Method for processing skin base station performance index data

Abstract

The application relates to a skin base station performance index data processing method. The method comprises the following steps: a data statistics unit sends registration information to a registration module of an operation and maintenance unit; after the registration module of the operation and maintenance unit receives the registration information, the registration module sends communication keys and data interaction port information to the data statistics unit; the data statistics unit reads a target configuration file of skin base station performance indexes, and creates an independent cache partition for the skin base station performance indexes according to an index type specified by the target configuration file; the data statistics unit collects index data of the skin base station performance indexes based on a target statistics period, and writes the index data into the independent cache partition; the data statistics unit classifies and processes the index data through an index statistics tool, and determines index statistics data; and the operation and maintenance unit processes the index statistics data based on an index type and an index level, and generates a performance index report. The above scheme can improve the overall stability and scalability of the system.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method for processing pico base station performance index data. Background Technology

[0002] Currently, when conducting data statistics for picocell base stations, performance data is often processed in a direct-write manner by the data statistics unit. This means the indicator data is directly written to the shared memory of the operation and maintenance unit, with a direct correlation between data writing and memory access. In indicator calculation, the operation and maintenance unit calculates average indicators based on a single set of reported data, unable to perform cumulative calculations on multiple sets of data, nor supporting iterative calculations of sample size and cumulative values. Furthermore, data statistics only involve the base station and cell levels, and can only handle integer and floating-point basic data. Technically, an inheritance architecture implemented through base class abstraction and subclasses is used, with the base class uniformly calling the statistical logic of different subclasses to complete indicator calculations. In terms of system operation, at startup, the operation and maintenance unit parses configuration files in formats such as XML (Extensible Markup Language) to allocate and initialize shared memory. During operation, the data statistics unit divides independent memory areas according to indicator type, directly filling the corresponding areas after statistically analyzing the raw data, and then triggering the data reporting process. Based on this, traditional picocell base station indicator statistics technology suffers from poor system stability, low accuracy of statistical indicators, incomplete functional coverage, and high architectural coupling. Therefore, improving the stability, accuracy, and completeness of the picocell base station indicator statistical system, as well as reducing the coupling of the picocell base station indicator statistical architecture, are problems that need to be solved. Summary of the Invention

[0003] Therefore, it is necessary to provide a method for processing picobase station performance index data that can improve the stability, accuracy, and completeness of the picobase station index statistical system and reduce the coupling of the picobase station index statistical architecture, in order to address the above-mentioned technical problems.

[0004] In a first aspect, this application provides a method for processing pico base station performance index data. The method is executed by a pico base station performance index processing system, which includes a data statistics unit and an operation and maintenance unit. The method includes:

[0005] After the two-way binding of identity information and interface is completed, the data statistics unit reads the target configuration file of the pico base station performance indicators and creates an independent cache partition for the pico base station performance indicators according to the indicator type specified in the target configuration file; the independent cache partition includes an increment counter area, an extreme value area, an average value area, a real-time value area, a cumulative value area, and an anomaly counter area;

[0006] The data statistics unit collects performance data of pico base stations based on the target statistical period, and writes the performance data into the independent cache partition based on the data type corresponding to the performance data.

[0007] The data statistics unit classifies the indicator data in the independent cache partition using indicator statistics tools, determines the indicator statistics data, and sends the indicator statistics data to the operation and maintenance unit through the data interaction port.

[0008] After receiving the statistical data of the indicators, the operation and maintenance unit processes the statistical data of the indicators based on the indicator type and indicator level, and generates a performance indicator report.

[0009] In one embodiment, the method for processing the pico base station performance index data further includes:

[0010] The data statistics unit sends registration information to the registration module of the operation and maintenance unit; the registration information includes a registration request, the indicator types and interface versions supported by the data statistics unit;

[0011] After receiving the registration information, the registration module of the operation and maintenance unit sends the communication key and data interaction port information to the data statistics unit, so that the data statistics unit and the operation and maintenance unit can perform two-way binding of identity information and two-way binding of interfaces based on the communication key and data interaction port.

[0012] In one embodiment, the operation and maintenance unit includes a configuration file parsing module, and the method further includes:

[0013] The configuration file parsing module in the operation and maintenance unit parses the target configuration file for base station performance indicators;

[0014] The operation and maintenance unit allocates memory to the shared memory of the pico base station based on the parsing results, and generates a memory mapping table based on the memory allocation results.

[0015] In one embodiment, the operation and maintenance unit further includes a configuration file verification module, wherein the configuration file parsing module in the operation and maintenance unit parses the target configuration file for base station performance indicators, including:

[0016] The operation and maintenance unit obtains the target configuration file of the pico base station performance indicators, and performs integrity verification on the target configuration file through the configuration file verification module in the operation and maintenance unit to determine whether the configuration parameters in the target configuration file are complete.

[0017] If so, the target configuration file is parsed by the configuration file parsing module in the operation and maintenance unit.

[0018] In one embodiment, after receiving the registration information, the registration module of the operation and maintenance unit sends a communication key and data interaction port information to the data statistics unit, including:

[0019] After receiving the registration information, the registration module of the operation and maintenance unit performs information verification on the registration information; the information verification includes interface version verification, indicator type matching verification, identity verification and parameter integrity verification;

[0020] If the information verification passes, the communication key and data interaction port information are sent to the data statistics unit.

[0021] In one embodiment, before the data statistics unit collects performance index data of the pico base station and writes the index data into the independent cache partition based on the data type corresponding to the index data, the method further includes:

[0022] The data statistics unit collects performance index data for pico base stations;

[0023] The data statistics unit performs data matching and verification on the indicator data to determine whether the data type of the indicator data matches the indicator data.

[0024] If so, perform data anomaly verification on the indicator data, determine the abnormal data from the indicator data based on the verification result, write the abnormal data into the anomaly counter area, and mark the anomaly level corresponding to the abnormal data.

[0025] In one embodiment, the indicator statistics tool includes: an incrementing counter, a real-time value counter, a maximum value counter, a minimum value counter, an average counter, a cumulative counter, an absolute value counter, and an anomaly counter. The data statistics unit uses the indicator statistics tool to classify the indicator data within the independent cache partition and determine the indicator statistics data, including:

[0026] The data statistics unit uses an incrementing counter to accumulate the indicator data stored in the incrementing counter area to determine the growth data;

[0027] The maximum and minimum values ​​are determined by comparing the index data stored in the extreme value region using a maximum value counter and a minimum value counter.

[0028] The cumulative value and sample count of the indicator data stored in the average value area are summed and counted by the average counter to determine the cumulative value and sample count of the indicator, and the mean data is determined based on the cumulative value and sample count of the indicator.

[0029] The real-time value counter is used to update the index data stored in the real-time value area in real time and determine the updated data.

[0030] The cumulative counter is used to overlay and calculate the index data stored in the cumulative value area to determine the accumulated data;

[0031] The growth data, maximum value, minimum value, average value, updated data, and accumulated data are used as indicator statistics.

[0032] In one embodiment, the step of performing cumulative value summation and sample count on the indicator data stored in the average value area using an average counter to determine the cumulative indicator value and the number of indicator samples, and determining the mean data based on the cumulative indicator value and the number of indicator samples, includes:

[0033] The data statistics unit performs cumulative value summation and sample count on the indicator data of the target statistical period stored in the average value area using an average counter, determines the cumulative value of the indicator and the number of indicator samples for the target statistical period, and synchronizes the cumulative value of the indicator and the number of indicator samples to the operation and maintenance unit.

[0034] The operation and maintenance unit determines the iterative cumulative value and iterative sample number based on the cumulative value and sample number of the indicator in the target statistical period, as well as the cumulative value and sample number of the indicator in the historical statistical period, using a two-parameter calculation model.

[0035] The operation and maintenance unit determines the mean data based on the cumulative value of the iteration and the number of iteration samples, and sends the mean data to the data statistics unit.

[0036] In one embodiment, the method for processing the above-mentioned pico base station performance index data further includes:

[0037] The data statistics unit generates an anomaly data report based on the indicator data stored in the anomaly counter area; the anomaly data report includes: indicator name, threshold value, and anomaly data generation time;

[0038] The abnormal data report is stored in the local abnormal log, and the abnormal data processing status is marked in the local abnormal log.

[0039] In one embodiment, after receiving the indicator statistics, the operation and maintenance unit processes the indicator statistics based on the indicator type and indicator level, and after generating a performance indicator report, it further includes:

[0040] The operation and maintenance unit reports the performance indicators to the network management system and records the reporting results of the performance indicators in the status log.

[0041] The indicator levels can include: channel level, base station level, cell level, and network-wide level.

[0042] The above-mentioned method for processing pico base station performance index data involves the data statistics unit sending registration information to the registration module of the operation and maintenance unit. The registration information includes a registration request, the index types supported by the data statistics unit, and the interface version. Upon receiving the registration information, the registration module of the operation and maintenance unit sends a communication key and data interaction port information to the data statistics unit, enabling the data statistics unit and the operation and maintenance unit to perform bidirectional identity information binding and interface binding based on the communication key and data interaction port. After the bidirectional identity information binding and interface binding are completed, the data statistics unit reads the target configuration file for the pico base station performance index and, according to the index types specified in the target configuration file, sets the pico base station performance index accordingly. An independent cache partition is created, comprising an incrementing counter area, an extreme value area, an average value area, a real-time value area, a cumulative value area, and an anomaly counter area. The data statistics unit collects performance data of the pico base station based on the target statistical period and writes the data into the independent cache partition according to the data type. The data statistics unit then classifies the data in the independent cache partition using a statistical tool to determine statistical data and sends it to the operation and maintenance unit via a data interaction port. Upon receiving the statistical data, the operation and maintenance unit processes it based on the indicator type and level to generate a performance indicator report. This approach solves the problems of poor system stability, low accuracy of statistical indicators, incomplete functional coverage, and high architectural coupling inherent in traditional pico base station performance statistics techniques. The above solution ensures data transmission security through two-way binding of identity and interface and communication keys during the registration phase, preventing data corruption or unauthorized access and improving system stability. It reduces data confusion and errors and improves the accuracy of statistical indicators by using methods such as type matching and anomaly verification before data collection, writing data to dedicated independent cache partitions according to data type, and categorized statistical processing. It covers the entire process of indicator registration, caching, collection, statistics, and report generation, overcoming the shortcomings of traditional technologies in terms of functional coverage. Furthermore, it adopts a collaborative model between data statistics units and operation and maintenance units, avoiding excessive dependence between modules, reducing architectural coupling, and further improving the overall stability and scalability of the system. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a method for processing pico base station performance index data in one embodiment.

[0044] Figure 2 This is a schematic diagram of the structure of a pico base station performance index processing system in one embodiment;

[0045] Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] In one embodiment, such as Figure 1 As shown, a method for processing pico base station performance index data is provided. The pico base station performance index processing method consists of... Figure 2 The pico base station performance index processing system shown in the diagram includes a data statistics unit and an operation and maintenance unit. The processing method for the aforementioned pico base station performance index data includes:

[0048] S110: After the two-way binding of identity information and interface is completed, the data statistics unit reads the target configuration file of the pico base station performance indicators and creates an independent cache partition for the pico base station performance indicators according to the indicator type specified in the target configuration file.

[0049] The independent cache partitions include an increment counter area, an extreme value area, an average value area, a real-time value area, a cumulative value area, and an anomaly counter area.

[0050] The system is divided into several sections: an incrementing counter area for storing continuously increasing count-based performance metrics; an extreme value area for storing the maximum and minimum values ​​of picocell base station performance metrics; an average value area for calculating and storing the average values ​​of picocell base station performance metrics; a real-time value area for storing the latest real-time data of picocell base station performance metrics; a cumulative value area for storing picocell base station performance metrics data accumulated and summarized across statistical periods; and an anomaly counter area for storing and recording anomalies such as picocell base station performance metrics exceeding thresholds. Picocell base station performance metrics can include incrementing counter metrics, extreme value metrics, average value metrics, real-time value metrics, cumulative value metrics, and anomaly metrics. The target configuration file is in XML format. The XML configuration file can include definitions, type identifiers, and hierarchical divisions of various performance metrics; rules for dividing independent cache partitions such as the incrementing counter area and extreme value area; mapping relationships between metric types and cache partitions; communication key generation rules; data interaction port allocation rules; performance metric report generation format and saving rules; and FTP (File Transfer Protocol) reporting parameters.

[0051] It should be noted that incrementing counter data is written to a pre-initialized incrementing counter area cache. The storage and calculation of accumulated values ​​are completed using the circular queue structure of this partition, ensuring the orderliness and efficiency of data operations. The core purpose of setting up the incrementing counter area cache is to provide dedicated and suitable storage space for incrementing counter type indicator data. This ensures that such indicators can complete the storage and calculation of accumulated values ​​within the cache period locally in the data statistics unit, according to the statistical logic of data accumulation within the period. Simultaneously, relying on the initialized circular queue structure, the orderliness and efficiency of data storage, retrieval, and accumulation operations for this type of data are guaranteed, laying the foundation for subsequent synchronization of accurate accumulated data to the operation and maintenance unit.

[0052] Furthermore, all independent cache partitions utilize a circular queue data storage structure to store their respective types of indicator data. This circular queue structure enables ordered enqueueing and dequeueing of data, adapting to the continuous collection and periodic processing characteristics of picocell base station indicator data. It also efficiently utilizes cache space, avoiding data fragmentation. The storage capacity of each cache partition can be flexibly adjusted directly through the system's XML configuration file without code modification. This adapts to the differences in data collection frequency and reporting cycles for various indicators across different operators and application scenarios, improving the adaptability of the cache module.

[0053] The above scheme allocates a dedicated independent cache partition for each pico base station performance indicator based on the statistical type of the indicator. This allows indicator data with different characteristics to be stored in a dedicated area, avoiding data mixing and providing a basis for subsequent targeted classification calculations.

[0054] S120: The data statistics unit collects performance data of pico base stations based on the target statistical period and writes the performance data into an independent cache partition based on the data type corresponding to the performance data.

[0055] Specifically, each data statistics unit receives indicator data through hardware and software interfaces. The hardware interfaces include base station hardware sensors and data acquisition cards, while the software interfaces include system APIs (Application Programming Interfaces) and communication protocol interfaces. Based on the data type of different indicators, the indicator data is written to corresponding independent cache partitions, thus achieving categorized data storage.

[0056] For example, before the data statistics unit collects performance index data of pico base stations and writes the index data into the independent cache partition based on the data type corresponding to the index data, it further includes:

[0057] The data statistics unit performs data matching and verification on the indicator data to determine whether the data type of the indicator data matches the indicator data. If so, it performs data anomaly verification on the indicator data, and based on the verification results, it identifies the abnormal data from the indicator data and writes the abnormal data into the anomaly counter area, marking the anomaly level corresponding to the abnormal data.

[0058] The abnormality level can be classified as mild, moderate, or severe.

[0059] Specifically, the data statistics unit collects various performance index data of the pico base station, performs data matching and verification on the collected index data, and confirms whether the actual content of the index data is consistent with the data type it is labeled. If the verification passes, the data anomaly verification is carried out on the index data. Based on the verification results, abnormal data that does not meet the standards is filtered out and written into the anomaly counter area in the independent cache partition. At the same time, the anomaly level corresponding to each abnormal data is marked.

[0060] The above solution not only ensures the accuracy and standardization of data written to the cache partition, but also provides a clear basis for marking and storing abnormal data for subsequent statistical analysis.

[0061] S130 The data statistics unit classifies the indicator data in the independent cache partition using the indicator statistics tool, determines the indicator statistics data, and sends the indicator statistics data to the operation and maintenance unit through the data interaction port.

[0062] Specifically, the data statistics unit, based on the data types corresponding to various indicators, uses an application programming interface (API) to write the data into matching independent cache partitions for categorized storage. Then, the indicator statistics tool processes the indicator data within each independent cache partition to obtain statistical data. This statistical data is then sent to the operation and maintenance unit via a two-way data interaction port, providing support for subsequent data merging and performance indicator report generation.

[0063] For example, statistical tools for metrics include: incrementing counters, real-time value counters, maximum value counters, minimum value counters, average counters, cumulative counters, absolute value counters, and anomaly counters.

[0064] For example, the data statistics logic and period reset strategies of each indicator statistical tool are shown in Table 1:

[0065] Table 1

[0066]

[0067] In this context, `sum` refers to the cumulative value of the indicator, and `count` refers to the number of indicator samples. The increment counter is used to accumulate data within a period, counting continuously increasing values ​​such as the number of calls and data packets. The real-time value counter updates and retains the latest data in real time, recording the current status value, such as the number of currently connected users and the current signal strength. The maximum value counter compares the performance indicator data collected within a period in real time and retains the maximum value, capturing extreme fluctuations in the indicator within the period, such as the maximum number of users and the minimum rate. The minimum value counter compares the performance indicator data collected within a period in real time and retains the minimum value. The average counter calculates the average value within a period based on `sum` and `count`, such as the average number of users and the average rate. The cumulative counter continuously accumulates data across statistical periods, calculating total daily traffic and cumulative call duration. The absolute value counter records absolute value data within a period, recording the absolute value of indicators such as power and voltage. The anomaly counter counts the number of times an indicator exceeds a threshold within a period, used for network anomaly monitoring and alarms. At the same time, a periodic reset strategy is set to clarify how the counter value should be handled after a statistical period ends, whether it should be cleared to zero, reset to an extreme value, or remain unchanged, in order to prepare for the statistics of the next period.

[0068] Different indicator data have different statistical requirements. Therefore, using different types of indicator statistical tools to perform performance indicator statistics on different indicator data can improve the reliability of the collected indicator data.

[0069] For example, the data statistics unit uses an indicator statistics tool to classify the indicator data within the independent cache partition to determine the indicator statistics data, including:

[0070] The data statistics unit uses an incrementing counter to accumulate the index data stored in the incrementing counter area to determine the growth data; it uses a maximum value counter and a minimum value counter to compare the index data stored in the extreme value area to determine the maximum and minimum values; it uses an average counter to perform cumulative value summation and sample count on the index data stored in the average value area to determine the cumulative index value and the number of index samples, and determines the mean data based on the cumulative index value and the number of index samples; it uses a real-time value counter to update the index data stored in the real-time value area and determine the updated data; and it uses a cumulative counter to superimpose the index data stored in the cumulative value area to determine the accumulated data; the growth data, maximum value, minimum value, mean data, updated data, and accumulated data are used as the index statistics data.

[0071] Wherein, the mean data = cumulative value of the indicator / number of indicator samples.

[0072] The above solution effectively ensures the accuracy and standardization of the statistical results by using corresponding counters to perform dedicated calculations on the indicator data of different independent cache partitions.

[0073] For example, by performing cumulative summation and sample count on the indicator data stored in the average value area using an average counter, the cumulative indicator value and the number of indicator samples are determined. Based on the cumulative indicator value and the number of indicator samples, the mean data is determined, including:

[0074] The data statistics unit uses an average counter to perform cumulative summation and sample count on the indicator data for the target statistical period stored in the average value area, determining the cumulative indicator value and the number of indicator samples for the target statistical period, and then synchronizes the cumulative indicator value and the number of indicator samples to the operation and maintenance unit. The operation and maintenance unit uses a two-parameter calculation model to determine the iterative cumulative value and the number of iterative samples based on the cumulative indicator value and the number of indicator samples for the target statistical period, as well as the cumulative indicator value and the number of indicator samples for historical statistical periods. The operation and maintenance unit determines the mean data based on the iterative cumulative value and the number of iterative samples, and then sends the mean data to the data statistics unit.

[0075] The two-parameter calculation model refers to a model that calculates the mean value of an indicator by using two parameters: the cumulative value of the indicator and the number of indicator samples. The iterative cumulative value is the sum of the cumulative values ​​of the indicator in the target statistical period and the cumulative values ​​of the indicator in the specified historical statistical period. The iterative sample size is the sum of the number of indicator samples in the target statistical period and the number of indicator samples in the specified historical statistical period. The mean value is the ratio of the iterative cumulative value to the iterative sample size.

[0076] The above scheme uses a two-parameter calculation model to combine current period and historical period data to calculate the cumulative value of iteration, the number of iteration samples, and the mean data, which reduces the computational burden on the data statistics unit. At the same time, it can achieve cross-period, high-precision mean iteration calculation, ensuring the accuracy, continuity and reliability of mean data statistics.

[0077] S140. After receiving the statistical data of the indicators, the operation and maintenance unit processes the statistical data of the indicators based on the indicator type and indicator level, and generates a performance indicator report.

[0078] Specifically, after receiving the statistical data of indicators reported by all data statistics units, the operation and maintenance unit first classifies, merges, and summarizes the data according to indicator type and indicator level, integrating the scattered data from each unit into a unified and standardized performance indicator report. At the same time, the report is saved to the local storage space in the format specified by the system. This not only realizes the centralized management of data, but also provides a unified and complete basic report for subsequent data reporting to the network management system via FTP protocol, ensuring the standardization and accuracy of data reporting.

[0079] For example, the method for processing the above-mentioned pico base station performance index data further includes:

[0080] S210, The data statistics unit sends registration information to the registration module of the operation and maintenance unit.

[0081] Registration information includes the registration request, the types of metrics supported by the data statistics unit, and the interface version.

[0082] The data statistics unit is capable of collecting performance index data of pico base stations through hardware and software interfaces. It initializes local cache partitions according to configuration, performs data classification caching and type / threshold verification, performs accumulation, comparison, averaging, and data updates on cached data by type, handles abnormal data, generates anomaly reports and records local anomaly logs, and reports the processed index data to the operation and maintenance unit. In the pico base station performance index processing system, there is at least one data statistics unit.

[0083] Specifically, the data statistics unit uses a Socket communication mechanism to initiate a registration request to the registration module of the operation and maintenance unit, and sends registration information such as the indicator types and interface versions supported by the data statistics unit to the operation and maintenance unit to complete the registration and binding with the operation and maintenance unit.

[0084] S220. After receiving the registration information, the registration module of the operation and maintenance unit sends the communication key and data interaction port information to the data statistics unit so that the data statistics unit and the operation and maintenance unit can perform bidirectional binding of identity information and bidirectional binding of interfaces based on the communication key and data interaction port.

[0085] The communication key is the identity verification information assigned to the data statistics unit by the registration module of the operation and maintenance unit, used for two-way authentication between the data statistics unit and the operation and maintenance unit. The data interaction port information can be the communication interface identifier assigned to the data statistics unit by the registration module of the operation and maintenance unit, used to determine the port number for data transmission between the data statistics unit and the operation and maintenance unit.

[0086] Specifically, after receiving the registration information sent by the data statistics unit, the registration module of the operation and maintenance unit will return the communication key and data interaction port information to the data statistics unit. Based on this, the data statistics unit and the operation and maintenance unit complete the two-way binding of identity information and interface.

[0087] For example, after receiving the registration information, the registration module of the operation and maintenance unit sends the communication key and data interaction port information to the data statistics unit, including:

[0088] After receiving the registration information, the registration module of the operation and maintenance unit verifies the registration information. The information verification includes interface version verification, indicator type matching verification, identity verification, and parameter integrity verification. If the information verification passes, the communication key and data interaction port information are sent to the data statistics unit.

[0089] The interface version verification compares the interface version reported by the data statistics unit with the list of compatible interface versions of the operation and maintenance unit to verify whether the version is within the supported range and excludes incompatible interface versions. The list of compatible interface versions of the operation and maintenance unit can be obtained from the parsing results of the target configuration file. The indicator type matching verification checks whether the indicator types declared by the data statistics unit match the indicator types corresponding to the memory blocks allocated during the initialization of the operation and maintenance unit, such as incrementing counters and real-time values, ensuring that the reported indicator types are within the system's preset range. The identity verification verifies whether the registration request contains the unique identifier of the data statistics unit and whether this unique identifier exists in the list of legal units pre-configured in the target configuration file, excluding registration requests from illegal units. The parameter integrity verification checks whether the registration request completely contains the required information such as the registration request instruction, supported indicator types, and interface version; if no key parameters are missing, the verification passes.

[0090] Specifically, after receiving the registration information sent by the data statistics unit, the registration module of the operation and maintenance unit verifies the registration information. The information verification may include interface version verification, indicator type matching verification, identity verification, and parameter integrity verification. If the information verification passes, the module sends a communication key and data interaction port to the data statistics unit. The data statistics unit and the operation and maintenance unit bind their identities and interfaces bidirectionally based on the communication key and data interaction port, establishing a secure and dedicated communication link for subsequent data transmission.

[0091] The above scheme uses the registration module of the operation and maintenance unit to perform multi-dimensional verification on the registration information reported by the data statistics unit, including interface version, indicator type matching, identity, and parameter integrity. After the verification is passed, a communication key and data interaction port are allocated to achieve two-way binding of the identities and interfaces of both parties, thereby building a secure, dedicated, stable and reliable communication link for subsequent data transmission.

[0092] For example, the method for processing the above-mentioned pico base station performance index data further includes:

[0093] The data statistics unit generates an abnormal data report based on the indicator data stored in the abnormal counter area. The abnormal data report includes the indicator name, the value exceeding the threshold, and the time when the abnormal data was generated. The abnormal data report is stored in the local abnormal log, and the abnormal data processing status is marked in the local abnormal log.

[0094] The status of abnormal data processing can include unprocessed, processing, and processed.

[0095] Specifically, the data statistics unit reads the abnormal data stored in the abnormal counter area of ​​the independent cache partition, and generates an abnormal data report based on this data. The abnormal data report includes the indicator name, the value exceeding the threshold, and the time when the abnormal data was generated. After generating the abnormal data report, it is stored in the local abnormal log, and the processing status of each abnormal data is marked in the local abnormal log.

[0096] The above solution not only achieves standardized retention of abnormal data, but also provides a clear basis for subsequent anomaly investigation and processing progress tracking, further improves the full-process function of indicator statistics, and makes up for the shortcomings of traditional technology in anomaly handling.

[0097] For example, the method for processing the above-mentioned pico base station performance index data further includes:

[0098] The operation and maintenance unit reports the performance indicators to the network management system and records the reporting results of the performance indicators in the status log.

[0099] The reporting results of performance metrics can include reporting success, reporting failure, and reporting timeout.

[0100] Specifically, after the operation and maintenance unit completes the merging and processing of the indicator data from each data statistics unit and generates a unified performance indicator report, it will upload the performance indicator report to the network management system via the FTP protocol to complete the final summary of the indicator data. At the same time, the operation and maintenance unit will record the upload result of this performance indicator report in the status log, which is convenient for subsequent tracking of the upload status.

[0101] The above solution can promptly detect, report, and handle anomalies, ensuring a closed loop in the entire indicator statistics and reporting process, and further improving the system's maintainability.

[0102] For example, the operation and maintenance unit includes a configuration file parsing module, and the above-mentioned method for processing pico base station performance index data further includes:

[0103] The configuration file parsing module in the operation and maintenance unit parses the target configuration file of the base station performance indicators; the operation and maintenance unit allocates memory for the shared memory of the pico base station according to the parsing results, and generates a memory mapping table based on the memory allocation results.

[0104] The parsing results can include indicator types, cache partitioning rules, and data interaction parameters. Memory allocation results can include the storage addresses and memory sizes for each indicator data type. A memory mapping table records shared memory allocation information, including the storage addresses and memory sizes for each type of indicator data.

[0105] Specifically, the configuration file parsing module of the operation and maintenance unit first parses the target configuration file of the pico base station performance indicators, extracting key information such as indicator types, cache partitioning rules, and data interaction parameters; then, based on the parsing results, it allocates corresponding storage space for the shared memory of the pico base station. A memory mapping table is then generated based on the specific memory allocation.

[0106] The above solution ensures that different types of indicator data have dedicated storage areas, avoiding memory usage conflicts. It also provides precise memory address guidance for subsequent data statistics units to read and write shared memory and transfer indicator data, guaranteeing the efficiency and accuracy of data reading and writing, and further optimizing the system's data processing architecture.

[0107] For example, the operation and maintenance unit further includes a configuration file verification module, wherein the configuration file parsing module in the operation and maintenance unit parses the target configuration file for base station performance indicators, including:

[0108] The operation and maintenance unit obtains the target configuration file of the pico base station performance indicators, and performs integrity verification on the target configuration file through the configuration file verification module in the operation and maintenance unit to determine whether the configuration parameters in the target configuration file are complete; if so, the target configuration file is parsed through the configuration file parsing module in the operation and maintenance unit.

[0109] The target configuration file includes parameters such as metric type, cache depth, and interaction port.

[0110] Specifically, the operation and maintenance unit obtains the target configuration file for the performance indicators of the pico base station. The configuration file verification module performs an integrity check on the target configuration file to check whether all necessary configuration parameters in the file are complete. If the verification confirms that the configuration parameters are complete, the configuration file parsing module in the operation and maintenance unit performs subsequent parsing of the target configuration file.

[0111] The above solution can avoid parsing errors and abnormal memory allocation caused by missing parameters in the configuration file.

[0112] In the above-mentioned method for processing pico base station performance index data, the data statistics unit sends registration information to the registration module of the operation and maintenance unit; the registration information includes a registration request, the index types supported by the data statistics unit, and the interface version; after receiving the registration information, the registration module of the operation and maintenance unit sends a communication key and data interaction port information to the data statistics unit, so that the data statistics unit and the operation and maintenance unit can perform two-way binding of identity information and two-way binding of interfaces based on the communication key and data interaction port; after the two-way binding of identity information and two-way binding of interfaces is completed, the data statistics unit reads the target configuration file of the pico base station performance index and, according to the index type specified in the target configuration file, sets the pico base station performance index... An independent cache partition is created, comprising an incrementing counter area, an extreme value area, an average value area, a real-time value area, a cumulative value area, and an anomaly counter area. The data statistics unit collects performance data of the pico base station based on the target statistical period and writes the data into the independent cache partition according to the data type. The data statistics unit then classifies the data in the independent cache partition using a statistical tool to determine statistical data and sends it to the operation and maintenance unit via a data interaction port. Upon receiving the statistical data, the operation and maintenance unit processes it based on the indicator type and level to generate a performance indicator report. This approach solves the problems of poor system stability, low accuracy of statistical indicators, incomplete functional coverage, and high architectural coupling inherent in traditional pico base station performance statistics techniques. The above solution ensures data transmission security through two-way binding of identity and interface and communication keys during the registration phase, preventing data corruption or unauthorized access and improving system stability. It reduces data confusion and errors and improves the accuracy of statistical indicators by using methods such as type matching and anomaly verification before data collection, writing data to dedicated independent cache partitions according to data type, and categorized statistical processing. It covers the entire process of indicator registration, caching, collection, statistics, and report generation, overcoming the shortcomings of traditional technologies in terms of functional coverage. Furthermore, it adopts a collaborative model between data statistics units and operation and maintenance units, avoiding excessive dependence between modules, reducing architectural coupling, and further improving the overall stability and scalability of the system.

[0113] For example, based on the above embodiments, the method for processing pico base station performance index data includes:

[0114] The operation and maintenance unit obtains the target configuration file for the performance indicators of the pico base station, and performs an integrity check on the target configuration file through the configuration file verification module in the operation and maintenance unit to determine whether the configuration parameters in the target configuration file are complete; if so, the target configuration file is parsed through the configuration file parsing module in the operation and maintenance unit; the operation and maintenance unit allocates memory to the shared memory of the pico base station according to the parsing result, and generates a memory mapping table according to the memory allocation result.

[0115] The data statistics unit uses a Socket communication mechanism to initiate a registration request to the registration module of the operation and maintenance unit, and sends registration information such as the indicator types and interface versions supported by the data statistics unit to the operation and maintenance unit to complete the registration and binding with the operation and maintenance unit.

[0116] After receiving the registration information sent by the data statistics unit, the registration module of the operation and maintenance unit verifies the registration information. The information verification may include interface version verification, indicator type matching verification, identity verification, and parameter integrity verification. If the information verification passes, the module sends the communication key and data interaction port to the data statistics unit. The data statistics unit and the operation and maintenance unit then perform bidirectional binding of identity and interface based on the communication key and data interaction port.

[0117] After the two-way binding of identity information and interface is completed, the data statistics unit reads the target configuration file of the pico base station performance indicators and creates an independent cache partition for the pico base station performance indicators according to the indicator type specified in the target configuration file. The independent cache partition includes an increment counter area, an extreme value area, an average value area, a real-time value area, a cumulative value area, and an anomaly counter area.

[0118] The data statistics unit collects various performance index data of the pico base station, performs data matching and verification on the collected index data, and confirms whether the actual content of the index data is consistent with the data type it is labeled. If the verification passes, the index data is then checked for data anomalies. Based on the verification results, abnormal data that does not meet the standards is filtered out and written into the anomaly counter area in the independent cache partition. At the same time, the anomaly level corresponding to each abnormal data is marked.

[0119] The data statistics unit collects performance data of pico base stations based on the target statistical period and writes the data into an independent cache partition based on the data type of the data.

[0120] The data statistics unit uses an incrementing counter to accumulate the indicator data stored in the incrementing counter area to determine the growth data; it uses a maximum value counter and a minimum value counter to compare the indicator data stored in the extreme value area to determine the maximum and minimum values; the data statistics unit uses an average counter to perform cumulative value summation and sample count on the indicator data stored in the average value area for the target statistical period to determine the cumulative indicator value and the number of indicator samples for the target statistical period, and synchronizes the cumulative indicator value and the number of indicator samples to the operation and maintenance unit; the operation and maintenance unit uses a two-parameter calculation model to determine the iterative cumulative value and the number of iterative samples based on the cumulative indicator value and the number of indicator samples for the target statistical period, as well as the cumulative indicator value and the number of indicator samples for historical statistical periods; the operation and maintenance unit determines the mean data based on the iterative cumulative value and the number of iterative samples, and sends the mean data to the data statistics unit. The real-time value counter updates the indicator data stored in the real-time value area in real time and determines the updated data; the cumulative counter performs superposition calculations on the indicator data stored in the cumulative value area to determine the accumulated data; the growth data, maximum value, minimum value, mean data, updated data, and accumulated data are used as the indicator statistics data.

[0121] After receiving the statistical data of indicators reported by all data statistics units, the operation and maintenance unit first classifies, merges and summarizes the data according to the indicator type and indicator level, integrates the scattered data of each unit into a unified and standardized performance indicator report, and saves the report to the local storage space in the format specified by the system.

[0122] The data statistics unit generates an abnormal data report based on the indicator data stored in the abnormal counter area. The abnormal data report includes the indicator name, the value exceeding the threshold, and the time when the abnormal data was generated. The abnormal data report is stored in the local abnormal log, and the abnormal data processing status is marked in the local abnormal log.

[0123] After the operation and maintenance unit completes the merging and processing of the indicator data from each data statistics unit and generates a unified performance indicator report, it will upload the performance indicator report to the network management system via the FTP protocol to complete the final summary of the indicator data. At the same time, the operation and maintenance unit will record the upload result of this performance indicator report in the status log, which will facilitate the subsequent tracking of the upload status.

[0124] In the above-mentioned method for processing pico base station performance index data, the data statistics unit sends registration information to the registration module of the operation and maintenance unit; the registration information includes a registration request, the index types supported by the data statistics unit, and the interface version; after receiving the registration information, the registration module of the operation and maintenance unit sends a communication key and data interaction port information to the data statistics unit, so that the data statistics unit and the operation and maintenance unit can perform two-way binding of identity information and two-way binding of interfaces based on the communication key and data interaction port; after the two-way binding of identity information and two-way binding of interfaces is completed, the data statistics unit reads the target configuration file of the pico base station performance index and, according to the index type specified in the target configuration file, sets the pico base station performance index... An independent cache partition is created, comprising an incrementing counter area, an extreme value area, an average value area, a real-time value area, a cumulative value area, and an anomaly counter area. The data statistics unit collects performance data of the pico base station based on the target statistical period and writes the data into the independent cache partition according to the data type. The data statistics unit then classifies the data in the independent cache partition using a statistical tool to determine statistical data and sends it to the operation and maintenance unit via a data interaction port. Upon receiving the statistical data, the operation and maintenance unit processes it based on the indicator type and level to generate a performance indicator report. This approach solves the problems of poor system stability, low accuracy of statistical indicators, incomplete functional coverage, and high architectural coupling inherent in traditional pico base station performance statistics techniques. The above solution ensures data transmission security through two-way binding of identity and interface and communication keys during the registration phase, preventing data corruption or unauthorized access and improving system stability. It reduces data confusion and errors and improves the accuracy of statistical indicators by using methods such as type matching and anomaly verification before data collection, writing data to dedicated independent cache partitions according to data type, and categorized statistical processing. It covers the entire process of indicator registration, caching, collection, statistics, and report generation, overcoming the shortcomings of traditional technologies in terms of functional coverage. Furthermore, it adopts a collaborative model between data statistics units and operation and maintenance units, avoiding excessive dependence between modules, reducing architectural coupling, and further improving the overall stability and scalability of the system.

[0125] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for processing picocell performance data. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or a key vector, trackball, or touchpad set on the computer device casing, or an external key vector disk, touchpad, or mouse, etc.

[0126] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0127] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0128] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0129] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for processing pico base station performance index data, characterized in that, The pico base station performance index processing method is executed by a pico base station performance index processing system, which includes a data statistics unit and an operation and maintenance unit. The method includes: After the two-way binding of identity information and interface is completed, the data statistics unit reads the target configuration file of the pico base station performance indicators and creates an independent cache partition for the pico base station performance indicators according to the indicator type specified in the target configuration file; the independent cache partition includes an increment counter area, an extreme value area, an average value area, a real-time value area, a cumulative value area, and an anomaly counter area; The data statistics unit collects performance data of pico base stations based on the target statistical period, and writes the performance data into the independent cache partition based on the data type corresponding to the performance data. The data statistics unit classifies the indicator data in the independent cache partition using indicator statistics tools, determines the indicator statistics data, and sends the indicator statistics data to the operation and maintenance unit through the data interaction port. After receiving the statistical data of the indicators, the operation and maintenance unit processes the statistical data of the indicators based on the indicator type and indicator level, and generates a performance indicator report.

2. The method according to claim 1, characterized in that, The method for processing the performance index data of the pico base station also includes: The data statistics unit sends registration information to the registration module of the operation and maintenance unit; the registration information includes a registration request, the indicator types and interface versions supported by the data statistics unit; After receiving the registration information, the registration module of the operation and maintenance unit sends the communication key and data interaction port information to the data statistics unit, so that the data statistics unit and the operation and maintenance unit can perform two-way binding of identity information and two-way binding of interfaces based on the communication key and data interaction port.

3. The method according to claim 1, characterized in that, The operation and maintenance unit includes a configuration file parsing module, and the method further includes: The configuration file parsing module in the operation and maintenance unit parses the target configuration file for base station performance indicators; The operation and maintenance unit allocates memory to the shared memory of the pico base station based on the parsing results, and generates a memory mapping table based on the memory allocation results.

4. The method according to claim 3, characterized in that, The operation and maintenance unit further includes a configuration file verification module. The configuration file parsing module in the operation and maintenance unit parses the target configuration file for base station performance indicators, including: The operation and maintenance unit obtains the target configuration file of the pico base station performance indicators, and performs integrity verification on the target configuration file through the configuration file verification module in the operation and maintenance unit to determine whether the configuration parameters in the target configuration file are complete. If so, the target configuration file is parsed by the configuration file parsing module in the operation and maintenance unit.

5. The method according to claim 2, characterized in that, After receiving the registration information, the registration module of the operation and maintenance unit sends the communication key and data interaction port information to the data statistics unit, including: After receiving the registration information, the registration module of the operation and maintenance unit performs information verification on the registration information; the information verification includes interface version verification, indicator type matching verification, identity verification and parameter integrity verification; If the information verification passes, the communication key and data interaction port information are sent to the data statistics unit.

6. The method according to claim 1, characterized in that, Before the data statistics unit collects performance index data of the pico base station and writes the index data into the independent cache partition based on the data type corresponding to the index data, it further includes: The data statistics unit collects performance index data for pico base stations; The data statistics unit performs data matching and verification on the indicator data to determine whether the data type of the indicator data matches the indicator data. If so, perform data anomaly verification on the indicator data, determine the abnormal data from the indicator data based on the verification result, write the abnormal data into the anomaly counter area, and mark the anomaly level corresponding to the abnormal data.

7. The method according to claim 6, characterized in that, The indicator statistical tools include: an incrementing counter, a real-time value counter, a maximum value counter, a minimum value counter, an average counter, a cumulative counter, an absolute value counter, and an anomaly counter. The data statistical unit uses these tools to classify the indicator data within the independent cache partition and determine the indicator statistical data, including: The data statistics unit uses an incrementing counter to accumulate the indicator data stored in the incrementing counter area to determine the growth data; The maximum and minimum values ​​are determined by comparing the index data stored in the extreme value region using a maximum value counter and a minimum value counter. The cumulative value and sample count of the indicator data stored in the average value area are summed and counted by the average counter to determine the cumulative value and sample count of the indicator, and the mean data is determined based on the cumulative value and sample count of the indicator. The real-time value counter is used to update the index data stored in the real-time value area in real time and determine the updated data. The cumulative counter is used to overlay and calculate the index data stored in the cumulative value area to determine the accumulated data; The growth data, maximum value, minimum value, average value, updated data, and accumulated data are used as indicator statistics.

8. The method according to claim 7, characterized in that, The step of performing cumulative summation and sample count on the indicator data stored in the average value area using an average counter to determine the cumulative indicator value and the number of indicator samples, and determining the mean data based on the cumulative indicator value and the number of indicator samples, includes: The data statistics unit performs cumulative value summation and sample count on the indicator data of the target statistical period stored in the average value area using an average counter, determines the cumulative value of the indicator and the number of indicator samples for the target statistical period, and synchronizes the cumulative value of the indicator and the number of indicator samples to the operation and maintenance unit. The operation and maintenance unit determines the iterative cumulative value and iterative sample number based on the cumulative value and sample number of the indicator in the target statistical period, as well as the cumulative value and sample number of the indicator in the historical statistical period, using a two-parameter calculation model. The operation and maintenance unit determines the mean data based on the cumulative value of the iteration and the number of iteration samples, and sends the mean data to the data statistics unit.

9. The method according to claim 1, characterized in that, Also includes: The data statistics unit generates an anomaly data report based on the indicator data stored in the anomaly counter area; The abnormal data report includes: indicator name, threshold value, and time when the abnormal data was generated; The abnormal data report is stored in the local abnormal log, and the abnormal data processing status is marked in the local abnormal log.

10. The method according to claim 1, characterized in that, After receiving the indicator statistics, the operation and maintenance unit processes the indicator statistics based on the indicator type and indicator level, and generates a performance indicator report. The system also includes: The operation and maintenance unit reports the performance indicators to the network management system and records the reporting results of the performance indicators in the status log.

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