CN116862202A - Enterprise management data management method based on big data analysis - Google Patents
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Abstract
The invention belongs to the technical field of enterprise management, in particular to an enterprise management data management method based on big data analysis, which comprises the following steps: establishing an enterprise data management framework, data quality management, data security management, data life cycle management, monitoring and optimization; the invention is based on the standard data management and operation flow, improves the accuracy and the integrity of the data, protects the privacy and the safety of the data, realizes the optimization and the standard management of the data, is beneficial to meeting the requirements of modern enterprises, generates corresponding early warning information based on the problems and risks existing in the data management when monitoring and optimizing, can automatically and reasonably regulate the display brightness of the early warning information when the problems and the risks are found, automatically selects the optimal manager when the display area is unmanned, and sends the early warning management notice to the optimal manager, and is beneficial to timely carrying out corresponding improvement measures.
Description
Technical Field
The invention relates to the technical field of enterprise management, in particular to an enterprise management data management method based on big data analysis.
Background
In the daily operation of an enterprise, a large amount of data can be generated, including business data, personnel information, financial data and the like, and enterprise management data management refers to a whole set of management behaviors related to data use, which are initiated and carried out by an enterprise data management department, including making and implementing a series of processes of business application and technical management for the data in the whole enterprise, wherein the aim of data management is to ensure that the data can be accurately collected, stored, managed, shared and used, so as to support decision making and business requirements of enterprise organizations;
at present, management of enterprise management data faces a plurality of challenges, such as problems of accuracy and completeness of data, data safety and privacy protection, data sharing and utilization and the like, and problems of irregular flow, undefined authority, low data quality and the like exist, so that the requirements of modern enterprises are difficult to meet, the display brightness of early warning information cannot be automatically and reasonably regulated when problems and risks are found, and optimal management personnel are automatically selected and sent to the display area when no person exists, so that corresponding improvement measures are not facilitated in time;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide an enterprise management data management method based on big data analysis, which solves the problems that the prior art has the problems of irregular flow, undefined authority, low data quality and the like, is difficult to meet the requirements of modern enterprises, can not automatically and reasonably regulate and control the display brightness of early warning information when problems and risks are found, automatically selects the optimal manager when no person exists in a display area, and sends early warning management notification to the optimal manager, and is not beneficial to timely carrying out corresponding improvement measures.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the enterprise management data management method based on big data analysis comprises the following steps:
step one, establishing an enterprise data management framework: determining a data owner, formulating data management and operation specifications, determining data security and privacy protection measures, determining data sharing and utilization rules, formulating data quality management, checking rules and determining a data life cycle management strategy;
step two, data quality management: the operations of data cleaning, checking and standardization are adopted, so that the accuracy, consistency, integrity and reliability of the data are ensured;
step three, data security management: adopting measures of data encryption, access control and audit to protect the privacy and safety of data;
fourth, data life cycle management: managing links of creation, storage, transmission, sharing and destruction of data, and ensuring effective utilization and compliance processing of the data;
step five, monitoring and optimizing: the data treatment process is monitored and optimized, the data treatment problem is found and solved in time, and the data treatment efficiency and quality are improved.
Further, in the first step, when determining the owner of the data, the creator, the owner, the manager and the visitor of the data are determined, and the responsibility and authority of the owner of the data are defined, so that unified management and control of the data are ensured; when the data management and operation specification is formulated, the data management and operation specification is formulated according to the business requirement and the data management requirement of an enterprise, wherein the data management and operation specification comprises the format, the standard, the storage mode, the transmission rule and the access control of data; when determining data security and privacy protection measures, the data security and privacy protection measures comprise data encryption, access control, identity authentication, data backup and restoration measures so as to ensure the security and privacy protection of the data;
when determining the data sharing and utilization rules, the data sharing range, purpose, mode and service life are included to ensure reasonable utilization and protection of the data; when formulating data quality management and check rules, including requirements of data integrity, accuracy and consistency, and flow and method of data check and correction, accuracy and reliability of data are ensured by formulating specifications and rules; when determining the data life cycle management strategy, the management requirements and the operation flow of the links including creation, storage, transmission, sharing and destruction of the data are included, so that the reasonable utilization and timely processing of the data are ensured.
Further, in the second step, the data cleaning is a data preprocessing process to remove noise and redundant information in the data, so that the data meets the requirements of subsequent processing, and the specific steps of data cleaning include removing null values, filling missing values, processing abnormal values and removing repeated data; the data verification is a process for verifying the accuracy, consistency and integrity of data, errors, inconsistencies and missing problems in the data are found through the data verification, corresponding processing and correction are carried out, and the specific method of the data verification comprises data range checking, rule checking, relation checking and consistency checking; the data normalization is to convert and adjust the data according to a certain standard to meet the requirement of subsequent processing, and the specific method comprises data normalization, discretization and coding, so that the comparability and operability of the data are improved through the data normalization.
Further, in the third step, the data encryption is to encrypt the data by an encryption algorithm to make the data become a ciphertext which cannot be read or understood, and in the data transmission and storage process, the data is encrypted to prevent the data from being illegally acquired and stolen, and the adopted encryption technology comprises symmetric encryption and asymmetric encryption; the access control is the control of the access right of the data, only users with corresponding rights can access and operate the data, and the formulated access control strategy comprises role-based access control and attribute-based access control so as to limit the access right of the data and prevent illegal access and data leakage; audit is the supervision and inspection of data security management to discover and correct problems and risks present in data security management, and established audit mechanisms include data security audit and security event audit.
Further, in the fourth step, the data creation is a data input, generation or collection process, and the accuracy and the integrity of the data are ensured by making standards and procedures for data creation; the data storage is a data storage and management process, and the safety and usability of the data are ensured by selecting a proper storage medium and a proper storage mode; the data transmission is the transmission process of data among different systems or nodes, and the reliability and the safety of the data transmission are ensured by establishing channels and rules of the data transmission; the data sharing is a sharing process of data to an external mechanism or a person, and reasonable utilization and protection of the data are ensured by making rules and flows of the data sharing; the data destruction is the process of deleting and destroying the data, and the complete deletion and privacy protection of the data are ensured by establishing the flow and standard of data destruction.
Further, the specific operation procedure of the fifth step is as follows:
and (3) establishing a monitoring system: establishing a monitoring system of a data treatment process, wherein the monitoring system comprises monitoring of data quality, data safety and data life cycle, and detecting occurrence and change conditions of data treatment problems in real time through the monitoring system; setting up indexes and standards: setting up indexes and standards of data governance for evaluating the effect and quality of the data governance, wherein the indexes and standards have scalability and comparability so as to accurately evaluate the level of the data governance in actual operation;
analysis and diagnosis were performed: analyzing and diagnosing the monitoring data to find out problems and risks existing in the data management, and finding out the defects and the aspects needing improvement of the data management through comparing indexes and standards; implementing optimization measures: according to analysis and diagnosis results, implementing targeted optimization measures, wherein the optimization measures comprise improving data treatment flow, perfecting data specification and enhancing data safety protection; periodic evaluation and adjustment: the effect and quality of data governance are evaluated regularly, optimization measures are adjusted and improved regularly, and the level and quality of service of data governance are improved through continuous optimization and adjustment.
Further, enterprise management data management is realized through a data management platform, and the data management platform generates corresponding early warning information based on problems and risks existing in data management when monitoring and optimizing, and sends the corresponding early warning information to a visual operation module for information display early warning; the visual operation module monitors in real time through the display area when information display early warning is carried out so as to judge whether management personnel exist in the display area, and if the management personnel exist in the display area, the brightness is automatically regulated and controlled through pre-display detection analysis; if no manager exists in the display area, generating an early warning pushing analysis signal and sending the early warning pushing analysis signal to the data management platform, and when the data management platform receives the early warning pushing analysis signal, carrying out early warning pushing analysis to determine the optimal manager and sending corresponding early warning information to an intelligent terminal of the optimal manager.
Further, the specific analysis process of the pre-display detection analysis is as follows:
the method comprises the steps of obtaining a visual condition value and a display table value through analysis, respectively comparing the visual condition value and the display table value with a preset visual condition threshold value and a preset display table threshold value, generating a high-brightness display signal if the visual condition value and the display table value exceed the corresponding preset threshold value, generating a low-brightness display signal if the visual condition value and the display table value do not exceed the corresponding preset threshold value, and generating a medium-brightness display signal if the visual condition value and the display table value do not exceed the corresponding preset threshold value; the method comprises the steps that a high-brightness display signal, a medium-brightness display signal and a low-brightness display signal are respectively corresponding to a group of brightness display ranges, the visual operation module determines an adaptive brightness display range based on the generated brightness display signals, if the actual brightness of the visual operation module is in the adaptive brightness display range, brightness regulation is not carried out, and otherwise, the display brightness is automatically regulated to be in the adaptive brightness display range.
Further, the specific process of obtaining the view condition value and the display table value through analysis is as follows:
collecting personnel positions of all management personnel in a display area, calculating the distance between the center point of the visual operation module and the corresponding personnel position to obtain a man-machine distance value, drawing a ray perpendicular to the visual operation module right in front of the center point of the visual operation module by taking the center point of the visual operation module as an endpoint, marking the ray as a forward vertical ray, connecting the middle point of the visual operation module with the corresponding personnel position, and marking the line segment as a man-machine path line segment; marking an included angle between a human-machine path line segment of a corresponding person and the forward vertical ray as a sight line oblique angle value;
analyzing and calculating the sight oblique angle value and the man-machine distance value to obtain a vision value, establishing a set of vision values of all management staff, marking a subset with the largest numerical value in the set as a vision upper limit value, carrying out average value calculation on all subsets in the set to obtain a vision average value, and analyzing and calculating the vision upper limit value and the vision average value to obtain a vision condition value; and setting a plurality of temperature measuring points on the display surface of the visual operation module, collecting temperature values of the temperature measuring points in real time, summing all the temperature values, calculating the average value to obtain a display temperature value, collecting the environmental temperature data, the environmental brightness data and the dust concentration data of the display area, and carrying out normalization calculation on the display temperature value, the environmental temperature data, the environmental brightness data and the dust concentration data to obtain a display value.
Further, the specific analysis process of the early warning pushing analysis is as follows:
acquiring intelligent terminal information of all managers, sending early warning instructions to all managers, receiving confirmation instructions of the managers, and marking the managers replying the confirmation instructions within a specified time as objects to be selected; collecting management time length of a corresponding object to be selected, collecting an average value of departure preparation time length and a delay arrival occupation value of the object to be selected for processing corresponding early warning operation, obtaining delay time length of each delay arrival, summing all delay time lengths, and taking the average value to obtain a delay analysis value;
performing numerical calculation on the management time length, the average value of the departure preparation time length, the delay arrival occupation ratio and the delay analysis value to obtain an analysis value, performing numerical comparison on the analysis value and a preset analysis threshold, and marking the corresponding object to be selected as a selectable object if the analysis value exceeds the preset analysis threshold; and collecting the path distance between the selectable object and the visual operation module, marking the path distance as a forward distance value, sequencing all the selectable objects according to the value of the forward distance value from large to small, and marking the selectable object positioned at the last position as an optimal manager.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, through establishing an enterprise data management framework, data quality management, data security management, data life cycle management, monitoring and optimization, based on standard data management and operation flow, the accuracy and the integrity of data are improved, the privacy and the security of the data are protected, the optimization and the standard management of the data are realized, a plurality of challenges facing the management of enterprise management data at present can be met, the problems of non-standard flow, undefined authority, low data quality and the like existing in the existing enterprise data management are solved, and the requirements of modern enterprises are met;
2. in the invention, corresponding early warning information is generated based on problems and risks in data treatment when monitoring and optimizing, the corresponding early warning information is sent to the visual operation module for information display early warning, and if a manager exists in a display area, the manager automatically regulates and controls the brightness through pre-display detection analysis, so that the display brightness of the early warning information can be automatically and reasonably regulated and controlled when the problems and the risks are found, and the early warning display content can be clearly seen by all the manager in the display area; and when no manager exists in the display area, the optimal manager is determined through early warning pushing analysis, so that corresponding improvement measures can be performed in time, and the intelligent degree of the manager is further improved.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: as shown in fig. 1, the enterprise management data management method based on big data analysis provided by the invention comprises the following steps:
step one, establishing an enterprise data management framework: determining a data owner, formulating data management and operation specifications, determining data security and privacy protection measures, determining data sharing and utilization rules, formulating data quality management, checking rules and determining a data life cycle management strategy;
specifically, the owner of the data is determined: firstly, determining owners of data, including creators, owners, managers, visitors and the like of the data, and defining responsibilities and rights of the owners of the data so as to ensure unified management and control of the data; formulating data management and operation specifications: and formulating data management and operation specifications according to business requirements and data management requirements of enterprises. Including data format, standard, storage mode, transmission rules, access control, etc. The specification should be as detailed and clear as possible so as to be accurately executed in actual operation; determining data security and privacy protection measures: in the data management framework, measures for ensuring data safety and privacy protection are required to be defined, including measures such as data encryption, access control, identity authentication, data backup and recovery, and the like, so as to ensure the data safety and privacy protection;
determining data sharing and utilization rules: enterprises often need to share data with external institutions or individuals, and thus, data sharing and utilization rules need to be formulated. The sharing range, purpose, mode, service life and other aspects of the data are included, so that reasonable utilization and protection of the data are ensured; formulating data quality management and verification rules: the data quality management and verification rules need to be included in the data governance framework. Including requirements in terms of data integrity, accuracy, consistency, etc., and flow and method of data checksum modification. By making a specification and a rule, the accuracy and the reliability of the data are ensured; determining a data lifecycle management policy: the data has different values and use modes at different stages, so that a data life cycle management strategy needs to be formulated, and the management requirements and operation flows of links such as creation, storage, transmission, sharing and destruction of the data are included, so that reasonable utilization and timely processing of the data are ensured;
step two, data quality management: the operations of data cleaning, checking and standardization are adopted, so that the accuracy, consistency, integrity and reliability of the data are ensured, and the quality and value of the data are improved;
specifically, the data cleaning is a data preprocessing process to remove noise and redundant information in the data, so that the data meets the requirement of subsequent processing, and the specific steps of the data cleaning comprise removing null values, filling missing values, processing abnormal values, removing repeated data and the like; the data verification is a process for verifying the accuracy, consistency and integrity of data, errors, inconsistencies and missing problems in the data are found through the data verification, corresponding processing and correction are carried out, and the specific method of the data verification comprises data range checking, rule checking, relation checking, consistency checking and the like; the data normalization is to convert and adjust the data according to a certain standard to meet the requirement of subsequent processing, and the specific method comprises data normalization, discretization, coding and the like, so that the comparability and operability of the data are improved through the data normalization;
step three, data security management: measures of data encryption, access control and audit are adopted, so that the privacy and safety of data are protected, and the data are prevented from being illegally acquired, revealed and abused;
specifically, the data encryption is to encrypt data by an encryption algorithm to make the data become a ciphertext which cannot be read or understood, and in the process of data transmission and storage, the data is encrypted to prevent the data from being illegally acquired and stolen, and enterprise data management can adopt various encryption technologies, such as symmetric encryption, asymmetric encryption and the like, so as to protect the privacy and safety of the data; the access control is the control of the access right of the data, only users with corresponding rights can access and operate the data, and enterprises limit the access right of the data by formulating access control strategies, including role-based access control, attribute-based access control and the like, so as to prevent illegal access and data leakage; the audit is supervision and inspection of data security management, and aims to find and correct problems and risks existing in the data security management, and enterprises can find and solve the data security problems in time by establishing an audit mechanism comprising data security audit, security event audit and the like, so that the security and reliability of the data are improved;
fourth, data life cycle management: managing the links of creation, storage, transmission, sharing and destruction of data, ensuring effective utilization and compliance processing of the data, and avoiding risks such as data leakage, abuse and loss;
specifically, the data creation is the process of inputting, generating or collecting the data, and the accuracy and the integrity of the data are ensured by making standards and procedures for data creation; the data storage is a data storage and management process, and the safety and usability of the data are ensured by selecting a proper storage medium and a proper storage mode; the data transmission is the transmission process of data among different systems or nodes, and the reliability and the safety of the data transmission are ensured by establishing channels and rules of the data transmission; the data sharing is a sharing process of data to an external mechanism or a person, and reasonable utilization and protection of the data are ensured by making rules and flows of the data sharing; the data destruction is the process of deleting and destroying the data, and the complete deletion and privacy protection of the data are ensured by establishing the flow and standard of data destruction;
step five, monitoring and optimizing: monitoring and optimizing the data treatment process, finding and solving the data treatment problem in time, improving the data treatment efficiency and quality, and realizing the optimization and standard management of the data; the specific operation process is as follows:
and (3) establishing a monitoring system: establishing a monitoring system of a data treatment process, wherein the monitoring system comprises monitoring of data quality, data safety and data life cycle, and detecting occurrence and change conditions of data treatment problems in real time through the monitoring system; setting up indexes and standards: setting up indexes and standards of data governance for evaluating the effect and quality of the data governance, wherein the indexes and standards have scalability and comparability so as to accurately evaluate the level of the data governance in actual operation;
analysis and diagnosis were performed: analyzing and diagnosing the monitoring data to find out problems and risks existing in the data management, and finding out the defects and the aspects needing improvement of the data management through comparing indexes and standards; implementing optimization measures: according to analysis and diagnosis results, implementing targeted optimization measures, wherein the optimization measures comprise improving data treatment flow, perfecting data specification and enhancing data safety protection; periodic evaluation and adjustment: the effect and quality of data governance are evaluated regularly, optimization measures are adjusted and improved regularly, and the level and quality of service of data governance are improved through continuous optimization and adjustment.
By the enterprise management data management method, based on the standard data management and operation flow, the accuracy and the integrity of the data are improved, the privacy and the safety of the data are protected, the optimization and the standard management of the data are realized, various challenges facing the management of enterprise management data at present, such as the problems of the accuracy and the integrity of the data, the safety and the privacy protection of the data, the sharing and the utilization of the data and the like, are solved, the problems of the non-standard flow, the undefined authority, the low quality of the data and the like existing in the existing enterprise data management are solved, and the requirements of modern enterprises are met.
Embodiment two: the difference between the embodiment and the embodiment 1 is that the enterprise management data management is realized through a data management platform, the data management platform generates corresponding early warning information based on the problems and risks existing in the data management when monitoring and optimizing, and the corresponding early warning information is sent to a visual operation module for information display early warning; the visual operation module monitors in real time through the display area when information display early warning is carried out so as to judge whether management staff exists in the display area, if so, the brightness is automatically regulated and controlled through pre-display detection analysis, the display brightness of early warning information can be automatically and reasonably regulated and controlled when problems and risks are found, all management staff in the display area can see early warning display content clearly, and the intelligent degree is high; the specific analytical process of the pre-display detection analysis is as follows:
the visual condition value and the display table value are obtained through analysis, and the visual condition value and the display table value are specifically: collecting personnel positions of all management personnel in a display area, calculating the distance between the center point of the visual operation module and the corresponding personnel position to obtain a man-machine distance value QT, drawing a ray vertical to the visual operation module right in front of the center point of the visual operation module by taking the center point of the visual operation module as an endpoint and marking the ray as a forward vertical ray, connecting the middle point of the visual operation module with the corresponding personnel position, and marking the line segment as a man-machine path line segment; marking an included angle between a human-machine path line segment of a corresponding person and the forward vertical ray as a sight line oblique angle value SX; the larger the value of the man-machine distance value QT of the corresponding manager is, the larger the value of the sight line oblique angle value SX is, the more difficult the corresponding manager is to see the display content, and the more the display brightness needs to be properly improved;
analyzing and calculating a sight line oblique angle value SX and a man-machine distance value QT through a formula QS=kq1×SX+kq2×QT to obtain a vision value QS, wherein kq1 and kq2 are preset weight coefficients, and kq1 is more than kq2 is more than 0; and the larger the value of the visual clarity value QS is, the more difficult the corresponding manager can see the display content clearly; establishing a set of vision values of all management staff, marking a subset with the largest numerical value in the set as a vision upper limit value, carrying out average value calculation on all subsets in the set to obtain a vision average value, and carrying out analysis calculation on the vision upper limit value SF1 and the vision average value SF2 through a formula QK= (kq3+kq4+SF2)/2 to obtain a vision value QK; wherein, kq3 and kq4 are preset proportionality coefficients, and kq4 is more than kq3 and more than 0; moreover, the larger the value of the condition value QK is, the more difficult it is for all managers to see the early warning display content;
setting a plurality of temperature measuring points on the display surface of the visual operation module, collecting temperature values of the temperature measuring points in real time, summing all the temperature values, calculating the average value to obtain a display temperature value, and displaying the larger the value of the display temperature value, the larger the damage and the running risk to the visual operation module caused by high-brightness display; the environmental temperature data, the environmental brightness data and the dust concentration data of the display area are acquired and are analyzed according to a normalization analysis formulaCarrying out normalization calculation on the display temperature value XW, the environmental temperature data QW, the environmental brightness data QH and the dust concentration data QF to obtain a display value XB; wherein vp1, vp2, vp3, vp4 are preset proportionality coefficients, vp1, vp2, vp3The values of vp4 are all larger than zero; the larger the value of the display table value XB, the more the display brightness needs to be properly improved;
respectively carrying out numerical comparison on the visual condition value QK and the display table value XB and a preset visual condition threshold value and a preset display table threshold value, if the visual condition value QK and the display table value XB both exceed the corresponding preset threshold values, generating a high-brightness display signal, if the visual condition value QK and the display table value XB do not exceed the corresponding preset threshold values, generating a low-brightness display signal, and otherwise, generating a medium-brightness display signal; the method comprises the steps that a high-brightness display signal, a medium-brightness display signal and a low-brightness display signal are preset to respectively correspond to a group of brightness display ranges, the visual operation module determines an adaptive brightness display range based on the generated brightness display signals, and if the actual brightness of the visual operation module is in the adaptive brightness display range, brightness regulation is not carried out; if the actual brightness of the visual operation module is not in the adaptive brightness display range, the display brightness is automatically adjusted to be in the adaptive brightness display range.
Embodiment III: the difference between the embodiment and the embodiment 1 and the embodiment 2 is that if no manager exists in the display area, an early warning pushing analysis signal is generated and sent to the data management platform, the data management platform performs early warning pushing analysis to determine the optimal manager when receiving the early warning pushing analysis signal, and sends corresponding early warning information to the intelligent terminal of the optimal manager, so that the optimal manager can be automatically selected and sent an early warning management notice when no person exists in the display area, corresponding improvement measures can be performed in time, and the intelligent degree of the intelligent manager is further improved; the specific analysis process of the early warning pushing analysis is as follows:
acquiring intelligent terminal information of all managers, sending early warning instructions to all managers, receiving confirmation instructions of the managers, and marking the managers replying the confirmation instructions within a specified time as objects to be selected; collecting the management time length of the corresponding object to be selected, wherein the larger the numerical value of the management time length is, the richer the management experience of the corresponding object to be selected is; the method comprises the steps of acquiring an average value of departure preparation time length and a delay arrival occupation ratio of corresponding early warning operation of object to be selected, wherein the delay arrival occupation ratio is a data value of which the number of times of the early warning operation is not arrived and processed on time is a percentage of the total number of times, acquiring delay time length of each delay arrival, summing all delay time lengths, and taking the average value to obtain a delay analysis value; it should be noted that, the larger the value of the average value of the departure preparation time length, the larger the value of the delay arrival occupation ratio and the larger the value of the delay analysis value, the less timely the processing of the corresponding object to be selected;
by the formulaCarrying out numerical calculation on the management time GS, the departure preparation time average value CS, the delay arrival occupation ratio YZ and the delay analysis value YF to obtain an analysis value TX; wherein, ap1, ap2, ap3 and ap4 are preset proportionality coefficients, and the values of ap1, ap2, ap3 and ap4 are all larger than zero; and, the larger the numerical value of the analysis value TX is, the more abundant and timely the processing experience of the corresponding object to be selected is; comparing the value of the analysis value TX with a preset analysis threshold value, and marking the corresponding object to be selected as a selectable object if the analysis value exceeds the preset analysis threshold value; and collecting the path distance between the selectable object and the visual operation module, marking the path distance as a forward distance value, sequencing all the selectable objects according to the value of the forward distance value from large to small, and marking the selectable object positioned at the last position as an optimal manager.
The working principle of the invention is as follows: when the method is used, the enterprise data management framework, the data quality management, the data safety management, the data life cycle management, the monitoring and the optimization are established, the accuracy and the integrity of data are improved based on the standard data management and the operation flow, the privacy and the safety of the data are protected, the optimization and the standard management of the data are realized, a plurality of challenges facing the management of the enterprise management data at present can be met, the problems of non-standard flow, undefined authority, low data quality and the like existing in the existing enterprise data management are solved, and the requirements of modern enterprises are met; and when monitoring and optimizing, corresponding early warning information is generated based on problems and risks existing in data management, the corresponding early warning information is sent to a visual operation module for information display early warning, if management personnel exist in a display area, automatic brightness regulation and control are performed through pre-display detection analysis, automatic and reasonable regulation and control of the display brightness of the early warning information can be performed when the problems and risks are found, and the early warning display content can be clearly seen by all the management personnel in the display area; and when no manager exists in the display area, determining the optimal manager through early warning pushing analysis, and sending corresponding early warning information to an intelligent terminal of the optimal manager, so that corresponding improvement measures can be carried out in time, and the intelligent degree of the intelligent terminal is further improved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The enterprise management data management method based on big data analysis is characterized by comprising the following steps:
step one, establishing an enterprise data management framework: determining a data owner, formulating data management and operation specifications, determining data security and privacy protection measures, determining data sharing and utilization rules, formulating data quality management, checking rules and determining a data life cycle management strategy;
step two, data quality management: the operations of data cleaning, checking and standardization are adopted, so that the accuracy, consistency, integrity and reliability of the data are ensured;
step three, data security management: adopting measures of data encryption, access control and audit to protect the privacy and safety of data;
fourth, data life cycle management: managing links of creation, storage, transmission, sharing and destruction of data, and ensuring effective utilization and compliance processing of the data;
step five, monitoring and optimizing: the data treatment process is monitored and optimized, the data treatment problem is found and solved in time, and the data treatment efficiency and quality are improved.
2. The method for managing data of enterprises based on big data analysis according to claim 1, wherein in the first step, when determining the owners of the data, the method comprises determining the creator, the owners, the manager and the visitors of the data, and defining the responsibilities and rights of the owners of the data, so as to ensure the unified management and control of the data; when the data management and operation specification is formulated, the data management and operation specification is formulated according to the business requirement and the data management requirement of an enterprise, wherein the data management and operation specification comprises the format, the standard, the storage mode, the transmission rule and the access control of data; when determining data security and privacy protection measures, the data security and privacy protection measures comprise data encryption, access control, identity authentication, data backup and restoration measures so as to ensure the security and privacy protection of the data;
when determining the data sharing and utilization rules, the data sharing range, purpose, mode and service life are included to ensure reasonable utilization and protection of the data; when formulating data quality management and check rules, including requirements of data integrity, accuracy and consistency, and flow and method of data check and correction, accuracy and reliability of data are ensured by formulating specifications and rules; when determining the data life cycle management strategy, the management requirements and the operation flow of the links including creation, storage, transmission, sharing and destruction of the data are included, so that the reasonable utilization and timely processing of the data are ensured.
3. The enterprise management data governance method based on big data analysis according to claim 1, wherein enterprise management data governance is implemented by a data governance platform, the data governance platform generates corresponding early warning information based on problems and risks existing in the data governance when monitoring and optimizing, and sends the corresponding early warning information to a visual operation module for information display early warning; the visual operation module monitors in real time through the display area when information display early warning is carried out so as to judge whether management personnel exist in the display area, and if the management personnel exist in the display area, the brightness is automatically regulated and controlled through pre-display detection analysis; if no manager exists in the display area, generating an early warning pushing analysis signal and sending the early warning pushing analysis signal to the data management platform, and when the data management platform receives the early warning pushing analysis signal, carrying out early warning pushing analysis to determine the optimal manager and sending corresponding early warning information to an intelligent terminal of the optimal manager.
4. The enterprise management data management method based on big data analysis according to claim 3, wherein the specific analysis process of the pre-display detection analysis is as follows:
the method comprises the steps of obtaining a visual condition value and a display table value through analysis, respectively comparing the visual condition value and the display table value with a preset visual condition threshold value and a preset display table threshold value, generating a high-brightness display signal if the visual condition value and the display table value exceed the corresponding preset threshold value, generating a low-brightness display signal if the visual condition value and the display table value do not exceed the corresponding preset threshold value, and generating a medium-brightness display signal if the visual condition value and the display table value do not exceed the corresponding preset threshold value; the method comprises the steps that a high-brightness display signal, a medium-brightness display signal and a low-brightness display signal are respectively corresponding to a group of brightness display ranges, the visual operation module determines an adaptive brightness display range based on the generated brightness display signals, if the actual brightness of the visual operation module is in the adaptive brightness display range, brightness regulation is not carried out, and otherwise, the display brightness is automatically regulated to be in the adaptive brightness display range.
5. The method for managing data of an enterprise based on big data analysis according to claim 4, wherein the specific process of obtaining the view value and the display value by the analysis is as follows:
collecting personnel positions of all management personnel in a display area, calculating the distance between the center point of the visual operation module and the corresponding personnel position to obtain a man-machine distance value, drawing a ray perpendicular to the visual operation module right in front of the center point of the visual operation module by taking the center point of the visual operation module as an endpoint, marking the ray as a forward vertical ray, connecting the middle point of the visual operation module with the corresponding personnel position, and marking the line segment as a man-machine path line segment; marking an included angle between a human-machine path line segment of a corresponding person and the forward vertical ray as a sight line oblique angle value;
analyzing and calculating the sight oblique angle value and the man-machine distance value to obtain a vision value, establishing a set of vision values of all management staff, marking a subset with the largest numerical value in the set as a vision upper limit value, carrying out average value calculation on all subsets in the set to obtain a vision average value, and analyzing and calculating the vision upper limit value and the vision average value to obtain a vision condition value; and setting a plurality of temperature measuring points on the display surface of the visual operation module, collecting temperature values of the temperature measuring points in real time, summing all the temperature values, calculating the average value to obtain a display temperature value, collecting the environmental temperature data, the environmental brightness data and the dust concentration data of the display area, and carrying out normalization calculation on the display temperature value, the environmental temperature data, the environmental brightness data and the dust concentration data to obtain a display value.
6. The enterprise management data management method based on big data analysis according to claim 3, wherein the specific analysis process of the early warning push analysis is as follows:
acquiring intelligent terminal information of all managers, sending early warning instructions to all managers, receiving confirmation instructions of the managers, and marking the managers replying the confirmation instructions within a specified time as objects to be selected; collecting management time length of a corresponding object to be selected, collecting an average value of departure preparation time length and a delay arrival occupation value of the object to be selected for processing corresponding early warning operation, obtaining delay time length of each delay arrival, summing all delay time lengths, and taking the average value to obtain a delay analysis value;
performing numerical calculation on the management time length, the average value of the departure preparation time length, the delay arrival occupation ratio and the delay analysis value to obtain an analysis value, performing numerical comparison on the analysis value and a preset analysis threshold, and marking the corresponding object to be selected as a selectable object if the analysis value exceeds the preset analysis threshold; and collecting the path distance between the selectable object and the visual operation module, marking the path distance as a forward distance value, sequencing all the selectable objects according to the value of the forward distance value from large to small, and marking the selectable object positioned at the last position as an optimal manager.
7. The enterprise management data governance method based on big data analysis of claim 1, wherein in step two, the data cleaning is a process of data preprocessing to remove noise and redundant information in the data, making the data conform to the requirements of subsequent processing, the specific steps of data cleaning include removing null values, filling missing values, processing outliers and removing duplicate data; the data verification is a process for verifying the accuracy, consistency and integrity of data, errors, inconsistencies and missing problems in the data are found through the data verification, corresponding processing and correction are carried out, and the specific method of the data verification comprises data range checking, rule checking, relation checking and consistency checking; the data normalization is to convert and adjust the data according to a certain standard to meet the requirement of subsequent processing, and the specific method comprises data normalization, discretization and coding, so that the comparability and operability of the data are improved through the data normalization;
in the third step, the data encryption is to encrypt the data through an encryption algorithm to change the data into a ciphertext which cannot be read or understood, and in the data transmission and storage process, the data is encrypted to prevent the data from being illegally acquired and stolen, and the adopted encryption technology comprises symmetric encryption and asymmetric encryption; the access control is the control of the access right of the data, only users with corresponding rights can access and operate the data, and the formulated access control strategy comprises role-based access control and attribute-based access control so as to limit the access right of the data and prevent illegal access and data leakage; the audit is the supervision and inspection of the data security management to find and correct problems and risks existing in the data security management, and the established audit mechanism comprises data security audit and security event audit;
in the fourth step, the data creation is the process of inputting, generating or collecting the data, and the accuracy and the integrity of the data are ensured by making the standard and the flow of the data creation; the data storage is a data storage and management process, and the safety and usability of the data are ensured by selecting a proper storage medium and a proper storage mode; the data transmission is the transmission process of data among different systems or nodes, and the reliability and the safety of the data transmission are ensured by establishing channels and rules of the data transmission; the data sharing is a sharing process of data to an external mechanism or a person, and reasonable utilization and protection of the data are ensured by making rules and flows of the data sharing; the data destruction is the process of deleting and destroying the data, and the complete deletion and privacy protection of the data are ensured by establishing the flow and standard of data destruction.
8. The enterprise management data governance method based on big data analysis of claim 1, wherein the specific operation procedure of step five is as follows:
and (3) establishing a monitoring system: establishing a monitoring system of a data treatment process, wherein the monitoring system comprises monitoring of data quality, data safety and data life cycle, and detecting occurrence and change conditions of data treatment problems in real time through the monitoring system; setting up indexes and standards: setting up indexes and standards of data governance for evaluating the effect and quality of the data governance, wherein the indexes and standards have scalability and comparability so as to accurately evaluate the level of the data governance in actual operation;
analysis and diagnosis were performed: analyzing and diagnosing the monitoring data to find out problems and risks existing in the data management, and finding out the defects and the aspects needing improvement of the data management through comparing indexes and standards; implementing optimization measures: according to analysis and diagnosis results, implementing targeted optimization measures, wherein the optimization measures comprise improving data treatment flow, perfecting data specification and enhancing data safety protection; periodic evaluation and adjustment: the effect and quality of data governance are evaluated regularly, optimization measures are adjusted and improved regularly, and the level and quality of service of data governance are improved through continuous optimization and adjustment.
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