CN115660509B - Factory building control method and system based on digital twin technology - Google Patents
Factory building control method and system based on digital twin technology Download PDFInfo
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Abstract
The invention provides a factory building control method and a system based on a digital twin technology, which relate to the technical field of engineering management, acquire and acquire factory building drawing information, acquire equipment execution function information, build equipment images, build a digital twin factory model, perform fitting on factory demands, perform layout optimization according to fitting results, further perform model adjustment to generate an adjustment supervision model, acquire images of factory building, and realize building control by supervising and identifying the image acquisition results, so that the technical problems that the final control is inaccurate due to insufficient intelligence of the factory building control method and insufficient evaluation dimension in the prior art, and deviation exists between the factory building results and expected values are solved.
Description
Technical Field
The invention relates to the technical field of engineering management, in particular to a factory building control method and system based on a digital twin technology.
Background
In the process of factory building, the whole factory and auxiliary facilities need to be built and planned in advance, the factory to be built can meet production requirements, and the factory building management and control is performed based on working condition planning. Meanwhile, certain uncontrollable factors inevitably exist in the factory building process so as to influence the engineering progress and the engineering quality. In order to ensure that the construction effect of a factory accords with requirements, working condition management is mainly performed by engineering management technicians based on experience or auxiliary related equipment, the current management and control method cannot ensure that the management and control effect accords with an expected construction plan, and certain subjective motility exists, so that construction deviation exists in working hours, working efficiency and the like, and further technical innovation is required.
In the prior art, due to the fact that the factory building control method is insufficient in intelligence and insufficient in evaluation dimension, final control is not accurate enough, and deviation exists between factory building results and expected values.
Disclosure of Invention
The application provides a factory building control method and a system based on a digital twin technology, which are used for solving the technical problems that in the prior art, due to the fact that the intelligent degree of the factory building control method is insufficient, the evaluation dimension is not comprehensive enough, the final control is not accurate enough, and the factory building result is deviated from an expected value.
In view of the above, the present application provides a method and system for factory building management and control based on digital twinning technology.
In a first aspect, the present application provides a method of factory building management and control based on digital twinning technology, the method comprising:
acquiring factory construction drawing information, wherein the construction drawing information comprises size information and structure information;
acquiring equipment execution function information, and constructing an equipment portrait according to the execution function information;
constructing a digital twin plant model based on the equipment portrait, the construction drawing information and the equipment executive function information;
performing plant demand execution fitting through the digital twin plant model, performing layout optimization according to a fitting result, and generating adjustment layout information based on the optimization result;
performing model adjustment on the digital twin plant model through the adjustment layout information to generate an adjustment supervision model;
and performing image acquisition of factory construction through the image acquisition device, performing supervision and identification on an image acquisition result through the adjustment supervision model, and performing construction management and control according to the supervision and identification result.
In a second aspect, the present application provides a plant building management and control system based on digital twinning technology, the system comprising:
the information acquisition module is used for acquiring factory construction drawing information, wherein the construction drawing information comprises size information and structure information;
the portrait construction module is used for obtaining equipment execution function information and constructing equipment portraits according to the execution function information;
the model construction module is used for constructing a digital twin plant model based on the equipment portrait, the construction drawing information and the equipment execution function information;
the layout information generation module is used for performing factory demand execution fitting through the digital twin factory model, performing layout optimization according to a fitting result, and generating adjustment layout information based on an optimization result;
the model adjustment module is used for carrying out model adjustment on the digital twin plant model through the adjustment layout information to generate an adjustment supervision model;
the monitoring and controlling module is used for collecting the image of the factory building through the image collecting device, monitoring and identifying the image collecting result through the adjusting monitoring model, and building and controlling according to the monitoring and identifying result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method for managing and controlling the factory building based on the digital twin technology comprises the steps of acquiring and obtaining factory building drawing information, including size information and structure information, obtaining equipment execution function information, constructing equipment portraits, constructing a digital twin factory model based on the equipment portraits, the building drawing information and the equipment execution function information, performing factory demand execution fitting based on the digital twin factory model, performing layout optimizing according to fitting results, generating adjustment layout information, performing model adjustment on the digital twin factory model, and generating an adjustment supervision model; the image acquisition device is used for acquiring the image of the factory building, the image acquisition result is supervised and identified through the adjustment supervision model, and building control is carried out according to the supervision and identification result, so that the technical problems that the final control is not accurate enough and deviation exists between the factory building result and an expected value due to insufficient intelligence of the factory building control method and insufficient evaluation dimension in the prior art are solved, the internal layout adaptability adjustment analysis is carried out by constructing the digital twin factory model, the building control reference is determined by determining the preferable layout result, the control accuracy can be effectively improved, and the fit degree between the building result and actual production requirements is ensured.
Drawings
FIG. 1 is a schematic flow diagram of a plant construction control method based on digital twinning technology;
FIG. 2 is a schematic diagram of a process for generating an optimizing result in a plant construction management and control method based on a digital twin technology;
FIG. 3 is a schematic diagram of a process for obtaining a supervision and identification result in a plant construction management and control method based on a digital twin technology;
fig. 4 is a schematic diagram of a plant building management and control system based on digital twin technology.
Reference numerals illustrate: the system comprises an information acquisition module 11, a portrait construction module 12, a model construction module 13, a layout information generation module 14, a model adjustment module 15 and a supervision and management module 16.
Detailed Description
According to the method and the system for managing and controlling the factory building based on the digital twin technology, factory building drawing information is acquired and obtained, equipment image is built by acquiring equipment execution function information, a digital twin factory model is built, fitting is carried out on factory requirements, layout optimizing is carried out according to fitting results, model adjustment is carried out, an adjustment supervision model is generated, image acquisition is carried out on factory building, and building management and control are achieved through supervision and identification on image acquisition results.
Example 1
As shown in fig. 1, the present application provides a method for factory building management and control based on digital twin technology, the method being applied to a factory building management and control system communicatively connected to an image acquisition device, the method comprising:
step S100: acquiring factory construction drawing information, wherein the construction drawing information comprises size information and structure information;
specifically, a certain uncontrollable factor inevitably exists in the process of factory building to influence the engineering progress and the engineering quality, and in order to ensure that the building condition of a factory is consistent with an expected planning, the factory building control method based on the digital twin technology is applied to a factory building control system, the factory building control system is a general control system for performing full period supervision of factory building, the system is in communication connection with an image acquisition device, and the image acquisition device is equipment for performing real-time image acquisition of the factory building and is used for performing real-time working condition analysis. The working condition design diagram of the target factory is collected, namely the dimension information and the structure information are included in the geometric reduction of the target factory, the target factory is the factory to be built, the factory coverage area, the construction and the layout condition can be determined based on the dimension information and the structure information, and the acquisition of factory construction drawing information provides a reference basis for the follow-up construction of a factory simulation model.
Step S200: acquiring equipment execution function information, and constructing an equipment portrait according to the execution function information;
step S300: constructing a digital twin plant model based on the equipment portrait, the construction drawing information and the equipment executive function information;
specifically, the process production equipment of the target factory is obtained, the process production equipment comprises all-cycle demand equipment, execution functions of all equipment are determined, such as polishing equipment, cutting equipment, testing equipment, measuring equipment and the like, the execution functions of all the process equipment are determined, such as testing equipment is used for sample quality inspection, the equipment and the execution functions are associated and correspond to be used as equipment execution function information, equipment portrait construction is carried out based on the execution function information, the equipment portrait construction can be completed by auxiliary work drawing software, the equipment portrait corresponds to production equipment in the target factory one by one, and real demand information is constructed for the factory. Further, a production scene under expected planning is determined based on the equipment portrait, the design drawing information and the equipment execution function information, the layout condition of each equipment under the design drawing information is determined, planning construction parameters are determined through performing simulation reduction, three-dimensional modeling is performed on the equipment portrait, the digital twin factory model is generated based on a digital twin technology, the digital twin factory model is a simulation construction model matched with the expected planning engineering information, production operation analysis and supervision can be performed based on the digital twin factory model, construction effects under the expected planning are verified, and the matching degree of the construction effects and requirements is guaranteed.
Step S400: performing plant demand execution fitting through the digital twin plant model, performing layout optimization according to a fitting result, and generating adjustment layout information based on the optimization result;
specifically, the data twin factory model, namely an analog factory under preliminary expected planning is obtained, factory demand information is determined based on the digital twin factory model, the factory demand information comprises layout demands of production equipment corresponding to each process step in a production process flow executed by the factory, such as equipment running environment influence, equipment demand environment, equipment relevance, path obstacle characteristics and the like, the layout demands are used as factory demands to perform fitting, the layout conditions of the production equipment are optimized on the basis of initial conditions, the optimizing result is ensured to meet the factory demands, the final optimizing result, namely preferable equipment layout information meeting the factory demands, is determined through multiple tabu optimizing iterations, the adjustment layout information is generated based on the optimizing result, and the obtaining of the adjustment layout information provides an optimization direction for the follow-up factory building planning.
Further, as shown in fig. 2, step S400 of the present application further includes:
step S410: acquiring and obtaining factory execution process flow information;
step S420: carrying out flow analysis on the process flow information to generate execution requirement information;
step S430: performing equipment interaction analysis through the execution requirement information, and generating equipment association data according to interaction frequency;
step S440: and carrying out layout optimization through the equipment association data to generate the optimizing result.
Further, step S440 of the present application further includes:
step S441: performing equipment operation pollution analysis according to the equipment execution function information to obtain equipment operation pollution analysis results;
step S442: performing equipment demand environment analysis based on the equipment execution function information to obtain an equipment demand environment analysis result;
step S443: performing equipment association evaluation according to the equipment operation pollution analysis result and the equipment demand environment analysis result to generate an equipment association evaluation result;
step S444: and carrying out layout optimization through the equipment association data and the equipment association degree evaluation result to generate the optimization result.
Specifically, the production process flow collection is carried out on the target factory, wherein the production process flow collection comprises a complete production line of a factory production target, and the complete production line is used as the factory execution process flow information. And analyzing the process flow information, determining the flow execution requirement under the normal production process of each process step, for example, ensuring the vibration-free and dust-less environment in the operation of the measuring equipment so as to ensure the execution effect of the equipment under the process step, and acquiring the execution requirement information by respectively carrying out requirement evaluation on each process flow. Further performing equipment interaction influence analysis on each flow step based on the execution requirement information, for example, the equipment with process flow connection has a sequential enabling relationship; the same frequency resonance of various devices corresponding to the same step is ensured; the remote process may not have equipment association or weak association, the equipment association or weak association may be appropriately ignored, the equipment interaction frequency is determined, the equipment association data is generated based on the equipment association, the equipment association data is in direct proportion to the equipment interaction frequency, preferably, a multi-level equipment association level can be set, the equipment association data is identified, the subsequent targeted analysis and evaluation are facilitated, and the compliance degree of subsequent equipment layout and production technology can be ensured through the equipment association analysis.
Specifically, the equipment operation pollution analysis is carried out based on the equipment association data, equipment operation pollution analysis including dust pollution, noise pollution, vibration pollution and the like is carried out based on the equipment execution function information, the equipment operation pollution identification can be carried out by setting multi-dimensional pollution levels, and the equipment operation pollution analysis result is generated; and then, carrying out demand environment analysis on the equipment based on the equipment execution function information, such as dust-free environment, noise reduction environment and the like, associating and corresponding the environment analysis result with production equipment, and generating the equipment demand environment analysis result. And carrying out relevance evaluation on production equipment based on the equipment operation pollution analysis result and the equipment demand environment analysis result, wherein the higher the equipment influence degree is, the higher the corresponding evaluation result is, and the equipment relevance evaluation result is obtained.
For example, when there is stronger association between devices, but there is a difference in operation of the devices, for example, the measurement device and the cutting device, dust-free and vibration-free needs to be ensured in the operation process of the measurement device, and a large amount of dust and stronger vibration can be generated in the operation process of the cutting device. And further carrying out layout optimizing on the equipment based on the equipment association data and the equipment association degree evaluation result, and obtaining the optimizing result as a final production equipment layout result so as to ensure that the equipment layout result is matched with actual production.
Further, step S444 of the present application further includes:
step S4441: constructing path obstacle characteristics according to the digital twin plant model;
step S4442: constructing optimizing evaluation parameters, and selecting any one of the equipment association degree evaluation result, the equipment association data and the path obstacle characteristics to perform characteristic direction optimization;
step S4443: when the optimizing result meets the optimizing evaluation parameters, adding the corresponding characteristics into a tabu list;
step S4444: and carrying out continuous iterative optimization according to the optimization evaluation parameters and the tabu list, and generating an optimization result according to the iterative optimization result.
Further, step S4444 of the present application further comprises:
step S44441-1: setting a tabu period;
step S44442-2: when the characteristic tabu time in the tabu table meets the tabu period limit, releasing the corresponding characteristic in the tabu table;
step S44443-3: and continuing to perform iterative optimization according to the release characteristics and the stored characteristics, completing the optimization when the iteration times meet a preset iteration times threshold, and taking the optimal result of the history optimization as the optimization result.
Specifically, the multi-dimensional optimizing direction is determined to perform layout optimizing, and the optimal plant equipment layout is realized. The path obstruction features, such as path traffic dimensions, path obstructions, etc., are constructed based on the digital twin plant model. And constructing the optimizing evaluation parameters, namely, laying expected critical values which are required to be met by optimizing results, taking the equipment association degree evaluation results, the equipment association data and the path obstacle characteristics as optimizing directions, and determining an optimizing target to carry out equipment layout optimizing. When the optimizing result meets the optimizing evaluation parameters, in order to avoid the final optimizing result from sinking into local optimum and affecting the final optimizing precision, adding the characteristics corresponding to the optimizing result into the tabu list, and continuing optimizing iteration based on the other characteristic directions. And performing the tabu characteristic determination based on the optimizing iteration result and the optimizing evaluation parameter to perform storage judgment of the tabu characteristic, and performing optimizing iteration for a plurality of times to perform optimizing result judgment.
Further setting the tabu period, namely adding the characteristic releasable time limit in the tabu table, when the characteristic tabu time in the tabu table meets the tabu period limit, indicating that the current optimizing process is stable and no local optimal risk exists, and releasing the tabu characteristic in the tabu table to be used as the current optimizing referent direction. Wherein, each optimization iteration can determine a tabu feature, namely an optimal feature direction, and the feature release is carried out until the tabu period is reached. And repeatedly storing the tabu list and optimizing iteration based on the mode, stopping target optimization when the iteration number reaches the preset iteration number threshold value, determining an optimal result in the historical optimizing results, and determining a device layout result as a final optimizing result. By optimizing equipment layout, the equipment layout accuracy can be improved, and the fit degree of the layout result and actual production condition is ensured.
Further, step S4444 of the present application further comprises:
step S44441-2: performing feature direction optimization based on features outside the tabu list;
step S44442-2: when the newly added optimizing result can meet the optimizing evaluation parameters, adding the newly added characteristics to the tabu list;
step S44443-2: generating an optimizing result comparison instruction, and comparing the optimizing result with the newly-added optimizing result through the optimizing result comparison instruction;
step S44444-2: when the newly-increased optimizing result is better than the optimizing result, replacing the optimizing result by the newly-increased optimizing result, and taking the newly-increased optimizing result as a local optimal solution to continue optimizing.
Specifically, by judging the optimizing evaluation parameters of the optimizing result, it is determined that a tabu feature is added to the tabu table. And carrying out characteristic direction optimizing again based on the characteristics outside the tabu list so as to avoid the final result from falling into local optimum and obtain the newly-increased optimizing result. Judging whether the newly-added optimizing result meets the optimizing evaluation parameters, adding the newly-added features into the tabu list when the newly-added optimizing result meets the optimizing evaluation parameters, and taking the feature adding time as initial time until the tabu deadline is met, and releasing the corresponding features. Generating the optimizing result comparison instruction synchronously along with the acquisition of the newly-increased optimizing result, namely, a starting instruction for optimizing result comparison, and comparing the optimizing result with the newly-increased optimizing result along with the receiving of the optimizing result comparison instruction, and replacing the optimizing result by the newly-increased optimizing result as a local optimal solution when the newly-increased optimizing result is better than the optimizing result; and when the newly added optimizing result is inferior to the optimizing result, continuing to take the optimizing result as a local optimal solution, and repeating optimizing iteration until a final optimizing result is obtained so as to ensure the preference of the final optimizing result.
Step S500: performing model adjustment on the digital twin plant model through the adjustment layout information to generate an adjustment supervision model;
step S600: and performing image acquisition of factory construction through the image acquisition device, performing supervision and identification on an image acquisition result through the adjustment supervision model, and performing construction management and control according to the supervision and identification result.
Specifically, the layout optimizing of the production equipment is carried out based on the digital twin plant model, and the adjustment layout information is obtained. And further, the adjustment layout information is used as an equipment layout adjustment direction, the constructed digital twin plant model is synchronously adjusted, and the adjustment supervision model is generated, and is a construction effect model which meets the production requirements of the plant and ensures the best production effect. Further, based on the image acquisition device, the live acquisition of factory construction is carried out, the image acquisition result is acquired, preferably, the division of working conditions of factory construction is carried out, a plurality of construction periods are determined, the evaluation analysis of the effect of construction is carried out for a plurality of times for each construction period, the image acquisition result is acquired and is matched with the adjustment supervision model in consistency, the working condition supervision and identification are carried out, the construction deviation existing in the working condition is timely adjusted based on the supervision and identification result, so that construction management and control are carried out, and the production requirement fitting degree of the final construction effect can be effectively ensured.
Further, as shown in fig. 3, step S600 of the present application further includes:
step S610: obtaining image position characteristic information according to the image acquisition result;
step S620: performing recognition feature matching on the adjustment supervision model through the image position feature information to obtain a recognition feature matching result;
step S630: and carrying out feature recognition on the image acquisition result based on the recognition feature matching result, and obtaining the supervision recognition result according to the feature recognition result.
Specifically, the image acquisition device is used for acquiring the construction working condition in real time, acquiring the image acquisition result, and extracting the image position characteristics of the image acquisition result, including layout, size and the like. And further carrying out feature consistency identification and matching on the image position feature information and the adjustment supervision model, carrying out key position scanning positioning on the image position feature information in the adjustment supervision model, and taking a positioning result as the identification feature matching result. And further, feature recognition is carried out on the image acquisition result based on the recognition feature matching result, the feature recognition result is determined to judge whether the current construction working condition reaches an expected target, the supervision recognition result is obtained, the supervision recognition result comprises normal or abnormal working conditions, and when the existing working conditions deviate, for example, construction deviation, working condition aging deviation and the like, construction planning adjustment is needed in time so as to ensure that the final construction effect reaches an expected value.
Example two
Based on the same inventive concept as one of the foregoing embodiments of the digital twin technology based factory building management and control method, as shown in fig. 4, the present application provides a digital twin technology based factory building management and control system, which includes:
the information acquisition module 11 is used for acquiring factory construction drawing information, wherein the construction drawing information comprises size information and structure information;
a portrait construction module 12, wherein the portrait construction module 12 is used for obtaining equipment execution function information and constructing an equipment portrait according to the execution function information;
a model building module 13, wherein the model building module 13 is used for building a digital twin plant model based on the equipment portrait, the construction drawing information and the equipment execution function information;
the layout information generation module 14 is used for performing factory demand execution fitting through the digital twin factory model, performing layout optimization according to a fitting result, and generating adjustment layout information based on an optimization result;
the model adjustment module 15 is used for performing model adjustment on the digital twin plant model through the adjustment layout information, and generating an adjustment supervision model;
the supervision and management module 16, wherein the supervision and management module 16 is used for performing image acquisition of factory building through the image acquisition device, performing supervision and identification on an image acquisition result through the adjustment supervision model, and performing building and management according to the supervision and identification result.
Further, the system further comprises:
the process acquisition module is used for acquiring and obtaining information of a factory execution process flow;
the demand information generation module is used for carrying out flow analysis on the process flow information to generate execution demand information;
the associated data generation module is used for carrying out equipment interaction analysis through the execution requirement information and generating equipment associated data according to the interaction frequency;
and the optimizing result generating module is used for carrying out layout optimizing through the equipment associated data to generate the optimizing result.
Further, the system further comprises:
the operation pollution analysis module is used for carrying out equipment operation pollution analysis according to the equipment execution function information to obtain an equipment operation pollution analysis result;
the demand environment analysis module is used for carrying out equipment demand environment analysis based on the equipment execution function information to obtain an equipment demand environment analysis result;
the relevance evaluation module is used for performing equipment relevance evaluation according to the equipment operation pollution analysis result and the equipment demand environment analysis result to generate an equipment relevance evaluation result;
the layout optimizing module is used for carrying out layout optimizing through the equipment association data and the equipment association degree evaluation result to generate the optimizing result.
Further, the system further comprises:
the feature construction module is used for constructing path obstacle features according to the digital twin plant model;
the characteristic direction optimizing module is used for constructing optimizing evaluation parameters, and selecting any one of the equipment association degree evaluation result, the equipment association data and the path obstacle characteristics to perform characteristic direction optimizing;
the tabu list adding module is used for adding corresponding characteristics to the tabu list when the optimizing result meets the optimizing evaluation parameters;
and the optimizing iteration module is used for carrying out continuous iterative optimizing according to the optimizing evaluation parameters and the tabu list and generating an optimizing result according to the iterative optimizing result.
Further, the system further comprises:
the time limit setting module is used for setting a tabu time limit;
the tabu list release module is used for releasing corresponding features in the tabu list when the feature tabu time in the tabu list meets the tabu period limit;
and the threshold judging module is used for continuing iterative optimization according to the release characteristics and the stored characteristics, and finishing the optimization when the iteration times meet the preset iteration times threshold, and taking the optimal result of the historical optimization as the optimizing result.
Further, the system further comprises:
the characteristic direction optimizing module is used for optimizing the characteristic direction based on the characteristics outside the tabu list;
the new feature adding module is used for adding the new features to the tabu list when the new optimizing result can meet the optimizing evaluation parameters;
the result comparison module is used for generating an optimizing result comparison instruction, and comparing the optimizing result with the newly-added optimizing result through the optimizing result comparison instruction;
and the local optimal solution replacement module is used for replacing the optimizing result by the newly-increased optimizing result when the newly-increased optimizing result is better than the optimizing result, and taking the newly-increased optimizing result as a local optimal solution to continue optimizing.
Further, the system further comprises:
the position characteristic acquisition module is used for acquiring image position characteristic information according to the image acquisition result;
the feature matching module is used for carrying out recognition feature matching on the adjustment supervision model through the image position feature information to obtain recognition feature matching results;
and the monitoring and identifying result acquisition module is used for carrying out characteristic identification on the image acquisition result based on the identifying characteristic matching result and acquiring the monitoring and identifying result according to the characteristic identification result.
The foregoing detailed description of a plant construction management and control method based on digital twin technology will be clear to those skilled in the art, and the description of the apparatus disclosed in this embodiment is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A method of factory building management and control based on digital twinning technology, the method being applied to a factory building management and control system communicatively coupled to an image acquisition device, the method comprising:
acquiring factory construction drawing information, wherein the construction drawing information comprises size information and structure information;
acquiring equipment execution function information, and constructing an equipment portrait according to the execution function information;
constructing a digital twin plant model based on the equipment portrait, the construction drawing information and the equipment executive function information;
performing plant demand execution fitting through the digital twin plant model, and performing layout optimizing according to a fitting result, wherein the method comprises the following steps:
acquiring and obtaining factory execution process flow information;
carrying out flow analysis on the process flow information to generate execution requirement information;
performing equipment interaction analysis through the execution requirement information, and generating equipment association data according to interaction frequency;
performing equipment operation pollution analysis according to the equipment execution function information to obtain equipment operation pollution analysis results;
performing equipment demand environment analysis based on the equipment execution function information to obtain an equipment demand environment analysis result;
performing equipment association evaluation according to the equipment operation pollution analysis result and the equipment demand environment analysis result to generate an equipment association evaluation result;
carrying out layout optimization through the equipment association data and the equipment association degree evaluation result, generating an optimization result, and generating adjustment layout information based on the optimization result;
performing model adjustment on the digital twin plant model through the adjustment layout information to generate an adjustment supervision model;
and performing image acquisition of factory construction through the image acquisition device, performing supervision and identification on an image acquisition result through the adjustment supervision model, and performing construction management and control according to the supervision and identification result.
2. The method of claim 1, wherein the method comprises:
constructing path obstacle characteristics according to the digital twin plant model;
constructing optimizing evaluation parameters, and selecting any one of the equipment association degree evaluation result, the equipment association data and the path obstacle characteristics to perform characteristic direction optimization;
when the optimizing result meets the optimizing evaluation parameters, adding the corresponding characteristics into a tabu list;
and carrying out continuous iterative optimization according to the optimization evaluation parameters and the tabu list, and generating an optimization result according to the iterative optimization result.
3. The method according to claim 2, wherein the method comprises:
setting a tabu period;
when the characteristic tabu time in the tabu table meets the tabu period limit, releasing the corresponding characteristic in the tabu table;
and continuing to perform iterative optimization according to the release characteristics and the stored characteristics, completing the optimization when the iteration times meet a preset iteration times threshold, and taking the optimal result of the history optimization as the optimization result.
4. A method according to claim 3, wherein the method comprises:
performing feature direction optimization based on features outside the tabu list;
when the newly added optimizing result can meet the optimizing evaluation parameters, adding the newly added characteristics to the tabu list;
generating an optimizing result comparison instruction, and comparing the optimizing result with the newly-added optimizing result through the optimizing result comparison instruction;
when the newly-increased optimizing result is better than the optimizing result, replacing the optimizing result by the newly-increased optimizing result, and taking the newly-increased optimizing result as a local optimal solution to continue optimizing.
5. The method of claim 1, wherein the method comprises:
obtaining image position characteristic information according to the image acquisition result;
performing recognition feature matching on the adjustment supervision model through the image position feature information to obtain a recognition feature matching result;
and carrying out feature recognition on the image acquisition result based on the recognition feature matching result, and obtaining the supervision recognition result according to the feature recognition result.
6. A plant-building management and control system based on digital twinning technology, characterized in that it is communicatively connected to an image acquisition device, said system comprising:
the information acquisition module is used for acquiring factory construction drawing information, wherein the construction drawing information comprises size information and structure information;
the portrait construction module is used for obtaining equipment execution function information and constructing equipment portraits according to the execution function information;
the model construction module is used for constructing a digital twin plant model based on the equipment portrait, the construction drawing information and the equipment execution function information;
the layout information generation module is used for performing factory demand execution fitting through the digital twin factory model, and performing layout optimization according to fitting results, and comprises the following steps:
acquiring and obtaining factory execution process flow information;
carrying out flow analysis on the process flow information to generate execution requirement information;
performing equipment interaction analysis through the execution requirement information, and generating equipment association data according to interaction frequency;
performing equipment operation pollution analysis according to the equipment execution function information to obtain equipment operation pollution analysis results;
performing equipment demand environment analysis based on the equipment execution function information to obtain an equipment demand environment analysis result;
performing equipment association evaluation according to the equipment operation pollution analysis result and the equipment demand environment analysis result to generate an equipment association evaluation result;
carrying out layout optimization through the equipment association data and the equipment association degree evaluation result, generating an optimization result, and generating adjustment layout information based on the optimization result;
the model adjustment module is used for carrying out model adjustment on the digital twin plant model through the adjustment layout information to generate an adjustment supervision model;
the monitoring and controlling module is used for collecting the image of the factory building through the image collecting device, monitoring and identifying the image collecting result through the adjusting monitoring model, and building and controlling according to the monitoring and identifying result.
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