CN112446623A - Enterprise management decision auxiliary system and method - Google Patents
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Abstract
The invention provides an enterprise management decision auxiliary system which comprises a data acquisition module, a data analysis module, a table generation module, a control module and a visualization module. The invention also provides an enterprise management decision auxiliary method, which comprises the following steps: s1: collecting basic data and classifying the basic data; s2: basic data are called to carry out analysis and calculation to obtain index data; s3: generating an energy efficiency table of the current stage according to the index data; s4: establishing a prediction model according to the historical index data to obtain an energy efficiency table of the next stage; s5: and displaying the energy efficiency tables of the current stage and the next stage. The invention provides an enterprise management decision-making auxiliary system and method, wherein an enterprise decision maker quickly grasps the operation condition of each level in an enterprise through an energy efficiency table displayed by a visualization submodule of each level, and the problems that the conventional medicine enterprise management needs to process a large amount of data and is low in efficiency by manually analyzing the data are solved.
Description
Technical Field
The invention relates to the field of enterprise management auxiliary equipment, in particular to an enterprise management decision auxiliary system and method.
Background
Business Intelligence (BI), Business Intelligence, is a complete solution for effectively integrating existing data in an enterprise, rapidly and accurately providing reports and providing decision bases, and helping the enterprise make intelligent Business operation decisions.
At present, the amount of data to be processed by medicine enterprise management is large, the defects of large workload, low efficiency and the like exist depending on manual data analysis, a decision maker cannot find problems in time often, cannot make decision response correctly, and the problem of serious resource waste is caused.
In the prior art, for example, chinese patent published in 2013, 6, month and 26, an intelligent decision support system for engineering project management, published under the number CN103177308A, uses a familiar business language to timely and accurately complete query, report and analysis of required information through simple and intuitive operations, and assists a user in making a more scientific decision, but the application range is not wide enough, and is not suitable for assisting a medical enterprise in managing decisions.
Disclosure of Invention
The invention provides an enterprise management decision-making auxiliary system and method for overcoming the technical defects that the existing medicine enterprise management needs large data amount to be processed and has low efficiency by manually analyzing data.
In order to solve the technical problems, the technical scheme of the invention is as follows:
an enterprise management decision auxiliary system comprises a data acquisition module, a data analysis module, a table generation module, a control module and a visualization module; wherein the visualization module comprises a plurality of levels of visualization submodules;
the output end of the data acquisition module is connected with the input end of the control module, the output end of the control module is respectively connected with the input ends of the visual sub-modules of each level, and the data analysis module and the table generation module are respectively connected with the control module;
the data acquisition module is used for acquiring basic data;
the data analysis module is used for analyzing and calculating the basic data acquired by the data acquisition module to obtain index data;
the table generating module is used for generating an energy efficiency table according to the index data obtained in the data analyzing module;
the control module is used for controlling each module to work;
the visualization submodules of all levels in the visualization module are respectively used for displaying the energy efficiency tables of all levels, so that a decision maker is helped to quickly master the operation conditions of all levels in an enterprise and make a decision correctly.
Preferably, the basic data includes enterprise production element data, employee business completion conditions, product release and sale conditions, new product development progress, and enterprise operation cost and profit data.
Preferably, the system also comprises a data classification module, wherein the input end of the data classification module is connected with the output end of the data acquisition module, and the output end of the data classification module is connected with the input end of the control module; the data classification module is used for classifying the basic data according to different levels.
Preferably, the device further comprises a data storage module, wherein the data storage module is connected with the control module; the data storage module is used for storing basic data, index data and an energy efficiency table.
Preferably, the visualization module comprises at least three levels of visualization submodules.
Preferably, the visualization module comprises four levels of visualization submodules, namely an enterprise level visualization submodule, a department level visualization submodule, a group level visualization submodule and an individual level visualization submodule;
the input ends of the enterprise layer visualization submodule, the department layer visualization submodule, the group layer visualization submodule and the individual layer visualization submodule are respectively connected with the output end of the control module;
the enterprise layer visualization submodule is used for displaying an energy efficiency table of the whole enterprise and helping a decision maker to quickly master the macroscopic operation condition of the enterprise;
the department layer visualization submodule is used for displaying energy efficiency tables of all departments in the enterprise and helping a decision maker to quickly master the operation conditions of all the departments in the enterprise;
the small group layer visualization submodule is used for displaying the energy efficiency tables of all groups in all departments and helping a decision maker to quickly master the operation conditions of all groups in the departments;
the individual layer visualization submodule is used for displaying the energy efficiency tables of all the employees in all the groups and helping a decision maker to quickly master the microcosmic operation condition of the enterprise.
Preferably, the device further comprises a prediction module, wherein the prediction module is connected with the control module; the prediction module is used for establishing a prediction model according to the historical index data, predicting the operation condition of the enterprise at the next stage, and displaying the prediction result of the corresponding level through the visualization sub-modules of each level.
Preferably, the system further comprises an optimization updating module, and the optimization updating module is connected with the control module; and the optimization updating module is used for comparing the prediction result with the corresponding actual operation condition to obtain a prediction error, and optimizing and updating the prediction model in the prediction module according to the prediction error.
Preferably, the system also comprises a login verification module, wherein the output end of the login verification module is connected with the input end of the control module; the login verification module is used for a decision maker to log in the enterprise management decision auxiliary system.
An enterprise management decision-making assistance method is realized based on the enterprise management decision-making assistance system, and comprises the following steps:
s1: acquiring basic data through a data acquisition module, classifying the acquired basic data through a data classification module, and storing the classified basic data in a data storage module;
s2: the basic data in the data storage module is called through the data analysis module to be analyzed and calculated to obtain index data, and the index data are stored in the data storage module;
s3: generating an energy efficiency table according to the index data stored in the data storage module through a table generation module, and storing the energy efficiency table in the data storage module;
s4: establishing a prediction model according to the historical index data in the data storage module through a prediction module, and predicting the operation condition of the enterprise at the next stage to obtain an energy efficiency table at the next stage;
s5: and displaying the energy efficiency tables of the corresponding levels in the current stage and the next stage through the visualization sub-modules of all the levels so as to assist enterprise management decisions.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention provides an enterprise management decision auxiliary system and method, wherein an enterprise decision maker quickly grasps the operation condition of each level in an enterprise through an energy efficiency table displayed by a visualization submodule of each level, thereby finding problems in time, correctly making a decision response and reasonably utilizing resources.
Drawings
FIG. 1 is a schematic diagram of the module connection of the present invention;
FIG. 2 is a flow chart of the steps for implementing the present invention;
wherein: 1. a data acquisition module; 2. a data analysis module; 3. a table generation module; 4. a control module; 5. a visualization module; 51. an enterprise level visualization submodule; 52. a department floor visualization submodule; 53. a small group layer visualization submodule; 54. an individual layer visualization submodule; 6. a data classification module; 7. a data storage module; 8. a prediction module; 9. an optimization updating module; 10. and a login verification module.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1, an enterprise management decision assistance system includes a data acquisition module 1, a data analysis module 2, a table generation module 3, a control module 4, and a visualization module 5; wherein the visualization module 5 comprises several levels of visualization submodules;
the output end of the data acquisition module 1 is connected with the input end of the control module 4, the output end of the control module 4 is respectively connected with the input ends of the visualization sub-modules of each level, and the data analysis module 2 and the table generation module 3 are respectively connected with the control module 4;
the data acquisition module 1 is used for acquiring basic data;
the data analysis module 2 is used for analyzing and calculating the basic data acquired by the data acquisition module 1 to obtain index data;
the table generating module 3 is used for generating an energy efficiency table according to the index data obtained in the data analyzing module 2;
the control module 4 is used for controlling each module to work;
the visualization submodules of the various levels in the visualization module 5 are respectively used for displaying the energy efficiency tables of the various levels, so that a decision maker is helped to quickly master the operation conditions of the various levels in an enterprise and make a decision correctly.
In the specific implementation process, the data acquisition module 1 is externally connected with data systems of all departments through data interfaces so as to realize data acquisition, the acquired data is analyzed and calculated by the data analysis module 2 to obtain profit index data, the form generation module 3 generates an energy efficiency form according to the profit index data, and an enterprise decision maker checks the energy efficiency form of a corresponding level through a visualization submodule of each level, so that the operation condition of each level in an enterprise can be rapidly mastered, problems can be found in time, decisions can be made correctly, and the resource distribution benefit can be improved.
More specifically, the basic data includes enterprise production element data, employee business completion, product delivery and sale, new product development progress, and enterprise operation cost and profit data.
In the specific implementation process, data such as the business completion condition of each employee in the enterprise, the putting-in and selling condition of each product, the research and development progress of a new product and the like are acquired every day, so that the analysis result is more accurate.
More specifically, the system further comprises a data classification module 6, wherein an input end of the data classification module 6 is connected with an output end of the data acquisition module 1, and an output end of the data classification module 6 is connected with an input end of the control module 4; the data classification module 6 is used for classifying the basic data according to different levels.
In the specific implementation process, the data classification module 6 classifies the basic data according to different levels by adopting a pre-trained classifier, so that the analysis and calculation at the later stage are facilitated, and the analysis efficiency is improved.
More specifically, the device further comprises a data storage module 7, wherein the data storage module 7 is connected with the control module 4; the data storage module 7 is used for storing basic data, index data and an energy efficiency table.
More specifically, the visualization module 5 includes at least three levels of visualization submodules.
In a specific implementation process, the visualization module 5 at least comprises three levels of visualization submodules, namely an enterprise level, a department level and an individual level.
More specifically, the visualization module 5 includes four levels of visualization submodules, namely an enterprise level visualization submodule 51, a department level visualization submodule 52, a group level visualization submodule 53 and an individual level visualization submodule 54;
the input ends of the enterprise layer visualization submodule 51, the department layer visualization submodule 52, the group layer visualization submodule 53 and the individual layer visualization submodule 54 are respectively connected with the output end of the control module 4;
the enterprise layer visualization submodule 51 is used for displaying an energy efficiency table of the whole enterprise and helping a decision maker to quickly master the macroscopic operation condition of the enterprise;
the department layer visualization submodule 52 is used for displaying energy efficiency tables of each department in the enterprise and helping a decision maker to quickly master the operation condition of each department in the enterprise;
the group layer visualization submodule 53 is used for displaying energy efficiency tables of each group in each department and helping a decision maker to quickly master the operation condition of each group in each department;
the individual layer visualization submodule 54 is configured to display an energy efficiency table of each employee in each group, and help a decision maker to quickly master the microscopic operation condition of an enterprise.
In the specific implementation process, visual submodules of four levels, namely an enterprise level, a department level, a small group level and an individual level, are arranged according to actual conditions, so that the omnibearing monitoring from macro to micro is realized, an enterprise decision maker can master the operation condition of an enterprise more comprehensively, and a specific link with a problem is found.
More specifically, the device further comprises a prediction module 8, wherein the prediction module 8 is connected with the control module 4; and the prediction module 8 is used for establishing a prediction model according to the historical index data, predicting the operation condition of the next stage of the enterprise, and displaying the prediction result of the corresponding level through the visualization sub-modules of each level.
In the specific implementation process, a decision maker predicts the operation condition of the enterprise at the next stage through the prediction module 8, so that the operation direction is determined more efficiently, the operation plan is made, and the risk avoidance capability is improved. The prediction result can also be stored in the data storage module 7, so that the prediction result is convenient to view and call.
More specifically, the system further comprises an optimization updating module 9, wherein the optimization updating module 9 is connected with the control module 4; and the optimization updating module 9 is used for comparing the prediction result with the corresponding actual operation condition to obtain a prediction error, and optimizing and updating the prediction model in the prediction module 8 according to the prediction error.
In the specific implementation process, the prediction model is optimized and updated through the prediction error, so that the prediction accuracy is improved, and the prediction function of the prediction module 8 has higher practicability.
More specifically, the system further comprises a login authentication module 10, wherein an output end of the login authentication module 10 is connected with an input end of the control module 4; the login verification module 10 is used for a decision maker to log in the enterprise management decision auxiliary system.
In the specific implementation process, the security of the enterprise management decision auxiliary system is improved through the login verification module 10, and confidential data is prevented from being leaked. The decision maker can perform login verification through the combination of the account, the password and the fingerprint, can perform login verification through the combination of the account, the password and the face identification, and can perform login verification through the fingerprint and the face identification, so that the safety level is improved.
Example 2
As shown in fig. 2, an enterprise management decision assistance method implemented based on the enterprise management decision assistance system includes the following steps:
s1: basic data are collected through a data collection module 1, the collected basic data are classified through a data classification module 6, and the classified basic data are stored in a data storage module 7;
s2: the basic data in the data storage module 7 is called through the data analysis module 2 for analysis and calculation to obtain index data, and the index data is stored in the data storage module 7;
s3: generating an energy efficiency table according to the index data stored in the data storage module 7 through the table generation module 3, and storing the energy efficiency table in the data storage module 7;
s4: establishing a prediction model according to the historical index data in the data storage module 7 through the prediction module 8, predicting the operation condition of the enterprise at the next stage, and obtaining an energy efficiency table at the next stage;
s5: and displaying the energy efficiency tables of the corresponding levels in the current stage and the next stage through the visualization sub-modules of all the levels so as to assist enterprise management decisions.
In the specific implementation process, the method further comprises the steps of comparing the prediction result with the corresponding actual operation condition through the optimization updating module 9 to obtain a prediction error, and performing optimization updating on the prediction model in the prediction module 8 according to the prediction error.
Example 3
This embodiment is implemented based on the enterprise management decision assistance system described in embodiment 1.
Firstly, an enterprise is divided according to levels, wherein the enterprise comprises an enterprise layer, a department layer, a small group layer and an individual layer, and each department, each small group and each individual are divided into independent subunits respectively on the department layer, the small group layer and the individual layer; then, with the profit data as guidance, establishing an internal market exchange relationship among the subunits, uniformly setting an exchange price and a floating range by a company, and carrying out 'market exchange' on the subunits according to uniform pricing; for example, a company purchases a product from a production department, a marketing department purchases a product from a company, a manufacturing department purchases a semi-finished product from a general department, a general department purchases a raw material from a purchasing department, and the like.
Acquiring data such as service completion conditions of various employees in an enterprise, putting and selling conditions of various products, research and development progress of new products and the like every day, and performing classified calculation processing to obtain profit index data of various subunits; meanwhile, different profit index weights are set for subunits of different levels, wherein the profit index weight of the subunit of the individual layer is the highest and is between 95% and 100%, and the profit index weights of the subunits of the subgroup layer, the department layer and the enterprise layer are sequentially decreased by 10% to 15%; and the energy efficiency table is obtained according to the profit index data and the profit index weight of the sub-units, and is visually displayed through the corresponding visualization sub-module.
A decision maker can intuitively know the profit condition of each subunit in real time by verifying and logging in the enterprise management decision auxiliary system, so that the operation condition of each level in an enterprise can be rapidly mastered. Meanwhile, the prediction module 8 can be used for predicting the operation condition of the enterprise at the next stage, and the exchange price and the profit index weight are adjusted in time according to the prediction result, so that the enthusiasm of each subunit is fully adjusted, and the resources are reasonably utilized and distributed.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. An enterprise management decision auxiliary system is characterized by comprising a data acquisition module (1), a data analysis module (2), a table generation module (3), a control module (4) and a visualization module (5); wherein the visualization module (5) comprises several levels of visualization sub-modules;
the output end of the data acquisition module (1) is connected with the input end of the control module (4), the output end of the control module (4) is respectively connected with the input end of each level of visualization sub-module, and the data analysis module (2) and the table generation module (3) are respectively connected with the control module (4);
the data acquisition module (1) is used for acquiring basic data;
the data analysis module (2) is used for analyzing and calculating the basic data acquired by the data acquisition module (1) to obtain index data;
the table generating module (3) is used for generating an energy efficiency table according to the index data obtained in the data analyzing module (2);
the control module (4) is used for controlling each module to work;
the visualization submodules of all levels in the visualization module (5) are respectively used for displaying the energy efficiency tables of all levels, so that a decision maker is helped to quickly master the operation conditions of all levels in an enterprise and make a decision correctly.
2. The system of claim 1, wherein the basic data comprises production factor data of the enterprise, completion of employee business, product release and sale, progress of new product development, and operation cost and profit data of the enterprise.
3. An enterprise management decision-making assistance system according to claim 1, further comprising a data classification module (6), an input of said data classification module (6) being connected to an output of said data collection module (1), an output of said data classification module (6) being connected to an input of said control module (4); the data classification module (6) is used for classifying the basic data according to different levels.
4. An enterprise management decision assistance system according to claim 1, further comprising a data storage module (7), said data storage module (7) being connected to said control module (4); the data storage module (7) is used for storing basic data, index data and an energy efficiency table.
5. An enterprise management decision assistance system according to claim 1, characterized in that said visualization module (5) comprises at least three levels of visualization submodules.
6. An enterprise management decision-making assistance system according to claim 5, characterized in that said visualization module (5) comprises four levels of visualization submodules, namely an enterprise level visualization submodule (51), a department level visualization submodule (52), a group level visualization submodule (53) and an individual level visualization submodule (54);
the input ends of the enterprise layer visualization submodule (51), the department layer visualization submodule (52), the group layer visualization submodule (53) and the individual layer visualization submodule (54) are respectively connected with the output end of the control module (4);
the enterprise layer visualization submodule (51) is used for displaying an energy efficiency table of the whole enterprise and helping a decision maker to quickly master the macroscopic operation condition of the enterprise;
the department floor visualization submodule (52) is used for displaying energy efficiency tables of all departments in the enterprise and helping a decision maker to quickly master the operation conditions of all the departments in the enterprise;
the small group layer visualization submodule (53) is used for displaying energy efficiency tables of all groups in all departments and helping a decision maker to quickly master the operation conditions of all groups in the departments;
the individual layer visualization sub-module (54) is used for displaying energy efficiency tables of all employees in all groups and helping decision makers to quickly master microscopic operation conditions of enterprises.
7. An enterprise management decision assistance system according to claim 1, further comprising a prediction module (8), said prediction module (8) being connected to said control module (4); the prediction module (8) is used for establishing a prediction model according to the historical index data, predicting the operation condition of the next stage of the enterprise, and displaying the prediction result of the corresponding level through the visualization sub-modules of each level.
8. An enterprise management decision assistance system according to claim 7, further comprising an optimization update module (9), wherein said optimization update module (9) is connected to said control module (4); and the optimization updating module (9) is used for comparing the prediction result with the corresponding actual operation condition to obtain a prediction error, and optimizing and updating the prediction model in the prediction module (8) according to the prediction error.
9. An enterprise management decision assistance system according to claim 1, further comprising a login authentication module (10), wherein an output of the login authentication module (10) is connected to an input of the control module (4); the login verification module (10) is used for a decision maker to log in the enterprise management decision auxiliary system.
10. An enterprise management decision assistance method, comprising the steps of:
s1: basic data are collected through a data collection module (1), the collected basic data are classified through a data classification module (6), and the classified basic data are stored in a data storage module (7);
s2: basic data in the data storage module (7) are called through the data analysis module (2) to be analyzed and calculated to obtain index data, and the index data are stored in the data storage module (7);
s3: generating an energy efficiency table of the current stage according to index data stored in a data storage module (7) through a table generation module (3), and storing the energy efficiency table in the data storage module (7);
s4: establishing a prediction model according to historical index data in the data storage module (7) through a prediction module (8), predicting the operation condition of the enterprise at the next stage, and obtaining an energy efficiency table at the next stage;
s5: and displaying the energy efficiency tables of the corresponding levels in the current stage and the next stage through the visualization sub-modules of all the levels so as to assist enterprise management decisions.
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