An intelligent management system and method based on enterprise data

Abstract

The present invention relates to the field of enterprise management technology, and in particular to an intelligent management system and method based on enterprise data, which solves the technical problem that the existing enterprise data management method lacks accurate matching of specific task requirements and dynamic management of permissions. It includes: configuring tasks to be executed for enterprise data attributes, constructing an enterprise data aggregation tree, aggregating enterprise data, and obtaining an enterprise data index tree; sorting the enterprise data index tree according to the number of the task to be executed, and obtaining a selected enterprise data index tree; configuring a first private key through a dynamic key library; configuring task scheduling through the number of the task to be executed and the execution time zone identifier, combined with the task allocation status of the target group, and generating a target executor; sending the first public key to the target executor to manage enterprise data. Dynamic permission allocation based on tasks is realized to ensure the security and rationality of data access, while improving the flexibility and efficiency of management.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise management, and in particular to an intelligent management system and method based on enterprise data. Background Art

[0002] With the rapid development of information technology, enterprise data management has become increasingly important. Traditional enterprise data management methods mainly rely on permission control, which manages data by assigning different access rights to employees at different levels. Although this method ensures data security to a certain extent, it has certain limitations. For example, permissions are set too broadly, allowing employees to access data that is not related to their duties, increasing the risk of data leakage.

[0003] And once employees are granted specific permissions, they can access all open permission data at any time and anywhere. This management method not only increases the risk of data leakage, but also may lead to data misuse or abuse. In addition, the static nature of permissions makes it difficult to adjust permissions when tasks change, and it is difficult to adapt to rapidly changing business needs.

[0004] In general, existing enterprise data management methods lack the technical issues of accurate matching of task-specific requirements and dynamic management of permissions. Summary of the invention

[0005] The present invention provides an intelligent management system and method based on enterprise data, which realizes task-based dynamic authority allocation, ensures the security and rationality of data access, and improves the flexibility and efficiency of management.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] In a first aspect, the present invention provides an intelligent management system based on enterprise data, comprising:

[0008] Aggregation tree construction module, used to configure tasks to be executed for enterprise data attributes and build enterprise data aggregation tree;

[0009] An index tree construction module, used to aggregate enterprise data according to the enterprise data aggregation tree to obtain an enterprise data index tree;

[0010] The to-be-executed task receiving module is used to receive the to-be-executed task number through the user terminal, wherein the to-be-executed task number has an execution time zone identifier;

[0011] An index tree selection module, used to sort the enterprise data index tree according to the number of the task to be executed, and obtain a selected enterprise data index tree;

[0012] A key configuration module, configured to configure a first private key for the selected enterprise data index tree through a dynamic key library, wherein the first private key and the first public key are uniquely corresponding;

[0013] A task scheduling configuration module is used to perform task scheduling configuration based on the task number to be executed and the execution time zone identifier, combined with the task allocation status of the target group, and generate a target executor;

[0014] An enterprise data management module is used to send the first public key to the target executor for enterprise data management, wherein the first public key is only valid within the execution time zone identifier.

[0015] In a second aspect, the present invention provides an intelligent management method based on enterprise data, comprising:

[0016] Configure tasks to be executed for enterprise data attributes and build an enterprise data aggregation tree;

[0017] Aggregate the enterprise data according to the enterprise data aggregation tree to obtain an enterprise data index tree;

[0018] Receiving, through a user terminal, a number of a task to be executed, wherein the number of the task to be executed has an execution time zone identifier;

[0019] Sorting the enterprise data index tree according to the to-be-executed task number to obtain a selected enterprise data index tree;

[0020] By means of a dynamic key library, configuring a first private key for the selected enterprise data index tree, wherein the first private key and the first public key are uniquely corresponding;

[0021] The task scheduling configuration is performed by combining the task number to be executed and the execution time zone identifier with the task allocation status of the target group to generate the target executor;

[0022] The first public key is sent to the target executor for enterprise data management, wherein the first public key is only valid within the execution time zone identifier.

[0023] The beneficial effects of the present invention are:

[0024] This intelligent management method based on enterprise data realizes the precise organization and management of data by configuring pending tasks for enterprise data attributes and constructing an enterprise data aggregation tree. The method further sorts the enterprise data index tree by receiving the pending task number with the execution time zone identifier, thereby ensuring the accuracy and security of data access. The first private key is configured through the dynamic key library to realize the dynamic management and verification of permissions. Combined with the task number and the execution time zone identifier, the task scheduling configuration is performed, and the target executor is generated to ensure the timely execution of the task. Finally, the first public key is sent to the target executor, so that data management is effective within the limited time zone, further improving the security and flexibility of data management. This method not only solves the potential security risks existing in traditional data management, but also achieves the technical effect of improving the efficiency and adaptability of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of an intelligent management method based on enterprise data provided by the present invention.

[0026] Figure 2 A structural diagram of intelligent management based on enterprise data provided by the present invention.

[0027] Figure 3 The present invention provides a schematic structural diagram of an electronic device.

[0028] Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0029] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0030] Electronic device 500 , memory 510 , processor 520 , computer program 511 , computer readable storage medium 600 , computer program 611 . DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0032] In the description of the present invention, 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, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0033] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0034] Embodiment 1

[0035] like Figure 1 As shown, the embodiment of the present invention provides an intelligent management method based on enterprise data, including the steps of:

[0036] S10: configure tasks to be executed for enterprise data attributes and build an enterprise data aggregation tree;

[0037] Specifically, enterprise data attributes refer to various types of information generated by enterprises during their operations, such as customer information, transaction records, product information, etc. These data are collected from a wide range of sources, which may come from customer management systems, sales systems, inventory management systems, etc. The role of enterprise data attributes is to provide support for enterprise decision-making and help enterprises better understand market dynamics, customer needs and internal operations.

[0038] Configuring tasks to be performed means translating the business requirements of an enterprise into specific operational instructions. These tasks may include data query, report generation, inventory management, etc. Each task needs to have clear goals and requirements so that accurate data aggregation can be performed in subsequent steps. The role of configuring tasks is to ensure that data management activities are consistent with the business goals of the enterprise and improve the pertinence and efficiency of data use.

[0039] Constructing an enterprise data aggregation tree is a complex process that combines data attributes with task requirements. In this process, the enterprise data must first be classified and organized, and then the data must be aggregated according to task requirements. The structure of an enterprise data aggregation tree is usually presented as a multi-level tree diagram, where each node represents a data set or data attribute. The purpose of constructing an enterprise data aggregation tree is to provide a clear data view, making data retrieval and management more efficient. Data aggregation refers to combining related data items to form a unified data set. This process requires screening and integration of data to ensure that the aggregated data can meet the requirements of specific tasks. The role of data aggregation is to reduce data redundancy and improve data availability and consistency.

[0040] Through these steps, enterprises can achieve refined management of data, ensure that the use of data is more in line with business needs, and improve data security and availability. This approach not only improves the efficiency of data management, but also enhances the control of enterprises over data, providing stronger support for enterprise decision-making.

[0041] S20: Aggregate the enterprise data according to the enterprise data aggregation tree to obtain an enterprise data index tree;

[0042] Specifically, according to the enterprise data aggregation tree, the attributes and sources of enterprise data required for each task can be clarified. Based on the enterprise data aggregation tree, enterprise data can be classified to obtain a specific enterprise data index tree. The enterprise data index tree is constructed on the basis of aggregation. It is a hierarchical data structure used to represent the logical relationship and hierarchical structure between data. Each node of the index tree represents a data set or a data attribute, and the connection between nodes represents the association between data. The role of the index tree is to provide an efficient data retrieval mechanism. Through the index tree, users can quickly locate the required data set without traversing the entire data set. In addition, the index tree also supports complex query operations, such as range query, fuzzy query, etc., which further improves the flexibility and accuracy of data retrieval.

[0043] S30: receiving a task number to be executed through a user terminal, wherein the task number to be executed has an execution time zone identifier;

[0044] Specifically, the user end refers to the interface or platform used by enterprise employees or authorized users, through which tasks can be submitted, received, and managed. The user end usually has a friendly interactive interface that enables users to operate easily. The task number to be executed is a unique identifier assigned to a specific task. Each task number is associated with a specific business activity or operation, such as data query, report generation, or the execution of a specific project. The task number is collected from the enterprise's task management system or workflow automation tool. The execution time zone identifier is a time attribute attached to the task number, which defines the effective time range for task execution. This identifier ensures that the task is completed within a specific time period, thereby improving the timeliness and security of task management.

[0045] S40: sorting the enterprise data index tree according to the to-be-executed task number to obtain a selected enterprise data index tree;

[0046] Specifically, each enterprise data index tree uniquely corresponds to a task number, so the enterprise data index tree can be sorted according to the task number to be executed to obtain the selected enterprise data index tree, which is convenient for the subsequent task authority allocation.

[0047] S50: configuring a first private key for the selected enterprise data index tree through a dynamic key library, wherein the first private key and the first public key are uniquely corresponding;

[0048] Further, by using the dynamic key library, a first private key is configured for the selected enterprise data index tree, and step S50 includes:

[0049] S51: According to the dynamic key library, randomly match a number of private key numbers for the task number to be executed;

[0050] S52: traverse the plurality of private key numbers, and count the duration of a plurality of matching intervals with the task numbers to be executed;

[0051] S53: traversing the plurality of private key numbers, and counting a plurality of matching frequency features with the to-be-executed task numbers in a preset distribution time zone;

[0052] S54: sorting the plurality of private key numbers according to the plurality of matching interval durations and the plurality of matching frequency characteristics to obtain the first private key.

[0053] Further, according to the plurality of matching interval durations and the plurality of matching frequency characteristics, the plurality of private key numbers are sorted to obtain the first private key, and step S54 includes:

[0054] S541: configuring a first weight for a normalized parameter of a matching interval duration, and configuring a second weight for a normalized parameter of a matching frequency feature;

[0055] S542: subtracting a second product parameter of the second weight and the normalized parameter of the matching frequency feature from a first product parameter of the first weight and the normalized parameter of the matching interval duration to obtain a sorting evaluation value;

[0056] S543: extract the sorting evaluation value, perform maximum value sorting on the plurality of private key numbers, and obtain the first private key.

[0057] Specifically, a dynamic key library is a system for storing and managing keys, which can dynamically generate, update, and revoke keys as needed. This library is a core component of enterprise data security management, ensuring the security and controllability of data access. The first private key is a key associated with a specific data index tree, which is used to encrypt and decrypt data to ensure that only authorized users can access and modify the data. The confidentiality of the private key is the key to data security, so it is usually strictly managed by the dynamic key library. The first public key is a key paired with the first private key, which is used to encrypt data or verify digital signatures. The public key can be shared publicly to verify the identity and authorization of the private key holder.

[0058] The process of configuring the first private key includes the following key actions:

[0059] The dynamic key library randomly matches several possible private key numbers according to the number of the task to be executed. This step increases the security of the system because randomness reduces predictability. Traverse these private key numbers and count the matching interval between each private key number and the number of the task to be executed. The interval length is the interval length from the last match to the current one. Further count the matching frequency characteristics of each private key number with the number of the task to be executed within the user-defined preset distribution time zone. According to the matching interval length and matching frequency characteristics, the system sorts the private key numbers to obtain the first private key that best suits the current task, as follows:

[0060] Configure the first weight for the normalized parameter of the matching interval duration, and configure the second weight for the normalized parameter of the matching frequency feature. The configuration of the weights reflects the importance the system places on different features. Multiply the first weight by the normalized parameter of the matching interval duration, and then subtract the result of multiplying the second weight by the normalized parameter of the matching frequency feature to obtain the sorting evaluation value of each private key number. This evaluation value is the basis for the selection of the private key number. Extract the sorting evaluation values ​​of all private key numbers, compare them, and select the private key number with the largest evaluation value as the first private key. This selection is based on the evaluation value, ensuring the optimal match of the private key.

[0061] S60: Perform task scheduling configuration based on the task number to be executed and the execution time zone identifier in combination with the task allocation status of the target group, and generate a target executor;

[0062] Further, task scheduling configuration is performed by combining the task number to be executed and the execution time zone identifier with the target group task allocation status to generate target executors. Step S60 includes:

[0063] S61: Matching the target group based on the employee database according to the task number to be executed;

[0064] S62: Obtaining a list of idle personnel identified by the execution time zone according to the task allocation status of the target group;

[0065] S63: According to the preset number of configured employees, select the idle personnel list to obtain the target executive personnel.

[0066] Further, according to the preset number of configured employees, the idle personnel list is selected to obtain the target executive personnel, and step S63 includes:

[0067] S631: Count the number of people processed in history according to the number of the task to be executed and the execution time zone identifier, and obtain a number of historical configuration numbers;

[0068] S632: Take the maximum value of the concentrated number of people among the several historical configuration numbers and set it as the preset configuration number of employees.

[0069] Specifically, the pending task number is a unique identifier assigned to each task, which contains detailed information about the task, such as task type, task content, priority, etc. The execution time zone identifier defines the effective time range for task execution, ensuring that the task is completed within the specified time, thereby improving the timeliness and accuracy of task management. The target group task allocation status refers to the current task allocation status of all target groups (such as corporate employees, teams, etc.), including assigned tasks, pending tasks, task completion status, etc.

[0070] Task scheduling configuration is the process of combining the tasks to be executed with the task allocation status of the target group for intelligent matching and scheduling. The following are the specific steps of this process:

[0071] Based on the number of the task to be performed, the system searches and matches the target group with the corresponding skills and qualifications in the employee database. The employee database is a database that stores employee information, including professional skills, experience, historical performance, etc. The system determines which personnel are free within the execution time zone identification based on the task assignment status of the target group. This involves checking the employee's schedule to ensure that they have enough time and ability to accept the new task. The system selects the most suitable execution personnel from the list of free personnel based on the preset configuration number of employees. This selection is based on the employee's availability, skill matching and task requirements. The system counts the number of employees required to handle similar tasks in the past based on the number of tasks to be performed and the execution time zone identification. This includes analyzing historical data to understand the human resource allocation pattern in different task types and time zones. The system finds the central trend from the historical configuration number, such as by calculating the mean, median or mode, to determine the most appropriate preset configuration number of employees. This number reflects the typical number of employees required to complete the task under similar circumstances. Through intelligent analysis and selection, human resources are ensured to be used most effectively, and historical data is used to predict and plan human resource needs, thereby improving the accuracy and efficiency of task scheduling. Through this step, enterprises can achieve precise control over task execution, ensure that tasks are completed on time, and at the same time ensure the rational allocation and use of human resources.

[0072] S70: Send the first public key to the target executor for enterprise data management, wherein the first public key is only valid within the execution time zone identifier.

[0073] Specifically, the first public key is a key paired with the first private key, which is usually used to encrypt data or verify digital signatures. The public key can be shared publicly, while the private key is kept confidential and can only be used by authorized users. The execution time zone identifier defines the effective time range for task execution, ensuring that the task is completed within the specified time and the relevant keys are valid within the time zone.

[0074] The process of sending the first public key includes the following key steps:

[0075] The task number is associated with the public key:

[0076] The system retrieves the corresponding first public key from the dynamic key library according to the task number to be executed. This public key is generated for a specific task and execution time zone identifier.

[0077] To perform time zone validation:

[0078] Before sending the public key, the system verifies the execution time zone identifier to ensure that the validity of the public key matches the execution time of the task.

[0079] Target Executive Identification:

[0080] The system identifies target executors based on the task scheduling configuration. These personnel are screened employees who have the skills and resources required to perform the task.

[0081] Public key sending:

[0082] The system securely sends the first public key to the target executive's device or system. The sending method may include email, internal corporate communication system or dedicated secure transmission protocol.

[0083] Access Control Implementation:

[0084] The target executor uses the first public key to encrypt or decrypt the enterprise data. Since the validity of the public key is limited to the execution time zone, the public key will become invalid after the time limit is exceeded, thereby enhancing the security of data access.

[0085] The role of public key validity restrictions: Time restrictions: By limiting the validity of the public key to the execution time zone, the system can control the time window for data access and prevent data from being accessed during unauthorized time. Security enhancement: Limiting the validity period of the public key can reduce the risk of data leakage and ensure that data is processed securely within the specified time. Flexibility: This approach allows enterprises to flexibly adjust data access rights based on the specific needs and schedule of the task.

[0086] Further, the tasks to be executed are configured for the enterprise data attributes to construct an enterprise data aggregation tree. Step S10 includes:

[0087] S11: performing first-level decomposition of the task to be executed to obtain a first-level decomposition data attribute set;

[0088] S12: traversing the first-level disassembled data attribute set to perform second-level disassembly of non-bottom-level data to obtain a second-level disassembled data attribute set;

[0089] S13: until all N-level disassembled data attribute sets are underlying data, construct the enterprise data aggregation tree according to the first-level disassembled data attribute set, the second-level disassembled data attribute set, and finally the N-level disassembled data attribute set; wherein the underlying data is data that can be directly collected.

[0090] Specifically, the tasks to be performed are initially decomposed into smaller and more specific data attribute sets, which are stored as first-level decomposition data attribute sets. The purpose of this step is to identify the key data elements involved in the task, such as customer information, transaction records, etc. The data attribute sets obtained by traversing the first-level decomposition are further decomposed for non-bottom-level data and stored as second-level decomposition data attribute sets. This step involves in-depth analysis of the first-level decomposition results, further refining the data attributes into more specific sub-attributes. The decomposition process is repeated until all levels of data attribute sets reach the bottom-level data. Bottom-level data refers to data that can be collected directly from the data source, such as specific transaction record entries or inventory unit information. The data attribute sets obtained from the first-level decomposition to the N-level decomposition are used to construct an enterprise data aggregation tree. The structure of the enterprise data aggregation tree is usually a multi-level tree diagram, with the top level being the task and the lower level being the data attribute sets related to the task.

[0091] Specific implementation example:

[0092] Assuming that an enterprise needs to perform a market analysis task, the implementation of step S10 may be as follows:

[0093] S11: First-level decomposition - Decompose the market analysis task into first-level data attribute sets such as "customer data", "sales data" and "product data".

[0094] S12: Secondary decomposition - further decompose the "customer data" to obtain secondary data attribute sets such as "customer basic information", "purchase history" and "preference settings".

[0095] S13: Multi-level decomposition - Continue decomposition until all data attribute sets are refined into underlying data, such as "Customer ID", "Purchase Date", "Product SKU", etc.

[0096] By disassembling the data level by level, it is ensured that the enterprise data aggregation tree can accurately reflect the task requirements and data structure. It allows enterprises to flexibly adjust the depth and breadth of the data attribute set disassembly according to the needs of different tasks. Building an enterprise data aggregation tree helps improve the efficiency of data retrieval and management, making data access more direct and faster. By rationally organizing the data attribute set, data access control and security policies can be better implemented.

[0097] Further, the enterprise data aggregation tree is constructed according to the first-level disassembled data attribute set, the second-level disassembled data attribute set, and finally the N-level disassembled data attribute set. Step S13 includes:

[0098] S131: Taking the task to be executed as the root node;

[0099] S132: Taking the first-level disassembled data attribute set as a first-level leaf node;

[0100] S133: taking the secondary disassembly data attribute set as a secondary leaf node;

[0101] S134: until the N-level disassembled data attribute set is an N-level leaf node;

[0102] S135: Construct the enterprise data aggregation tree according to the root node, the first-level leaf node, the second-level leaf node, and finally the N-level leaf node.

[0103] Specifically, the task to be executed is used as the root node of the enterprise data aggregation tree. The root node represents the core objectives and requirements of the entire task and is the starting point of the enterprise data aggregation tree structure. The first-level disassembled data attribute set is used as the first-level leaf node of the enterprise data aggregation tree. These nodes are directly expanded from the root node and represent the key components of the task. The second-level disassembled data attribute set is used as the second-level leaf node of the enterprise data aggregation tree. These nodes are further expanded from the first-level leaf node to provide more detailed data attributes. Continue to recursively add each level of disassembled data attribute set as a leaf node of the corresponding level to the enterprise data aggregation tree. As the level increases, the data attributes represented by the node become more specific and low-level. Using the root node and leaf nodes at all levels, a complete enterprise data aggregation tree is constructed. This tree reflects the complete hierarchical structure from tasks to underlying data, which is easy to manage and retrieve.

[0104] Specific implementation example:

[0105] Assuming that an enterprise needs to perform a "customer satisfaction survey" task, the implementation of step S13 may be as follows:

[0106] S131: Set the root node - "Customer Satisfaction Survey" as the root node of the enterprise data aggregation tree.

[0107] S132: Adding a first-level leaf node - the first-level disassembly data attribute set may include "basic customer information", "historical transaction records", "service contact points", etc.

[0108] S133: Adding secondary leaf nodes - performing secondary decomposition on "basic customer information" to obtain secondary leaf nodes such as "name", "age", and "contact information".

[0109] S134: Recursively add until N-level leaf nodes - continue to decompose, for example, "historical transaction records" can be decomposed into more specific nodes such as "purchase date", "purchased products", and "transaction amount".

[0110] S135: The enterprise data aggregation tree is constructed. Finally, the enterprise data aggregation tree expands downward from the root node of "Customer Satisfaction Survey", including all relevant data attribute sets, forming a complete data structure.

[0111] The enterprise data aggregation tree provides a clear and structured way to represent and manage enterprise data. The enterprise data aggregation tree, which is built with tasks as the starting point, ensures a high degree of consistency between data management and enterprise business goals. As business needs change, the enterprise data aggregation tree can be easily expanded or adjusted to adapt to new data attributes or tasks. Through the hierarchical structure, the required data can be quickly located, improving the efficiency of data retrieval. Through this step, the enterprise can build an enterprise data aggregation tree that reflects task requirements and data hierarchical relationships, providing a solid foundation for effective management and secure access to data.

[0112] An intelligent management method based on enterprise data provided by an embodiment of the present invention has at least the following technical effects:

[0113] Through the intelligent management method based on enterprise data, by configuring the tasks to be executed for the enterprise data attributes and constructing the enterprise data aggregation tree, the precise organization and management of data is achieved. The method further receives the task number to be executed with the execution time zone identifier, sorts the enterprise data index tree, and ensures the accuracy and security of data access. The first private key is configured through the dynamic key library to realize the dynamic management and verification of permissions. Combined with the task number and the execution time zone identifier, the task scheduling configuration is performed, and the target executor is generated to ensure the timely execution of the task. Finally, the first public key is sent to the target executor, so that data management is effective within the limited time zone, further improving the security and flexibility of data management. This method not only solves the potential security problems existing in traditional data management, but also achieves the technical effect of improving the efficiency and adaptability of management.

[0114] Embodiment 2

[0115] like Figure 2 As shown, based on the same inventive concept as the intelligent management method based on enterprise data provided in Embodiment 1, the embodiment of the present invention further provides an intelligent management system based on enterprise data, including:

[0116] Aggregation tree construction module, used to configure tasks to be executed for enterprise data attributes and build enterprise data aggregation tree;

[0117] An index tree construction module, used to aggregate enterprise data according to the enterprise data aggregation tree to obtain an enterprise data index tree;

[0118] The to-be-executed task receiving module is used to receive the to-be-executed task number through the user terminal, wherein the to-be-executed task number has an execution time zone identifier;

[0119] An index tree selection module, used to sort the enterprise data index tree according to the number of the task to be executed, and obtain a selected enterprise data index tree;

[0120] A key configuration module, configured to configure a first private key for the selected enterprise data index tree through a dynamic key library, wherein the first private key and the first public key are uniquely corresponding;

[0121] A task scheduling configuration module is used to perform task scheduling configuration based on the task number to be executed and the execution time zone identifier, combined with the task allocation status of the target group, and generate a target executor;

[0122] An enterprise data management module is used to send the first public key to the target executor for enterprise data management, wherein the first public key is only valid within the execution time zone identifier.

[0123] Furthermore, the aggregation tree construction module executes the steps including:

[0124] Performing a first-level decomposition on the task to be executed to obtain a first-level decomposition data attribute set;

[0125] Traversing the first-level disassembled data attribute set to perform second-level disassembly of non-bottom-level data to obtain a second-level disassembled data attribute set;

[0126] When all the N-level disassembled data attribute sets are bottom-level data, the enterprise data aggregation tree is constructed according to the first-level disassembled data attribute set, the second-level disassembled data attribute set, and finally the N-level disassembled data attribute set;

[0127] Among them, the underlying data is the data that can be collected directly.

[0128] Furthermore, the aggregation tree construction module executes the steps including:

[0129] Taking the task to be executed as the root node;

[0130] The first-level disassembly data attribute set is used as a first-level leaf node;

[0131] The secondary disassembly data attribute set is used as the secondary leaf node;

[0132] Until the N-level disassembly data attribute set is an N-level leaf node;

[0133] The enterprise data aggregation tree is constructed according to the root node, the first-level leaf node, the second-level leaf node, and finally the N-level leaf node.

[0134] Furthermore, the key configuration module executes the steps including:

[0135] According to the dynamic key library, a number of private key numbers are randomly matched for the task number to be executed;

[0136] Traversing the plurality of private key numbers, and counting the duration of a plurality of matching intervals with the number of the task to be executed;

[0137] Traversing the plurality of private key numbers, and counting a plurality of matching frequency features with the number of the task to be executed in a preset distribution time zone;

[0138] The plurality of private key numbers are sorted according to the plurality of matching interval durations and the plurality of matching frequency characteristics to obtain the first private key.

[0139] Furthermore, the key configuration module executes the steps including:

[0140] A first weight is configured for a normalized parameter of a matching interval duration, and a second weight is configured for a normalized parameter of a matching frequency feature;

[0141] subtracting a second product parameter of the second weight and the normalized parameter of the matching frequency feature from a first product parameter of the first weight and the normalized parameter of the matching interval duration to obtain a sorting evaluation value;

[0142] The sorting evaluation value is extracted, and the plurality of private key numbers are sorted by maximum value to obtain the first private key.

[0143] Furthermore, the task scheduling configuration module executes the steps including:

[0144] According to the task number to be performed, the target group is matched based on the employee database;

[0145] According to the task allocation status of the target group, a list of idle personnel identified by the execution time zone is obtained;

[0146] According to the preset number of configured employees, the idle personnel list is selected to obtain the target executive personnel.

[0147] Furthermore, the task scheduling configuration module executes the steps including:

[0148] Perform historical processing headcount statistics according to the task number to be executed and the execution time zone identifier to obtain a number of historical configuration headcounts;

[0149] The maximum value of the concentrated number of people among the several historical configuration numbers is taken as the preset configuration number of employees.

[0150] Embodiment 3

[0151] See also Figure 3The embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 511, the following steps are implemented:

[0152] Configure tasks to be executed for enterprise data attributes and build an enterprise data aggregation tree;

[0153] Aggregate the enterprise data according to the enterprise data aggregation tree to obtain an enterprise data index tree;

[0154] Receiving, through a user terminal, a number of a task to be executed, wherein the number of the task to be executed has an execution time zone identifier;

[0155] Sorting the enterprise data index tree according to the to-be-executed task number to obtain a selected enterprise data index tree;

[0156] By means of a dynamic key library, configuring a first private key for the selected enterprise data index tree, wherein the first private key and the first public key are uniquely corresponding;

[0157] The task scheduling configuration is performed by combining the task number to be executed and the execution time zone identifier with the task allocation status of the target group to generate the target executor;

[0158] The first public key is sent to the target executor for enterprise data management, wherein the first public key is only valid within the execution time zone identifier.

[0159] Embodiment 4

[0160] See also Figure 4 This embodiment provides a computer-readable storage medium 600, on which a computer program 611 is stored. When the computer program 611 is executed by a processor, the following steps are implemented:

[0161] Configure tasks to be executed for enterprise data attributes and build an enterprise data aggregation tree;

[0162] Aggregate the enterprise data according to the enterprise data aggregation tree to obtain an enterprise data index tree;

[0163] Receiving, through a user terminal, a number of a task to be executed, wherein the number of the task to be executed has an execution time zone identifier;

[0164] Sorting the enterprise data index tree according to the to-be-executed task number to obtain a selected enterprise data index tree;

[0165] By means of a dynamic key library, configuring a first private key for the selected enterprise data index tree, wherein the first private key and the first public key are uniquely corresponding;

[0166] The task scheduling configuration is performed by combining the task number to be executed and the execution time zone identifier with the task allocation status of the target group to generate the target executor;

[0167] The first public key is sent to the target executor for enterprise data management, wherein the first public key is only valid within the execution time zone identifier.

[0168] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0169] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0170] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 computer, 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.

[0171] 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 comprising 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.

[0172] 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.

[0173] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0174] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. An intelligent management system based on enterprise data, characterized in that: include: Aggregation tree construction module, used to configure tasks to be executed for enterprise data attributes and build enterprise data aggregation tree; An index tree construction module, used to aggregate enterprise data according to the enterprise data aggregation tree to obtain an enterprise data index tree; The to-be-executed task receiving module is used to receive the to-be-executed task number through the user terminal, wherein the to-be-executed task number has an execution time zone identifier, the to-be-executed task number is a unique identifier assigned to a specific task, and each task number is associated with a specific business activity or operation; An index tree selection module, used to sort the enterprise data index tree according to the number of the task to be executed, and obtain a selected enterprise data index tree; A key configuration module, configured to configure a first private key for the selected enterprise data index tree through a dynamic key library, wherein the first private key and the first public key are uniquely corresponding; A task scheduling configuration module is used to perform task scheduling configuration based on the task number to be executed and the execution time zone identifier, combined with the task allocation status of the target group, and generate a target executor; An enterprise data management module, used for sending the first public key to the target executor for enterprise data management, wherein the first public key is only valid within the execution time zone identifier; The steps of executing the aggregation tree construction module include: Performing a first-level decomposition on the task to be executed to obtain a first-level decomposition data attribute set; Traversing the first-level disassembled data attribute set to perform second-level disassembly of non-bottom-level data to obtain a second-level disassembled data attribute set; When all the N-level disassembled data attribute sets are bottom-level data, the enterprise data aggregation tree is constructed according to the first-level disassembled data attribute set, the second-level disassembled data attribute set, and finally the N-level disassembled data attribute set; Among them, the underlying data is the data that can be directly collected; The steps of executing the aggregation tree construction module include: Taking the task to be executed as the root node; The first-level disassembly data attribute set is used as a first-level leaf node; The secondary disassembly data attribute set is used as the secondary leaf node; Until the N-level disassembly data attribute set is an N-level leaf node; Constructing the enterprise data aggregation tree according to the root node, the first-level leaf node, the second-level leaf node, and finally the N-level leaf node; The key configuration module executes the following steps: According to the dynamic key library, a number of private key numbers are randomly matched for the task number to be executed; Traversing the plurality of private key numbers, and counting the duration of a plurality of matching intervals with the task numbers to be executed; Traversing the plurality of private key numbers, and counting a plurality of matching frequency features with the to-be-executed task numbers in a preset distribution time zone; sorting the plurality of private key numbers according to the plurality of matching interval durations and the plurality of matching frequency characteristics to obtain the first private key; The key configuration module executes the following steps: A first weight is configured for a normalized parameter of a matching interval duration, and a second weight is configured for a normalized parameter of a matching frequency feature; subtracting a second product parameter of the second weight and the normalized parameter of the matching frequency feature from a first product parameter of the first weight and the normalized parameter of the matching interval duration to obtain a sorting evaluation value; Extracting the sorting evaluation value, performing maximum value sorting on the plurality of private key numbers, and obtaining the first private key; The task scheduling configuration module executes the following steps: According to the task number to be performed, the target group is matched based on the employee database; According to the task allocation status of the target group, a list of idle personnel identified by the execution time zone is obtained; According to the preset number of configured employees, the idle personnel list is selected to obtain the target executive personnel.

2. An intelligent management system based on enterprise data as claimed in claim 1, characterized in that: The task scheduling configuration module execution steps include: Perform historical processing headcount statistics according to the task number to be executed and the execution time zone identifier to obtain a number of historical configuration headcounts; The maximum value of the concentrated number of people among the several historical configuration numbers is taken as the preset configuration number of employees.

3. An intelligent management method based on enterprise data, applied to an intelligent management system based on enterprise data as claimed in any one of claims 1 to 2, characterized in that: include: Configure tasks to be executed for enterprise data attributes and build an enterprise data aggregation tree; Aggregate the enterprise data according to the enterprise data aggregation tree to obtain an enterprise data index tree; Receiving, through a user terminal, a task number to be executed, wherein the task number to be executed has an execution time zone identifier, the task number to be executed is a unique identifier assigned to a specific task, and each task number is associated with a specific business activity or operation; Sorting the enterprise data index tree according to the to-be-executed task number to obtain a selected enterprise data index tree; By means of a dynamic key library, configuring a first private key for the selected enterprise data index tree, wherein the first private key and the first public key are uniquely corresponding; The task scheduling configuration is performed by combining the task number to be executed and the execution time zone identifier with the task allocation status of the target group to generate the target executor; Sending the first public key to the target executor for enterprise data management, wherein the first public key is only valid within the execution time zone identifier; The steps of executing the aggregation tree construction module include: Performing a first-level decomposition on the task to be executed to obtain a first-level decomposition data attribute set; Traversing the first-level disassembled data attribute set to perform second-level disassembly of non-bottom-level data to obtain a second-level disassembled data attribute set; When all the N-level disassembled data attribute sets are bottom-level data, the enterprise data aggregation tree is constructed according to the first-level disassembled data attribute set, the second-level disassembled data attribute set, and finally the N-level disassembled data attribute set; Among them, the underlying data is the data that can be directly collected; The steps of executing the aggregation tree construction module include: Taking the task to be executed as the root node; The first-level disassembly data attribute set is used as a first-level leaf node; The secondary disassembly data attribute set is used as the secondary leaf node; Until the N-level disassembly data attribute set is an N-level leaf node; Constructing the enterprise data aggregation tree according to the root node, the first-level leaf node, the second-level leaf node, and finally the N-level leaf node; The key configuration module executes the following steps: According to the dynamic key library, a number of private key numbers are randomly matched for the task number to be executed; Traversing the plurality of private key numbers, and counting the duration of a plurality of matching intervals with the task numbers to be executed; Traversing the plurality of private key numbers, and counting a plurality of matching frequency features with the to-be-executed task numbers in a preset distribution time zone; sorting the plurality of private key numbers according to the plurality of matching interval durations and the plurality of matching frequency characteristics to obtain the first private key; The key configuration module executes the following steps: A first weight is configured for a normalized parameter of a matching interval duration, and a second weight is configured for a normalized parameter of a matching frequency feature; subtracting a second product parameter of the second weight and the normalized parameter of the matching frequency feature from a first product parameter of the first weight and the normalized parameter of the matching interval duration to obtain a sorting evaluation value; Extracting the sorting evaluation value, performing maximum value sorting on the plurality of private key numbers, and obtaining the first private key; The task scheduling configuration module executes the following steps: According to the task number to be performed, the target group is matched based on the employee database; According to the task allocation status of the target group, a list of idle personnel identified by the execution time zone is obtained; According to the preset number of configured employees, the idle personnel list is selected to obtain the target executive personnel.

4. An electronic device, characterized in that: include: Memory for storing computer software programs; The processor is used to read and execute the computer software program, thereby implementing the intelligent management method based on enterprise data as described in claim 3.

5. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, which, when executed by a processor, implements an intelligent management method based on enterprise data as claimed in claim 3.

Images