Job data processing method and device, equipment, storage medium and program product

Abstract

The invention relates to a job data processing method and device, equipment, a storage medium and a program product, and relates to the technical field of computers. The method comprises the steps that the exhibition stand device obtains current homework data obtained by recognizing current homework of a student; the exhibition stand device performs font feature extraction on characters in the current job data to obtain target stroke features, target structure features and target geometric features of the current job data; the exhibition stand device determines a stroke similarity between the target stroke feature and a standard stroke feature of a preset standard font, determines a structural similarity between the target structural feature and a standard structural feature of the standard font, and determines a geometric similarity between the target geometric feature and a standard geometric feature of the standard font; and generating a current writing evaluation report of the student based on the stroke similarity, the structural similarity and the geometric similarity. By adopting the method, the utilization rate of computer hardware resources can be improved.

Description

Operational data processing methods, apparatus, equipment, storage media, and program products Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, device, storage medium, and program product for processing job data. Background Technology

[0002] Document camera equipment, also known as a document camera, connects to projectors and televisions to clearly display student assignments, materials, handouts, and physical objects, making it an indispensable piece of teaching equipment in multimedia classrooms. Traditionally, document camera equipment only supports the recognition and display of student assignments, lacking the capacity for further analysis and processing. This underutilization of the equipment leads to a waste of computer hardware resources intended for teaching. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, apparatus, device, storage medium, and program product for processing job data that can improve the utilization rate of computer hardware resources, in response to the above-mentioned technical problems.

[0004] In a first aspect, this application provides a method for processing job data, the method comprising:

[0005] Obtain the current assignment data obtained by identifying the student's current assignment;

[0006] Font features are extracted from the text in the current task data to obtain the target stroke features, target structural features, and target geometric features of the current task data;

[0007] The similarity between the target stroke feature and the standard stroke feature of a pre-set standard font is determined; the structural similarity between the target structural feature and the standard structural feature of the standard font is determined; and the geometric similarity between the target geometric feature and the standard geometric feature of the standard font is determined. Based on the stroke similarity, structural similarity, and geometric similarity, a current handwriting assessment report for the student is generated.

[0008] In one embodiment, the method further includes:

[0009] Obtain a historical handwriting assessment report generated based on the font features of the student's historical assignment data;

[0010] By comparing and analyzing the current writing assessment report and the historical writing assessment report, data on the student's recent learning status can be obtained.

[0011] In the above embodiments, by comparing and analyzing students' current and historical writing assessment reports, data on students' recent learning status can be obtained, thereby further improving the utilization rate of computer hardware resources. This also helps educators improve the efficiency of their teaching work.

[0012] In one embodiment, the current assignment data includes student identity information, subject information, and assignment content; obtaining the current assignment data obtained by identifying the student's current assignment includes:

[0013] Acquire the current task image obtained by image acquisition for the student's current task;

[0014] The current assignment image is sent to the server; the sent current assignment image is used to instruct the server to input the current assignment image into a convolutional neural network to extract image features from the current assignment image through the convolutional neural network to obtain the student identity information and the subject information, and to input the current assignment image into a long short-term memory network to extract text information from the current assignment image through the long short-term memory network to obtain the assignment content;

[0015] Receive the student identity information, subject information, and homework content sent by the server.

[0016] In the above embodiments, using a convolutional neural network to extract image features from the current assignment image to obtain student identity information and subject information can improve the accuracy of the obtained student identity information and subject information. Using a long short-term memory network to extract text information from the current assignment image to obtain the assignment content can improve the accuracy of the obtained assignment content.

[0017] In one embodiment, the method further includes:

[0018] The current assignment data is uploaded to the server; the uploaded current assignment data is used to instruct the server to obtain classroom teaching data from the interactive smart whiteboard device, and to generate a classroom summary report based on the current assignment data and the classroom teaching data; the interactive smart whiteboard device is set up in the classroom where the student is located.

[0019] In the above embodiments, by uploading the current assignment data to the server, the server can generate a classroom summary report based on the current assignment data and classroom teaching data obtained from the interactive smart tablet device, which can further improve the utilization rate of computer hardware resources. It can also further help educators improve the efficiency of their teaching work.

[0020] In one embodiment, the method further includes:

[0021] The current assignment data is uploaded to the server; the uploaded current assignment data is used to instruct the server to obtain the student's classroom performance data from at least one teaching aid, and input the current assignment data and the classroom performance data into a sentiment analysis network, so as to extract the assignment features of the current assignment data and the classroom performance features of the classroom performance data through the sentiment analysis network, fuse the assignment features and the classroom performance features to obtain fused features, and predict the student's personality type based on the fused features; the teaching aid is set in the classroom where the student is located.

[0022] In the above embodiments, by uploading the current assignment data to the server, the server inputs the current assignment data and classroom performance data obtained from at least one teaching aid into the sentiment analysis network to predict the student's personality type, which can further improve the utilization rate of computer hardware resources. It can also further help educators improve the efficiency of their teaching work.

[0023] In one embodiment, the teaching aid includes at least one of a microphone device and a camera device.

[0024] In the above embodiments, by limiting the teaching aids to include at least one of a microphone device and a camera device, richer classroom performance data can be obtained, thereby improving the accuracy of predicting students' personality types.

[0025] Secondly, this application provides a work data processing device for use in exhibition booth equipment, the device comprising:

[0026] The acquisition module is used to acquire the current assignment data obtained by identifying the student's current assignment.

[0027] The extraction module is used to extract font features from the text in the current task data to obtain the target stroke features, target structural features and target geometric features of the current task data;

[0028] The generation module is used to determine the stroke similarity between the target stroke feature and the standard stroke feature of a pre-set standard font, determine the structural similarity between the target structural feature and the standard structural feature of the standard font, and determine the geometric similarity between the target geometric feature and the standard geometric feature of the standard font; based on the stroke similarity, the structural similarity, and the geometric similarity, it generates the student's current handwriting assessment report.

[0029] Thirdly, this application provides a booth device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the various method embodiments of this application.

[0030] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the various method embodiments of this application.

[0031] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the various method embodiments of this application.

[0032] The aforementioned homework data processing method, apparatus, equipment, storage medium, and program product acquire current homework data obtained by recognizing students' current homework; extract font features from the text in the current homework data to obtain target stroke features, target structural features, and target geometric features; determine the stroke similarity between the target stroke features and the standard stroke features of a pre-set standard font, determine the structural similarity between the target structural features and the standard structural features of the standard font, and determine the geometric similarity between the target geometric features and the standard geometric features of the standard font; and generate a student's current handwriting evaluation report based on the stroke similarity, structural similarity, and geometric similarity. Compared to traditional homework data processing methods, this application, while supporting the recognition and display of students' homework, extracts features from the text in the recognized current homework data to obtain font features of the current homework data, and generates a student's current handwriting evaluation report based on these font features. This allows for a deeper analysis of students' handwriting, enabling more efficient use of the display equipment and improving the utilization rate of computer hardware resources, i.e., the hardware resources of the display equipment, thus avoiding waste of computer hardware resources used to support teaching. Attached Figure Description

[0033] Figure 1 is an application environment diagram of a job data processing method in one embodiment;

[0034] Figure 2 is a flowchart illustrating a job data processing method in one embodiment;

[0035] Figure 3 is a structural block diagram of a job data processing device in one embodiment;

[0036] Figure 4 is a structural block diagram of the job data processing device in another embodiment;

[0037] Figure 5 is an internal structural diagram of the booth equipment in one embodiment. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0039] In traditional technologies, display equipment can only identify and display student assignments, but cannot perform further analysis and processing. As a result, the display equipment cannot be fully utilized, leading to a waste of computer hardware resources used to support teaching.

[0040] However, this application obtains current assignment data by recognizing students' current assignments; extracts font features from the text in the current assignment data to obtain target stroke features, target structural features, and target geometric features; determines the stroke similarity between the target stroke features and the standard stroke features of a pre-set standard font, determines the structural similarity between the target structural features and the standard structural features of the standard font, and determines the geometric similarity between the target geometric features and the standard geometric features of the standard font; and generates a student's current handwriting evaluation report based on the stroke similarity, structural similarity, and geometric similarity. Compared to traditional assignment data processing methods, this application, while supporting the recognition and display of students' assignments, extracts features from the text in the recognized current assignment data to obtain font features, and generates a student's current handwriting evaluation report based on these font features. This allows for a deeper analysis of students' handwriting, enabling more efficient use of the display equipment and improving the utilization rate of computer hardware resources, i.e., the hardware resources of the display equipment, thus avoiding waste of computer hardware resources used to support teaching. It also helps educators improve the efficiency of their teaching work.

[0041] The data processing method provided in this application can be applied to the application environment shown in Figure 1. The booth device 102 communicates with the server 104 via a network. The data storage system can be set up separately and can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104 or placed on the cloud or other servers. The server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, cloud security, host security and other network security services, CDN, and big data and artificial intelligence platforms. The booth device 102 and the server 104 can be connected directly or indirectly via wired or wireless communication, which is not limited herein.

[0042] The booth device 102 can acquire current assignment data obtained by recognizing the student's current assignment, and extract features from the text in the current assignment data to obtain the font features of the current assignment data. The booth device 102 can generate a current handwriting assessment report for the student based on the font features of the current assignment data.

[0043] It is understood that the booth device 102 can store the generated current writing evaluation report uploaded to the server 104. This embodiment does not limit this. It is understood that the application scenario in Figure 1 is only for illustration and is not limited thereto.

[0044] In one embodiment, as shown in Figure 2, a job data processing method is provided, applied to a booth equipment, including the following steps:

[0045] Step 202: Obtain the current assignment data obtained by identifying the student's current assignment.

[0046] The current assignment refers to the assignment completed by the student at the current moment. The current assignment data is the assignment data obtained by identifying the current assignment.

[0047] In one embodiment, a display stand device equipped with a camera is deployed on a classroom display stand. The teacher can place a student's current assignment below the camera. The display stand device can then capture an image of the student's current assignment through the camera. The display stand device can then recognize the image of the assignment to obtain the student's current assignment data.

[0048] Step 204: Extract font features from the text in the current task data to obtain the target stroke features, target structural features, and target geometric features of the current task data.

[0049] Among them, stroke features are used to characterize the thickness of strokes; structural features are used to characterize the spacing between characters; and geometric features are used to characterize the aspect ratio and position of the font.

[0050] In one embodiment, the booth device can input the current work data into a convolutional neural network to extract text features from the text in the current work data and output the font features of the current work data, namely the target stroke features, target structural features, and target geometric features.

[0051] Step 206: Determine the stroke similarity between the target stroke features and the standard stroke features of the pre-set standard font, determine the structural similarity between the target structural features and the standard structural features of the standard font, and determine the geometric similarity between the target geometric features and the standard geometric features of the standard font; based on the stroke similarity, structural similarity, and geometric similarity, generate the student's current handwriting assessment report.

[0052] The current writing assessment report is a writing assessment report obtained by analyzing the font features of the current assignment data, namely the target stroke features, target structural features, and target geometric features.

[0053] In one embodiment, the display device can determine the stroke similarity between the target stroke features and the standard stroke features of a pre-set standard font, determine the structural similarity between the target structural features and the standard structural features of the standard font, and determine the geometric similarity between the target geometric features and the standard geometric features of the standard font; based on the stroke similarity, structural similarity, and geometric similarity, it obtains the student's current handwriting assessment report and stores it in a database. It is understood that the current handwriting assessment report can characterize the correctness and standardization of the student's handwriting in the current assignment. For example, the current handwriting assessment report can characterize whether the student's handwriting in the current assignment is sloppy and whether it conforms to preset handwriting standards, etc.

[0054] In one embodiment, the display device can acquire pre-set stroke weights for stroke similarity, pre-set structural weights for structural similarity, and pre-set geometric weights for geometric similarity. Based on stroke similarity and its stroke weights, structural similarity and its structural weights, and geometric similarity and its geometric weights, a current handwriting assessment report for the student is generated.

[0055] In the aforementioned homework data processing method, current homework data is obtained by identifying the student's current homework; font features are extracted from the text in the current homework data to obtain target stroke features, target structural features, and target geometric features; the stroke similarity between the target stroke features and the standard stroke features of a pre-set standard font is determined, as are the structural similarity between the target structural features and the standard structural features of the standard font, and the geometric similarity between the target geometric features and the standard geometric features of the standard font; based on the stroke similarity, structural similarity, and geometric similarity, a current handwriting evaluation report for the student is generated. Compared to traditional homework data processing methods, this application, while supporting the identification and display of student homework, extracts features from the text in the identified current homework data to obtain font features, and generates a current handwriting evaluation report based on these font features. This allows for a deeper analysis of the student's handwriting, enabling more efficient use of the display equipment and improving the utilization rate of computer hardware resources, i.e., the hardware resources of the display equipment, thus avoiding waste of computer hardware resources used to support teaching. It also helps educators improve the efficiency of their teaching work.

[0056] In one embodiment, the method further includes: obtaining a historical handwriting assessment report generated based on the font features of the student's historical homework data; and comparing and analyzing the current handwriting assessment report and the historical handwriting assessment report to obtain the student's recent learning status data.

[0057] Among them, historical assignments are assignments completed by students at various historical points in time. Historical assignment data is assignment data obtained by identifying students' past assignments. Historical handwriting assessment reports are handwriting assessment reports obtained by analyzing the font characteristics of historical assignment data. Recent learning status data is data representing students' recent learning status.

[0058] In one embodiment, the display device can acquire current assignment data obtained by recognizing the student's current assignment, extract features from the text in the current assignment data to obtain font features, and generate a current handwriting assessment report for the student based on the font features of the current assignment data. Furthermore, the display device can also acquire historical handwriting assessment reports generated based on the font features of the student's historical assignment data, and compare and analyze the current handwriting assessment report and the historical handwriting assessment report to obtain the student's recent learning status data. It can be understood that the student's recent learning status data can reflect the student's recent learning progress, for example, whether the student's handwriting in recent assignments has improved.

[0059] For example, if student A used to write along the lines of the manuscript, but later it was found that student A's writing was going over the lines or off the lines, then it can be concluded that student A's handwriting in recent assignments has not improved, but has actually regressed.

[0060] In the above embodiments, by comparing and analyzing students' current and historical writing assessment reports, data on students' recent learning status can be obtained, thereby further improving the utilization rate of computer hardware resources. This also helps educators improve the efficiency of their teaching work.

[0061] In one embodiment, the current assignment data includes student identity information, subject information, and assignment content; obtaining the current assignment data obtained by identifying the student's current assignment includes: obtaining the current assignment image obtained by image acquisition of the student's current assignment; sending the current assignment image to the server; the sent current assignment image is used to instruct the server to input the current assignment image into a convolutional neural network to extract image features from the current assignment image through the convolutional neural network to obtain student identity information and subject information, and inputting the current assignment image into a long short-term memory network to extract text information from the current assignment image through the long short-term memory network to obtain assignment content; and receiving the student identity information, subject information, and assignment content sent by the server.

[0062] In one embodiment, the display stand device can acquire an image of the student's current assignment. The display stand device can send this image to a server; the sent image instructs the server to input it into a convolutional neural network (CNN) to extract image features, output student identity information and subject information, and send these to the display stand device. The server can also input the image into a long short-term memory (LSTM) network to extract text information, output assignment content, and send it to the display stand device. The display stand device can receive the student identity information, subject information, and assignment content from the server. The device can extract features from the text in the assignment content, obtain font features, generate a writing assessment report based on these features, and bind and store the report with the student identified by the student identity information and the subject identified by the subject information.

[0063] In one embodiment, student identification information may include at least one of student ID or student name. Subject information may include at least one of subject code or subject name.

[0064] In the above embodiments, using a convolutional neural network to extract image features from the current assignment image to obtain student identity information and subject information can improve the accuracy of the obtained student identity information and subject information. Using a long short-term memory network to extract text information from the current assignment image to obtain the assignment content can improve the accuracy of the obtained assignment content.

[0065] In one embodiment, the method further includes: uploading current assignment data to a server; the uploaded current assignment data is used to instruct the server to obtain classroom teaching data from the interactive smart whiteboard device, and to generate a classroom summary report based on the current assignment data and the classroom teaching data; the interactive smart whiteboard device is set up in the classroom where the students are located.

[0066] In one embodiment, a display stand and an interactive whiteboard are deployed in the classroom. The display stand can upload students' current homework data to a server, while the server can retrieve classroom teaching data from the interactive whiteboard, such as teaching presentations displayed on the whiteboard. Furthermore, the server can generate a classroom summary report based on the current homework data and classroom teaching data, allowing teachers to conduct a more comprehensive summary and analysis of the lesson.

[0067] In the above embodiments, by uploading the current assignment data to the server, the server can generate a classroom summary report based on the current assignment data and classroom teaching data obtained from the interactive smart tablet device, which can further improve the utilization rate of computer hardware resources. It can also further help educators improve the efficiency of their teaching work.

[0068] In one embodiment, the method further includes: uploading current assignment data to a server; the uploaded current assignment data is used to instruct the server to obtain student classroom performance data from at least one teaching aid, and inputting the current assignment data and classroom performance data into a sentiment analysis network to extract assignment features of the current assignment data and classroom performance features of the classroom performance data through the sentiment analysis network, fusing the assignment features and classroom performance features to obtain fused features, and predicting the student's personality type based on the fused features; the teaching aid is set up in the classroom where the student is located.

[0069] Classroom performance data refers to data reflecting students' performance in the classroom.

[0070] In one embodiment, a display stand and at least one teaching aid are deployed in the classroom. The display stand can upload current homework data to a server. The server can obtain student classroom performance data from the at least one teaching aid and input the current homework data and classroom performance data into a sentiment analysis network. The sentiment analysis network extracts homework features from the current homework data and classroom performance features from the classroom performance data, fuses the homework features and classroom performance features to obtain a fused feature, and predicts and outputs the student's personality type based on the fused feature, so that teachers can better tailor their teaching to individual students. The personality type may include an outgoing type or an introverted type, etc.

[0071] In the above embodiments, by uploading the current assignment data to the server, the server inputs the current assignment data and classroom performance data obtained from at least one teaching aid into the sentiment analysis network to predict the student's personality type, which can further improve the utilization rate of computer hardware resources. It can also further help educators improve the efficiency of their teaching work.

[0072] In one embodiment, the teaching aid includes at least one of a microphone device and a camera device.

[0073] In one embodiment, a display stand, microphone, and camera are deployed in the classroom. Classroom performance data may include audio data of the student collected by the microphone and video data of the student collected by the camera. The display stand can upload current assignment data to a server, which can retrieve the audio data of the student collected by the microphone and the video data of the student collected by the camera. The server can input the current assignment data, audio data, and video data into a sentiment analysis network to predict and output the student's personality type based on the current assignment data, audio data, and video data.

[0074] In the above embodiments, by limiting the teaching aids to include at least one of a microphone device and a camera device, richer classroom performance data can be obtained, thereby improving the accuracy of predicting students' personality types.

[0075] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially, these steps are not necessarily executed in that order. Unless otherwise expressly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the above embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0076] In one embodiment, as shown in FIG3, a work data processing device 300 is provided, applied to a booth device, the device specifically including:

[0077] The acquisition module 302 is used to acquire the current assignment data obtained by identifying the student's current assignment.

[0078] Extraction module 304 is used to extract font features from the text in the current task data to obtain the target stroke features, target structure features and target geometric features of the current task data;

[0079] The generation module 306 is used to determine the stroke similarity between the target stroke features and the standard stroke features of the pre-set standard font, the structural similarity between the target structural features and the standard structural features of the standard font, and the geometric similarity between the target geometric features and the standard geometric features of the standard font; based on the stroke similarity, structural similarity and geometric similarity, it generates the student's current handwriting assessment report.

[0080] In one embodiment, the generation module 306 is further configured to obtain a historical writing assessment report generated based on the font features of the student's historical homework data; and to compare and analyze the current writing assessment report and the historical writing assessment report to obtain the student's recent learning status data.

[0081] In one embodiment, the current assignment data includes student identity information, subject information, and assignment content; the acquisition module 302 is further configured to acquire the current assignment image obtained by image acquisition of the student's current assignment; send the current assignment image to the server; the sent current assignment image is used to instruct the server to input the current assignment image into a convolutional neural network to extract image features from the current assignment image through the convolutional neural network to obtain student identity information and subject information, and to input the current assignment image into a long short-term memory network to extract text information from the current assignment image through the long short-term memory network to obtain assignment content; and receive the student identity information, subject information, and assignment content sent by the server.

[0082] In one embodiment, as shown in FIG4, the job data processing device 300 further includes:

[0083] Upload module 308 is used to upload the current assignment data to the server; the uploaded current assignment data is used to instruct the server to obtain classroom teaching data from the interactive smart whiteboard device, and generate a classroom summary report based on the current assignment data and classroom teaching data; the interactive smart whiteboard device is set up in the classroom where the students are located.

[0084] In one embodiment, as shown in FIG4, the job data processing device 300 further includes:

[0085] Upload module 308 is used to upload current assignment data to the server; the uploaded current assignment data is used to instruct the server to obtain student classroom performance data from at least one teaching aid, and input the current assignment data and classroom performance data into a sentiment analysis network, so as to extract assignment features of the current assignment data and classroom performance features of the classroom performance data through the sentiment analysis network, fuse the assignment features and the classroom performance features to obtain fused features, and predict the student's personality type based on the fused features; the teaching aid is set in the classroom where the student is located.

[0086] In one embodiment, the teaching aid includes at least one of a microphone device and a camera device.

[0087] The aforementioned homework data processing device acquires current homework data obtained by recognizing students' current homework; extracts font features from the text in the current homework data to obtain target stroke features, target structural features, and target geometric features; determines the stroke similarity between the target stroke features and the standard stroke features of a pre-set standard font, determines the structural similarity between the target structural features and the standard structural features of the standard font, and determines the geometric similarity between the target geometric features and the standard geometric features of the standard font; and generates a current handwriting evaluation report for the student based on the stroke similarity, structural similarity, and geometric similarity. Compared to traditional homework data processing methods, this application, while supporting the recognition and display of students' homework, extracts features from the text in the recognized current homework data to obtain font features, and generates a current handwriting evaluation report based on these font features. This allows for a deeper analysis of students' handwriting, enabling more efficient use of the display equipment and improving the utilization rate of computer hardware resources, i.e., the hardware resources of the display equipment, thus avoiding waste of computer hardware resources used to support teaching. It also helps educators improve the efficiency of their teaching work.

[0088] Each module in the aforementioned data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the booth equipment in hardware form or independent of it, or stored in the memory of the booth equipment in software form, so that the processor can call and execute the operations corresponding to each module.

[0089] In one embodiment, a display stand device is provided, the internal structure of which can be shown in Figure 5. The display stand device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor of the display stand device provides computing and control capabilities. The memory of the display stand device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the display stand device is used for exchanging information between the processor and external devices. The communication interface of the display stand device is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a job data processing method. The display unit of the booth equipment is used to form a visually visible image. It can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the booth equipment can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the shell of the booth equipment, or external keyboards, touchpads, or mice, etc.

[0090] Those skilled in the art will understand that the structure shown in Figure 5 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the booth equipment to which the present application is applied. Specific booth equipment may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0091] In one embodiment, a booth device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0092] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0093] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0094] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0095] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0096] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for processing job data, characterized in that, The method, applied to exhibition booth equipment, includes: acquiring current assignment data obtained by recognizing a student's current assignment; extracting font features from the text in the current assignment data to obtain target stroke features, target structural features, and target geometric features of the current assignment data; determining the stroke similarity between the target stroke features and the standard stroke features of a pre-set standard font, determining the structural similarity between the target structural features and the standard structural features of the standard font, and determining the geometric similarity between the target geometric features and the standard geometric features of the standard font; and generating a current handwriting assessment report for the student based on the stroke similarity, the structural similarity, and the geometric similarity.

2. The method according to claim 1, characterized in that, The method further includes: obtaining a historical handwriting assessment report generated based on the font features of the student's historical homework data; comparing and analyzing the current handwriting assessment report and the historical handwriting assessment report to obtain the student's recent learning status data.

3. The method according to claim 1, characterized in that, The current assignment data includes student identity information, subject information, and assignment content; The step of acquiring current assignment data obtained by identifying the student's current assignment includes: acquiring a current assignment image obtained by image acquisition of the student's current assignment; sending the current assignment image to a server; the sent current assignment image is used to instruct the server to input the current assignment image into a convolutional neural network to extract image features from the current assignment image through the convolutional neural network to obtain the student's identity information and the subject information, and inputting the current assignment image into a long short-term memory network to extract text information from the current assignment image through the long short-term memory network to obtain the assignment content; and receiving the student's identity information, the subject information, and the assignment content sent by the server.

4. The method according to claim 1, characterized in that, The method further includes: uploading the current assignment data to a server; the uploaded current assignment data is used to instruct the server to obtain classroom teaching data from the interactive smart whiteboard device, and to generate a classroom summary report based on the current assignment data and the classroom teaching data; the interactive smart whiteboard device is set up in the classroom where the student is located.

5. The method according to claim 1, characterized in that, The method further includes: uploading the current assignment data to a server; the uploaded current assignment data is used to instruct the server to obtain the student's classroom performance data from at least one teaching aid, and inputting the current assignment data and the classroom performance data into a sentiment analysis network to extract assignment features of the current assignment data and classroom performance features of the classroom performance data through the sentiment analysis network, fusing the assignment features and the classroom performance features to obtain fused features, and predicting the student's personality type based on the fused features; the teaching aid is set in the classroom where the student is located.

6. The method according to claim 5, characterized in that, The teaching aids include at least one of a microphone and a camera.

7. A job data processing device, characterized in that, An application to exhibition booth equipment includes: an acquisition module for acquiring current assignment data obtained by recognizing a student's current assignment; an extraction module for extracting font features from the text in the current assignment data to obtain target stroke features, target structural features, and target geometric features of the current assignment data; a generation module for determining the stroke similarity between the target stroke features and the standard stroke features of a pre-set standard font, determining the structural similarity between the target structural features and the standard structural features of the standard font, and determining the geometric similarity between the target geometric features and the standard geometric features of the standard font; and generating a current handwriting assessment report for the student based on the stroke similarity, the structural similarity, and the geometric similarity.

8. A booth device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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