CN115616997A - Thermal control state monitoring and knowledge base fusion method and system - Google Patents
Thermal control state monitoring and knowledge base fusion method and system Download PDFInfo
- Publication number
- CN115616997A CN115616997A CN202211264137.9A CN202211264137A CN115616997A CN 115616997 A CN115616997 A CN 115616997A CN 202211264137 A CN202211264137 A CN 202211264137A CN 115616997 A CN115616997 A CN 115616997A
- Authority
- CN
- China
- Prior art keywords
- knowledge base
- thermal control
- diagnosis
- state monitoring
- fault
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41845—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by system universality, reconfigurability, modularity
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/33—Director till display
- G05B2219/33273—DCS distributed, decentralised controlsystem, multiprocessor
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention relates to a thermal control state monitoring and knowledge base fusion method and system. According to the method, after a diagnosis knowledge base model is constructed, the operation data are input into the diagnosis knowledge base model, so that a diagnosis result, a fault processing suggestion and a thermal control equipment operation suggestion can be obtained, a trend prejudgment can be formed during the operation of a subsequent system, and early warning is realized. In addition, the invention carries out systematic and comprehensive fault diagnosis on the main control unit by fusing the fault diagnosis knowledge base, replaces part of the original fault troubleshooting method depending on personnel experience and skills, avoids the occurrence of human problem events caused by reason analysis errors and unreasonable processing measures due to the difference of personnel technical levels, and further improves the real-time performance and the accuracy of thermal control state monitoring.
Description
Technical Field
The invention relates to the technical field of thermal control state monitoring, in particular to a method and a system for thermal control state monitoring and knowledge base fusion.
Background
The thermal control state monitoring is to monitor and diagnose the states of a DCS control center and a thermal control secondary device of a power plant, establish a thermal control state detection system, perform real-time online diagnosis on the adjusting performances of an Automatic Gain Control (AGC), a primary frequency modulation and an automatic control loop of a thermal generator set, provide optimized setting parameters for a control system by combining a diagnosis rule knowledge base and based on technologies such as big data analysis, flow calculation, AI prediction models and the like, and improve the reliability of the thermal control professional overall device and the accurate and rapid response of the adjusting system.
Currently, the world is still in a pure monitoring mode for monitoring the thermal control state, and the closed-loop processing of diagnosis and optimal control cannot be realized on a control system. The monitoring means is single, a mature and stable diagnosis knowledge base is not used as a support, no accumulation means for diagnosis rules and experiences is provided, a diagnosis rule knowledge base of the system cannot be formed, trend prejudgment cannot be formed during the operation of a subsequent system, and early warning is realized.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a thermal control state monitoring and knowledge base fusion method and system.
In order to achieve the purpose, the invention provides the following scheme:
a thermal control state monitoring and knowledge base fusion method comprises the following steps:
constructing a diagnosis knowledge base model;
acquiring operation data of the thermal control equipment;
inputting the operating data into the diagnosis knowledge base model to obtain a thermal control state monitoring result; the thermal control state monitoring result comprises: diagnosis results, fault handling suggestions and thermal control equipment operation suggestions.
Preferably, the constructing a diagnosis knowledge base model specifically includes:
establishing a platform database; the platform database comprises roll names of all control units in the thermal control equipment and historical operating data corresponding to the roll names;
defining a diagnosis rule;
generating a diagnostic result based on the historical operating data and the diagnostic rule; the diagnostic result includes: fault alarm information and fault early warning information;
acquiring a quality code of a DCS (distributed control System) control center; the quality code is used for expressing each operation state in the DCS control center;
determining a fault cause based on the quality code;
generating a fault handling suggestion and a thermal control equipment operation suggestion based on the fault reason;
and generating a diagnosis knowledge base model based on the diagnosis rule, the diagnosis result, the quality code, the fault treatment suggestion and the thermal control equipment operation suggestion.
Preferably, the method further comprises the following steps: and performing visual editing processing on the diagnosis rule.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the thermal control state monitoring and knowledge base fusion method provided by the invention, after a diagnosis knowledge base model is constructed, the operation data is input into the diagnosis knowledge base model, so that a diagnosis result, a fault processing suggestion and a thermal control equipment operation suggestion can be obtained, a trend prejudgment can be formed during the operation of a subsequent system, and early warning is realized. In addition, the invention carries out systematic and comprehensive fault diagnosis on the main control unit by fusing the fault diagnosis knowledge base, replaces part of the original fault troubleshooting method depending on personnel experience and skills, avoids the occurrence of human problem events caused by reason analysis errors and unreasonable processing measures due to the difference of personnel technical levels, and further improves the real-time performance and the accuracy of thermal control state monitoring.
Corresponding to the provided method for monitoring the control state and fusing the knowledge base, the invention also provides two systems for monitoring the thermal control state and fusing the knowledge base, which specifically comprise the following steps:
one of them thermal control state monitoring and knowledge base integration system includes:
the diagnosis knowledge base model building module is used for building a diagnosis knowledge base model;
the operation data acquisition module is used for acquiring operation data of the thermal control equipment;
the state monitoring result determining module is used for inputting the operating data into the diagnosis knowledge base model to obtain a thermal control state monitoring result; the thermal control state monitoring result comprises the following steps: the diagnosis result, the fault processing suggestion and the thermal control equipment operation suggestion.
Another thermal control state monitoring and knowledge base fusion system, comprising:
the collector is used for collecting the operating data of the thermal control equipment;
the memory is connected with the collector and used for storing the logic instruction; the logic instruction is used for implementing the thermal control state monitoring and knowledge base fusion method;
and the processor is connected with the memory and used for calling and executing the logic instructions.
Preferably, the memory is a computer-readable storage medium.
Preferably, the method further comprises the following steps:
and the display is used for displaying the operation page of the logic instruction.
Preferably, the display and the processor are integrally provided.
Since the technical effect achieved by the two thermal control state monitoring and knowledge base fusion systems provided by the invention is the same as the technical effect achieved by the two thermal control state monitoring and knowledge base fusion methods provided by the invention, further description is omitted here.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a thermal control state monitoring and knowledge base fusion method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a thermal control state monitoring and knowledge base fusion system according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a thermal control state monitoring and knowledge base fusion system according to a third embodiment of the present invention;
FIG. 4 is a framework diagram of a diagnostic knowledge base model according to the fourth embodiment of the present invention;
FIG. 5 is a diagram illustrating a database page of the platform according to the fourth embodiment of the present invention;
fig. 6 is a display diagram of a combined operation page during visual editing according to a fourth embodiment of the present invention;
fig. 7 is a diagram illustrating a wireless nested grouping logic page during visual editing according to a fourth embodiment of the present invention;
fig. 8 is a display diagram of a variable designation page of standard data types during visual editing according to the fourth embodiment of the present invention;
fig. 9 is a display diagram of a page for selecting and specifying a point quality code during visual editing according to the fourth embodiment of the present invention;
fig. 10 is a display diagram of a page of various logic computations during visual editing according to the fourth embodiment of the present invention;
fig. 11 is a timing condition page display diagram during visual editing according to the fourth embodiment of the present invention;
fig. 12 is a display diagram of a display output setting page during visual editing according to a fourth embodiment of the present invention;
fig. 13 is a display diagram of an automated push page during visual editing according to the fourth embodiment of the present invention;
fig. 14 is a diagram illustrating quality code information definition provided in accordance with a fourth embodiment of the present invention;
fig. 15 is an application scenario diagram of quality codes according to the fourth embodiment of the present invention;
FIG. 16 is a diagnostic rules page display diagram provided in accordance with a fourth embodiment of the present invention;
fig. 17 is a display diagram of a page showing a result of establishing the diagnostic rule according to the fourth embodiment of the present invention;
fig. 18 is a data interaction diagram of the thermal control state overhaul and DCS control center according to the fourth embodiment of the present invention;
fig. 19 is a display diagram of a visualization rule editing page provided in the fifth embodiment of the present invention;
fig. 20 is a diagram illustrating a page read by the computing module according to the fifth embodiment of the present invention;
FIG. 21 is a diagram of a rule selection page provided in accordance with a fifth embodiment of the present invention;
fig. 22 is a page display diagram of a rule calculation result provided by the fifth embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for monitoring a thermal control state and fusing a knowledge base, which can form trend prejudgment during the operation of a subsequent system, realize early warning and further improve the real-time property and the accuracy of monitoring the thermal control state.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The method comprises the steps of carrying out omnibearing data acquisition on thermal control equipment of a power plant through an industrial protocol of industry standards, obtaining thermal control production operation data in real time, establishing a visual rule editor, carrying out standardized definition and recording on verified diagnosis experience to form a standard diagnosis knowledge base, realizing rule judgment and triggering early warning based on a rule engine, wherein the rule engine supports standard logic analysis, rule analysis of complex scenes, big data trend analysis and AI model processing.
Based on this setting background, the present invention provides the following technical solutions:
example one
In this embodiment, a method for fusing a thermal control state monitoring and a knowledge base is provided, and as shown in fig. 1, the method for fusing a thermal control state monitoring and a knowledge base includes:
step 100: and constructing a diagnosis knowledge base model. In the embodiment, a DCS manufacturer knowledge base, historical fault cases and maintenance experience of professional technicians are stored in a diagnosis knowledge base in a fault model mode, a systematic fault diagnosis model is formed, fault reason analysis and processing suggestions are provided for a user at the first time, and the timeliness and accuracy of fault processing are improved. Wherein, the implementation process of the step specifically comprises the following steps:
step 1001: and establishing a platform database. The platform database comprises roll names of all control units in the thermal control equipment and historical operating data corresponding to the roll names. The established platform database is used as an information source of the whole diagnosis system, and the unique point name and the corresponding information of each main control unit are determined. The unique roll call of each main control unit and the corresponding information thereof mainly comprise information such as equipment roll call, roll description, disc cabinet number, model specification, commissioning date, serial number, correlation system, interlocking protection and the like, are imported in an equipment account form, and are searched by adopting the equipment roll call to match with the related information.
Step 1002: diagnostic rules are defined. The diagnostic rule defined in this embodiment is a description of a computational and judgment logic, which may be combined to form a complex computational judgment logic. The process of defining diagnostic rules therefore requires a simple and intuitive form to implement.
The diagnostic rules are defined based on a visual mode, and standardized rule definitions are formed for the rules through a browser interface, so that the rule definition process can be greatly simplified, a business user can read the rules in a more visual mode, and based on the rule definition process, the diagnostic rules can be visually edited. In this embodiment, the combination is supported by the visual rule editing, and multiple rules may be combined in an and-or manner. The visualization rule editing supports infinite nested grouping logic, a sub-grouping can be established, a plurality of logic rules are established in the grouping, and the grouping rules are unified to support AND-OR logic gate judgment. Visualization rule editing supports passing rules, and common rule usage matches all diagnostic logic. And editing the visualization rules to support the input filtering matching and the type filtering screening of the acquisition points. Visualization rule editors support variable specification of standard data types. The visual rule editing supports the selection and the designation of the quality codes of the measuring points, and comprises quality code attribute values and quality code bit numbers. Visualization rule editing supports a variety of logical computations, including: switching, equal to, greater than or equal to, less than or equal to, average, rate of change surge greater than or equal to, rate of change surge less than or equal to the change rate sudden drop is larger than, the change rate sudden drop is larger than or equal to, the change rate sudden drop is smaller than or equal to, the maximum value, the minimum value, the differential quantity, the deviation is larger than, the deviation is smaller than, self-defined and the like. Visualization rule editing supports timing conditions, including: within a period and continuously. The number of times that the rule is established in the specified period is represented in the period, the duration of the current rule is represented, and the time unit comprises millisecond, second, minute, hour, day, week, month, season, year and the like. The visual rule editing supports display output setting, and the output format can be set in a user-defined mode according to actual requirements. The visual rule editing supports automatic data pushing, and related data and events are triggered when the rules are established, so that the early warning purpose is achieved.
Step 1003: a diagnostic result is generated based on the historical operating data and the diagnostic rules. The diagnosis result comprises: fault alarm information and fault early warning information.
Step 1004: and acquiring a quality code of the DCS control center. The quality code is used to express each operating state in the DCS control center. In the DCS control center, for each station (including a main control unit, an engineer station, an operator station, a history station, etc.), a module card, a network node, an I/O point, etc., a quality code is usually used to express its different states and possible reasons for the states. The information can present the state and reason of the current point position, and accurate and reliable basic information is provided for further fault diagnosis. Through screening, combining and analyzing the information, a comprehensive fault diagnosis model is formed, and comprehensive, prospective and comprehensive intelligent early warning diagnosis is realized on the monitored equipment.
The quality code analysis method refers to a DCS control center record type reference manual to arrange a quality code information list, and defines the meaning and application scene of quality code information according to a DCS fault manual.
In this embodiment, the quality code is obtained in the following manner: and establishing communication butt joint with a DCS (distributed control system) control center, and usually realizing system communication in an OPC (optical proximity correction) communication mode. And realizing data docking by retrieving the roll names of the database, and extracting corresponding quality code information according to the site state field number and the reason bit number. And extracting the quality code information of the corresponding point according to the retrieval sequence of the point name, the field number and the bit number. For example, acquiring current control mode information of the #1 disk cabinet # 1 master: firstly, when the point name is 'DROP 1' and the field FB and the bit number 4 are 'true' are inquired in a database, the current state is the master control mode.
Step 1005: the cause of the fault is determined based on the quality code. Part of the quality code information can be directly used for state display and early warning of the main control unit, such as a control mode, a standby mode, network overtime, primary and secondary control mismatching and the like. The information can visually present the current state of the main control unit, and after the user receives the alarm information, the user can make judgment and processing at the first time, so that secondary processing is not needed, and when the quality code state is true, the user can directly give an alarm.
And when the other part of the quality codes are sent out, a user cannot directly know the problems of the system according to the information, and needs to comprehensively study, judge and analyze the other information at the same time, and generate state early warning information after secondary diagnosis. For example, "detect this station and break down", need to search for the power state, network state, master control state, IOIC card state, I/O card state that the present site contains synchronously, and other quality code information that change. And determining the fault cause of the station according to different state transactions, thereby providing intuitive comprehensive fault information for users. For example, if the state abnormality of the IOIC card is synchronously detected, it can be determined that the state abnormality of the IOIC card causes a site fault alarm.
Step 1006: and generating a fault processing suggestion and a thermal control equipment operation suggestion based on the fault reason.
Step 1007: and generating a diagnosis knowledge base model based on the diagnosis rule, the diagnosis result, the quality code, the fault processing suggestion and the operation suggestion of the thermal control equipment.
The diagnosis knowledge base model reads related test data (including process parameters and the like) through the data acquisition module, runs diagnosis rules, generates diagnosis conclusions (including fault conclusions and severity levels) and fault processing/running suggestions, and achieves interpretation and evaluation of fault information of the monitoring equipment.
Step 101: and acquiring the operation data of the thermal control equipment.
Step 102: and inputting the operation data into a diagnosis knowledge base model to obtain a thermal control state monitoring result. The thermal control state monitoring result comprises the following steps: the diagnosis result, the fault processing suggestion and the thermal control equipment operation suggestion.
Example two
This embodiment provides a thermal control state monitoring and knowledge base fusion system, as shown in fig. 2, the system includes:
and the diagnosis knowledge base model building module 200 is used for building a diagnosis knowledge base model.
And an operation data acquiring module 201, configured to acquire operation data of the thermal control device.
And the state monitoring result determining module 202 is used for inputting the operation data into the diagnosis knowledge base model to obtain a thermal control state monitoring result. The thermal control state monitoring result comprises the following steps: the diagnosis result, the fault processing suggestion and the thermal control equipment operation suggestion.
The specific operation functions of each module in the second embodiment may refer to the contents disclosed in the first embodiment, and are not described herein again.
EXAMPLE III
In this embodiment, another system for integrating thermal control state monitoring and knowledge base is provided, as shown in fig. 3, the system includes:
and the collector 300 is used for collecting the operation data of the thermal control equipment.
And the memory 301 is connected with the collector and used for storing the logic instruction. The logic instructions are used for implementing a thermal control state monitoring and knowledge base fusion method provided by the embodiment.
The memory 301 preferably employs a computer-readable storage medium.
And the processor 302 is connected with the memory 301 and used for calling and executing the logic instructions.
Further, a display is also provided in the system for displaying an operation page of the logic instruction. Preferably, the display and the processor may be in an integrated arrangement.
Example four
In this embodiment, the thermal control state monitoring and knowledge base fusion system provided in the third embodiment is used as a hardware carrier, and a specific implementation process of the thermal control state monitoring and knowledge base fusion method provided in the third embodiment is described.
Through deep analysis and comprehensive integration and study and judgment of the state quality codes of the main control unit, the system state codes are converted into languages which are easy to identify and use by common users, so that the users can clearly and intuitively acquire related state information. In the initial stage of abnormal change of the controller, the early abnormality can be found according to the state of the quality code. And provides the user with the reason for generating the abnormity and the suggestion for processing the abnormity.
And 2, storing experts in the diagnosis knowledge base in a fault model mode according to the maintenance experience of the DCS manufacturer knowledge base, the historical fault cases and the professional technical personnel to form a systematic fault diagnosis model, providing fault reason analysis and treatment suggestions for a user at the first time, and improving the timeliness and accuracy of fault treatment.
The diagnosis rule is defined based on a visual mode, and the rule is formed into a standardized rule definition through a browser interface, so that the rule definition process can be greatly simplified, and a business user can read and understand the rule in a more intuitive mode
Collecting state information of other parts of the DCS control center, such as a power supply, a network, an interface card, an I/O (input/output) card, a disk cabinet and the like, searching for a rule related to a main control unit, fully integrating discrete alarm information, and forming an all-dimensional multi-angle comprehensive studying and judging algorithm. And presenting centralized state early warning through scattered information data.
The execution process of the closed-loop early warning scheme based on the diagnosis knowledge base and combined with the diagnosis rule visualization is specifically as follows:
A. diagnostic knowledge base
The diagnosis knowledge base reads related test data (including process parameters and the like) through the data acquisition module, runs diagnosis rules, generates diagnosis conclusions (including fault conclusions and severity levels) and fault processing/running suggestions, and achieves the purpose of reading and evaluating fault information of the monitoring equipment. The principle is shown in fig. 4.
a) Establishing a platform database as an information source of the whole diagnosis system, determining the unique roll name of each main control unit and corresponding information thereof, leading contents mainly comprising information such as equipment roll names, roll descriptions, disk cabinet numbers, model specifications, commissioning dates, serial numbers, association systems, interlocking protection and the like into the system in an equipment account form, and adopting equipment roll name retrieval to match related information. The display page of the building platform database in the display is shown in fig. 5.
b) Diagnostic rule visualization parsing
A diagnostic rule is a description of computational and judgment logic, and rules may be combined to form complex computational judgment logic. The process of defining the diagnostic rules therefore requires a simple and intuitive form to implement. The diagnostic rules are defined based on a visual mode, and the rules are defined in a standardized mode through a browser interface, so that the rule definition process can be greatly simplified, and a business user can read and understand the rules in a more visual mode.
1. The visualization rule editing supports combination, and a plurality of rules can be combined in a AND-OR mode, as shown in FIG. 6.
2. The visualization rule editing supports infinite nested grouping logic, a sub-group can be established, a plurality of logic rules are created in the group, and the rules of the group uniformly support AND-OR logic gate judgment, as shown in FIG. 7.
3. Visualization rule editing supports rule passing, universal rule using all diagnostic logic.
4. And editing the visualization rules to support the input filtering matching and the type filtering screening of the acquisition points.
5. Visualization rule editors support variable specification of standard data types, as shown in FIG. 8.
6. The visual rule editing supports the selection and the specification of the quality code of the measuring point, and comprises a quality code attribute value and a quality code bit number, as shown in figure 9.
7. <xnotran> , : , , , , , , , , , , , , , , , , , , , , , 10 . </xnotran>
8. Visualization rule editing supports timing conditions, including periodic, persistent. The number of times that the rule is established in the specified period is represented in the period, the duration of the current rule is continuously represented, and the time unit comprises millisecond, second, minute, hour, day, week, month, season and year. This operation is illustrated in fig. 11.
9. Visualization rule editing supports display output settings that may specify that the associated data item appear highlighted when the rule is established, as shown by the solid gray box in fig. 12.
10. The visualization rule editing supports automatic data pushing, and when the rule is established, related data and events are triggered to achieve the purpose of early warning, as shown in fig. 13.
c) Principle of quality code analysis
In a DCS control center, for each station (including a master control unit, an engineer station, an operator station, a history station, etc.), a module card, a network node, and an I/O point, etc., a quality code is usually used to express its different states and possible causes for the states.
For example, the overview system of emerson supports four quality states: GOOD (Normal), FAIR (forced), POOR (Algorithm status), and BAD (Fault).
Each state indicates the operation and operating conditions of the point. A point of a certain quality state may have multiple causes stored in the 16 cause bits of the state word (e.g., 1w \2wfield). Possible reasons for quality include: point values are maintained, hardware errors, point oscillations, sensor calibration, scanning stopped, data link failures, engineering value limits, ground faults, power supply loss, etc.
The information can present the state and reason of the current point position, and accurate and reliable basic information is provided for further fault diagnosis. Through screening, combining and analyzing the information, a comprehensive fault diagnosis model is formed, and comprehensive, prospective and comprehensive intelligent early warning diagnosis is realized on the monitored equipment.
The quality code analysis method comprises the following steps: referring to a DCS control center record type reference manual, a quality code information list is sorted, and according to a DCS fault manual, the meaning and application scenario of quality code information are defined as shown in fig. 14 and 15 below.
The quality code acquisition mode is as follows: a communication interface is established with the DCS control system (the interface is illustrated by taking the network architecture shown in fig. 18 as an example), and the system communication is usually realized by an OPC communication method. And realizing data docking by retrieving the roll names of the database, and extracting corresponding quality code information according to the site state field number and the reason bit number. And extracting the quality code information of the corresponding point according to the retrieval sequence of the point name, the field number and the bit number. For example, obtain the current control mode information of the #1 disk cabinet # 1 master: firstly, when the point name is 'DROP 1' and the field FB and the bit number 4 are 'true' are inquired in a database, the current state is the master control mode.
Part of the quality code information can be directly used for state display and early warning of the main control unit, such as 'control mode', 'standby mode', 'network overtime', 'primary and secondary control mismatching' and the like, the information can visually present the current state of the main control unit, and a user can make judgment and processing at the first time after receiving the alarm information, so that secondary processing is not needed, and when the quality code state is true, the user can directly give an alarm;
and when the other part of the quality codes are sent out, a user cannot directly know the problems of the system according to the information, and needs to comprehensively study, judge and analyze the other information at the same time, and generate state early warning information after secondary diagnosis. For example, "detect this station and break down", need to search for the power state, network state, master control state, IOIC card state, I/O card state that the present station contains synchronously, and other quality code information that change. And determining the fault cause of the station according to different state transactions, thereby providing intuitive comprehensive fault information for users. For example, if the state abnormality of the IOIC card is synchronously detected, it can be determined that the state abnormality of the IOIC card causes a site fault alarm.
d) Real-time streaming computing
After the quality code is acquired, whether the current state is normal or not needs to be judged, so that the establishment of the model is a key step for determining whether the equipment is abnormal or not, and the judgment reference of the main control unit normal and the main control unit fault is established by respectively establishing a main control unit normal model and a main control unit fault model.
The stream computing performs computation verification on the rules configured by the rule editing, and if the rules are established, an alarm is triggered, and a typical rule in logic of the rule computing is illustrated as an example, and the rule is shown in fig. 16.
The computational logic is performed as follows:
1. it is determined what the conditions of all the rule entries are, and a plurality of patterns including all being true, one of them being true, all not being true, etc. are given in the rule, as shown in fig. 17.
2. And analyzing each rule parameter, wherein the parameters comprise acquisition points, data types, conditions, bit numbers, periods, times and the like.
3. And the background calculation program carries out real-time calculation according to the input parameters, and the data is acquired by a sensor in real time.
4. If the rule is once established in the calculation process, the current rule is established, and then an alarm is given.
e) Rule calculation trigger early warning
Through the steps of database establishment, quality code analysis, normal and fault model establishment and the like of a) to d), all diagnosis algorithm models are put into the system, a fault diagnosis knowledge base is established in the system, and when fault diagnosis conditions are met, early warning or alarm is sent out according to the algorithm models; and the real-time state monitoring and early warning of the main control unit are realized.
EXAMPLE five
In this embodiment, the method for monitoring the thermal control state and fusing the knowledge bases provided in the first embodiment is described in detail by taking the emerson ovision system as an example and citing a mode of combining part of the diagnostic logic with the knowledge bases.
Protection analysis logic description: two of the oil pressure 1 (60 HNC11CP 101) of the induced draft fan hydraulic oil station A, the oil pressure 2 (60 HNC11CP 102) of the induced draft fan hydraulic oil station A and the oil pressure 3 (60 HNC11CP 103) of the induced draft fan hydraulic oil station A are less than 0.7; delaying for 1200s; and the 9 th position of the switching state IDFATRIPLOCK is 1, A induced draft fan protection first IDFATRIPFST is 9.
Based on the rule description above, the visualization rule is used for editing, as shown in fig. 19.
According to the visual configuration of the diagnosis rule, the real-time calculation module reads the whole rule structure, and the calculation logic is as follows:
1) Reading the value of the measuring point of the 'fully closed state of the flue gas electric door at the outlet of the induced draft fan A', if the value is equal to 1, indicating that the first rule is established, otherwise, if the value is not established, calculating a regression origin, and starting the data acquisition and calculation of the next round, as shown in fig. 20.
2) The second part of rules is a group and comprises three sub-rules, if any two of the sub-rules are determined to be satisfied, the second group rule is identified to be satisfied, if not, the regression origin is calculated, and the next round of data acquisition and calculation is started, as shown in fig. 21.
The three sub-rules are as follows:
(1) and reading that the value of the oil pressure 1 of the hydraulic oil station of the induced draft fan A is less than 0.7, and indicating that the sub rule is established.
(2) And reading that the value of the oil pressure 2 of the hydraulic oil station of the induced draft fan A is less than 0.7, and indicating that the sub rule is established.
(3) And reading that the value of the oil pressure 3 of the hydraulic oil station of the induced draft fan A is less than 0.7, and showing that the sub-rule is established.
And according to the automatic pushing configuration in the diagnosis rule configuration information, selecting a mode of triggering early warning, and pushing timely and accurate early warning information to business personnel.
Based on the description of the above embodiments one to five, the present invention also has the following advantages over the prior art:
1. according to the invention, through deep analysis and comprehensive study and judgment of the state quality code of the main control unit, more accurate, clear, visual and comprehensive state information can be provided for a user, and the timeliness and accuracy of processing of abnormity and faults are improved.
2. The invention integrates the fault diagnosis knowledge base of the thermal control industry expert, DCS manufacturer and electric academy debugging expert experience model, carries out systematic and comprehensive fault diagnosis on the main control unit, replaces part of the original fault troubleshooting method depending on personnel experience and skills, and avoids the occurrence of human problem events of wrong analysis of reasons and unreasonable treatment measures caused by the difference of personnel technical levels.
3. The comprehensive diagnosis algorithm for internal and external factors of the system integrates multiple factors such as DCS (distributed control system) associated equipment, process flow parameters, equipment account information, environmental factors and the like, comprehensively and comprehensively judges the health state of the main control unit in multiple angles, can find hidden dangers in time, intelligently early warn, reduce or eliminate sudden faults of the main control unit, further carry out state overhaul, preventive maintenance and replacement for users, and provide actual data and theoretical basis.
4. According to the invention, the display is adopted to generate the fault popup window during alarming so as to provide clear equipment names, fault contents, possible reasons and processing suggestions, thereby greatly improving the timeliness and accuracy of fault processing and solving the problems of insufficient skills of new people, faults of old and new technologies and the like.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. A thermal control state monitoring and knowledge base fusion method is characterized by comprising the following steps:
constructing a diagnosis knowledge base model;
acquiring operation data of the thermal control equipment;
inputting the operating data into the diagnosis knowledge base model to obtain a thermal control state monitoring result; the thermal control state monitoring result comprises: the diagnosis result, the fault processing suggestion and the thermal control equipment operation suggestion.
2. The thermal control state monitoring and knowledge base fusion method according to claim 1, wherein the constructing a diagnostic knowledge base model specifically comprises:
establishing a platform database; the platform database comprises roll names of all control units in the thermal control equipment and historical operating data corresponding to the roll names;
defining a diagnosis rule;
generating a diagnostic result based on the historical operating data and the diagnostic rule; the diagnostic result includes: fault alarm information and fault early warning information;
acquiring a quality code of a DCS (distributed control System) control center; the quality code is used for expressing each operation state in the DCS control center;
determining a fault cause based on the quality code;
generating a fault handling suggestion and a thermal control equipment operation suggestion based on the fault reason;
and generating a diagnosis knowledge base model based on the diagnosis rule, the diagnosis result, the quality code, the fault treatment suggestion and the thermal control equipment operation suggestion.
3. The thermal control state monitoring and knowledge base fusion method according to claim 2, further comprising: and performing visual editing processing on the diagnosis rule.
4. A thermal control state monitoring and knowledge base fusion system is characterized by comprising:
the diagnosis knowledge base model building module is used for building a diagnosis knowledge base model;
the operation data acquisition module is used for acquiring operation data of the thermal control equipment;
the state monitoring result determining module is used for inputting the operation data into the diagnosis knowledge base model to obtain a thermal control state monitoring result; the thermal control state monitoring result comprises the following steps: diagnosis results, fault handling suggestions and thermal control equipment operation suggestions.
5. A thermal control state monitoring and knowledge base fusion system is characterized by comprising:
the collector is used for collecting the operating data of the thermal control equipment;
the memory is connected with the collector and used for storing the logic instruction; the logic instructions are used for implementing the thermal control state monitoring and knowledge base fusion method according to any one of claims 1-3;
and the processor is connected with the memory and used for calling and executing the logic instructions.
6. The thermal state monitoring and knowledge base fusion system of claim 5, wherein the memory is a computer readable storage medium.
7. The thermal state monitoring and knowledge base fusion system of claim 5, further comprising:
and the display is used for displaying the operation page of the logic instruction.
8. The thermal state monitoring and knowledge base fusion system of claim 7, wherein the display and the processor are in an integrated arrangement.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211264137.9A CN115616997A (en) | 2022-10-17 | 2022-10-17 | Thermal control state monitoring and knowledge base fusion method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211264137.9A CN115616997A (en) | 2022-10-17 | 2022-10-17 | Thermal control state monitoring and knowledge base fusion method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115616997A true CN115616997A (en) | 2023-01-17 |
Family
ID=84862676
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211264137.9A Pending CN115616997A (en) | 2022-10-17 | 2022-10-17 | Thermal control state monitoring and knowledge base fusion method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115616997A (en) |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06110690A (en) * | 1992-09-29 | 1994-04-22 | Mitsubishi Heavy Ind Ltd | Knowledge base construction supporting device |
| US5408412A (en) * | 1992-04-09 | 1995-04-18 | United Technologies Corporation | Engine fault diagnostic system |
| CN104679828A (en) * | 2015-01-19 | 2015-06-03 | 云南电力调度控制中心 | Rules-based intelligent system for grid fault diagnosis |
| CN105302112A (en) * | 2015-10-23 | 2016-02-03 | 中国电子科技集团公司第十研究所 | Intelligent fault diagnosis system for ICNI system |
| US20160116892A1 (en) * | 2014-10-22 | 2016-04-28 | Industrial Technology Research Institute | Method and system of cause analysis and correction for manufacturing data |
| CN107703912A (en) * | 2017-09-13 | 2018-02-16 | 陕西千山航空电子有限责任公司 | A kind of method for diagnosing faults based on Aerial Electronic Equipment |
| CN108008332A (en) * | 2017-11-29 | 2018-05-08 | 国网山东省电力公司电力科学研究院 | A kind of new energy Remote testing device method for diagnosing faults based on data mining |
| CN108038547A (en) * | 2017-10-27 | 2018-05-15 | 上海卫星工程研究所 | Diagnosis rule editing system based on graphics for satellite telemetry data |
| CN108388950A (en) * | 2018-01-29 | 2018-08-10 | 杭州安脉盛智能技术有限公司 | Intelligent transformer O＆M method and system based on big data |
| CN109636139A (en) * | 2018-11-26 | 2019-04-16 | 华南理工大学 | A kind of smart machine method for diagnosing faults based on semantic reasoning |
| CN110377001A (en) * | 2019-06-04 | 2019-10-25 | 上海华电奉贤热电有限公司 | Industrial equipment intelligent Fault Diagnose Systems and method based on data fusion |
| CN110798348A (en) * | 2019-10-28 | 2020-02-14 | 海南电网有限责任公司 | Fault warning method, server and system for power distribution communication network |
| CN113705924A (en) * | 2021-09-07 | 2021-11-26 | 北京必可测科技股份有限公司 | Intelligent diagnosis method and system for thermal control equipment |
| CN114037307A (en) * | 2021-11-16 | 2022-02-11 | 中瑞恒（北京）科技有限公司 | Visual topological reasoning automatic diagnosis tool and method for building energy system |
| CN114721352A (en) * | 2022-04-11 | 2022-07-08 | 华能威海发电有限责任公司 | Method and system for state monitoring and fault diagnosis of DCS (distributed control system) |
-
2022
- 2022-10-17 CN CN202211264137.9A patent/CN115616997A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5408412A (en) * | 1992-04-09 | 1995-04-18 | United Technologies Corporation | Engine fault diagnostic system |
| JPH06110690A (en) * | 1992-09-29 | 1994-04-22 | Mitsubishi Heavy Ind Ltd | Knowledge base construction supporting device |
| US20160116892A1 (en) * | 2014-10-22 | 2016-04-28 | Industrial Technology Research Institute | Method and system of cause analysis and correction for manufacturing data |
| CN104679828A (en) * | 2015-01-19 | 2015-06-03 | 云南电力调度控制中心 | Rules-based intelligent system for grid fault diagnosis |
| CN105302112A (en) * | 2015-10-23 | 2016-02-03 | 中国电子科技集团公司第十研究所 | Intelligent fault diagnosis system for ICNI system |
| CN107703912A (en) * | 2017-09-13 | 2018-02-16 | 陕西千山航空电子有限责任公司 | A kind of method for diagnosing faults based on Aerial Electronic Equipment |
| CN108038547A (en) * | 2017-10-27 | 2018-05-15 | 上海卫星工程研究所 | Diagnosis rule editing system based on graphics for satellite telemetry data |
| CN108008332A (en) * | 2017-11-29 | 2018-05-08 | 国网山东省电力公司电力科学研究院 | A kind of new energy Remote testing device method for diagnosing faults based on data mining |
| CN108388950A (en) * | 2018-01-29 | 2018-08-10 | 杭州安脉盛智能技术有限公司 | Intelligent transformer O＆M method and system based on big data |
| CN109636139A (en) * | 2018-11-26 | 2019-04-16 | 华南理工大学 | A kind of smart machine method for diagnosing faults based on semantic reasoning |
| CN110377001A (en) * | 2019-06-04 | 2019-10-25 | 上海华电奉贤热电有限公司 | Industrial equipment intelligent Fault Diagnose Systems and method based on data fusion |
| CN110798348A (en) * | 2019-10-28 | 2020-02-14 | 海南电网有限责任公司 | Fault warning method, server and system for power distribution communication network |
| CN113705924A (en) * | 2021-09-07 | 2021-11-26 | 北京必可测科技股份有限公司 | Intelligent diagnosis method and system for thermal control equipment |
| CN114037307A (en) * | 2021-11-16 | 2022-02-11 | 中瑞恒（北京）科技有限公司 | Visual topological reasoning automatic diagnosis tool and method for building energy system |
| CN114721352A (en) * | 2022-04-11 | 2022-07-08 | 华能威海发电有限责任公司 | Method and system for state monitoring and fault diagnosis of DCS (distributed control system) |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5264828B2 (en) | How to generate integrated warnings in process plants | |
| CN101989087B (en) | On-line real-time failure monitoring and diagnosing system device for industrial processing of residual oil | |
| CN115657617A (en) | Implementation method of intelligent monitoring alarm system for thermal power plant | |
| CN112561736A (en) | Fault diagnosis system and method for relay protection device of intelligent substation | |
| CN116300561A (en) | System, computer readable storage medium and method for monitoring manufacturing | |
| CN109948808A (en) | Database construction method, fault diagnosis method and system for substation equipment fault case database | |
| KR20140130543A (en) | Method and system for condition monitoring of a group of plants | |
| CN108051709A (en) | Transformer state online evaluation analysis method based on artificial intelligence technology | |
| CN114721352B (en) | State monitoring and fault diagnosis method and system of DCS (distributed control system) | |
| CN107066500B (en) | Power grid mass data quality verification method based on PMS model | |
| CN113848843B (en) | Self-diagnosis analysis system based on intelligent optimization energy-saving system | |
| CN118333347B (en) | Steelmaking wastewater environmental protection treatment monitored control system | |
| CN117560300B (en) | Intelligent internet of things flow prediction and optimization system | |
| CN118132996B (en) | Adaptive production scheduling optimization method based on industrial digital twin | |
| CN111627199A (en) | Hydropower station dam safety monitoring system and monitoring method | |
| CN112580858A (en) | Equipment parameter prediction analysis method and system | |
| CN102929241B (en) | Safe operation guide system of purified terephthalic acid device and application of safe operation guide system | |
| CN115238929A (en) | Predictive maintenance method and system for petroleum refining system | |
| CN113919151B (en) | Electrical measurement equipment acceleration reliability test method based on active triggering | |
| CN114756394A (en) | Intelligent diagnosis system for quality of full-link production data | |
| CN115616997A (en) | Thermal control state monitoring and knowledge base fusion method and system | |
| WO2024208180A1 (en) | Automatic monitoring method and system for technical specification of nuclear power plant operation | |
| CN113033166B (en) | Electronic report generation method and system based on PI system | |
| CN113836203A (en) | Network data diagnosis detection analysis system | |
| CN105184686B (en) | A Method for Signal Lean Analysis Using Dynamic Rule Engine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20230117 |