CN112966924A - Data management system and method based on risk map - Google Patents
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Abstract
The invention discloses a data management system and a method based on a risk map, wherein the related data management system based on the risk map comprises the following steps: the construction module is used for constructing a risk map based on risk knowledge, risk information and risk data; and the treatment module is used for treating the data to be treated according to the constructed risk map. The invention forms a management mechanism for data management by designing a map with layered data, information and knowledge, by using map constraint rules of a knowledge layer to an information layer and an information layer to a data layer, and combining map combination rules among data (data types and class rules) and information (information types and class rules). The high unification of business management and data management is formed, and a direction can be provided for better management cognition, so that the real target of data management is realized: and (5) business innovation.
Description
Technical Field
The invention relates to the technical field of data management, in particular to a data management system and method based on a risk map.
Background
As society develops, social problems and social risks can collectively burst in the process from failure to normative, from chaotic to orderly, which can also seriously affect unit business.
Data has become an important asset for enterprises (governments) and also an important weapon for enterprise competitiveness. The success of enterprise business requires timely, complete and accurate data provision support. But such phenomena are often seen: when the director of several departments in the enterprise works in reporting, the data of the same index is different and may even be opposite, and at the end, which data is the correct data? There is no slave acknowledgement. The reasons for this are many, such as data quality issues, statistical aperture issues, etc. How can it be ensured that timely, relevant and trustworthy data can be provided for important services?
Data governance is currently primarily in compliance with DAMA (international data management association) specifications, which is an active set of exercises power and control over data asset management, i.e., data governance is not a simple behavioral action, but rather a form of management. The system comprises all core business systems, an operation data storage or data warehouse, an ECIF (enterprise customer information integration), risk control and many other systems, needs a technology and a soft content platform to provide support, covers the fields of data strategy, metadata, data quality, data security, main data and reference data, data warehouse and business intelligence, data integration, data operation, files and content and the like, finally realizes all-round supervision of all data, realizes end-to-end data combing and management, and ensures the effectiveness, accessibility, high quality, consistency, auditability and safety of the data.
Therefore, the data management process is complex and high in cost, and if only the data assets are realized, the data assets have knowledge in different periods at present according to the management effect, so that the data assets can only be promoted all the time, namely the assets are extremely high in depreciation speed.
High investment needs high return, and the existing mechanism is difficult to have enough return, so data governance needs to be integrated into business, and the governance level is to achieve better business risk management and business innovation so as to seek for greater benefit return.
The business process can involve various risk problems, for example, the risk problems to be solved in fire-fighting include repeated fire-alarm points, wrong fire-alarm points, regular false alarm of fire alarms, and the like, which are improved along with business cognition, but not only the problems that can be solved by a data management department, but the problems need to be normalized and responsible from the perspective of data management, at present, data management is more managed from data acquisition, cleaning, conversion, quality control, and the like, and effective business information and business knowledge are not formed. Therefore, the layer-by-layer specification of data level, information level and knowledge level can not be realized from the cognitive level of the service. It is difficult to form a cognitive architecture of business and an extension of cognitive innovation.
Therefore, aiming at the defects of the prior art, a data management system and a data management method based on a risk map are provided.
Disclosure of Invention
The invention aims to provide a data management system and a data management method based on a risk map, aiming at the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a risk profile-based data governance system, comprising:
the construction module is used for constructing a risk map based on risk knowledge, risk information and risk data;
and the treatment module is used for treating the data to be treated according to the constructed risk map.
Further, the building module specifically includes:
the risk data module is used for acquiring data factors corresponding to the risk data and constructing a risk data map according to the acquired data factors;
the risk information module is used for generating risk information according to the acquired data factors and constructing a risk information map according to the risk data map and the generated risk information;
and the risk knowledge module is used for determining risk knowledge according to the generated risk information and constructing a risk knowledge graph according to the risk information graph and the determined risk knowledge.
Further, the risk knowledge determined in the risk knowledge module includes a risk knowledge set of a person, a risk knowledge set of a management, a risk knowledge set of an object, and a risk knowledge set of an environment.
Further, the risk information module further includes expanding the risk information.
Furthermore, the processing of the data to be processed in the processing module is specifically to process the data to be processed by constraining the risk information and the map of the risk information to the risk data through the risk knowledge.
Correspondingly, a data management method based on the risk map is also provided, and comprises the following steps:
s1, constructing a risk map based on risk knowledge, risk information and risk data;
and S2, processing the data to be treated according to the constructed risk map.
Further, the step S1 specifically includes:
s11, acquiring a data factor corresponding to the risk data, and constructing a risk data map according to the acquired data factor;
s12, generating risk information according to the acquired data factors, and constructing a risk information map according to the risk data map and the generated risk information;
and S13, determining risk knowledge according to the generated risk information, and constructing a risk knowledge graph according to the risk information graph and the determined risk knowledge.
Further, the risk knowledge determined in step S13 includes a risk knowledge set of a person, a risk knowledge set of a management, a risk knowledge set of an object, and a risk knowledge set of an environment.
Further, the step S12 includes performing an expansion process on the risk information.
Further, the step S2 of processing the data to be treated specifically includes processing the data to be treated by constraining the risk information and the map of the risk information to the risk data through the risk knowledge.
Compared with the prior art, the invention forms a management mechanism for data management by designing the maps of data, information and knowledge layering, by the map constraint rule of the knowledge layer to the information layer and the map constraint rule of the information layer to the data layer, and by combining the map combination rule between data (data type and class rule) and information (information type and class rule). The high unification of business management and data management is formed, and a direction can be provided for better management cognition, so that the real target of data management is realized: and (5) business innovation. And through knowledge map type bidirectional step-by-step management from the knowledge layer, the information layer to the data layer, data management can be carried out more comprehensively and effectively, and the knowledge value is ensured.
Drawings
FIG. 1 is a block diagram of a risk profile-based data management system according to an embodiment;
FIG. 2 is a schematic representation of a risk profile provided in the first embodiment;
FIG. 3 is a schematic view of the abatement structure of colleges and universities provided in the fourth embodiment.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
The invention aims to provide a data management system and a data management method based on a risk map, aiming at the defects of the prior art.
Example one
The present embodiment provides a data management system based on a risk profile, as shown in fig. 1, including:
the construction module 11 is used for constructing a risk map based on risk knowledge, risk information and risk data;
and the treatment module 12 is used for processing the data to be treated according to the constructed risk map.
In the construction module 11, a risk map based on risk knowledge, risk information and risk data is constructed.
The risk map is mainly a management mechanism of data treatment so as to realize the treatment of the data to be treated.
The method specifically comprises the following steps: risk knowledge module (four subsets of knowledge system to risk knowledge, forming risk knowledge tree), risk information module (risk information refinement, forming graph between risk information), risk data module (forming more complex graph from information to data), as shown in fig. 2.
The risk data module is used for acquiring data factors corresponding to the risk data and constructing a risk data map according to the acquired data factors;
data factors to form information are obtained, including data 1, data 2, data 3 … …, data n.
And forming a map related to the risk data according to the acquired data.
In this embodiment, the risk data includes data classes, data objects, and the like, where the data types are: fire detectors, data objects such as: the room is coded as a fire detector of 0101002.
The risk information module is used for generating risk information according to the acquired data factors and constructing a risk information map according to the risk data map and the generated risk information;
the risk information is generated based on risk data, such as: the risk data is coded by the detector, and the risk information can be information corresponding to the coded detector.
In this embodiment, the risk information generated by the risk data includes the information of the risk data itself, and also includes risk information generated by combining words of the risk data in the manners of nouns, adjectives, predicates, and the like.
When information is generated according to data, corresponding responsibility is generated.
It should be noted that the risk information includes information corresponding to the risk data, and also includes information generated after exhaustion and expansion based on the risk data.
And the risk knowledge module is used for determining risk knowledge according to the generated risk information and constructing a risk knowledge graph according to the risk information graph and the determined risk knowledge.
The risk knowledge module comprises a plurality of itemized risk knowledge, such as a human risk knowledge set, a managed risk knowledge set, an object risk knowledge set and an environment risk knowledge set.
The risk knowledge set of the person comprises the events which are related to the risk information and need to be processed by the person, and the result of processing the events by related personnel can be confirmed through the risk map, and the person is responsible.
The risk knowledge set of the object includes the relevant events of the product itself.
The managed risk knowledge set includes events related to managed personnel, products, etc.
The risk knowledge set of the environment includes relevant events of the weather, the surrounding environment, etc.
Wherein one risk information may correspond to one or more itemized risk knowledge.
In this embodiment, data, information, and knowledge may be extensible, for example, new information may be formed by extending data; the extension information generates new data; expanding the knowledge generates new information.
In the abatement module 12, the data to be abated is processed according to the constructed risk profile.
By designing a data, information and knowledge layered map, by the map constraint rule of the knowledge layer to the information layer and the map constraint rule of the information layer to the data layer, and by combining the map combination rule between data and information, a management mechanism for data management is formed.
In the embodiment, through the knowledge map type bidirectional step-by-step management from the knowledge layer, the information layer to the data layer, the data management can be carried out more comprehensively and effectively, the knowledge value and the expandability of information application are ensured, and the high integration of the data management and the unit management is ensured.
Example two
The difference between the data management system based on the risk map provided by the embodiment and the embodiment I is that:
the present embodiment is described with a fire alarm as an example.
In the risk data map, the related risk data classes comprise detection point positions, detector codes, detector signals, detection devices, detection signal transmission lines, duty personnel, patrol personnel, maintenance personnel, supervision personnel, management personnel, linkage devices and the like.
In the risk information graph, the risk information class generated through the risk data class comprises an effective combination of the following data: the detector codes and signals are triggered; the signal has trigger and transmission line; transmission lines, operators on duty; patrol personnel and maintenance personnel; person on duty, linkage; person on duty, supervisor; supervisory personnel, management personnel, etc.
The risk information generated through the risk data expansion comprises the following steps: collecting, setting, inputting, verifying and adjusting corresponding responsible persons of important data; a verification period of the verifier; and (4) repairing the fault.
In the risk knowledge graph, the total risk knowledge includes a timely and correct technical management implementation mode for each fire alarm.
Itemized risk knowledge: the risk of people is controllable every time of fire alarm; the risk of the object is acceptable every time of fire alarm; the risk of the environment is controllable every time of fire alarm; the managed risk is controllable every time of fire alarm.
Human risk control: the person on duty notifies the patrolman to confirm on site in time; the person on duty receives the confirmation condition of the patrolman in time; the person on duty correctly processes the confirmation condition; and so on.
The risk of the object is controllable: the fire alarm host is correctly connected; reporting the fire alarm in time; correct positioning of fire alarm; the false alarm rate of fire alarms is acceptable (and in part is a human risk); and so on.
The risk of the environment is controllable: severe climates such as drought, snowfall and the like can be normal; some special circumstances: misstatement is increased due to large fog of seaside, a traditional Chinese medicine hot moxibustion room and the like; and so on.
Managed risk control: whether the training of the person is in place; there is no mechanism to deal with once there is additional risk; (there are many risks managed here as well as human risks); managing synonym and near synonym information; and so on.
In fire alarm, the rules of risk knowledge for risk information are: each fire alarm needs to be reflected to the operator on duty from the detection point in time, and the position of each detection point is required to be accurate; if the fire alarm cannot be timely transmitted to the person on duty from the detection point, the maintenance personnel needs to be informed to overhaul the fire alarm, and the fire alarm can be in good condition and effective in a specified time; the designer needs to make each detector code uniquely correspond to the position of the detection point; the attendant needs to process each message, inform patrol personnel within a specified time, and supervise the patrol personnel to confirm within the specified time; patrolmen are required to confirm within a prescribed time, and so on.
The rule of risk information to risk data can be decomposed into the collection rule of risk data according to the above rule: a fire alarm detection point condition; detecting a device condition; detecting the condition of the information transmission line; maintaining personnel conditions; maintenance schedule; the coding positions of all detectors are unique and accurate; the person on duty situation; a notification time; confirming the condition of the patrol personnel; confirmation time, etc. Information combinations for different data, such as nouns, adjectives, predicates, etc., can be combined by a semantic mechanism.
The design rule formed finally is specifically as follows: false alarm control of the fire alarm detection point; alarming and transmitting by a detection device; a bidirectional feedback mechanism for information transmission; maintenance ability and service ability of maintenance personnel; timely detecting in-place maintenance and actively registering by maintenance personnel; the detector code is unique or the host number and the detector code are unique; the detector position has a positioning mechanism or an error correction mechanism (which has difficulty); the name of the current operator on duty and the performance assessment condition of the current operator on duty at ordinary times; each time of fire alarm, effective notification time and effective confirmation time; the name of a patrol worker and the performance assessment condition of patrol at ordinary times; the time of each fire alarm notification (requiring confirmation, requiring a specific location), the effective confirmation time.
For problems (hidden danger, faults or accidents) occurring in the process, the information processing is considered to be insufficient, the data class (information) needs to be verified, and meanwhile, related data needs to be verified. For example, if a fire alarm occurs in a certain room, but no specific position is displayed on the interface of the attendant, that is, no correct notification is given, the data type information is verified: whether each detector corresponds to the position correctly or not is judged, and as a result, the detector with the room number of 01010202 does not correspond to the 'building name, floor and room position', then relevant personnel in the input link and the verification link at that time are verified, other responsible data are required to be verified, and meanwhile, the principal responsible person can be asked for responsibility or pursue responsibility through management personnel and supervision personnel.
After the rules are introduced, better innovation can be achieved, the information is indexed, for example, an information transmission line is intact, a detector can be installed, a test device is installed at the tail end of each loop, data needs to be synchronized once every hour, and if the back end does not receive the data in time, the fault is considered; for example, the nouns adopted by different people are different, namely, the nouns used by the tool automatic specification can be carried out according to synonyms and synonym sets, for example, fire detectors, smoke senses and smoke sensing points are synonyms, temperature senses are synonyms of smoke senses, but synonyms of fire detectors; for example, the operator on duty can be mechanized, and the mechanization of the information effective processing mechanism can be replaced after being verified.
The embodiment generates responsibility from data to information, and the data verification is that the information needs to be verified, a generator needs to take charge, and each regulator needs to take charge; thereby the information is also perfected; not all data of the responsible person. The high integration of the business is realized through risk information and risk knowledge in the aspect of data management, and the business requirement is better and more detailed technical cognition, so that a comprehensive promotion mechanism is provided for business innovation in the future.
EXAMPLE III
The embodiment provides a data management system based on a risk map, which is different from the first embodiment in that:
this embodiment is described by taking environmental regulations as an example.
In the risk data map, the related risk data includes the possible large environmental accidents (pollution source detection concentration, area, expansion channel at different time points) caused by the emission of the pollution source of the unit, and the like.
In the risk information map, the risk information generated through the risk data includes various environmental elements (atmosphere, water, soil, ocean, nuclear, solid waste and chemicals, noise, natural ecology), pollution sources, pollution abatement facilities, patrols, operators on duty, various posts, and the like. Example (c): the heavy metals in the water exceed the standard, and the treatment of the operators on duty is in place.
In the risk profile, the overall risk knowledge includes ensuring environmental remediation ecology.
And (3) treatment risk knowledge of each item: ensuring the environmental management risk of people in place; ensuring the environmental management risk of the object in place; ensuring the treatment risk of environmental influence in place; ensuring the managed environmental management risk in place, and the like.
Ensuring the environmental management risk of people in place: the law enforcement officer can supervise the unit in place, the unit can patrol the district in place, and the unit attendant can alarm and manage the district in place.
Ensuring the environmental management risk of the object in place: the method comprises the steps of in-place pollutant monitoring of a pollution source, in-place atmospheric environment monitoring, in-place water environment monitoring, in-place soil environment monitoring and the like.
Ensuring the treatment risk of environmental impact in place: earthquake, environmental abnormality caused by abnormal weather, etc.
Ensuring the managed environmental management risks in place: whether the training of the person is in place; there is no mechanism to deal with once there is additional risk; (there are many risks managed here and also human risks).
In the embodiment, the data can be managed more comprehensively and effectively by the knowledge map type bidirectional step-by-step management from the knowledge layer, the information layer and the data layer, and the knowledge value is ensured.
Example four
The embodiment provides a data management system based on a risk map, which is different from the first embodiment in that:
this embodiment will be described by taking the unit management software data management as an example.
In the risk data map, the related risk data includes performance and personnel management.
In the risk information map, risk information generated through risk data includes performance information and personnel management information.
In the risk profile, the overall risk knowledge includes the person's performance management in place.
Knowledge of the subentries: personnel risk to performance management; risk of asset-to-performance management; risk of environmental to performance management; risk of performance management itself
Personnel risk to performance management: the degree of interest of different personnel in performance objectives; self-management ability of different personnel to reach performance standards; related behaviors of different persons on performance closed loop
Risk of asset to performance management: the person who is responsible needs material stimulation, and the person who is disliability needs material punishment; whether or not certain types of work can be replaced by machines of an automated degree
Environmental risk to performance management: the impact of large economic environment on customers;
risk of performance management itself: whether performance management needs to timely inform of disliability or not and who needs to be reminded
In this embodiment, the universities and universities are generally the university governance shown in fig. 3, and three blocks of governance mechanisms (combined in a responsibility networking manner) shared by co-construction and co-governance (democratic governance, legal governance and civilization sharing), business governance of each business department and IT governance of each information department are integrated into the data governance, so that the comprehensive fusion of the risks of knowledge, information and data layers is really realized, and after the knowledge forms a real valuable data asset, cognitive innovation and business innovation are further formed through the bidirectional stage-by-stage governance of the risk map type from the knowledge layer, the information layer to the data layer.
EXAMPLE five
The embodiment provides a data management method based on a risk map, which comprises the following steps:
s1, constructing a risk map based on risk knowledge, risk information and risk data;
and S2, processing the data to be treated according to the constructed risk map.
Further, the step S1 specifically includes:
s11, acquiring a data factor corresponding to the risk data, and constructing a risk data map according to the acquired data factor;
s12, generating risk information according to the acquired data factors, and constructing a risk information map according to the risk data map and the generated risk information;
and S13, determining risk knowledge according to the generated risk information, and constructing a risk knowledge graph according to the risk information graph and the determined risk knowledge.
Further, the risk knowledge determined in step S13 includes a risk knowledge set of a person, a risk knowledge set of a management, a risk knowledge set of an object, and a risk knowledge set of an environment.
Further, the step S12 includes performing an expansion process on the risk information.
Further, the step S2 of processing the data to be treated specifically includes processing the data to be treated by constraining the risk information and the map of the risk information to the risk data through the risk knowledge.
It should be noted that the data management method based on the risk map provided in this embodiment is similar to the embodiment, and is not described herein again.
Compared with the prior art, the embodiment forms a management mechanism for data management by designing the maps of data, information and knowledge layering, by the map constraint rule of the knowledge layer to the information layer and the map constraint rule of the information layer to the data layer, and by combining the map combination rule between data and information; and through knowledge map type bidirectional step-by-step management from the knowledge layer, the information layer to the data layer, data management can be carried out more comprehensively and effectively, and the knowledge value is ensured.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. A data governance system based on a risk profile, comprising:
the construction module is used for constructing a risk map based on risk knowledge, risk information and risk data;
and the treatment module is used for treating the data to be treated according to the constructed risk map.
2. The risk map-based data governance system according to claim 1, wherein the building module specifically comprises:
the risk data module is used for acquiring data factors corresponding to the risk data and constructing a risk data map according to the acquired data factors;
the risk information module is used for generating risk information according to the acquired data factors and constructing a risk information map according to the risk data map and the generated risk information;
and the risk knowledge module is used for determining risk knowledge according to the generated risk information and constructing a risk knowledge graph according to the risk information graph and the determined risk knowledge.
3. The risk profile-based data governance system of claim 2, wherein the risk knowledge determined in the risk knowledge module comprises a human risk knowledge set, a managed risk knowledge set, an object risk knowledge set, an environmental risk knowledge set.
4. The risk profile-based data governance system according to claim 2, wherein the risk information module further comprises an expansion process for risk information.
5. The risk map-based data governance system according to claim 2, wherein the handling of the data to be governed in the governance module is specifically handling of the data to be governed by constraining the risk information, the map of the risk information to the risk data by the risk knowledge.
6. A data governance method based on a risk map is characterized by comprising the following steps:
s1, constructing a risk map based on risk knowledge, risk information and risk data;
and S2, processing the data to be treated according to the constructed risk map.
7. The risk map-based data governance method according to claim 6, wherein the step S1 specifically comprises:
s11, acquiring a data factor corresponding to the risk data, and constructing a risk data map according to the acquired data factor;
s12, generating risk information according to the acquired data factors, and constructing a risk information map according to the risk data map and the generated risk information;
and S13, determining risk knowledge according to the generated risk information, and constructing a risk knowledge graph according to the risk information graph and the determined risk knowledge.
8. The risk profile-based data governance method according to claim 7, wherein the risk knowledge determined in step S13 comprises a human risk knowledge set, a managed risk knowledge set, an object risk knowledge set, and an environmental risk knowledge set.
9. The method for data governance based on risk profiles according to claim 7, wherein the step S12 further comprises performing expansion processing on the risk information.
10. The risk map-based data governance method according to claim 7, wherein the processing of the data to be governed in step S2 is specifically to process the data to be governed by restricting the map of risk data to risk information and risk information through risk knowledge.
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