Comb type tobacco clamp quantitative intelligent operation table

Abstract

The quantitative intelligent operation table comprises a support, a table top and a rotating frame, one end of the rotating frame is connected with the rear end of the support through a first rotating shaft, a pressing rod is arranged on the rotating frame, and the two ends of the pressing rod are connected with a left rod and a right rod of the rotating frame respectively; the two ends of the operation table are provided with guide grooves used for containing tobacco clamps, one of the two guide rods forming the same guide groove is fixed to the support, and the other guide rod is fixed to the table top. The tabletop is rotationally connected with the left and right ends of the bracket through second rotating shafts; a weighing sensor is arranged on the support below the table top and located on the rear side of the second rotating shaft, and the lower surface of the table top abuts against a loading platform of the weighing sensor. A display screen is installed at one end of the support, connected with the weighing sensor and used for displaying the quality of the laid tobacco leaves in real time.

Description

Technical Field

[0001] The present application relates to the technical field of tobacco leaf processing, and in particular to a comb-type tobacco clip quantitative intelligent operating table. Background Art

[0002] With the widespread promotion and trial use of comb-type tobacco clips in major tobacco-growing areas, the standardization of tobacco harvesting and tobacco weaving has been greatly improved, especially in large-scale cluster curing houses such as cooperative curing factories, which fully use tobacco comb clips to weave and cure tobacco leaves. The standardized and normative use of tobacco comb clips plays a vital role in the quality of tobacco weaving and baking.

[0003] However, the requirements for the use of tobacco clips vary from region to region, and different tobacco weavers have different control over the quantity and weight of the tobacco leaves weaved into the clips. This results in different densities of the tobacco leaves weaved into the clips, which in turn affects the baking quality. Summary of the invention

[0004] The purpose of the present application is to provide a comb-type tobacco clip quantitative intelligent operating table to solve the problem in the prior art that different regions have different requirements for the use of tobacco comb clips, and different tobacco editors have different control over the quantity and weight of tobacco leaves, which results in different density of tobacco clips and affects the baking quality.

[0005] To achieve the above-mentioned purpose, the embodiment of the present application provides a comb-type cigarette clip quantitative intelligent operation table, including: a bracket, a table top, and a rotating frame, wherein:

[0006] One end of the rotating frame is connected to the rear end of the bracket through a first rotating shaft, and a pressure rod is provided on the rotating frame, and the two ends of the pressure rod are respectively connected to the left and right rods of the rotating frame;

[0007] Guide grooves for placing cigarette clips are provided at both ends of the operating table, one of the two guide rods constituting the same guide groove is fixed on the bracket, and the other is fixed on the table top;

[0008] The table top is rotatably connected to the left and right ends of the bracket via a second rotating shaft;

[0009] A weighing sensor is provided on the bracket below the table, the weighing sensor is located at the rear side of the second rotating shaft, and the lower surface of the table abuts against the loading platform of the weighing sensor;

[0010] A display screen is installed at one end of the bracket, and the display screen is connected to a weighing sensor for displaying the quality of the laid tobacco leaves in real time.

[0011] Optionally, it also includes: an alarm,

[0012] The alarm is connected to the weighing sensor.

[0013] Optionally, the weighing sensor is connected to the cloud.

[0014] Optionally, it also includes: a camera,

[0015] The camera is installed above or at one end of the bracket.

[0016] Optionally, the camera is connected to the cloud.

[0017] Optionally, the weighing sensor includes a loading platform, an elastomer, a sensing element, a signal processing circuit, a connecting piece and a data output interface. The elastomer is arranged below the loading platform. The sensing element includes a strain gauge or a piezoresistive sensor and is fixed on the elastomer. The signal processing circuit is electrically connected to the sensing element and the data output interface. The upper end of the connecting piece is connected to the housing of the weighing sensor, and the lower end is connected to the bracket.

[0018] The embodiments of the present application have the following advantages:

[0019] The embodiment of the present application provides a comb-type quantitative intelligent operating table for cigarette clips, comprising: a bracket, a table top, and a rotating frame, wherein one end of the rotating frame is connected to the rear end of the bracket through a first rotating shaft, and a pressure rod is provided on the rotating frame, and the two ends of the pressure rod are respectively connected to the left and right rods of the rotating frame; guide grooves for placing cigarette clips are provided at both ends of the operating table, and one of the two guide rods constituting the same guide groove is fixed to the bracket, and the other is fixed to the table top; the table top is rotatably connected to the left and right ends of the bracket through a second rotating shaft; a weighing sensor is provided on the bracket below the table top, and the weighing sensor is located on the rear side of the second rotating shaft, and the lower surface of the table top is abutted against the loading platform of the weighing sensor; a display screen is installed at one end of the bracket, and the display screen is connected to the weighing sensor for real-time display of the quality of the laid tobacco leaves.

[0020] Compared with the prior art, the comb-type tobacco clamp quantitative intelligent operating table provided by the above technical solution can take the following measures to make up for the shortcomings in baking production management without changing the specifications and dimensions of the original operating table. By improving the comb-type tobacco clamp operating table and adding a quantitative weighing function, it is equipped with IoT functions such as data uploading platform statistics, and the quantitative standard of fresh tobacco leaves before clipping is achieved, so that the quantitative quantity of each tobacco rod is unified, and the quality and homogeneity of flue-cured tobacco are further improved. This solves the problem that the various regions in the prior art have different requirements for the use of tobacco combs and clamps, and different tobacco clippers have different control over the quantity and weight of clipped tobacco leaves, which results in different density of tobacco clips and affects the baking quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings described below are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0022] Figure 1 A schematic diagram of the three-dimensional structure of a comb-type cigarette holder quantitative intelligent operating table provided in at least one embodiment of the present application;

[0023] Figure 2 A side view of a comb-type cigarette holder quantitative intelligent operating table provided in at least one embodiment of the present application. DETAILED DESCRIPTION

[0024] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0025] In the description of the present application, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance. Unless otherwise clearly specified and limited, the terms "set", "install", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0026] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0027] The present application embodiment provides a comb-type cigarette holder quantitative intelligent operation table, referring to Figure 1 to Figure 2 ,include:

[0028] Bracket 1, table 2, rotating frame 3, wherein:

[0029] One end of the rotating frame 3 is connected to the rear end of the bracket 1 through a first rotating shaft 5. A pressure rod 4 is provided on the rotating frame 3. The two ends of the pressure rod 4 are respectively connected to the left and right rods of the rotating frame 3.

[0030] Guide grooves for placing cigarette clips are provided at both ends of the operating table, one of the two guide rods 6 constituting the same guide groove is fixed on the bracket 1, and the other is fixed on the table top 2;

[0031] The tabletop 2 is rotatably connected to the left and right ends of the bracket 1 via a second rotating shaft 7;

[0032] A weighing sensor 8 is provided on the bracket 1 below the table 2. The weighing sensor 8 is located at the rear side of the second rotating shaft 7. The lower surface of the table 2 abuts against the loading platform of the weighing sensor 8.

[0033] A display screen 9 is installed at one end of the bracket 1, and the display screen 9 is connected to a weighing sensor 8 for displaying the quality of the laid fresh tobacco leaves in real time.

[0034] In some embodiments, it also includes: an alarm,

[0035] The alarm is connected to the weighing sensor 8. When the acquired mass exceeds a preset value, an alarm sounds.

[0036] In some embodiments, the weighing sensor 8 is connected to the cloud to count the tobacco leaf harvest of each management area through the cloud.

[0037] In some embodiments, the invention further comprises: a camera 10,

[0038] The camera 10 is installed above or at one end of the bracket 1. It is used to take photos and identify the grouped tobacco leaves on the table 2 in real time, and use a visual analysis model to evaluate the quality of fresh tobacco leaves based on the obtained recognition image data.

[0039] In some embodiments, the camera 10 is connected to the cloud to collect and record the quality data of fresh tobacco leaves in each management area.

[0040] In some embodiments, the weighing sensor 8 includes a loading platform, an elastomer, a sensing element, a signal processing circuit, a connector and a data output interface. The elastomer is arranged below the loading platform. The sensing element includes a strain gauge or a piezoresistive sensor and is fixed on the elastomer. The signal processing circuit is electrically connected to the sensing element and the data output interface. The upper end of the connector is connected to the housing of the weighing sensor 8, and the lower end is connected to the bracket 1.

[0041] Specifically, the weighing sensor 8 is a key component in the comb-type tobacco clip quantitative operation table, responsible for real-time and accurate measurement of the weight of the tobacco leaves on the table 2. The following is a detailed structural description of the weighing sensor 8:

[0042] Elastomer: The elastomer is the core component of the weighing sensor 8, and is usually made of materials with high elasticity, high strength and good stability, such as metal alloys or special plastics. When the tobacco leaves are placed on the table 2, the gravity on the table 2 will be transferred to the elastomer, causing it to produce corresponding elastic deformation. The degree of deformation of the elastomer is proportional to the weight it bears, which is the basic principle of the weighing sensor 8 to measure weight.

[0043] Sensing element: The sensing element usually adopts electronic components such as strain gauges or piezoresistive sensors, which are tightly fixed on the elastic body. When the elastic body is deformed by external force (i.e. the weight of tobacco leaves), the sensing element will also deform. The deformation of the sensing element will cause its resistance value to change, and this change is proportional to the weight it bears.

[0044] Signal processing circuit: The signal processing circuit is an important part of the weighing sensor 8. It is responsible for receiving the electrical signal output by the sensor element and amplifying, filtering and digitizing it. The signal processing circuit can convert weak electrical signals into measurable voltage or current signals for subsequent data processing and analysis.

[0045] Housing and connectors: The weighing sensor 8 usually has a sturdy housing to protect the internal elastic body, sensor element and signal processing circuit from the influence of the external environment. The connector is used to connect the weighing sensor 8 with other parts of the operating table (such as the table 2) to ensure that the sensor can work stably.

[0046] Temperature compensation and calibration: Since temperature changes may affect the measurement accuracy of the weighing sensor 8, a temperature compensation function is usually added to the signal processing circuit to eliminate the influence of temperature on the measurement results. At the same time, in order to ensure the accuracy of the measurement results, the weighing sensor 8 needs to be calibrated regularly to ensure that its measurement accuracy meets the requirements.

[0047] Data output and interface: The weighing sensor 8 usually has a digital or analog signal output interface for transmitting the measurement results to the control system or display device of the operating console. The interface type can be selected according to specific needs, such as RS-232, RS-485, USB, Ethernet, etc.

[0048] The method for using the operating console provided in the above embodiment includes:

[0049] First, turn the table 2 to a horizontal state and abut it against the loading platform of the weighing sensor 8 below, and place the beam of the cigarette holder in the guide groove;

[0050] After the beam is fixed, the tobacco leaves are spread flat on the table 2 above the beam, and the display screen 9 dynamically displays the weight of the tobacco leaves;

[0051] When the weight and thickness of the tobacco leaves laid reach the set value, the laying can be stopped;

[0052] Insert the needle beam of the cigarette clip into the tobacco leaves on the grinding beam through the guide groove;

[0053] Press down the rotating frame 3 to make the pressure rod 4 act on the needle beam until the needle beam is completely locked in the grinding beam to realize the cigarette loading operation.

[0054] In some embodiments, the use of a visual analysis model to evaluate the quality of fresh tobacco leaves based on the acquired recognition image data includes:

[0055] Load and preprocess tobacco leaf recognition image data;

[0056] Establish a model framework to define the visual analysis model architecture for tobacco leaf recognition;

[0057] Training Model Train the tobacco leaf recognition visual analysis model;

[0058] Evaluate the performance of the tobacco leaf recognition visual analysis model and obtain a visual analysis model that meets the requirements.

[0059] During the model training process, machine learning algorithms are used to optimize the model to improve the accuracy and stability of tobacco leaf recognition. At the same time, during the use of the model, the recognition precision and accuracy are improved through continuous learning and adjustment.

[0060] Specifically: Phase 1: Fusion algorithm, model building

[0061] Regarding the equipment, it is particularly important to note that due to the differences in camera parameters and fill light colors that lead to differences in imaging effects, in order to avoid the loss of early data accumulation caused by subsequent camera model changes, the camera equipment selection work needs to be completed at the beginning of model establishment. In addition, it is also necessary to focus on issues such as equipment installation location, shooting angle, and viewing range, and try to complete them in the early stage to avoid data waste caused by later adjustments.

[0062] In terms of algorithms, tobacco leaf recognition will be based on convolutional neural networks to achieve visual analysis in the tobacco leaf recognition process. Convolutional neural networks are a type of feedforward neural network that includes convolution calculations and has a deep structure. It is one of the representative algorithms of deep learning.

[0063] The process of establishing a visual analysis model is as follows:

[0064] Step 1: Load and preprocess tobacco leaf recognition image data

[0065] First, the construction of the deep learning model for visual analysis of tobacco leaf recognition images is very critical. If there are a large number of images in the training set, the image classification model will have a greater chance of achieving better classification results and better visual analysis accuracy. In addition, depending on the framework used and the dimension of the data, the effect will also be different.

[0066] In short, the model is trained on the training set and then validated on the validation set. If the results on the validation set are satisfactory, it can be used to predict the data of the test set.

[0067] Step 2: Establish the model framework and define the visual analysis model architecture for tobacco leaf recognition

[0068] This is another important step in the process of building a deep learning model. In this process, you need to think about several questions: How many convolutional layers are needed? What is the activation function of each layer? How many hidden units are there in each layer? But these are basically the hyperparameters of the model, and they play an important role in the prediction results. How to determine the values ​​of these hyperparameters? One way is to choose these values ​​based on existing research. Another idea is to keep trying these values ​​until you find the best one, but this can be a very time-consuming process.

[0069] Step 3: Training the model Training the tobacco leaf recognition visual analysis model

[0070] To train the model, you need training images and their true labels, validation set images and their true labels, and you need to define the number of iterations.

[0071] Step 4: Evaluate the performance of the visual analysis model for tobacco leaf quality recognition

[0072] Finally, we load the test data (images) and complete the preprocessing steps. We then use the trained model to predict the categories of these images, and the final results will indicate the progress of the model building.

[0073] Phase 2: Results verification, machine learning

[0074] Based on the completion of the first stage, the second stage will enter the results verification stage. During the tobacco leaf identification process, visual analysis of the tobacco leaf identification images will be carried out, and the key factors of tobacco leaf color and state will be integrated to correct the tobacco leaf identification (in addition, real-time data for tobacco leaf quality identification will be generated to grasp the correlation between tobacco leaf identification quality and provide data support for subsequent real-time adjustments). After the tobacco leaf identification is completed, the overlap of the real tobacco leaves generated or corrected by visual analysis will be calculated to obtain the final tobacco leaf evaluation information. When the evaluation is high, machine learning will be started to correct self-operation problems and correct image labels with misjudgments. The recognition precision and accuracy will be continuously improved until the overlap with 90% of the high-scoring tobacco leaf identification batches is higher than 95%. The corrected visual analysis model is obtained, and then the third stage of tobacco leaf identification visual analysis can be entered.

[0075] Phase 3: Automatic tobacco leaf recognition

[0076] The third phase will be dedicated to the realization of automated tobacco leaf identification. First, in order to ensure the stability of tobacco leaf identification, the frequency of image recognition visual analysis and tobacco leaf identification parameter reporting will be consistent in the early stage, and the correction operation will be realized once every preset time. The accuracy is still considered, and multiple result calibrations are carried out. Corrections are made at a preset number of times. The correction results are pushed to the front end for manual confirmation. After confirmation, they are sent to the controller for correction operations. If there are manual objections and the record is not executed, the real-time quality of tobacco leaf identification is identified, and the real-time quality evaluation information is integrated to analyze the reasons for non-execution. If there are problems with the correction, machine learning is started, and the correction operations under the same factors are subsequently improved. If the failure to perform the operation leads to a decline in the quality of tobacco leaf identification or a poor final evaluation result, the learning of subsequent tobacco leaf identification quality recovery operations will be strengthened in combination with this batch.

[0077] Phase 4: Blockchain Technology Application

[0078] Establish data centers and apply blockchain technology to improve data reliability and ensure data security.

[0079] Specifically, a data center is established to store and manage data such as tobacco leaf harvest volume and grade. The data center uses high-performance servers and storage devices to ensure data reliability and security. Blockchain technology is used to improve data reliability and ensure the integrity and non-tamperability of data during transmission and storage. At the same time, the distributed storage and encryption technology of blockchain is used to ensure data security.

[0080] In summary, compared with the prior art, the comb-type cigarette clip quantitative intelligent operating table provided in the present application can take the following measures to make up for the shortcomings in baking production management without changing the specifications and dimensions of the original operating table.

[0081] By improving the comb-type tobacco clamp operating table and adding a quantitative weighing function, it is equipped with IoT functions such as data uploading platform statistics, and the quantitative standard of fresh tobacco leaves before clamping can be achieved, so that the quantitative quantity of each tobacco rod can be unified, and the quality and homogeneity of flue-cured tobacco can be further improved. It is only necessary to modify and process the existing operating table. You can choose the most widely used specifications and models of operating tables on the market to modify and add weighing sensors, and add a display screen to display the weight of the laid tobacco leaves in real time. It is also equipped with an alarm to realize the over-quantity alarm function.

[0082] An installation position is added above or on the side of the operating table, and a visual acquisition device (camera) is installed. A special camera that matches the baking system is used to take real-time photos of the grouped tobacco leaves for evaluation and filing. The visually acquired image data can be connected and uploaded in conjunction with the baking system. The quality of tobacco leaves can be statistically summarized through the cloud and provided to the baker and baking system for reference to the corresponding baking curve.

[0083] Note that, unless otherwise directly stated, all features disclosed in this specification (including any attached claims, abstracts and drawings) may be replaced by alternative features for achieving the same, equivalent or similar purposes. Therefore, unless otherwise explicitly stated, each feature disclosed is only an example of a group of equivalent or similar features. Where used, further, preferably, further and more preferably are simple beginnings for elaborating another embodiment based on the aforementioned embodiment, and the content of the further, preferably, further or more preferably followed by the combination with the aforementioned embodiment constitutes a complete construction of another embodiment. Several further, preferably, further or more preferably settings following the same embodiment can be arbitrarily combined to form another embodiment.

[0084] In the implementation of functions and steps, the corresponding functions and steps in various embodiments may also occur in a different order than shown. For example, two consecutive functions and steps may actually be performed or implemented substantially in parallel, and they may sometimes be performed or implemented in the opposite order, depending on the functions involved.

[0085] Although the present application has been described in detail above with general descriptions and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements may be made to the present application. Therefore, these modifications or improvements made without departing from the spirit of the present application are within the scope of protection claimed by the present application.

Claims

1. A comb-type cigarette holder quantitative intelligent operation table, characterized in that: include: Brackets, tables, rotating racks, including: One end of the rotating frame is connected to the rear end of the bracket through a first rotating shaft, and a pressure rod is provided on the rotating frame, and the two ends of the pressure rod are respectively connected to the left and right rods of the rotating frame; Guide grooves for placing cigarette clips are provided at both ends of the operating table, one of the two guide rods constituting the same guide groove is fixed on the bracket, and the other is fixed on the table top; The table top is rotatably connected to the left and right ends of the bracket via a second rotating shaft; A weighing sensor is provided on the bracket below the table, the weighing sensor is located at the rear side of the second rotating shaft, and the lower surface of the table abuts against the loading platform of the weighing sensor; A display screen is installed at one end of the bracket, and the display screen is connected to a weighing sensor for displaying the quality of the laid tobacco leaves in real time.

2. The comb-type cigarette holder quantitative intelligent operation table according to claim 1 is characterized in that: Also includes: Alarm, The alarm is connected to the weighing sensor.

3. The comb-type cigarette holder quantitative intelligent operation table according to claim 1 is characterized in that: The weighing sensor is connected to the cloud.

4. The comb-type cigarette holder quantitative intelligent operation table according to claim 1 is characterized in that: Also includes: Camera, The camera is installed above or at one end of the bracket.

5. The comb-type cigarette holder quantitative intelligent operation table according to claim 4 is characterized in that: The camera is connected to the cloud.

6. The comb-type cigarette holder quantitative intelligent operation table according to claim 1 is characterized in that: The weighing sensor includes a loading platform, an elastomer, a sensing element, a signal processing circuit, a connector and a data output interface. The elastomer is arranged below the loading platform. The sensing element includes a strain gauge or a piezoresistive sensor and is fixed on the elastomer. The signal processing circuit is electrically connected to the sensing element and the data output interface. The upper end of the connector is connected to the housing of the weighing sensor, and the lower end is connected to the bracket.

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