Method, device and storage medium for determining abnormal network equipment

Abstract

The present application provides a method, device, and storage medium for determining abnormal network devices, relating to the field of communication technology, and capable of accurately determining abnormal network devices. The method comprises: determining a test location point of each grid in a plurality of grids in a target area; the test location point is a location point with a problem determined by testing the target area within a preset period; for each grid, determining a problem index of the test location point of the grid and an attribute index of the grid; the problem index of the test location point of the grid is determined based on a coverage problem index, a load problem index, and an alarm problem index; the attribute index of the grid is determined based on an importance index, an urgency index, and a complaint index; based on the problem index and the attribute index, determining a target grid among the plurality of grids, and determining that a network device whose coverage includes the target grid is abnormal; the target grid is the grid with the highest priority among the plurality of grids.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, apparatus, and storage medium for determining abnormal network equipment. Background Art

[0002] In related technologies, network quality in different scenarios across multiple regions is generally analyzed at a fine-grained level through network coverage, load, and communication quality, thereby ensuring accurate planning, construction, and maintenance of the network.

[0003] Currently, network equipment detection is generally done manually on-site, which results in low efficiency. Therefore, how to accurately identify abnormal network equipment is an urgent problem to be solved. Summary of the Invention

[0004] The present application provides a method, apparatus, and storage medium for determining abnormal network devices, which can accurately determine abnormal network devices.

[0005] To achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a method for determining abnormal network equipment, the method comprising: determining a test location point of each grid in a plurality of grids of a target area; the test location point is a location point where a problem is determined by testing the target area within a preset period; for each grid, determining a problem index of the test location point of the grid and an attribute index of the grid; the problem index of the test location point of the grid is determined based on a coverage problem index, a load problem index, and an alarm problem index; the attribute index of the grid is determined based on an importance index, an urgency index, and a complaint index; based on the problem index and the attribute index, determining a target grid among the plurality of grids, and determining that the network equipment whose coverage includes the target grid is abnormal; the target grid is the grid with the highest priority among the plurality of grids; wherein the priority of the grid is determined based on the problem index of the test location point of the grid and the attribute index of the grid.

[0007] Based on the above technical solution, the method for determining abnormal network devices provided in the embodiment of the present application first determines the test location points where problems exist in each grid in the multiple grids of the target area, and determines the problem indicators of the test location points of the grid and the attribute indicators of the grid; then, based on the two indicators, determines the target grid that needs to be processed first, that is, the problem in the target grid is the most urgent, and finally determines that the network device whose coverage includes the target grid is abnormal.

[0008] In combination with the first aspect, in a possible implementation method, determining the test position point of each grid in multiple grids of the target area includes: obtaining the position information of the multiple test position points in the target area; and determining the test position point of each grid in the multiple grids of the target area based on the position information of each grid and the position information of the multiple test position points.

[0009] In combination with the first aspect, in a possible implementation method, before obtaining the location information of multiple test location points in the target area, it also includes: obtaining multi-source data of multiple test points in the target area; the multi-source data is used to characterize whether there is a problem with the test point; the multi-source data includes: signal strength, utilization, and noise interference; based on the multi-source data, determine the test location points among the multiple test points in the target area.

[0010] In combination with the first aspect, in a possible implementation, the coverage problem index is determined based on the coverage type of the test location point of the grid; the coverage types include: weak coverage, overlapping coverage, cross-zone coverage, and coverage holes; the load problem index is determined based on the utilization rate of the network equipment whose coverage range includes the test location point; the alarm problem index is determined based on the alarm information of the network equipment whose coverage range includes the test location point.

[0011] In combination with the first aspect, in a possible implementation, the importance index is determined based on the number of network devices within the grid, the number of terminals accessing the network devices within the grid, and the scenario type of the grid; the urgency index is determined based on the number of test location points within the grid; and the complaint index is determined based on the number of complaints from the terminals.

[0012] In a second aspect, the present application provides an apparatus for determining abnormal network equipment, the apparatus comprising: a processing unit; the processing unit is used to determine a test location point of each grid in a plurality of grids of a target area; the test location point is a location point where a problem is determined by testing the target area within a preset period; for each grid, the processing unit is also used to determine a problem index of the test location point of the grid and an attribute index of the grid; the problem index of the test location point of the grid is determined based on a coverage problem index, a load problem index, and an alarm problem index; the attribute index of the grid is determined based on an importance index, an urgency index, and a complaint index; the processing unit is also used to determine a target grid among the plurality of grids based on the problem index and the attribute index, and determine that a network device whose coverage includes the target grid is abnormal; the target grid is the grid with the highest priority among the plurality of grids; wherein the priority of the grid is determined based on the problem index of the test location point of the grid and the attribute index of the grid.

[0013] In combination with the second aspect, in a possible implementation, the device further includes: an acquisition unit; the acquisition unit is used to obtain the position information of multiple test position points in the target area; the processing unit is also used to determine the test position points of each grid in the multiple grids of the target area based on the position information of each grid and the position information of the multiple test position points.

[0014] In combination with the second aspect, in a possible implementation, the acquisition unit is further used to acquire multi-source data of multiple test points in the target area; the multi-source data is used to characterize whether there is a problem with the test point; the multi-source data includes: signal strength, utilization, and noise interference; the processing unit is further used to determine the test location points among the multiple test points in the target area based on the multi-source data.

[0015] In combination with the second aspect, in a possible implementation method, the coverage problem index is determined based on the coverage type of the test location point of the grid; the coverage types include: weak coverage, overlapping coverage, cross-zone coverage, and coverage holes; the load problem index is determined based on the utilization rate of the network equipment whose coverage range includes the test location point; the alarm problem index is determined based on the alarm information of the network equipment whose coverage range includes the test location point.

[0016] In combination with the second aspect, in one possible implementation, the importance index is determined based on the number of network devices within the grid, the number of terminals accessing the network devices within the grid, and the scenario type of the grid; the urgency index is determined based on the number of test location points within the grid; and the complaint index is determined based on the number of complaints from the terminals.

[0017] In a third aspect, the present application provides a device for determining abnormal network devices, the device comprising: a processor and a communication interface; the communication interface and the processor are coupled, and the processor is used to run a computer program or instructions to implement the method for determining abnormal network devices as described in the first aspect and any possible implementation of the first aspect.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed on a terminal, the terminal executes the method for determining abnormal network devices as described in the first aspect and any possible implementation of the first aspect.

[0019] In this application, the name of the apparatus for determining abnormal network devices does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they fall within the scope of the claims of this application and their equivalents.

[0020] These and other aspects of the present application will become more readily apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the structure of a system for identifying abnormal network devices provided by this application;

[0022] Figure 2 A schematic diagram of the structure of an apparatus for determining abnormal network devices provided by this application;

[0023] Figure 3 A flowchart of a method for determining abnormal network devices provided by this application;

[0024] Figure 4 A flowchart of another method for determining abnormal network devices provided by this application;

[0025] Figure 5 A flowchart of another method for determining abnormal network devices provided by this application;

[0026] Figure 6 A schematic diagram of cross-area coverage provided by this application;

[0027] Figure 7 A schematic diagram of a covered cavity provided in this application;

[0028] Figure 8 A schematic diagram of the structure of an apparatus for determining abnormal network devices provided in this application. DETAILED DESCRIPTION

[0029] The following describes in detail the method and apparatus for determining abnormal network devices provided in the embodiments of the present application with reference to the accompanying drawings.

[0030] The term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0031] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0032] Furthermore, the terms "including," "having," and any variations thereof, as used in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or apparatus.

[0033] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being more preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0034] Among related technologies, network signal testing and problem analysis play a crucial role in mobile communication networks. Therefore, ensuring network quality and providing a positive user experience is crucial. Comprehensive assessments of mobile communication network performance can be achieved by testing metrics such as signal quality, coverage, and data rates to understand network performance at different locations and times, identifying weaknesses and bottlenecks. Furthermore, signal testing can help identify network problems and anomalies. For example, testing can reveal weak signals, dropped calls, and congestion. It can also detect sources of interference and the causes of performance degradation within the network. Problems can then be located and resolved.

[0035] Currently, analyzing signal test data can quickly locate network problems and provide solutions. This analysis can reveal the root cause of the problem, helping operators or network managers take appropriate measures to resolve the issue and improve network performance and user experience. Signal testing and problem analysis can provide valuable data for network planning and deployment. By analyzing test data, areas with insufficient network coverage and capacity can be identified, providing guidance for network planning and equipment deployment. The quality of mobile communication networks directly impacts user experience and satisfaction. Through signal testing and problem analysis, network coverage and performance can be improved, call dropouts, data loss, and other issues can be reduced, providing more stable, high-quality communication services and enhancing user experience and loyalty.

[0036] Currently, traditional mobile signal testing and problem-solving methods often rely on manual operations and analysis, which presents several technical limitations and shortcomings. In traditional methods, test data from mobile communication networks is collected manually, and testers must manually record and process test results. Problem detection and resolution also rely on human judgment and experience. This approach has several disadvantages. Time-consuming manual operations: In traditional methods, both test data collection and processing require extensive manual operations and record-keeping. This is not only time-consuming and labor-intensive, but also prone to human error and inconsistencies. Subjectivity and uncertainty: In traditional methods, problem detection and resolution often rely on the subjective judgment and experience of testers. This subjectivity can lead to missed detections or misdiagnosis of problems, and can also vary between testers. Data processing and analysis limitations: In traditional methods, test data processing and analysis are often performed manually, failing to fully leverage the advantages of large-scale data and complex algorithms. This results in limited accuracy and efficiency in analysis results, potentially failing to fully identify problems and provide accurate resolution recommendations. Data security and credibility: In traditional methods, test data and problem-solving results are typically stored and transmitted in the form of files or documents. This method can easily lead to data tampering and loss, posing a threat to data security and credibility.

[0037] In summary, traditional mobile signal testing and problem-solving methods suffer from shortcomings such as time-consuming manual operations, subjectivity and uncertainty, data processing and analysis limitations, and data security and reliability issues. To overcome these issues, a blockchain-based mobile signal testing, analysis, and processing system offers an innovative solution. By leveraging blockchain technology, automated analysis, and priority management, it improves the efficiency, accuracy, and security of testing and processing.

[0038] In order to solve the problems in the prior art, the method for determining abnormal network devices provided in the embodiment of the present application first determines the test location points where problems exist in each grid of multiple grids in the target area, and determines the problem indicators of the test location points of the grid and the attribute indicators of the grid; then, based on the two indicators, determines the target grid that needs to be processed first, that is, the problem in the target grid is the most urgent, and finally determines that the network device whose coverage includes the target grid is abnormal.

[0039] like Figure 1 1 is a schematic diagram of the structure of a system 100 for determining abnormal network devices provided by an embodiment of the present application. The system includes a signal testing unit 101, a signal analyzing unit 102, a problem processing unit 103, and a monitoring unit 104.

[0040] The signal testing unit 101 may be a road test system, which includes a laptop computer, test software installed in the laptop computer, a dongle plugged into the USB port of the laptop computer, a terminal device for receiving signals, and a GPS positioning device installed on the top of a vehicle. In the application scenario of the embodiment of the present application, the coverage of multiple base stations 105 includes a test point. The road test system can test the downlink signals of wireless networks such as WCDMA, TDSCDMA, LTE, and NR in the area where the signal needs to be tested, that is, the air interface (Um) of each wireless network. It is mainly used to obtain the following data of the test point: the latitude and longitude of the test location, the RSRP and SINR of the serving cell and the neighboring cell, the signaling process of switching and access, the cell identification code, the area identification code, the service establishment success rate, the switching ratio, the average uplink and downlink throughput, the geographical location of the mobile phone, call management, mobile management\service establishment delay, etc. Its main function is to evaluate the network quality and optimize the wireless network.

[0041] Signal Analysis Unit 102 is used to automatically analyze signals at the test location based on the test information, identifying signal issues. The goal of Signal Analysis Unit 102 is to improve the quality and performance of mobile communication networks through automated signal analysis and problem identification. It helps operators and related technical teams better understand network issues and provides guidance and decision support to improve network coverage, capacity, and user experience. The system automatically executes analysis algorithms, processes test data, generates analysis results, and uniquely numbers each identified problem point.

[0042] The problem processing unit 103 is used to arrange processing according to grid priority, track the processing process and results of the problem, record the operations and processing plans of the processing personnel, and regularly upload the information of the problem points and processing results to the blockchain.

[0043] The supervisory unit 104 is responsible for auditing and spot-checking the signal problem handling content, and plays an important supervisory and assessment role in the blockchain's mobile signal testing, analysis, and processing system. The following is a detailed description of the supervisory unit:

[0044] The supervisory unit 104 uses the query function provided by the system for determining abnormal network devices to retrieve the processing results submitted by the problem processing unit from the blockchain, and determines whether the processing is appropriate by comparing the data of the original problem point and the processing results. It also evaluates the processing process and verifies the integrity, accuracy and compliance of the processing results.

[0045] The monitoring unit 104 can randomly select a portion of the processing results for verification to check whether the processing personnel's operations and processing plans meet the requirements and compare them with the actual processing results. The results of the random inspection can be used to evaluate the ability and level of the processing personnel.

[0046] After completing the audit and spot check, the supervisory unit 104 can conduct a performance evaluation of the issue handler based on indicators such as the quality of the results, processing time, and customer satisfaction. The evaluation results are used to assess the handler's overall performance and work attitude, and provide feedback and improvement suggestions. Furthermore, based on the handler's performance and evaluation results, the supervisory unit 104 provides training and guidance, shares best practices, and helps handlers improve their skills and knowledge. The supervisory unit can also provide individual guidance to address weaknesses of the handler.

[0047] The supervisory unit 104 utilizes the data analysis function provided by the system to conduct an overall analysis and report on the processing results and the performance of the processing personnel. The report can provide decision support for the management, help optimize the processing process and improve the overall system performance.

[0048] It can be understood that the supervisory unit 104 plays a role in quality oversight and improvement within the system. By reviewing and evaluating problem resolution results, it promotes the development of processing personnel and helps ensure the efficiency and reliability of system operation. The supervisory unit 104 can use the blockchain's query function to verify the integrity and correctness of records and conduct audits and evaluations based on the recorded results. Data query and retrieval: Through the query function of the smart contract or using tools such as blockchain browsers, data on problem points and resolution results can be retrieved from the blockchain. This makes the data publicly accessible and verifiable, ensuring its immutability.

[0049] Figure 2 A schematic diagram of the structure of a transmission power adjustment device provided in an embodiment of the present application is shown as follows: Figure 2As shown, the transmit power adjustment device 200 includes at least one processor 201, a communication line 202, and at least one communication interface 204, and may also include a memory 203. The processor 201, the memory 203, and the communication interface 204 may be connected via the communication line 202.

[0050] The processor 201 can be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application, such as one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs).

[0051] The communication link 202 may include a pathway for transmitting information between the aforementioned components.

[0052] The communication interface 204 is used to communicate with other devices or communication networks and can use any transceiver-like device, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0053] The memory 203 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to include or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0054] In one possible design, the memory 203 can exist independently of the processor 201, that is, the memory 203 can be a memory external to the processor 201. In this case, the memory 203 can be connected to the processor 201 via the communication line 202, and is used to store execution instructions or application code, and the execution is controlled by the processor 201 to implement the network quality determination method provided in the following embodiment of this application. In another possible design, the memory 203 can also be integrated with the processor 201, that is, the memory 203 can be the internal memory of the processor 201. For example, the memory 203 is a cache that can be used to temporarily store some data and instruction information.

[0055] As an implementation method, the processor 201 may include one or more CPUs, such as Figure 2 As another implementation method, the transmission power adjustment device 200 may include multiple processors, such as Figure 2 As another implementation, the transmit power adjustment apparatus 200 may further include an output device 205 and an input device 206.

[0056] Through the description of the above embodiments, those skilled in the art will clearly understand that for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the network node can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-described systems, modules, and network nodes can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0057] like Figure 3 As shown, it is a flow chart of a method for determining abnormal network devices provided by an embodiment of the present application. The method for determining abnormal network devices provided by an embodiment of the present application can be applied to the following examples: Figure 2 In the apparatus for determining abnormal network devices shown, the device positioning method provided in the embodiment of the present application can be implemented through the following steps.

[0058] S301: Determine a test location point of each grid in a plurality of grids in a target area.

[0059] The test location points are locations where problems are determined by testing the target area within a preset period.

[0060] In a possible implementation, the target area may be a city or a block; the apparatus for determining abnormal network devices may determine the test location point in each grid based on the latitude and longitude information of the test location point and the coverage of each grid.

[0061] S302: For each grid, determine the problem index of the test location point of the grid and the attribute index of the grid.

[0062] Among them, the problem index of the test location point of the grid is determined based on the coverage problem index, load problem index, and alarm problem index; the attribute index of the grid is determined based on the importance index, urgency index, and complaint index.

[0063] As a possible implementation manner, the implementation process of the above S302 may be: determining the problem index of the corresponding test location point in each grid and the attribute index of each grid.

[0064] S303: Determine a target grid among the multiple grids based on the problem indicator and the attribute indicator, and determine that a network device whose coverage includes the target grid is abnormal.

[0065] Among them, the target grid is the grid with the highest priority among multiple grids; the priority of the grid is determined according to the problem index of the test location point of the grid and the attribute index of the grid.

[0066] In one possible implementation, the priority of each grid is determined based on its problem indicators and attribute indicators. The grid priority satisfies the following formula: Grid Priority = Coverage Problem Index * Coverage Problem Weight + Load Problem Index * Load Problem Weight + Alarm Problem Index * Alarm Problem Weight + Importance Index * Weight + Urgency Index * Urgency Weight + Complaint Index * Complaint Weight. The priorities of multiple grids are then determined. A target grid is identified based on the priority ranking. Network device anomalies are then determined based on the problem indicators of the test locations in the target grid and the attribute indicators of the target grid.

[0067] Based on the above technical solution, the method for determining abnormal network devices provided in the embodiment of the present application first determines the test location points where problems exist in each grid in the multiple grids of the target area, and determines the problem indicators of the test location points of the grid and the attribute indicators of the grid; then, based on the two indicators, determines the target grid that needs to be processed first, that is, the problem in the target grid is the most urgent, and finally determines that the network device whose coverage includes the target grid is abnormal.

[0068] It is understood that after identifying the target grid, issues at the test locations within the target grid are prioritized. Specifically, after receiving the target grid, the problem handling unit in the device for identifying abnormal network devices can create a processing transaction, encapsulating the relevant information about the target grid, the operator's actions, and the resolution as transaction data. Next, the operator digitally signs the processing transaction to ensure its integrity and authentication. The digital signature can be encrypted using the operator's private key and verified using their public key. The transaction processing process can be broadcast to nodes in the blockchain network for confirmation, and the nodes will verify the transaction's validity, signature, and data integrity. Once the transaction is confirmed by multiple nodes, it is added to a block on the blockchain, ensuring data storage and immutability. This means that the operator's actions and resolution are permanently recorded on the blockchain, becoming an unalterable record. Due to the characteristics of blockchain, any attempt to tamper with the record will be detected. The blockchain's recording process ensures the immutability and credibility of the operator's actions and resolution. The distributed nature of blockchain and its consensus algorithm ensure distributed storage and verification of the record, increasing data security and reliability. At the same time, digital signatures provide a mechanism for authentication and data integrity protection, ensuring that only authorized processors can create valid processing transactions.

[0069] It is worth noting that after completing the processing of the test location points within the grid, the supervisory unit can review and evaluate the operations and processing plans of the processing personnel through the records on the blockchain. It is responsible for reviewing the processing results submitted by the problem processing unit and evaluating and guiding the problem handling personnel.

[0070] In one possible implementation, combining Figure 3 ,like Figure 4 As shown, the above S301, determining the test location point of each grid in the multiple grids of the target area, can be specifically implemented through the following S401-S402.

[0071] S401: Obtain location information of multiple test locations in a target area.

[0072] In a possible implementation, the apparatus for determining an abnormal network device obtains latitude and longitude information of a problematic test location point.

[0073] S402 : Determine a test location point of each grid in the plurality of grids in the target area based on the location information of each grid and the location information of the plurality of test location points.

[0074] As a possible implementation method, the implementation process of the above S402 can be: divide the target area into 500*500 meter grids on the map, number each grid, and divide the corresponding test points into the corresponding grids according to the latitude and longitude of the test location points and the location range of each grid.

[0075] It is understandable that there may be no test location point in a grid, or there may be multiple test location points in a grid.

[0076] Based on the above technical solution, the embodiment of the present application can correspond multiple test location points to the position range of the grid to which they belong, so as to facilitate subsequent further processing of the test location points.

[0077] In one possible implementation, combining Figure 4 ,like Figure 5 As shown, before obtaining the position information of multiple test points in the target area in the above S401, the device needs to determine the test point among the multiple test points, which can be specifically achieved through the following S501-S502.

[0078] S501: Acquire multi-source data of multiple test points in a target area.

[0079] The multi-source data is used to characterize whether there is a problem at the test point; the multi-source data includes: signal strength, utilization, and noise interference.

[0080] In one possible implementation, testers can use a drive test system to obtain data from multiple test points within a target area. By receiving base station signals, they can visualize these data on a map, displaying indicators such as the base station number, signal strength, utilization, and communication quality. Furthermore, the multi-source data from these test points can be uploaded to a blockchain for storage, ensuring immutability and traceability.

[0081] For example, if the target area is a block, the tester uses the drive test system to move 50 meters every 100 seconds to obtain data from the test point.

[0082] S502: Determine a test location point among multiple test points in a target area based on multi-source data.

[0083] In one possible implementation, the above-mentioned S502 may be implemented as follows: A signal analysis unit in a system that identifies abnormal network devices may first preprocess multi-source data. This preprocessing may include steps such as data cleaning, noise removal, and data format conversion to ensure data accuracy and consistency. Then, by applying signal processing and feature extraction algorithms, the signal analysis unit extracts valuable data such as signal strength, coverage, interference level, signal-to-noise ratio, and other features related to mobile communication network performance from the preprocessed data. Based on the extracted features, the signal analysis unit utilizes machine learning, data mining, and other techniques to perform automated signal analysis, identifying potential signal quality issues such as signal weaknesses, coverage issues, and interference sources.

[0084] For example, regarding coverage issues at test points, the signal analysis unit analyzes the signal strengths of multiple test points. If the coverage of the serving cell and the strongest neighboring cell at a test point is less than or equal to a threshold, the test point is considered to have weak coverage. For example, if the signal strength at test point A is -108dBm, where the 4G threshold is -110dBm and the 5G threshold is -105dBm, then test point A has weak coverage under the 5G base station.

[0085] Furthermore, if the RSRP of the serving cell and the adjacent cell of the test point within the grid is greater than 100dBm, the number of cells is greater than three, and the level value difference is six dB, it is defined as an overlapping coverage test point.

[0086] Furthermore, when the main service cell signal of the test point comes from a distant base station (the distance is determined according to the specific network, for example, for a 4G urban network, the distance can be defined as 500 meters, and for a 5G urban network, the distance can be defined as 400 meters), it is called cross-zone coverage.

[0087] Specifically, such as Figure 6 As shown in the figure, for some reason, a strong signal area of ​​base station D is generated within the coverage area of ​​distant cell A. Because this area is beyond the actual coverage range of cell D and often lacks neighbor relationships with surrounding cells, it creates an island of coverage, causing interference to cell A. Alternatively, a terminal device initiating a call in the island area cannot be handed off to cell A, resulting in weak coverage or dropped calls. This can easily lead to problems such as saturated uplink transmit power and chaotic handover relationships on mobile phones, severely impacting download speeds and even causing call drops.

[0088] Furthermore, the test location may have coverage holes. Coverage holes refer to areas where no signal can be detected or the signal is very weak, preventing the terminal from accessing the network. To determine this, the RSRP of the strongest cell obtained from the test can be compared with the set threshold. A coverage hole is defined as an area where RSRP < -120dBm. Figure 7Shown is a schematic diagram of a scenario where coverage holes may exist.

[0089] As another example, whether the service cell of the test point has a load problem can be determined based on performance indicators such as the PRB utilization rate of the base station covering the test point, the number of RRC connections, and the average downlink traffic. As shown in Table 1 below, the service cell of the test point is considered to be highly loaded when it meets the following conditions, that is, the service cell of the test point has a load problem and needs to be expanded.

[0090] Table 1 Service cell load table of test points

[0091]

[0092] In another example, whether there is an alarm problem in the service cell of the test point can be determined based on the alarm information of the base station covering the test point. If an alarm occurs, it means that the base station covering the test point has a fault.

[0093] As another example, whether a serving cell at a test point has a quality problem can be determined based on the SINR ratio at the test point. If the ratio is below a threshold, a quality problem is considered to exist. For example, if the base station's sinr ≤ 0, a quality problem is considered to exist. It is understood that the SINR ratio at the test point refers to the ratio of the strength of the received useful signal to the strength of the received interference signal (noise and interference).

[0094] It is worth noting that after the test locations are identified in the embodiments of the present application, problems at the test locations can be located and visualized: the signal analysis unit locates the discovered problems and plots the test locations on a map or provides accurate geographic location information. This allows processing personnel to better understand the spatial distribution of the test locations and take appropriate measures.

[0095] The signal analysis unit can also generate detailed signal analysis reports, including the test location's network type, base station element number, CID, test location name, longitude and latitude, responsible department, problem category, and problem description. These reports can be used as a reference for processing personnel and to demonstrate signal quality to management and other stakeholders.

[0096] Based on the above technical solution, the embodiment of the present application can accurately determine the test location point with problems among multiple test points.

[0097] The above problem indicators and attribute indicators are explained below with reference to specific examples / data.

[0098] 1. Problem indicators.

[0099] The problem indicators include, but are not limited to, the coverage problem index, the load problem index, and the alarm problem index. The coverage problem index is used for characterization; the load problem index is used for characterization; and the alarm problem index is used for characterization. The following details the determination method for each index.

[0100] 1. Coverage problem index.

[0101] For coverage of the test points on the grid, you can determine the scores for different coverage types based on Table 2 below. Higher scores indicate a greater impact of the coverage issue. You can also determine the corresponding scores for each type of coverage issue yourself.

[0102] Table 2 Coverage of question types

[0103] Serial number Override question types Score 1 Overlapping coverage 4 2 Cross-area coverage 3 3 Weak coverage 2 4 Covering holes 1

[0104] Coverage Problem Index = the sum of the coverage problem scores of all base stations covering the test location within the grid. It is worth noting that the coverage problem index is less than or equal to 10.

[0105] 2. Load problem index.

[0106] The load problem index can be determined based on the PRB utilization of different base stations corresponding to all load test locations within the grid. Load problem index = sum of the PRB utilization of different base stations corresponding to all load test locations within the grid * 10. The load problem index is less than or equal to 10.

[0107] For example, if three test locations within a grid have load issues, the primary serving base station corresponding to the first and second test locations is the same base station A, and the third test location corresponds to base station B. Since the calculations are performed for different primary serving base stations, the load issue index for this grid = (PRB utilization of base station A + PRB utilization of base station B) * 10.

[0108] 3. Alarm problem index.

[0109] The alarm problem index of the test location point refers to the failure of the base station covering the test location point. Such failure and other problems will usually be prompted in the network management system. The alarm of the base station is usually an important reason for the failure of the test location point.

[0110] Based on the impact of a fault on the base station system, base station alarms are categorized into four levels: severe, major, minor, and light. Alarms are assigned a weight based on their severity, as shown in Table 3 below. A higher weight indicates a more severe alarm. The corresponding Alarm Problem Index weight can be adjusted.

[0111] Table 3 Alarm level table

[0112]

[0113] At the same time, the alarms that may appear in the base station can be classified as shown in Table 4. In fact, the base station alarms are far more than those in Table 4, and there may be dozens or even hundreds of them. The alarms here are just examples.

[0114] Table 4 Alarm type table

[0115] Serial number Alarm Name Alarm level 1 Network element disconnection alarm serious 2 Abnormal air inlet temperature main 3 Abnormal temperature secondary 4 Authorization limit is about to be exceeded slight …… …… ……

[0116] Alarm Problem Index = the sum of the scores of all currently occurring alarms at all base stations in the grid. It is important to note that the Alarm Problem Index is less than or equal to 10.

[0117] It is worth noting that the problem indicators of the test location points of the grid may also include quality problem indicators.

[0118] The quality problem index can be calculated based on the quality index (sinr) corresponding to all test locations in the grid. The quality problem index = the absolute value of the sum of the quality indexes corresponding to all test locations in the grid, and the quality problem index is less than or equal to 10.

[0119] It is understandable that since the quality problem is only considered when sinr is less than 0, the quality index (sinr) is always a negative number, so there is an absolute value sign above.

[0120] Based on the above technical solution, the embodiment of the present application uses the above method to determine the coverage problem index, load problem index, and alarm problem index of the test location point in each grid, and adds up the multiple indexes to determine the problem index of the test location point in each grid.

[0121] 2. Attribute indicators.

[0122] The attribute indicators include, but are not limited to, the importance index, the urgency index, and the complaint index. The importance index is used for characterization; the urgency index is used for characterization; and the complaint index is used for characterization. The following describes in detail how each index is determined.

[0123] 1. Importance index.

[0124] First, determine the number of sites in the grid, the grid type, and the number of resident terminals in the grid.

[0125] Number of sites: refers to the number of base stations within a grid. Parameters such as longitude and latitude are recorded during base station construction. By obtaining these parameters and matching them with the network, the number of base stations within the grid can be determined. Generally, the more base stations there are, the more important the grid is.

[0126] Grid type: refers to the main scene type of buildings within the grid, as shown in Table 5 below.

[0127] Table 5 Coverage scenario types

[0128]

[0129]

[0130] As can be seen from Table 5 above, each grid can be given a score to represent its importance, and the larger the score, the more important it is.

[0131] Resident Terminals in a Grid: This refers to the number of terminals connected to network devices within the grid. The number of users accessing each base station within the grid is calculated. Users who access the base station three days out of a week and spend more than two hours per day are considered resident users. The total number of resident users for each base station within a grid is then calculated.

[0132] In one possible implementation, the importance index of the grid satisfies the following formula: Importance Index = Network Device Quantity Weight * Network Device Quantity + Grid Scenario Type Weight * Grid Scenario Type Score + Terminal Quantity Weight of Network Devices Accessed in the Grid * Terminal Quantity of Network Devices Accessed in the Grid.

[0133] For example, grid A has 8 sites, a grid type of tertiary hospitals (10), and 65 resident users; grid B has 5 sites, a grid type of residential communities (8), and 130 resident users. The weight coefficients are set as follows: site number weight: 0.4, grid type weight: 0.5, and resident user weight: 0.1.

[0134] Considering that the number of resident users is usually larger than the number of sites and the grid type score, it is recommended to set the resident user weight to 0.1. Considering that the number of resident users may be large, resulting in a large value of resident user weight * number of resident users, it is recommended to set an upper limit, such as 15.

[0135] According to the above given formula, the importance index of grid A and grid B can be calculated.

[0136] The importance index of grid A = (8 × 0.4 + 10 × 0.5 + 65 × 0.1) = 14.7;

[0137] The importance index of grid B = (5 × 0.4 + 8 × 0.5 + 130 × 0.1) = 19;

[0138] According to the calculation results, we can conclude that grid B is more important, while grid A is less important.

[0139] 2. Urgency index.

[0140] The grid's urgency index indicates the degree to which problems within the grid need to be resolved immediately. This index mainly considers the number of problem points within the grid. The grid's urgency index = the number of problem points within the grid.

[0141] 3. Complaint index.

[0142] The complaint index represents the complaints and demands of terminals within a grid regarding an issue. It captures all complaints from terminals within a grid during a specific time period. Complaint information includes, but is not limited to, the terminal number, user package, latitude and longitude of the complaint location, and complaint type. Based on the grid's coverage and latitude and longitude, complaint information for each grid can be obtained. The number of complaints varies for different terminals and is set based on the package price. As shown in Table 6 below, if a terminal with a Level 1 package files one complaint, the complaint count is 1. If a terminal with a Level 5 package files one complaint, the complaint count is 5. Therefore, the complaint index equals the total number of complaints for the grid.

[0143] Table 6 Complaint Form

[0144] Package Level Number of complaints Level 1 1 Level 2 2 Level 3 3 Level 4 4 Level 5 5

[0145] Based on the above technical solution, the embodiment of the present application determines the importance index, urgency index, and complaint index of each grid through the above method, and adds the three indexes to determine the attribute index of each grid.

[0146] In the embodiment of the present application, the device for determining abnormal network devices can be divided into functional modules or functional units according to the above method example. For example, each functional module or functional unit can be divided according to each function, or two or more functions can be integrated into one processing module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software functional modules or functional units. Among them, the division of modules or units in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0147] like Figure 8As shown, a structural schematic diagram of an apparatus 800 for determining abnormal network equipment provided by an embodiment of the present application is provided, wherein the apparatus includes: a processing unit 801; the processing unit 801 is used to determine a test location point of each grid in a plurality of grids of a target area; the test location point is a location point where a problem is determined by testing the target area within a preset period; for each grid, the processing unit 801 is further used to determine a problem index of the test location point of the grid and an attribute index of the grid; the problem index of the test location point of the grid is determined based on a coverage problem index, a load problem index, and an alarm problem index; the attribute index of the grid is determined based on an importance index, an urgency index, and a complaint index; the processing unit 801 is further used to determine a target grid among the plurality of grids based on the problem index and the attribute index, and determine that a network device having a coverage range including the target grid is abnormal; the target grid is the grid with the highest priority among the plurality of grids; wherein the priority of the grid is determined based on the problem index of the test location point of the grid and the attribute index of the grid.

[0148] Optionally, the device further includes: an acquisition unit 802; the acquisition unit 802 is used to obtain the position information of multiple test position points in the target area; the processing unit 801 is also used to determine the test position points of each grid in the multiple grids of the target area based on the position information of each grid and the position information of the multiple test position points.

[0149] Optionally, the acquisition unit 802 is further used to acquire multi-source data of multiple test points in the target area; the multi-source data is used to characterize whether there is a problem with the test point; the multi-source data includes: signal strength, utilization, and noise interference; the processing unit 801 is further used to determine the test location points among the multiple test points in the target area based on the multi-source data.

[0150] Optionally, the coverage problem index is determined based on the coverage type of the test location point of the grid; the coverage types include: weak coverage, overlapping coverage, cross-zone coverage, and coverage holes; the load problem index is determined based on the utilization rate of the network equipment whose coverage range includes the test location point; the alarm problem index is determined based on the alarm information of the network equipment whose coverage range includes the test location point.

[0151] Optionally, the importance index is determined based on the number of network devices within the grid, the number of terminals accessing the network devices within the grid, and the scenario type of the grid; the urgency index is determined based on the number of test location points within the grid; and the complaint index is determined based on the number of complaints from the terminals.

[0152] When implemented by hardware, the communication unit in the embodiment of the present application can be integrated into the communication interface, and the processing unit 801 can be integrated into the processor. The above is only a specific embodiment of the present application, but the scope of protection of the present application is not limited to this. Any changes or replacements within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for determining abnormal network devices, characterized in that: The method comprises: Determine a test location point of each grid in a plurality of grids of a target area; the test location point is a location point where a problem is determined by testing the target area within a preset period; For each grid, determining a problem index of a test location point of the grid and an attribute index of the grid; the problem index of the test location point of the grid is determined based on a coverage problem index, a load problem index, and an alarm problem index; and the attribute index of the grid is determined based on an importance index, an urgency index, and a complaint index; Based on the problem indicators and attribute indicators, a target grid among the multiple grids is determined, and the network device anomaly whose coverage includes the target grid is determined; the target grid is the grid with the highest priority among the multiple grids; wherein the priority of the grid is determined based on the problem indicators of the test location points of the grid and the attribute indicators of the grid.

2. The method according to claim 1, characterized in that Determine test location points for each of multiple grids in the target area, including: Acquire location information of a plurality of test locations in the target area; Based on the position information of each grid and the position information of the plurality of test position points, a test position point of each grid in the plurality of grids of the target area is determined.

3. The method according to claim 2, characterized in that Before obtaining the location information of the plurality of test locations in the target area, the method further includes: Acquire multi-source data of multiple test points in the target area; the multi-source data is used to characterize whether there is a problem at the test point; the multi-source data includes: signal strength, utilization, and noise interference; Based on the multi-source data, a test location point among a plurality of test points in the target area is determined.

4. The method according to any one of claims 1 to 3, characterized in that The coverage problem index is determined based on the coverage type of the test location point of the grid; the coverage type includes: weak coverage, overlapping coverage, cross-area coverage, and coverage hole; The load problem index is determined based on the utilization rate of network devices whose coverage area includes the test location point; The alarm problem index is determined based on alarm information of network devices whose coverage includes the test location point.

5. The method according to any one of claims 1 to 3, characterized in that The importance index is determined based on the number of network devices in the grid, the number of terminals accessing the network devices in the grid, and the scene type of the grid; The urgency index is determined based on the number of test location points within the grid; The complaint index is determined based on the number of complaints received by the terminal.

6. A device for determining abnormal network equipment, characterized in that: The device comprises: a processing unit; The processing unit is configured to determine a test location point of each grid in a plurality of grids of a target area; the test location point is a location point where a problem is determined by testing the target area within a preset period; For each grid, the processing unit is further configured to determine a problem index of a test location point of the grid and an attribute index of the grid; the problem index of the test location point of the grid is determined based on a coverage problem index, a load problem index, and an alarm problem index; and the attribute index of the grid is determined based on an importance index, an urgency index, and a complaint index; The processing unit is further used to determine a target grid among the multiple grids based on the problem indicators and attribute indicators, and determine that the network device anomaly whose coverage includes the target grid; the target grid is the grid with the highest priority among the multiple grids; wherein the priority of the grid is determined based on the problem indicators of the test location points of the grid and the attribute indicators of the grid.

7. The device according to claim 6, characterized in that The device further includes: an acquisition unit; The acquiring unit is configured to acquire location information of a plurality of test locations in the target area; The processing unit is further configured to determine a test location point of each grid in the plurality of grids in the target area based on the location information of each grid and the location information of the plurality of test location points.

8. The device according to claim 7, characterized in that The acquisition unit is further configured to acquire multi-source data of multiple test points in the target area; the multi-source data is used to characterize whether there is a problem at the test point; the multi-source data includes: signal strength, utilization rate, and noise interference; The processing unit is further configured to determine a test location point among a plurality of test points in the target area based on the multi-source data.

9. The device according to any one of claims 6 to 8, characterized in that The coverage problem index is determined based on the coverage type of the test location point of the grid; the coverage type includes: weak coverage, overlapping coverage, cross-area coverage, and coverage hole; The load problem index is determined based on the utilization rate of network devices whose coverage area includes the test location point; The alarm problem index is determined based on alarm information of network devices whose coverage includes the test location point.

10. The device according to any one of claims 6 to 8, characterized in that: The importance index is determined based on the number of network devices in the grid, the number of terminals accessing the network devices in the grid, and the scene type of the grid; The urgency index is determined based on the number of test location points within the grid; The complaint index is determined based on the number of complaints received by the terminal.

11. A device for determining abnormal network equipment, characterized in that: include: A processor and a communication interface; the communication interface is coupled to the processor, and the processor is configured to execute a computer program or instruction to implement the method for determining an abnormal network device as described in any one of claims 1 to 5.

12. A computer-readable storage medium storing instructions, characterized in that: When a computer executes the instruction, the computer executes the method for determining abnormal network devices as described in any one of claims 1 to 5.

Images