Content revision method, apparatus, and electronic device

Abstract

Embodiments of the present disclosure disclose content revision methods, devices and electronic devices. A specific implementation of the method includes: in response to receiving to-be-revised content, obtaining content features of the to-be-revised content and user features of a user from which the to-be-revised content is derived; obtaining revision user features of revision users in a candidate revision user set; selecting a target revision user from the candidate revision user set based on the content features, the user features and the revision user features; sending the to-be-revised content to a user terminal of the target revision user, receiving revised content returned by the user terminal, determining final revised content based on the revised content; and sending the final revised content to a user terminal of the user from which the to-be-revised content is derived. The implementation can improve the accuracy of content revision.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, specifically to methods, apparatus, and electronic devices for content modification. Background Technology

[0002] Currently, in some scenarios, such as content editing, users are typically required to edit the content. In existing content editing methods, each piece of content to be edited is usually assigned to only one user, and errors may occur during the editing process, which could mislead the user who originally provided the content. Summary of the Invention

[0003] This disclosure is provided to briefly introduce the concepts, which will be described in detail in the subsequent Detailed Description section. This disclosure is not intended to identify key or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0004] This disclosure provides a content editing method, apparatus, and electronic device that can improve the accuracy of content editing.

[0005] In a first aspect, embodiments of this disclosure provide a content modification method, comprising: in response to receiving content to be modified, obtaining content features of the content to be modified and user features of the user from which the content to be modified originates; obtaining modification user features of modification users in a candidate modification user set; selecting a target modification user from the candidate modification user set based on the content features, user features, and modification user features; sending the content to be modified to the user terminal of the target modification user; receiving modified content returned by the user terminal; determining the final modified content based on the modified content; and sending the final modified content to the user terminal from which the content to be modified originates.

[0006] Secondly, embodiments of this disclosure provide a content editing device, comprising: a first acquisition unit, configured to, in response to receiving content to be edited, acquire content features of the content to be edited and user features of the user from whom the content to be edited originates; a second acquisition unit, configured to acquire editing user features of editing users in a candidate editing user set; a selection unit, configured to, based on content features, user features, and editing user features, select a target editing user from the candidate editing user set, send the content to be edited to the target editing user's user terminal, receive edited content returned by the user terminal, and determine the final edited content based on the edited content; and a sending unit, configured to send the final edited content to the user terminal from which the content to be edited originates.

[0007] Thirdly, embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the content modification method as described in the first aspect.

[0008] Fourthly, embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon that, when executed by a processor, implements the steps of the content modification method as described in the first aspect.

[0009] The content editing method, apparatus, and electronic device provided in this disclosure, in response to receiving content to be edited, acquire the content characteristics of the content to be edited and the user characteristics of the user from whom the content to be edited originates; then, acquire the editing user characteristics of editing users in a candidate editing user set; then, based on the content characteristics, the user characteristics, and the editing user characteristics, select a target editing user from the candidate editing user set, send the content to be edited to the target editing user's user terminal, receive the edited content returned by the user terminal, determine the final edited content based on the edited content, and finally send the final edited content to the user terminal from which the content to be edited originates. In this way, the target editing user can be selected by utilizing the content characteristics of the content to be edited, the user characteristics of the user from whom the content to be edited originates, and the user characteristics of the editing user, and the content to be edited can be sent to the target editing user for editing, thereby improving the accuracy of content editing. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0011] Figure 1 These are exemplary system architecture diagrams to which the various embodiments of this disclosure can be applied;

[0012] Figure 2 This is a flowchart of an embodiment of the correction method based on the content of this disclosure;

[0013] Figure 3 This is a flowchart of yet another embodiment of the correction method based on the content of this disclosure;

[0014] Figure 4 This is a flowchart of an embodiment of the method for selecting a target user for modification based on the content of this disclosure;

[0015] Figure 5It is a bipartite graph representing the relationship between the user being modified and the content to be modified, according to the content modification method disclosed herein;

[0016] Figure 6 This is a schematic diagram of the structure of one embodiment of the correction device according to the present disclosure;

[0017] Figure 7 This is a schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present disclosure. Detailed Implementation

[0018] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0019] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0020] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0021] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0022] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0023] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0024] Figure 1An exemplary system architecture 100 is shown, to which embodiments of the content correction methods of this disclosure can be applied.

[0025] like Figure 1 As shown, system architecture 100 may include terminal devices 1011 and 1012, networks 1021 and 1022, server 103, and modification terminal devices 1041 and 1042. Network 1021 serves as the medium for providing a communication link between terminal devices 1011 and 1012 and server 103. Network 1022 serves as the medium for providing a communication link between server 103 and modification terminal devices 1041 and 1042. Networks 1021 and 1022 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0026] Users can use terminal devices 1011 and 1012 to interact with server 103 via network 1021 to send or receive messages. For example, users can use terminal devices 1011, 1012, and 1013 to send content to be edited to server 103, and server 103 can send the final edited content to terminal devices 1011, 1012, and 1013. Users can use editing terminal devices 1041 and 1042 to interact with server 103 via network 1022 to send or receive messages. For example, server 103 can send content to be edited to editing terminal devices 1041 and 1042, and users can use editing terminal devices 1041 and 1042 to return the edited content to server 103. Various communication client applications can be installed on terminal devices 1011 and 1012 and editing terminal devices 1041 and 1042, such as image editing applications, instant messaging software, and content editing applications.

[0027] Terminal devices 1011 and 1012 can be either hardware or software. When terminal devices 1011 and 1012 are hardware, they can be various electronic devices with cameras and supporting information interaction, including but not limited to smart lamps, smartphones, tablets, and laptops. When terminal devices 1011 and 1012 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services) or as a single software program or software module. No specific limitations are imposed here.

[0028] The rating terminal devices 1041 and 1042 can be either hardware or software. When the rating terminal devices 1041 and 1042 are hardware, they can be various electronic devices with a display screen and supporting information interaction, including but not limited to smartphones, tablets, and laptops. When the rating terminal devices 1041 and 1042 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services) or as a single software program or software module. No specific limitations are imposed here.

[0029] Server 103 can be a server that provides various services. For example, after receiving the content to be modified sent by terminal devices 1011 and 1012, server 103 can determine the user who needs to modify the content and then send the content to be modified to that user's terminal device. In response to receiving the content to be modified sent by terminal devices 1011 and 1012, server 103 can obtain the content characteristics of the content to be modified and the user characteristics of the user from which the content to be modified originates. Then, server 103 can obtain the modification user characteristics of the modification users in the candidate modification user set. Next, based on the content characteristics, user characteristics, and modification user characteristics, server 103 can select the target modification user from the candidate modification user set, send the content to be modified to the target modification user's user terminal (i.e., modification terminal devices 1041 and 1042), receive the modified content returned by modification terminal devices 1041 and 1042, and determine the final modification content based on the modified content. Finally, server 103 can send the final modification content to the user terminal (i.e., terminal devices 1011 and 1012) from which the content to be modified originates.

[0030] It should be noted that server 103 can be either hardware or software. When server 103 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When server 103 is software, it can be implemented as multiple software programs or software modules (e.g., used to provide distributed services), or as a single software program or software module. No specific limitations are made here.

[0031] It should also be noted that the content modification method provided in this embodiment is usually executed by server 103, and the content modification device is usually set in server 103.

[0032] It should be understood that Figure 1 The number of terminal devices, networks, servers, and approval / correction terminal devices shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, servers, and approval / correction terminal devices can be included.

[0033] Continue to refer to Figure 2 The diagram illustrates a flow 200 of an embodiment of a content editing method according to the present disclosure. The content editing method includes the following steps:

[0034] Step 201: In response to receiving the content to be modified, obtain the content characteristics of the content to be modified and the user characteristics of the user from whom the content to be modified originates.

[0035] In this embodiment, the execution subject of the content modification method (e.g.) Figure 1 The server shown can determine whether it has received the content to be revised. The content to be revised can be in the form of images and videos. If the content to be revised is received, the executing entity can obtain the content characteristics of the content to be revised and the user characteristics of the user from whom the content to be revised originates. The content characteristics may include, but are not limited to, at least one of the following: the number of pages in the content to be revised, the number of words in the content to be revised, and the submission time of the content to be revised. The user characteristics may include, but are not limited to, at least one of the following: the familiarity between the user from whom the content to be revised originates and the user revising it (which can be represented by the number of interactions), the duration of the user from whom the content to be revised originates being online, and the historical behavior information of the user from whom the content to be revised originates.

[0036] Step 202: Obtain the user characteristics of the users who need to be graded in the candidate set of graded users.

[0037] In this embodiment, the executing entity can obtain the modification user characteristics of modification users from the candidate modification user set. The candidate modification user set can be a pre-set modification user set. The modification user characteristics can include: the modification user's historical behavior information.

[0038] Step 203: Based on content features, user features, and grading user features, select the target grading user from the candidate grading user set, send the content to be graded to the target grading user's user terminal, receive the graded content returned by the user terminal, and determine the final graded content based on the graded content.

[0039] In this embodiment, the execution entity can select a target user for grading from the candidate grading user set based on the content features, user features, and grading user features obtained in step 201 and in step 202. The target grading user is typically the user who will grade the content to be graded. As an example, the execution entity can input the content features, user features, and grading user features from the candidate grading user set into a pre-trained grading user selection model to obtain the user identifier of the target grading user. The grading user selection model can be used to represent the correspondence between the content features, user features, and grading user features from the grading user set, and the identifiers of the users in the grading user set who will grade the content.

[0040] Subsequently, the aforementioned executing entity can send the content to be corrected to the user terminal of the target user. Upon receiving the content, the target user can correct it and obtain the corrected content. Specifically, the target user can use correction tools such as a pen, eraser, and rectangular annotation boxes to annotate the content. If the accuracy of the correction is high, the target user can paste a preset image (e.g., a "like" image) into the content.

[0041] It should be noted that if the content to be corrected is presented as an image, after the target user completes the corrections, clicking the "Save" button will combine the content to be corrected with the correction marks into a single corrected image. During the process of combining the corrected image, the user terminal can first save the correction marks generated by the target user as a set of correction coordinates. Then, using a canvas, these correction coordinate sets are gradually drawn onto the image of the content to be corrected, with feathering applied for a more natural blend. After drawing is complete, the user terminal can upload the generated corrected image to the execution entity for saving and marking it as a composite correction image for subsequent review and viewing.

[0042] Then, the aforementioned executing entity can receive the modified content returned by the aforementioned user terminal. Based on the modified content, the final modified content can be determined. For example, the aforementioned executing entity can determine the modified content as the final modified content.

[0043] Step 204: Send the final revised content to the user terminal from which the content to be revised originated.

[0044] In this embodiment, the executing entity can send the final revised content to the user terminal from which the content to be revised originates. The user from which the content to be revised originates can then view the final revised content.

[0045] It should be noted that the aforementioned implementing entity may also send the final revised content to other electronic devices of the user from which the content to be revised originated. These other electronic devices may include, but are not limited to, at least one of the following: mobile phones, computers, and smartwatches.

[0046] The method provided in the above embodiments of this disclosure improves the accuracy of content grading by utilizing the content characteristics of the content to be graded, the user characteristics of the user from whom the content to be graded originates, and the user characteristics of the grading user.

[0047] In some alternative implementations, the user terminal from which the content to be modified originates may include a desk lamp terminal. Here, the desk lamp terminal typically includes a camera and supports information interaction. The desk lamp terminal can use the camera to acquire the content to be modified, and then send the acquired content to the executing entity for further processing.

[0048] Continue to refer to Figure 3 This illustrates a flow 300 of yet another embodiment of the content correction method. Flow 300 of the content correction method includes the following steps:

[0049] Step 301: In response to receiving the content to be modified, obtain the content characteristics of the content to be modified and the user characteristics of the user from whom the content to be modified originates.

[0050] Step 302: Obtain the user characteristics of the users who need to be graded in the candidate set of graded users.

[0051] In this embodiment, steps 301-302 can be performed in a similar manner to steps 201-202, and will not be described again here.

[0052] Step 303: Obtain the correct results corresponding to the questions contained in the content to be graded.

[0053] In this embodiment, the content to be graded may include the question and the answer information. The entity executing the content grading method (e.g., Figure 1 The server shown can obtain the correct results for the questions contained in the above-mentioned content to be corrected.

[0054] Here, the aforementioned executing entity can use OCR (Optical Character Recognition) text recognition technology to identify the question stem information in the content to be corrected; then, a feature recognition algorithm can be used to extract keywords from the question stem; and then, the keywords can be compared with an existing question bank to obtain the correct result for the question.

[0055] Step 304: Perform the following correction steps: Based on content features, user features, and correction user features, select the target correction user from the candidate correction user set, send the content to be corrected to the target correction user's user terminal, and receive the corrected content returned by the user terminal; use the correct result to determine whether there are any errors in the corrected content; if there are no errors in the corrected content, then determine the corrected content as the final correction content.

[0056] In this embodiment, the aforementioned execution entity can perform the following modification steps.

[0057] In this embodiment, the correction step 304 may include sub-steps 3041, 3042, and 3043. Wherein:

[0058] Step 3041: Based on content features, user features, and grading user features, select the target grading user from the candidate grading user set, send the content to be graded to the target grading user's user terminal, and receive the graded content returned by the user terminal.

[0059] In this embodiment, the executing entity can select a target user for grading from the candidate user set based on the content characteristics of the content to be graded, the user characteristics of the user from whom the content to be graded originates, and the grading user characteristics of the grading users in the candidate user set. The target user is typically the user who will grade the content to be graded.

[0060] As an example, the aforementioned executing entity can input the aforementioned content features, user features, and the user characteristics of the users in the candidate set of users who are making corrections into a pre-trained user selection model to obtain the user identifier of the target user. The aforementioned user selection model can be used to represent the correspondence between the content features, user features, and user characteristics of the users in the set of users who are making corrections to the content, and the identifiers of the users in that set who are making corrections to the content.

[0061] Subsequently, the aforementioned executing entity can send the content to be corrected to the user terminal of the target user. Upon receiving the content, the target user can correct it and obtain the corrected content. Specifically, the target user can use correction tools such as a pen, eraser, and rectangular annotation boxes to annotate the content. If the accuracy of the correction is high, the target user can paste a preset image into the content.

[0062] It should be noted that if the content to be corrected is presented as an image, after the target user completes the corrections, clicking the "Save" button will combine the content to be corrected with the correction marks into a single corrected image. During the process of combining the corrected image, the user terminal can first save the correction marks generated by the target user as a set of correction coordinates. Then, using a canvas, these correction coordinate sets are gradually drawn onto the image of the content to be corrected, with feathering applied for a more natural blend. After drawing is complete, the user terminal can upload the generated corrected image to the execution entity for saving and marking it as a composite correction image for subsequent review and viewing.

[0063] Then, the aforementioned executing entity can receive the modified content returned by the aforementioned user terminal.

[0064] Step 3042: Using the correct results, determine whether there are any errors in the corrected content.

[0065] In this embodiment, the executing entity can use the correct result obtained in step 303 to determine whether there are any errors in the modified content.

[0066] Here, the implementing entity can compare the solutions to the questions in the content to be corrected, the corrections to the questions in the already corrected content, and the correct answers to the questions to determine whether there are any errors in the already corrected content. Specifically, if the solutions to the questions in the content to be corrected are consistent with the correct answers, then the solutions to the questions are correct. In this case, if the corrections to the questions in the already corrected content indicate that the solutions are correct, then the already corrected content is correct; if the corrections to the questions in the already corrected content indicate that the solutions are incorrect, then the already corrected content is incorrect. Conversely, if the solutions to the questions in the content to be corrected are inconsistent with the correct answers, then the solutions to the questions are incorrect. In this case, if the corrections to the questions in the already corrected content indicate that the solutions are correct, then the already corrected content is incorrect; if the corrections to the questions in the already corrected content indicate that the solutions are incorrect, then the already corrected content is correct.

[0067] It should be noted that if the above-mentioned content to be corrected contains at least two questions, then for each of the at least two questions, the above-mentioned executing entity can compare the solution result of the question in the above-mentioned content to be corrected, the correction result of the question in the above-mentioned content to be corrected, and the correct result of the question, so as to determine whether there is an error in the question in the above-mentioned content to be corrected.

[0068] Step 3043: If there are no errors in the corrected content, then the corrected content is determined as the final corrected content.

[0069] In this embodiment, if it is determined in step 3042 that the above-mentioned modified content does not contain any errors, the above-mentioned execution entity can determine the above-mentioned modified content as the final modified content.

[0070] Step 305: Send the final revised content to the user terminal from which the content to be revised originated.

[0071] In this embodiment, step 305 can be performed in a similar manner to step 204, and will not be described again here.

[0072] from Figure 3 It can be seen from this that, with Figure 2 Compared to the corresponding embodiments, the content correction method in this embodiment, in its process 300, involves using the correct results corresponding to the questions in the content to be corrected to determine whether there are any errors in the corrected content. If no errors are found, the corrected content is determined as the final corrected content. Therefore, the solution described in this embodiment can check the corrected content returned by the target user, thereby further improving the accuracy of content correction.

[0073] In some optional implementations, after determining whether the corrected content contains errors using the correct results, if errors exist, the executing entity can continue executing the correction steps (i.e., sub-steps 3041-3043) until the corrected content is completely correct. This method allows for the selection of a new target user to correct the content when errors exist, thereby correcting the errors.

[0074] In some optional implementations, the aforementioned user characteristics for grading may include grading accuracy. If the graded content contains errors, the executing entity can adjust the grading accuracy of the target user. As an example, if the graded content contains errors, the executing entity can lower the grading accuracy of the target user by a preset value (e.g., 1%). As another example, if the graded content contains errors, the executing entity can increment the total number of grading attempts for the target user by one, and determine the grading accuracy as the ratio of the number of correct grading attempts to the incremented total number of grading attempts.

[0075] In some optional implementations, the aforementioned content characteristics may include the subject to which the content to be graded belongs. The executing entity can select a target grading user from the candidate grading user set based on the aforementioned content characteristics, user characteristics, and grading user characteristics, send the content to be graded to the target grading user's user terminal, and receive the graded content returned by the user terminal. The executing entity can determine whether the subject to which the content to be graded belongs is the target subject. Here, the target subject is usually a subject with many subjective questions, such as Chinese or English. If the subject to which the content to be graded belongs is the target subject, the executing entity can select a first target grading user from the candidate grading user set based on the content characteristics of the content to be graded, the user characteristics of the user from whom the content to be graded originates, and the grading user characteristics of the grading users in the candidate grading user set. The first target grading user is usually the user who grades the content to be graded. As an example, the aforementioned executing entity can input the aforementioned content features, user features, and the modification user features of modification users in the candidate modification user set into a pre-trained modification user selection model to obtain the user identifier of the first target modification user. The modification user selection model can be used to represent the correspondence between the content features, user features, and modification user features of modification users in the modification user set, and the identifiers of the users in the modification user set who are modifying the content. Then, the aforementioned executing entity can send the content to be modified to the user terminal of the first target modification user. After receiving the content to be modified, the first target modification user can modify the content to be modified, obtaining the first batch of modified content. Then, the aforementioned executing entity can receive the first batch of modified content returned by the user terminal of the first target modification user.

[0076] Then, the executing entity can select a second target grading user from the candidate grading user set based on the aforementioned content features, user features, and grading user features. This second target grading user is typically used to check the first batch of revised content obtained by the first target grading user. Similarly, the executing entity can input the aforementioned content features, user features, and grading user features from the candidate grading user set into a pre-trained grading user selection model to obtain the user identifier of the second target grading user. Afterwards, the executing entity can send the content to be graded and the first batch of revised content to the user terminal of the second target grading user. Upon receiving the content to be graded and the first batch of revised content, the user terminal of the second target grading user can use the content to be graded to check the first batch of revised content and obtain the second graded content. It should be noted that if the first batch of revised content is error-free, then the second graded content is the same as the first batch of revised content; if the first batch of revised content contains errors, then the second target grading user can grade it based on the first batch of revised content to obtain the second graded content. Then, the aforementioned executing entity can receive the second batch of corrected content returned by the user terminal of the second target user as the corrected content. In this way, the content to be corrected in subjects containing a large number of subjective questions can be re-checked, thereby reducing the error rate in the correction process.

[0077] Further reference Figure 4 This illustrates a flow 400 of an embodiment of a content grading method for selecting a target grading user. The flow 400 of this target grading user selection method includes the following steps:

[0078] Step 401: Based on content features and grading user features, select a subset of grading users from the candidate grading user set.

[0079] In this embodiment, the execution subject of the content modification method (e.g.) Figure 1 The server shown can select a subset of users to be modified from the candidate set of users to be modified based on the above-mentioned content features and the above-mentioned user modification features. As an example, the above-mentioned user modification features may include the amount of unmodified content of the user to be modified. The execution entity can select users to be modified whose amount of unmodified content is less than a preset content amount threshold from the candidate set of users to be modified, thus obtaining a subset of users to be modified.

[0080] Step 402: For each user in the subset of users to be reviewed, determine the matching degree between the user to be reviewed and the content to be reviewed based on the content features, user features and the review user features of the user to be reviewed.

[0081] In this embodiment, for each user in the subset of users selected in step 401, the execution entity can determine the matching degree between the user and the content to be modified based on the content features, user features, and the user's modification features. Here, the execution entity can input the content features, user features, and the user's modification features into a pre-trained matching degree prediction model to obtain the matching degree between the user and the content to be modified. The matching degree prediction model can be used to represent the correspondence between the content features, user features, and user's modification features, and the matching degree between the user and the content.

[0082] Step 403: Sort the users in the subset of users to be modified according to the matching degree from high to low, and use the sorting result to select the target users to be modified from the subset of users to be modified.

[0083] In this embodiment, the execution entity can sort the users in the subset of users to be modified according to their matching degree from high to low, and obtain a sorting result. Then, the target user to be modified can be selected from the subset of users to be modified using the sorting result.

[0084] Here, since the aforementioned execution entity typically assigns multiple pending modification requests to the modification users simultaneously, it can utilize a preemptive scheduling algorithm to distribute these requests. During the execution of the current process, if an important or urgent process arrives (its state must be ready), the current process will be forced to relinquish the processor, and the system will immediately allocate the processor to the newly arrived process. This process is called preemption.

[0085] If the allocation is successful, the content to be modified can be sent to the assigned user (i.e., the target user) for modification. If the preemption fails, the subsequent users in the sorted results will be traversed to attempt allocation. If the allocation still fails, it will be marked that no target user was found in this matching process, and the next allocation scheduling will continue after a preset time.

[0086] The method provided in the above embodiments of this disclosure improves the accuracy of selecting target users for modification by dividing the matching process into three stages: screening, sorting, and decision-making.

[0087] In some optional implementations, the aforementioned content characteristics may include the subject of the content to be graded, and the aforementioned grading user characteristics may include the subject of the grading user, whether the grading user has grading permissions, and the current status of the grading user. The subject of the grading user is usually a subject that the grading user is proficient in. The current status of the grading user may include online / idle, online / non-idle, and offline. The aforementioned executing entity may select a subset of grading users from the aforementioned candidate grading user set based on the aforementioned content characteristics and the aforementioned grading user characteristics in the following way: The aforementioned executing entity may select grading users that meet the target conditions from the aforementioned candidate grading user set to obtain the subset of grading users. Wherein, the aforementioned target conditions may include at least one of the following: the subject of the content to be graded matches the subject of the grading user, the grading user has grading permissions, and the grading user's current status is online and idle.

[0088] As an example, if the target condition is that the subject of the content to be corrected matches the subject of the user who is correcting it, and if the subject of the content to be corrected is Chinese, then the executing entity needs to select the user whose subject is Chinese from the candidate user set to obtain the user subset.

[0089] As another example, if the above target conditions are that the subject of the content to be graded matches the subject of the grading user, the grading user has grading permissions, and the grading user is currently online and idle, and if the subject of the content to be graded is English, then the executing entity needs to select from the above candidate grading user set the corresponding subject is English, the grading user has grading permissions, and the grading user is currently online and idle, thereby obtaining a subset of grading users.

[0090] In some optional implementations, the number of received content to be modified is at least two. The aforementioned execution entity can sort the users in the aforementioned subset of users to be modified according to their matching degree from high to low in the following way, obtain the sorting result, and use the sorting result to select the target user to be modified from the aforementioned subset of users to be modified: The aforementioned execution entity can construct a bipartite graph. A bipartite graph, also known as a bipartite graph, is a special model in graph theory. Let G = (V, E) be an undirected graph. If vertex V can be partitioned into two disjoint subsets (A, B), and each edge (i, j) in the graph is associated with two vertices i and j belonging to these two different vertex sets (i in A, j in B), then graph G can be called a bipartite graph. Here, the vertices in the first vertex set of the aforementioned bipartite graph can be used to represent the content to be modified, the vertices in the second vertex set of the aforementioned bipartite graph can be used to represent the user to be modified, and the numerical values ​​corresponding to the edges in the aforementioned bipartite graph can be used to represent the matching degree between the user to be modified and the content to be modified. Subsequently, the aforementioned executing entity can use the bipartite graph and the Kuhn Munkras algorithm to determine the matching result between the user to be modified and the content to be modified. The bipartite graph maximum weight matching algorithm finds a matching in a weighted bipartite graph such that the sum of the weights on the matching edges is maximized.

[0091] like Figure 5 As shown, Figure 5 This illustrates a bipartite graph representing the relationship between the user making the corrections and the content to be corrected. Figure 5 In the bipartite graph, nodes represent content to be corrected, such as nodes representing content to be corrected 1, content to be corrected 2, ..., content to be corrected N. The bipartite graph also includes nodes representing users who perform the corrections, such as nodes representing user 1, user 2, ..., user N. The matching degree between content to be corrected 1 and user 1 is 0.3, between content to be corrected 1 and user 2 is 0.6, between content to be corrected 2 and user 2 is 0.7, between content to be corrected 2 and user N is 0.7, between content to be corrected N and user 1 is 0.1, and between content to be corrected N and user N is 0.9.

[0092] Further reference Figure 6 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a content editing device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0093] like Figure 6As shown, the content editing device 600 of this embodiment includes: a first acquisition unit 601, a second acquisition unit 602, a selection unit 603, and a sending unit 604. The first acquisition unit 601 is used to acquire the content features of the content to be edited and the user features of the user from whom the content to be edited originates in response to receiving the content to be edited. The second acquisition unit 602 is used to acquire the editing user features of the editing users in the candidate editing user set. The selection unit 603 is used to select the target editing user from the candidate editing user set based on the content features, user features, and editing user features, send the content to be edited to the target editing user's user terminal, receive the edited content returned by the user terminal, and determine the final edited content based on the edited content. The sending unit 604 is used to send the final edited content to the user terminal from which the content to be edited originates.

[0094] In this embodiment, the specific processing of the first acquisition unit 601, the second acquisition unit 602, the selection unit 603, and the sending unit 604 of the content editing device 600 can be referred to Figure 2 The corresponding steps are 201, 202, 203 and 204 in the embodiment.

[0095] In some optional implementations, the content to be corrected may include questions; and the selection unit 603 may be further configured to select a target user from the candidate user set based on the content features, user features, and user characteristics, send the content to be corrected to the target user's user terminal, receive the corrected content returned by the user terminal, and determine the final corrected content based on the corrected content: the selection unit 603 may obtain the correct results corresponding to the questions included in the content to be corrected; then, the following correction steps may be performed: based on the content features, user features, and user characteristics, select a target user from the candidate user set, send the content to be corrected to the target user's user terminal, receive the corrected content returned by the user terminal; the correct results may be used to determine whether there are errors in the corrected content; if there are no errors in the corrected content, the corrected content may be determined as the final corrected content.

[0096] In some optional implementations, the selection unit 603 may be further configured to select a target user for grading from the candidate user set based on the content features, user features, and grading user features, send the content to be graded to the user terminal of the target user for grading, receive the graded content returned by the user terminal, and determine the final graded content based on the graded content. If the graded content contains errors, the selection unit 603 may continue to execute the grading steps.

[0097] In some optional implementations, the aforementioned user characteristics may include a correction accuracy rate; and the method may further include an adjustment unit (not shown in the figure). The adjustment unit is used to adjust the correction accuracy rate of the target user if errors exist in the corrected content.

[0098] In some optional implementations, the selection unit 603 can be further configured to select a target user for review from the candidate user set based on the content features, user features, and review user features as follows: the selection unit 603 can select a subset of review users from the candidate user set based on the content features and review user features; then, for each review user in the subset of review users, the selection unit 603 can determine the matching degree between the review user and the content to be reviewed based on the content features, user features, and the review user features of that review user; then, the review users in the subset of review users can be sorted in descending order of matching degree to obtain a sorting result, and the target user for review can be selected from the subset of review users using the sorting result.

[0099] In some optional implementations, the aforementioned content features may include the subject to which the content to be graded belongs, and the aforementioned grading user features may include the subject corresponding to the grading user, whether the grading user has grading permissions, and the current status of the grading user. The aforementioned selection unit 603 may be further configured to select a subset of grading users from the aforementioned candidate grading user set based on the aforementioned content features and the aforementioned grading user features in the following manner: the aforementioned selection unit 603 may select grading users that meet the target conditions from the aforementioned candidate grading user set to obtain a subset of grading users, wherein the aforementioned target conditions include at least one of the following: the subject to which the content to be graded belongs matches the subject corresponding to the grading user; the grading user has grading permissions; and the current status of the grading user is online and idle.

[0100] In some optional implementations, the number of received content to be modified can be at least two. The selection unit 603 can be further used to sort the users in the subset of users to be modified according to the matching degree from high to low in the following manner, and use the sorting result to select the target user to be modified from the subset of users to be modified: The selection unit 603 can construct a bipartite graph, wherein the vertices in the first vertex set of the bipartite graph are used to represent the content to be modified, the vertices in the second vertex set of the bipartite graph are used to represent the user to be modified, and the values ​​corresponding to the edges in the bipartite graph are used to represent the matching degree between the user to be modified and the content to be modified; then, based on the bipartite graph, the matching result between the user to be modified and the content to be modified can be determined using the bipartite graph maximum weight matching algorithm.

[0101] In some optional implementations, the aforementioned content features may include the subject to which the content to be corrected belongs. The selection unit 603 may be further configured to select a target correction user from the candidate correction user set based on the aforementioned content features, user features, and correction user features, and send the content to be corrected to the target correction user's user terminal, and receive the corrected content returned by the user terminal: If the subject is the target subject, the selection unit 603 may select a first target correction user from the candidate correction user set based on the aforementioned content features, user features, and correction user features, send the content to be corrected to the first target correction user's user terminal, and receive the first batch of corrected content returned by the first target correction user's user terminal; subsequently, a second target correction user may be selected from the candidate correction user set based on the aforementioned content features, user features, and correction user features, send the content to be corrected and the first batch of corrected content to the second target correction user's user terminal, and receive the second batch of corrected content returned by the second target correction user's user terminal as corrected content.

[0102] In some alternative implementations, the user terminal from which the aforementioned content to be modified originates may include a desk lamp terminal.

[0103] The following is for reference. Figure 7 It illustrates an electronic device suitable for implementing embodiments of the present disclosure (e.g., Figure 1 The structural diagram of the server (700) in the middle. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0104] like Figure 7As shown, the electronic device 700 may include a processing unit (e.g., a central processing unit, a graphics processor, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device 700. The processing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0105] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic device 700 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7 An electronic device 700 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 7 Each box shown can represent a device or multiple devices as needed.

[0106] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a storage device 708, or installed from a ROM 702. When the computer program is executed by a processing device 701, it performs the functions defined in the methods of embodiments of this disclosure. It should be noted that the computer-readable medium described in embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0107] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: in response to receiving content to be modified, acquire the content characteristics of the content to be modified and the user characteristics of the user from whom the content to be modified originates; acquire the modification user characteristics of modification users in a candidate modification user set; based on the content characteristics, user characteristics, and modification user characteristics, select a target modification user from the candidate modification user set, send the content to be modified to the target modification user's user terminal, receive the modified content returned by the user terminal, determine the final modified content based on the modified content, and send the final modified content to the user terminal from which the content to be modified originates.

[0108] Computer program code for performing the operations of embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. 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 indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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 the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0110] According to one or more embodiments of this disclosure, a content correction method is provided, the method comprising: in response to receiving content to be corrected, obtaining content features of the content to be corrected and user features of the user from which the content to be corrected originates; obtaining correction user features of correction users in a candidate correction user set; selecting a target correction user from the candidate correction user set based on the content features, user features, and correction user features; sending the content to be corrected to the user terminal of the target correction user; receiving corrected content returned by the user terminal; determining the final correction content based on the corrected content; and sending the final correction content to the user terminal from which the content to be corrected originates.

[0111] According to one or more embodiments of this disclosure, the content to be corrected includes questions; and based on content features, user features, and correcting user features, a target correcting user is selected from a set of candidate correcting users, the content to be corrected is sent to the target correcting user's user terminal, the corrected content is received from the user terminal, and the final corrected content is determined based on the corrected content, including: obtaining the correct results corresponding to the questions contained in the content to be corrected; performing the following correction steps: based on content features, user features, and correcting user features, a target correcting user is selected from a set of candidate correcting users, the content to be corrected is sent to the target correcting user's user terminal, the corrected content is received from the user terminal; using the correct results, it is determined whether there are any errors in the corrected content; if there are no errors in the corrected content, the corrected content is determined as the final corrected content.

[0112] According to one or more embodiments of this disclosure, a target user is selected from a set of candidate users for grading based on content features, user features, and grading user features. The content to be graded is sent to the user terminal of the target user for grading. The graded content returned by the user terminal is received. Based on the graded content, the final graded content is determined. The method also includes: if there are errors in the graded content, the grading steps are continued.

[0113] According to one or more embodiments of this disclosure, the user characteristics for grading include grading accuracy; and the method further includes: if there are errors in the graded content, adjusting the grading accuracy of the target user.

[0114] According to one or more embodiments of this disclosure, selecting a target user for review from a set of candidate users based on content features, user features, and reviewer features includes: selecting a subset of reviewers from the set of candidate users based on content features and reviewer features; for each reviewer in the subset of reviewers, determining the matching degree between the reviewer and the content to be reviewed based on content features, user features, and the reviewer features of that reviewer; sorting the reviewers in the subset of reviewers according to the matching degree from high to low, obtaining a sorting result, and using the sorting result to select a target user for review from the subset of reviewers.

[0115] According to one or more embodiments of this disclosure, the content features include the subject to which the content to be corrected belongs, and the user features include the subject to which the user corrects, whether the user has correction permissions, and the current status of the user; and based on the content features and user features, selecting a subset of users from a set of candidate users includes: selecting users who meet target conditions from the set of candidate users to obtain a subset of users, wherein the target conditions include at least one of the following: the subject to which the content to be corrected belongs matches the subject to which the user corrects; the user corrects has correction permissions; and the user's current status is online and idle.

[0116] According to one or more embodiments of this disclosure, the number of received content to be modified is at least two; and the modification users in the modification user subset are sorted in descending order of matching degree to obtain a sorting result, and the target modification user is selected from the modification user subset using the sorting result, including: constructing a bipartite graph, wherein the vertices in the first vertex set of the bipartite graph are used to represent the content to be modified, the vertices in the second vertex set of the bipartite graph are used to represent the modification user, and the numerical values ​​corresponding to the edges in the bipartite graph are used to represent the matching degree between the modification user and the content to be modified; based on the bipartite graph, the matching result between the modification user and the content to be modified is determined using the bipartite graph maximum weight matching algorithm.

[0117] According to one or more embodiments of this disclosure, the content features include the subject to which the content to be corrected belongs; and the process of selecting a target correcting user from a set of candidate correcting users based on the content features, user features, and correcting user features, sending the content to be corrected to the user terminal of the target correcting user, and receiving the corrected content returned by the user terminal includes: if the subject is the target subject, then selecting a first target correcting user from the set of candidate correcting users based on the content features, user features, and correcting user features, sending the content to be corrected to the user terminal of the first target correcting user, and receiving the first batch of corrected content returned by the user terminal of the first target correcting user; selecting a second target correcting user from the set of candidate correcting users based on the content features, user features, and correcting user features, sending the content to be corrected and the first batch of corrected content to the user terminal of the second target correcting user, and receiving the second batch of corrected content returned by the user terminal of the second target correcting user as the corrected content.

[0118] According to one or more embodiments of this disclosure, the user terminal from which the content to be modified originates includes a desk lamp terminal.

[0119] According to one or more embodiments of this disclosure, a content editing apparatus is provided, the apparatus comprising: a first acquisition unit, configured to, in response to receiving content to be edited, acquire content features of the content to be edited and user features of the user from whom the content to be edited originates; a second acquisition unit, configured to acquire editing user features of editing users in a candidate editing user set; a selection unit, configured to, based on the content features, user features, and editing user features, select a target editing user from the candidate editing user set, send the content to be edited to the target editing user's user terminal, receive edited content returned by the user terminal, and determine the final edited content based on the edited content; and a sending unit, configured to send the final edited content to the user terminal from which the content to be edited originates.

[0120] According to one or more embodiments of this disclosure, the content to be corrected includes questions; and the selection unit is further configured to select a target user from a set of candidate users for correction based on content features, user features, and correction user features, send the content to be corrected to the user terminal of the target user for correction, receive the corrected content returned by the user terminal, and determine the final corrected content based on the corrected content: obtaining the correct results corresponding to the questions contained in the content to be corrected; performing the following correction steps: selecting a target user from a set of candidate users for correction based on content features, user features, and correction user features, sending the content to be corrected to the user terminal of the target user for correction, receiving the corrected content returned by the user terminal; using the correct results, determining whether there are any errors in the corrected content; if there are no errors in the corrected content, then determining the corrected content as the final corrected content.

[0121] According to one or more embodiments of this disclosure, the selection unit is further configured to select a target user for review from a set of candidate users for review based on content features, user features, and review user features, send the content to be reviewed to the user terminal of the target user for review, receive the reviewed content returned by the user terminal, and determine the final reviewed content based on the reviewed content. If there are errors in the reviewed content, the review steps are continued.

[0122] According to one or more embodiments of this disclosure, the user characteristics for grading include grading accuracy; and the apparatus further includes: an adjustment unit for adjusting the grading accuracy of the target user if there are errors in the graded content.

[0123] According to one or more embodiments of this disclosure, the selection unit is further configured to select a target user for review from a set of candidate users for review in the following manner, based on content features, user features, and review user features: selecting a subset of users for review from the set of candidate users for review based on content features and review user features; for each user for review in the subset of users for review, determining the matching degree between the user for review and the content to be reviewed based on content features, user features, and the review user features of the user for review; sorting the users for review in the subset of users for review according to the matching degree from high to low, obtaining a sorting result, and using the sorting result to select the target user for review from the subset of users for review.

[0124] According to one or more embodiments of this disclosure, the content features include the subject to which the content to be corrected belongs, and the user characteristics include the subject corresponding to the user, whether the user has correction permissions, and the current status of the user; and the selection unit is further configured to select a subset of users from the candidate user set based on the content features and the user characteristics in the following manner: selecting users who meet the target conditions from the candidate user set to obtain the subset of users, wherein the target conditions include at least one of the following: the subject to which the content to be corrected belongs matches the subject corresponding to the user; the user has correction permissions; and the user's current status is online and idle.

[0125] According to one or more embodiments of this disclosure, the number of received content to be modified is at least two; and the selection unit is further configured to sort the users in the user modification subset in descending order of matching degree in the following manner, to obtain a sorting result, and to select the target user to be modified from the user modification subset using the sorting result: constructing a bipartite graph, wherein the vertices in the first vertex set of the bipartite graph are used to represent the content to be modified, the vertices in the second vertex set of the bipartite graph are used to represent the user to be modified, and the numerical values ​​corresponding to the edges in the bipartite graph are used to represent the matching degree between the user to be modified and the content to be modified; based on the bipartite graph, using the bipartite graph maximum weight matching algorithm, determining the matching result between the user to be modified and the content to be modified.

[0126] According to one or more embodiments of this disclosure, the content features include the subject to which the content to be corrected belongs; and the selection unit is further configured to select a target correcting user from a set of candidate correcting users based on the content features, user features, and correcting user features in the following manner, send the content to be corrected to the user terminal of the target correcting user, and receive the corrected content returned by the user terminal: if the subject is the target subject, then based on the content features, user features, and correcting user features, select a first target correcting user from the set of candidate correcting users, send the content to be corrected to the user terminal of the first target correcting user, and receive the first batch of corrected content returned by the user terminal of the first target correcting user; based on the content features, user features, and correcting user features, select a second target correcting user from the set of candidate correcting users, send the content to be corrected and the first batch of corrected content to the user terminal of the second target correcting user, and receive the second batch of corrected content returned by the user terminal of the second target correcting user as the corrected content.

[0127] According to one or more embodiments of this disclosure, the user terminal from which the content to be modified originates includes a desk lamp terminal.

[0128] The units described in the embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a first acquisition unit, a second acquisition unit, a selection unit, and a sending unit. The names of these units do not necessarily limit the specific unit; for example, a sending unit may also be described as "a unit that sends the final modified content to the user terminal from which the content to be modified originates."

[0129] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of technical features, but should also cover other technical solutions formed by arbitrary combinations of technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A content correction method, characterized in that, include: In response to receiving content to be modified, the content characteristics of the content to be modified and the user characteristics of the user from whom the content to be modified originates are obtained; Obtain the user characteristics of the users who made corrections in the candidate set of users who made corrections; Based on the content features and the grading user features, a subset of grading users is selected from the candidate grading user set; For each user in the subset of users to be reviewed, the matching degree between the user to be reviewed and the content to be reviewed is determined based on the content features, the user features, and the review user features of the user to be reviewed. When the number of received items to be corrected is at least two, a bipartite graph is constructed, wherein the vertices in the first vertex set of the bipartite graph are used to represent the items to be corrected, the vertices in the second vertex set of the bipartite graph are used to represent the users who make corrections, and the numerical values ​​corresponding to the edges in the bipartite graph are used to represent the degree of matching between the users who make corrections and the items to be corrected. Based on the bipartite graph, the matching degree between the user to be modified and the content to be modified is determined using the bipartite graph maximum weight matching algorithm. The users in the subset of users to be modified are sorted in descending order of matching degree. The target user is selected from the candidate set of users to be modified based on the sorting result. The content to be modified is sent to the user terminal of the target user. The modified content is received from the user terminal. Based on the modified content, the final modified content is determined. The final revised content is sent to the user terminal from which the content to be revised originates.

2. The method according to claim 1, characterized in that, The content to be graded includes questions, and the method further includes: Obtain the correct results corresponding to the questions contained in the content to be corrected; Using the correct result, determine whether there are any errors in the corrected content; if there are no errors in the corrected content, then determine the corrected content as the final corrected content.

3. The method according to claim 2, characterized in that, The method further includes: If the corrected content contains errors, continue with the correction process.

4. The method according to claim 3, characterized in that, The user characteristics for grading include grading accuracy rate; and The method further includes: If the corrected content contains errors, the correction accuracy rate of the target correction user will be adjusted.

5. The method according to claim 1, characterized in that, The content features include the subject to which the content to be corrected belongs, and the user features include the subject to which the user corrects, whether the user has correction authority, and the current status of the user. as well as The step of selecting a subset of users to be graded from the candidate set of users based on the content features and the grading user features includes: From the candidate set of users who have submitted comments, users who meet the target criteria are selected to obtain a subset of users who have submitted comments. The target criteria include at least one of the following: The subject of the content to be corrected matches the subject of the user being corrected; Users who need to make corrections have correction permissions. The user being reviewed is currently online and idle.

6. The method according to claim 2, characterized in that, The content features include the subject to which the content to be corrected belongs; the process of sorting the correction users in the correction user subset according to the matching degree from high to low, selecting the target correction user from the candidate correction user set using the sorting result, sending the content to be corrected to the target correction user's user terminal, and receiving the corrected content returned by the user terminal includes: If the subject is the target subject, then based on the content features, the user features and the grading user features, a first target grading user is selected from the candidate grading user set, the content to be graded is sent to the user terminal of the first target grading user, and the first batch of graded content returned by the user terminal of the first target grading user is received. Based on the content features, user features, and grading user features, a second target grading user is selected from the candidate grading user set. The content to be graded and the first batch of graded content are sent to the user terminal of the second target grading user. The second graded content returned by the user terminal of the second target grading user is received as the graded content.

7. The method according to claim 1, characterized in that, The user terminals from which the content to be corrected originates include desk lamp terminals.

8. A content correction device, characterized in that, include: The first acquisition unit is configured to, in response to receiving content to be modified, acquire the content characteristics of the content to be modified and the user characteristics of the user from which the content to be modified originates; The second acquisition unit is used to acquire the user characteristics of the users who need to be graded in the candidate set of graded users. The selection unit is configured to select a subset of review users from the candidate review user set based on the content features and the review user features. The selection unit is further configured to: for each review user in the subset of review users, determine the matching degree between the review user and the content to be reviewed based on the content features, the user features and the review user features of the review user. When the number of received items to be corrected is at least two, a bipartite graph is constructed, wherein the vertices in the first vertex set of the bipartite graph are used to represent the items to be corrected, the vertices in the second vertex set of the bipartite graph are used to represent the users who make corrections, and the numerical values ​​corresponding to the edges in the bipartite graph are used to represent the degree of matching between the users who make corrections and the items to be corrected. Based on the bipartite graph, the matching degree between the user to be reviewed and the content to be reviewed is determined using the bipartite graph maximum weight matching algorithm. The users in the subset of users to be reviewed are sorted in descending order of matching degree. The target user to be reviewed is selected from the candidate set of users to be reviewed using the sorting result. The content to be reviewed is sent to the user terminal of the target user to be reviewed. The reviewed content is received from the user terminal. Based on the reviewed content, the final reviewed content is determined. as well as The sending unit is used to send the final revised content to the user terminal from which the content to be revised originates.

9. An electronic device, characterized in that, include: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

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