator is monitored according to a preset alarm threshold, and an alarm message is generated when the performance indicator exceeds the preset threshold, and the alarm message is sent to an alarm platform.

8. An operation and maintenance management device for a wireless access device, characterized in that: The method comprises a unit for executing the method according to any one of claims 1 to 7.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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