CN116341899A

CN116341899A - Intelligent risk index management system and method based on disduty and dead duty

Info

Publication number: CN116341899A
Application number: CN202310120684.8A
Authority: CN
Inventors: 裘炅; 张林梓; 裘瑱寅; 李安宁
Original assignee: Zhejiang Zhilian Technology Co ltd
Current assignee: Zhejiang Zhilian Technology Co ltd
Priority date: 2023-02-16
Filing date: 2023-02-16
Publication date: 2023-06-27

Abstract

The invention discloses a risk index intelligent management system based on disduty and dead duty, comprising: the acquisition module is used for acquiring a risk index related to the target; the building module is used for building a risk index management system based on the disclaimer reminding and the disclaimer excitation; the risk index management system comprises a disduty reminding index and a disduty excitation index; and the responsibility-performing module is used for realizing quantitative supervision of target responsibility-performing based on the risk index management system. According to the invention, by establishing a related efficiency algorithm of the failure index and the failure index, the basis of the failure index and the basis of the failure index are processed according to the efficiency algorithm, and the risk index is systemized, responsibilitized and strategically unified, so that effective risk responsibilities are performed for the whole process management (such as acquisition failure, acquisition error, inconsistent names and the like) of the targets; and effectively extends to the disciplinary intelligent management of various models (programming, business models, artificial intelligence, data, etc.) and multiple models.

Description

Intelligent risk index management system and method based on disduty and dead duty

Technical Field

The invention relates to the technical field of index management and control, in particular to an intelligent risk index management system and method based on disduty and dead duty.

Background

Various evaluation index systems are established in the current finance, IT research and development effectiveness and the like, and are standardized, and various related international standards, national standards, industry standards or unit standards exist.

However, at present, the data sources, index classification, index pools, index derivation (or derivation), dynamic visualization of indexes, index monitoring, alarm and the like of the indexes are basically managed, and the system is more used for visualization in a cockpit and other modes; or through the layering risk decomposition and indexing of the targets, the scientization corresponding to the indexes is realized by adopting a comprehensive risk and grading post mode, and the incompleteness of a target strategy or a plan is avoided.

There are also simplified processes in the process of index responsibility (responsibility allocation), such as matrix responsibility allocation, or setting of responsibility allocation conditions, or fast identification of whether related responsibility allocation conditions are met from media such as images (such as directly informing related responsible persons after identifying work sites), the existing method is difficult to realize that indexes in the process are not out of control, only provides a visual decision basis, and a large number of so-called managers are needed to manage the indexes, so that once the indexes are oversized, efficiency and benefit cannot be achieved.

From the standpoint of human resource performance management, after-the-fact responsibility following is of course necessary, but not the responsibility following and responsibility following can solve the fundamental problem of humanization, but the problem needs to be realized from the standpoint of stimulating the intrinsic power of the person, from the standpoint of combining risks and responsibilities, namely, the responsibilities (risks) need to be reminded and the responsibilities (inexhaustible risks) need to be stimulated, and the back of the reminding and stimulating actions needs to be a very scientific index base, and the related index rules are used for correspondingly prompting the responsibilities and stimulating the responsibilities, so that the intelligent management and control of the whole process is the problem to be solved urgently.

Therefore, the invention provides an intelligent management system and method for risk indexes based on failure and dead responsibility.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides an intelligent risk index management system and method based on disduty and dead duty.

In order to achieve the above purpose, the present invention adopts the following technical scheme:

the intelligent risk index management system based on the disduties and the dead duties comprises:

the acquisition module is used for acquiring a risk index related to the target;

the building module is used for building a risk index management system based on the disclaimer reminding and the disclaimer excitation; the risk index management system comprises a disduty reminding index and a disduty excitation index;

and the responsibility-performing module is used for realizing quantitative supervision of target responsibility-performing based on the risk index management system. Further, the establishment module is characterized in that the index of the disduty reminding comprises the time of the disduty reminding, the space of the disduty reminding, the threshold value of the disduty reminding and the relationship of the disduty reminding.

Further, the establishment module is used for establishing the relationship among the accountability excitation indexes including time of accountability excitation, space of accountability excitation, threshold value of accountability excitation and accountability excitation.

Further, the disclaimer alert or disclaimer incentive is expressed as:

wherein status represents either failure or failure; ir _i Representing a certain index; fd (fd) ^t () Indicating a notice of loss of responsibilityTime of responsibility excitation; fd (fd) ^s () A space representing an alarm of failure/incentive of failure; fd (fd) ^C () A threshold value representing an alarm of failure/incentive of failure; fd (fd) ^r () Representing the relationship of the disclaimer reminder/disclaimer incentive; n represents the number of risk indicators associated with the target.

Furthermore, the acquisition module is also used for acquiring the evaluation data of the risks of different models and the index refinement of the models.

Furthermore, the obtaining module is further configured to obtain risk indexes after different models are superimposed.

Further, before the module is built, the method further comprises:

and the optimizing module is used for optimizing the risk index.

Correspondingly, the intelligent management method for the risk index based on the discipline and the dead duty is also provided, and comprises the following steps:

s1, acquiring a risk index related to a target;

s2, constructing a risk index management system based on the disclaimer reminding and the disclaimer excitation; the risk index management system comprises a disduty reminding index and a disduty excitation index;

s3, realizing quantitative supervision of target responsibility based on a risk index management system.

Further, the index of the alarm of the failure in the step S2 includes the time of the alarm of the failure, the space of the alarm of the failure, the threshold of the alarm of the failure, and the relation of the alarm of the failure; the discipline excitation index comprises the relationship of time of discipline excitation, space of discipline excitation, threshold value of discipline excitation and discipline excitation.

Further, the disclaimer alert or disclaimer incentive is expressed as:

wherein status represents either failure or failure; ir _i Representing a certain index; fd (fd) ^t () Time representing the disclaimer alert/disclaimer incentive; fd (fd) ^s () A space representing an alarm of failure/incentive of failure; fd (fd) ^C () A threshold value representing an alarm of failure/incentive of failure; fd (fd) ^r () Representing the relationship of the disclaimer reminder/disclaimer incentive; n represents the number of risk indicators associated with the target.

Compared with the prior art, the invention processes the basis of the index of failure and the basis of the index of full responsibility according to the efficiency algorithm by establishing the related efficiency algorithm of the index of failure and the index of full responsibility, and systemizes, returns and strategically unifies the risk indexes, and returns the effective risk for the whole process management (such as acquisition failure, acquisition error, inconsistent name and the like) of the targets; and effectively extends to the disciplinary intelligent management of various models (programming, business models, artificial intelligence, data, etc.) and multiple models.

Drawings

FIG. 1 is a block diagram of a risk indicator intelligent management system based on discipline and discipline provided in accordance with one embodiment;

FIG. 2 is a schematic diagram of a risk model provided in accordance with an embodiment;

FIG. 3 is a schematic diagram of ChatGPT assisted programming provided in the second embodiment;

FIG. 4 is a schematic diagram of a HAZOP risk workflow provided by embodiment four;

fig. 5 is a schematic diagram of a judgment mechanism for whether the risk matrix provided in the fourth embodiment is controllable;

fig. 6 is a schematic diagram of a mechanism related to optimizing risk indicators provided in the fourth embodiment.

Detailed Description

Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.

Aiming at the defects of the prior art, the invention provides an intelligent management system and method for risk indexes based on disduty and dead duty.

Example 1

The present embodiment provides an intelligent management system for risk indexes based on failure and responsibility, which is mainly used for managing data quality in data management, as shown in fig. 1-2, and includes:

an acquiring module 11, configured to acquire a risk indicator related to a target;

the building module 12 is used for building a risk index management system based on the disclaimer reminding and the disclaimer motivation; the risk index management system comprises a disduty reminding index and a disduty excitation index;

and the responsibility performing module 13 is used for realizing quantitative supervision of target responsibility performing based on the risk index management system.

In the acquisition module 11, a risk indicator associated with the target is acquired.

The goal is the overall effect that all people or enterprises want to achieve in each activity, just as the goal of enterprise operation would be to pursue sustainable profit maximization; as in data governance with respect to data quality, the goal is to ensure that the database quality, which is valuable for data, meets 98%; the qualitative objectives involved therein include: tube use, add: dare to use and love; economic objectives include: synergistic effect, quality improvement and cost reduction.

Specific risk indexes are obtained as in application number 2022115882593, in this embodiment, risks in data quality include corresponding overall risks, multiple layers and risk space-time based, and the target risks are specifically:

overall layers such as overall manager, the target risks involved are: source data error risk, important source data error detection risk, quality closed loop management risk, responsibility to post management risk.

Preliminary indexes: the data quality risk closed-loop rate is 100%; important data quality responsibility reaches 100 percent; the design rate of the problem discovery is 95%, and the design rate is refined later.

The main data, as well as the valuable incremental data, require dynamic design or corresponding processing criteria.

Management layers such as IT department manager, business department manager, the target risks involved are: data integrity risk, monitoring coverage risk, alarm response risk and data accuracy risk.

The implementation layer, such as a business department team leader and an IT department quality team leader, involves the target risks of: the integrity rule risk of the table, the integrity rule risk of the field, the integrity risk of the quality monitoring, the alarm response out-of-place risk and the operation process management risk.

The execution layer comprises a business department collector, a business department check-up member, an IT department quality manager, an IT department developer (data processing), a business department developer (data utilization processing) and a tester, and the target risk is as follows: the method comprises the steps of incomplete risk of a table, incomplete risk of a field, effective risk of monitoring operation, alarm response timeliness risk, alarm response correction in place risk, operation accuracy risk, operation stability risk, operation timeliness risk and operation performance substandard risk.

In the establishing module 12, a risk index management system is established based on the disclaimer reminding and the disclaimer motivation; the risk index management system comprises a disduty reminding index and a disduty excitation index.

The risk indexes comprise index contents of risk time and space, and the index contents of the risk time and space also relate to some atom indexes, some derivative indexes and the like.

The risk index is associated with a space-time comprising time, space, threshold, relationship.

The embodiment is based on the technical index of the data quality target, the time of generating the disclaimer by the service index, the space of the disclaimer, the threshold value of the disclaimer and the disclaimer relationship of the disclaimer, so as to obtain the index of the disclaimer.

The technical index based on the data quality target, the time of generating the discipline excitation, the space of the discipline excitation, the threshold value of the discipline excitation and the discipline relation of the discipline excitation are generated by the service index, so that the discipline excitation index is obtained.

The disduty reminding index and the disduty excitation index obtained by the embodiment can be applied to disduty reminding and disduty excitation of a single post; multiple posts are subjected to disclaimer reminding and disclaimer excitation; the decompensation reminding and the disciplinary incentive of the target.

The decompensation alert indicator and the decompensation incentive indicator can be quantitatively defined as follows:

five-tuple ds: (id, person, status, level, demand_value), where id is the number, person is the person (or post), status is the level of failure, responsibility or normal, level is the quantized value of failure, responsibility.

The level of disclaimer includes personal disclaimer, department disclaimer, and whole organization disclaimer.

The level of accountability includes personal growth accountability, personal short-term value realization, medium-term value realization, long-term value realization, and accountability of target completion.

The decompensation reminding needs to be advanced, and the residual workload needs to be counted and subjected to a mechanism; the accountability incentive requires an incentive term such as a positive value added, or a positive demand: personal risk quality has improved, short term benefit index, mid term benefit index (mainly sustainability) and long term benefit index.

In this embodiment, the following configuration is required for the dead space of the dead space:

1) Time: the prior planning is not in place, and the prior planning is in place; the progress is not ideal, the progress is ideal, the completion is not performed on schedule, the completion is performed on schedule, and the completion is performed in advance; the post summary is not in place and the post summary is in place.

The definition is as follows: fd (fd) ^t (ir _i ) Which is provided withZhongir _i For a certain index, judging item by item, and obtaining a ds five-tuple as a result.

2) Space: when planning, there are responsibility cross and responsibility blank points; the space relation is optimized when the specified space is not in place and the specified space is in place.

The definition is as follows: fd (fd) ^s (ir _i ) Wherein ir _i For a certain index, judging item by item, and obtaining a ds five-tuple as a result.

3) Relationship: single subject liability, department liability (with administrative liability), organization liability (with leadership).

The definition is as follows: fd (fd) ^r (ir _i ) Wherein ir _i For a certain index, judging item by item, and obtaining a ds five-tuple as a result.

4) Threshold of performance information: the risk index is required to be judged, and the method can be further extended to: cost, quality, security, business efficiency and benefit, strategic optimization (discipline), innovation level.

The definition is as follows: fd (fd) ^c (ir _i ) Wherein ir _i For a certain index, judging item by item, and obtaining a ds five-tuple as a result.

The disclaimer alert for a person is expressed as:

wherein status represents decompensation; ir _i Representing a certain index; fd (fd) ^t () Time (or time sequence or combination) representing a disclaimer alert; fd (fd) ^s () A space (or combination of spaces) representing a discipline alert; fd (fd) ^c () Watch (watch)A threshold (or combination of thresholds) showing a disclaimer alert; fd (fd) ^r () A relationship (or a combination of relationships) representing a disclaimer alert; n represents the number of risk indicators associated with the target.

The motivation for the responsibility of a person is expressed as:

wherein status represents accountability; ir _i Representing a certain index; fd (fd) ^t () Time (or time sequence or combination) representing the discipline stimulus; fd (fd) ^s () A space (or combination of spaces) representing a dead stimulus; fd (fd) ^c () A threshold (or combination of thresholds) representing a dead stimulus; fd (fd) ^r () Relationships (or combinations of relationships) representing the discipline stimulus; n represents the number of risk indicators associated with the target.

If the accountability condition is low, the accountability excitation, namely the add-divide, can be performed singly.

Through the above, the embodiment completes the index intellectualization of the responsibility, namely, constructs a risk index management system.

In the responsibility performing module 13, quantitative supervision of target responsibility performing is realized based on a risk index management system.

Based on the constructed risk index management system, a user can realize the execution of a task when executing a target task, and can know whether the task is in failure execution or in failure execution when executing the task, so that the user is subjected to failure reminding and failure excitation, and the task is strongly supervised when executing the task.

In the quality control in the data management of this embodiment, the data metadata risk (i.e., the risk related to the target) is a DAMA quality model, or a DCMM (DMM) model, and an index system and a risk management and control system are established, where the index corresponds to a person, corresponds to a failure and corresponds to a full responsibility, so as to implement reminding and excitation of the index, and the index corresponds to a strategy, so as to implement strategic reminding and excitation, and further improve accuracy, completeness, consistency, normalization, and timeliness of the target.

Correspondingly, the embodiment also provides a risk index management method based on the discipline and the dead responsibility, which comprises the following steps:

s1, acquiring a risk index related to a target;

Further, the index of the alarm of the failure in the step S2 includes the time of the alarm of the failure, the space of the alarm of the failure, the threshold of the alarm of the failure, and the relation of the alarm of the failure.

Further, the accountability excitation index in step S2 includes a relationship among time of accountability excitation, space of accountability excitation, threshold of accountability excitation, and accountability excitation.

Further, the disclaimer alert or disclaimer incentive is expressed as:

wherein status represents either failure or failure; ir _i Representing a certain index; fd (fd) ^t () Time (or time sequence or combination) representing the disclaimer/disclaimer reminder; fd (fd) ^s () A space (or combination of spaces) representing an disclaimer/disclaimer reminder; fd (fd) ^c () A threshold (or a combination of multiple thresholds) representing a disclaimer/disclaimer reminder; fd (fd) ^r () A relationship (or a combination of relationships) representing a disclaimer/disclaimer reminder; n represents the number of risk indicators associated with the target.

According to the embodiment, through establishing a related efficiency algorithm of the failure index and the failure index, the basis of the failure index and the basis of the failure index are processed according to the efficiency algorithm, and the risk index is systemized, responsibilitized and strategically unified, so that effective risk responsibilities are performed on the whole process management (such as acquisition failure, acquisition errors, inconsistent names and the like) of the target; and effectively extends to the disciplinary intelligent management of various models (programming, business models, artificial intelligence, data, etc.) and multiple models.

Example two

The risk index intelligent management system based on the disciplinary and the dead responsibility provided in this embodiment is different from the first embodiment in that:

this embodiment is described by taking the accountability mechanism of the ChatGPT, the gittub Copilot, the alphacode knowledge model as an example.

the acquisition module is also used for acquiring assessment data of risks of different models and refining indexes of the models.

The targets of the ChatGPT, gitHub Copilot, alphacode knowledge model are: a model of the knowledge may be automatically generated.

The integration takes ChatGPT assisted programming as an example, as shown in fig. 3, with the black box mechanism on the left and several modules that have been further tested (including integrated management) on the right.

The ChatGPT is regarded as a black box model with auxiliary programming on the left side in fig. 3, and the risk of the model is detected and evaluated, and the detection mechanism adopts the API of the ChatGPT as shown in the following table 1:

TABLE 1

This evaluates the use of the model on an implementation layer that is manageable by the implementation layer, and then lists the corresponding indices, namely:

code semi-automatization risk

Code quality risk

Code understandability

Code execution efficiency

Code full path test verification

·…

Risk of understanding demand (prompt)

Find uncertainty in full demand

Logic capability for demand understanding

Demand detailed design assistance capability

Promt's wording, combine in place

Promtt input and output length

·…

These are all reflected on the design index of Prompt (Prompt) (i.e. the words of Prompt are combined in place), the Prompt needs to be fastened to the target, the other party needs to understand the risk role and cannot be ambiguous, the interface can only be used for the task, in addition, the internal ChatGPT prescribes the keys not longer than 2048, only 750 words are converted, so that the length of each interaction cannot be longer than 750 words, and therefore, the long codes can be interacted with multiple times and context, but the effect is definitely not ideal. A series of hint template libraries (prompt template library) need to be designed.

Taking the index of the understandability of the code as an example (the main meaning of the code is read by the personnel, the command specification of the code is compliant by the personnel, the personnel is automatically checked, the code format is easy to see, the personnel is automatically checked), the three indexes are changed into the personnel, and meanwhile, some indexes can be manually checked, and some indexes need to be automatically checked, for example, the main meaning of the code can be designed by keywords, so that the code can be automatically checked.

The manual risk auditing method has the advantages that the indexes are insufficient, and the manual risk auditing method corresponds to the disclaimer reminding and disclaimer excitation of the manual auditing, so that the efficiency can be greatly accelerated, and the indexes can be effectively applied to better realization: improving quality, enhancing efficiency and reducing cost.

The right side of fig. 3 is more refined, so that a work of a half-implementation layer can be realized, that is, a small team (hereinafter referred to as four-person group) of programming knowledge can be realized, and a corresponding flow and mechanism can be generated, so that the risk index can be further refined to four-person group:

code semi-automatization risk

Risk of interaction (social)

Indicators of dialog fineness

Index of demand description

Indication of under-planning

Inference risk

Programming target resolution capability

Technical details of non-standard standardization

·…

Knowledge base and learning risk

Knowledge completion of code flaws

Knowledge of code quality

Complete experience

Instruction management risk

Alignment force of target and promt

Target and progressive management forces

·…

In addition, the method can be quickly added in four groups: the known risk and the unknown risk of errors are corrected in different modules, and a corrected risk management and control mechanism (index and index intelligence) can be quickly built and combined after the correction.

with the development of ChatGPT technology, a higher-level risk mechanism and management system can be formed, such as adding voice, image (video), brain-computer interface and the like, so as to realize further target intelligent coordination of people and computers.

Based on the indexes, the generation of the model is realized, and information about whether the model is out of responsibility or dead in the generation process can be generated.

Example III

the risk index management system of the present embodiment is also applicable to a risk-based programming mechanism and the like.

Unlike the previous embodiments, the following are: in this embodiment, different risks of different models need to be coordinated, because risks generated by different models are superimposed into new risks, and indexes have contradictory indexes, which are mainly used for processing the contradictory indexes; therefore, the acquisition module can also acquire risk indexes after different models are overlapped.

Risk management of models in risk-based programming mechanisms:

the model comprises the following steps: process-oriented programming (pop), object-oriented programming (oop), aspect-oriented programming (aop), low codes (e.g., nailed teasers), artificial intelligence models (e.g., machine learning models and deep learning models, which can see embodiment two), data assets (extending applications on the basis of embodiment one), etc.;

the risk management mechanism of the model is similar to embodiment two, including model risk and management risk for the model, and corresponding interfaces, which have code instrumentation, risk detection, code modification, process control, interface interactions, etc. in addition to the API calls of embodiment two.

The overall risk at present is the following: quality risk, security risk, compiler-induced risk; the maintenance is carried out according to development: demand risk, design risk, coding risk, development testing risk, implementation risk, maintenance risk, and the like.

Risk indicators for risk-based programming mechanisms, such as security risk, in a hybrid programming product:

object oriented (c++), process oriented (c++ in c statement), aspect_c, spike should be used in one product, artificial intelligence learning model, data driven model, risk model, as shown in table 2.

TABLE 2

The above risk indexes can also be combined, for example, an AI learning model is applied in a c++ program, and the indexes are superimposed on some indexes, for example, fake data in the AI learning model, replay attacks and message attacks in the c++ are combined, the indexes need to be realized by combining the program, if the indexes are used alone, then only sequence relations are needed, if the indexes are calling mechanisms, then the indexes are concurrent relations, and the risk index points and the risks in the codes need to be given in a correlated manner, as described below.

C++ code: sqlcommand ("select id, part from product _ bom where classtype = '" +ai_model auto_generate () + "'").

The result produced in AI learning model ai_model. Auto_generate () is originally an optimized solution of one cluster: the direct current high-speed electric motor has a certain autonomy in the direct current high-speed electric motor, and is added as follows: direct current high-speed electric power; drop table product _bom; . This presents a dangerous list deletion hazard. So that risk prompt or further evaluation index of the AI learning model is needed at this time.

The following is a management system of risks which need to be introduced in the mixed model management based on the programming of risks, and comprises model management risks and risk index management:

expansion of risk models (model middle table)

1. Model interface of existing model: networking-capable modeling language mechanism: XMI, index formula and index visualization;

2. new model mechanism: depth, breadth, toughness (time) of risk.

Management risk assessment of the self: disabled, time, …, can be done to alert of the loss of responsibility, indicator of relevant risk of the due incentive;

in this way a URML (universal risk modeling language) can be formed, by means of which a risk based programming language (ROP) is re-imported, which enables a combination of automatic programming and manual programming of the machine, with the following integration:

1) Modeling language: expanding on the basis of SysML and RAAML;

2) The index management is expanded on the basis of the R language;

3) Data and system integration: extensions are made on DMM, UAF of OMG.

And managing the programming execution process based on the constructed risk index management system to obtain the results of disclaimer or disclaimer.

Example IV

The risk index management system based on the discipline and the discipline provided in this embodiment is different from the first embodiment in that:

the risk index management system of the present embodiment may also be applied to hazop analysis and the like.

Hazop analysis-based accountability (indexing of risk assessment model):

1) Target object

HAZOP analysis requires a major concern for process safety issues, particularly those that are susceptible to leaks, fires, explosions, or upgrades resulting in leaking fire explosion events.

2) Risk and index

The risk index includes:

setting of alarm, whether alarm value is appropriate

Whether the setting of the interlock, action logic is reasonable

Control whether system settings are reasonable

Emergency shut-off and emergency bleed system

Secure accessory settings

Operability problems, whether or not to facilitate site handling and operation

● Whether the design change can bring new risk points or not, and whether the site is consistent with the drawing or not

The device is designed whether to facilitate driving feeding, stopping and dumping, and normal maintenance and emergency treatment are humanized or not.

HAZOP analysis includes two aspects, namely hazard analysis and operability analysis. The former is for safety purposes; the latter concerns whether the process system is capable of normal operation, and whether maintenance or repair is facilitated.

HAZOP analysis considers both safety issues and operability issues; because operability issues directly affect safety issues, HAZOP analysis generally does not take into account quality and yield issues.

Sources of risk indicators are:

design basis for project or process installation

Description of Process/Process characteristics

Pipeline and instrument flow diagram (P & ID)

Previous hazard source identification or security analysis report

Material and heat balance

Interlocking logic diagram or causal relationship table

Total view of whole plant

Device layout

Material Safety Data Sheet (MSDS)

Device data table

Relief valve relief operating mode and data table

Pipeline Material class specification

Pipeline meter

Operational procedure and maintenance requirements

Emergency parking scheme

Control scheme and safety instrumented System specification

Equipment Specification

Evaluation agency and government security requirements

Accident reporting on process safety aspects of similar processes

3) Relevant responsibility post

HAZOP analysis chairman

Secretary or recorder

Process engineer

Instrument engineer

Equipment/mechanical engineer

Representative of the patent or supplier (as needed)

Operator expert/representative

Safety engineer

Other professionals

4) Simulation calculation of metrics

In this embodiment, the system further includes an optimization module, configured to optimize or control the risk indicator.

As shown in fig. 4, which illustrates a HAZOP risk workflow, the calculation of risk is performed by pressing fig. 4, and redesigning or readjusting (i.e., optimizing) the facility for the risk uncontrollable need may be performed several hundred to several thousand times until the risk is controllable. And to place these risks on each responsibility post in a disduty reminder, a disduty incentive.

Fig. 5 is a mechanism for determining whether the risk matrix is controllable.

Fig. 6 is a relevant mechanism (onion model) that can optimize risk indicators.

Some risk index optimization methods:

1) The safety measures of hardware and the safety valve should be prioritized; an alarm system; an emergency shut-off valve; fire dikes; safety Instrumented Systems (SIS); a fire protection system; a flame arrester; rupture disk

2) The safety index can be compared with an onion model, and the index is optimized layer by layer.

And managing the HAZOP analysis process based on the constructed risk index management system to obtain a result of disclaimer or dead.

The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions thereof without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.

Claims

1. The intelligent risk index management system based on the disduties and the dead duties is characterized by comprising the following components:

and the responsibility-performing module is used for realizing quantitative supervision of target responsibility-performing based on the risk index management system.

2. The intelligent management system for risk indexes based on the failure and the dead responsibility according to claim 1, wherein the failure reminding indexes in the building module comprise the relationship of the time of the failure reminding, the space of the failure reminding, the threshold of the failure reminding and the failure reminding.

3. The system of claim 2, wherein the establishment of the module includes a relationship of time of accountability excitation, space of accountability excitation, threshold of accountability excitation, and accountability excitation.

4. The decompensation-based, risk indicator intelligent management system according to claim 3, wherein the decompensation reminder or decompensation incentive is expressed as:

5. The intelligent management system of risk indexes based on disduty and dead duty according to claim 1, wherein the obtaining module is further configured to obtain evaluation data of risks of different models and index refinement of the models.

6. The intelligent management system for risk indexes based on disduty and dead duty according to claim 1, wherein the obtaining module is further configured to obtain risk indexes after different models are superimposed.

7. The intelligent management system for risk indicators based on decompensation and accountability according to claim 1, wherein the building module further comprises:

and the optimizing module is used for optimizing the risk index.

8. The intelligent risk index management method based on the disduty and the dead duty is characterized by comprising the following steps:

s1, acquiring a risk index related to a target;

9. The intelligent management method for risk indexes based on the failure and the dead responsibility according to claim 8, wherein the failure reminding indexes in the step S2 include the relationship of the time of the failure reminding, the space of the failure reminding, the threshold of the failure reminding and the failure reminding; the discipline excitation index comprises the relationship of time of discipline excitation, space of discipline excitation, threshold value of discipline excitation and discipline excitation.

10. The intelligent management method for risk indexes based on disclaimer and disclaimer as claimed in claim 9, wherein the disclaimer alert or disclaimer incentive is expressed as: