Data acquisition method and system

Abstract

The invention provides a data acquisition method and system. The data acquisition system comprises electronic equipment, a first server, a second server and a third server, and the method comprises the steps that the electronic equipment receives input update data, responds to update operation triggered by a user and sends the update data to the first server; the first server updates target data of the preset information system according to the received update data to obtain updated target data; the electronic equipment monitors a preset tool and sends a storage path to a second server in response to the storage path of the updated target data monitored by the preset tool; and the second server analyzes the updated target data based on the storage path according to the received storage path, and stores the analyzed structured data to the third server. By using the method, the real-time performance and synchronism of data acquisition can be realized, so that the intelligent effect of artificial intelligence can be improved.

Description

Technical Field

[0001] This application relates to the field of computers, and in particular, to a data acquisition method and system. Background Art

[0002] With the rapid development of artificial intelligence, the demand for artificial intelligence is increasing day by day. In order to enable artificial intelligence to meet the intelligent needs of daily life, it is necessary to train artificial intelligence with data. Currently, the main data acquisition method is to grab data through scheduling or timing. However, this data acquisition method often has a lag in timeliness, cannot be synchronized with the user's operations in a timely manner, and is difficult to collect data in real time, resulting in an impact on the intelligent effect of artificial intelligence. Summary of the Invention

[0003] In view of the above, it is necessary to provide a data acquisition method and system that can solve the technical problem that the intelligent effect of artificial intelligence is affected due to the difficulty of collecting data in real time.

[0004] On the one hand, this application provides a data acquisition method, which is applied to a data acquisition system. The data acquisition system includes an electronic device and multiple servers. The multiple servers include a first server, a second server, and a third server. The data acquisition method includes: the electronic device obtains updated data, and in response to an update operation triggered by the user, sends the updated data to the first server. The first server receives the updated data sent from the electronic device and updates the target data of a preset information system in the first server according to the received updated data to obtain updated target data. The electronic device monitors a preset tool in the electronic device, and in response to the storage path of the updated target data monitored in the preset tool, sends the storage path to the second server. The second server parses the updated target data according to the storage path received from the electronic device to obtain structured data, and the second server sends the structured data to the third server for storage.

[0005] In some embodiments of this application, the first server includes an integrated artificial intelligence learner, and the target data is the data output by the artificial intelligence learner. The method further includes: the first server obtains the structured data from the third server and trains the artificial intelligence learner according to the obtained structured data to obtain an artificial intelligence model.

[0006] In some embodiments of the present application, the second server parses the updated target data based on the storage path to obtain the structured data, which includes: the second server determines a corresponding parsing algorithm according to the category of the updated target data, and parses the updated target data according to the parsing algorithm to obtain the structured data.

[0007] In some embodiments of the present application, the category of the updated target data includes one or more of text data, image data, video data, and audio data.

[0008] In some embodiments of the present application, if the target data is audio data, the second server parses the updated target data according to the parsing algorithm to obtain the structured data, which includes: the second server transcribes the audio data into text information, generates multiple fields based on the text information, the second server identifies the field type corresponding to each field, and determines each field and the corresponding field type as the structured data.

[0009] In some embodiments of the present application, the monitoring methods for the update operation and the preset tool include one or more of microservices, threads, and scheduling.

[0010] On the other hand, the present application provides a data acquisition method, which is applied to an electronic device. The electronic device is connected to multiple servers, and the multiple servers include a first server and a second server. The method includes: obtaining updated data, in response to an update operation triggered by a user, sending the updated data to the first server, causing the first server to update the target data in a preset information system in the first server according to the received updated data to obtain updated target data, monitoring a preset tool in the electronic device, and in response to the storage path of the updated target data monitored in the preset tool, sending the storage path to the second server, causing the second server to parse the updated target data based on the storage path to obtain structured data.

[0011] In some embodiments of the present application, the monitoring methods for the update operation and the preset tool include one or more of microservices, threads, and scheduling.

[0012] On the other hand, the present application provides an electronic device, which includes: a memory storing at least one instruction; and a processor executing at least one instruction to implement the data acquisition method described above.

[0013] On the other hand, the present application provides a computer-readable storage medium storing at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the data acquisition method.

[0014] Through the above embodiments, when receiving an update operation triggered by a user, the electronic device sends update data to the first server, enabling the first server to update the target data in a timely manner according to the update data, and synchronously storing the storage path of the updated target data in a preset tool. Since the electronic device can monitor the preset tool in real time, when it monitors the storage path of the updated target data in the preset tool, the electronic device can timely send the storage path to the second server, enabling the second server to parse the updated target data based on the received storage path and store the parsed structured data in the third server, thereby achieving the real-time and synchronous data acquisition in the third server. When training an artificial intelligence learner with structured data having real-time and synchronous properties, since the structured data has higher accuracy, comprehensibility, and processability compared to the target data, the intelligence level of the trained artificial intelligence model can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is an application scenario diagram of the data acquisition method provided by an embodiment of the present application.

[0016] Figure 2 is an interaction flowchart of the data acquisition method provided by an embodiment of the present application.

[0017] Figure 3 is a flowchart of the method for generating structured data provided by an embodiment of the present application.

[0018] Figure 4 is a flowchart of the data acquisition method provided by another embodiment of the present application.

[0019] Figure 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0020] Figure 6 is a schematic structural diagram of a server provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] It should be noted that in this application, "at least one" means one or more, and "a plurality" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims, and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0023] In the embodiments of this application, words such as "exemplary" or "for example" are used to give examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0024] With the rapid development of artificial intelligence, the demand for artificial intelligence is increasing day by day. In order to enable artificial intelligence to meet the intelligent needs of daily life, it is necessary to train artificial intelligence through data. Currently, the main data collection method is through scheduling or timed scraping. However, this data collection method often has a lag in timeliness, cannot be synchronized with the user's operations in a timely manner, and is difficult to collect data in real time, resulting in an impact on the intelligent effect of artificial intelligence.

[0025] To solve the above problems, the embodiments of this application provide a data collection method and system, which can achieve the real-time and synchronous data collection. The following is an introduction in combination with the application scenarios provided in the embodiments of this application.

[0026] As Figure 1As shown in the figure, it is an application scenario diagram of a data collection method provided by an embodiment of the present application. The data collection method is applied to a data collection system. The data collection system includes an electronic device 10 and a server 20. In this embodiment, the server 20 may be a server group, including a first server, a second server, and a third server. Among them, the electronic device 10 is communicatively connected to the first server and the second server, and the first server is communicatively connected to the second server and the third server. The ways in which the electronic device 10 is communicatively connected to the first server and the second server, and the ways in which the first server is communicatively connected to the second server and the third server include, but are not limited to: Bluetooth, hotspot, Wireless Fidelity (Wi-Fi). The electronic device 10 takes the data input by the user on a preset file as update data, and in response to an update operation triggered by the user on the preset file, sends the update data to the first server. The first server updates the target data according to the received update data to obtain the updated target data. The electronic device 10 sends the storage path of the updated target data to the second server. The second server parses the updated target data based on the received storage path to obtain structured data. The second server stores the parsed structured data in the third server, so that the first server can timely obtain the structured data from the third server to train the artificial intelligence learner in the first server, thereby improving the intelligence level of the trained artificial intelligence model.

[0027] In some embodiments of the present application, Figure 1 All the devices presented are only for illustrative purposes, and are not limited thereto in actual applications. For example, the first server, the second server, and the third server may be a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of hosts or network servers based on Cloud Computing. The electronic device 10 may be any electronic product that can perform human-computer interaction with the user, such as a mobile phone, a computer, a tablet computer, and other electronic devices.

[0028] The networks where the first server, the second server, the third server, and the electronic device 10 are located include, but are not limited to: the Internet, a wide area network, a metropolitan area network, a local area network, a Virtual Private Network (VPN), etc. The embodiments of the present application do not impose any restrictions on the specific type of the electronic device.

[0029] To more clearly introduce the data interaction between the electronic device, the first server, the second server, and the third server for implementing the data collection method, the following combines Figure 2 to elaborate on the interaction process in detail.

[0030] As Figure 2As shown in the figure, it is an interaction flowchart of a data acquisition method provided by an embodiment of the present application. According to different requirements, the order of each step in the flowchart can be adjusted according to actual detection requirements, and some steps can be omitted.

[0031] S11, the electronic device obtains updated data.

[0032] In some embodiments of the present application, the electronic device may use the data input by the user in a preset file or a dialog box of a preset application as the updated data. In other embodiments, the data in the clipboard may be used as the updated data. In practical applications, it is not limited to the above examples. Among them, the preset file may be a file under a preset storage path, and the preset storage path can be set by itself. The present application does not limit this.

[0033] The preset file includes, but is not limited to: table files and document files. The preset application in the electronic device can be used for data interaction with the first server, etc. The present application does not limit this. The electronic device may be Figure 1 the electronic device 10 in

[0034] S12, in response to the update operation triggered by the user, the electronic device sends the updated data to the first server.

[0035] In some embodiments of the present application, the update operation may be an edit operation, a save operation, a send operation, or a submit operation triggered by the user on a preset file or a preset application. The electronic device can monitor the update operation through one or more of microservices, threads, and scheduling.

[0036] In some embodiments of the present application, the electronic device monitors the update operation through microservices, including: if an update time request sent by a preset script for the update operation is received through the interface of the microservice, it is determined that the user has triggered an update operation on the preset file. Among them, the preset script is used to monitor whether the user triggers an update operation in the preset file, and when it monitors that the user triggers an update operation, it sends an update event request to the interface of the microservice. The preset script includes callable functions, and the functions can be set by themselves. The present application does not limit this. The update event request includes the user identity identification code (Identity Document, ID) corresponding to the update operation, the modification time, and the storage path of the preset file, etc.

[0037] In other embodiments of the present application, the electronic device may also monitor the update operation through a server configured with microservices, or the electronic device may monitor the update operation through other means. The present application does not limit this.

[0038] In this embodiment, the electronic device sends the updated data to the first server in response to the triggered update operation, which can provide a basis for the first server to update the target data in a timely manner.

[0039] S13. The first server updates the target data of the preset information system according to the updated data to obtain the updated target data.

[0040] The first server receives the updated data sent from the electronic device and updates the target data of a preset information system in the first server according to the received updated data to obtain the updated target data.

[0041] In some embodiments of the present application, the first server includes a preset information system and an artificial intelligence learner. The artificial intelligence learner is applied to the preset information system, and the specific application process will be described in detail below. Among them, the preset information system can be a Manufacturing Execution System (MES). The manufacturing execution system is a management system for workshop production. The manufacturing execution system collects and integrates production data, converts the production plan into operation instructions for production equipment, and interacts with production equipment, personnel, and materials to realize the monitoring, control, and coordination of the production process. The artificial intelligence learner includes, but is not limited to: Spark large model and Chat Generative Pre-trained Transformer (ChatGPT), etc.

[0042] In some embodiments of the present application, the target data includes the data output after the artificial intelligence learner analyzes the data of the preset information system. For example, the target data can be the production plan output by the artificial intelligence learner for data such as production resource data and production demand data input into the preset information system. The update of the target data by the first server includes, but is not limited to: replacement, deletion, addition, and modification, etc.

[0043] In this embodiment, the first server updates the target data to make the updated target data more accurate.

[0044] S14. When the preset tool monitors the storage path of the updated target data, the electronic device sends the storage path to the second server.

[0045] The electronic device monitors the preset tool in the electronic device and sends the storage path to the second server in response to the storage path of the updated target data monitored by the preset tool.

[0046] In some embodiments of the present application, since the electronic device is communicatively connected to the first server, after the first server updates the target data, the electronic device can obtain the storage path of the updated target data from the first server and send the storage path of the updated target data to a preset tool in the electronic device.

[0047] The preset tool may be a clipboard, or a preset directory or folder of a hard disk, a flash memory, or a storage medium in the electronic device. The preset directory and folder can be set by oneself, and the present application does not limit this. The storage path may be the storage path of the updated target data in the first server.

[0048] In some embodiments of the present application, the preset tool (such as a clipboard) can be monitored by means of microservices, threads, and scheduling. The process of monitoring the preset tool through microservices can refer to the process of monitoring the update operation through microservices in step S12.

[0049] In some embodiments of the present application, since after the target data is updated, the electronic device will send the storage path of the updated target data to a preset tool in the electronic device, the electronic device can send the storage path to a second server (such as Figure 1 the server 20 in).

[0050] In this embodiment, since the first server will not automatically send the updated target data to the second server, and the electronic device can know the storage path of the updated target data and send the storage path of the updated target data to a preset tool in the electronic device, the electronic device monitors the preset tool and can timely send the storage path of the updated target data to the second server.

[0051] S15. The second server generates a data acquisition request according to the storage path received from the electronic device.

[0052] In some embodiments of the present application, the data acquisition request is used to instruct the first server to send the updated target data to the second server. The data acquisition request may include the storage path of the updated target data and the sending method. The data acquisition request includes, but is not limited to: Http request, FTP request, POP request, etc.

[0053] S16. The second server sends the data acquisition request to the first server.

[0054] In some embodiments of the present application, the second server sends the data acquisition request to the first server to obtain the updated target data.

[0055] S17. In response to the data acquisition request, the first server sends the updated target data to the second server.

[0056] In some embodiments of the present application, since the data acquisition request instructs the first server to send the updated target data to the second server, when the first server receives the data acquisition request sent from the second server, the first server sends the updated target data to the second server by the sending method in the data acquisition request.

[0057] S18. The second server parses the updated target data to obtain structured data.

[0058] In some embodiments of the present application, the second server includes multiple parsing algorithms. Structured data refers to data with a standard format and clear definition. Each field in the structured data has a clear meaning and data type, such as integers, strings, dates, etc. Compared with unstructured data, structured data has higher comprehensibility and processability.

[0059] When receiving the storage path sent from the electronic device, the second server can determine the parsing algorithm corresponding to the category of the updated target data, and call the corresponding parsing algorithm to parse the updated target data to obtain structured data. Among them, the category of the updated target data includes one or more of text data, image data, video data, and audio data, and the present application does not limit this.

[0060] In this embodiment, parsing the updated target data into structured data with higher comprehensibility and processability can facilitate the recognition of the artificial intelligence learner, thereby improving the training effect on the artificial intelligence learner. Since the storage path of the updated target data is sent to the second server in a timely manner, the generation speed of the structured data can be improved.

[0061] S19. The second server sends the structured data to the third server for storage.

[0062] The second server sends the structured data to the third server for storage.

[0063] In some embodiments of the present application, storing the structured data in the third server can save the memory of the first server and the second server, thereby improving the operation fluency. In addition, the third server (such as Figure 1 the server 30) in is communicatively connected to the first server. Therefore, storing the structured data in the third server enables the first server to obtain the structured data in a timely manner to train the artificial intelligence learner, so the intelligence level of the trained artificial intelligence model can be improved, and thus an artificial intelligence model with a higher intelligence level can be better applied to the preset information system.

[0064] For example, the applications of the artificial intelligence model in the preset information system include: the artificial intelligence model can analyze the operating conditions and historical data of industrial equipment collected by the preset information system, predict the faults that occur in the industrial equipment to take control measures, so as to avoid greater losses. Or, the artificial intelligence model can analyze the production resource data and production demand data collected by the preset information system, and output a better energy usage strategy to reduce production costs. Or, the artificial intelligence model can analyze the production resource data, order demand data, and production workshop data collected by the preset information system, and output a better production plan to improve production efficiency. Among them, the above examples of the application of the artificial intelligence model in the preset information system are only examples, and the actual applications are not limited to this.

[0065] Through the above implementation manner, when receiving the update operation triggered by the user, the electronic device will send the update data to the first server, so that the first server can update the target data in a timely manner according to the update data, and synchronously store the storage path of the updated target data to the preset tool. Since the electronic device can monitor the preset tool in real time, when it monitors the storage path of the updated target data in the preset tool, the electronic device can timely send the storage path to the second server, so that the second server can parse the updated target data based on the received storage path, and store the parsed structured data to the third server, so as to achieve the real-time and synchronization of data collection in the third server. When training the artificial intelligence learner with the structured data with real-time and synchronization, since the structured data has higher accuracy, understandability, and processability than the target data, the intelligence level of the trained artificial intelligence model can be improved.

[0066] In some embodiments of the present application, if the target data is audio data, the second server parses the updated target data according to the parsing algorithm to obtain structured data. As Figure 3 shown, it is a flowchart of a method for generating structured data provided by an embodiment of the present application, including the following steps:

[0067] S181, the second server transcribes the audio data into text information.

[0068] In some embodiments of the present application, the second server may perform filtering processing on the audio data, and use a pre-trained speech recognition model to recognize the filtered audio data to obtain text information. Among them, the speech recognition algorithms include, but are not limited to: Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), etc.

[0069] S182. The second server generates multiple fields based on the text information.

[0070] In some embodiments of the present application, the second server performs cleaning and normalization processing on the text information, and performs word segmentation and part-of-speech tagging on the cleaned and normalized text information to obtain multiple fields. Among them, the multiple fields include words, phrases, etc.

[0071] In this embodiment, performing cleaning and normalization processing on the text information can remove incorrect punctuation marks in the text information and correct the semantics of the text information.

[0072] S183. The second server identifies the field type corresponding to each field, and determines each field and the corresponding field type as structured data.

[0073] In some embodiments of the present application, the second server can identify the field type of each field according to a regular expression or a pre-trained deep learning model. Among them, the deep learning model can be pre-trained through training data. The training data includes text information, context information of the text information, semantic structure, multiple fields in the text information, and the field type corresponding to each field.

[0074] In some embodiments of the present application, as Figure 4 shown, it is a flowchart of a data acquisition method provided by another embodiment of the present application. According to different requirements, the order of each step in the flowchart can be adjusted according to actual detection requirements, and some steps can be omitted.

[0075] S21. Obtain updated data.

[0076] In some embodiments of the present application, the method for obtaining updated data can refer to step S11, and the present application will not repeat the description.

[0077] S22. In response to an update operation triggered by a user, send the update data to the first server, and cause the first server to update the target data of the preset information system in the first server according to the received update data to obtain the updated target data.

[0078] In some embodiments of the present application, the method for the first server to update the target data may refer to the relevant content of step S12, and the present application will not repeat the description.

[0079] S23. Monitor the preset tools in the electronic device.

[0080] In some embodiments of the present application, the method for monitoring the preset tools may refer to step S14, and the present application will not repeat the description.

[0081] S24. In response to the storage path of the updated target data monitored in the preset tool, send the storage path to the second server, and cause the second server to parse the updated target data based on the storage path to obtain the structured data.

[0082] In some embodiments of the present application, the method for the second server to parse the updated target data may refer to the relevant content of step S18, and the present application will not repeat the description.

[0083] As Figure 5 shown, it is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 5 shown, the electronic device 10 may include a communication module 101, a memory 102, a processor 103, an input / output (I / O) interface 104, and a bus 105. The processor 103 is respectively coupled to the communication module 101, the memory 102, and the input / output interface 104 through the bus 105.

[0084] The communication module 101 may include a wired communication module and / or a wireless communication module. The wired communication module may provide one or more of the solutions for wired communication such as a universal serial bus (USB), a controller area network bus (CAN, Controller Area Network), etc. The wireless communication module may provide one or more of the solutions for wireless communication such as wireless fidelity (Wi-Fi), Bluetooth (BT), a mobile communication network, frequency modulation (FM), near field communication technology (NFC), infrared technology (IR), etc.

[0085] The memory 102 may include one or more random access memories (RAM) and one or more non-volatile memories (NVM). The random access memory can be directly read and written by the processor 103 and can be used to store the executable programs (such as machine instructions) of other running programs, and can also be used to store user and application data, etc. The random access memory can include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc.

[0086] The non-volatile memory can also store executable programs and store user and application data, etc., and can be pre-loaded into the random access memory for direct reading and writing by the processor 103. The non-volatile memory can include disk storage devices and flash memory.

[0087] The memory 102 is used to store one or more computer programs. The one or more computer programs are configured to be executed by the processor 103. The one or more computer programs include a plurality of instructions, and when the plurality of instructions are executed by the processor 103, a data acquisition method executable on the electronic device 10 can be realized.

[0088] In other embodiments, as Figure 5 shown, the electronic device 10 further includes an external memory interface for connecting to an external memory to implement the expansion of the storage capacity of the electronic device 10.

[0089] The processor 103 may include one or more processing units. For example, the processor 103 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0090] The processor 103 provides computing and control capabilities. For example, the processor 103 is used to execute the computer program stored in the memory 102 to implement the above data acquisition method.

[0091] The input / output interface 104 is used to provide channels for user input or output. For example, the input / output interface 104 can be used to connect various input and output devices, such as a mouse, a keyboard, a touch device, a display screen, etc., so that the user can input information or visualize the information.

[0092] The bus 105 is at least used to provide a communication channel for the communication module 101, the memory 102, the processor 103, and the input / output interface 104 in the electronic device 10 to communicate with each other.

[0093] As Figure 6 shown, it is a schematic structural diagram of a server provided by an embodiment of the present application. As Figure 6 shown, the server 20 may include a communication module 201, a storage device 202, a processing device 203, an input / output (I / O) interface 204, and a bus 205. The processing device 203 is respectively coupled to the communication module 201, the storage device 202, and the input / output interface 204 through the bus 205.

[0094] The communication module 201 may include a wired communication module and / or a wireless communication module. The wired communication module may provide one or more of the solutions for wired communication such as universal serial bus (USB), Controller Area Network (CAN), etc. The wireless communication module may provide one or more of the solutions for wireless communication such as wireless fidelity (Wi-Fi), Bluetooth (BT), mobile communication network, frequency modulation (FM), near field communication (NFC), infrared (IR), etc.

[0095] The storage device 202 may include one or more random access memory (RAM) devices and one or more non-volatile memory (NVM) devices. The random access memory device can be directly read and written by the processing device 203, can be used to store executable programs (such as machine instructions) of other running programs, and can also be used to store user and application data, etc. The random access memory device may include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc.

[0096] The non-volatile memory device can also store executable programs and store user and application data, etc., and can be pre-loaded into the random access memory device for direct reading and writing by the processing device 203. The non-volatile memory device may include a disk storage device, a flash memory device.

[0097] The storage device 202 is used to store one or more computer programs. The one or more computer programs are configured to be executed by the processing device 203. The one or more computer programs include a plurality of instructions, and when the plurality of instructions are executed by the processing device 203, a data acquisition method executable on the server 20 can be implemented.

[0098] In other embodiments, such as Figure 6 the server 20 shown also includes an external storage device interface for connecting an external storage device to expand the storage capacity of the server 20.

[0099] The processing device 203 may include one or more processing units. For example, the processing device 203 may include an application processor (AP), a modem processing device, a graphics processing unit (GPU), an image signal processor (ISP), a control device, a video codec device, a digital signal processor (DSP), and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processing devices.

[0100] The processing device 203 provides computing and control capabilities. For example, the processing device 203 is used to execute the computer program stored in the storage device 202 to implement the above data acquisition method.

[0101] The input / output interface 204 is used to provide channels for user input or output. For example, the input / output interface 204 can be used to connect various input / output devices, such as a mouse, a keyboard, a touch device, a display screen, etc., so that the user can input information or visualize information.

[0102] The bus 205 is at least used to provide a communication channel for mutual communication among the communication module 201, the storage device 202, the processing device 203, and the input / output interface 204 in the server 20.

[0103] It can be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the server 20. In other embodiments of the present application, the server 20 may include more or fewer components than shown, or combine certain components, or split certain components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0104] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions, and the method implemented when the program instructions are executed may refer to the methods in the above various embodiments of the present application.

[0105] Among them, the computer-readable storage medium may be the internal memory or storage device of the electronic device / server described in the foregoing embodiments, such as the hard disk or memory of the electronic device / server. The computer-readable storage medium may also be an external storage device of the electronic device / server, such as a plug-in hard disk equipped on the electronic device / server, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0106] In some embodiments, the computer-readable storage medium may include a storage program area and a storage data area. Among them, the storage program area may store an operating system, applications required for at least one function, etc.; the storage data area may store data created according to the use of the electronic device / server, etc.

[0107] In the foregoing embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0108] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0109] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, the functional modules in the various embodiments of the present application may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above-mentioned integrated unit may be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0111] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present application is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed in the present application. Any reference signs attached to the claims should not be regarded as limiting the claimed rights.

[0112] In addition, it is obvious that the term "including" does not exclude other units or steps, and the singular form does not exclude the plural form. The multiple units or devices described in this application can also be implemented by one unit or device through software or hardware. Terms such as first and second are used to denote names and do not denote any particular order.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A data acquisition method, applied to a data acquisition system, characterized in that, The data acquisition system includes an electronic device and multiple servers. The multiple servers include a first server, a second server, and a third server. The data acquisition method includes: The electronic device obtains updated data and, in response to an update operation triggered by a user, sends the updated data to the first server; The first server receives the updated data sent from the electronic device and updates the target data of a preset information system in the first server based on the received updated data to obtain updated target data; The electronic device monitors a preset tool in the electronic device and, in response to the storage path of the updated target data monitored in the preset tool, sends the storage path to the second server; The second server parses the updated target data according to the storage path received from the electronic device to obtain structured data; The second server sends the structured data to the third server for storage.

2. The data acquisition method according to claim 1, characterized in that The first server includes an integrated artificial intelligence learner, and the target data is the data output by the artificial intelligence learner. The method further includes: The first server obtains the structured data from the third server and trains the artificial intelligence learner based on the obtained structured data to obtain an artificial intelligence model.

3. The data acquisition method according to claim 1, wherein The second server parses the updated target data based on the storage path to obtain the structured data, including: The second server determines a corresponding parsing algorithm according to the category of the updated target data and parses the updated target data according to the parsing algorithm to obtain the structured data.

4. The data acquisition method according to claim 3, wherein, The category of the updated target data includes one or more of text data, image data, video data, and audio data.

5. The data acquisition method according to claim 4, wherein If the target data is audio data, the second server parses the updated target data according to the parsing algorithm to obtain the structured data, including: The second server transcribes the audio data into text information and generates multiple fields based on the text information; The second server identifies the field type corresponding to each field and determines each field and the corresponding field type as the structured data.

6. The data acquisition method according to claim 1, wherein The monitoring methods for the update operation and the preset tool include one or more of microservices, threads, and scheduling.

7. A data acquisition method, applied to an electronic device, characterized in that, The electronic device is connected to multiple servers. The multiple servers include a first server and a second server. The method includes: Obtain updated data; In response to an update operation triggered by a user, send the updated data to the first server, and cause the first server to update the target data of a preset information system in the first server based on the received updated data to obtain updated target data; Monitor a preset tool in the electronic device; In response to the storage path of the updated target data monitored by the preset tool, send the storage path to the second server, and instruct the second server to parse the updated target data based on the storage path to obtain structured data.

8. The data acquisition method according to claim 7, wherein The monitoring methods for the update operation and the preset tool include one or more of microservices, threads, and scheduling.

9. An electronic device, characterized in that, The electronic device includes: a memory storing at least one instruction; and a processor executing the at least one instruction to implement the data acquisition method according to any one of claims 7 to 8.

10. A computer-readable storage medium, characterized in that: At least one instruction is stored in the computer-readable storage medium, and when the at least one instruction is executed by a processor in an electronic device, the data acquisition method according to any one of claims 7 to 8 is implemented.

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