CN113514239A - Air distribution plate online detection method and system and storage medium - Google Patents
Air distribution plate online detection method and system and storage medium Download PDFInfo
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Abstract
The application provides an air distribution plate online detection method, an air distribution plate online detection system and a storage medium, which are used for detecting the blocking condition of an air distribution plate in real time, quickly and accurately. The online detection method of the air distribution plate comprises the following steps: at least obtaining state information of an air distribution plate and a discharging scraper in the fluidized bed, wherein the state information comprises operation state information and/or structure state information; preprocessing the state information to obtain target characteristic data; inputting the target characteristic data into a detection model, wherein the detection model is at least used for detecting the hole blocking condition of the air distribution plate; and obtaining hole blocking information which is fed back by the detection model and related to the air distribution plate. By adopting the scheme provided by the application, the labor and time cost are saved, and the air distribution plate can be rapidly and effectively detected in real time.
Description
Technical Field
The application relates to the technical field of dry-method dense medium fluidized bed coal separation, in particular to an air distribution plate online detection method, equipment and a storage medium.
Background
The dry-process heavy-medium fluidized bed technology is a technology for forming a quasi-fluid gas-solid suspension with a certain density in a bed layer by utilizing the fluidization property of gas-solid two phases, the selected materials realize floating and sinking layering in the bed layer according to the density difference of the selected materials, and the floating/sinking products are collected by a scraper and other discharge devices to realize the separation process of the materials, and the dry-process heavy-medium fluidized bed technology is successfully applied in the field of coal dressing and has obvious technical advantages.
With the industrial popularization and application of the dry-method heavy-medium fluidized bed in the field of coal dressing, the air distribution effect and the blockage problem of the step air distribution plate are more and more concerned. The plugging of the step register generally comprises: leakage of fine fraction particles and accumulation of gangue. On one hand, the air distribution effect is an important factor directly influencing the stability of the bed density, and the blockage of the step air distribution plate can cause the local instability of the step air distribution of the bed, reduce the air distribution capacity and be not beneficial to the stability of the bed density; on the other hand, in the actual continuous sorting process, the accumulation of the gangue layer on the air distribution plate not only can cause certain resistance to the gangue discharge scraper blade and increase energy consumption, but also can sometimes cause abrasion and fracture of the scraper blade; meanwhile, the wind distribution effect of the step wind distribution plate can be seriously influenced by a gangue layer with a certain thickness, and secondary wind distribution formed by the gangue layer can disturb the wind distribution effect of the step to a certain extent. At present, whether the step air distribution plate is blocked or not is checked by a manual checking mode, and when the step air distribution plate is blocked, the hole blocking position and the hole blocking area are also checked by manual checking, so that a large amount of manpower and time cost are consumed, and the production is extremely not facilitated.
Disclosure of Invention
The application provides an air distribution plate online detection method, an air distribution plate online detection system and a storage medium, which are used for detecting the blocking condition of an air distribution plate in real time, quickly and accurately.
The application provides an online detection method for an air distribution plate, which comprises the following steps:
at least obtaining state information of an air distribution plate and a discharging scraper in the fluidized bed, wherein the state information comprises operation state information and/or structure state information;
preprocessing the state information to obtain target characteristic data;
inputting the target characteristic data into a detection model, wherein the detection model is at least used for detecting the hole blocking condition of the air distribution plate;
and obtaining hole blocking information which is fed back by the detection model and related to the air distribution plate.
Optionally, the method further comprises:
establishing a target model;
acquiring historical state information of the air distribution plate and the discharge scraper and hole plugging information of the air distribution plate corresponding to the historical state information;
and training the target model at least based on the historical state information and the hole blocking information to obtain the detection model.
Optionally, the state information includes one or more of air tightness information of the air distribution plate, pressure drop information, thickness of material stacked on the air distribution plate, motor torque information of the discharging scraper, and information of acting force of the discharging scraper on the air distribution plate.
Optionally, the hole plugging information includes hole plugging area and hole plugging position information.
Optionally, the preprocessing the state information to obtain target feature data includes:
and extracting the characteristics of the state information based on characteristic engineering to obtain the target characteristic data, wherein the target characteristic data comprises information which is directly influenced by the hole plugging state of the air distribution plate in the state information.
Optionally, the method further comprises:
obtaining a pressure signal of the pressure applied to the fluidized bed layer corresponding to the historical hole plugging information;
the training the target model based on at least the historical state information and the hole plugging information to obtain the detection model comprises:
and training the target model based on the historical state information, the hole plugging information and the pressure signal to obtain a detection model capable of detecting the hole plugging condition of the air distribution plate and the influence of a hole plugging area on the bed pressure fluctuation.
Optionally, before the preprocessing the state information to obtain the target feature data, the method further includes:
determining the air tightness of the air distribution plate and the pressure drop conditions of different areas based on the state information, and simultaneously determining the motor power and the motor torque of the discharging scraper blade;
and when one or more of the air tightness, the pressure drop conditions of different areas, the motor power and the motor torque are determined to be abnormal, adjusting the air distribution plate and/or the discharging scraper plate based on abnormal data and abnormal positions, and preprocessing the state information to obtain the target characteristic data if the data generated at the abnormal positions after adjustment are still abnormal.
Optionally, the method further comprises:
determining whether the hole plugging information is consistent with the actual hole plugging condition of the air distribution plate or not, and obtaining a corresponding result;
training the detection model based on the target feature data, the hole plugging information and the corresponding results.
Another embodiment of the present application simultaneously provides an online detecting system for a grid plate, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the processor, and the instructions are executed by the at least one processor to implement the online detection method of the air distribution plate in any of the above embodiments.
Another embodiment of the present application further provides a computer-readable storage medium, where when instructions in the storage medium are executed by a processor corresponding to the air distribution plate online detection system, the air distribution plate online detection system can implement the air distribution plate online detection method described in any of the above embodiments.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present application is further described in detail by the accompanying drawings and examples.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiment(s) of the application and together with the description serve to explain the application and not limit the application. In the drawings:
fig. 1 is a flowchart of an online detection method for an air distribution plate in an embodiment of the present application;
FIG. 2 is a flow chart of an online detection method for an air distribution plate according to another embodiment of the present application;
FIG. 3 is a flow chart of an online detection method for an air distribution plate according to another embodiment of the present application;
FIG. 4 is a flow chart of an online detection method for an air distribution plate according to another embodiment of the present application;
FIG. 5 is a flowchart illustrating an on-line detection method for an air distribution plate according to another embodiment of the present disclosure;
fig. 6 is a schematic diagram illustrating a connection relationship between components of an online detection system of an air distribution plate in an embodiment of the present application.
Detailed Description
The preferred embodiments of the present application will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein only to illustrate and explain the present application and not to limit the present application.
Fig. 1 is a flowchart of an online detection method for a wind distribution plate according to an embodiment of the present application, and as shown in fig. 1, the method may be implemented as the following steps S11-S14:
in step S11, at least obtaining status information of a grid plate and a discharge scraper in the fluidized bed, wherein the status information includes operation status information and/or structural status information;
in step S12, preprocessing the state information to obtain target feature data;
in step S13, inputting the target feature data into a detection model, where the detection model is at least used to detect a hole blockage condition of the wind distribution plate;
in step S14, hole plugging information about the grid plate fed back by the detection model is obtained.
The scheme provided by the embodiment of the application is suitable for a fluidized bed system, such as a dry-method heavy-medium fluidized bed system, and can also be suitable for a platform for providing the air distribution plate on-line detection for the fluidized bed.
In this application, when carrying out the detection of air distribution plate, the state information of air distribution plate and row material scraper blade in the fluidized bed needs to be obtained at least, and this state information can include the use/running state information of air distribution plate, and structural state information, for example including the pressure information that air distribution plate receives, whether deformation appears in the structure, the gas tightness, the aperture ratio of all gas pockets etc. can also include the running state information that is used for removing the row material scraper blade in order to scrape row the material at air distribution plate, for example including the motor running state information that the scraper blade was arranged, confirm to arrange the material scraper blade whether high-efficient the operation, whether have the condition that is obstructed and influence efficiency. The state information can be obtained by arranging sensors with different functions, and for devices such as a motor, the running state of the devices can also be obtained by being in communication connection with central control equipment, a processing platform and the like of the fluidized bed, and the specific mode is uncertain. And after the required state information is obtained, preprocessing the state information to obtain target characteristic data which can be input into the detection model and calculated and used by the detection model, wherein the target characteristic data is characteristic data closely related to the blockage condition of the air holes of the air distribution plate. And the detection model is at least used for detecting the hole blocking condition of the air distribution plate, based on the detection model, the hole blocking condition of the air distribution plate can be quickly and accurately detected, hole blocking data is obtained, based on the hole blocking data, a worker can timely know the state of the air distribution plate and take corresponding cleaning measures, the overhauling efficiency of the air distribution plate is improved, the time and labor cost are saved, and the normal operation of the fluidized bed can be effectively ensured.
Optionally, the air distribution plate may be a common air distribution plate, or may be a step air distribution plate. In the present embodiment, a step wind distribution plate is taken as an example for description. Before step S11 is executed, the step air distribution plate needs to be assembled to ensure that the aperture ratio of the air distribution plate at the raw coal feeding end is greater than that of the clean coal discharging end, so as to ensure the coal dressing effect and precision. And then, carrying out air tightness inspection on the assembled air distribution plate to ensure that no side edge air leakage exists in the air distribution plate area of the dry heavy medium fluidized bed, then determining the pressure drop of the air distribution plate in each area of the fluidized bed, and debugging the pressure drop of the air distribution plate in different areas as required to ensure that the pressure drop of the air distribution plate at the raw coal feeding end is small and the pressure drop of the air distribution plate at the clean coal discharging end is large, thereby achieving the step air distribution effect.
Further, the fluidized bed in this embodiment is provided with a plurality of sensors with different functions, so as to at least sense the structural state and/or the operating state information of the air distribution plate and the discharging scraper, so that the processing equipment/processing device (such as the central control equipment, the detection platform, the moving equipment, etc. described above) executing the method of this embodiment obtains the required state information. Specifically, this embodiment still can utilize the sensor on-line measuring to pile up the thickness of material on the grid plate before carrying out the grid plate and detecting, for example the thickness of accumulational waste rock layer, and then adjust waste rock row material scraper blade motor power based on the thickness of this material, make its normal operating. In addition, the torque of the driving motor of the gangue discharge scraper can be detected on line, so that the fluidizing gas speed of the fluidized bed can be controlled based on the torque, and the fluidizing quality of the bed layer can be improved. Optionally, this embodiment also performs online real-time detection of the bed density of the fluidized bed, so that the bed can maintain the optimal sorting density in real time. The sort density may refer to a theoretical sort density determined on a selectable curve based on the gravity cleaned coal ash requirement.
Fig. 2 is a flowchart of an online detection method for an air distribution plate in another embodiment of the present application, and as shown in fig. 2, the method in this embodiment further includes:
step S15: establishing a target model;
step S16: acquiring historical state information of the air distribution plate and the discharge scraper and hole plugging information of the air distribution plate corresponding to the historical state information;
step S17: and training the target model at least based on the historical state information and the hole blocking information to obtain the detection model.
Specifically, in order to realize the online and real-time detection of the grid plate, the detection process is required to be efficient and accurate, so that the detection model is trained in the embodiment to realize the real-time online detection of the grid plate based on the detection model. When the method is applied, a target model needs to be established first, and the target model can be any model for realizing prediction or any model for realizing classification, including a convolutional neural network model, a binary neural network model, and specifically, a multiple linear regression model and the like. After the target model is determined and the target model architecture is obtained, the obtained historical state information of the air distribution plate and the discharging scraper, namely the historical operation information and/or the structural state information of the air distribution plate and the discharging scraper, and the hole blocking information of the air distribution plate corresponding to the historical state information, namely the information of whether the air distribution plate is blocked or not are preprocessed and then input into the target model to train the target model, for example, the target model is trained and learned from a large amount of preprocessed data through a random forest algorithm, and then the detection model which can predict whether the air distribution plate is blocked or not based on the state information of the air distribution plate and the discharging scraper is obtained.
The preprocessing of the historical state information described in the above embodiment and the preprocessing of the currently obtained state information described in step S12 include the same processing steps, which are both used to obtain the target feature data, and the preprocessing process specifically includes:
and extracting the characteristics of the state information based on characteristic engineering to obtain the target characteristic data, wherein the target characteristic data comprises information which is directly influenced by the hole plugging state of the air distribution plate in the state information.
The characteristic engineering is to carry out a series of engineering processing on the original data, refine the original data into characteristics, and use the characteristics as input for algorithms and models. Essentially, feature engineering is a process of representing and exposing data. In actual work, feature engineering aims to remove impurities and redundancy in raw data, and more efficient features are designed to characterize the relationship between the solved problem and the prediction model. In this embodiment, the obtained state information is preprocessed based on the feature engineering, for example, the state information is subjected to feature normalization processing, so as to obtain information directly affected by the hole blocking state of the air distribution plate in the state information, that is, target feature data, such as motor power of the discharging scraper, motor torque, normal operation, pressure drop of the air distribution plate, pressure applied to the air distribution plate, and the like. The target model is trained based on the acquired historical target feature data as input data and the hole blocking condition of the air distribution plate corresponding to the historical target feature data as output data, and the trained detection model also predicts the hole blocking condition of the current air distribution plate based on the input target feature data.
Further, the state information in this embodiment includes one or more of air tightness information of the air distribution plate, pressure drop information, material thickness stacked on the air distribution plate, motor torque information of the discharging scraper, and information of an acting force of the material on the air distribution plate. For example, take place to block up when the grid plate, lead to the unable normal discharge of material, it receives the material influence of piling up on the grid plate to arrange the material scraper blade, and during unable normal row's material, the material is piled up and can is extrudeed the grid plate, make the grid plate produce abnormal pressure, the effort that the grid plate received can surpass the predetermined range promptly (this pressure value can assist and confirm grid plate stifled hole position and stifled hole area), receive this influence simultaneously, arrange the motor torque of material scraper blade, power all produces unusually, lead to not being in required within range, detection model alright this moment predict whether current grid plate blocks up according to the eigenvalue of above-mentioned characteristic.
Optionally, the hole plugging information in this embodiment includes hole plugging area and hole plugging position information. After the detection model is input with target characteristic data, calculation and prediction are carried out according to the target characteristic data so as to obtain information whether the current air distribution plate is blocked or not, if the current air distribution plate is blocked, the area of the blocked holes is output simultaneously, namely, the number of the blocked holes is the total area, and the positions of the blocked holes are output, so that auxiliary workers can quickly determine and find the air distribution plate which is blocked and the positions on the air distribution plate which are blocked, and timely clean the air distribution plate, thereby ensuring the normal operation of the fluidized bed. Through the method of this embodiment, not only can realize the real-time detection in air distribution plate stifled hole, can effectively promote the detection and the maintenance efficiency in air distribution plate stifled hole moreover, saved manpower and time cost, compare in artifical the detection, the precision is also stronger.
Further, in order to enrich the function of the detection model, so that the detection model not only can detect the blockage condition of the air distribution plate, but also can determine the influence of the fluctuation of the bed pressure of the fluidized bed when the air distribution plate is blocked, fig. 3 is a flowchart of an air distribution plate detection method according to another embodiment of the present application, as shown in fig. 3, in the embodiment, when the detection model is trained, the method further includes:
step S18, obtaining a pressure signal of the pressure applied to the fluidized bed layer corresponding to the historical hole plugging information;
the training the target model based on at least the historical state information and the hole plugging information to obtain the detection model comprises:
and S19, training the target model based on the historical state information, the hole blocking information and the pressure signal to obtain the detection model capable of detecting the hole blocking condition of the air distribution plate and the influence of the hole blocking area on the bed pressure fluctuation.
For example, when the fluidized bed operates in historical time, the pressure applied to the bed can be detected through a pressure sensor arranged on the bed, and specifically, whether the pressure applied to the bed is qualified or not can be detected through receiving a pressure signal and utilizing a time domain and a frequency domain of the pressure signal. The time domain and the frequency domain are basic properties of signals, and the mutual influence between the signals and third-party objects such as interconnection lines can be clearly reflected on the basis of the time domain and the frequency domain. When the air distribution plate is blocked, the pressure applied to the bed layer can fluctuate corresponding to the position and the area of the blocked hole, so that when a user collects training data, pressure signals of the pressure applied to the fluidized bed layer corresponding to historical blocked hole information and relevant attribute information of the pressure signals can be collected simultaneously, the pressure signals are integrated into the training data, namely the training data comprise pressure signal information, historical state information and blocked hole information, and the detection model is trained through the training data, so that the detection model can have the capability of detecting the influence of the blocked hole area on the bed pressure fluctuation.
Further, the method in this embodiment further includes:
determining whether the hole plugging information is consistent with the actual hole plugging condition of the air distribution plate or not, and obtaining a corresponding result;
training the detection model based on the target feature data, the hole plugging information and the corresponding results.
For example, when the hole blocking information output by the detection model is checked by a worker on the spot and the data obtained by the detection of the detection model is consistent with the hole blocking condition of the actual air distribution plate, the data input into the detection model and the data output by the detection model can be added into training data to further train the detection model. And only the data with detection errors, namely the hole blocking information output by the detection model is inconsistent with the hole blocking condition of the actual air distribution plate, at the moment, the input data, the output data and the actual hole blocking data of the current time can be input into the detection model so as to further train the detection model, correct the weight of the detection model and refine the detection precision of the detection model.
Optionally, fig. 4 is a flowchart of a method for detecting a wind distribution plate according to another embodiment of the present application, in order to reduce a detection load of a detection model and achieve faster and targeted detection of blockage of the wind distribution plate, in this embodiment, before performing detection, that is, before preprocessing currently obtained state information to obtain target feature data, as shown in fig. 4, the method further includes:
step S20, determining the air tightness of the air distribution plate and the pressure drop conditions of different areas based on the state information, and simultaneously determining the motor power and the motor torque of the discharging scraper;
and step S21, when one or more of the air tightness, the pressure drop condition of different areas, the motor power and the motor torque is determined to be abnormal, adjusting the air distribution plate and/or the discharging scraper plate based on abnormal data and abnormal positions, and preprocessing the state information to obtain the target characteristic data if the data generated at the abnormal positions after adjustment are still abnormal.
For example, before the air distribution plate is subjected to detection, the air tightness and pressure drop condition of the air distribution plate can be determined based on the obtained state information, and the motor power and the motor torque of the discharging scraper plate are determined at the same time. That is, functional devices such as an air distribution plate of the fluidized bed are adjusted through investigation, if the condition is improved after adjustment, it is indicated that the air distribution plate is not blocked, and if the condition cannot be improved after adjustment, it is determined that the problem is the air distribution plate, and hole blocking detection needs to be performed on the problem. Therefore, the processing load of the detection model can be reduced, the operation of the fluidized bed can be adjusted more quickly, and the normal operation of the whole fluidized bed is ensured.
Specifically, fig. 5 is a flow chart of an actual application of the online detection method for the air distributor in the embodiment of the present application, and as shown in fig. 5, in order to better explain the method of the embodiment, the following description is made in more detail with reference to specific embodiments:
taking the fluidized bed as a dry dense medium fluidized bed and the air distribution plate as a step air distribution plate as an example, firstly, the step air distribution plate needs to be assembled, and the opening rate of the air distribution plate at the raw coal feeding end is ensured to be greater than that of the clean coal discharging end;
carrying out air tightness inspection on the assembled step air distribution plate to ensure that no side edge air leakage exists in the air distribution plate area of the dry-method heavy medium fluidized bed;
pressure drops of the air distribution plates in different areas are adjusted, so that the pressure drop of the air distribution plate at the raw coal feeding end is small, the pressure drop of the air distribution plate at the clean coal discharging end is large, and a step air distribution effect is achieved;
after the step air distribution plate is assembled, the thickness of a gangue layer accumulated on the air distribution plate can be detected on line by using a sensor, and the power of a motor of a gangue discharge scraper blade is adjusted to enable the gangue discharge scraper blade to operate normally;
the torque of a driving motor of the waste discharge scraper is detected on line, the fluidization gas speed is controlled, and the fluidization quality of a bed layer is improved;
performing on-line real-time detection on the density of the bed layer to enable the bed layer to reach the optimal sorting density;
when the air distribution plate is blocked, acquiring torque information of a motor of a gangue discharge scraper;
acquiring the acting force of the gangue layer on the step air distribution plate by using a strain sensor arranged in the air distribution plate, and acquiring information of a blocking position and area according to the acting force;
acquiring original data of influences of the area and the position of the plugged hole on time domain and frequency domain analysis of the bed pressure signal;
characterizing the obtained data based on a characteristic project, namely preprocessing the data;
training and learning the machine from a large amount of preprocessed data by adopting a random forest algorithm, for example, training a target model by taking motor torque, detection data of a wind distribution plate strain sensor, hole plugging area and hole plugging position data, and data of influence of the hole plugging area and the hole plugging position data on bed pressure fluctuation as training data;
correcting the preliminarily established target model, dividing the obtained training data into a training set and a test set, and connectingAnalyzing the result of the corrected target model based on the training set and the test set, and determining the coefficient R2Judging the effectiveness of the target model, finally forming a stable and reliable detection model, wherein the detection model can be seen as being formed by combining a motor torque model, a bed pressure fluctuation model and a grid plate strain sensor model as shown in figure 5, the motor torque model is used for determining whether a grid plate is blocked according to motor torque characteristic parameters, the bed pressure fluctuation model is used for determining the influence of bed pressure according to bed pressure characteristic parameters and further determining whether the grid plate is blocked, the grid plate strain sensor model is used for obtaining material accumulation thickness parameters on the grid plate, determining whether the grid plate is blocked according to the parameters and can assist in determining the blocking position and area, the detection model comprehensively analyzes the detection results of the three sub-models to determine whether the grid plate is blocked, and if the grid plate is blocked, determining the blocking position and area information, that is, the detection model in this embodiment has the functions of the three models at the same time, so as to further realize the blockage positioning of the air distribution plate, of course, the sub-model is only the internal structure of the detection model, and the detection model in this embodiment are the same detection model;
when the detection model is used for online detection of the blockage condition of the air distribution plate, the relevant information of the basic data obtained online in real time is used as input characteristics, namely, the obtained state information is processed to form target characteristic data which is used as input data of the model;
inputting the input features into a trained detection model;
obtaining the blocking information of the grid plate fed back by the detection model, and enabling a worker to quickly and accurately position the area and the position of the blocked hole on the grid plate based on the blocking information;
and characterizing the basic data obtained on line in real time, and inputting the characterized data and the data output by the detection model into the detection model again to retrain the detection model, so as to refine the detection accuracy of the detection model for the blockage positioning of the air distribution plate.
Fig. 6 is a schematic hardware structure diagram of an online detecting system 600 for a wind distribution plate in an embodiment of the present application, as shown in fig. 6, including:
at least one processor 602; and the number of the first and second groups,
a memory 604 communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the processor, and the instructions are executed by the at least one processor to implement the online detection method of the air distribution plate in any of the above embodiments.
Referring to fig. 6, the grid plate online detection system 600 may include one or more of the following components: processing component 602, memory 604, power component 606, multimedia component 608, audio component 610, input/output (I/O) interface 612, sensor component 614, and communication component 616.
The processing component 602 generally controls the overall operation of the air distribution plate online detection system 600, such as obtaining status information of the air distribution plate and the discharge scraper in the fluidized bed, preprocessing the status information to obtain target characteristic data, and the like. The processing component 602 may include one or more processors 620 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 602 can include one or more modules that facilitate interaction between the processing component 602 and other components. For example, the processing component 602 can include a multimedia module to facilitate interaction between the multimedia component 608 and the processing component 602.
The memory 604 is configured to store various types of data to support the operation of the grid plate online detection system 600. Examples of such data include instructions for any application or method operating on the grid board online detection system 600, such as text, pictures, video, and so forth. The memory 604 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 606 provides power to the various components of the grid plate online detection system 600. Power components 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power supplies for in-vehicle control system 600.
The multimedia component 608 includes a screen that provides an output interface between the grid plate online detection system 600 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 608 may also include a front facing camera and/or a rear facing camera. When the air distribution board online detection system 600 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 610 is configured to output and/or input audio signals. For example, the audio component 610 includes a Microphone (MIC) configured to receive an external audio signal when the windguard online detection system 600 is in an operational mode, such as an alarm mode, a recording mode, a voice recognition mode, and a voice output mode. The received audio signal may further be stored in the memory 604 or transmitted via the communication component 616. In some embodiments, audio component 610 further includes a speaker for outputting audio signals.
The I/O interface 612 provides an interface between the processing component 602 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 614 includes one or more sensors for providing various aspects of condition assessment for the grid online detection system 600. For example, the sensor component 614 may include an acoustic sensor. In addition, the sensor component 614 can detect the open/close state of the air distribution plate online detection system 600, the relative positioning of the components, for example, the components are a display and a keypad of the air distribution plate online detection system 600, the sensor component 614 can also detect the operation state of the air distribution plate online detection system 600 or one component of the air distribution plate online detection system 600, such as the operation state, the structural state, the operation state of a discharging scraper, and the like of the air distribution plate, the orientation or acceleration/deceleration of the air distribution plate online detection system 600, and the temperature change of the air distribution plate online detection system 600. The sensor assembly 614 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, a material stack thickness sensor, or a temperature sensor.
The communication component 616 is configured to enable the wind panel online detection system 600 to provide a wired or wireless communication capability with other devices and cloud platforms. The wind distribution plate online detection system 600 may access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 616 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 616 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the air distribution plate online detection system 600 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components, and is configured to perform the air distribution plate online detection method described in any of the above embodiments.
The present application further provides a computer-readable storage medium, and when instructions in the storage medium are executed by a processor corresponding to the air distribution plate online detection system, the air distribution plate online detection system can implement the air distribution plate online detection method described in any of the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. An online detection method for a wind distribution plate is characterized by comprising the following steps:
at least obtaining state information of an air distribution plate and a discharging scraper in the fluidized bed, wherein the state information comprises operation state information and/or structure state information;
preprocessing the state information to obtain target characteristic data;
inputting the target characteristic data into a detection model, wherein the detection model is at least used for detecting the hole blocking condition of the air distribution plate;
and obtaining hole blocking information which is fed back by the detection model and related to the air distribution plate.
2. The method of claim 1, further comprising:
establishing a target model;
acquiring historical state information of the air distribution plate and the discharge scraper and hole plugging information of the air distribution plate corresponding to the historical state information;
and training the target model at least based on the historical state information and the hole blocking information to obtain the detection model.
3. The method of claim 2, wherein the status information comprises one or more of air tightness information of the grid plate, pressure drop information, thickness of material stacked on the grid plate, motor torque information of the discharge scraper, and force information of the material stacked on the grid plate.
4. The method of claim 3, wherein the plugged hole information comprises plugged hole area and plugged hole location information.
5. The method of claim 4, wherein the preprocessing the state information to obtain target feature data comprises:
and extracting the characteristics of the state information based on characteristic engineering to obtain the target characteristic data, wherein the target characteristic data comprises information which is directly influenced by the hole plugging state of the air distribution plate in the state information.
6. The method of claim 2, further comprising:
obtaining a pressure signal of the pressure applied to the fluidized bed layer corresponding to the historical hole plugging information;
the training the target model based on at least the historical state information and the hole plugging information to obtain the detection model comprises:
and training the target model based on the historical state information, the hole plugging information and the pressure signal to obtain a detection model capable of detecting the hole plugging condition of the air distribution plate and the influence of a hole plugging area on the bed pressure fluctuation.
7. The method according to claim 1, wherein before preprocessing the state information to obtain target feature data, further comprising:
determining the air tightness and pressure drop condition of the air distribution plate based on the state information, and simultaneously determining the motor power and the motor torque of the discharging scraper plate;
and when one or more of the air tightness, the pressure drop conditions of different areas, the motor power and the motor torque are determined to be abnormal, adjusting the air distribution plate and/or the discharging scraper plate based on abnormal data and abnormal positions, and preprocessing the state information to obtain the target characteristic data if the data generated at the abnormal positions after adjustment are still abnormal.
8. The method of claim 1, further comprising:
determining whether the hole plugging information is consistent with the actual hole plugging condition of the air distribution plate or not, and obtaining a corresponding result;
training the detection model based on the target feature data, the hole plugging information and the corresponding results.
9. The utility model provides a grid plate on-line measuring system which characterized in that includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the one processor to perform the method of online test of a wind panel according to any one of claims 1 to 8.
10. A computer-readable storage medium, wherein when executed by a processor corresponding to the air distribution plate online detection system, the instructions enable the air distribution plate online detection system to implement the air distribution plate online detection method according to any one of claims 1 to 8.
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