Method, device and equipment for evaluating production quality of cigarette product packaging machine

Abstract

The invention discloses a method, a device and equipment for evaluating the production quality of a cigarette product packaging machine. The method comprises the following steps: acquiring running temperature data of the cigarette packaging machine; obtaining an operating temperature score according to the operating temperature data and a preset scoring rule; according to the operation temperature score and a preset risk evaluation weight, a production quality score is obtained, and the preset risk evaluation weight is obtained by dynamically optimizing a weight model according to weight influence factors; and obtaining a production quality evaluation score of the cigarette product packaging machine according to the production quality score. According to the scheme, the evaluation content is formulated, the corresponding weight is dynamically given according to the algorithm, the final score is calculated in a weight distribution mode, the quality level is reflected through a quantitative result, the requirements for timely, accurate and real quality feedback are met, and the detection efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of product quality detection, and in particular to a method, device and equipment for evaluating the production quality of a cigarette product packaging machine. Background Art

[0002] After changing the brand of cigarette packaging equipment, packaging quality can be significantly impacted by the different packaging materials. This requires an evaluation of the equipment. A common approach involves running the equipment normally and having quality inspectors randomly check the appearance and internal quality of cigarette products. This destructive testing wastes raw materials and impacts production schedules. Manual inspection is inefficient (each person can inspect ≤ 2,000 packs per day), and static weighting cannot adapt to changes in material properties. Summary of the Invention

[0003] In view of the above, the present invention aims to provide a method, device and equipment for evaluating the production quality of a cigarette product packaging machine to solve the above-mentioned technical problems.

[0004] The technical solution adopted in the present invention is as follows:

[0005] The present invention provides a method for evaluating the production quality of a cigarette product packaging machine, which includes:

[0006] Obtaining operating temperature data of the cigarette packaging machine, the operating temperature data including at least the temperature of the small box soldering iron, the heat-sealing temperature of the long side of the biaxially oriented polypropylene film of the small box, the heat-sealing temperature of the bottom of the biaxially oriented polypropylene film of the small box, the heat-sealing temperature of the top of the biaxially oriented polypropylene film of the small box, the temperature of the small box cosmetic device, the temperature of the soldering iron of the long side of the biaxially oriented polypropylene film of the strip box, the soldering iron temperature of the end of the biaxially oriented polypropylene film of the strip box, and the temperature of the cosmetic device of the strip box;

[0007] Obtaining an operating temperature score based on the operating temperature data and a preset scoring rule;

[0008] Obtaining a production quality score based on the operating temperature score and a preset risk assessment weight, wherein the preset risk assessment weight is obtained by dynamically optimizing a weight model based on weight influencing factors;

[0009] A production quality evaluation score of the cigarette product packaging machine is obtained based on the production quality score.

[0010] Optionally, obtain operating temperature data of the cigarette packaging machine, including:

[0011] The current temperature data of the cigarette packaging machine is obtained by distributing temperature sensors installed at multiple locations of the cigarette packaging machine;

[0012] The current temperature data is preprocessed to obtain operating temperature data.

[0013] Optionally, obtaining an operating temperature score based on the operating temperature data and a preset scoring rule includes:

[0014] Get the preset scoring rule table;

[0015] An operating temperature score is obtained according to a mapping relationship between the preset scoring rule table and the operating temperature data.

[0016] Optionally, a production quality score is obtained based on the operating temperature score and a preset risk assessment weight, including:

[0017] The product of the operating temperature score and the preset risk assessment weight is summed to obtain the production quality score.

[0018] Optionally, the preset risk assessment weights are dynamically optimized based on weight influencing factors, including:

[0019] Establish expert weight model;

[0020] The expert weight model is dynamically optimized through subjective weight influencing factors and objective weight influencing factors to obtain preset risk assessment weights.

[0021] Optionally, the preset risk assessment weight is:

[0022] ;

[0023] in, ;

[0024] in, Group risk assessment matrix; is the weight influencing factor; is the objective weight influencing factor; is the subjective weight influencing factor; is the balance parameter between objective and subjective coordination, .

[0025] Optionally, obtaining a cigarette product packaging machine production quality evaluation score based on the production quality score includes:

[0026] The production quality scores of cigarette packaging machines are summed to obtain the production quality evaluation score of cigarette product packaging machines.

[0027] The present invention also provides a device for evaluating the production quality of a cigarette product packaging machine, comprising:

[0028] An acquisition module, used to obtain operating temperature data of the cigarette packaging machine;

[0029] a processing module, configured to obtain an operating temperature score based on the operating temperature data and a preset scoring rule;

[0030] Obtaining a production quality score based on the operating temperature score and a preset risk assessment weight, wherein the preset risk assessment weight is obtained by dynamically optimizing a weight model based on weight influencing factors;

[0031] A production quality evaluation score of the cigarette product packaging machine is obtained based on the production quality score.

[0032] The present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program executes the method described above when executed by the processor.

[0033] The present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0034] The above solution of the present invention includes at least the following beneficial effects:

[0035] The above-mentioned solution of the present invention obtains operating temperature data of the cigarette packaging machine; determines an operating temperature score based on the operating temperature data and preset scoring rules; determines a production quality score based on the operating temperature score and preset risk assessment weights, wherein the preset risk assessment weights are obtained by dynamically optimizing a weight model based on weight influencing factors; and determines a production quality evaluation score for the cigarette packaging machine based on the production quality score. The solution of the present invention formulates evaluation content and dynamically assigns corresponding weights according to the above-mentioned algorithm. The final score is calculated by weight distribution, and the quantitative results reflect the quality level, meeting the requirements of timely, accurate, and authentic quality feedback, thereby improving detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described below with reference to the accompanying drawings, in which:

[0037] Figure 1 This is a flow chart of a method for evaluating production quality of a cigarette product packaging machine provided by an embodiment of the present invention.

[0038] Figure 2 A schematic diagram of a module of a device for evaluating the production quality of a cigarette product packaging machine provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0040] The present invention proposes an embodiment of a method for evaluating the production quality of a cigarette product packaging machine. Specifically, Figure 1 shown, including:

[0041] Step 11, obtaining operating temperature data of the cigarette packaging machine;

[0042] Step 12: Obtain an operating temperature score based on the operating temperature data and a preset scoring rule;

[0043] Step 13: obtaining a production quality score based on the operating temperature score and a preset risk assessment weight, wherein the preset risk assessment weight is obtained by dynamically optimizing a weight model based on weight influencing factors;

[0044] Step 14: Obtain a production quality evaluation score of the cigarette product packaging machine based on the production quality score.

[0045] The main function of a cigarette packaging machine is to wrap cigarettes with trademark paper. Taking hard-pack cigarettes as an example, the materials required for wrapping and forming are outer trademark paper, inner cardboard, and inner aluminum foil. These three materials are wrapped and formed through cutting, folding, gluing, and heating. Cutting and folding are fixed molds, and gluing is a fixed position. The heat shrinkage of the biaxially oriented polypropylene film (BOPP) film and heating and drying after gluing are related to whether the wrapping is firm and whether the cigarette pack will deform and spread in the future. Therefore, temperature parameters play a very critical role in the appearance quality of the packaging machine products.

[0046] This embodiment mainly uses the various heating temperature parameters of the cigarette packaging machine, calculates the score by comparing the actual operating parameters with the set parameters, and calculates the total score by weighting all parameter scores. The total score is used to determine whether the packaging machine can produce cigarette products with stable quality, and the weight distribution can be dynamically optimized to achieve the upgrade of quality evaluation from passive detection to active prediction.

[0047] The evaluation method for the production quality of cigarette product packaging machines in this embodiment formulates evaluation content and dynamically assigns corresponding weights according to the above algorithm, calculates the final score by weight distribution, and reflects the quality level with quantitative results, meeting the needs of timely, accurate and true quality feedback, and improving detection efficiency.

[0048] In an optional embodiment of the present invention, step 11 includes:

[0049] Step 111, obtaining current temperature data of the cigarette packaging machine through distributed temperature sensors installed at multiple locations of the cigarette packaging machine;

[0050] Step 112: pre-process the current temperature data to obtain operating temperature data.

[0051] In this embodiment, distributed temperature sensors are set at multiple key positions of the cigarette packaging machine to obtain the current temperature data of the packaging machine in real time. The current temperature data includes one or more temperature data. Data preprocessing is further performed. First, the original sensor signal (such as vibration, current waveform) is smoothed to remove high-frequency noise or power frequency interference, thereby improving the accuracy of subsequent analysis. Secondly, feature extraction, compression and dimensionality reduction are performed on the processed data. This can reduce the amount of data that needs to be transmitted or processed while ensuring that key information is not lost. The preprocessed data is then input into the LSTM algorithm to perform temperature trend prediction, generate a prediction curve, and obtain operating temperature data.

[0052] In this step, you need to set the upper and lower limits of the sensor threshold. When the detected temperature data is greater than 165°C and less than 130°C, the data is considered abnormal and an alarm is issued.

[0053] Furthermore, a scoring rule table was established based on the operating temperature data, set parameters, and weights detected by the distributed temperature sensors at various locations on the packaging machine. Using the scoring rules for each operating temperature data item in the scoring rule table, a single operating temperature score was calculated for each operating temperature data item. Next, the single operating temperature scores were statistically analyzed, multiplied by the risk assessment weight, and summed to calculate a total score, resulting in a production quality evaluation score for the cigarette packaging machine.

[0054] In a specific embodiment, temperature sensors are used to detect the temperature of the small box soldering iron of the packaging machine, the heat sealing temperature of the long side of the small box BOPP film, the heat sealing temperature of the bottom of the small box BOPP film, the heat sealing temperature of the top of the small box BOPP film, the temperature of the small box beautifier, the temperature of the long side soldering iron of the strip box BOPP film, the temperature of the end soldering iron of the strip box BOPP film, and the temperature of the strip box beautifier.

[0055] Create scoring rules:

[0056] 1) Heat sealing temperature of the long side of BOPP film for small boxes: full marks will be awarded if the actual value is within the range of 145℃-150℃; 10 points will be deducted if the actual value is less than 145℃ and greater than 135℃, or greater than 150℃ and less than 160℃; 20 points will be deducted if the actual value is less than 135℃ and greater than 130℃, or greater than 160℃ and less than 165℃; 0 points will be awarded if the actual value is greater than 165℃ and less than 130℃.

[0057] 2) Heat sealing temperature of the bottom of the BOPP film for small boxes: full marks will be awarded if the actual value is within the range of 145℃-150℃; 10 points will be deducted if the actual value is less than 145℃ and greater than 135℃, or greater than 150℃ and less than 160℃; 20 points will be deducted if the actual value is less than 135℃ and greater than 130℃, or greater than 160℃ and less than 165℃; 0 points will be awarded if the actual value is greater than 165℃ and less than 130℃.

[0058] 3) Heat sealing temperature of the top of the BOPP film of the small box: full marks will be awarded if the actual value is within the range of 145℃-150℃; 10 points will be deducted if the actual value is less than 145℃ and greater than 135℃, or greater than 150℃ and less than 160℃; 20 points will be deducted if the actual value is less than 135℃ and greater than 130℃, or greater than 160℃ and less than 165℃; 0 points will be awarded if the actual value is greater than 165℃ and less than 130℃.

[0059] 4) Soldering iron temperature of the long side of BOPP film for strip boxes: full marks will be awarded if the actual value is within the range of 145℃-150℃; 10 points will be deducted if the actual value is less than 145℃ and greater than 135℃, or greater than 150℃ and less than 160℃; 20 points will be deducted if the actual value is less than 135℃ and greater than 130℃, or greater than 160℃ and less than 165℃; 0 points will be awarded if the actual value is greater than 165℃ and less than 130℃.

[0060] 5) Soldering iron temperature of BOPP film end of strip box: full marks will be awarded if the actual value is within the range of 145℃-150℃; 10 points will be deducted if the actual value is less than 145℃ and greater than 135℃, or greater than 150℃ and less than 160℃; 20 points will be deducted if the actual value is less than 135℃ and greater than 130℃, or greater than 160℃ and less than 165℃; 0 points will be awarded if the actual value is greater than 165℃ and less than 130℃.

[0061] 6) Temperature of the beauty device in the box: full marks will be awarded if the actual value is within 110℃±5℃; 10 points will be deducted if the actual value is within 110℃±10℃; 20 points will be deducted if the actual value is within 110℃±15℃; 0 points will be awarded if the actual value is less than 85℃ and greater than 125℃.

[0062] 7) Temperature of small beauty device: if the actual value is within 105℃±5℃, full marks will be awarded; if the actual value is within 105℃±10℃, 10 points will be deducted; if the actual value is within 105℃±15℃, 30 points will be deducted; if the actual value is less than 90℃ and greater than 120℃, 0 points will be awarded.

[0063] 8) Small box soldering iron temperature: full marks will be given if the actual value is within 105℃±5℃; 10 points will be deducted if the actual value is within 105℃±10℃; 30 points will be deducted if the actual value is within 105℃±15℃; 0 points will be given if the actual value is less than 90℃ and greater than 120℃.

[0064] According to the above rules, a scoring rule table is established:

[0065]

[0066] Total score: X=100 11.5%+90 11%+90 9.5%+100 11%+90 +100 8.5%+100 18.5%+100 =97.05, and the evaluation score of the batch cigarette product packaging is 97.3 points, which is consistent with the situation in the actual production process.

[0067] In the table, the maximum score for each item is 100 points, the weighted score is the item score multiplied by the weight, and the total score is the sum of all item weights. If the total score is found to be too low, you can check the individual scores and adjust the corresponding items.

[0068] In an optional embodiment of the present invention, the preset risk assessment weights dynamically optimize the weight model according to weight influencing factors, including:

[0069] Establish expert weight model;

[0070] The expert weight model is dynamically optimized through subjective weight influencing factors and objective weight influencing factors to obtain preset risk assessment weights.

[0071] In this embodiment, in order to reduce the impact of evaluation information bias and ensure that the majority of expert evaluation results can support the final risk ranking, an expert weight model is established by minimizing the difference between expert opinions and group evaluations:

[0072] min:D= ;

[0073] Subject to ;

[0074] in, The weight of the expert rating, Score the group. is the expert score, D represents the difference between the individual score and the group score, m is the number of experts, and n is the number of groups.

[0075] According to the expert weight model, the group risk assessment matrix is ​​calculated:

[0076] ;

[0077] Calculate the subjective weights of risk factors. First, the expert group specifies the best and worst risk factors. Second, each expert selects the best and worst risk factors and compares the best and worst risk factors with all other risk factors in pairs to determine the best and worst risk factors against other vectors. Specifically, it can be expressed as and , represents the value of the risk factor determined by the Kth expert compared with the optimal risk factor, Represents the value of the risk factor determined by the Kth expert compared with the worst risk factor.

[0078] , represents the optimal vector of the group; , represents the worst vector of the group.

[0079] According to the above influencing factors, determine the subjective weight influencing factors ,satisfy:

[0080] Subject to ;

[0081] in, is the constraint value.

[0082] Furthermore, the entropy weight method is used to determine the objective weight influencing factors:

[0083] The entropy method quantifies the importance of each factor based on its degree of differentiation. The higher the degree of differentiation of a factor, the more information it can bring. Therefore, a greater weight should be assigned to that factor, and vice versa. First, the entropy value can be calculated according to the following formula:

[0084] ;

[0085] Then, the objective weighted influencing factors are obtained as follows:

[0086] ;

[0087] In practical problems, only considering the experts' preferences for the importance of risk factors will make the weighting results too subjective, while only considering objective risk information will ignore the experts' personal tendencies. Therefore, it is necessary to coordinate the subjective and objective aspects. The comprehensive calculation formula for weighted image factors is as follows:

[0088] ;

[0089] Where, is the balance parameter between objective and subjective coordination, , which can be adjusted flexibly according to specific problems, and is generally taken as 0.5.

[0090] The final risk assessment weight is:

[0091] ;

[0092] in, Group risk assessment matrix; is the weight influencing factor; is the objective weight influencing factor; It is a subjective weight influencing factor.

[0093] In an optional embodiment of the present invention, an intelligent quality prediction system is constructed by integrating an IoT sensor network and a machine learning model. This system deploys an infrared temperature sensor array to collect real-time equipment operating data, combines it with an LSTM time series prediction network to predict parameter deviation trends, and uses computer vision algorithms to detect cosmetic defects in cigarette packages and provide real-time feedback.

[0094] Specifically, we use YOLOv5 to detect cigarette pack defects and determine whether different temperature parameters affect the packaging and molding process of rigid cigarette packs and whether they correspond to actual conditions.

[0095] (1) Input: Mosaic data augmentation. The Mosaic data augmentation algorithm combines multiple images into a single image at a certain ratio, enabling the model to identify objects within a smaller range. Using Mosaic data augmentation increases the diversity of the dataset, improves the robustness of the model, and improves small object detection performance.

[0096] (2) Backbone network: Focus module and CSP module. The Focus module slices the image before it enters the backbone network, that is, it takes a value for every pixel in an image, thereby obtaining four images, thereby reducing the width and height of the input to half of the previous ones, and expanding the input channel fourfold. In this way, the new image is subjected to a convolution operation to obtain a double-downsampled feature map without information loss. The CSP module uses staged feature reuse and branching structure to enable the model to extract image features more effectively while maintaining a low computational overhead. Adding the CSP module to the backbone network optimizes the transmission of deep features, avoids the gradient vanishing problem in traditional deep networks, and thus improves the training efficiency and accuracy of the model.

[0097] (3) Neck network: FPN-PAN structure. The main purpose of FPN is to improve the network's detection ability for targets of different sizes by fusing multiple layers of features. It extracts features from the backbone network (Backbone) and upsamples them layer by layer through a top-down process, forming a pyramid structure. PAN further optimizes the feature pyramid network, more efficiently transferring context information to different layers of the network, making feature fusion more comprehensive. Combining FPN and PAN structures achieves efficient context information flow and multi-scale feature fusion. This greatly enhances the network's multi-scale detection capability. It not only improves the detection performance of small targets, but also improves the overall detection accuracy in complex scenarios. This efficient feature fusion design makes YOLOv5 a fast and accurate target detection algorithm.

[0098] On the cigarette packaging production line, cigarette package image data is acquired at various temperature ranges under different temperature parameters. Defects on the cigarette packages are annotated using tools such as LabelImg and CVAT, generating label files in the YOLOv5 format. The defect type is determined based on the actual process, for example, a small box of BOPP film with a missing seal on the long side. The image data is fed into a trained YOLOv5 model, which performs inference on the test set images at each temperature range. The number of detection boxes and confidence distribution for each defect type are counted, and a bar chart or heat map is plotted showing how defect types change over temperature. The correlation between temperature and defects is determined. Based on this correlation, detection feedback is provided to determine whether the set risk assessment weights meet current requirements.

[0099] An embodiment of the present invention further provides a device 20 for evaluating the production quality of a cigarette product packaging machine, comprising:

[0100] An acquisition module 21 is used to acquire operating temperature data of a cigarette packaging machine;

[0101] A processing module 22 is configured to obtain an operating temperature score based on the operating temperature data and a preset scoring rule;

[0102] Obtaining a production quality score based on the operating temperature score and a preset risk assessment weight, wherein the preset risk assessment weight is obtained by dynamically optimizing a weight model based on weight influencing factors;

[0103] A production quality evaluation score of the cigarette product packaging machine is obtained based on the production quality score.

[0104] Optionally, obtain operating temperature data of the cigarette packaging machine, including:

[0105] The current temperature data of the cigarette packaging machine is obtained by distributing temperature sensors installed at multiple locations of the cigarette packaging machine;

[0106] The current temperature data is preprocessed to obtain operating temperature data.

[0107] Optionally, obtaining an operating temperature score based on the operating temperature data and a preset scoring rule includes:

[0108] Get the preset scoring rule table;

[0109] An operating temperature score is obtained according to a mapping relationship between the preset scoring rule table and the operating temperature data.

[0110] Optionally, a production quality score is obtained based on the operating temperature score and a preset risk assessment weight, including:

[0111] The product of the operating temperature score and the preset risk assessment weight is summed to obtain the production quality score.

[0112] Optionally, the preset risk assessment weights are dynamically optimized based on weight influencing factors, including:

[0113] Establish expert weight model;

[0114] The expert weight model is dynamically optimized through subjective weight influencing factors and objective weight influencing factors to obtain preset risk assessment weights.

[0115] Optionally, the preset risk assessment weight is:

[0116] ;

[0117] in, ;

[0118] in, Group risk assessment matrix; is the weight influencing factor; is the objective weight influencing factor; is the subjective weight influencing factor; is the balance parameter between objective and subjective coordination, .

[0119] Optionally, obtaining a cigarette product packaging machine production quality evaluation score based on the production quality score includes:

[0120] The production quality scores of cigarette packaging machines are summed to obtain the production quality evaluation score of cigarette product packaging machines.

[0121] It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0122] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described in the above embodiment. All implementations of the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0123] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to execute the method described in the above embodiment. All implementations of the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0124] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0125] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0126] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0127] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0128] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0129] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0130] In addition, it should be pointed out that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but they do not necessarily need to be performed in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it can be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in hardware, firmware, software or a combination thereof in any computing device (including a processor, storage medium, etc.) or a network of computing devices. This can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0131] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0132] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for evaluating the production quality of a cigarette product packaging machine, characterized in that: include: Obtaining operating temperature data of the cigarette packaging machine, the operating temperature data including at least the temperature of the small box soldering iron, the heat-sealing temperature of the long side of the biaxially oriented polypropylene film of the small box, the heat-sealing temperature of the bottom of the biaxially oriented polypropylene film of the small box, the heat-sealing temperature of the top of the biaxially oriented polypropylene film of the small box, the temperature of the small box cosmetic device, the temperature of the soldering iron of the long side of the biaxially oriented polypropylene film of the strip box, the soldering iron temperature of the end of the biaxially oriented polypropylene film of the strip box, and the temperature of the cosmetic device of the strip box; Obtaining an operating temperature score based on the operating temperature data and a preset scoring rule; Obtaining a production quality score based on the operating temperature score and a preset risk assessment weight, wherein the preset risk assessment weight is obtained by dynamically optimizing a weight model based on weight influencing factors; A production quality evaluation score of the cigarette product packaging machine is obtained based on the production quality score.

2. The method for evaluating the production quality of a cigarette product packaging machine according to claim 1, characterized in that: Obtain operating temperature data of cigarette packaging machines, including: The current temperature data of the cigarette packaging machine is obtained by distributing temperature sensors installed at multiple locations of the cigarette packaging machine; The current temperature data is preprocessed to obtain operating temperature data.

3. The method for evaluating the production quality of a cigarette product packaging machine according to claim 1, characterized in that: According to the operating temperature data and the preset scoring rules, an operating temperature score is obtained, including: Get the preset scoring rule table; An operating temperature score is obtained according to a mapping relationship between the preset scoring rule table and the operating temperature data.

4. The method for evaluating the production quality of a cigarette product packaging machine according to claim 1, characterized in that: According to the operating temperature score and the preset risk assessment weight, a production quality score is obtained, including: The product of the operating temperature score and the preset risk assessment weight is summed to obtain the production quality score.

5. The method for evaluating the production quality of a cigarette product packaging machine according to claim 1, characterized in that: The preset risk assessment weights dynamically optimize the weight model according to weight influencing factors, including: Establish expert weight model; The expert weight model is dynamically optimized through subjective weight influencing factors and objective weight influencing factors to obtain preset risk assessment weights.

6. The method for evaluating the production quality of a cigarette product packaging machine according to claim 1, characterized in that: The preset risk assessment weights are: ； in, ; in, Group risk assessment matrix; is the weight influencing factor; is the objective weight influencing factor; is the subjective weight influencing factor; is the balance parameter between objective and subjective coordination, .

7. The method for evaluating the production quality of a cigarette product packaging machine according to claim 1, characterized in that: According to the production quality score, the cigarette product packaging machine production quality evaluation score is obtained, including: The production quality scores of cigarette packaging machines are summed to obtain the production quality evaluation score of cigarette product packaging machines.

8. A device for evaluating the production quality of a cigarette product packaging machine, characterized in that: include: An acquisition module, used to obtain operating temperature data of the cigarette packaging machine; a processing module, configured to obtain an operating temperature score based on the operating temperature data and a preset scoring rule; Obtaining a production quality score based on the operating temperature score and a preset risk assessment weight, wherein the preset risk assessment weight is obtained by dynamically optimizing a weight model based on weight influencing factors; A production quality evaluation score of the cigarette product packaging machine is obtained based on the production quality score.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: The device stores instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 7.

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