Data processing method, device and electronic equipment for offline service

Abstract

This application provides a data processing method, apparatus, and electronic device for offline services, relating to the field of data processing technology, and alleviating the technical problem of low monitoring efficiency for offline services. The method includes: in response to a startup event for the offline service, collecting time data, location data, and video data of the offline service during its execution process via a data acquisition unit; recognizing audio and images in the video data to obtain speech-text data and motion data in the images; comparing specified service standard data with the time data, location data, motion data, and speech-text data to obtain a comparison result, and determining the execution monitoring result of the offline service based on the comparison result; wherein the specified service standard data is used to characterize the service standard corresponding to the offline service.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data processing method, apparatus, and electronic device for offline services. Background Technology

[0002] Currently, online services are often intertwined with offline services, and sometimes they need to be executed together. For example, when a consumer successfully books a car pick-up service online, an offline service representative needs to pick up the car at the customer's location at the scheduled time. To ensure the quality of service for customers, the system needs to monitor, analyze, and control the service process in real time to standardize offline services. However, the current monitoring efficiency for offline services is low, making it difficult to standardize them. Summary of the Invention

[0003] The purpose of this invention is to provide a data processing method, apparatus, and electronic device for offline services, so as to alleviate the current technical problem of low monitoring efficiency for offline services.

[0004] In a first aspect, embodiments of this application provide a data processing method for offline services, which provides a data collection unit for the offline services through a terminal device; the method includes:

[0005] In response to a startup event for the offline service, the data acquisition unit collects time data, location data, and video data of the offline service during its execution.

[0006] The audio and images in the video data are identified to obtain speech text data and motion data in the images;

[0007] The specified service standard data is compared with the time data, the location data, the action data, and the voice / text data to obtain a comparison result, and the execution monitoring result of the offline service is determined based on the comparison result; wherein, the specified service standard data is used to characterize the service standard corresponding to the offline service.

[0008] In one possible implementation, it also includes:

[0009] Obtain the current time and current location data of the offline service during its execution;

[0010] In response to the current time data not conforming to the time standard data in the specified service standard data, and / or the current location data not conforming to the location standard data in the specified service standard data, a service standard prompt message is issued through the terminal device.

[0011] In one possible implementation, the service standard corresponding to the offline service includes any one or more of the following:

[0012] Specify the time standard, location standard, voice-text similarity judgment standard, image action similarity judgment standard, timeliness standard, and business process standard in the SOP standard database.

[0013] In one possible implementation, the step of recognizing audio and images in the video data to obtain speech-text data and motion data in the images includes:

[0014] Based on the start time node in the specified business process, the audio and images in the video data are denoised using timestamps to obtain key segment audio and key frame images.

[0015] The key audio segments and key frame images are identified to obtain speech text data and motion data in the images.

[0016] In one possible implementation, the step of recognizing audio and images in the video data to obtain speech-text data and motion data in the images includes:

[0017] The images in the video data are identified, and the target time period in the video data containing the target object is determined based on the identification results.

[0018] The audio and images corresponding to the target time period in the video data are identified to obtain speech text data and motion data in the images.

[0019] In one possible implementation, the step of comparing the specified service standard data with the voice-text data to obtain a comparison result includes:

[0020] The voice text data is matched with specified keywords in the specified service standard data, and the number of times the specified keywords appear in the voice text data is determined based on the matching results;

[0021] The similarity data between the specified service standard data and the voice text data is obtained by weighted summation based on the multiple occurrence counts corresponding to multiple specified keywords and the preset weights corresponding to multiple specified keywords.

[0022] The similarity enhancement data between the specified service standard data and the voice text data is calculated using an unsupervised text similarity calculation method.

[0023] Based on the basic similarity data and the enhanced similarity data, the voice-text comparison results between the specified service standard data and the voice-text data are obtained.

[0024] In one possible implementation, the terminal device also provides a graphical user interface; the method further includes:

[0025] In response to the start operation of the service for the graphical user interface, determine the start event of the offline service;

[0026] In response to a service completion operation for the graphical user interface, an end event for the offline service is determined.

[0027] Secondly, a data processing device for offline services is provided, which provides a data acquisition unit for the offline services through a terminal device; the device includes:

[0028] The data acquisition module is used to respond to the start event of the offline service by acquiring time data, location data and video data of the offline service during its execution through the data acquisition unit.

[0029] The recognition module is used to recognize the audio and images in the video data to obtain voice text data and motion data in the images;

[0030] The comparison module is used to compare the specified service standard data with the time data, the location data, the action data, and the voice and text data to obtain a comparison result, and to determine the execution monitoring result of the offline service based on the comparison result; wherein, the specified service standard data is used to characterize the service standard corresponding to the offline service.

[0031] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described in the first aspect above.

[0032] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method described in the first aspect above.

[0033] The embodiments of this application bring the following beneficial effects:

[0034] This application provides a data processing method, apparatus, and electronic device for offline services. Responding to a service initiation event, the device collects time data, location data, and video data during the service's execution process via a data acquisition unit. It then identifies audio and images from the video data to obtain speech-text data and motion data from the images. The device compares specified service standard data with the time data, location data, motion data, and speech-text data to obtain a comparison result. Based on this comparison result, it determines the offline service's execution monitoring result. The specified service standard data characterizes the service standard corresponding to the offline service. This solution not only uses audio for quality inspection but also comprehensively utilizes video data. This solution aims to standardize offline services by collecting and comparing data on frequency and the time and location of personnel. By comparing this data with business data, it can also standardize operational timeliness, achieving the goal of offline service standardization. In the offline service process, by introducing an online service quality control system, offline service standards are mapped to computer space. Through the integration of business flow data, service standard data, time and location data, voice and text data comparison, and action data comparison, service quality is integrated, monitored, and analyzed to ensure offline service quality. This solution enables effective, real-time, and efficient monitoring of the service process, ensuring the quality of vehicle pickup and delivery services and alleviating the current technical problem of low monitoring efficiency for offline services.

[0035] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 A flowchart illustrating the data processing method for offline services provided in this application embodiment;

[0038] Figure 2 Another flowchart illustrating the data processing method for offline services provided in the embodiments of this application;

[0039] Figure 3 Another flowchart illustrating the data processing method for offline services provided in the embodiments of this application;

[0040] Figure 4Another flowchart illustrating the data processing method for offline services provided in the embodiments of this application;

[0041] Figure 5 This illustration shows a graphical user interface diagram of the analysis script standards and action standards provided in an embodiment of this application;

[0042] Figure 6 This illustration shows a graphical user interface diagram of an embodiment of the present application for analyzing text and action similarity;

[0043] Figure 7 A schematic diagram of the structure of a data processing device for offline services provided in an embodiment of this application;

[0044] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this application, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0047] Currently, in vehicle-related offline services, the most important value propositions for users are convenience and ease of use. For example, providing pick-up and delivery services during car repairs greatly enhances user experience. However, these services occur outdoors, making direct management difficult. Existing technologies allow for video surveillance and offline manual inspections by the business side, but this is not real-time and inefficient due to high labor costs. Another approach is to combine pre-service personnel training with post-service outcome control, but this lacks clarity regarding the process and hinders continuous service quality optimization. Furthermore, while quality inspection robots can be used, current robots only support voice detection, not video, and their accuracy for specific business scenarios falls short of requirements. Therefore, the current monitoring efficiency for offline services is low, making standardization difficult.

[0048] Based on this, embodiments of this application provide a data processing method, apparatus, and electronic device for offline services, which can alleviate the current technical problem of low monitoring efficiency for offline services.

[0049] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0050] Figure 1 This is a flowchart illustrating a data processing method for offline services provided in an embodiment of this application. The method can be applied to a terminal device, which provides a data collection unit for offline services. Figure 1 As shown, the method includes:

[0051] Step S110: In response to the start event for the offline service, the data acquisition unit collects time data, location data, and video data of the offline service during its execution.

[0052] In practical applications, for example, such as Figure 2 As shown, after a customer successfully books a car pick-up service online, staff can provide door-to-door pick-up service according to the customer's appointment time. To ensure the quality of customer service, such as... Figure 3 As shown in the embodiments of this application, the service process can be monitored, analyzed, and controlled in real time.

[0053] Step S120: Recognize the audio and images in the video data to obtain speech text data and motion data in the images.

[0054] In this step, audio from the video data can be identified using Natural Language Processing (NLP) to obtain speech-text data. The key to processing natural language is enabling computers to "understand" natural language; therefore, natural language processing is also called Natural Language Understanding (NLU) or Computational Linguistics.

[0055] Step S130: Compare the specified service standard data with time data, location data, action data, and voice / text data to obtain the comparison results, and determine the execution monitoring results of the offline service based on the comparison results.

[0056] Among them, the designated service standard data is used to characterize the service standards corresponding to offline services.

[0057] In this embodiment, not only audio is used for quality inspection, but video and the time and location data of operators are also comprehensively utilized to achieve the goal of offline service standardization. By collecting and comparing this data with business data, the timeliness of operations can also be standardized, thus achieving the goal of offline service standardization. Figure 2 As shown, in the offline service process, by introducing an online service quality control system, offline service standards are mapped to computer space. By integrating business flow data, service standard data, time and location data, voice and text data comparison, and action data comparison, service quality is monitored and analyzed to ensure offline service quality. The solution provided in this application embodiment enables real-time control of the service process, ensuring the quality of vehicle pick-up and delivery services.

[0058] The steps described above will be explained in detail below.

[0059] In some embodiments, the method may further include the following steps:

[0060] Step a) Obtain the current time and current location data of the offline service during its execution;

[0061] Step b), in response to the current time data not conforming to the time standard data in the specified service standard data, and / or the current location data not conforming to the location standard data in the specified service standard data, a service standard prompt message is issued through the terminal device.

[0062] For example, such as Figure 4 The "Timely Reminder Service" shown in section 2. Time / Location prompts can be used to check the difference between the scheduled location and the work location when the current time is close to the scheduled time, reminding the workers to arrive at the work location as soon as possible. It can also remind the workers to start the work by operating the APP when the current time is equal to the scheduled time if the work has not yet started.

[0063] In some embodiments, the service standards corresponding to offline services include any one or more of the following:

[0064] Specify the time standard, location standard, voice-text similarity judgment standard, image action similarity judgment standard, timeliness standard, and business process standard in the SOP standard database.

[0065] like Figure 3 and Figure 4 The “Process Step Standards” and “Work Procedure Data” shown include, for example: 12. Appointment time, etc. can include: appointment time, appointment location, current time, current location, work start / end time and work start / end location, etc.; 14. Process steps, etc. can include: process steps, script standards, action standards, importance, etc.

[0066] In this embodiment of the application, by performing structured modeling of the service SOP, the work procedures of offline service personnel can be standardized at the operational end. By integrating business flow data, service standard data, time and location data, similarity of dialogue, similarity of actions and various indicators of user evaluation, the service quality can be integrated and scored, thereby more effectively achieving the goal of ensuring the quality of offline services.

[0067] In some embodiments, step S120 above may include the following steps:

[0068] Step c) Based on the start time node in the specified business process, use the timestamp to perform noise reduction processing on the audio and image in the video data to obtain key segment audio and key frame images.

[0069] Step d) involves recognizing key audio segments and key frame images to obtain speech text data and motion data in the images.

[0070] like Figure 3 and Figure 4 The "timestamps and keyframe images" shown may include process steps, start times, and end times. Figure 4 The 1. Appointment time and appointment location can include: appointment time, appointment location, current time, current location, start / end time of the task, start / end location of the task, etc.

[0071] By introducing the temporal sequence and recording procedures of audio and video, audio and video can be effectively segmented. Noise reduction is achieved through image and audio cropping, specifically by using timestamps (between the start and end times of a specified business process) for cropping (targeting the specific business scenario by introducing the temporal sequence and recording procedures of audio and video). During comparison, text and action comparisons within the same business process can be performed, making the comparison more targeted and improving accuracy.

[0072] In some embodiments, step S120 above may include the following steps:

[0073] Step e) Identify the images in the video data and determine the target time period in the video data where the target object exists based on the identification results;

[0074] Step f) involves recognizing the audio and images corresponding to the target time period in the video data to obtain speech text data and motion data in the images.

[0075] For example, action recognition and speech recognition in scenarios containing objects such as vehicles (under limited scenarios). Limiting the scenario can improve the accuracy of speech and action similarity calculation.

[0076] In the embodiments of this application, such as Figure 3 and Figure 4 As shown, by introducing the temporal sequence of audio and video and the recording procedure, audio and video can be effectively separated. By integrating business flow data, service standard data, time and location data, script similarity, action similarity, and user evaluation indicators, service quality can be comprehensively scored, thereby more efficiently ensuring the quality of offline services.

[0077] In some embodiments, step S130 above may include the following steps:

[0078] Step g) Match the voice text data with the specified keywords in the specified service standard data, and determine the number of times the specified keywords appear in the voice text data based on the matching results;

[0079] Step h) is to perform a weighted sum based on the multiple occurrences of multiple specified keywords and the preset weights of multiple specified keywords to obtain the basic similarity data between the specified service standard data and the voice text data.

[0080] Step i) Calculate the similarity enhancement data between the specified service standard data and the speech text data using an unsupervised text similarity calculation method;

[0081] Step j) Based on the basic similarity data and the similarity enhancement data, obtain the voice-text comparison results between the specified service standard data and the voice-text data.

[0082] In practical applications, similarity can include keyword matching (from a keyword database) rather than comparing the similarity of the entire text. Different keywords can also have different weights. The similarity calculation algorithm can be implemented through the following process:

[0083] By obtaining the keyword database preset by the script template, matching the number of times each keyword appears in the text, and weighted summing, a basic similarity score is obtained;

[0084] This paper employs various unsupervised short text similarity calculation methods to calculate the similarity between the input text and the standard utterance, and obtains the similarity score by weighted summation. The steps are as follows: the input text is segmented based on a professional lexicon; features are constructed for the input text and the standard utterance using TF-IDF, word vectors, sentence vectors, and simhash respectively; and text similarity is calculated using Euclidean distance, cosine distance, Jacard similarity, and Hamming distance respectively.

[0085] Among them, Euclidean distance: Text similarity calculation based on Euclidean distance: cosine distance Text similarity based on cosine distance: similarity = 1 - cos; Jacard similarity: Haiming Distance Text similarity based on Hamming distance:

[0086] In the algorithm implementation provided in this application embodiment, for application scenarios of standardized speech, keyword matching can be selected as the basic scoring method, such as... Figure 5 As shown, by setting weights for basic scores (such as the importance of each step), the problem of strong control over core business requirements can be solved; for example... Figure 6 As shown, by calculating various text similarities, we can further evaluate the differences from the standard script, more accurately assess the standardization of offline service scripts, and thus obtain more accurate service quality monitoring results.

[0087] like Figure 6 As shown, by integrating business flow data, text similarity, action similarity, and scores of various indicators, service quality is monitored in a comprehensive manner to ensure offline service quality. By incorporating timeliness scores and user ratings from each process stage into the scoring model, the overall accuracy of service ratings is improved.

[0088] In some embodiments, a graphical user interface is also provided via a terminal device; the method may further include the following steps:

[0089] Step k), in response to the service start operation for the graphical user interface, determines the start event of the offline service;

[0090] Step 1) In response to a service completion operation for the graphical user interface, determine the end event of the offline service.

[0091] For example, such as Figure 2 As shown, clicking "Start Service" and "Complete Service" respectively initiates and terminates offline services. Interacting with the graphical user interface makes it easier for monitoring staff to determine the timeliness of services provided.

[0092] Figure 7 A schematic diagram of a data processing device for offline services is provided. This device can be applied to terminal devices, which provide data acquisition units for the offline services.

[0093] like Figure 7 As shown, the data processing device 700 for offline services includes:

[0094] The acquisition module 701 is used to collect time data, location data and video data of the offline service during its execution process through the data acquisition unit in response to the start event of the offline service.

[0095] The recognition module 702 is used to recognize the audio and images in the video data to obtain voice text data and motion data in the images;

[0096] The comparison module 703 is used to compare the specified service standard data with the time data, the location data, the action data, and the voice and text data to obtain a comparison result, and to determine the execution monitoring result of the offline service based on the comparison result; wherein, the specified service standard data is used to characterize the service standard corresponding to the offline service.

[0097] In some embodiments, the device further includes:

[0098] The acquisition module is used to acquire the current time data and current location data of the offline service during its execution.

[0099] The issuing module is used to issue a service standard prompt message through the terminal device in response to the current time data not conforming to the time standard data in the specified service standard data, and / or the current location data not conforming to the location standard data in the specified service standard data.

[0100] In some embodiments, the service standards corresponding to the offline services include any one or more of the following:

[0101] Specify the time standard, location standard, voice-text similarity judgment standard, image action similarity judgment standard, timeliness standard, and business process standard in the SOP standard database.

[0102] In some embodiments, the identification module is specifically used for:

[0103] Based on the start time node in the specified business process, the audio and images in the video data are denoised using timestamps to obtain key segment audio and key frame images.

[0104] The key audio segments and key frame images are identified to obtain speech text data and motion data in the images.

[0105] In some embodiments, the identification module is further configured to:

[0106] The images in the video data are identified, and the target time period in the video data containing the target object is determined based on the identification results.

[0107] The audio and images corresponding to the target time period in the video data are identified to obtain speech text data and motion data in the images.

[0108] In some embodiments, the comparison module is specifically used for:

[0109] The voice text data is matched with specified keywords in the specified service standard data, and the number of times the specified keywords appear in the voice text data is determined based on the matching results;

[0110] The similarity data between the specified service standard data and the voice text data is obtained by weighted summation based on the multiple occurrence counts corresponding to multiple specified keywords and the preset weights corresponding to multiple specified keywords.

[0111] The similarity enhancement data between the specified service standard data and the voice text data is calculated using an unsupervised text similarity calculation method.

[0112] Based on the basic similarity data and the enhanced similarity data, the voice-text comparison results between the specified service standard data and the voice-text data are obtained.

[0113] In some embodiments, the terminal device also provides a graphical user interface; the device further includes:

[0114] The first determining module is configured to determine the startup event of the offline service in response to a service start operation for the graphical user interface.

[0115] The second determining module is used to determine the end event of the offline service in response to a service completion operation for the graphical user interface.

[0116] The data processing device for offline services provided in this application embodiment has the same technical features as the data processing method for offline services provided in the above embodiments, so it can also solve the same technical problems and achieve the same technical effects.

[0117] An electronic device provided in this application embodiment, such as Figure 8 As shown, the electronic device 800 includes a processor 802 and a memory 801. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method provided in the above embodiments.

[0118] See Figure 8The electronic device also includes a bus 803 and a communication interface 804. The processor 802, the communication interface 804 and the memory 801 are connected through the bus 803. The processor 802 is used to execute executable modules, such as computer programs, stored in the memory 801.

[0119] The memory 801 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 804 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0120] Bus 803 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0121] The memory 801 is used to store programs. After receiving an execution instruction, the processor 802 executes the program. The method executed by the apparatus defined by the process disclosed in any of the preceding embodiments of this application can be applied to the processor 802 or implemented by the processor 802.

[0122] The processor 802 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 802 or by instructions in software form. The processor 802 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 801, and processor 802 reads the information from memory 801 and, in conjunction with its hardware, completes the steps of the above method.

[0123] Corresponding to the data processing method for the offline service described above, this application embodiment also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are invoked and executed by a processor, the computer-executable instructions cause the processor to perform the steps of the data processing method for the offline service described above.

[0124] The data processing device for offline services provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The device provided in this application embodiment has the same implementation principle and technical effects as the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0125] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0126] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0127] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0129] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the data processing method for offline services described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0130] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0131] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A data processing method for offline services, characterized in that, The method includes providing a data collection unit for the offline service via a terminal device; the method includes: In response to a startup event for the offline service, the data acquisition unit collects time data, location data, and video data of the offline service during its execution. The audio and images in the video data are identified to obtain speech text data and motion data in the images; The specified service standard data is compared with the time data, the location data, the action data, and the voice / text data to obtain a comparison result, and the execution monitoring result of the offline service is determined based on the comparison result; wherein, the specified service standard data is used to characterize the service standard corresponding to the offline service; The step of comparing the specified service standard data with the voice-text data to obtain the comparison result includes: The voice text data is matched with specified keywords in the specified service standard data, and the number of times the specified keywords appear in the voice text data is determined based on the matching results; The similarity data between the specified service standard data and the voice text data is obtained by weighted summation based on the multiple occurrence counts corresponding to multiple specified keywords and the preset weights corresponding to multiple specified keywords. The similarity enhancement data between the specified service standard data and the voice text data is calculated using an unsupervised text similarity calculation method. Based on the basic similarity data and the enhanced similarity data, the voice-text comparison results between the specified service standard data and the voice-text data are obtained.

2. The method according to claim 1, characterized in that, Also includes: Obtain the current time and current location data of the offline service during its execution; In response to the current time data not conforming to the time standard data in the specified service standard data, and / or the current location data not conforming to the location standard data in the specified service standard data, a service standard prompt message is issued through the terminal device.

3. The method according to claim 1, characterized in that, The service standards corresponding to the offline services include any one or more of the following: Specify the time standard, location standard, voice-text similarity judgment standard, image action similarity judgment standard, timeliness standard, and business process standard in the SOP standard database.

4. The method according to claim 1, characterized in that, The step of recognizing audio and images in the video data to obtain speech text data and motion data in the images includes: Based on the start time node in the specified business process, the audio and image in the video data are denoised using the timestamp to obtain key segment audio and key frame image. The key audio segments and key frame images are identified to obtain speech text data and motion data in the images.

5. The method according to claim 1, characterized in that, The step of recognizing audio and images in the video data to obtain speech text data and motion data in the images includes: The images in the video data are identified, and the target time period in the video data containing the target object is determined based on the identification results. The audio and images corresponding to the target time period in the video data are identified to obtain speech text data and motion data in the images.

6. The method according to claim 1, characterized in that, The terminal device also provides a graphical user interface; the method further includes: In response to the start operation of the service for the graphical user interface, determine the start event of the offline service; In response to a service completion operation for the graphical user interface, an end event for the offline service is determined.

7. A data processing device for offline services, characterized in that, The device includes a data collection unit for the offline service provided via a terminal device; the device includes: The data acquisition module is used to respond to the start event of the offline service by acquiring time data, location data and video data of the offline service during its execution through the data acquisition unit. The recognition module is used to recognize the audio and images in the video data to obtain voice text data and motion data in the images; The comparison module is used to compare the specified service standard data with the time data, the location data, the action data, and the voice and text data to obtain a comparison result, and to determine the execution monitoring result of the offline service based on the comparison result; wherein, the specified service standard data is used to characterize the service standard corresponding to the offline service; The comparison module is specifically used for: The voice text data is matched with specified keywords in the specified service standard data, and the number of times the specified keywords appear in the voice text data is determined based on the matching results; The similarity data between the specified service standard data and the voice text data is obtained by weighted summation based on the multiple occurrence counts corresponding to multiple specified keywords and the preset weights corresponding to multiple specified keywords. The similarity enhancement data between the specified service standard data and the voice text data is calculated using an unsupervised text similarity calculation method. Based on the basic similarity data and the enhanced similarity data, the voice-text comparison results between the specified service standard data and the voice-text data are obtained.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

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