Network data processing method, apparatus, device, and storage medium

Abstract

The present disclosure provides a network data processing method and device, equipment and storage medium, and relates to the technical field of communication. In some embodiments of the present disclosure, preset network management monitoring data and a resource to be audited are obtained; the preset network management monitoring data and the resource to be audited are automatically compared for consistency to obtain a data comparison result, improve data processing efficiency, and improve the accuracy of data processing; in the case that the data comparison result is inconsistent data, the abnormality degree state of abnormal data in the resource to be audited is determined; in the case that the abnormality degree state of the abnormal data is serious abnormality, an artificial survey work order is automatically issued to perform artificial survey processing on the abnormal data, thereby improving the data processing efficiency and reducing the labor cost.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and particularly relates to a network data processing method and device, equipment and a storage medium. BACKGROUND

[0002] The resource system manages device resources, line resources, and inter-device relationship resources of transmission networks such as core networks, wireless networks, and data networks. Resource data of the resource system, such as resources corresponding to machine rooms, ONUs, and OBDs of a building, are initially manually entered by local management personnel. The accuracy of these manually entered resource data directly affects user opening and daily operation. With the gradual expansion of communication networks, higher requirements are put forward for the effectiveness and accuracy of resource data from automatic configuration at opening to accurate fault judgment and intelligent analysis of pre-inspection and pre-repair.

[0003] Currently, resource data corresponding to a building is manually processed, and the accuracy of data processing is low, and the labor cost is high. SUMMARY

[0004] The present disclosure provides a network data processing method, device, equipment and storage medium to at least solve the problems of low accuracy and high labor cost of existing data processing.

[0005] The technical solution of the present disclosure is as follows:

[0006] The present disclosure provides a network data processing method, comprising:

[0007] obtaining preset network management monitoring data and to-be-audited resources;

[0008] performing consistency comparison on the preset network management monitoring data and the to-be-audited resources to obtain a data comparison result;

[0009] in a case where the data comparison result is inconsistent data, determining an abnormality degree state of abnormal data in the to-be-audited resources;

[0010] in a case where the abnormality degree state of the abnormal data is serious abnormality, issuing a manual survey work order to perform manual survey processing on the abnormal data.

[0011] Optionally, the determining of the abnormality degree state of the abnormal data in the to-be-audited resources comprises:

[0012] counting an abnormality number of the to-be-audited resources in a preset historical period; and

[0013] determining whether the to-be-audited resources will be abnormal in a set future period according to attribute information of the to-be-audited resources;

[0014] If the number of anomalies is greater than or equal to a set number, and the resource to be audited experiences an anomaly within the set future time period, then the anomaly status of the anomaly data is determined to be a severe anomaly.

[0015] Optionally, determining whether the resource to be audited will experience an anomaly within a set future time period based on its attribute information includes:

[0016] The attribute information of the resource to be audited is input into the trained anomaly identification model to obtain the identification result of whether the resource to be audited will experience an anomaly within a set future time period.

[0017] Optionally, before using the anomaly detection model, the method further includes:

[0018] Obtain sample resources to be audited;

[0019] The sample auditable resources are filtered to obtain historical data of sample anomaly alarms and the results of whether each of the sample anomaly alarm historical data has anomalies within a set future time period;

[0020] Based on the historical data of the sample anomaly alarms and the results of whether anomalies occur in each of the historical data of the sample anomaly alarms within a set future time period, the initial random forest model is trained to obtain the anomaly identification model.

[0021] Optionally, determining the degree of anomalousness of the abnormal data in the resource to be audited includes:

[0022] Identify the buildings to be audited corresponding to the abnormal data;

[0023] Obtain the abnormal association attributes of other buildings that have wiring connections with the building to be audited;

[0024] The abnormal association attributes are input into the abnormal frequency category identification model to obtain the abnormal frequency category of the area where the resource to be audited is located;

[0025] The degree of abnormality is determined based on the abnormality frequency category.

[0026] Optionally, determining the degree of abnormality based on the abnormality frequency category includes:

[0027] If the abnormal frequency category is medium abnormal frequency or high abnormal frequency, the abnormality level is determined to be severe abnormality; or,

[0028] The number of manual audits of the other buildings within a set time period is counted; if the number of manual audits is less than the number of audits required for the abnormal frequency category, the abnormality status is determined to be a serious abnormality.

[0029] Optionally, before using the abnormal frequency category identification model, the method further comprises:

[0030] obtaining sample abnormal association attributes of other sample buildings having a wiring connection relationship with the sample building;

[0031] determining a sample abnormal frequency category of each of the other sample buildings according to the number of manual audits of each of the other sample buildings;

[0032] training an initial logistic regression model according to the sample abnormal association attributes and the sample abnormal frequency category of each of the other sample buildings to obtain the abnormal frequency category identification model.

[0033] The embodiments of the present disclosure further provide a network data processing apparatus, comprising:

[0034] an acquisition module configured to acquire preset network management monitoring data and a resource to be audited;

[0035] a comparison module configured to perform consistency comparison on the preset network management monitoring data and the resource to be audited to obtain a data comparison result;

[0036] a determination module configured to determine an abnormal degree state of abnormal data in the resource to be audited in a case where the data comparison result is inconsistent data;

[0037] an issuing module configured to issue a manual survey work order to perform manual survey processing on the abnormal data in a case where the abnormal degree state of the abnormal data is serious abnormality.

[0038] The embodiments of the present disclosure further provide an electronic device, comprising:

[0039] a processor;

[0040] a memory configured to store processor-executable instructions;

[0041] The processor is configured to execute the instructions to implement each step in the above method.

[0042] The embodiments of the present disclosure further provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement each step in the above method.

[0043] The embodiments of the present disclosure provide at least the following beneficial effects:

[0044] In some embodiments of the present disclosure, preset network management monitoring data and a resource to be audited are acquired; the preset network management monitoring data and the resource to be audited are automatically compared for consistency to obtain a data comparison result, improve data processing efficiency, and improve data processing accuracy; in a case where the data comparison result is inconsistent data, an abnormality degree state of abnormal data in the resource to be audited is determined; in a case where the abnormality degree state of the abnormal data is serious abnormality, an artificial survey work order is automatically issued to perform artificial survey processing on the abnormal data, improve data processing efficiency, and reduce labor cost.

[0045] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0046] The accompanying drawings incorporated in and forming a part of the specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure without imposing on the disclosure limitations that are inconsistent with the principles of the disclosure.

[0047] Figure 1 A flowchart of a network data processing method according to an exemplary embodiment of the present disclosure;

[0048] Figure 2 An architecture diagram of a resource auditing system according to an exemplary embodiment of the present disclosure;

[0049] Figure 3 A resource auditing flowchart according to an exemplary embodiment of the present disclosure;

[0050] Figure 4 A resource auditing flowchart according to an exemplary embodiment of the present disclosure;

[0051] Figure 5 A schematic diagram of a network data processing device according to an exemplary embodiment of the present disclosure;

[0052] Figure 6 A structural schematic diagram of an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0053] In order for those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings.

[0054] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0055] It should be noted that the user information involved in the present disclosure includes but is not limited to user equipment information and user personal information; the collection, storage, use, processing, transmission, provision and disclosure of user information in the present disclosure comply with the provisions of relevant laws and regulations, and do not violate public order and good customs.

[0056] The traditional building surveying has the problem that the auditing personnel do not go to the surveying site to backfill information, the authenticity of the auditing result lacks guarantee, and the accuracy of the data is low. Moreover, the auditing personnel need to manually verify the building surveying of all abnormal situations, which seriously consumes manpower.

[0057] In order to solve the above technical problems, in some embodiments of the present disclosure, preset network management monitoring data and to-be-audited resources are obtained; the preset network management monitoring data and the to-be-audited resources are automatically compared for consistency to obtain a data comparison result, improve the data processing efficiency, and improve the accuracy of data processing; in the case that the data comparison result is inconsistent data, the abnormal degree state of abnormal data in the to-be-audited resources is determined; in the case that the abnormal degree state of the abnormal data is serious abnormality, an artificial survey work order is automatically issued to perform artificial survey processing on the abnormal data, improve the data processing efficiency, and reduce the labor cost.

[0058] The technical solutions provided by the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0059] Figure 1 A flowchart of a network data processing method provided by an exemplary embodiment of the present disclosure is shown in FIG. Figure 1 As shown in the figure, the method comprises:

[0060] S101: obtaining preset network management monitoring data and to-be-audited resources;

[0061] S102: comparing the preset network management monitoring data and the to-be-audited resources for consistency to obtain a data comparison result;

[0062] S103: in the case that the data comparison result is inconsistent data, determining the abnormal degree state of abnormal data in the to-be-audited resources;

[0063] S104: In the case that the abnormality degree state of the abnormal data is a serious abnormality, an artificial survey work order is issued to perform artificial survey processing on the abnormal data.

[0064] In the embodiment, the execution subject of the method can be a terminal device or a server.

[0065] The terminal device includes but is not limited to a mobile station (MS), a mobile terminal, a mobile telephone, a handset, a portable equipment, and the like. The terminal device can communicate with one or more core networks through a radio access network (RAN). For example, the terminal device can be a mobile phone (also referred to as a "cellular" phone), a computer with wireless communication function, and the like. The terminal device can also be a computer with wireless transceiver function, a virtual reality (VR) terminal device, an AR terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical treatment, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, and the like. An operating system installed on the terminal device includes but is not limited to an IOS, an Android, a windows, a linux, a Mac OS, and the like. The terminal device can be referred to by different names in different networks. For example, the terminal device can be referred to as a user equipment, a mobile station, a subscriber unit, a station, a cellular phone, a personal digital assistant, a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop station, a television, and the like. For the sake of convenience, the terminal device is referred to as a terminal device in the embodiment.

[0066] In the embodiment, the implementation form of the server is not limited. For example, the server can be a conventional server, a cloud server, a cloud host, a virtual center, and the like. The server mainly includes a processor, a hard disk, a memory, a system bus, and the like, and has a general computer architecture type.

[0067] In the embodiment, preset network management monitoring data and to-be-audited resources are acquired; the preset network management monitoring data and the to-be-audited resources are automatically compared for consistency, to obtain a data comparison result, improve data processing efficiency, and improve data processing accuracy; in the case that the data comparison result is inconsistent data, an abnormality degree state of abnormal data in the to-be-audited resources is determined; in the case that the abnormality degree state of the abnormal data is a serious abnormality, an artificial survey work order is automatically issued to perform artificial survey processing on the abnormal data, to improve data processing efficiency and reduce labor costs.

[0068] Figure 2 An architecture diagram of a resource auditing system is provided for the exemplary embodiments of the present disclosure. As shown in the figure, the resource auditing system interfaces with a professional network management terminal to collect online data of to-be-audited resources, and performs data deduplication and cleaning after data collection, such as collecting, analyzing, and integrating resource data of an operator network through task management. The resource data collection range is a building with customer acquisition business in the whole network, and the collection granularity is to the building level. An end-to-end resource tree is constructed in this way, and online resource data auditing is performed based on the full amount of customer acquisition dedicated line building auditing as the auditing strategy. Finally, the problem data is output as an alarm through the client of the auditing system. Figure 2

[0069] Referring to Figure 2 , online auditing of to-be-audited resources is implemented on the resource auditing system, and the preset network management monitoring data and the to-be-audited resources are automatically compared and audited by the auditing background of the resource auditing system. The resource link from the preset network management monitoring data to the building to-be-audited resources is established by the end-to-end resource tree. The network resources such as ONU, router, switch, IAD, IP-PBX, and PTN, optical transceiver, Metro, MSAP, and program-controlled switch are audited. The data consistency between the resource auditing system and the existing network is automatically audited and compared. The query and export functions are provided for inconsistent data, and an alarm is formed for abnormal resources of inconsistent data. The specific implementation mode of the alarm operation is not limited in the embodiments of the present disclosure, and the existing alarm operation can be used, which will not be described here.

[0070] In some embodiments of the present disclosure, an abnormality degree state of abnormal data in to-be-audited resources is determined; in the case that the abnormality degree state of the abnormal data is a serious abnormality, an artificial survey work order is issued to perform artificial survey processing on the abnormal data. The embodiments of the present disclosure preferentially perform online auditing on abnormal resources, but directly issue an artificial survey work order for abnormal resources with relatively serious abnormal conditions. The abnormality degree state of abnormal data in to-be-audited resources includes but is not limited to the following several abnormality determination methods:

[0071] ​The abnormality determination manner one is: counting the number of times of abnormality of the to-be-audited resource in a preset historical period; and determining whether the to-be-audited resource is abnormal in a set future period according to attribute information of the to-be-audited resource; in the case that the number of times of abnormality is greater than or equal to a set number of times and the to-be-audited resource is abnormal in the set future period, it is determined that the abnormality degree state of the abnormal data is a serious abnormality. It should be noted that the preset historical period, the set future period and the set number of times are not limited in the embodiment of the present disclosure, and can be adjusted according to actual conditions. The preset historical period is, for example, one year or two years. The set number of times is, for example, 2 or 3. The set future period is, for example, 30 minutes in the future or one hour in the future. The attribute information of the to-be-audited resource includes but is not limited to the following: business type, business state and transmission type.

[0072] The abnormality determination manner two is: determining the to-be-audited building corresponding to the abnormal data; obtaining abnormal association attributes of other buildings having a wiring connection relationship with the to-be-audited building; inputting the abnormal association attributes into an abnormal frequency category identification model to obtain an abnormal frequency category of a region where the to-be-audited resource is located; and determining the abnormality degree state according to the abnormal frequency category. It should be noted that the abnormal association attributes include but are not limited to the following: the number of abnormalities of other buildings, time intervals and wiring connection distances.

[0073] In the above abnormality determination manner one, the to-be-audited resource has occurred abnormality for a set number of times (such as ≥2 times) in a past preset period (such as one year), and the to-be-audited resource is predicted to be abnormal in a set future period by an abnormality prediction algorithm, and a manual survey work order is issued.

[0074] In the above abnormality determination manner one, the attribute information of the to-be-audited resource is input into an abnormality identification model that has been trained to obtain an identification result of whether the to-be-audited resource is abnormal in a set future period.

[0075] In the embodiment of the present disclosure, the abnormality prediction algorithm adopts a random forest algorithm to construct an abnormality identification model of whether abnormality occurs in a short future time. The random forest algorithm in the embodiment of the present disclosure models a data set in which the business of the to-be-audited resource changes, and predicts whether abnormality occurs in a short future time for the to-be-audited resource. The specific process includes data processing, model training, prediction evaluation and the like.

[0076] 1) Data processing, abnormal alarm history data of all buildings in which the business type and transmission type of the area where the to-be-audited resource is located change within two years, and tagging these resource data with business type, business status and transmission type tags through automated tools. Business type tags include data line, Internet line and voice line; business status tags include the change direction of the business, increase or decrease; transmission type tags include bare fiber, pon, ptn / spn, sdh / mstp and otn / vc-otn.

[0077] The above three tags are used to classify the data set of business changes, and the meanings of the three fields of the data set are as follows.

[0078]

[0079] 2) Model training and prediction

[0080] In some embodiments of the present disclosure, a sample to-be-audited resource is obtained; sample abnormal alarm history data and whether each sample abnormal alarm history data occurs abnormally within a set future period are screened out from the sample to-be-audited resource; and an initial random forest model is trained according to the sample abnormal alarm history data and the result of whether each sample abnormal alarm history data occurs abnormally within a set future period, to obtain an abnormality identification model.

[0081] Among them, the data set is divided into training set and test set in the ratio of 8:2, the training set is imported into the existing random forest model, and the fit function is used for model training. Finally, the test set is used for prediction, and the mean square error loss function MSE is used to evaluate the performance of the model, and the model is adjusted to improve the prediction accuracy. The model training process of the present embodiment adopts the general training method of the random forest algorithm model, which is not described here. The trained model has the ability to predict the possibility of abnormal occurrence in the near future.

[0082] In the above abnormality determination method two, the abnormality occurrence frequency of the area where the to-be-audited resource is located is confirmed according to the abnormality of other buildings in the network device wiring network of the area which are closely related to the to-be-audited resource.

[0083] a) Establish a softmax logistic regression model.

[0084] b) Train the model.

[0085] In some embodiments of the present disclosure, sample abnormal association attributes of other sample buildings having a wiring connection relationship with the sample building are acquired; a sample abnormal frequency category of each other sample building is determined according to the number of artificial audits of each other sample building; and the initial logistic regression model is trained according to the sample abnormal association attributes and the sample abnormal frequency category of each other sample building to obtain an abnormal frequency category identification model.

[0086] In the formula, the building with a resource scale and magnitude similar to that of the building where the resource to be audited is located is taken as a reference building, resource data of all reference buildings in the past half year is retrieved, and abnormal conditions of other buildings having a wiring connection relationship with the reference building are acquired, and three data sets of abnormal quantity, time interval and wiring connection distance of the other buildings are extracted as feature inputs of the softmax logistic regression model. The number of artificial audits of the abnormal conditions of the other buildings is acquired as the output category of the softmax logistic regression model. The number of artificial audits of 1, 2-3 times and more than 3 times are respectively marked as low abnormal frequency, medium abnormal frequency and high abnormal frequency.

[0087] In the formula, the abnormal quantity refers to the total number of resource data abnormalities of the other buildings in the past half year, such as less than 3 times, 3-5 times and more than 5 times. The time interval distance refers to the time interval between the occurrence of abnormalities of the other buildings and the occurrence of abnormalities of the similar building, such as 7 days, 30 days, within the past half year, and the similar building has not occurred abnormality. The wiring connection distance refers to the wiring connection distance between the other abnormal buildings and the similar building, such as direct wiring connection, wiring connection with one building in between, and wiring connection with two to three buildings in between.

[0088] c) Model application.

[0089] For the building to be audited that has appeared problem data and has issued an abnormal alarm, other buildings having a wiring connection relationship with the building to be audited are retrieved to form a wiring relationship network of the building to be audited. The data sets of three features of abnormal quantity, time interval and wiring connection distance in the past half year are acquired in the wiring relationship network, input into the trained abnormal frequency category identification model, and the category of the district abnormal frequency category is identified.

[0090] In some embodiments of the present disclosure, in the case where the abnormal frequency category is medium abnormal frequency or high abnormal frequency, the abnormal degree state is determined to be serious abnormality. In the case where the abnormal frequency category is medium abnormal frequency or high abnormal frequency, it is determined that the abnormal occurrence frequency of the district where the resource to be audited is located reaches a preset degree, the resource data abnormal condition of the resource to be audited is relatively serious, and an artificial investigation work order is issued.

[0091] In some embodiments of the present disclosure, the number of manual audits of other buildings in a set time period is counted; and in the case that the number of manual audits is less than the number of audits required by the abnormal frequency category, the abnormality degree state is determined to be a serious abnormality. The number of manual audits of other buildings connected to the building to be audited in the past six months is retrieved, and it is determined whether the output category of the model matches the actual number of manual audits. If the actual number of manual audits is less than the number of manual audits required by the output category of the model, the resource data abnormality of the resource to be audited is relatively serious, and the manual survey work order is issued.

[0092] For the above two abnormality degree state determination methods, one is selected according to the actual resource data of the area where the resource to be audited is located, or the manual survey work order is issued only when both methods determine that the resource data abnormality of the resource to be audited is relatively serious.

[0093] Figure 3 A resource auditing process diagram is provided for the exemplary embodiments of the present disclosure. As shown in Figure 3 , the present disclosure performs online auditing on abnormal resources first, but for abnormal resources with relatively serious abnormality, the auditing background directly issues a manual survey work order. The hardware structure of the manual survey work order is shown in Figure 3 , and the auditing system has three levels of field device, interface service and auditing background. The interface service supports the management system, thereby providing resource data for the auditing background.

[0094] In the above method embodiments of the present disclosure, the preset network management monitoring data and the resource to be audited are obtained; the preset network management monitoring data and the resource to be audited are automatically compared for consistency to obtain a data comparison result, improve the data processing efficiency, and improve the accuracy of data processing; in the case that the data comparison result is inconsistent data, the abnormality degree state of the abnormal data in the resource to be audited is determined; and in the case that the abnormality degree state of the abnormal data is a serious abnormality, a manual survey work order is automatically issued to perform manual survey processing on the abnormal data, thereby improving the data processing efficiency and reducing the labor cost.

[0095] Figure 4 A resource auditing process diagram is provided for the exemplary embodiments of the present disclosure. The execution process of the manual survey work order is as follows: the auditing personnel carries the field device with the auditing system or the mobile terminal with the installed auditing APP / miniprogram to the site for manual survey. The present disclosure introduces visual building information data based on the existing map location function in the system to establish a GIS-based location mode. As shown in Figure 4 , the resource auditing process includes:

[0096] S401: For abnormal resources, a manual survey work order is issued;

[0097] S402: After the auditor arrives at the site, start the auditing system, auditing APP or small program, etc. Auditing application;

[0098] S403: The auditor inputs the building name to be audited in the auditing application interface, or selects the building to be audited in the map tool provided by the auditing application;

[0099] S404: After receiving the building information, the auditing application starts address positioning, and compares the location of the auditing site recorded by the system and the positioning address of the building when the building is actually surveyed.

[0100] S405: The positioning address is within the set distance range of the target building, and the auditing is allowed to be initiated.

[0101] In this embodiment, the problem that the auditor fills in the information without going to the survey site is solved. It should be noted that the set distance range is not limited in the present disclosure, and can be adjusted according to the actual situation. The set distance range is, for example, 1 km, 500 m, etc. The present disclosure performs position verification on the auditing site and the actual survey building, and allows the auditing to be initiated within the set distance range of the target building, thereby ensuring the authenticity of the auditing result. The backfilled information after manual auditing is automatically compared and audited again, so as to realize the monitoring and closed-loop feedback of the resource auditing work.

[0102] Figure 5 A schematic diagram of a network data processing device 50 provided by an exemplary embodiment of the present disclosure is shown. As shown in the figure, the network data processing device 50 includes an acquisition module 51, a comparison module 52, a determination module 53 and a delivery module 54. Figure 5

[0103] The acquisition module 51 is configured to acquire preset network management monitoring data and a resource to be audited.

[0104] The comparison module 52 is configured to perform consistency comparison on the preset network management monitoring data and the resource to be audited, to obtain a data comparison result.

[0105] The determination module 53 is configured to determine an abnormality degree state of abnormal data in the resource to be audited in the case of inconsistent data.

[0106] The delivery module 54 is configured to deliver a manual survey work order to perform manual survey processing on the abnormal data in the case of serious abnormality of the abnormality degree state of the abnormal data.

[0107] Optionally, when determining the abnormality degree state of the abnormal data in the resource to be audited, the determination module 53 is configured to:

[0108] count the number of times of abnormality of the resource to be audited in a preset historical period; and ​

[0109] determine whether the to-be-audited resource has an abnormality in a set future period according to attribute information of the to-be-audited resource;

[0110] In a case where the number of abnormalities is greater than or equal to the set number of times and the to-be-audited resource has an abnormality in the set future period, the abnormality degree state of the abnormal data is determined to be a serious abnormality.

[0111] Optionally, when determining whether the to-be-audited resource has an abnormality in a set future period according to attribute information of the to-be-audited resource, the determining module 53 is configured to:

[0112] input the attribute information of the to-be-audited resource into an already trained abnormality recognition model to obtain a recognition result of whether the to-be-audited resource has an abnormality in the set future period.

[0113] Optionally, before using the abnormality recognition model, the determining module 53 is further configured to:

[0114] obtain a sample to-be-audited resource;

[0115] screen sample abnormality alarm historical data and a result of whether each piece of sample abnormality alarm historical data has an abnormality in a set future period from the sample to-be-audited resource;

[0116] train an initial random forest model according to the sample abnormality alarm historical data and the result of whether each piece of sample abnormality alarm historical data has an abnormality in the set future period to obtain the abnormality recognition model.

[0117] Optionally, when determining an abnormality degree state of abnormal data in the to-be-audited resource, the determining module 53 is configured to:

[0118] determine a to-be-audited building corresponding to the abnormal data;

[0119] obtain an abnormality association attribute of another building having a wiring connection relationship with the to-be-audited building;

[0120] input the abnormality association attribute into an abnormality frequency category recognition model to obtain an abnormality frequency category of a region where the to-be-audited resource is located;

[0121] determine the abnormality degree state according to the abnormality frequency category.

[0122] Optionally, when determining the abnormality degree state according to the abnormality frequency category, the determining module 53 is configured to:

[0123] in a case where the abnormality frequency category is a medium abnormality frequency or a high abnormality frequency, determine that the abnormality degree state is a serious abnormality; or

[0124] The number of manual audits of other buildings in a set time period is counted; in the case where the number of manual audits is less than the number of audits required by the abnormal frequency category, the abnormality degree state is determined to be a serious abnormality.

[0125] Optionally, the determining module 53 can also be used to:

[0126] Obtain sample abnormal association attributes of other sample buildings having a wiring connection relationship with the sample building;

[0127] According to the number of manual audits of each other sample building, determine the sample abnormal frequency category of each other sample building;

[0128] According to the sample abnormal association attributes and the sample abnormal frequency category of each other sample building, train the initial logistic regression model to obtain the abnormal frequency category identification model.

[0129] As to the apparatus in the above-described embodiments, the specific manners in which various modules perform operations have been described in detail in the embodiments related to the method, and thus will not be described in detail here.

[0130] Figure 6 A structural schematic diagram of an electronic device is provided for the exemplary embodiments of the present disclosure. As shown in Figure 6 The electronic device includes a memory 61 and a processor 62. In addition, the electronic device also includes a power supply component 63 and a communication component 64.

[0131] The memory 61 is used to store computer programs and can be configured to store other various data to support operations on the electronic device. Examples of these data include instructions for any application program or method operating on the electronic device.

[0132] The memory 61 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0133] The communication component 64 is used for data transmission with other devices.

[0134] The processor 62 can execute computer instructions stored in the executable memory 61 to: acquire preset network management monitoring data and a resource to be audited; perform consistency comparison on the preset network management monitoring data and the resource to be audited to obtain a data comparison result; in a case where the data comparison result is inconsistent data, determine an abnormality degree state of abnormal data in the resource to be audited; and in a case where the abnormality degree state of the abnormal data is serious abnormality, issue a manual survey work order for manual survey processing of the abnormal data.

[0135] Optionally, the processor 62, when determining the abnormality degree state of the abnormal data in the resource to be audited, is configured to:

[0136] count an abnormality number of the resource to be audited in a preset historical period; and

[0137] determine, according to attribute information of the resource to be audited, whether the resource to be audited is abnormal in a set future period.

[0138] In a case where the abnormality number is greater than or equal to a set number and the resource to be audited is abnormal in the set future period, the abnormality degree state of the abnormal data is determined as serious abnormality.

[0139] Optionally, the processor 62, when determining, according to the attribute information of the resource to be audited, whether the resource to be audited is abnormal in the set future period, is configured to:

[0140] input the attribute information of the resource to be audited into an already trained abnormality recognition model to obtain a recognition result of whether the resource to be audited is abnormal in the set future period.

[0141] Optionally, before using the abnormality recognition model, the processor 62 can further be configured to:

[0142] acquire a sample resource to be audited;

[0143] select, from the sample resource to be audited, sample abnormality alarm historical data and a result of whether each piece of sample abnormality alarm historical data is abnormal in a set future period;

[0144] train an initial random forest model according to the sample abnormality alarm historical data and the result of whether each piece of sample abnormality alarm historical data is abnormal in the set future period to obtain the abnormality recognition model.

[0145] Optionally, the processor 62, when determining the abnormality degree state of the abnormal data in the resource to be audited, is configured to:

[0146] determine a building to be audited corresponding to the abnormal data;

[0147] acquire an abnormality association attribute of another building having a wiring connection relationship with the building to be audited;

[0148] inputting the abnormal association attribute into the abnormal frequency category identification model to obtain an abnormal frequency category of a region where the to-be-audited resource is located;

[0149] determine an abnormal degree state according to the abnormal frequency category.

[0150] Optionally, when determining the abnormal degree state according to the abnormal frequency category, the processor 62 is configured to:

[0151] determine that the abnormal degree state is a serious abnormality in a case where the abnormal frequency category is a medium abnormal frequency or a high abnormal frequency; or

[0152] count a number of manual audits of other buildings in a set time period; and determine that the abnormal degree state is a serious abnormality in a case where the number of manual audits is less than a number of audit requirements corresponding to the abnormal frequency category.

[0153] Optionally, before using the abnormal frequency category identification model, the processor 62 is further configured to:

[0154] obtain sample abnormal association attributes of other sample buildings having a wiring connection relationship with the sample building;

[0155] determine a sample abnormal frequency category of each of the other sample buildings according to a number of manual audits of each of the other sample buildings;

[0156] train the initial logistic regression model according to the sample abnormal association attributes and the sample abnormal frequency category of each of the other sample buildings to obtain the abnormal frequency category identification model.

[0157] Correspondingly, the embodiments of the present disclosure further provide a computer readable storage medium storing a computer program. When the computer readable storage medium stores the computer program and the computer program is executed by one or more processors, the one or more processors are caused to execute Figure 1 the steps in the method embodiments.

[0158] Correspondingly, the embodiments of the present disclosure further provide a computer program product, which includes a computer program / instruction. When the computer program / instruction is executed by a processor Figure 1 the steps in the method embodiments.

[0159] The above Figure 6The communication component is configured to facilitate wired or wireless communication between the device containing the communication component and other devices. The device containing the communication component can access wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G / LTE, 5G, or combinations thereof. In one exemplary embodiment, the communication component receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component also includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID), Infrared Data Association (IrDA) technology, Ultra-Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0160] The above Figure 6 The power supply component provides power to the various components of the device in which it resides. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device in which it resides.

[0161] The aforementioned electronic devices also include a display screen and audio components.

[0162] The display includes a screen, which may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touchscreen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation.

[0163] An audio component may be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC) configured to receive external audio signals when the device containing the audio component is in an operating mode, such as call mode, recording mode, or voice recognition mode. The received audio signals may be further stored in memory or transmitted via a communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals.

[0164] In the embodiments of the above-disclosed apparatus, equipment, storage medium, and program products, preset network management monitoring data and resources to be audited are acquired; the preset network management monitoring data and resources to be audited are automatically compared for consistency to obtain data comparison results, thereby improving data processing efficiency and accuracy; in the case where the data comparison results show inconsistent data, the degree of abnormality of the abnormal data in the resources to be audited is determined; in the case where the degree of abnormality of the abnormal data is severe, a manual inspection work order is automatically issued to manually inspect and process the abnormal data, thereby improving data processing efficiency and reducing labor costs.

[0165] Those skilled in the art will appreciate that embodiments of the disclosure can be supplied as a method, a system, or a computer program product. Accordingly, the disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the disclosure can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0166] The disclosure is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the disclosure. It should be understood that each flow and / or block in the flowchart and / or block diagrams, and a combination of flows and / or blocks in the flowchart and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowchart and / or block diagrams of the flowchart and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart and / or block diagram.

[0167] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufacture product including an instruction means, which implements the functions specified in the flowchart and / or block diagrams of the flowchart and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart and / or block diagram.

[0168] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowchart and / or block diagrams of the flowchart and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart and / or block diagram.

[0169] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0170] Memory can include non-persistent memory, such as volatile random access memory (RAM) and / or non-volatile memory, such as read only memory (ROM), electrically erasable read only memory (EEPROM), flash memory, or the like. Memory is an example of computer readable media.

[0171] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile discs (DVDs) or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0172] It should be noted that, in the present document, relational terms such as "first" and "second", and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus.

[0173] The above specification, examples and data provide a complete description of the manufacture and use of the composition and method. Since many embodiments of the disclosure can be made without departing from the spirit and scope of the disclosure, the disclosure is not to be limited to the embodiments disclosed.

Claims

1. A network data processing method, characterized by, The method comprises the following steps: obtaining preset network management monitoring data and a resource to be audited; performing consistency comparison on the preset network management monitoring data and the resource to be audited to obtain a data comparison result; in a case where the data comparison result is inconsistent data, determining an abnormality degree state of abnormal data in the resource to be audited; in a case where the abnormality degree state of the abnormal data is serious abnormality, issuing a manual survey work order to perform manual survey processing on the abnormal data; the determination of the abnormality degree state of the abnormal data in the resource to be audited comprises: counting the number of times of abnormality of the resource to be audited in a preset historical period; and determining whether the resource to be audited will be abnormal in a set future period according to attribute information of the resource to be audited, comprising: inputting the attribute information of the resource to be audited into an already trained abnormality recognition model to obtain a recognition result of whether the resource to be audited will be abnormal in the set future period; in a case where the number of times of abnormality is greater than or equal to a set number of times and the resource to be audited is abnormal in the set future period, it is determined that the abnormality degree state of the abnormal data is serious abnormality.

2. The method of claim 1, wherein, Before using the abnormality recognition model, the method further comprises: obtaining a sample resource to be audited; screening sample abnormal alarm historical data and a result of whether each piece of the sample abnormal alarm historical data will be abnormal in a set future period from the sample resource to be audited; training an initial random forest model according to the sample abnormal alarm historical data and the result of whether each piece of the sample abnormal alarm historical data will be abnormal in a set future period to obtain the abnormality recognition model.

3. The method of claim 1, wherein, The determination of the abnormality degree state of the abnormal data in the resource to be audited comprises: determining a building to be audited corresponding to the abnormal data; obtaining an abnormal association attribute of another building having a wiring connection relationship with the building to be audited; inputting the abnormal association attribute into an abnormal frequency category recognition model to obtain an abnormal frequency category of a region where the resource to be audited is located; determining the abnormality degree state according to the abnormal frequency category.

4. The method of claim 3, wherein, The determination of the abnormality degree state according to the abnormal frequency category comprises: in a case where the abnormal frequency category is medium abnormal frequency or high abnormal frequency, determining that the abnormality degree state is serious abnormality, wherein manual audit times of 2-3 times and more than 3 times are respectively marked as medium abnormal frequency and high abnormal frequency; or counting the number of manual audits of the other building in a set time period; in a case where the number of manual audits is less than a number of audit requirements corresponding to the abnormal frequency category, determining that the abnormality degree state is serious abnormality.

5. The method of claim 4, wherein, Before using the abnormal frequency category recognition model, the method further comprises: obtaining a sample abnormal association attribute of another sample building having a wiring connection relationship with the sample building; determining a sample abnormal frequency category of each of the other sample buildings according to the number of manual audits of each of the other sample buildings; According to the sample abnormal correlation attribute and the sample abnormal frequency category of each other sample building, an initial logistic regression model is trained to obtain the abnormal frequency category identification model.

6. A network data processing device, characterized by The method comprises the steps of: An acquisition module is configured to acquire preset network management monitoring data and a resource to be audited. A comparison module is configured to compare the preset network management monitoring data and the resource to be audited for consistency to obtain a data comparison result. A determination module is configured to determine an abnormality degree state of abnormal data in the resource to be audited when the data comparison result is inconsistent data. An issuing module is configured to issue an artificial survey work order to perform artificial survey processing on the abnormal data when the abnormality degree state of the abnormal data is serious abnormality. The determination module is configured to count the number of times of abnormality of the resource to be audited in a preset historical period. And According to the attribute information of the resource to be audited, it is determined whether the resource to be audited will be abnormal in a set future period, which comprises: inputting the attribute information of the resource to be audited into an abnormality identification model that has been trained to obtain an identification result of whether the resource to be audited will be abnormal in the set future period. When the number of times of abnormality is greater than or equal to a set number of times and the resource to be audited is abnormal in the set future period, it is determined that the abnormality degree state of the abnormal data is serious abnormality.

7. An electronic device, comprising: The method comprises the steps of: A processor; A memory for storing processor-executable instructions; The processor is configured to execute the instructions to implement each step in the method of any one of claims 1-5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement each step in the method of any one of claims 1-5.

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