CN116610169A - Intelligent maintenance system and method for target object - Google Patents
Intelligent maintenance system and method for target object Download PDFInfo
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- CN116610169A CN116610169A CN202310884610.1A CN202310884610A CN116610169A CN 116610169 A CN116610169 A CN 116610169A CN 202310884610 A CN202310884610 A CN 202310884610A CN 116610169 A CN116610169 A CN 116610169A
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- 238000012423 maintenance Methods 0.000 title claims abstract description 43
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000009826 distribution Methods 0.000 claims abstract description 35
- 238000004891 communication Methods 0.000 claims abstract description 3
- 238000003860 storage Methods 0.000 claims description 4
- 238000001723 curing Methods 0.000 description 64
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The specification relates to an intelligent maintenance system and method for a target object, wherein the system comprises: the device comprises a steam generator, a data acquisition device and a control device, wherein the data acquisition device is arranged in a curing area and an object to be cured; the control device is in communication connection with the data acquisition device, and comprises a processor and a controller, wherein the data acquisition device comprises an environment sensor, and the environment sensor is used for acquiring maintenance environment data and/or steam data; the steam data includes at least one of steam temperature, flow rate, and output; the processor acquires parameters of an object to be maintained and/or steam nozzle data from the terminal; determining the current temperature distribution of the object to be maintained based on at least one of maintenance environment data, the object to be maintained parameters, steam nozzle data and steam data; generating an adjustment instruction based on the current temperature distribution of the object to be maintained and steam data, wherein the adjustment instruction comprises a steam adjustment strategy; the controller executes the adjustment instructions to control the operating parameters of the steam generator.
Description
Technical Field
The specification relates to the field of equipment control, in particular to an intelligent maintenance system and method for a target object.
Background
The curing steam is used for continuously acting higher temperature and humidity on part of the structure and promoting the change of structural characteristics, so that the object to be cured can obtain proper strength and good compactness. For example, the concrete precast beam slab generally needs to be cured for a long time to meet the requirement of tensioning, so that the waiting time before tensioning is shortened for saving the construction period, the turnover of the precast beam slab is quickened, the production efficiency of the concrete beam slab is improved, and the concrete precast beam slab can be treated by utilizing a steam curing mode.
However, for curing objects with larger sizes (for example, the objects are longer than 50 meters, the objects are higher than 5 meters or the objects are wider than 6 meters, etc.), the problems that the steam curing is not in place and the curing conditions of different areas are different may exist, so that the overall quality of the curing objects is affected. Therefore, it is desirable to provide an intelligent maintenance method for a target object, which can better adapt to a large-size object to be maintained and ensure the maintenance effect.
Disclosure of Invention
One of the embodiments of the present specification provides an intelligent maintenance system for a target object, the system including: the system comprises a steam generator, a data acquisition device and a control device, wherein the data acquisition device is arranged in a curing area and an object to be cured; the control device is in communication connection with the data acquisition device, and the control device comprises a processor and a controller, wherein the data acquisition device comprises an environment sensor, and the environment sensor is used for acquiring maintenance environment data and/or steam data; the steam data includes at least one of steam temperature, flow rate, and output; the processor acquires parameters of an object to be maintained and/or steam nozzle data from the terminal; determining a current temperature distribution of the object to be cured based on at least one of the curing environment data, the object to be cured parameter, the steam nozzle data and the steam data; generating an adjustment instruction based on the current temperature distribution of the object to be maintained and the steam data, wherein the adjustment instruction comprises a steam adjustment strategy; the controller executes the adjustment instructions to control the operating parameters of the steam generator.
One of the embodiments of the present disclosure provides an intelligent maintenance method for a target object, including: acquiring maintenance environment data and/or steam data; the steam data includes at least one of steam temperature, flow rate, and output; acquiring parameters of an object to be maintained and/or steam nozzle data; determining the current temperature distribution of the object to be maintained based on at least one of the maintenance environment data, the object to be maintained parameters, the steam nozzle data and the steam data; generating an adjustment instruction based on the current temperature distribution of the object to be maintained and the steam data; the adjustment instructions include a steam adjustment strategy; and executing the adjustment instruction.
One of the embodiments of the present specification provides a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs a target object intelligent maintenance method.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a block diagram of a platform of a target object intelligent maintenance system according to some embodiments of the present description;
FIG. 2 is an exemplary flow chart of a target object intelligent maintenance method according to some embodiments of the present description;
FIG. 3 is an exemplary flow chart for determining a current temperature profile of an object to be cured according to some embodiments of the present description;
FIG. 4 is a schematic diagram of a sub-region temperature prediction model shown in accordance with some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
In the existing scheme, the temperature difference between the inside and the outside of the object to be maintained caused by the factors such as the temperature and the flow rate of steam is not well considered, and cracks are easy to occur in the maintenance process of the object to be maintained with a larger size. In view of this, in some embodiments of the present disclosure, an intelligent curing method for a target object is provided, where the working state of each curing steam nozzle is precisely controlled, and the temperature difference between the steam temperature and the target temperature in the curing process is controlled, so as to ensure uniformity of steam curing.
FIG. 1 is a block diagram of a platform of a target object intelligent maintenance system according to some embodiments of the present description.
As shown in fig. 1, the target object intelligent maintenance system 100 may include a steam generator 110, a data acquisition device 120, and a control device 130.
The steam generator 110 may also be referred to as a steam heat source machine, and is a mechanical device that heats water or other medium with heat energy of fuel or other energy sources to obtain steam. In some embodiments, the steam generator 110 may be connected to various steam vents.
The data acquisition device 120 is arranged in the maintenance area and the object to be maintained. For example, the curing area may be a curing shed, and the object to be cured may be a concrete precast slab, a concrete precast beam, or the like.
In some embodiments, the data acquisition device may include an environmental sensor for acquiring maintenance environmental data and/or steam data. The environmental sensor may include a temperature sensor, a humidity sensor, a flow sensor, and the like.
In some embodiments, the environmental sensor is configured to acquire curing environment data and/or steam data including at least one of steam temperature, flow rate, and output. For more description of curing environment data and steam data, see description relating to step 210.
Control apparatus 130 is communicatively coupled to data acquisition apparatus 120, and control apparatus 130 may include one or more sub-processing devices (e.g., single-core processing devices or multi-core processing devices). By way of example only, the control device 130 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a special purpose instruction processor (ASIP), a microprocessor, or the like, or any combination thereof.
In some embodiments, the control device 130 may include a processor and a controller.
The processor acquires parameters of an object to be maintained and/or steam nozzle data from the terminal; determining a current temperature distribution of the object to be cured based on at least one of the curing environment data, the object to be cured parameter, the steam nozzle data and the steam data; and generating an adjustment instruction based on the current temperature distribution of the object to be maintained and the steam data, wherein the adjustment instruction comprises a steam adjustment strategy.
In some embodiments, for more content of the processor, reference may be made to step 240 and its corresponding content, which are not described herein.
The processor may execute computer instructions (e.g., program code) and send the instructions to the controller. Computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform particular functions described herein. Wherein the terminal may obtain data from other data sources (e.g., database, input device, etc.), such as curing object parameters and/or steam nozzle data, etc.
The controller executes the adjustment instructions to control the operating parameters of the steam generator.
In some embodiments, for more content of the controller, refer to step 250 and corresponding content thereof, which are not described herein. The controller may include, but is not limited to, a single chip or application specific integrated circuit, for example. In some embodiments, the controller may include a master controller located at the steam generator and slave controllers located at the respective steam vents.
FIG. 2 is an exemplary flow chart of a target object intelligent maintenance method according to some embodiments of the present description. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, the process 200 may be performed by the control device 130 in the target object intelligent maintenance system 100.
At step 210, curing environment data and/or steam data is obtained. In some embodiments, the curing environment data and/or steam data may be acquired by the data acquisition device 120 in the target object intelligent curing system 100.
The curing environment data refers to data related to a curing area in which an object to be cured is located. For example, the curing environment data may include one or more of curing booth room temperature, curing booth air humidity, covering material, height of the covering material from the object to be cured, insulation, area of the object to be cured controlled by the steam port.
In some embodiments, curing environment data including curing booth room temperature, curing booth air humidity, etc. may be automatically acquired by means of sensors, etc., for example using hygrothermographs, temperature sensors, etc. In some embodiments, curing environment data including covering material, height of covering material from object to be cured, thermal insulation material, area of object to be cured controlled by the steam port, etc. may be set by a user according to actual conditions, or may be acquired based on relevant process conditions of the object to be cured.
Steam data refers to operating parameters during steam curing. In some embodiments, the steam data may include at least one of steam temperature, flow rate, and output.
The steam temperature refers to the temperature of the steam ejected from the steam nozzle. The steam flow rate refers to the flow rate of steam ejected from the steam nozzle. The steam output is the output of steam sprayed out from the steam nozzle. In some embodiments, the steam temperature, flow rate, and output may be preset or obtained by a user.
And 220, acquiring parameters of an object to be maintained and/or steam nozzle data.
In some embodiments, the object parameters to be cured and/or the steam vents data may be obtained from the terminal by a processor.
The object parameters to be maintained refer to the technological parameters of the object to be maintained. For example, water to ash ratio, etc. In some embodiments, the object to be cured parameters may be obtained based on the relevant process conditions of the object to be cured.
The steam vents data refers to data related to the configuration of the steam vents. Such as the distribution of the steam vents, the size of the steam vents, etc. In some embodiments, the steam vents data may be determined based on field reality.
In some embodiments, the steam vents data may include steam vents distribution data and steam vents coverage data.
The steam nozzle distribution data refers to the distribution position of the steam nozzle with respect to the object to be cured. In some embodiments, the steam vents distribution data may include position data for each steam vent, such as coordinate information or relative position with the object to be cured.
The steam jet coverage data refers to the range that the steam jet can cover the object to be maintained, and in some embodiments, the coverage of different steam jets may be intersected, and the intersection of each steam jet and the other steam jets calculates the coverage of each steam jet. Illustratively, if the coverage areas of steam vents a and B intersect, the coverage area of steam vents a includes this intersection and the coverage area of steam vents B also includes this intersection.
In some embodiments, the intersection of each steam vent with the remaining steam vents may not be repeated, continuing with the previous example, where the coverage of steam vent a and steam vent B intersect, and if the coverage of steam vent a includes such intersection, the coverage of steam vent B does not include such intersection.
By acquiring the steam nozzle distribution data and the steam nozzle coverage data, the heating range during steam curing can be more accurately determined, and the method is beneficial to accurately determining the actual temperature and the heating condition of an object to be cured.
Step 230, determining the current temperature distribution of the object to be maintained based on at least one of maintenance environment data, the parameter of the object to be maintained, steam nozzle data and steam data.
The current temperature distribution of the object to be maintained refers to the temperature conditions of different parts of the object to be maintained. For example, the object to be cured can be divided into a plurality of sub-areas, and the temperature condition of each sub-area can be determined.
In some embodiments, after determining the current temperature distribution of the object to be cured, different steam curing strategies adopted by different areas can be controlled, so that the object to be cured is prevented from generating cracks due to temperature differences of the parts.
In some embodiments, the current temperature profile of the object to be maintained may be determined in a number of ways. For example, the current temperature distribution of the object to be maintained is determined according to the temperature sensors configured in the subareas of the object to be maintained, or the parameters based on the object heat conduction characteristics (such as the heat conduction coefficient of concrete) and the like are further calculated.
Fig. 3 is an exemplary flow chart for determining a current temperature profile of an object to be cured according to some embodiments of the present description. As shown in fig. 3, one or more steps in flow 300 may be performed by a processor, including the steps of:
at step 310, at least one sub-region of the object to be cured is determined based on the steam nozzle data and the object to be cured parameters.
The to-be-maintained object subregion refers to an area obtained by dividing the to-be-maintained object based on the coverage range of the steam nozzle.
In some embodiments, determining the sub-region of the object to be cured may facilitate determining a current temperature distribution of the object to be cured and thereby facilitate controlling steam temperatures at different locations of the object to be cured.
In some embodiments, the sub-region of the object to be cured may be determined in a variety of ways. For example, the object to be maintained is divided into a preset number (such as 10 or 20) of subareas or a preset area (such as 250 cm) based on subareas 2 ) Dividing objects to be maintained.
For the object to be maintained with larger size, the object to be maintained is divided into a plurality of subareas, and the steam nozzle is controlled, so that the maintenance effect can be remarkably improved.
In some embodiments, the sub-region of the object to be cured may be determined based on the steam nozzle distribution data, the steam nozzle coverage data, and the object to be cured parameter.
In some embodiments, the sub-areas of the object to be maintained may be divided according to coverage areas of steam nozzles, one nozzle corresponds to one sub-area of the object to be maintained, and the size of the sub-area of the object to be maintained is equal to the coverage area of the nozzle. In other words, during curing of the object to be cured, the number of steam vents is consistent with the number of subregions of the object to be cured.
In general, the temperature is higher near the steam nozzle, and the temperature condition of each subarea can be better determined by determining the subarea of the object to be maintained in the mode.
Step 320, determining the current temperature of at least one sub-area of the object to be cured based on the curing environment data, the sub-area of the object to be cured, and the steam data.
In some embodiments, the current temperature of the sub-zone may include the highest temperature and the lowest temperature of the sub-zone, and may also include data such as a center temperature. In some embodiments, the current temperature of any sub-region may be calculated by a formula or a preset algorithm. For example, the total heat may be determined based on the steam data and the current temperature calculated in combination with the object thermal conductivity characteristics (e.g., concrete thermal conductivity).
In some embodiments, the steam vents to be adjusted and the steam vent data may be determined based on the current temperature of the sub-region. For example, the steam vents may be closed when the maximum temperature of the sub-zone is too high.
In some embodiments, the temperature difference of the subarea can be determined by the highest temperature and the lowest temperature of the subarea, and the difference between the steam temperature and the temperature of the object to be maintained and the temperature difference of different positions of the object to be maintained can be further determined by the current temperature and the temperature difference data. For more description of the temperature difference, see later description regarding step 230.
As previously described, the current temperature of the sub-region may include the highest temperature and the lowest temperature of the sub-region, and in some embodiments, the highest temperature and the lowest temperature of each sub-region of the object to be cured may be determined based on the curing environment data, the object to be cured sub-region, and the steam data.
In some embodiments, the highest temperature and the lowest temperature of each region of the object to be maintained can be determined through a sub-region temperature prediction model based on the maintenance environment data, the sub-region of the object to be maintained and the steam data. Wherein the sub-region temperature prediction model is a machine learning model, in some embodiments the sub-region temperature prediction model may be a GNN (graph neural network) model.
FIG. 4 is a schematic diagram of a sub-region temperature prediction model shown in accordance with some embodiments of the present description. The input of the subarea temperature prediction model is a curing steam nozzle distribution diagram of the object to be cured, specifically, the curing steam nozzle distribution diagram of the object to be cured can comprise nodes and edges among the nodes, the nodes are subareas corresponding to the steam nozzles, and the edges are established among the adjacent steam nozzles. The node characteristics comprise maintenance environment data, sub-areas of objects to be maintained, steam data and the like; the edge features are the temperature coefficient of influence between the spouts. The output of the subarea temperature prediction model is the current temperature of each subarea, namely each node can output the highest temperature and the lowest temperature of the corresponding subarea.
The relevant data in the maintenance process of the object to be maintained is processed through machine learning, so that the current temperature of each subarea can be rapidly and accurately predicted.
Curing environment data, sub-areas of objects to be cured, steam data may be as described in connection with step 210 above. The temperature coefficient of influence between the spouts can be determined in a variety of ways. In some embodiments, the processor may determine the inter-nozzle temperature influence coefficient based on historical operating data of the curing steam nozzle. In some embodiments, the processor may also determine the inter-nozzle temperature coefficient of influence by simulating the operating conditions of the curing steam nozzle.
Taking the case of determining the inter-spout temperature influence coefficient through historical working data as an example, assuming that the current temperatures of the two areas are historically predicted by the area temperature prediction model, wherein the current temperatures comprise the predicted highest temperature (marked as A1), the predicted lowest temperature (marked as A2) and the predicted highest temperature (marked as B1) and the predicted lowest temperature (marked as B2) of the area A, the actual highest temperature (marked as A3) and the actual lowest temperature (marked as A4) of the area B and the actual lowest temperature (marked as B4) of the area B, the inter-spout temperature influence coefficient can be obtained by calculating (|A3-A1|/A1|A4-A2|/A2+|B3-B1+|/B4-B2|/4) as the curing inter-spout temperature influence coefficient of the area A, namely the edge between the corresponding nodes of the two steam spouts. The historical predicted temperature of the regional temperature prediction model may be that a temperature influence coefficient between steam vents is initialized (for example, the coefficient is set to be 1), and iteration is performed through the method to obtain a new temperature influence coefficient between steam vents.
By determining the temperature influence coefficient between the steam nozzles, the factors of the temperature influence between the nozzles can be removed when the current temperature of each subarea is predicted, and the accuracy of the current temperature prediction of each subarea is ensured.
The subarea temperature prediction model can be obtained based on training samples, wherein the training samples comprise a sample graph, the sample graph at least comprises subareas corresponding to steam nozzles, corresponding historical maintenance environment data, historical to-be-maintained object subareas, historical steam data and the like, and the graph also comprises establishment edges between adjacent steam nozzles and temperature influence coefficients between the nozzles. The actual temperatures (including the highest temperature and the lowest temperature) of each subarea in the sample graph are used as labels. The temperature influence coefficient between the nozzles, the history curing environment data, the history object subregion to be cured and the history steam data can be determined based on the history data, and the label can be obtained by a temperature sensor or the like or obtained by computer simulation (finite element analysis).
In some embodiments, the sub-region temperature prediction model may be trained from the labeled sample map described above. Specifically, a sample graph with a label can be input into an initial sub-region temperature prediction model, a loss function is constructed through the label and the result of the initial sub-region temperature prediction model, and parameters of the initial time prediction model are iteratively updated through gradient descent or other methods based on the loss function. And when the preset conditions are met, model training is completed, and a trained sub-region temperature prediction model is obtained. The preset condition may be that the loss function converges, the number of iterations reaches a threshold value, etc.
The relevant data in the maintenance process of the object to be maintained is processed through machine learning, so that the current temperature of each subarea can be rapidly and accurately predicted.
Step 330, determining the current temperature distribution of the object to be maintained based on the current temperature of the at least one sub-area of the object to be maintained.
According to the number of sub-areas of the object to be maintained, which are actually divided by the object to be maintained, and the current temperature of each sub-area, the current temperature distribution which can reflect the current state of the object to be maintained can be obtained.
In some embodiments, the current temperature profile may be in the form of a matrix or vector, with each element in the matrix or vector corresponding to the current temperature of one of the sub-regions.
Step 240, generating an adjustment instruction based on the current temperature distribution of the object to be maintained and the steam data.
The adjustment instructions may be computer instructions or electrical signals generated by the processor for manipulating the controller to perform actions, and the adjustment instructions may be instructions for adjusting the steam generator or steam nozzle. In some embodiments, the adjustment instructions include a steam adjustment strategy.
The steam adjusting strategy refers to a strategy for adjusting the working parameters or the working time of the steam nozzle to be adjusted. In some embodiments, the steam conditioning strategy may include determining locations where supplemental steam curing is desired, parameters of supplemental steam (e.g., temperature, flow rate), etc. For example, steam jet operation time can be increased or supplementary steam curing can be performed in a region with lower temperature; reducing steam jet flow rate in areas of higher temperature or greater temperature differential, and so forth.
In some embodiments, the steam adjustment strategy may include steam vents to be adjusted and steam adjustment parameters thereof. The controller may determine the steam vents to be adjusted based on the highest temperature and the lowest temperature of the at least one sub-area of the object to be cured, and determine the steam adjustment parameters based on current steam data of the steam vents to be adjusted.
The steam vents to be adjusted correspond to the sub-areas to be adjusted, and the steam vents to be adjusted in the steam adjustment strategy can include an ID or a position (e.g., a position in a matrix or vector) of the steam vents.
The steam adjustment parameter is an adjustable parameter of steam used for curing, and may include an increase or decrease in steam temperature, an increase or decrease in steam flow rate, and the like. For example, when the difference between the highest temperature and the lowest temperature is too large, the steam temperature and the flow rate of the corresponding steam vents are reduced.
In some embodiments, the controller may determine the steam vents to be adjusted by combining a preset strategy with a maximum temperature and a minimum temperature of at least one sub-region of the object to be cured.
In some embodiments, the preset strategy may include a preset temperature difference threshold and a temperature change gradient threshold, and when the temperature difference and/or the temperature change gradient of the sub-region of the object to be maintained do not meet the temperature difference threshold and/or the temperature change gradient threshold, the corresponding steam nozzle needs to be adjusted, that is, the steam nozzle to be adjusted.
In some embodiments, the temperature difference of the subarea of the object to be maintained can be determined by the difference between the highest temperature of the subarea and the lowest temperature of the subarea, the temperature difference can reflect the temperature difference of different positions of the object to be maintained in the subarea, and if the temperature difference is too large, stress is generated in the subarea, so that defects such as cracks and the like appear.
The temperature change gradient may include a maximum temperature change gradient and/or a minimum temperature change gradient, which may be expressed as: current time highest temperature of the subarea-last time highest temperature of the subarea/last time highest temperature of the subarea; the lowest temperature change gradient can be expressed as: current time the sub-region minimum temperature-last time the sub-region minimum temperature/last time the sub-region minimum temperature.
The temperature gradient relates to the temperature of the object to be maintained at the current moment and the temperature of the object to be maintained at the next moment, and the temperature of the object to be maintained at the next moment is related to the current steam data, namely the action of the current steam data can generate the appearance of the temperature of the object to be maintained at the next moment, so that the temperature change gradient can intuitively reflect the difference between the current steam temperature and the temperature of the object to be maintained. It should be noted that, the temperature difference threshold and the temperature change gradient threshold may be determined according to parameters such as the characteristics (such as the concrete label) and the volume of the to-be-maintained object, and different to-be-maintained objects may correspond to different temperature difference thresholds and temperature change gradient thresholds, which are not limited in the present specification.
Through the preset strategy, the temperature condition of the object to be maintained in the steam maintenance process can be comprehensively considered, internal stress is avoided, and then cracks appear.
After determining the steam vents to be adjusted, in some embodiments, the controller may predict, based on the candidate steam adjustment data, the temperatures of the sub-regions of the object to be cured adjusted based on the candidate steam adjustment data through the sub-region temperature prediction model, and determine preferred candidate steam adjustment parameters through the adjusted temperatures of the sub-regions of the object to be cured.
In some embodiments, the candidate steam adjustment data may include one or more sets of adjustment parameters corresponding to the current steam data, and since the adjustment parameters do not affect the curing environment data and the sub-area of the object to be cured, the candidate steam adjustment data may be input to the sub-area temperature prediction model in combination with the current steam data, and the model output is based on the temperatures of each sub-area of the object to be cured after the adjustment of the candidate steam adjustment data, and the sub-area temperature prediction model may be described in step 320 above, which is not repeated herein.
Based on the temperature of each sub-region of the object to be maintained, which is adjusted by the candidate steam adjustment data obtained through model prediction, whether the temperature meets the threshold requirement can be judged again based on a preset strategy, and then the optimal candidate steam adjustment parameters meeting the actual requirement are determined by the temperature of each sub-region of the object to be maintained.
In some embodiments, candidate steam conditioning data may be preset by a technician or determined by initialization; the actual requirement to be met may be a priority condition in the curing of the object to be cured currently, for example, if the curing period of the object to be cured is expected to be shorter, a group with the largest temperature difference change or the largest temperature change gradient may be selected as a preferred candidate steam adjustment parameter, or if the occurrence of cracks due to the temperature difference is expected to be avoided as much as possible, a group with the smallest temperature difference change or the smallest temperature change gradient may be selected as a preferred candidate steam adjustment parameter, or the two conditions are compromised, and a group with moderate values is selected.
In some embodiments, the steam adjusting policy further includes steam curing time, and if a set of preferred candidate steam adjusting parameters meeting actual requirements is obtained in the process of determining the preferred candidate steam adjusting parameters according to the temperatures of the subregions of the object to be cured after adjustment, a set of the steam adjusting parameters with the shortest steam curing time can be selected as the actual steam adjusting parameters in the steam adjusting policy, so as to accelerate curing progress and reduce energy consumption. The steam curing time is the time required for the entire steam curing, and in some embodiments, the steam curing time may be characterized based on the time the curing steam vents are operational.
In some embodiments, the steam curing time may be determined based on parameters of the object to be cured, steam data, for example, based on the size and volume of the object to be cured, in combination with the steam temperature and flow rate, calculating the amount of heat absorbed by the object to be cured and determining the steam curing time based on the time required for the absorbed heat to reach a preset temperature.
Referring to fig. 4 at the same time, in some embodiments, the parameters of the object to be cured and the steam data at the current moment may be input into a sub-region temperature prediction model, the model outputs the predicted sub-region temperature, and the predicted sub-region temperature is re-input into the sub-region temperature prediction model as one of the parameters of the object to be cured at the current moment to obtain the predicted sub-region temperature at the next moment, and the foregoing steps are repeated, and based on the sub-region temperatures of each iteration, the temperature gradient sequence data of each sub-region of the object to be cured may be obtained.
Furthermore, the controller can obtain a temperature function by adopting a parameter regression method based on the temperature gradient sequence data, and further obtain steam curing time based on the temperature function. It should be noted that, the parameter regression method may be a common linear regression method, and will not be described herein.
The steam curing time can be estimated more accurately by the temperature gradient sequence data obtained through iteration of the subarea temperature prediction model.
Step 250, execute the adjustment instruction.
The controller obtains the adjustment instruction generated by the processor, adjusts the steam generator and the steam nozzle to carry out steam maintenance.
In some embodiments of the specification, parameters of each curing steam nozzle are accurately controlled and adjusted in real time according to actual conditions through an intelligent curing method of a target object, so that temperature differences between steam temperature and object temperature in the curing process and temperature differences at various positions inside an object to be cured are accurately controlled, uniformity of steam curing is guaranteed, cracks are avoided, and performance of the object to be cured is improved.
It should be noted that the above description of the flow 200 and the flow 300 is for illustration and description only, and is not intended to limit the scope of applicability of the present description. Various modifications and changes to flow 200 and flow 300 may be made by those skilled in the art under the guidance of this specification. However, such modifications and variations are still within the scope of the present description.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (9)
1. An intelligent maintenance system for a target object, the system comprising: the system comprises a steam generator, a data acquisition device and a control device, wherein the data acquisition device is arranged in a curing area and an object to be cured; the control device is in communication connection with the data acquisition device, the control device comprises a processor and a controller, wherein,
the data acquisition device comprises an environment sensor, wherein the environment sensor is used for acquiring maintenance environment data and/or steam data; the steam data includes at least one of steam temperature, flow rate, and output;
the processor acquires parameters of an object to be maintained and/or steam nozzle data from the terminal; determining a current temperature distribution of the object to be cured based on at least one of the curing environment data, the object to be cured parameter, the steam nozzle data and the steam data; generating an adjustment instruction based on the current temperature distribution of the object to be maintained and the steam data, wherein the adjustment instruction comprises a steam adjustment strategy;
the controller executes the adjustment instructions to control the operating parameters of the steam generator.
2. The system of claim 1, wherein the steam vents data comprises steam vents distribution data and steam vents coverage data; the processor is further configured to:
determining at least one sub-region of the object to be maintained based on the steam nozzle data and the object to be maintained parameters; determining the current temperature of the at least one sub-area of the object to be maintained based on the maintenance environment data, the sub-area of the object to be maintained and the steam data; and determining the current temperature distribution of the object to be maintained based on the current temperature of the at least one sub-area of the object to be maintained.
3. The system of claim 2, wherein the current temperature of the at least one sub-region of the object to be cured comprises a highest temperature and a lowest temperature of the sub-region; the controller is further configured to:
and determining the highest temperature and the lowest temperature of the at least one sub-area of the object to be maintained based on the maintenance environment data, the sub-area of the object to be maintained and the steam data.
4. The system of claim 1, wherein the steam tuning strategy comprises steam vents to be tuned and steam tuning parameters thereof; the processor is further configured to: determining the steam nozzle to be adjusted based on the highest temperature and the lowest temperature of at least one sub-region of the object to be maintained; and determining steam adjusting parameters based on the current steam data of the steam vents to be adjusted.
5. An intelligent maintenance method for a target object is characterized by comprising the following steps:
acquiring maintenance environment data and/or steam data; the steam data includes at least one of steam temperature, flow rate, and output;
acquiring parameters of an object to be maintained and/or steam nozzle data;
determining the current temperature distribution of the object to be maintained based on at least one of the maintenance environment data, the object to be maintained parameters, the steam nozzle data and the steam data; the method comprises the steps of,
generating an adjustment instruction based on the current temperature distribution of the object to be maintained and the steam data; the adjustment instructions include a steam adjustment strategy;
and executing the adjustment instruction.
6. The method of claim 5, wherein the steam vents data comprises steam vents distribution data and steam vents coverage data;
the determining the current temperature distribution of the object to be maintained based on at least one of the maintenance environment data, the object to be maintained parameter, the steam nozzle data and the steam data further includes:
determining at least one sub-region of the object to be maintained based on the steam nozzle data and the object to be maintained parameters;
determining the current temperature of the at least one sub-area of the object to be maintained based on the maintenance environment data, the sub-area of the object to be maintained and the steam data;
and determining the current temperature distribution of the object to be maintained based on the current temperature of the at least one sub-area of the object to be maintained.
7. The method of claim 6, wherein the current temperature of the at least one sub-region of the object to be cured comprises a highest temperature and a lowest temperature of the sub-region;
the determining the current temperature of the at least one sub-area of the object to be cured based on the curing environment data, the sub-area of the object to be cured, and the steam data further includes:
and determining the highest temperature and the lowest temperature of the at least one sub-area of the object to be maintained based on the maintenance environment data, the sub-area of the object to be maintained and the steam data.
8. The method of claim 5, wherein the steam tuning strategy comprises steam vents to be tuned and steam tuning parameters thereof;
the generating an adjustment instruction based on the current temperature distribution of the object to be maintained and the steam data further comprises:
determining steam vents to be adjusted based on the highest temperature and the lowest temperature of at least one sub-region of the object to be maintained;
and determining steam adjusting parameters based on the current steam data of the steam vents to be adjusted.
9. A computer-readable storage medium storing computer instructions, wherein when the computer instructions in the storage medium are read by a computer, the computer executes the target object intelligent maintenance method according to any one of claims 5 to 8.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| NL7214881A (en) * | 1971-11-05 | 1973-05-08 | ||
| US5118200A (en) * | 1990-06-13 | 1992-06-02 | Varian Associates, Inc. | Method and apparatus for temperature measurements |
| EP0744836A2 (en) * | 1995-05-25 | 1996-11-27 | Kabushiki Kaisha Meidensha | Temperature compensated crystal oscillator |
| CN101709869A (en) * | 2009-12-18 | 2010-05-19 | 杭州电子科技大学 | Hybrid control method for superheat steam temperature system of coal-fired boiler |
| US20130009622A1 (en) * | 2011-07-07 | 2013-01-10 | Min-Hung Hu | Device and Module of Triggering and Generating Temperature Coefficient Current |
| CN103576553A (en) * | 2013-11-06 | 2014-02-12 | 华北电力大学（保定） | Fractional-order self-adjusting control method for steam temperature of coal-fired boiler |
| CN112507586A (en) * | 2020-12-02 | 2021-03-16 | 中国航发沈阳发动机研究所 | Rapid assessment method for two-dimensional temperature and strength of turbine air cooling blade |
| CN114290503A (en) * | 2021-12-07 | 2022-04-08 | 河北雄安京德高速公路有限公司 | Power control method and device for T-beam steam health-preserving generator |
| CN115328228A (en) * | 2022-10-13 | 2022-11-11 | 新乡市合力鑫电源有限公司 | High-frequency switching power supply |
| CN115464759A (en) * | 2022-10-28 | 2022-12-13 | 国网四川省电力公司天府新区供电公司 | Concrete spraying maintenance control method and system, electronic equipment and medium |
| CN115879498A (en) * | 2021-10-20 | 2023-03-31 | 西南交通大学 | Marine environment parameter rapid refined forecasting method based on graph neural network |
| CN116186978A (en) * | 2022-12-07 | 2023-05-30 | 中国人民解放军军事科学院国防科技创新研究院 | Data and physical dual-drive temperature field prediction method for complex geometric area of aircraft |
| CN116446300A (en) * | 2023-06-19 | 2023-07-18 | 四川公路桥梁建设集团有限公司 | Control method and system of slope regulator |
-
2023
- 2023-07-19 CN CN202310884610.1A patent/CN116610169B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| NL7214881A (en) * | 1971-11-05 | 1973-05-08 | ||
| US5118200A (en) * | 1990-06-13 | 1992-06-02 | Varian Associates, Inc. | Method and apparatus for temperature measurements |
| EP0744836A2 (en) * | 1995-05-25 | 1996-11-27 | Kabushiki Kaisha Meidensha | Temperature compensated crystal oscillator |
| CN101709869A (en) * | 2009-12-18 | 2010-05-19 | 杭州电子科技大学 | Hybrid control method for superheat steam temperature system of coal-fired boiler |
| US20130009622A1 (en) * | 2011-07-07 | 2013-01-10 | Min-Hung Hu | Device and Module of Triggering and Generating Temperature Coefficient Current |
| CN103576553A (en) * | 2013-11-06 | 2014-02-12 | 华北电力大学（保定） | Fractional-order self-adjusting control method for steam temperature of coal-fired boiler |
| CN112507586A (en) * | 2020-12-02 | 2021-03-16 | 中国航发沈阳发动机研究所 | Rapid assessment method for two-dimensional temperature and strength of turbine air cooling blade |
| CN115879498A (en) * | 2021-10-20 | 2023-03-31 | 西南交通大学 | Marine environment parameter rapid refined forecasting method based on graph neural network |
| CN114290503A (en) * | 2021-12-07 | 2022-04-08 | 河北雄安京德高速公路有限公司 | Power control method and device for T-beam steam health-preserving generator |
| CN115328228A (en) * | 2022-10-13 | 2022-11-11 | 新乡市合力鑫电源有限公司 | High-frequency switching power supply |
| CN115464759A (en) * | 2022-10-28 | 2022-12-13 | 国网四川省电力公司天府新区供电公司 | Concrete spraying maintenance control method and system, electronic equipment and medium |
| CN116186978A (en) * | 2022-12-07 | 2023-05-30 | 中国人民解放军军事科学院国防科技创新研究院 | Data and physical dual-drive temperature field prediction method for complex geometric area of aircraft |
| CN116446300A (en) * | 2023-06-19 | 2023-07-18 | 四川公路桥梁建设集团有限公司 | Control method and system of slope regulator |
Non-Patent Citations (1)
| Title |
|---|
| 张中礼;李明海;胡绍全;: "外壁热流响应计算的导热反问题方法及其验证", 强度与环境, no. 04 * |
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| Publication number | Publication date |
|---|---|
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