Data monitoring method and device, electronic equipment and computer readable storage medium

Abstract

The application provides a data monitoring method and device, electronic equipment and a computer readable storage medium. The method determines each speech processing link according to a voice request after receiving the voice request, then obtains and integrates target business tables corresponding to each speech processing link to obtain a full-link wide table, and finally monitors the full-link wide table according to a preset data monitoring model and a preset monitoring period to obtain a monitoring result. The method integrates the target business tables of each speech processing link to form a full-link wide table, and then obtains a monitoring result by monitoring the full-link wide table, so that the staff can quickly locate the data abnormal place in the full-link wide table according to the monitoring result to feed back the problem, and the accuracy and timeliness of data monitoring are improved.

Description

Technical Field

[0001] This application relates to the field of big data processing technology, and in particular to a data monitoring method, apparatus, electronic device, and computer-readable storage medium. Background Technology

[0002] As terminal devices are endowed with more and more AI capabilities, monitoring the data generated during the processing of AI requests becomes particularly important.

[0003] From the moment a user issues a voice request to the final response, a series of processing steps are involved. This process generates a large amount of data from multiple sources, potentially involving different technical teams. However, current technology lacks end-to-end data monitoring for the entire request processing flow, making it impossible to quickly pinpoint data anomalies and resulting in low accuracy and timeliness of data monitoring. This hinders timely optimization and resolution of issues raised by staff.

[0004] Therefore, there is a need to provide a data monitoring method to monitor the entire request processing flow, so as to quickly locate the problems reported at the point of data anomaly, thereby improving the accuracy and timeliness of data monitoring. Summary of the Invention

[0005] This application provides a data monitoring method, apparatus, electronic device, and computer-readable storage medium for monitoring the entire data flow of a request processing process, so as to quickly locate the problems reported at the point of data anomaly, thereby improving the accuracy and timeliness of data monitoring.

[0006] To address the aforementioned technical problems, this application provides the following technical solution:

[0007] This application provides a data monitoring method, including:

[0008] Receive a voice request and determine each voice processing step based on the voice request;

[0009] Obtain the target business table corresponding to each voice processing stage;

[0010] By integrating the target business tables, a full-link wide table is obtained;

[0011] The full-link wide table is monitored according to the preset data monitoring model and preset monitoring period to obtain monitoring results.

[0012] Accordingly, this application also provides a data monitoring device, including:

[0013] The request receiving module is used to receive voice requests and determine each voice processing step based on the voice requests.

[0014] The business table acquisition module is used to acquire the target business table corresponding to each voice processing stage;

[0015] The business table integration module is used to integrate the target business tables to obtain a full-link wide table;

[0016] The monitoring module is used to monitor the full-link wide table according to a preset data monitoring model and a preset monitoring period, and obtain monitoring results.

[0017] Meanwhile, this application provides an electronic device including a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to run the computer program in the memory to perform the steps in the above-described data monitoring method.

[0018] In addition, this application also provides a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute the steps in the above-described data monitoring method.

[0019] Beneficial Effects: This application provides a data monitoring method, apparatus, electronic device, and computer-readable storage medium. Specifically, upon receiving a voice request, the method determines each voice processing stage based on the request, then obtains the target business table corresponding to each stage, integrates these tables to obtain a full-link wide table, and finally monitors the full-link wide table according to a preset data monitoring model and a preset monitoring period to obtain monitoring results. This method obtains a full-link wide table by integrating the target business tables of each voice processing stage and monitors it to obtain monitoring results. Based on the monitoring results, data problems in the full-link wide table can be located to data problems in the target business table of the voice processing stage, thereby achieving the goal of quickly locating abnormal processing stages, improving the accuracy and timeliness of data monitoring. Simultaneously, staff can optimize and process abnormal processing stages promptly and accurately based on the location results, improving the processing performance of voice requests. Attached Figure Description

[0020] The technical solution and other beneficial effects of this application will become apparent from the following detailed description of specific embodiments in conjunction with the accompanying drawings.

[0021] Figure 1 This is a system architecture diagram of the data monitoring system provided in the embodiments of this application.

[0022] Figure 2 This is a flowchart illustrating the data monitoring method provided in the embodiments of this application.

[0023] Figure 3 This is a schematic diagram of the entire voice processing flow provided in the embodiments of this application.

[0024] Figure 4 This is a schematic diagram of the data warehouse structure provided in an embodiment of this application.

[0025] Figure 5 This is a schematic diagram of the data monitoring device provided in the embodiments of this application.

[0026] Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0028] The terms "comprising" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusion; the division of modules appearing in this application is merely a logical division, and in actual application, there may be other division methods, such as multiple modules being combined into or integrated into another system, or some features being ignored or not executed.

[0029] In this application, a voice request refers to a request formed by a voice message sent by a person to a terminal device, such as "I want to watch XX movie".

[0030] In this application, the voice processing stage mainly includes the signal processing stage on the client side, the voice recognition processing stage on the voice recognition terminal, the voice control processing stage on the central control domain terminal, and the semantic search processing stage on the search server side.

[0031] In this application, the target business table refers to the business table corresponding to each voice processing stage. For example, the signal processing stage corresponds to the client table, the speech recognition stage corresponds to the ASR table, the voice central control processing stage corresponds to the central control table, and the semantic search stage corresponds to the search table, etc.

[0032] In this application, a full-link wide table refers to a database table that links together metrics, dimensions, and attributes related to a business theme; that is, a database table with many fields formed by combining all the aforementioned target business tables. It should be noted that, compared to scattered target business tables, a full-link wide table has the advantages of high query performance and ease of use.

[0033] In this application, the preset data monitoring model is mainly configured using the open-source big data component Griffin.

[0034] This application provides a data monitoring method, apparatus, electronic device, and computer-readable storage medium.

[0035] Please see Figure 1 , Figure 1 This is a schematic diagram of the system architecture of the data monitoring system provided in this application, such as... Figure 1 As shown, the data monitoring system includes at least a terminal device 101 and a monitoring server 102, wherein:

[0036] A communication link is established between the terminal device 101 and the monitoring server 102 to enable information exchange. The type of communication link may include wired, wireless communication links, or fiber optic cables, etc., and this application does not impose any restrictions.

[0037] Terminal device 101 can be a smart device with AI functions, such as an audio module and a communication module; for example, a smartphone, a smart tablet, a smart speaker, a smart refrigerator, and a smart TV.

[0038] The monitoring server 102 can be a standalone server, or a server network or server cluster composed of servers; for example, the server described in this application includes, but is not limited to, computers, network hosts, database servers, and application servers, or cloud servers composed of multiple servers, wherein the cloud server is composed of a large number of computers or network servers based on cloud computing.

[0039] This application proposes a data monitoring system, which includes a terminal device 101 and a monitoring server 102. Specifically, the terminal device 101 receives voice requests from users, determines the voice processing steps required to process the voice request, obtains the target business tables corresponding to these voice processing steps, integrates all target business tables to obtain a full-link wide table, and finally, the monitoring server 102 monitors the full-link wide table according to a preset data monitoring model and a preset monitoring period to obtain monitoring results.

[0040] During the aforementioned data monitoring process, big data technology is used to integrate various target business tables along the AI ​​voice processing chain to form a full-link wide table. This effectively integrates the data generated along a single link in processing AI voice requests. Combined with the configuration of relevant monitoring content using big data monitoring components, the full-link wide table is monitored to obtain monitoring results. Based on these results, data problems in the full-link wide table can be located to problems in the target business table data of the voice processing stage. This achieves the goal of quickly locating the anomaly handling stage, improving the accuracy and timeliness of data monitoring. Simultaneously, staff can promptly and accurately optimize and handle the anomaly handling stage based on the location results. They can also propose optimization or supplementary suggestions for technical issues involved in the voice processing stage, achieving the effect of data-driven AI business.

[0041] It should be noted that, Figure 1 The system architecture diagram shown is merely an example. The servers, terminals, devices, and scenarios described in this application are for the purpose of more clearly illustrating the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of systems and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems. Detailed descriptions are provided below. It should be noted that the order of description of the following embodiments is not intended to limit the preferred order of the embodiments.

[0042] Based on the system architecture of the data monitoring system described above, the data monitoring method in this application will be described in detail below. Please refer to [link / reference]. Figure 2 As shown, Figure 2 This is a flowchart illustrating the data monitoring method provided in this application. The data monitoring method of this application will be described in detail below, and the method includes at least the following steps:

[0043] S201: Receive a voice request and determine the various voice processing steps based on the voice request.

[0044] A user issues a voice request, such as "Watch XX movie." A smart terminal device with AI capabilities receives this voice request and then determines the various voice processing steps based on it. For example... Figure 3 As shown, Figure 3 This is a schematic diagram of the entire voice processing flow provided in the embodiments of this application. For a voice request to "watch XX movie", the process involves the voice processing stage on the client side, the voice recognition processing stage on the voice recognition server (ASR server), the voice control processing stage on the central control domain, and the semantic search service stage on the search server. Finally, the request result is returned through the client.

[0045] S202: Obtain the target business table corresponding to each voice processing stage.

[0046] In one embodiment, after determining each voice processing stage, it is necessary to obtain the target business table corresponding to each voice processing stage. The specific steps include: obtaining a preset data processing cycle and source log data for each voice processing stage; processing the source log data according to the preset data processing cycle to obtain the original business table corresponding to each voice processing stage; processing the original business table according to the preset data processing cycle to obtain the detailed business table corresponding to each voice processing stage; and processing the detailed business table according to the preset data processing cycle to obtain the target business table corresponding to each voice processing stage. The source log data includes client log data, ASR service log data, central control log data, and search log data; such as... Figure 4 As shown, Figure 4 This is a schematic diagram of the data warehouse structure provided in an embodiment of this application. The data warehouse mainly includes an Operation Data Store (OSD layer), a Data Warehouse Details (DWD layer), a Data Warehouse Service (DWS layer), and an Application Data Service (ADS layer). The ODS layer is used to store the original business tables, the DWD layer is used to store the detailed business tables, the DWS layer is used to store the target business tables, and the ADS layer is used to store the full-link wide tables. The acquisition of the business tables in each layer will be described in detail below.

[0047] In one embodiment, the specific steps for obtaining the original business tables include: obtaining a preset data format; storing the source log data in the corresponding table of the operation data layer of the data warehouse according to the preset data processing cycle and the preset data format, thereby obtaining the original business tables for each voice processing stage. The preset data format includes JSON format; the data warehouse can be a Hive data warehouse; the operation data layer (OSD layer), also known as the source layer, is the most original data directly collected from the business system, containing all business change processes, and has the finest data granularity.

[0048] The process of obtaining the raw business tables involves periodically processing data from multiple source sources and synchronizing it to the OSD layer of the Hive data warehouse. Specifically, using big data technologies Spark and Flink, source log data such as client logs, ASR service logs, central control logs, and search logs are processed according to a preset data processing cycle. The logs are stored in the corresponding tables of the data warehouse's operational data layer in complete JSON format, resulting in the raw business tables for each voice processing stage. This ensures compatibility with the addition or removal of fields from the data source. In addition, each table also retains a request ID (query-id) to identify the AI ​​voice request or processing, a version field to distinguish logs, and a date field to identify the data time concept data warehouse partition. The request ID (query-id) is unique; when a specific SQL statement is compiled, the request ID (query-id) is generated to identify that specific SQL statement.

[0049] It should be noted that while processing the source data to the Hive data warehouse, it is also necessary to use the big data technology Airflow to set up scheduled tasks for subsequent monitoring.

[0050] In one embodiment, the specific steps for obtaining the detailed business table include: parsing the data in the original business table to obtain key-value pairs; storing the key-value pairs in the table corresponding to the detailed data layer of the data warehouse; and performing data cleaning processing on each business table in the operational data layer to obtain the detailed business table data corresponding to each voice processing stage. Here, the key-value pairs refer to the key and value in JSON log format in the original business table; data cleaning processing refers to removing null values, removing dirty data, removing data exceeding the limit, deduplication, and IP resolution; the detailed data layer (Data Warehouse Details, DWD layer) is a real-time fact detail layer modeled on the ODS layer based on the business process. For data such as access logs, it will be fed back to the offline system for downstream use, maximizing the consistency between real-time and offline data in the ODS layer and the DWD layer.

[0051] The process of obtaining detailed business tables involves periodically processing the raw business tables in the Hive data warehouse ODS layer to the DWD layer. Specifically, Spark is used to parse the preset data format of each raw business table in the ODS layer to obtain the key and value values ​​of the log data in the preset data format. The full fields of the key values ​​are retained and sent to the DWD layer, and the value values ​​are imported. In addition, after data cleaning processing such as removing nulls, deduplication, and IP resolution is performed on each business table in the ODS layer, the detailed business tables in the Hive data warehouse DWD layer are formed.

[0052] In one embodiment, the specific steps for obtaining the target business table include: obtaining preset fields; extracting data from the detailed business tables according to the preset data processing cycle and preset fields to form the target business tables corresponding to each voice processing stage. The preset fields include the main fields of each detailed business table in the DWD layer.

[0053] The process of obtaining the target business tables involves periodically processing the detailed business tables of the Hive Data Warehouse (DWD) layer to the Data Warehouse Service (DWS) layer. Specifically, the main fields of each detailed business table in the Hive DWD layer are extracted to form the target business tables such as the client table, ASR table, central control table, and search table in the Hive DWS layer. After subscribing to data from the DWD layer, the DWS layer calculates summary metrics for each dimension in real-time computing tasks. If a dimension is common to various vertical business lines, it will be placed in the DWS layer as a general data model.

[0054] S203: Integrate the target business tables to obtain a full-link wide table.

[0055] After obtaining the various target business tables (including client tables, ASR tables, central control tables, and search tables) from the DWS layer, big data technology is needed to integrate these tables using Spark SQL based on the query ID of the voice request, forming a full-link wide table. It's important to note that this full-link wide table is formed within the Hive data warehouse's Application Data Service (ADS) layer. This layer primarily provides data for data products and data analysis, and is typically stored in systems like Elasticsearch and MySQL for use by online systems.

[0056] S204: Monitor the entire link wide table according to the preset data monitoring model and preset monitoring period, and obtain the monitoring results.

[0057] In one embodiment, after forming a full-link wide table corresponding to a certain voice request, it is necessary to monitor the full-link wide table. The specific steps include: obtaining preset monitoring content and scheduling the task completion time of the full-link wide table; configuring a preset data monitoring model according to the preset monitoring content; determining a preset monitoring period according to the task completion time; monitoring the preset monitoring content in the full-link wide table according to the preset monitoring period and the preset data monitoring model to obtain monitoring results. The preset monitoring content can be manually configured or set to system default. The monitoring content includes: the data volume of the full-link wide table, missing data issues in full-link wide table fields, duplicate value issues in full-link wide table fields, and a comparison of the data volume of each original business table in the source ODS layer with the full-link wide table in the data warehouse terminal ADS layer. The task completion time of scheduling the full-link wide table is calculated based on the daily scheduling time of the full-link wide table by Airflow. The data monitoring model uses the open-source big data component Griffin. The data monitoring model is configured according to the preset monitoring content, and a corresponding scheduled monitoring program is configured on Griffin according to the task completion time, thus enabling scheduled monitoring of the preset monitoring content of the full-link wide table.

[0058] Specifically, for the data volume of the full-link wide table, monitoring can be carried out by configuring the total daily partition data volume of the full-link wide table in the data monitoring model. The judgment basis can be as follows: First, if the partition data volume on the current day is 0, it is regarded as abnormal. Second, compare the total partition data volume on the current day with the historical partition data volumes of the past few days. If it exceeds the preset threshold, it is regarded as abnormal; for the problem of missing field data in the full-link wide table, monitor the important fields corresponding to different business tables in the full-link wide table by configuring in the data monitoring model. Especially for the dimensional fields related to the basic information of terminal devices, the judgment basis can be as follows: If the record of the selected monitoring field is empty, it is regarded as abnormal. If the number of empty records of the selected monitoring field exceeds a certain range, it is regarded as abnormal. If the proportion of empty selected monitoring fields in the total amount exceeds the preset threshold, it is regarded as abnormal; for the problem of duplicate values in the fields of the full-link wide table, configure in the data monitoring model to select the duplicate values of certain fields in the full-link wide table. The judgment basis can be as follows: If the proportion of duplicate data in the monitored field in the total amount exceeds the preset threshold, it is regarded as abnormal. If the number of duplicate data in the monitored field exceeds the preset threshold, it is regarded as abnormal; for the comparison of the data volume between the full-link wide table and each original business table in the OSD layer, configure in the data monitoring model to compare the data volumes of the client table, ASR table, central control table, search table in the OSD layer and the full-link wide table in the ADS layer. The judgment basis can be as follows: Subtract the data total amount of each original business table from the data total amount of the full-link wide table respectively to obtain the corresponding data differences of each original business table. If the difference exceeds the preset threshold, it is regarded as abnormal. By monitoring the full-link data, the change of business volume and the consistency problem of data volume from end to end can be observed, forming a monitoring closed loop of log data.

[0059] In one embodiment, after obtaining the monitoring result, an alarm can be issued for the abnormal part indicated by the monitoring result. The specific steps include: when the monitoring result indicates an abnormality, trigger a warning message; according to the warning message, send a warning email. Specifically, it can be configured on the open-source big data component Griffin for personnel to receive the monitoring result alarm email, so as to analyze the monitoring result in time, locate the abnormal processing link in the entire voice processing process, and solve the problem in time, ensuring the accuracy and reliability of the data warehouse data.

[0060] It should be noted that in addition to sending a warning email to notify the staff of the abnormal monitoring result, other methods such as voice can also be used for alarm. The present application does not limit the specific alarm method here.

[0061] Based on the content of the above embodiment, an embodiment of the present application provides a data monitoring device. This data monitoring device is used to execute the data monitoring method provided in the above method embodiment. Specifically, please refer to Figure 5 and this device includes:

[0062] The request receiving module 501 is used to receive voice requests and determine each voice processing step based on the voice requests.

[0063] The business table acquisition module 502 is used to acquire the target business table corresponding to each voice processing stage.

[0064] The business table integration module 503 is used to integrate the target business table to obtain a full-link wide table.

[0065] The monitoring module 504 is used to monitor the full-link wide table according to the preset data monitoring model and the preset monitoring period, and obtain the monitoring results.

[0066] In one embodiment, the business table retrieval module 502 includes:

[0067] The data acquisition module is used to acquire source log data for preset data processing cycles and various voice processing stages;

[0068] The original table acquisition module is used to process the source log data according to the preset data processing cycle to obtain the original business table corresponding to each voice processing stage.

[0069] The detail table acquisition module is used to process the original business table according to the preset data processing cycle to obtain the detail business table corresponding to each voice processing stage.

[0070] The target table acquisition module is used to process the detailed business table according to the preset data processing cycle to obtain the target business table corresponding to each voice processing stage.

[0071] In one embodiment, the original table retrieval module includes:

[0072] The format acquisition module is used to acquire preset data formats;

[0073] The original table generation module is used to save the source log data in the table corresponding to the operation data layer of the data warehouse according to the preset data processing cycle and the preset data format, so as to obtain the original business table of each voice processing link.

[0074] In one embodiment, the detail table retrieval module includes:

[0075] The data parsing module is used to parse the data in the original business table to obtain the key-value pairs of the data in the original business table;

[0076] The detail table generation module is used to store the key-value pairs into the tables corresponding to the detail data layer of the data warehouse, and to perform data cleaning processing on each business table in the operation data layer to obtain the detail business table data corresponding to each voice processing stage.

[0077] In one embodiment, the target table acquisition module includes:

[0078] The field retrieval module is used to retrieve preset fields;

[0079] The target table generation module is used to extract data from the detailed business table according to the preset data processing cycle and the preset fields, and form the target business table corresponding to each voice processing stage.

[0080] In one embodiment, the monitoring module includes:

[0081] The first acquisition module is used to acquire preset monitoring content and the task completion time of the full-link wide table.

[0082] The model configuration module is used to configure a preset data monitoring model based on the preset monitoring content.

[0083] The cycle determination module is used to determine a preset monitoring cycle based on the task completion time.

[0084] The monitoring submodule is used to monitor the preset monitoring content in the full-link wide table according to the preset monitoring period and the preset data monitoring model, and obtain the monitoring results.

[0085] In one embodiment, the data monitoring device further includes:

[0086] The early warning triggering module is used to trigger an early warning message when the monitoring results indicate an anomaly.

[0087] The email sending module is used to send warning emails based on the warning information.

[0088] The data monitoring device of this application embodiment can be used to execute the technical solution of the foregoing method embodiment. Its implementation principle and technical effect are similar, and will not be repeated here.

[0089] Unlike current technologies, the data monitoring device provided in this application includes a business table integration module and a monitoring module. The business table integration module clarifies the data throughout the entire voice request processing chain, thereby integrating multi-source data from different teams, which helps in data value discovery and business optimization. The monitoring module enables monitoring of the entire data chain, allowing observation of changes in business volume, data consistency issues from end to end, etc., forming a closed loop for log data monitoring. Based on the monitoring results, data problems in the entire chain can be quickly located to data problems in the target business table of the voice processing stage, achieving the goal of quickly locating the anomaly handling stage and improving the accuracy and timeliness of data monitoring.

[0090] Accordingly, embodiments of this application also provide an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 601 with one or more processing cores, a wireless (WiFi, Wireless Fidelity) module 602, a memory 603 with one or more computer-readable storage media, an audio circuit 604, a display unit 605, an input unit 606, a sensor 607, a power supply 608, and a radio frequency (RF) circuit 609, etc. Those skilled in the art will understand that... Figure 6 The structure of the electronic device shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0091] The processor 601 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 603, and by calling data stored in the memory 603, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. In one embodiment, the processor 601 may include one or more processing cores; preferably, the processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 601.

[0092] WiFi is a short-range wireless transmission technology. Electronic devices, through the wireless module 602, can help users send and receive emails, browse web pages, and access streaming media, providing users with wireless broadband internet access. Although Figure 6 The wireless module 602 is shown, but it is understood that it is not an essential component of the terminal and can be omitted as needed without changing the nature of the invention.

[0093] The memory 603 can be used to store software programs and modules. The processor 601 executes various functional applications and data processing by running the computer programs and modules stored in the memory 603. The memory 603 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the terminal (such as audio data, phone book, etc.). In addition, the memory 603 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 603 may also include a memory controller to provide access to the memory 603 for the processor 601 and the input unit 606.

[0094] Audio circuit 604 includes a speaker, which provides an audio interface between the user and the electronic device. Audio circuit 604 can convert received audio data into electrical signals and transmit them to the speaker, where the speaker converts them into sound signals for output. Conversely, the speaker can convert collected sound signals into electrical signals, which are then received by audio circuit 604, converted back into audio data, and then processed by processor 601 before being transmitted via radio frequency circuit 609 to, for example, another electronic device, or output to memory 603 for further processing. Audio circuit 604 may also include an earphone jack to facilitate communication between peripheral headphones and the electronic device.

[0095] Display unit 605 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the terminal. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Display unit 605 may include a display panel, which in one embodiment can be configured as a liquid crystal display (LCD), organic light-emitting diode (OLED), or similar form. Furthermore, a touch-sensitive surface can cover the display panel. When the touch-sensitive surface detects a touch operation on or near it, it transmits the information to processor 601 to determine the type of touch event. Subsequently, processor 601 provides corresponding visual output on the display panel according to the type of touch event. Although in Figure 6 In this context, the touch-sensitive surface and the display panel are two separate components for implementing input and output functions. However, in some embodiments, the touch-sensitive surface and the display panel can be integrated to achieve input and output functions.

[0096] The input unit 606 can be used to receive input numerical or character information, and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control. Specifically, in one embodiment, the input unit 606 may include a touch-sensitive surface and other input devices. A touch-sensitive surface, also known as a touch display or touchpad, can collect user touch operations on or near it (e.g., user operations using fingers, styluses, or any suitable object or accessory on or near the touch-sensitive surface) and drive corresponding connection devices according to a pre-set program. In one embodiment, the touch-sensitive surface may include a touch detection device and a touch controller. The touch detection device detects the user's touch orientation and the signal generated by the touch operation, transmitting the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, sends it to the processor 601, and can receive and execute commands from the processor 601. Furthermore, various types of touch-sensitive surfaces, such as resistive, capacitive, infrared, and surface acoustic wave, can be used. In addition to the touch-sensitive surface, the input unit 606 may also include other input devices. Specifically, other input devices may include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0097] The electronic device may also include at least one sensor 607, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel according to the ambient light level (the proximity sensor can turn off the display panel and / or backlight when the terminal is moved to the ear). As a type of motion sensor, a gravity acceleration sensor can detect the magnitude of acceleration in various directions (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used for applications that recognize the phone's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping, etc.); as for other sensors that may be configured in the electronic device, such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be described in detail here.

[0098] The electronic device also includes a power supply 608 (such as a battery) that supplies power to various components. Preferably, the power supply can be logically connected to the processor 601 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 608 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0099] The radio frequency (RF) circuit 609 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink information from the base station and hands it over to one or more processors 601 for processing; additionally, it transmits uplink data to the base station. Typically, the RF circuit 609 includes, but is not limited to, an antenna, at least one amplifier, a tuner, one or more oscillators, a Subscriber Identity Module (SIM) card, a transceiver, a coupler, a low-noise amplifier (LNA), a duplexer, etc. Furthermore, the RF circuit 609 can also communicate wirelessly with networks and other devices. Wireless communication can use any communication standard or protocol, including but not limited to GSM, GPRS, CDMA, WCDMA, LTE, email, and SMS.

[0100] Although not shown, the electronic device may also include a camera, Bluetooth module, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 601 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 603 according to the following instructions, and the processor 601 runs the applications stored in the memory 603 to achieve the following functions:

[0101] Receive a voice request and determine each voice processing step based on the voice request;

[0102] Obtain the target business table corresponding to each voice processing stage;

[0103] By integrating the target business tables, a full-link wide table is obtained;

[0104] The full-link wide table is monitored according to the preset data monitoring model and preset monitoring period to obtain monitoring results.

[0105] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0106] Therefore, embodiments of this application provide a computer-readable storage medium storing multiple instructions that can be loaded by a processor to implement the functions of the aforementioned data monitoring method.

[0107] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0108] The data monitoring method, apparatus, electronic device, and computer-readable storage medium provided in the embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A data monitoring method, characterized in that, include: Receive a voice request and determine each voice processing step based on the voice request; The voice processing stage includes at least one of the following: signal processing stage, voice recognition stage, voice central control processing stage, and semantic search stage; Obtain the target service table corresponding to each voice processing stage; the target service table includes at least one of the following: the client table corresponding to the signal processing stage, the ASR table corresponding to the voice recognition stage, the central control table corresponding to the voice central control processing stage, and the search table corresponding to the semantic search stage; Based on the request ID of the voice request, the target service table is integrated to obtain a full-link wide table; The full-link wide table is monitored according to the preset data monitoring model and preset monitoring period to obtain monitoring results.

2. The data monitoring method according to claim 1, characterized in that, The step of obtaining the target business table corresponding to each voice processing stage includes: Obtain source log data for the preset data processing cycle and each voice processing stage; The source log data is processed according to the preset data processing cycle to obtain the original business table corresponding to each voice processing stage; The original business table is processed according to the preset data processing cycle to obtain the detailed business table corresponding to each voice processing stage. The detailed business table is processed according to the preset data processing cycle to obtain the target business table corresponding to each voice processing stage.

3. The data monitoring method according to claim 2, characterized in that, The step of processing the source log data according to the preset data processing cycle to obtain the original business tables corresponding to each voice processing stage includes: Get the preset data format; According to the preset data processing cycle and the preset data format, the source log data is stored in the table corresponding to the operation data layer of the data warehouse to obtain the original business table of each voice processing link.

4. The data monitoring method according to claim 2, characterized in that, The step of processing the original business table according to the preset data processing cycle to obtain the detailed business table corresponding to each voice processing stage includes: Parse the data in the original business table to obtain the key-value pairs of the data in the original business table; The key-value pairs are stored in the tables corresponding to the detailed data layer of the data warehouse, and the data of each business table in the operation data layer is cleaned to obtain the detailed business table data corresponding to each voice processing stage.

5. The data monitoring method according to claim 2, characterized in that, The step of processing the detailed business table according to the preset data processing cycle to obtain the target business table corresponding to each voice processing stage includes: Retrieve preset fields; Data is extracted from the detailed business table according to the preset data processing cycle and the preset fields to form the target business table corresponding to each voice processing stage.

6. The data monitoring method according to claim 1, characterized in that, The step of monitoring the entire link wide table according to a preset data monitoring model and a preset monitoring period to obtain monitoring results includes: Obtain the preset monitoring content and schedule the task completion time of the full-link wide table; Configure a preset data monitoring model based on the preset monitoring content; Based on the task completion time, a preset monitoring period is determined; The preset monitoring content in the full-link wide table is monitored according to the preset monitoring period and the preset data monitoring model to obtain monitoring results.

7. The data monitoring method according to claim 1, characterized in that, After the step of monitoring the entire link wide table according to a preset data monitoring model and a preset monitoring period to obtain monitoring results, the method further includes: When the monitoring results indicate an anomaly, an early warning message is triggered; Based on the aforementioned warning information, a warning email will be sent.

8. A data monitoring device, characterized in that, include: The request receiving module is used to receive voice requests and determine each voice processing step based on the voice requests. The voice processing stage includes at least one of the following: signal processing stage, voice recognition stage, voice central control processing stage, and semantic search stage; The business table acquisition module is used to acquire the target business table corresponding to each voice processing stage; the target business table includes at least one of the following: the client table corresponding to the signal processing stage, the ASR table corresponding to the voice recognition stage, the central control table corresponding to the voice central control processing stage, and the search table corresponding to the semantic search stage. The business table integration module is used to integrate the target business table according to the request ID of the voice request to obtain a full-link wide table; The monitoring module is used to monitor the full-link wide table according to a preset data monitoring model and a preset monitoring period, and obtain monitoring results.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program, and the processor being used to run the computer program in the memory to perform the steps of the data monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted for loading by a processor to perform the steps of the data monitoring method according to any one of claims 1 to 7.

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