Error checking method and device of wideband equipment, equipment and storage medium

Abstract

The method, apparatus, device, and storage medium for detecting errors in broadband equipment provided in this application involve a cloud server retrieving data from multiple target broadband devices from pre-stored spatial data of existing devices in a data center, based on received data of the broadband device to be verified. Then, the addresses of the broadband device to be verified and the addresses of the multiple target broadband devices are input into a pre-trained neural network model for processing to obtain a first verification result. Next, the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices are input into a spatial computing model for calculation to obtain a second verification result. Finally, the first and second verification results are sent to the user equipment. This method utilizes a neural network model and a spatial computing model to verify the address and spatial location of the broadband device to be verified against multiple existing broadband devices, achieving rapid error detection for broadband equipment and improving troubleshooting speed.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, apparatus, device and storage medium for error detection of broadband equipment. Background Technology

[0002] With the rapid development of telecommunications services, the requirements for the accuracy of broadband equipment data are becoming increasingly stringent. In practical applications, when precise service processing or fault handling is needed in a designated area, the low accuracy of broadband equipment data and location offsets prevent accurate positioning, ultimately affecting service processing efficiency.

[0003] The existing data from a large number of broadband devices suffers from inconsistencies in coordinate systems and a lack of rigorous verification of the entered information, resulting in low data accuracy and location discrepancies. Current technology necessitates manual searching and correction, leading to extremely low efficiency and unsatisfactory results in the investigation and rectification of broadband device data.

[0004] Therefore, how to quickly identify broadband devices with location deviations is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for troubleshooting broadband equipment, in order to solve the problem of low efficiency in manual troubleshooting of broadband equipment.

[0006] Firstly, this application provides a method for error detection in broadband equipment, including:

[0007] Receive data from a broadband device to be verified sent by a user equipment, wherein the data of the broadband device to be verified includes the address and latitude and longitude information of the broadband device to be verified;

[0008] Based on the latitude and longitude information of the broadband device to be verified, data of multiple target broadband devices are obtained from the existing device spatial data pre-stored in the data center. The spatial distance between each target broadband device and the broadband device to be verified is less than a preset value.

[0009] The address of the broadband device to be verified and the addresses of the multiple target broadband devices are input into a pre-trained neural network model for processing to obtain a first verification result. The first verification result is used to indicate whether the address of the broadband device to be verified is abnormal. The neural network model includes a natural language decomposition module and a data comparison module. The natural language decomposition module is used to decompose the device address into text, and the data comparison module is used to compare the text decomposed by natural language.

[0010] The latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices are input into the spatial calculation model for calculation and processing to obtain a second verification result. The second verification result is used to indicate whether the spatial location of the broadband device to be verified is abnormal. The spatial calculation model is used to calculate the number of devices within a preset distance range of the broadband device to be verified.

[0011] The first verification result and the second verification result are sent to the user equipment.

[0012] In conjunction with the first aspect, in one specific implementation, the step of inputting the address of the broadband device to be verified and the addresses of the plurality of target broadband devices into a pre-trained neural network model for processing to obtain a first verification result includes:

[0013] The language decomposition module of the neural network model performs natural language decomposition and parsing on the address of the broadband device to be verified and the addresses of the multiple target broadband devices to obtain the feature data and common data corresponding to each address;

[0014] Based on the feature data and common data corresponding to each address, the target address with the highest matching degree with the address of the broadband device to be verified is obtained from the addresses of the multiple target broadband devices through the data comparison module of the neural network model;

[0015] Calculate the similarity between the address of the broadband device to be verified and the target address;

[0016] Based on the similarity, a probability judgment is made to obtain the first verification result.

[0017] In conjunction with the first aspect, in one specific implementation, the step of inputting the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the plurality of target broadband devices into a spatial computing model for calculation processing to obtain a second verification result includes:

[0018] Based on the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices, the spatial distance calculation module in the spatial calculation model determines the number of devices among the multiple target broadband devices whose distance from the broadband device to be verified is less than a preset distance.

[0019] Based on the number of devices, obtain the second verification result.

[0020] In conjunction with the first aspect, in one specific embodiment, the method further includes:

[0021] Based on the latitude and longitude information of the broadband device to be verified and the district / county branch office area data obtained from the map, point-area matching is performed through the point-area matching module in the spatial calculation model to obtain the area and branch office information to which the broadband device to be verified belongs; the second verification result also includes the area and branch office information to which the broadband device to be verified belongs.

[0022] In conjunction with the first aspect, in one specific implementation, before obtaining data of multiple target broadband devices from pre-stored spatial data of existing devices in the data center based on the latitude and longitude information of the broadband device to be verified, the method further includes:

[0023] Based on the latitude and longitude information of the broadband device to be verified, the address of the device to be verified is verified by means of reverse address encoding to obtain the verified address information.

[0024] Secondly, this application also provides an error-detecting device for broadband equipment, comprising:

[0025] The receiving module is used to receive data from the broadband device to be verified sent by the user equipment. The data of the broadband device to be verified includes the address and latitude and longitude information of the broadband device to be verified.

[0026] The acquisition module is used to acquire data of multiple target broadband devices from the existing equipment spatial data pre-stored in the data center based on the latitude and longitude information of the broadband device to be verified, wherein the spatial distance between each target broadband device and the broadband device to be verified is less than a preset value.

[0027] The first verification module is used to input the address of the broadband device to be verified and the addresses of the multiple target broadband devices into a pre-trained neural network model for processing to obtain a first verification result. The first verification result is used to indicate whether the address of the broadband device to be verified is abnormal. The neural network model includes a natural language decomposition module and a data comparison module. The natural language decomposition module is used to decompose the device address into text, and the data comparison module is used to compare the text decomposed by natural language.

[0028] The second verification module is used to input the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices into the spatial calculation model for calculation and processing to obtain the second verification result. The second verification result is used to indicate whether the spatial location of the broadband device to be verified is abnormal. The spatial calculation model is used to calculate the number of devices within a preset distance range of the broadband device to be verified.

[0029] An interactive feedback module is used to send the first verification result and the second verification result to the user equipment.

[0030] In conjunction with the second aspect, in one specific implementation, the first verification module is specifically used for:

[0031] The language decomposition module of the neural network model performs natural language decomposition and parsing on the address of the broadband device to be verified and the addresses of the multiple target broadband devices to obtain the feature data and common data corresponding to each address;

[0032] Based on the feature data and common data corresponding to each address, the target address with the highest matching degree with the address of the broadband device to be verified is obtained from the addresses of the multiple target broadband devices through the data comparison module of the neural network model;

[0033] Calculate the similarity between the address of the broadband device to be verified and the target address;

[0034] Based on the similarity, a probability judgment is made to obtain the first verification result.

[0035] In conjunction with the second aspect, in one specific implementation, the second verification module is specifically used for:

[0036] Based on the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices, the spatial distance calculation module in the spatial calculation model determines the number of devices among the multiple target broadband devices whose distance from the broadband device to be verified is less than a preset distance.

[0037] Based on the number of devices, obtain the second verification result.

[0038] In conjunction with the second aspect, in one specific implementation, the second verification module is further configured to:

[0039] Based on the latitude and longitude information of the broadband device to be verified and the district / county branch office area data obtained from the map, point-area matching is performed through the point-area matching module in the spatial calculation model to obtain the area and branch office information to which the broadband device to be verified belongs; the second verification result also includes the area and branch office information to which the broadband device to be verified belongs.

[0040] In conjunction with the second aspect, in one specific embodiment, the device further includes:

[0041] The address verification module is used to verify the address of the broadband device to be verified by means of inverse address encoding based on the latitude and longitude information of the device to be verified, and to obtain the verified address information.

[0042] Thirdly, this application also provides an electronic device, including: a processor, a memory, a communication interface for communicating with other devices, and computer program instructions stored in the memory and executable on the processor, wherein the processor executes the computer program instructions to implement the error-checking method of the broadband device as described in any of the first aspects.

[0043] Fourthly, this application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the error-checking method for a broadband device as described in any of the first aspects.

[0044] This application provides a method, apparatus, device, and storage medium for detecting errors in broadband equipment. A cloud server receives data from a user device regarding a broadband device to be verified. This data includes the address and latitude / longitude information of the broadband device to be verified. Based on the latitude / longitude information of the broadband device to be verified, data from multiple target broadband devices are obtained from pre-stored spatial data of existing devices in a data center. The spatial distance between each target broadband device and the broadband device to be verified is less than a preset value. Then, the address of the broadband device to be verified and the addresses of the multiple target broadband devices are input into a pre-trained neural network model for processing to obtain a first verification result. Next, the latitude / longitude information of the broadband device to be verified and the latitude / longitude information of the multiple target broadband devices are input into a spatial calculation model for calculation processing to obtain a second verification result. Finally, the first and second verification results are sent to the user device. The user only needs to upload broadband equipment data; the cloud server verifies the address data of the broadband device through the neural network model and verifies the spatial location data of the broadband device through the spatial calculation model, outputting verification results. This achieves rapid error detection of broadband equipment and improves the speed of troubleshooting by comparing the broadband device to be verified with multiple existing broadband devices. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] Figure 1 A schematic diagram illustrating an application scenario of the error-detecting method for broadband devices provided in this application embodiment;

[0047] Figure 2 A flowchart illustrating an embodiment of the error-detection method for broadband devices provided in this application;

[0048] Figure 3 A flowchart illustrating Embodiment 2 of the error-detection method for broadband devices provided in this application;

[0049] Figure 4A flowchart illustrating Embodiment 3 of the error-detection method for broadband equipment provided in this application;

[0050] Figure 5 A flowchart illustrating Embodiment 4 of the error-detection method for broadband devices provided in this application;

[0051] Figure 6 This is a schematic diagram of the structure of an embodiment of the error-detecting device for broadband equipment provided in this application.

[0052] Figure 7 This is a schematic diagram of an electronic device structure provided in an embodiment of this application.

[0053] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0054] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0055] First, let me introduce the background information involved in this application:

[0056] Geographic Information System (GIS) is a technological system capable of collecting, managing, processing, analyzing, modeling, and displaying spatial data. It combines geography, cartography, remote sensing, and computer science, integrating map visualization and geographic analysis functions with general database operations.

[0057] Existing broadband device location data contains many anomalies, such as a large number of devices located in rivers or along highways, or a number of devices in a small area exceeding a reasonable range. Current technology necessitates manual searching and correction, resulting in extremely low efficiency.

[0058] To address the above problems, the inventors discovered during their research in this technical field that by combining broadband device data with GIS technology, and effectively calculating and processing the spatial data of broadband devices, the address and spatial location of broadband devices can be verified, thus achieving automated error detection of broadband device locations. Based on this, this application provides a method, apparatus, device, and storage medium for detecting errors in broadband devices.

[0059] Figure 1 This is a schematic diagram illustrating an application scenario of the error-detecting method for broadband devices provided in this application embodiment. For example... Figure 1 As shown, this application scenario includes at least one user device and a cloud server. The user device has an application (APP) tracking function and the ability to collect and upload data. The user device can be a mobile phone, a computer with a mobile terminal, a smartwatch, a tablet computer, etc., and is not limited here.

[0060] In this scheme, "broadband device to be verified" and "device to be verified" both refer to the same broadband device.

[0061] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0062] Figure 2 This is a flowchart illustrating an embodiment of the error-detecting method for broadband devices provided in this application. Figure 2 As shown, the error-detection method for this broadband device may include the following steps:

[0063] S101. Receive data from the user equipment for the broadband device to be verified, including the address and latitude and longitude information of the broadband device to be verified.

[0064] In this step, to verify the accuracy of the information recorded by the broadband device on the cloud server, it is first necessary to obtain the data of the broadband device to be verified. This data is then compared with the already recorded broadband device data to determine if the location of the broadband device to be verified is off. The user obtains the data of the broadband device to be verified through the tracking tool on their device's app. This data includes the address and latitude / longitude information of the broadband device to be verified. Then, the user sends the data of the broadband device to be verified to the cloud server. The cloud server receives the data of the broadband device to be verified sent by the user device.

[0065] In one possible implementation, the address of the broadband device to be verified is verified by using inverse address encoding based on the latitude and longitude information of the device to be verified, and the verified address information is obtained.

[0066] Specifically, after receiving the broadband device data to be verified sent by the user equipment, the system uses the latitude and longitude information to perform address resolution service through the Application Programming Interface (API) of the map application to obtain the query address of the device to be verified. The query address obtained by reverse address encoding is compared with the address information sent by the user to determine whether the address information uploaded by the user and the latitude and longitude are matched normally.

[0067] S102. Based on the latitude and longitude information of the broadband device to be verified, obtain the data of multiple target broadband devices from the existing spatial data of the devices pre-stored in the data center. The spatial distance between each target broadband device and the broadband device to be verified is less than a preset value.

[0068] In this step, the cloud server's data center pre-stores spatial data of existing broadband devices. This spatial data includes the latitude and longitude information, address text data, and data on the district / county branch company to which the broadband devices belong. After receiving the data of the broadband device to be verified, multiple target broadband devices within a specific range are identified from the existing spatial data based on this data. Specifically, based on the latitude and longitude of the broadband device to be verified, multiple target broadband devices within a preset distance from that latitude and longitude are retrieved from the existing data.

[0069] In one possible implementation, based on the address of the broadband device to be tested, data of multiple target broadband devices belonging to the region of the address of the broadband device to be tested are obtained from the existing devices.

[0070] It should be noted that selecting multiple target broadband devices from the existing equipment and comparing and verifying these multiple target broadband devices with the broadband device to be verified can reduce the verification workload and improve the query and verification efficiency compared to comparing and verifying all existing equipment.

[0071] S103. Input the address of the broadband device to be verified and the addresses of multiple target broadband devices into a pre-trained neural network model for processing to obtain a first verification result. The first verification result is used to indicate whether the address of the broadband device to be verified is abnormal.

[0072] The neural network model includes a natural language decomposition module and a data comparison module. The natural language decomposition module is used to decompose the device address into text, and the data comparison module is used to compare the text decomposed by natural language.

[0073] In this step, the address of the broadband device to be verified and the addresses of multiple target broadband devices are input into a neural network model. The neural network model decomposes and compares the address text to determine whether the address of the broadband device to be verified is among the addresses of the multiple target broadband devices, and outputs the first verification result. The first verification result indicates whether the address of the broadband device to be verified is abnormal. If the address of the broadband device to be verified cannot be found among the multiple target broadband device addresses, it indicates that the address of the broadband device to be verified is incorrect, and the user needs to check whether the address information of the broadband device to be verified or the existing device data is abnormal.

[0074] Specifically, firstly, the address of the broadband device to be verified and multiple target broadband device addresses are processed into key fields through the natural language decomposition module of a neural network model. Then, the address field of the broadband device to be verified (after word segmentation) is compared with the location field of the target broadband devices (after word segmentation). The target broadband device address with the highest similarity is selected, and the first verification result (whether the address of the broadband device to be verified is incorrect) is determined based on the similarity. If the similarity is less than a preset value, it indicates that the address of the broadband device to be verified cannot be found among the multiple target broadband devices, and the probability value of the address being incorrect is output.

[0075] The pre-trained neural network model includes a tokenizer and a Transformer model, which can obtain the device address with the highest matching degree among multiple target devices based on the address of the broadband device to be verified, and has the function of calculating the similarity of address text.

[0076] S104. Input the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of multiple target broadband devices into the spatial calculation model for calculation and processing to obtain the second verification result. The second verification result is used to indicate whether the spatial location of the broadband device to be verified is abnormal.

[0077] In this scheme, the spatial calculation model is mainly used to determine the number of devices within a preset distance range of the broadband device to be verified. Based on this number, it can be determined whether the spatial location of the broadband device to be verified is abnormal.

[0078] Optionally, in one specific implementation, the spatial calculation model includes a spatial distance calculation module and a point-to-surface matching module. The spatial distance calculation module is used to calculate the number of devices within a preset distance range of the broadband device to be verified, and the point-to-surface matching module is used to match the district / county and branch company to which the broadband device to be verified belongs.

[0079] In this step, to verify the accuracy of the spatial location of the broadband device to be verified, it is necessary to collect and judge the number of target broadband devices within a preset distance range around the spatial location of the broadband device to be verified. If there are a large number of target broadband devices in the vicinity, exceeding the set reasonable range, it indicates that the spatial location of the device to be verified or the target broadband devices within the range is incorrect. Specifically, the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of multiple target broadband devices are input into the spatial calculation model to calculate the number of target broadband devices within the preset range. The number of target broadband devices is compared with the preset reasonable number, and a second verification result is output. The second verification result can indicate whether the spatial location of the broadband device to be verified is abnormal.

[0080] For example, if there are more than 5 target broadband devices within a 10m radius of the device to be verified, it indicates that the spatial location of the device to be verified is too dense and is within an unreasonable range. The user needs to investigate the spatial location of the device to be verified and the target broadband devices.

[0081] In one specific implementation, the latitude and longitude information of the broadband device to be verified can be matched with the regional surface data of the district and county through a spatial computing model. This will determine whether the location of the broadband device on the map is within the area of ​​the district or county where the device is located, thereby determining whether the spatial location of the broadband device to be verified is normal.

[0082] S105. Send the first verification result and the second verification result to the user equipment.

[0083] In this step, the first and second verification results are sent to the user equipment. The user can use the first and second verification results to determine whether the address and spatial location of the device to be verified are abnormal. In case of abnormality, the user can manually check the data of the broadband device to be verified.

[0084] The broadband device error detection method provided in this embodiment involves a cloud server receiving data from a user device regarding a broadband device to be verified. This data includes the address and latitude / longitude information of the broadband device. Based on this data, data for multiple target broadband devices is retrieved from pre-stored spatial data of existing devices in the data center. Then, the address of the broadband device to be verified and the addresses of the multiple target broadband devices are input into a pre-trained neural network model for processing to obtain a first verification result. Next, the latitude / longitude information of the broadband device to be verified and the latitude / longitude information of the multiple target broadband devices are input into a spatial computing model for calculation to obtain a second verification result. Finally, the first and second verification results are sent to the user device. This method allows users to simply upload data about the device to be verified. The cloud server uses a neural network model and a spatial computing model to determine the address and spatial location of the device to be verified and outputs the verification result back to the user, achieving automatic error detection for broadband devices. Furthermore, by comparing the broadband device to be verified with multiple existing broadband devices, the detection speed is improved.

[0085] Figure 3 This is a flowchart illustrating a second embodiment of the error-detecting method for broadband devices provided in this application. Figure 3 As shown, step S103 involves inputting the address of the broadband device to be verified and the addresses of multiple target broadband devices into a pre-trained neural network model for processing to obtain the first verification result. Specifically, this includes the following steps:

[0086] S201. The language decomposition module of the neural network model performs natural language decomposition and parsing on the address of the broadband device to be verified and the addresses of multiple target broadband devices to obtain the feature data and common data corresponding to each address.

[0087] In this step, since the format and expression of the broadband device address and the existing device address input by the user may differ, it is necessary to perform word segmentation on the input address to obtain the characteristic and common data of the address field. Based on the comparison of characteristic and common data, the characteristic data refers to the region name in the address, and the common data refers to "city", "district", etc. Specifically, the address is decomposed and parsed using a natural language decomposition module. A feature scanning method can be used, for example, a province can be decomposed into province. The natural language decomposition module can be constructed using a word segmenter, such as the IK segmenter, the Lucence segmenter, or a custom segmenter. This application does not restrict the specific word segmenter selection and segmentation method.

[0088] S202. Based on the feature data and common data corresponding to each address, the target address with the highest matching degree with the address of the broadband device to be verified is obtained from the addresses of multiple target broadband devices through the data comparison module of the neural network model.

[0089] In this step, based on the characteristic data and common data formed by address segmentation, the characteristic data of the device address to be verified is compared with the characteristic data of multiple target broadband device addresses through a data comparison module. The common data is not compared. The target address with the best matching characteristic data is obtained from the comparison of multiple target broadband device addresses.

[0090] The neural network model data comparison module can adopt a transformer model structure, containing multiple encoders, multiple decoders, linear layers, and softmax layers. The encoders and decoders have identical structures, but their weight parameters differ. When inputting into the neural network model data comparison module, a positional encoding needs to be added to each decomposed word. The address with the highest probability is then used as the target address by the data comparison module.

[0091] S203. Calculate the similarity between the address of the broadband device to be verified and the target address.

[0092] In this step, after obtaining the most matching target address, the data comparison module calculates the text similarity between the target address and the address of the device to be verified, and obtains the similarity result. Specifically, the target address text and the address text of the device to be verified are compared by string similarity to obtain the similarity result. Alternatively, a deep learning model can be used to compare the address text similarity; there are no restrictions on this approach.

[0093] S204. Based on the similarity, a probability judgment is made to obtain the first verification result.

[0094] In this step, based on the similarity results and the preset similarity and address anomaly probability comparison table, the address anomaly probability is looked up. Finally, the neural network model outputs the address anomaly probability value of the broadband device to be verified as the first verification result. The similarity and address anomaly probability comparison table is obtained based on prior experiments.

[0095] For example, with a similarity result of 90%, the similarity and address anomaly probability comparison table is consulted to determine the probability anomaly value of 5%. The first verification result is "the probability of address anomaly of the device to be verified is 5%".

[0096] The broadband device error detection method provided in this embodiment firstly involves a neural network model's language decomposition module performing natural language decomposition and parsing on the address of the broadband device to be verified and the addresses of multiple target broadband devices. Based on the feature data and common data after address decomposition, the neural network model's data comparison module then selects the target address with the highest matching degree from the addresses of the multiple target broadband devices. Finally, the similarity between the address of the broadband device to be verified and the target address is calculated, and a probability judgment is made based on the similarity to obtain the first verification result. This scheme achieves automatic error detection of broadband device addresses, and by utilizing a neural network model for word segmentation and comparison, it can prevent interference caused by different address formats and expressions, thus improving the accuracy of address recognition and error detection.

[0097] Figure 4 This is a flowchart illustrating Embodiment 3 of the error-detection method for broadband devices provided in this application. Figure 4 As shown, step S104 inputs the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of multiple target broadband devices into the spatial calculation model for calculation and processing to obtain the second verification result, including the following steps:

[0098] S301. Based on the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of multiple target broadband devices, the spatial distance calculation module in the spatial calculation model is used to determine the number of devices among the multiple target broadband devices whose distance from the broadband device to be verified is less than a preset distance.

[0099] In this step, to verify the spatial location of the broadband device to be verified, a spatial calculation model of the broadband device is first constructed. This model includes a spatial distance calculation module, which calculates the actual distance between the broadband device to be verified and the target broadband devices, and also calculates the number of target broadband devices within a preset distance. The latitude and longitude information of the broadband device to be verified and the latitude and longitude information of multiple target broadband devices are input into the spatial calculation model. The spatial distance calculation module then calculates the number of target broadband devices within a preset distance threshold. The preset distance can be 10 meters, 12 meters, or 15 meters; there are no restrictions on this.

[0100] S302. Obtain the second verification result based on the number of devices.

[0101] In this step, after obtaining the number of target broadband devices, the spatial distance calculation module judges the obtained number of target broadband devices and compares it with a preset number threshold, outputting a second verification result. The preset number threshold can be set to 5, 6, or 7 devices. The setting of the preset number threshold is related to the preset distance threshold; when the preset distance is modified, the preset number threshold also needs to be changed. The preset number threshold can be determined based on the spatial density of broadband devices. This application does not restrict the configuration of the preset number threshold.

[0102] If the number of target broadband devices exceeds a preset threshold, a large number of broadband devices exist in the same location area, and the second verification result is: the spatial distance between the broadband devices to be verified is abnormal data. If the number of target broadband devices is less than or equal to the preset threshold, an allowed number of broadband devices exist in the same location area, and the second verification result is: the spatial distance between the broadband devices to be verified is normal data. Taking a preset threshold of 5 as an example, when the number of target broadband devices is greater than 5, the second verification result is: the spatial distance between the broadband devices to be verified is abnormal data; when the number of target broadband devices is less than or equal to 5, the second verification result is: the spatial distance between the broadband devices to be verified is normal data. When the second verification result is abnormal data, the user can rectify the spatial location of the device to be verified and adjacent broadband devices based on the abnormal data.

[0103] In one implementation, when the number of target broadband devices is less than or equal to a preset threshold, the spatial distance calculation module stores the latitude and longitude data of the device to be verified and the data of the target broadband devices within the range in a preset format as an associated data table. The second verification result includes the associated data table and the result that the data is normal. Based on the associated data table, the existing device data in the cloud database can be marked, and the broadband devices in the associated data table can be associated in the GIS map.

[0104] The broadband device error detection method provided in this embodiment, based on the latitude and longitude information of the broadband device to be verified and multiple target broadband devices, uses the spatial distance calculation module in the spatial calculation model to determine the number of target broadband devices whose distance from the broadband device to be verified is less than a preset distance. Based on this number, a second verification result is obtained. This method can query the density of broadband devices around the broadband device to be verified, quickly detect anomalies in the device to be verified and its surrounding devices, and improve error detection efficiency.

[0105] Figure 5 This is a flowchart illustrating Embodiment 4 of the error-detection method for broadband devices provided in this application. Figure 5 As shown, based on Embodiment 3, the following steps are also included:

[0106] S401. Based on the latitude and longitude information of the broadband device to be verified and the district / county branch office area data obtained from the map, point-area matching is performed through the point-area matching module in the spatial calculation model to obtain the area and branch office information to which the broadband device to be verified belongs.

[0107] In one implementation, the second verification result also includes information about the plane and branch company to which the broadband device to be verified belongs.

[0108] In this step, the location of the broadband device to be verified needs to be checked. First, the latitude and longitude information of the broadband device to be verified and multiple target broadband devices are obtained. Then, a pre-stored GIS map is retrieved. Based on the latitude and longitude information of the multiple target broadband devices, information about them is obtained from the GIS map. This information includes the district / county and branch area to which each target broadband device belongs. Each area represents a target polygon, thus multiple target polygon data can be obtained. Finally, the location of the broadband device to be verified is marked on the GIS map according to its latitude and longitude. The point-polygon matching module then determines whether the location point of the broadband device to be verified belongs to a target polygon.

[0109] In one implementation, a buffer zone with a preset distance is constructed for the location of the broadband device to be verified. If the buffer zone falls within the plane, then the point-plane matching is successful.

[0110] If the point-to-area matching is successful, the data is considered normal. The area and branch company information to which the broadband device to be verified belongs are then obtained and sent to the user as part of the second verification result. In other words, the second verification result also includes the area and branch company information to which the broadband device to be verified belongs. When the device to be verified is a newly added broadband device, the user can associate, bind, and update it in the GIS based on the area and branch company information to which the broadband device to be verified belongs.

[0111] If the point-to-surface matching fails, it is considered abnormal data. The abnormal point-to-surface matching result is output as part of the second verification result and sent to the user. Based on the abnormal point-to-surface matching result, the user can query the spatial location of the broadband device to be verified and whether the regional surface data in the GIS map is drawn normally.

[0112] The broadband device error-checking method provided in this embodiment uses the latitude and longitude information of the broadband device to be checked and the district / county branch office area data obtained from the GIS map. It then uses a point-area matching module in the spatial calculation model to perform point-area matching to obtain the area and branch office information to which the broadband device to be checked belongs. This method allows for rapid error checking of the spatial location of the device to be checked and the collection of area and branch office information.

[0113] Figure 6 This is a schematic diagram of the structure of an embodiment of the error-detecting device for broadband equipment provided in this application, as shown in the following figure. Figure 6 As shown, the error-detecting device 300 of the broadband equipment includes:

[0114] The receiving module 311 is used to receive data of the broadband device to be verified sent by the user equipment, wherein the data of the broadband device to be verified includes the address and latitude and longitude information of the broadband device to be verified.

[0115] The acquisition module 312 is used to acquire data of multiple target broadband devices from the existing equipment spatial data pre-stored in the data center based on the latitude and longitude information of the broadband device to be verified, wherein the spatial distance between each target broadband device and the broadband device to be verified is less than a preset value.

[0116] The first verification module 313 is used to input the address of the broadband device to be verified and the addresses of the plurality of target broadband devices into a pre-trained neural network model for processing to obtain a first verification result. The first verification result is used to indicate whether the address of the broadband device to be verified is abnormal. The neural network model includes a natural language decomposition module and a data comparison module. The natural language decomposition module is used to decompose the device address into text, and the data comparison module is used to compare the text decomposed by natural language.

[0117] The second verification module 314 is used to input the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices into the spatial calculation model for calculation and processing to obtain a second verification result. The second verification result is used to indicate whether the spatial location of the broadband device to be verified is abnormal. The spatial calculation model is used to calculate the number of devices within a preset distance range of the broadband device to be verified.

[0118] The interactive feedback module 315 is used to send the first verification result and the second verification result to the user equipment.

[0119] Optionally, the first verification module 313 is specifically used for:

[0120] The language decomposition module of the neural network model performs natural language decomposition and parsing on the address of the broadband device to be verified and the addresses of the multiple target broadband devices to obtain the feature data and common data corresponding to each address;

[0121] Based on the feature data and common data corresponding to each address, the target address with the highest matching degree with the address of the broadband device to be verified is obtained from the addresses of the multiple target broadband devices through the data comparison module of the neural network model;

[0122] Calculate the similarity between the address of the broadband device to be verified and the target address;

[0123] Based on the similarity, a probability judgment is made to obtain the first verification result.

[0124] Optionally, the second verification module 314 is specifically used for:

[0125] Based on the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices, the spatial distance calculation module in the spatial calculation model determines the number of devices among the multiple target broadband devices whose distance from the broadband device to be verified is less than a preset distance.

[0126] Based on the number of devices, obtain the second verification result.

[0127] Optionally, the second verification module 314 is further configured to:

[0128] Based on the latitude and longitude information of the broadband device to be verified and the district / county branch office area data obtained from the map, point-area matching is performed through the point-area matching module in the spatial calculation model to obtain the area and branch office information to which the broadband device to be verified belongs; the second verification result also includes the area and branch office information to which the broadband device to be verified belongs.

[0129] Optionally, the device further includes:

[0130] The address verification module 316 is used to perform address verification on the broadband device to be verified by means of inverse address encoding based on the latitude and longitude information of the device to be verified, and obtain the verified address information.

[0131] The error-checking device for broadband equipment provided in this application is used to execute the technical solution of the error-checking method for broadband equipment provided in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0132] Figure 7 This application provides a schematic diagram of an electronic device structure, such as... Figure 7 As shown, the electronic device 400 includes:

[0133] The processor 411, the memory 412 communicatively connected to the processor, and the communication interface 413 for interacting with other devices;

[0134] The memory 412 stores computer-executed instructions;

[0135] The processor executes the computer program instructions stored in the memory to implement the error-checking method for the broadband device described in any of the above method embodiments.

[0136] When the electronic device 400 is implemented as a server, it needs to communicate wirelessly with user equipment.

[0137] Optionally, the various devices described above in the electronic device 400 can be connected via a system bus.

[0138] The memory 412 can be a separate memory unit or a memory unit integrated into the processor 411. The number of processors 411 can be one or more.

[0139] The error-checking device for broadband equipment provided in this application is used to execute the technical solution of the error-checking method for broadband equipment provided in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0140] Optionally, the electronic device can also be a display for showing the processor's processing results and for human-computer interaction. In some embodiments, the display can be the front panel of the electronic device; in other embodiments, the display can be a flexible display screen, or even a non-rectangular, irregularly shaped display screen, i.e., a non-rectangular screen. The display can be made of materials such as liquid crystal display (LCD) or organic light-emitting diode (OLED).

[0141] It should be understood that the processor 411 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0142] The system bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0143] All or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0144] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the error-checking method for the broadband device described in any of the foregoing method embodiments.

[0145] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0146] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0147] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for detecting errors in broadband equipment, characterized in that, include: Receive data from a broadband device to be verified sent by a user equipment, wherein the data of the broadband device to be verified includes the address and latitude and longitude information of the broadband device to be verified; Based on the latitude and longitude information of the broadband device to be verified, data of multiple target broadband devices are obtained from the existing device spatial data pre-stored in the data center. The spatial distance between each target broadband device and the broadband device to be verified is less than a preset value. The address of the broadband device to be verified and the addresses of the multiple target broadband devices are input into a pre-trained neural network model for processing to obtain a first verification result. The first verification result is used to indicate whether the address of the broadband device to be verified is abnormal. The neural network model includes a natural language decomposition module and a data comparison module. The natural language decomposition module is used to decompose the device address into text, and the data comparison module is used to compare the text decomposed by natural language. The latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices are input into the spatial calculation model for calculation and processing to obtain a second verification result. The second verification result is used to indicate whether the spatial location of the broadband device to be verified is abnormal. The spatial calculation model is used to calculate the number of devices within a preset distance range of the broadband device to be verified. The first verification result and the second verification result are sent to the user equipment.

2. The method according to claim 1, characterized in that, The step of inputting the address of the broadband device to be verified and the addresses of the multiple target broadband devices into a pre-trained neural network model for processing to obtain a first verification result includes: The language decomposition module of the neural network model performs natural language decomposition and parsing on the address of the broadband device to be verified and the addresses of the multiple target broadband devices to obtain the feature data and common data corresponding to each address; Based on the feature data and common data corresponding to each address, the target address with the highest matching degree with the address of the broadband device to be verified is obtained from the addresses of the multiple target broadband devices through the data comparison module of the neural network model; Calculate the similarity between the address of the broadband device to be verified and the target address; Based on the similarity, a probability judgment is made to obtain the first verification result.

3. The method according to claim 1 or 2, characterized in that, The step involves inputting the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices into a spatial computing model for calculation and processing to obtain a second verification result, including: Based on the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices, the spatial distance calculation module in the spatial calculation model determines the number of devices among the multiple target broadband devices whose distance from the broadband device to be verified is less than a preset distance. Based on the number of devices, obtain the second verification result.

4. The method according to claim 3, characterized in that, The method further includes: Based on the latitude and longitude information of the broadband device to be verified and the district / county branch office area data obtained from the map, point-area matching is performed through the point-area matching module in the spatial calculation model to obtain the area and branch office information to which the broadband device to be verified belongs; the second verification result also includes the area and branch office information to which the broadband device to be verified belongs.

5. The method according to claim 1 or 2, characterized in that, Before obtaining data of multiple target broadband devices from pre-stored spatial data of existing devices in the data center based on the latitude and longitude information of the broadband device to be verified, the method further includes: Based on the latitude and longitude information of the broadband device to be verified, the address of the device to be verified is verified by means of reverse address encoding to obtain the verified address information.

6. A fault-detection device for broadband equipment, characterized in that, include: The receiving module is used to receive data from the broadband device to be verified sent by the user equipment. The data of the broadband device to be verified includes the address and latitude and longitude information of the broadband device to be verified. The acquisition module is used to acquire data of multiple target broadband devices from the existing equipment spatial data pre-stored in the data center based on the latitude and longitude information of the broadband device to be verified, wherein the spatial distance between each target broadband device and the broadband device to be verified is less than a preset value. The first verification module is used to input the address of the broadband device to be verified and the addresses of the multiple target broadband devices into a pre-trained neural network model for processing to obtain a first verification result. The first verification result is used to indicate whether the address of the broadband device to be verified is abnormal. The neural network model includes a natural language decomposition module and a data comparison module. The natural language decomposition module is used to decompose the device address into text, and the data comparison module is used to compare the text decomposed by natural language. The second verification module is used to input the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices into the spatial calculation model for calculation and processing to obtain the second verification result. The second verification result is used to indicate whether the spatial location of the broadband device to be verified is abnormal. The spatial calculation model is used to calculate the number of devices within a preset distance range of the broadband device to be verified. An interactive feedback module is used to send the first verification result and the second verification result to the user equipment.

7. The apparatus according to claim 6, characterized in that, The first verification module is specifically used for: The language decomposition module of the neural network model performs natural language decomposition and parsing on the address of the broadband device to be verified and the addresses of the multiple target broadband devices to obtain the feature data and common data corresponding to each address; Based on the feature data and common data corresponding to each address, the target address with the highest matching degree with the address of the broadband device to be verified is obtained from the addresses of the multiple target broadband devices through the data comparison module of the neural network model; Calculate the similarity between the address of the broadband device to be verified and the target address; Based on the similarity, a probability judgment is made to obtain the first verification result.

8. The apparatus according to claim 6 or 7, characterized in that, The second verification module is specifically used for: Based on the latitude and longitude information of the broadband device to be verified and the latitude and longitude information of the multiple target broadband devices, the spatial distance calculation module in the spatial calculation model determines the number of devices among the multiple target broadband devices whose distance from the broadband device to be verified is less than a preset distance. Based on the number of devices, obtain the second verification result.

9. The apparatus according to claim 8, characterized in that, The second verification module is also used for: Based on the latitude and longitude information of the broadband device to be verified and the district / county branch office area data obtained from the map, point-area matching is performed through the point-area matching module in the spatial calculation model to obtain the area and branch office information to which the broadband device to be verified belongs; the second verification result also includes the area and branch office information to which the broadband device to be verified belongs.

10. The apparatus according to claim 6 or 7, characterized in that, The device further includes: The address verification module is used to verify the address of the broadband device to be verified by means of inverse address encoding based on the latitude and longitude information of the device to be verified, and to obtain the verified address information.

11. An electronic device, characterized in that, include: The processor, memory, communication interface for communicating with other devices, and computer program instructions stored in the memory and executable on the processor, wherein the processor executes the computer program instructions to implement the error-checking method for the broadband device as described in any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the error-checking method for a broadband device as described in any one of claims 1 to 5.

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