An intelligent processing method and system for multi-person online collaboration on a data visualization platform

Abstract

The present invention relates to the field of computer technology, and discloses an intelligent processing method and system for multi-person online collaboration on a data visualization platform. The method verifies the logged-in user based on login information; when the verification is passed, the collaboration request is analyzed to obtain an account authority value, and the corresponding collaboration data and collaboration results are presented based on the account authority value; a real-time communication interface is established, and data visualization is performed on an online collaboration platform, including real-time viewing of data, real-time modification of data, real-time update of data, and real-time notification of data; the generated data is stored in real time; the real-time stored data is encrypted to obtain encrypted data, thereby realizing intelligent processing and visualization of collaboration data, providing a simpler and more efficient online collaboration process, ensuring data security and visualization information security, and completing intelligent visualization based on mutual collaboration among team members. The method is not limited to large screens, but is more suitable for applications in digital cockpits based on large model analysis.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an intelligent processing method and system for multi-person online collaboration on a data visualization platform. Background Art

[0002] Online collaboration on the data visualization platform means that multiple people can participate in a project or task at the same time through the Internet, share and collaborate on all work materials. Online collaboration allows multiple people to view and edit online at the same time, save in real time, and control security. The data visualization platform also provides intelligent file management, file search, cloud encrypted storage, cloud synchronization and other functions to help users collaborate and manage files efficiently.

[0003] In traditional document design, design documents need to be transmitted with the help of third-party tools such as USB flash drives, emails, and instant messaging tools, which is time-consuming and laborious. Later, it developed into online design, where files can be shared directly within the website. However, if you want to see the modifications made by other collaborators, you must reopen the design document and cannot see the modifications in real time. Modifications can only be made alternately. This causes certain defects in the current online design, and it is impossible to achieve real-time collaboration, real-time viewing, modification, etc. These defects will reduce the overall work efficiency and affect the overall work progress. Summary of the invention

[0004] The embodiments of the present invention provide an intelligent processing method and system for online collaboration of multiple people on a data visualization platform, which is used to solve the technical problems in the prior art that online design cannot achieve real-time collaboration and real-time viewing, modification and other functions, resulting in a decrease in overall work efficiency, affecting the overall work progress, and being unable to achieve intelligent processing and visualization of collaborative data, and unable to ensure data security and visualization information security.

[0005] In order to achieve the above object, the present invention provides an intelligent processing method for multi-person online collaboration on a data visualization platform, comprising:

[0006] Furthermore, when the login information of the login user is obtained and the identity of the login user is verified based on the login information, it includes:

[0007] Receiving login information of a login user sent by the online collaboration platform, and parsing the login information to determine a login user name and a login password of the login user;

[0008] Verify the login user name and the login password, and search in the database whether there is a network identity record that matches the login user name and the login password.

[0009] If it exists, the identity authentication is judged to be successful;

[0010] If it does not exist, the identity authentication is judged to have failed.

[0011] Further, when receiving a collaboration request from a logged-in user, analyzing the collaboration request according to a preset rule to obtain the account authority value of the logged-in user includes:

[0012] Analyze the collaboration request according to preset rules to determine the analysis result, wherein the analysis result includes the account authorization identifier and the system authorization identifier of the logged-in user;

[0013] A traversal is performed in a preset account authority mapping relationship table according to the account authorization identifier and the system authorization identifier, and the account authority value of the logged-in user is determined.

[0014] Furthermore, before presenting the corresponding collaboration data and collaboration results to the logged-in user based on the account authority value, it also includes:

[0015] Determine all users who have passed verification within a preset time period and the historical feature information corresponding to each user, and determine the user's historical participation factor sequence based on the historical feature information;

[0016] The historical participation factor sequence is screened based on a preset screening factor to obtain a screening factor set, and tasks are allocated according to the historical participation factors in the screening factor set.

[0017] Furthermore, when determining all users who have passed verification within a preset time period and the historical characteristic information corresponding to each user, and determining the historical participation factor of the user according to the historical characteristic information, it includes:

[0018] Obtaining historical feature information corresponding to each user, wherein the historical feature information includes the number of task processing times, the average task processing time, and the number of successful task processing times;

[0019] A first allocation set is constructed according to the number of task processing times corresponding to each user, a second allocation set is constructed according to the average task processing time corresponding to each user, and a third allocation set is constructed according to the number of successful task processing times corresponding to each user;

[0020] Get the task processing times threshold, task average processing time threshold and task successful processing times threshold respectively;

[0021] Eliminate the task processing times less than the task processing times threshold in the first allocation set to obtain a fourth allocation set, eliminate the task average processing times less than the task average processing time threshold in the second allocation set to obtain a fifth allocation set, and eliminate the task successful processing times less than the task successful processing times threshold in the third allocation set to obtain a sixth allocation set;

[0022] Calculate the historical participation factor of the user according to the fourth allocation set, the fifth allocation set, and the sixth allocation set, and construct the historical participation factor sequence, wherein the historical participation factor sequence is A, A={h1, h2, h3, ..., hg}, hi is the historical participation factor of the i-th user, and g is the number of users;

[0023] The historical participation factor is calculated according to the following formula:

[0024] ;

[0025] Among them, hi is the historical participation factor of the i-th user, a1 is the weight corresponding to the number of task processing times, n is the number of task processing times in the fourth allocation set, xi is the number of task processing times corresponding to the i-th user in the fourth allocation set, xmax is the maximum number of task processing times in the fourth allocation set, b1 is the weight corresponding to the average task processing time, m is the number of average task processing times in the fifth allocation set, yi is the average task processing time corresponding to the i-th user in the fifth allocation set, ymax is the maximum average task processing time in the fifth allocation set, c1 is the weight corresponding to the number of successful task processing times, f is the number of successful task processing times in the sixth allocation set, zi is the number of successful task processing times corresponding to the i-th user in the sixth allocation set, and zmax is the maximum number of successful task processing times in the sixth allocation set.

[0026] Further, when the historical participation factor sequence is screened based on a preset screening factor to obtain a screening factor set, and task allocation is performed according to the historical participation factors in the screening factor set, it includes:

[0027] Sorting the historical participation factor sequence by numerical value to obtain a sorting result sequence;

[0028] Interpolating the preset screening factor into the sorting result sequence based on a numerical value rule;

[0029] All historical participation factors between the head historical participation factor in the sorting result sequence and the preset screening factor are taken as the screening factor set, and the remaining historical participation factors in the sorting result sequence are deleted.

[0030] Furthermore, when the historical participation factor sequence is screened based on a preset screening factor to obtain a screening factor set, and tasks are allocated according to the historical participation factors in the screening factor set, it also includes:

[0031] Collecting historical behavior information of each user in the screening factor set, and determining the task bias coefficient of the corresponding user based on the historical behavior information; setting the task allocation quantity of the corresponding user according to the historical participation factor in the screening factor set;

[0032] The task allocation quantity is compensated based on the task bias coefficient to obtain the target task allocation quantity of the corresponding user.

[0033] Furthermore, when collecting the historical behavior information of each user in the screening factor set and determining the task bias coefficient of the corresponding user based on the historical behavior information, it includes:

[0034] Perform bias analysis on the historical behavior information based on a preset bias analysis model to determine indirect bias behavior and continuous bias behavior corresponding to each user;

[0035] Obtain the tasks to be assigned distributed by the data visualization platform and determine the corresponding types of tasks to be assigned;

[0036] Determining the indirect bias correlation between the indirect bias behavior and the type of task to be assigned based on a preset correlation analysis model;

[0037] Determining the persistence bias correlation between the persistence bias behavior and the type of task to be assigned based on a preset correlation analysis model;

[0038] When the indirect bias correlation degree and the continuous bias correlation degree are equal, the continuous bias correlation degree or the indirect bias correlation degree is used as the task bias coefficient;

[0039] When the indirect bias correlation is greater than or equal to the continuous bias correlation, the indirect bias correlation is used as the task bias coefficient;

[0040] When the indirect bias correlation is less than or equal to the continuous bias correlation, the continuous bias correlation is used as the task bias coefficient.

[0041] Furthermore, it also includes:

[0042] When a logged-in user edits data on the online collaboration platform, the generated data is automatically saved, and the automatic saving process has the function of viewing the data modifier, data saving time, and data modification time.

[0043] In order to achieve the above object, the present invention provides an intelligent processing system for multi-person online collaboration on a data visualization platform, comprising:

[0044] An identity verification module, used to obtain the login information of the logged-in user and verify the identity of the logged-in user based on the login information;

[0045] The permission acquisition module is used to receive the collaboration request of the logged-in user when the identity authentication is passed, analyze the collaboration request according to the preset rules to obtain the account permission value of the logged-in user, and present the corresponding collaboration data and collaboration results to the logged-in user based on the account permission value;

[0046] A data collaboration module, used to establish a real-time communication interface between the user terminal and the online collaboration platform, and to perform data visualization on the online collaboration platform based on the real-time communication interface, wherein the data visualization includes real-time data viewing, real-time data modification, real-time data updating and real-time data notification;

[0047] A data storage module, used for storing the data generated on the online collaboration platform in real time when the logged-in user completes the online collaboration;

[0048] The data encryption module is used to obtain the data transmission requirements of the online collaboration platform in real time, obtain an encryption key using the data transmission requirements, encrypt the real-time stored data according to the encryption key to obtain encrypted data, and complete data transmission according to the encrypted data.

[0049] The present invention provides an intelligent processing method and system for multi-person online collaboration on a data visualization platform, which has the following beneficial effects compared with the prior art:

[0050] The present invention discloses an intelligent processing method and system for multi-person online collaboration on a data visualization platform, which verifies the logged-in user based on login information; when the verification is passed, analyzes the collaboration request to obtain the account authority value, and presents the corresponding collaboration data and collaboration results based on the account authority value; establishes a real-time communication interface, and performs data visualization on an online collaboration platform, including real-time data viewing, real-time data modification, real-time data updating, and real-time data notification; stores the generated data in real time; encrypts the real-time stored data to obtain encrypted data, thereby realizing intelligent processing and visualization of collaboration data, providing a simpler and more efficient online collaboration process, ensuring data security and visualization information security, and completing intelligent visualization based on mutual collaboration among team members, which is not limited to large screens, but is more suitable for applications in digital cockpits based on large model analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0052] Figure 1 A schematic flow chart of an intelligent processing method for multi-person online collaboration on a data visualization platform in an embodiment of the present invention is shown;

[0053] Figure 2 A structural schematic diagram of an intelligent processing system for multi-person online collaboration on a data visualization platform in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0054] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0055] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0056] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0057] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0058] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0059] like Figure 1As shown, an embodiment of the present invention discloses an intelligent processing method for multi-person online collaboration on a data visualization platform, comprising:

[0060] S110: Acquire login information of a login user, and verify the identity of the login user based on the login information;

[0061] In some embodiments of the present application, when the login information of the login user is obtained and the identity of the login user is verified based on the login information, it includes:

[0062] Receiving the login information of the login user sent by the terminal, and parsing the login information to determine the login user name and login password of the login user;

[0063] Verify the login user name and the login password, and search in the database whether there is a network identity record that matches the login user name and the login password.

[0064] If it exists, the identity authentication is judged to be successful;

[0065] If it does not exist, the identity authentication is judged to have failed.

[0066] In this embodiment, the network identity record refers to an identifier of whether the logged-in user is allowed to perform multi-person collaboration.

[0067] The beneficial effect of the above technical solution is: the present invention can ensure the data security of the multi-person collaboration process by judging whether the identity authentication of the logged-in user is passed. At the same time, based on the same display goal, multiple people collaborate to create the same large visualization screen and complete the same task. In the process of joint creation, different data and different results are displayed according to account permissions.

[0068] S120: When the identity authentication is passed, receiving a collaboration request from the logged-in user, analyzing the collaboration request according to a preset rule to obtain an account authority value of the logged-in user, and presenting corresponding collaboration data and collaboration results to the logged-in user based on the account authority value;

[0069] In some embodiments of the present application, when receiving a collaboration request from a logged-in user and analyzing the collaboration request according to a preset rule to obtain an account authority value of the logged-in user, the process includes:

[0070] Analyze the collaboration request according to preset rules to determine the analysis result, wherein the analysis result includes the account authorization identifier and the system authorization identifier of the logged-in user;

[0071] A traversal is performed in a preset account authority mapping relationship table according to the account authorization identifier and the system authorization identifier, and the account authority value of the logged-in user is determined.

[0072] In this embodiment, the system authorization identifier refers to the identifier generated after the above-mentioned login user passes the identity authentication.

[0073] In this embodiment, the account authority value of the logged-in user can be obtained by traversing the pre-set account authority mapping relationship table through the account authorization identifier.

[0074] The beneficial effect of the above technical solution is: the present invention presents corresponding collaboration data and collaboration results to the logged-in user based on the account authority value, which can make data processing targeted and secure, and ensure that different users process different data.

[0075] In some embodiments of the present application, before presenting corresponding collaboration data and collaboration results to the logged-in user based on the account authority value, the method further includes:

[0076] Determine all users who have passed verification within a preset time period and the historical feature information corresponding to each user, and determine the user's historical participation factor sequence based on the historical feature information;

[0077] The historical participation factor sequence is screened based on a preset screening factor to obtain a screening factor set, and tasks are allocated according to the historical participation factors in the screening factor set.

[0078] In some embodiments of the present application, when determining all users who have passed verification within a preset time period and the historical feature information corresponding to each user, and determining the historical participation factor of the user according to the historical feature information, it includes:

[0079] Obtaining historical feature information corresponding to each user, wherein the historical feature information includes the number of task processing times, the average task processing time, and the number of successful task processing times;

[0080] A first allocation set is constructed according to the number of task processing times corresponding to each user, a second allocation set is constructed according to the average task processing time corresponding to each user, and a third allocation set is constructed according to the number of successful task processing times corresponding to each user;

[0081] Get the task processing times threshold, task average processing time threshold and task successful processing times threshold respectively;

[0082] Eliminate the task processing times less than the task processing times threshold in the first allocation set to obtain a fourth allocation set, eliminate the task average processing times less than the task average processing time threshold in the second allocation set to obtain a fifth allocation set, and eliminate the task successful processing times less than the task successful processing times threshold in the third allocation set to obtain a sixth allocation set;

[0083] Calculate the historical participation factor of the user according to the fourth allocation set, the fifth allocation set, and the sixth allocation set, and construct the historical participation factor sequence, wherein the historical participation factor sequence is A, A={h1, h2, h3, ..., hg}, hi is the historical participation factor of the i-th user, and g is the number of users;

[0084] The historical participation factor is calculated according to the following formula:

[0085] ;

[0086] Among them, hi is the historical participation factor of the i-th user, a1 is the weight corresponding to the number of task processing times, n is the number of task processing times in the fourth allocation set, xi is the number of task processing times corresponding to the i-th user in the fourth allocation set, xmax is the maximum number of task processing times in the fourth allocation set, b1 is the weight corresponding to the average task processing time, m is the number of average task processing times in the fifth allocation set, yi is the average task processing time corresponding to the i-th user in the fifth allocation set, ymax is the maximum average task processing time in the fifth allocation set, c1 is the weight corresponding to the number of successful task processing times, f is the number of successful task processing times in the sixth allocation set, zi is the number of successful task processing times corresponding to the i-th user in the sixth allocation set, and zmax is the maximum number of successful task processing times in the sixth allocation set.

[0087] In this embodiment, the preset time period can be set according to the urgency of the task to be assigned. When the task to be assigned is more urgent, the preset time period is shortened. When the task to be assigned is not urgent, the preset time period is extended. The setting is made according to the actual situation.

[0088] In this embodiment, the number of successful task processing times refers to the number of successful multi-person collaborations. In actual situations, when users are performing multi-person collaborations, there may be situations where task processing fails.

[0089] In this embodiment, according to the task processing times threshold, the task average processing time threshold and the task successful processing times threshold, some users with few task processing times, short average task processing times and few successful task processing times can be eliminated to improve processing efficiency.

[0090] In this embodiment, g is the total number of users in the fourth allocation set, the fifth allocation set, and the sixth allocation set.

[0091] The beneficial effect of the above technical solution is that the present invention calculates the user's historical participation factor according to the fourth allocation set, the fifth allocation set, and the sixth allocation set, and constructs a historical participation factor sequence, thereby laying a foundation for the targeted allocation of the number of tasks to be allocated.

[0092] In some embodiments of the present application, when the historical participation factor sequence is screened based on a preset screening factor to obtain a screening factor set, and tasks are allocated according to the historical participation factors in the screening factor set, the process includes:

[0093] Sorting the historical participation factor sequence by numerical value to obtain a sorting result sequence;

[0094] Interpolating the preset screening factor into the sorting result sequence based on a numerical value rule;

[0095] All historical participation factors between the head historical participation factor in the sorting result sequence and the preset screening factor are taken as the screening factor set, and the remaining historical participation factors in the sorting result sequence are deleted.

[0096] In this embodiment, when all historical participation factors between the head historical participation factor in the sorting result sequence and the preset screening factor are taken as the screening factor set, the screening factor set includes the head historical participation factor in the sorting result sequence and the preset screening factor.

[0097] The beneficial effect of the above technical solution is that the present invention can further improve the efficiency of multi-person collaboration by obtaining a set of screening factors and deleting the remaining historical participating factors in the sorting result sequence.

[0098] When the historical participation factor sequence is screened based on the preset screening factor to obtain a screening factor set, and tasks are allocated according to the historical participation factors in the screening factor set, the method further includes:

[0099] Collecting historical behavior information of each user in the screening factor set, and determining the task bias coefficient of the corresponding user based on the historical behavior information; setting the task allocation quantity of the corresponding user according to the historical participation factor in the screening factor set;

[0100] The task allocation quantity is compensated based on the task bias coefficient to obtain the target task allocation quantity of the corresponding user.

[0101] In some embodiments of the present application, when collecting the historical behavior information of each user in the screening factor set and determining the task bias coefficient of the corresponding user based on the historical behavior information, it includes:

[0102] Perform bias analysis on the historical behavior information based on a preset bias analysis model to determine indirect bias behavior and continuous bias behavior corresponding to each user;

[0103] Obtain the tasks to be assigned distributed by the data visualization platform and determine the corresponding types of tasks to be assigned;

[0104] Determining the indirect bias correlation between the indirect bias behavior and the type of task to be assigned based on a preset correlation analysis model;

[0105] Determining the persistence bias correlation between the persistence bias behavior and the type of task to be assigned based on a preset correlation analysis model;

[0106] When the indirect bias correlation degree and the continuous bias correlation degree are equal, the continuous bias correlation degree or the indirect bias correlation degree is used as the task bias coefficient;

[0107] When the indirect bias correlation is greater than or equal to the continuous bias correlation, the indirect bias correlation is used as the task bias coefficient;

[0108] When the indirect bias correlation is less than or equal to the continuous bias correlation, the continuous bias correlation is used as the task bias coefficient.

[0109] In this embodiment, the bias analysis model is trained in advance, and the bias analysis model can analyze historical behavior information to determine indirect bias behavior and continuous bias behavior.

[0110] In this embodiment, the indirect biased behavior refers to the biased behavior of the user within a period of time.

[0111] In this embodiment, the continuous bias behavior is the bias behavior of the user between the first multi-person collaboration and the current multi-person collaboration.

[0112] In this embodiment, the correlation analysis model is trained in advance, and the correlation analysis model can output the correlation between the indirect bias behavior and the continuous bias behavior.

[0113] In this embodiment, when the indirect bias correlation degree and the continuous bias correlation degree are equal, the continuous bias correlation degree or the indirect bias correlation degree can be randomly selected as the task bias coefficient.

[0114] In this embodiment, the task preference coefficient refers to a measurement value of which type of task the user prefers to handle.

[0115] The beneficial effect of the above technical solution is that the present invention can determine the task bias coefficient according to the indirect bias correlation and the continuous bias correlation, thereby ensuring the allocation accuracy of the tasks to be allocated and further improving the efficiency of multi-person collaboration.

[0116] In some embodiments of the present application, when setting the number of tasks assigned to the corresponding user according to the historical participation factor in the screening factor set, it includes:

[0117] Presetting a first preset historical participation factor and a second preset historical participation factor;

[0118] Setting the number of tasks assigned to the corresponding user according to the relationship between the historical participation factor in the screening factor set, the first preset historical participation factor, and the second preset historical participation factor;

[0119] When the historical participation factor is less than the first preset historical participation factor, the number of tasks assigned to the corresponding user is set to k1;

[0120] When the historical participation factor is greater than or equal to the first preset historical participation factor, and the historical participation factor is less than the second preset historical participation factor, the number of tasks assigned to the corresponding user is set to k2;

[0121] When the historical participation factor is greater than or equal to the second preset historical participation factor, the number of tasks assigned to the corresponding user is set to k3; wherein k1 <k2<k3;

[0122] When the number of tasks assigned to the corresponding user is set to ki, i=1,2,3.

[0123] The beneficial effect of the above technical solution is: the present invention sets the number of task assignments for the corresponding user according to the relationship between the historical participation factor, the first preset historical participation factor and the second preset historical participation factor in the screening factor set. By setting the number of task assignments for the corresponding user, accurate allocation of the assigned tasks can be achieved, shortening the progress of multi-person collaborative processing.

[0124] In some embodiments of the present application, when compensating the task allocation quantity based on the task bias coefficient to obtain the corresponding user's target task allocation quantity, it includes:

[0125] Presetting a first preset task bias coefficient and a second preset task bias coefficient;

[0126] Compensating the task allocation quantity ki according to the relationship between the task bias coefficient, the first preset task bias coefficient and the second preset task bias coefficient to obtain the corresponding target task allocation quantity for the user;

[0127] When the task bias coefficient is less than the first preset task bias coefficient, the task allocation quantity ki is compensated based on the preset first compensation factor d1 to obtain the corresponding user's target task allocation quantity ki*d1;

[0128] When the task bias coefficient is greater than or equal to the first preset task bias coefficient, and the task bias coefficient is less than the second preset task bias coefficient, the task allocation quantity ki is compensated based on the preset second compensation factor d2 to obtain the corresponding user's target task allocation quantity ki*d2;

[0129] When the task bias coefficient is greater than or equal to the second preset task bias coefficient, the task allocation quantity ki is compensated based on the preset third compensation factor d3 to obtain the corresponding user's target task allocation quantity ki*d3, where d1 <d2<d3。

[0130] The beneficial effect of the above technical solution is: the present invention compensates the task assignment quantity ki according to the relationship between the task bias coefficient, the first preset task bias coefficient and the second preset task bias coefficient, and obtains the target task assignment quantity of the corresponding user. By obtaining the target task assignment quantity of the corresponding user, the user's task processing initiative can be improved and the possibility of errors in the multi-person collaboration process can be reduced.

[0131] S130: establishing a real-time communication interface between the user terminal and the online collaboration platform, and performing data visualization on the online collaboration platform based on the real-time communication interface, wherein the data visualization includes real-time data viewing, real-time data modification, real-time data updating, and real-time data notification;

[0132] The beneficial effects of the above technical solution are: establishing a real-time communication interface between the user end and the online collaboration platform, providing users with the function of sharing charts, data and annotations, ensuring that team members can view and modify data in real time. Realizing real-time communication between the user end and the online collaboration platform so that when a team member operates on the data, other members can immediately see the update.

[0133] S140: When the logged-in user completes the online collaboration, the data generated on the online collaboration platform is stored in real time;

[0134] The beneficial effect of the above technical solution is that the present invention can store the data generated by the logged-in user in real time to avoid data loss.

[0135] S150: acquiring data transmission requirements of the online collaboration platform in real time, obtaining an encryption key using the data transmission requirements, encrypting the data stored in real time according to the encryption key to obtain encrypted data, and completing data transmission according to the encrypted data.

[0136] The beneficial effect of the above technical solution is that by encrypting the data in the process of multi-person online collaboration, it can be further ensured that the data will not be leaked, so as to ensure the data security when multiple people and multiple departments work together.

[0137] In some embodiments of the present application, it also includes:

[0138] When a logged-in user edits data on the online collaboration platform, the generated data is automatically saved, and the automatic saving process has the function of viewing the data modifier, data saving time, and data modification time.

[0139] The beneficial effect of the above technical solution is that the present invention automatically saves the generated data, and the automatic saving process has the function of viewing the data modifier, data saving time, and data modification time, realizing the versatility of online collaborative processing, meeting user needs, and completing intelligent visualization display based on mutual collaboration between team members. It is not limited to large screens, but is more suitable for applications in digital cockpits based on large model analysis.

[0140] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0141] Correspondingly, such as Figure 2 As shown, the present application also provides an intelligent processing system for multi-person online collaboration on a data visualization platform, including:

[0142] An identity verification module, used to obtain the login information of the logged-in user and verify the identity of the logged-in user based on the login information;

[0143] The permission acquisition module is used to receive the collaboration request of the logged-in user when the identity authentication is passed, analyze the collaboration request according to the preset rules to obtain the account permission value of the logged-in user, and present the corresponding collaboration data and collaboration results to the logged-in user based on the account permission value;

[0144] A data collaboration module, used to establish a real-time communication interface between the user terminal and the online collaboration platform, and to perform data visualization on the online collaboration platform based on the real-time communication interface, wherein the data visualization includes real-time data viewing, real-time data modification, real-time data updating and real-time data notification;

[0145] A data storage module, used for storing the data generated on the online collaboration platform in real time when the logged-in user completes the online collaboration;

[0146] The data encryption module is used to obtain the data transmission requirements of the online collaboration platform in real time, obtain an encryption key using the data transmission requirements, encrypt the real-time stored data according to the encryption key to obtain encrypted data, and complete data transmission according to the encrypted data.

[0147] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0148] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

[0149] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent processing method for multi-person online collaboration on a data visualization platform, characterized in that: include: Obtaining login information of a logged-in user, and verifying the identity of the logged-in user based on the login information; When the identity authentication is passed, a collaboration request of the logged-in user is received, the collaboration request is analyzed according to preset rules to obtain the account authority value of the logged-in user, and corresponding collaboration data and collaboration results are presented to the logged-in user based on the account authority value; Establishing a real-time communication interface between the user terminal and the online collaboration platform, and performing data visualization on the online collaboration platform based on the real-time communication interface, wherein the data visualization includes real-time data viewing, real-time data modification, real-time data updating and real-time data notification; When the logged-in user completes the online collaboration, the data generated on the online collaboration platform is stored in real time; Acquire the data transmission requirements of the online collaboration platform in real time, obtain an encryption key using the data transmission requirements, encrypt the data stored in real time according to the encryption key to obtain encrypted data, and complete the data transmission according to the encrypted data; Before presenting corresponding collaboration data and collaboration results to the logged-in user based on the account authority value, the method further includes: Determine all users who have passed verification within a preset time period and the historical feature information corresponding to each user, and determine the user's historical participation factor sequence based on the historical feature information; The historical participation factor sequence is screened based on a preset screening factor to obtain a screening factor set, and tasks are assigned according to the historical participation factors in the screening factor set; When determining all users who have passed verification within a preset time period and the historical characteristic information corresponding to each user, and determining the historical participation factor sequence of the user according to the historical characteristic information, it includes: Obtaining historical feature information corresponding to each user, wherein the historical feature information includes the number of task processing times, the average task processing time, and the number of successful task processing times; A first allocation set is constructed according to the number of task processing times corresponding to each user, a second allocation set is constructed according to the average task processing time corresponding to each user, and a third allocation set is constructed according to the number of successful task processing times corresponding to each user; Get the task processing times threshold, task average processing time threshold and task successful processing times threshold respectively; Eliminate the task processing times less than the task processing times threshold in the first allocation set to obtain a fourth allocation set, eliminate the task average processing times less than the task average processing time threshold in the second allocation set to obtain a fifth allocation set, and eliminate the task successful processing times less than the task successful processing times threshold in the third allocation set to obtain a sixth allocation set; Calculate the historical participation factor of the user according to the fourth allocation set, the fifth allocation set, and the sixth allocation set, and construct the historical participation factor sequence, wherein the historical participation factor sequence is A, A={h1, h2, h3, ..., hg}, hi is the historical participation factor of the i-th user, and g is the number of users; The historical participation factor is calculated according to the following formula: ； Among them, hi is the historical participation factor of the i-th user, a1 is the weight corresponding to the number of task processing times, n is the number of task processing times in the fourth allocation set, xi is the number of task processing times corresponding to the i-th user in the fourth allocation set, xmax is the maximum number of task processing times in the fourth allocation set, b1 is the weight corresponding to the average task processing time, m is the number of average task processing times in the fifth allocation set, yi is the average task processing time corresponding to the i-th user in the fifth allocation set, ymax is the maximum average task processing time in the fifth allocation set, c1 is the weight corresponding to the number of successful task processing times, f is the number of successful task processing times in the sixth allocation set, zi is the number of successful task processing times corresponding to the i-th user in the sixth allocation set, and zmax is the maximum number of successful task processing times in the sixth allocation set.

2. The intelligent processing method for multi-person online collaboration on a data visualization platform according to claim 1 is characterized in that: When the login information of the login user is obtained and the identity of the login user is verified based on the login information, it includes: Receiving login information of a login user sent by the online collaboration platform, and parsing the login information to determine a login user name and a login password of the login user; Verify the login user name and the login password, and search in the database whether there is a network identity record that matches the login user name and the login password. If it exists, the identity authentication is judged to be successful; If it does not exist, the identity authentication is judged to have failed.

3. The intelligent processing method for multi-person online collaboration on a data visualization platform according to claim 1 is characterized in that: When receiving a collaboration request from a logged-in user and analyzing the collaboration request according to a preset rule to obtain the account authority value of the logged-in user, the method includes: Analyze the collaboration request according to preset rules to determine the analysis result, wherein the analysis result includes the account authorization identifier and the system authorization identifier of the logged-in user; A traversal is performed in a preset account authority mapping relationship table according to the account authorization identifier and the system authorization identifier, and the account authority value of the logged-in user is determined.

4. The intelligent processing method for multi-person online collaboration on a data visualization platform according to claim 1 is characterized in that: When the historical participation factor sequence is screened based on the preset screening factor to obtain a screening factor set, and task allocation is performed according to the historical participation factors in the screening factor set, it includes: Sorting the historical participation factor sequence by numerical value to obtain a sorting result sequence; Interpolating the preset screening factor into the sorting result sequence based on a numerical value rule; All historical participation factors between the head historical participation factor in the sorting result sequence and the preset screening factor are taken as the screening factor set, and the remaining historical participation factors in the sorting result sequence are deleted.

5. The intelligent processing method for multi-person online collaboration on a data visualization platform according to claim 4 is characterized in that: When the historical participation factor sequence is screened based on the preset screening factor to obtain a screening factor set, and tasks are allocated according to the historical participation factors in the screening factor set, the method further includes: Collecting historical behavior information of each user in the screening factor set, and determining the task bias coefficient of the corresponding user based on the historical behavior information; setting the task allocation quantity of the corresponding user according to the historical participation factor in the screening factor set; The task allocation quantity is compensated based on the task bias coefficient to obtain the target task allocation quantity of the corresponding user.

6. The intelligent processing method for multi-person online collaboration on a data visualization platform according to claim 5 is characterized in that: When collecting the historical behavior information of each user in the screening factor set and determining the task bias coefficient of the corresponding user based on the historical behavior information, it includes: Perform bias analysis on the historical behavior information based on a preset bias analysis model to determine indirect bias behavior and continuous bias behavior corresponding to each user; Obtain the tasks to be assigned distributed by the data visualization platform and determine the corresponding types of tasks to be assigned; Determining the indirect bias correlation between the indirect bias behavior and the type of task to be assigned based on a preset correlation analysis model; Determining the persistence bias correlation between the persistence bias behavior and the type of task to be assigned based on a preset correlation analysis model; When the indirect bias correlation degree and the continuous bias correlation degree are equal, the continuous bias correlation degree or the indirect bias correlation degree is used as the task bias coefficient; When the indirect bias correlation is greater than or equal to the continuous bias correlation, the indirect bias correlation is used as the task bias coefficient; When the indirect bias correlation is less than or equal to the continuous bias correlation, the continuous bias correlation is used as the task bias coefficient.

7. The intelligent processing method for multi-person online collaboration on a data visualization platform according to claim 1 is characterized in that: Also includes: When a logged-in user edits data on the online collaboration platform, the generated data is automatically saved, and the automatic saving process has the function of viewing the data modifier, data saving time, and data modification time.

8. An intelligent processing system for multi-person online collaboration on a data visualization platform, applied to the intelligent processing method for multi-person online collaboration on a data visualization platform as claimed in any one of claims 1 to 7, characterized in that: include: An identity verification module, used to obtain the login information of the logged-in user and verify the identity of the logged-in user based on the login information; The permission acquisition module is used to receive the collaboration request of the logged-in user when the identity authentication is passed, analyze the collaboration request according to the preset rules to obtain the account permission value of the logged-in user, and present the corresponding collaboration data and collaboration results to the logged-in user based on the account permission value; A data collaboration module, used to establish a real-time communication interface between the user terminal and the online collaboration platform, and to perform data visualization on the online collaboration platform based on the real-time communication interface, wherein the data visualization includes real-time data viewing, real-time data modification, real-time data updating and real-time data notification; A data storage module, used for storing the data generated on the online collaboration platform in real time when the logged-in user completes the online collaboration; The data encryption module is used to obtain the data transmission requirements of the online collaboration platform in real time, obtain an encryption key using the data transmission requirements, encrypt the real-time stored data according to the encryption key to obtain encrypted data, and complete data transmission according to the encrypted data.

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