Method and system for setting approval data items based on three-dimensional structure

Abstract

The present invention discloses a method and system for setting approval data items based on a three-dimensional structure, the method comprising: when an approval node receives form data corresponding to an approval process, obtaining the form data; judging whether the approval node belongs to an initial node according to the form data; when judging that the approval node is an initial node, generating an approval data item of a three-dimensional vector structure; the approval data item includes an approval user, approval content and approval time; and writing the approval data item into the form data so that the form data updates the approval data item when it is sent to the next approval node. It can be seen that the present invention can reduce the storage space of node approval records through data items of a three-dimensional vector structure, effectively improve the storage efficiency of node approval records, and improve the working efficiency of the approval system.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for setting approval data items based on a three-dimensional structure. Background Art

[0002] Process approval business is an unavoidable demand in the process of information system construction. It is generally implemented by workflow technology, BPN technology or BPMN technology. However, these technologies only solve the process problems of each node of process approval, and the management of data at each node requires form technology. In a process approval business, a process generally needs to be bound to a specific form to complete its approval business. The process controls the approval node, and the form is used to manage and store the approval content data of each node. A form consists of multiple data items for managing different approval contents. These data items are generally divided into fixed data items, one-time approval data items and multiple approval data items. For multiple approval data items, in order to allow subsequent approvers to understand the content of the previous approval, that is, the historical record, the usual practice is to generate an approval record for each approval. In this way, when a certain approval item is approved N times, there must be N records. This practice leads to great storage redundancy in the system, which will increase the load of the system, slow the system response speed and poor user experience. It can be seen that the existing technology has defects and needs to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for setting approval data items based on a three-dimensional structure, which can reduce the storage space of node approval records through data items of a three-dimensional vector structure, effectively improve the storage efficiency of node approval records, and improve the working efficiency of the approval system.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for setting approval data items based on a three-dimensional structure, the method comprising:

[0005] When the approval node receives the form data corresponding to the approval process, the form data is obtained;

[0006] According to the form data, determining whether the approval node belongs to an initial node;

[0007] When the approval node is determined to be an initial node, an approval data item of a three-dimensional vector structure is generated; the approval data item includes an approval user, approval content and approval time;

[0008] The approval data item is written into the form data, so that the form data updates the approval data item when being sent to the next approval node.

[0009] As an optional implementation, in the first aspect of the present invention, the approval data item is data in YAML format.

[0010] As an optional implementation, in the first aspect of the present invention, the approval data item is data in JSON format.

[0011] As an optional implementation, in the first aspect of the present invention, the approval data item is data in XML format.

[0012] As an optional implementation, in the first aspect of the present invention, judging whether the approval node belongs to an initial node according to the form data includes:

[0013] Determine whether there is a fixed data item in the form data to obtain a first determination result;

[0014] When the first judgment result is yes, judging whether there are corresponding multiple approval data items in the form data according to the node type of the approval node, and obtaining a second judgment result;

[0015] When the second judgment result is no, it is determined that the approval node belongs to an initial node.

[0016] As an optional implementation, in the first aspect of the present invention, judging whether there are corresponding multiple approval data items in the form data according to the node type of the approval node to obtain a second judgment result includes:

[0017] Determine in a preset database a plurality of historical multiple approval data items corresponding to the node type of the approval node; the node type includes the node level, the node organization, the node personnel information and the node name;

[0018] Calculate the weighted sum average of the similarities between all the historical multiple-approved data items and the form data to obtain the existence probability parameter corresponding to the form data; wherein the weighted calculation weight corresponding to the similarity between each of the historical multiple-approved data items and the form data is inversely proportional to the data volume of the historical multiple-approved data item;

[0019] It is determined whether the existence probability parameter is greater than a preset parameter threshold to obtain a second determination result.

[0020] As an optional implementation, in the first aspect of the present invention, the generation of the approval data item of the three-dimensional vector structure includes:

[0021] According to the approval node and the form data, based on a neural network algorithm, predicting the data item capacity corresponding to the form data;

[0022] According to the data item capacity, an approval data item with a marking parameter of the data item capacity is generated in a three-dimensional vector structure, so that when the approval data item is subsequently filled, if the data item capacity is exceeded, a data overflow alarm will be issued for the filling.

[0023] As an optional implementation, in the first aspect of the present invention, predicting the data item capacity corresponding to the form data based on the approval node and the form data based on a neural network algorithm includes:

[0024] The historical approval records and node types of the approval nodes and the form data are input into a trained CNN prediction neural network to obtain the data item capacity corresponding to the output form data; the CNN prediction neural network is trained using a training data set including multiple training approval node types and corresponding approval record annotations, approval form data annotations, and data item final capacity annotations.

[0025] A second aspect of an embodiment of the present invention discloses a system for setting approval data items based on a three-dimensional structure, the system comprising:

[0026] An acquisition module, used for acquiring the form data corresponding to the approval process when the approval node receives the form data;

[0027] A judgment module, used for judging whether the approval node belongs to an initial node according to the form data;

[0028] A generating module, for generating an approval data item of a three-dimensional vector structure when judging that the approval node is an initial node; the approval data item includes an approval user, approval content and approval time;

[0029] The writing module is used to write the approval data item into the form data, so that the form data updates the approval data item when it is sent to the next approval node.

[0030] As an optional implementation, in the second aspect of the present invention, the approval data item is data in YAML format.

[0031] As an optional implementation, in the second aspect of the present invention, the approval data item is data in JSON format.

[0032] As an optional implementation, in the second aspect of the present invention, the approval data item is data in XML format.

[0033] As an optional implementation, in the second aspect of the present invention, the specific manner in which the judgment module judges whether the approval node belongs to the initial node according to the form data includes:

[0034] Determine whether there is a fixed data item in the form data to obtain a first determination result;

[0035] When the first judgment result is yes, judging whether there are corresponding multiple approval data items in the form data according to the node type of the approval node, and obtaining a second judgment result;

[0036] When the second judgment result is no, it is determined that the approval node belongs to an initial node.

[0037] As an optional implementation, in the second aspect of the present invention, the judgment module judges whether there are corresponding multiple approval data items in the form data according to the node type of the approval node, and the specific manner of obtaining the second judgment result includes:

[0038] Determine in a preset database a plurality of historical multiple approval data items corresponding to the node type of the approval node; the node type includes the node level, the node organization, the node personnel information and the node name;

[0039] Calculate the weighted sum average of the similarities between all the historical multiple-approved data items and the form data to obtain the existence probability parameter corresponding to the form data; wherein the weighted calculation weight corresponding to the similarity between each of the historical multiple-approved data items and the form data is inversely proportional to the data volume of the historical multiple-approved data item;

[0040] It is determined whether the existence probability parameter is greater than a preset parameter threshold to obtain a second determination result.

[0041] As an optional implementation, in the second aspect of the present invention, the specific manner in which the generation module generates the approval data item of the three-dimensional vector structure includes:

[0042] According to the approval node and the form data, based on a neural network algorithm, predicting the data item capacity corresponding to the form data;

[0043] According to the data item capacity, an approval data item with a marking parameter of the data item capacity is generated in a three-dimensional vector structure, so that when the approval data item is subsequently filled, if the data item capacity is exceeded, a data overflow alarm will be issued for the filling.

[0044] As an optional implementation, in the second aspect of the present invention, the specific manner in which the generation module predicts the data item capacity corresponding to the form data based on the approval node and the form data and based on a neural network algorithm includes:

[0045] The historical approval records and node types of the approval nodes and the form data are input into a trained CNN prediction neural network to obtain the data item capacity corresponding to the output form data; the CNN prediction neural network is trained using a training data set including multiple training approval node types and corresponding approval record annotations, approval form data annotations, and data item final capacity annotations.

[0046] The third aspect of the present invention discloses another approval data item setting system based on a three-dimensional structure, the system comprising:

[0047] A memory storing executable program code;

[0048] a processor coupled to the memory;

[0049] The processor calls the executable program code stored in the memory to execute part or all of the steps in the three-dimensional structure-based approval data item setting method disclosed in the first aspect of the present invention.

[0050] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the three-dimensional structure-based approval data item setting method disclosed in the first aspect of the present invention.

[0051] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0052] The present invention can generate an approval data item with a three-dimensional vector structure of the approval user, approval content and approval time for the form data at the initial approval node, so that the form data can update the approval data item at the next approval node to achieve efficient recording of the approval record, thereby reducing the storage space of the node approval record through the data item of the three-dimensional vector structure, effectively improving the storage efficiency of the node approval record, and improving the working efficiency of the approval system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 It is a flowchart of a method for setting approval data items based on a three-dimensional structure disclosed in an embodiment of the present invention.

[0055] Figure 2It is a structural diagram of a three-dimensional structure-based approval data item setting system disclosed in an embodiment of the present invention.

[0056] Figure 3 It is a structural diagram of another three-dimensional structure-based approval data item setting system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0059] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0060] The present invention discloses a method and system for setting approval data items based on a three-dimensional structure, which can generate approval data items with a three-dimensional vector structure of approval users, approval content and approval time for form data at an initial approval node, so that the form data can update the approval data items at the next approval node to achieve efficient recording of approval records, thereby reducing the storage space of node approval records through data items with a three-dimensional vector structure, effectively improving the storage efficiency of node approval records, and improving the working efficiency of the approval system. The following are detailed descriptions.

[0061] Embodiment 1

[0062] See also Figure 1 , Figure 11 is a flow chart of a method for setting approval data items based on a three-dimensional structure disclosed in an embodiment of the present invention. Figure 1 The described method for setting approval data items based on a three-dimensional structure can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the approval data item setting method based on the three-dimensional structure may include the following operations:

[0063] 101. When the approval node receives the form data corresponding to the approval process, the form data is obtained.

[0064] 102. According to the form data, determine whether the approval node belongs to the initial node.

[0065] 103. When it is determined that the approval node is an initial node, an approval data item of a three-dimensional vector structure is generated.

[0066] Optionally, the approval data items include the approving user, approval content and approval time.

[0067] 104. Write the approval data item into the form data so that the form data updates the approval data item when it is sent to the next approval node.

[0068] It can be seen that the above-mentioned embodiment of the invention can generate an approval data item with a three-dimensional vector structure of the approval user, approval content and approval time for the form data at the initial approval node, so that the form data can update the approval data item at the next approval node to achieve efficient recording of the approval record, thereby reducing the storage space of the node approval record through the data item of the three-dimensional vector structure, effectively improving the storage efficiency of the node approval record, and improving the working efficiency of the approval system.

[0069] As an optional embodiment, in the above steps, the approval data item is data in YAML format.

[0070] Specifically, the YAML notation is:

[0071] dataitem:

[0072] - Approving user: Approving user_1;

[0073] Approval content: Approval content_1;

[0074] Approval time: approval time_1;

[0075] - Approving user: Approving user_2;

[0076] Approval content: Approval content_2;

[0077] Approval time: approval time_2; .................

[0079] - Approving user: approvinguser_n;

[0080] Approval content: approval content_n;

[0081] Approval time: Approval time_n.

[0082] It can be seen that through the above optional embodiments, the data type of the approval data item is limited, and YAML, a technology that is highly readable and can express data in a serialized manner, can be used to further assist in reducing the storage space of node approval records, effectively improve the storage efficiency of node approval records, and improve the working efficiency of the approval system.

[0083] As an optional embodiment, in the above steps, the approval data item is data in JSON format.

[0084] Specifically, the JSON representation is:

[0085] { dataitem :

[0086] [{Approval user: Approval user_1,

[0087] Approval content: Approval content_1,

[0088] Approval time: Approval time_1},

[0089] {Approval user: Approval user_2,

[0090] Approval content: Approval content_2,

[0091] Approval time: Approval time_2},

[0092] ...........................,

[0093] {Approving user: Approving user_n,

[0094] Approval content: Approval content_n,

[0095] Approval time: approval time_n}]}.

[0096] It can be seen that through the above optional embodiments, the data type of the approval data item is limited, and the highly readable and lightweight data exchange format such as JSON can be used to further assist in reducing the storage space of the node approval records, effectively improve the storage efficiency of the node approval records, and improve the working efficiency of the approval system.

[0097] As an optional embodiment, in the above steps, the approval data item is data in XML format.

[0098] Specifically, the XML representation is:

[0099] <?xml version="1.0" encoding="UTF-8"?>

[0100] <!DOCTYPE xml>

[0101] <xml>

[0102] <dataitem>

[0103] <Approving User>Approving User_1< / Approving User>

[0104] <Approval Content>Approval Content_1< / Approval Content>

[0105] <Approval Time>Approval Time_1< / Approval Time>

[0106] < / dataitem>

[0107] <dataitem>

[0108] <Approving User>Approving User_2< / Approving User>

[0109] <Approval Content>Approval Content_2< / Approval Content>

[0110] <Approval Time>Approval Time_2< / Approval Time>

[0111] < / dataitem> .......................

[0113] <dataitem>

[0114] <Approving User>Approving User_n< / Approving User>

[0115] <Approval Content>Approval Content_n< / Approval Content>

[0116] <Approval Time>Approval Time_n< / Approval Time>

[0117] < / dataitem>

[0118] < / xml> .

[0119] It can be seen that through the above optional embodiments, the data type of the approval data item is limited, and the highly readable and lightweight extensible markup language such as XML can further assist in reducing the storage space of the node approval records, effectively improve the storage efficiency of the node approval records, and improve the working efficiency of the approval system.

[0120] Specifically, the YAML, JSON, and XML technologies in the present invention can realize efficient management of the approval content (history records) of each stage of the process approval documents, and are used to identify the content of any approval data item, and can describe the content of any approval data item any number of times in the form of text, thereby solving problems such as system storage redundancy, load, slow response speed, and poor user experience, and enhancing system flexibility and versatility.

[0121] The technical solution of the present invention is for any process approval data item (dataitem), which is set as a text (TEXT) or binary (Binary) data item in the form, and any one of YAML, JSON, and XML technologies is used to set the expression form of the approval data item content.

[0122] In a specific implementation scheme, the process approval steps are as follows:

[0123] (1) Initiate the process and fill in fixed data items.

[0124] (2) Enter the approval node and load the YAML / JSON / XML text of each existing approval content.

[0125] (3) Fill in the approval content and generate a vector of the approval content (approver, approval content, approval time).

[0126] (4) Add the approval content vector (approver, approval content, approval time) to the YAML / JSON / XML text of the existing approval content.

[0127] (5) When it is the first approval node, there is no existing YAML / JSON / XML text of the approval content, but a corresponding YAML / JSON / XML text is created. Taking the YAML text as an example, its approval data items are as follows:

[0128] dataitem_1:

[0129] - Approving user: Approving user_1

[0130] Approval content: Approval content_1

[0131] Approval time: Approval time_1

[0132] dataitem_2:

[0133] - Approving user: Approving user_1

[0134] Approval content: Approval content_1

[0135] Approval time: Approval time_1

[0136] ………………………………

[0137] dataitem_m:

[0138] - Approving user: Approving user_1

[0139] Approval content: Approval content_1

[0140] Approval time: Approval time_1.

[0141] (6) Store the YAML texts of the m approval items into the corresponding data items of the form respectively.

[0142] (7) This process repeats until the process is approved.

[0143] (8) When the Nth approval is performed, the following YAML text will be generated and stored in the corresponding data items:

[0144] dataitem_1:

[0145] - Approving user: Approving user_1

[0146] Approval content: Approval content_1

[0147] Approval time: Approval time_1

[0148] - Approving user: Approving user_2

[0149] Approval content: Approval content_2

[0150] Approval time: Approval time_2

[0151] …………………………

[0152] - Approving user: Approving user_n

[0153] Approval content: Approval content_n

[0154] Approval time: Approval time_n

[0155] dataitem_2:

[0156] - Approving user: Approving user_1

[0157] Approval content: Approval content_1

[0158] Approval time: Approval time_1

[0159] - Approving user: Approving user_2

[0160] Approval content: Approval content_2

[0161] Approval time: Approval time_2

[0162] …………………………

[0163] - Approving user: Approving user_k

[0164] Approval content: Approval content_k

[0165] Approval time: Approval time_k

[0166] ………………………………

[0167] Dataitem_m:

[0168] - Approving user: Approving user_1

[0169] Approval content: Approval content_1

[0170] Approval time: Approval time_1

[0171] - Approving user: Approving user_2

[0172] Approval content: Approval content_2

[0173] Approval time: Approval time_2

[0174] …………………………

[0175] - Approving user: Approving user_u

[0176] Approval content: Approval content_u

[0177] Approval time: Approval time_u.

[0178] Note: Not all approval items require N approvals, so k<= N, u<= N.

[0179] As described above, the YAML, JSON, and XML technologies used in the present invention have the following advantages for managing process approval content, including:

[0180] (1) Realize the flexibility and passability of the system.

[0181] (2) Reduce system load and shorten response time.

[0182] (3) Eliminate system storage redundancy.

[0183] (4) Improve user experience.

[0184] As an optional embodiment, in the above step, judging whether the approval node belongs to the initial node according to the form data includes:

[0185] Determine whether there is a fixed data item in the form data, and obtain a first determination result;

[0186] When the first judgment result is yes, judging whether there are corresponding multiple approval data items in the form data according to the node type of the approval node, and obtaining a second judgment result;

[0187] When the second judgment result is no, it is determined that the approval node belongs to the initial node.

[0188] It can be seen that through the above optional embodiments, it is possible to determine whether the approval node belongs to the initial node by determining whether there are fixed data items in the form data and multiple approval data items corresponding to the node type of the approval node, so as to accurately determine the timing of generating approval data items for the form data, thereby assisting in reducing the storage space of the node approval records, effectively improving the storage efficiency of the node approval records, and improving the working efficiency of the approval system.

[0189] As an optional embodiment, in the above step, judging whether there are corresponding multiple approval data items in the form data according to the node type of the approval node, and obtaining the second judgment result includes:

[0190] Determine in a preset database a plurality of historical multiple approval data items corresponding to the node type of the approval node; optionally, the node type includes the node level, the node organization, the node personnel information and the node name;

[0191] Calculate the weighted sum average of the similarities between all historical multiple-approved data items and the form data to obtain the existence probability parameter corresponding to the form data; optionally, the weighted calculation weight corresponding to the similarity between each historical multiple-approved data item and the form data is inversely proportional to the data volume of the historical multiple-approved data item;

[0192] It is determined whether the existence probability parameter is greater than a preset parameter threshold to obtain a second determination result.

[0193] It can be seen that through the above optional embodiments, it is possible to obtain an existence probability parameter by calculating the similarity between multiple historical approval data items corresponding to the node type of the approval node and the form data, so as to accurately determine whether the approval node belongs to the initial node, to accurately determine the timing of generating approval data items for the form data, to assist in reducing the storage space of the node approval records, to effectively improve the storage efficiency of the node approval records, and to improve the working efficiency of the approval system.

[0194] As an optional embodiment, in the above step, generating the approval data item of the three-dimensional vector structure includes:

[0195] According to the approval nodes and form data, based on the neural network algorithm, the data item capacity corresponding to the form data is predicted;

[0196] According to the data item capacity, an approval data item with a marking parameter of the data item capacity is generated in a three-dimensional vector structure, so that when the approval data item is subsequently filled, if the data item capacity is exceeded, a data overflow alarm will be issued for the current filling.

[0197] It can be seen that through the above optional embodiments, it is possible to predict the data item capacity corresponding to the form data based on the neural network algorithm, and then based on the generation of the approval data items with a three-dimensional vector structure, the generated approval data items include data item capacity prediction parameters, so that the subsequent process approval content can be filled within the range of the data item capacity, and when the data item capacity is exceeded, a data overflow alarm will be issued for the current filling, so as to improve the security of the process approval data and reduce system security incidents.

[0198] As an optional embodiment, in the above step, predicting the data item capacity corresponding to the form data based on the approval node and the form data and based on the neural network algorithm includes:

[0199] The historical approval records, node types and form data of the approval nodes are input into the trained CNN prediction neural network to obtain the data item capacity corresponding to the output form data; the CNN prediction neural network is trained through a training data set including multiple training approval node types and corresponding approval record annotations, approval form data annotations and data item final capacity annotations.

[0200] It can be seen that through the above optional embodiments, the data item capacity corresponding to the form data can be accurately predicted based on the trained CNN prediction neural network according to the node information and form data information, so that the subsequent process approval content can be filled within the range of the data item capacity, and when the data item capacity is exceeded, a data overflow alarm will be issued for the current filling, so as to improve the security of the process approval data and reduce security incidents of the system.

[0201] Embodiment 2

[0202] See also Figure 2 , Figure 2 Schematic diagram of a system for setting approval data items based on a three-dimensional structure disclosed in an embodiment of the present invention. Figure 2 The described approval data item setting system based on the three-dimensional structure can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the approval data item setting system based on the three-dimensional structure may include:

[0203] The acquisition module 201 is used to acquire the form data when the approval node receives the form data corresponding to the approval process.

[0204] The judgment module 202 is used to judge whether the approval node belongs to the initial node according to the form data.

[0205] The generating module 203 is used to generate an approval data item of a three-dimensional vector structure when the approval node is determined to be an initial node.

[0206] Optionally, the approval data items include the approving user, approval content and approval time.

[0207] The writing module 204 is used to write the approval data item into the form data, so that the form data updates the approval data item when it is sent to the next approval node.

[0208] It can be seen that the above-mentioned embodiment of the invention can generate an approval data item with a three-dimensional vector structure of the approval user, approval content and approval time for the form data at the initial approval node, so that the form data can update the approval data item at the next approval node to achieve efficient recording of the approval record, thereby reducing the storage space of the node approval record through the data item of the three-dimensional vector structure, effectively improving the storage efficiency of the node approval record, and improving the working efficiency of the approval system.

[0209] As an optional embodiment, the approval data item is data in YAML format.

[0210] It can be seen that through the above optional embodiments, the data type of the approval data item is limited, and YAML, a technology that is highly readable and can express data in a serialized manner, can be used to further assist in reducing the storage space of node approval records, effectively improve the storage efficiency of node approval records, and improve the working efficiency of the approval system.

[0211] As an optional embodiment, the approval data item is data in JSON format.

[0212] It can be seen that through the above optional embodiments, the data type of the approval data item is limited, and the highly readable and lightweight data exchange format such as JSON can be used to further assist in reducing the storage space of the node approval records, effectively improve the storage efficiency of the node approval records, and improve the working efficiency of the approval system.

[0213] As an optional embodiment, the approval data item is data in XML format.

[0214] It can be seen that through the above optional embodiments, the data type of the approval data item is limited, and the highly readable and lightweight extensible markup language such as XML can further assist in reducing the storage space of the node approval records, effectively improve the storage efficiency of the node approval records, and improve the working efficiency of the approval system.

[0215] As an optional embodiment, the specific manner in which the judgment module judges whether the approval node belongs to the initial node according to the form data includes:

[0216] Determine whether there is a fixed data item in the form data, and obtain a first determination result;

[0217] When the first judgment result is yes, judging whether there are corresponding multiple approval data items in the form data according to the node type of the approval node, and obtaining a second judgment result;

[0218] When the second judgment result is no, it is determined that the approval node belongs to the initial node.

[0219] It can be seen that through the above optional embodiments, it is possible to determine whether the approval node belongs to the initial node by determining whether there are fixed data items in the form data and multiple approval data items corresponding to the node type of the approval node, so as to accurately determine the timing of generating approval data items for the form data, thereby assisting in reducing the storage space of the node approval records, effectively improving the storage efficiency of the node approval records, and improving the working efficiency of the approval system.

[0220] As an optional embodiment, the judgment module judges whether there are corresponding multiple approval data items in the form data according to the node type of the approval node, and the specific manner of obtaining the second judgment result includes:

[0221] Determine in a preset database a plurality of historical multiple approval data items corresponding to the node type of the approval node; optionally, the node type includes the node level, the node organization, the node personnel information and the node name;

[0222] Calculate the weighted sum average of the similarities between all historical multiple-approved data items and the form data to obtain the existence probability parameter corresponding to the form data; optionally, the weighted calculation weight corresponding to the similarity between each historical multiple-approved data item and the form data is inversely proportional to the data volume of the historical multiple-approved data item;

[0223] It is determined whether the existence probability parameter is greater than a preset parameter threshold to obtain a second determination result.

[0224] It can be seen that through the above optional embodiments, it is possible to obtain an existence probability parameter by calculating the similarity between multiple historical approval data items corresponding to the node type of the approval node and the form data, so as to accurately determine whether the approval node belongs to the initial node, to accurately determine the timing of generating approval data items for the form data, to assist in reducing the storage space of the node approval records, to effectively improve the storage efficiency of the node approval records, and to improve the working efficiency of the approval system.

[0225] As an optional embodiment, the specific manner in which the generation module generates the approval data item of the three-dimensional vector structure includes:

[0226] According to the approval nodes and form data, based on the neural network algorithm, the data item capacity corresponding to the form data is predicted;

[0227] According to the data item capacity, an approval data item with a marking parameter of the data item capacity is generated in a three-dimensional vector structure, so that when the approval data item is subsequently filled, if the data item capacity is exceeded, a data overflow alarm will be issued for the current filling.

[0228] It can be seen that through the above optional embodiments, it is possible to predict the data item capacity corresponding to the form data based on the neural network algorithm, and then based on the generation of the approval data items with a three-dimensional vector structure, the generated approval data items include data item capacity prediction parameters, so that the subsequent process approval content can be filled within the range of the data item capacity, and when the data item capacity is exceeded, a data overflow alarm will be issued for the current filling, so as to improve the security of the process approval data and reduce system security incidents.

[0229] As an optional embodiment, the specific manner in which the generation module predicts the data item capacity corresponding to the form data based on the approval node and the form data and based on the neural network algorithm includes:

[0230] The historical approval records, node types and form data of the approval nodes are input into the trained CNN prediction neural network to obtain the data item capacity corresponding to the output form data; the CNN prediction neural network is trained through a training data set including multiple training approval node types and corresponding approval record annotations, approval form data annotations and data item final capacity annotations.

[0231] It can be seen that through the above optional embodiments, the data item capacity corresponding to the form data can be accurately predicted based on the trained CNN prediction neural network according to the node information and form data information, so that the subsequent process approval content can be filled within the range of the data item capacity, and when the data item capacity is exceeded, a data overflow alarm will be issued for the current filling, so as to improve the security of the process approval data and reduce security incidents of the system.

[0232] Embodiment 3

[0233] See also Figure 3 , Figure 3 It is another approval data item setting system based on a three-dimensional structure disclosed in an embodiment of the present invention. Figure 3 The described approval data item setting system based on the three-dimensional structure is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the approval data item setting system based on the three-dimensional structure may include:

[0234] A memory 301 storing executable program codes;

[0235] a processor 302 coupled to the memory 301;

[0236] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the method for setting approval data items based on the three-dimensional structure described in the first embodiment.

[0237] Embodiment 4

[0238] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the method for setting approval data items based on a three-dimensional structure as described in the first embodiment.

[0239] Embodiment 5

[0240] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the three-dimensional structure-based approval data item setting method described in the first embodiment.

[0241] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0242] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0243] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0244] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0245] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0246] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0247] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0248] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0249] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0250] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0251] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0252] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0253] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0254] Finally, it should be noted that the method and system for setting approval data items based on a three-dimensional structure disclosed in the embodiment of the present invention disclose only the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for setting approval data items based on a three-dimensional structure, characterized in that: The method comprises: When the approval node receives the form data corresponding to the approval process, the form data is obtained; Judging whether the approval node is an initial node according to the form data includes: Determine whether there is a fixed data item in the form data to obtain a first determination result; When the first judgment result is yes, a plurality of historical multiple approval data items corresponding to the node type of the approval node are determined in a preset database; the node type includes the node level, the node organization, the node personnel information and the node name; Calculate the weighted sum average of the similarities between all the historical multiple-approved data items and the form data to obtain the existence probability parameter corresponding to the form data; wherein the weighted calculation weight corresponding to the similarity between each of the historical multiple-approved data items and the form data is inversely proportional to the data volume of the historical multiple-approved data item; Determine whether the existence probability parameter is greater than a preset parameter threshold, and obtain a second determination result; When the second judgment result is no, determining that the approval node is an initial node; When the approval node is determined to be an initial node, an approval data item of a three-dimensional vector structure is generated; the approval data item includes an approval user, approval content and approval time; The approval data item is written into the form data, so that the form data updates the approval data item when being sent to the next approval node.

2. The method for setting approval data items based on a three-dimensional structure according to claim 1, characterized in that: The approval data item is data in YAML format.

3. The method for setting approval data items based on a three-dimensional structure according to claim 1, characterized in that: The approval data item is data in JSON format.

4. The method for setting approval data items based on a three-dimensional structure according to claim 1, characterized in that: The approval data item is data in XML format.

5. The method for setting approval data items based on a three-dimensional structure according to claim 1, characterized in that: The approval data items for generating the three-dimensional vector structure include: According to the approval node and the form data, based on a neural network algorithm, predicting the data item capacity corresponding to the form data; According to the data item capacity, an approval data item with a marking parameter of the data item capacity is generated in a three-dimensional vector structure, so that when the approval data item is subsequently filled, if the data item capacity is exceeded, a data overflow alarm will be issued for the filling.

6. The method for setting approval data items based on a three-dimensional structure according to claim 5, characterized in that: The step of predicting the data item capacity corresponding to the form data based on the approval node and the form data and based on a neural network algorithm includes: The historical approval records and node types of the approval nodes and the form data are input into a trained CNN prediction neural network to obtain the data item capacity corresponding to the output form data; the CNN prediction neural network is trained using a training data set including multiple training approval node types and corresponding approval record annotations, approval form data annotations, and data item final capacity annotations.

7. A system for setting approval data items based on a three-dimensional structure, characterized in that: The system comprises: An acquisition module, used for acquiring the form data corresponding to the approval process when the approval node receives the form data; A judgment module, used to judge whether the approval node belongs to an initial node according to the form data, including: Determine whether there is a fixed data item in the form data to obtain a first determination result; When the first judgment result is yes, a plurality of historical multiple approval data items corresponding to the node type of the approval node are determined in a preset database; the node type includes the node level, the node organization, the node personnel information and the node name; Calculate the weighted sum average of the similarities between all the historical multiple-approved data items and the form data to obtain the existence probability parameter corresponding to the form data; wherein the weighted calculation weight corresponding to the similarity between each of the historical multiple-approved data items and the form data is inversely proportional to the data volume of the historical multiple-approved data item; Determine whether the existence probability parameter is greater than a preset parameter threshold, and obtain a second determination result; When the second judgment result is no, determining that the approval node is an initial node; A generating module, for generating an approval data item of a three-dimensional vector structure when judging that the approval node is an initial node; the approval data item includes an approval user, approval content and approval time; The writing module is used to write the approval data item into the form data, so that the form data updates the approval data item when it is sent to the next approval node.

8. A system for setting approval data items based on a three-dimensional structure, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the three-dimensional structure-based approval data item setting method according to any one of claims 1 to 6.

Images