An intelligent adjustment and efficient flap system and method

Abstract

The present invention relates to the technical field of a flap system, and in particular to an intelligent adjustment and efficient flap system and method. The flap system includes: a log recording module; a judgment module for responding to a calibration check signal and judging whether there is a historical calibration timestamp; an acquisition module for acquiring the historical flipped sheet material models from the latest historical calibration timestamp to the current time; a matching module for matching in a database to obtain the reference weights of the sheet materials corresponding to the respective historical flipped sheet material models; a statistics module for statistically calculating the total flipping duration of the sheet materials of the respective historical flipped sheet material models from the latest historical calibration timestamp to the current time; a set generation module for obtaining a calibration data array set based on the calibration data arrays corresponding to the respective historical flipped sheet material models; a calculation module for calculating the wear error degree R based on the calibration data array set; and an alarm module for sending out a calibration alarm message. This application has the effect of relatively accurately judging the wear condition of the device and timely sending out a calibration alarm.

Description

Technical Field

[0001] The present invention relates to the technical field of a flap system, and in particular to an intelligent adjustment and efficient flap system and method. Background Art

[0002] With the continuous development of automation and intelligent technologies, the control system of a flapper also faces increasingly high requirements for accuracy and efficiency. Usually, the staff inputs the size of the board, or selects the board model, sets the flapping speed, and the flapper automatically configures the operating parameters of the clamping and conveying mechanism and the flipping mechanism, such as track parameters, etc. to adapt to the model or size, or is manually configured by the staff.

[0003] Traditional control systems of flappers often rely on fixed program settings and lack a dynamic adjustment and feedback mechanism for the actual operating state. This results in the difficulty of timely detecting and correcting performance deviations caused by factors such as wear and load changes after long-term operation, thereby affecting efficiency and quality.

[0004] Most existing intelligent adjustment systems focus on the optimization control of a single parameter and lack the comprehensive evaluation and prediction ability for the overall performance of the equipment. Especially in the case of dealing with various board models and complex flipping requirements, how to accurately judge the equipment wear condition, issue a correction warning in time, and avoid production interruption and product quality problems. Summary of the Invention

[0005] In order to accurately judge the equipment wear condition, issue a correction warning in time, and avoid production interruption and product quality problems, the present application provides an intelligent adjustment and efficient flap system.

[0006] The first invention object of the present application is achieved through the following technical solutions:

[0007] An intelligent adjustment and efficient flap system, comprising:

[0008] A log recording module, configured to record historical correction timestamps, the number of flipping batches, and record the board model and flipping duration of each flip;

[0009] A judgment module, configured to respond to a correction check signal and judge whether there is a historical correction timestamp;

[0010] An acquisition module, configured to, if any, acquire the historical flipped board models from the latest historical correction timestamp to the current time;

[0011] A matching module, which is used to match and obtain the reference weight of each historical flipped sheet model in a database. The database pre-stores a model-weight mapping table, and the model-weight mapping table represents the mapping relationship between the sheet model and the reference weight of the sheet. The reference weight of the sheet is calculated based on the sheet size;

[0012] A statistical module, which is used to count the total flipping duration of the sheets of each historical flipped sheet model from the latest historical calibration timestamp to the current time;

[0013] A set generation module, which is used to use the reference weight of the sheet and the total flipping duration of the sheet of the same historical flipped sheet model as a calibration data array, and obtain a set of calibration data arrays based on the calibration data arrays corresponding to each historical flipped sheet model;

[0014] A calculation module, which is used to calculate the wear error degree R based on the set of calibration data arrays;

[0015] An alarm module, which is used to send a calibration alarm message if the wear error degree R and the preset value R1 satisfy a preset relationship.

[0016] In a preferred example of the present application, it can be further configured that: calculating the wear error degree R based on the set of calibration data arrays, and obtaining it by using the following formula:

[0017]

[0018] Wherein, R is the wear error degree, n is the number of elements in the set of calibration data arrays, W i is the reference weight of the sheet of the i-th element in the set of calibration data arrays, T i is the total flipping duration of the sheet of the i-th element in the set of calibration data arrays, W max is the maximum reference weight of the sheet in the set of calibration data arrays; a is an influence proportionality coefficient.

[0019] In a preferred example of the present application, it can be further configured that: the satisfaction of the preset relationship means R + E * R / d ≥ R1; d is the number of flipping batches from the latest historical calibration timestamp to the current time, where E is a preset constant.

[0020] In a preferred example of the present application, it can be further configured that: the value range of a is 0.005 to 0.02.

[0021] In a preferred example of the present application, it can be further configured that: a is 0.01.

[0022] In a preferred example of the present application, it can be further configured that: the unit of the reference weight of the sheet is Kg, and the unit of the total flipping duration of the sheet is h.

[0023] In a preferred example, the present application can be further configured as: if not present, the obtaining module is further used to obtain the historical turnover sheet material types from the starting working time to the current time.

[0024] An intelligent adjustment and efficient turning method for performing turning alarm, comprising the following steps:

[0025] S1. Respond to the calibration check signal and determine whether there is a historical calibration timestamp;

[0026] S2. If present, obtain the historical turnover sheet material types from the latest historical calibration timestamp to the current time;

[0027] S3. Match in the database to obtain the reference weights of the sheets corresponding to each historical turnover sheet material type, and a model-weight mapping table is pre-stored in the database;

[0028] S4. Statistically calculate the total turnover duration of the sheets of each historical turnover sheet material type from the latest historical calibration timestamp to the current time;

[0029] S5. Take the reference weight of the sheet and the total turnover duration of the sheet of the same historical turnover sheet material type as a calibration data array, and obtain a calibration data array set based on the calibration data arrays corresponding to each historical turnover sheet material type;

[0030] S6. Calculate the wear error degree R based on the calibration data array set;

[0031] S7. If the wear error degree R and the preset value R1 satisfy a preset relationship, send a calibration alarm message.

[0032] In a preferred example, the present application can be further configured as: calculating the wear error degree R based on the calibration data array set, and calculating it using the following formula:

[0033]

[0034] wherein, R is the wear error degree, n is the number of elements in the calibration data array set, W i is the reference weight of the sheet of the i-th element in the calibration data array set, T i is the total turnover duration of the sheet of the i-th element in the calibration data array set, W max is the largest reference weight of the sheet in the calibration data array set; a is the influence proportionality coefficient.

[0035] In a preferred example, the present application can be further configured as: the satisfaction of the preset relationship means R + E * R / d ≥ R1; d is the number of turnover batches from the latest historical calibration timestamp to the current time, where E is a preset constant.

[0036] In summary, the present application includes at least one of the following beneficial technical effects:

[0037] 1. By integrating functional modules such as log recording, data matching, and statistical analysis, comprehensive monitoring and precise calibration of the operating status of the flipping mechanism of the tipping machine are achieved. The system can automatically calculate the wear error degree according to different sheet material models and flipping data, and promptly send out calibration warning information. It can automatically trigger a calibration prompt. After the staff performs timely calibration and maintenance, its stable operation can be ensured. Thereby effectively extending the service life of the equipment and helping to avoid production interruptions caused by equipment failures.

[0038] 2. The proposed wear error degree algorithm can map the original data to a unified range (usually between 0 and 1), thereby eliminating the influence of different dimensions and data ranges on the estimation results. A normalized logarithmic function is used to capture the slightly increasing influence of the sheet weight on the scrap degree. When the sheet weight Wi is small, the value of the logarithmic function is also small, indicating a small influence on the scrap degree; when the sheet weight Wi approaches Wmax, the value of the logarithmic function approaches 1, indicating that the influence on the scrap degree approaches the maximum value, but still shows a slight increase. And by adding a coefficient a, the overall amplitude of the influence of both the weight and the flipping duration on the wear error degree can be adjusted simultaneously.

[0039] 3. Enables effective comparison and analysis of the scrap degree data of different devices or at different time points. Moreover, the logarithmic function has the characteristic of smoothing data, which can reduce the influence of extreme values or outliers in the data on the estimation results. This helps to improve the stability and accuracy of the equipment scrap degree estimation; and the normalized logarithmic function can retain the changing trend in the original data, enabling the estimation result of the scrap degree to reflect the changing trend of the equipment performance. Description of the Drawings

[0040] Figure 1 is a schematic diagram of the overall structure of the tipping machine applied in the intelligent adjustment and efficient tipping system according to an embodiment of the present application;

[0041] Figure 2 is a schematic diagram of the module connection of the intelligent adjustment and efficient tipping system according to an embodiment of the present application;

[0042] Figure 3 is a flowchart of the implementation of the intelligent adjustment and efficient tipping warning method according to an embodiment of the present application.

[0043] Reference Numerals: 1, frame; 2, transmission tipping mechanism; 21, clamping and conveying mechanism; 22, flipping mechanism; 23, mounting seat. Detailed Embodiments

[0044] The exemplary embodiments of the present application will be described below in conjunction with the accompanying drawings. Various details of the embodiments of the present application are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0045] It should be noted that the terms "first", "second", etc. in the present invention are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure.

[0046] In addition, the term "and / or" herein is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0047] Figure 1 is a schematic diagram according to the first embodiment of the present application. As Figure 1 shown, the intelligent adjustment and efficient flap system is applied to the control system of a flap machine. The flap machine generally includes a frame 1 and a driving flap mechanism 2 installed on the frame 1. The driving flap mechanism 2 further includes two clamping and conveying mechanisms 21 and a flipping mechanism 22. The two clamping mechanisms are respectively arranged on both sides of the conveying space. The clamping and conveying mechanism 21 includes two parallel conveyor belts and a driving member for driving the two conveyor belts to run synchronously so that the sheet between the two conveyor belts moves in one direction. While the two conveyor belts carry the sheet, they play a clamping role on the sheet. Generally, the clamping and conveying mechanism 21 is installed in the mounting seat 23 of the flipping mechanism 22. The clamping and conveying mechanism 21 is used to convey the sheets and the like sent by the host computer. The flipping mechanism 22 realizes the flipping function by flipping the mounting seat 23 on which the clamping and conveying mechanism 21 is installed. And the control system of the flap machine in the related art usually provides the user with the option to select the sheet type or size to automatically adapt the operating parameters, such as track parameters (the spacing of the clamping and conveying mechanism 21), the flipping speed of the flipping mechanism 22, etc., or can also be adjusted and set by the staff themselves.

[0048] Combined with Figure 2, the intelligent adjustment and efficient flap system includes a log recording module, a judgment module, an acquisition module, a matching module, a statistics module, a set generation module, a calculation module, and an alarm module. Among them, the log recording module is used to record the historical calibration timestamp, the number of flipping batches, and record the sheet material model and flipping duration for each flip.

[0049] Specifically, recording the sheet material model and flipping duration for each flip means that during the operation of the equipment, record the start timestamp and end timestamp of the flipping action of the flipping mechanism to calculate the time difference to record the single flip duration, and associate the sheet material model currently being flipped. Generally speaking, the heavier the sheet material, the longer the set flipping duration; the historical calibration timestamp refers to the timestamp when the equipment has been calibrated and repaired in the past. Calibration and repair refer to correcting and repairing the flipping mechanism for the flipping angle deviation problem. The flipping batch refers to the same equipment, the same model, and the same shift as a flipping batch.

[0050] The judgment module is used to respond to the calibration inspection signal and judge whether there is a historical calibration timestamp.

[0051] The calibration inspection signal can be sent by the staff or the system is set to send the calibration inspection signal after each flipping batch is completed, so as to automatically judge once for each batch of flipped sheet materials. Judging whether there is a historical calibration timestamp is to check whether there is a historical calibration timestamp in the information recorded in the log recording module.

[0052] The acquisition module is used to, if it exists, acquire the historical flipped sheet material models from the latest historical calibration timestamp to the current time; if it does not exist, acquire the historical flipped sheet material models from the start working timestamp to the current time.

[0053] It can be understood that if there is a historical calibration timestamp, it means that calibration and repair have been carried out, so the records before the latest historical calibration timestamp can be ignored because the deviation returns to zero or is adjusted to the reference value after calibration and repair; the start working timestamp refers to the timestamp when the equipment is first run. If it does not exist, it means that no calibration and repair have been carried out, so acquire the historical flipped sheet material models from the start working timestamp to the current time.

[0054] The matching module is used to match the corresponding sheet material reference weights for each historical flipped sheet material model in the database. The database pre-stores a model-weight mapping table, and the model-weight mapping table represents the mapping relationship between the sheet material model and the sheet material reference weight. The sheet material reference weight is calculated based on the sheet material size.

[0055] Specifically, the reference weight of the board can be calculated by multiplying the length × width × thickness × reference density. The reference density can be obtained by actually weighing a certain board and then calculating the ratio with the value of the length × width × thickness of the board. It can be understood that in this embodiment, it is not necessary for each board to have a very precise weight value, and only an approximate reference weight is required to meet the subsequent calculation requirements.

[0056] The statistical module is used to count the total flipping duration of the boards of each historical flipped board model from the latest historical calibration timestamp to the current time.

[0057] The total flipping duration of the boards of a certain board model from the latest historical calibration timestamp to the current time in the calculation log recording module can be calculated to obtain the total flipping duration of the boards of each historical flipped board model.

[0058] The set generation module is used to take the reference weight and total flipping duration of the boards of the same historical flipped board model as a calibration data array, and obtain a calibration data array set based on the calibration data arrays corresponding to each historical flipped board model.

[0059] Specifically, a calibration data array is [W, T], where W is the reference weight of the boards of a certain historical flipped board model, T is the total flipping duration of the boards of this historical flipped board model, there are n elements in the calibration data array set, and each element in the calibration data array set is a calibration data array.

[0060] The calculation module is used to calculate the wear error degree R based on the calibration data array set.

[0061] Specifically, it is calculated using the following formula:

[0062]

[0063] Among them, R is the wear error degree, n is the number of elements in the calibration data array set, W i is the reference weight of the boards of the i-th element in the calibration data array set, T i is the total flipping duration of the boards of the i-th element in the calibration data array set, W max is the maximum reference weight of the boards in the calibration data array set; a is the influence proportionality coefficient. The unit of the reference weight of the boards is Kg, and the unit of the total flipping duration of the boards is h.

[0064] The normalization logarithmic function can map the original data to a unified range (usually between 0 and 1), thereby eliminating the influence of different dimensions and data ranges on the estimation results. This enables effective comparison and analysis of the scrapping degree data of different devices or at different time points. Moreover, the logarithmic function has the characteristic of smoothing data, which can reduce the influence of extreme values or outliers in the data on the estimation results. This helps to improve the stability and accuracy of equipment scrapping degree estimation; and, the normalization logarithmic function can retain the changing trend in the original data, making the estimation results of the scrapping degree reflect the trend of equipment performance changes.

[0065] It should be noted that due to the influence of weight and duration on wear, that is, during the flipping process of different plates, the heavier the plate weight and the longer the flipping duration, the slightly greater the wear impact on the flipping mechanism. Therefore, the normalization logarithmic function is used to capture the slightly increased influence of plate weight on the scrapping degree. When the plate weight Wi is small, the value of the logarithmic function is also small, indicating a small influence on the scrapping degree; when the plate weight Wi approaches Wmax, the value of the logarithmic function approaches 1, indicating that the influence on the scrapping degree approaches the maximum value, but still shows a slight increase. And by adding a coefficient a, it can be used to adjust the overall amplitude of the influence of weight and flipping duration on the wear error degree at the same time.

[0066] Based on the above constructed formula, the coefficient a and the reference wear error degree R2 are trained through multiple sets of calibration data array set samples. The reference wear error degree R2 is calculated using the same formula as R. Each set of calibration data array set is obtained from the historical data when the equipment is used until the deviation reaches the failing range; the failing range is determined according to the actual flipping requirements and equipment accuracy. For example, it can be 1°, or other standards can be adopted, such as the axis deviation not exceeding ±3mm as the judgment standard.

[0067] Then, through a machine learning model, such as a non - linear regression model or a neural network, fitting or training is carried out. In this embodiment, a neural network is used for training.

[0068] Through multiple sets of problem data sample sets, the neural network is trained to obtain a formula model, thereby obtaining the values of the coefficient a and the wear error degree R2. The value range of a is between 0.005 and 0.02. In one embodiment, a is 0.01. The value of R2 is 0.84.

[0069] An alarm module is used to send a calibration alarm message if the wear error degree R and the preset value R1 satisfy a preset relationship.

[0070] Specifically, the satisfaction of the preset relationship means that R + E * R / d ≥ R1; where R1 = R2 + E * R2 / d, where d is the number of flipping batches from the latest historical calibration timestamp to the current time, and E is a preset constant. E can be set to 5, and its function is to provide a margin for prediction and a certain degree of fault tolerance, and can further issue an alarm earlier.

[0071] This application also provides an intelligent adjustment and efficient flap alarm method. Refer to Figure 3 , including:

[0072] S1. Respond to the calibration check signal and determine whether there is a historical calibration timestamp.

[0073] S2. If it exists, obtain the historical flipping sheet model from the latest historical calibration timestamp to the current time.

[0074] S3. Match the corresponding sheet reference weights of each historical flipping sheet model in the database, and a model-weight mapping table is pre-stored in the database.

[0075] Among them, the model-weight mapping table represents the mapping relationship between the sheet model and the sheet reference weight, and the sheet reference weight is calculated based on the sheet size.

[0076] S4. Statistically calculate the total flipping duration of each historical flipping sheet model from the latest historical calibration timestamp to the current time.

[0077] S5. Use the sheet reference weight and the total flipping duration of the same historical flipping sheet model as a calibration data array, and obtain a calibration data array set based on the calibration data arrays corresponding to each historical flipping sheet model.

[0078] S6. Calculate the wear error degree R based on the calibration data array set.

[0079] S7. If the wear error degree R and the preset value R1 satisfy the preset relationship, issue a calibration alarm message.

[0080] Specific technical limitations regarding the intelligent adjustment and efficient flap alarm method can adopt the limitations for the intelligent adjustment and efficient flap system in the above text, which will not be elaborated here. Each step of the above intelligent adjustment and efficient flap alarm method can be implemented in whole or in part through software, hardware, and their combination.

[0081] The embodiments of the present invention have the following technical effects: 1. By integrating functional modules such as log recording, data matching, and statistical analysis, comprehensive monitoring and precise calibration of the operating state of the flipping mechanism of the flipper are achieved. The system can automatically calculate the wear error degree according to different sheet material models and flipping data, and promptly send out calibration warning messages. It can automatically trigger calibration prompts. After the staff makes timely calibration and repair, its stable operation can be ensured. Thereby effectively extending the service life of the equipment and helping to avoid production interruptions caused by equipment failures.

[0082] 2. The proposed wear error degree algorithm can map the original data to a unified range (usually between 0 and 1), thereby eliminating the influence of different dimensions and data ranges on the estimation results. A normalized logarithmic function is used to capture the slightly increasing influence of the sheet weight on the scrap degree. When the sheet weight Wi is small, the value of the logarithmic function is also small, indicating a small influence on the scrap degree; when the sheet weight Wi approaches Wmax, the value of the logarithmic function approaches 1, indicating that the influence on the scrap degree approaches the maximum value, but still increases slightly. And by adding a coefficient a, it can be used to adjust the overall amplitude of the influence of weight and flipping duration on the wear error degree at the same time.

[0083] 3. It enables effective comparison and analysis of the scrap degree data of different devices or at different time points, and the logarithmic function has the characteristic of smoothing data, which can reduce the influence of extreme values or outliers in the data on the estimation results. This helps to improve the stability and accuracy of the equipment scrap degree estimation; moreover, the normalized logarithmic function can retain the change trend in the original data, making the estimation result of the scrap degree reflect the trend of equipment performance changes.

[0084] The various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application-specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0085] These computing procedures (also known as programs, software, software applications, or code) include machine instructions for a programmable processor and can implement these computing procedures using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0086] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0087] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0088] It should be understood that the various forms of the processes shown above can be reordered, added to, or deleted. For example, the steps recited in this application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved, and no limitation is made herein.

[0089] The above specific embodiments do not constitute a limitation on the protection scope of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. An intelligent adjustment and efficient flap system, applied to the control system of a flapper machine, is characterized in that Including: A log recording module, configured to record historical calibration timestamps, the number of flipping batches, and record the sheet model and flipping duration for each flip; A judgment module, configured to respond to a calibration check signal and determine whether there is a historical calibration timestamp; An acquisition module, configured to, if there is one, acquire the historical flipped sheet models from the latest historical calibration timestamp to the current time; A matching module, configured to match in a database to obtain the reference sheet weights corresponding to the respective historical flipped sheet models. A model-weight mapping table is pre-stored in the database, and the model-weight mapping table represents the mapping relationship between the sheet model and the reference sheet weight. The reference sheet weight is calculated based on the sheet size; A statistics module, configured to count the total flipping duration of the sheets of each historical flipped sheet model from the latest historical calibration timestamp to the current time; An array set generation module, configured to use the reference sheet weight and the total flipping duration of the sheets of the same historical flipped sheet model as a calibration data array, and obtain a calibration data array set based on the calibration data arrays corresponding to the respective historical flipped sheet models; A calculation module, configured to calculate a wear error degree R based on the calibration data array set; An alarm module, configured to send a calibration alarm message if the wear error degree R and a preset value R1 satisfy a preset relationship; Wherein, the calculation of the wear error degree R based on the calibration data array set is obtained by using the following formula: ； Wherein, R is the wear error degree, and n is the number of elements in the calibration data array set. is the reference weight of the sheet for the i-th element in the calibration data array set. is the total flipping duration of the sheet for the i-th element in the calibration data array set. is the maximum reference weight of the sheet in the calibration data array set; a is the influence proportionality coefficient.

2. The intelligent adjustment and efficient flap system according to claim 1, wherein The satisfaction of the preset relationship means R + E * R / d ≥ R1; d is the number of flipping batches from the latest historical calibration timestamp to the current time, where E is a preset constant.

3. The intelligent adjustment and efficient flap system according to claim 1, wherein The value range of a is between 0.005 and 0.

02.

4. The intelligent adjustment and efficient flap system according to claim 1 or 3, characterized in that, a is 0.

01.

5. The intelligent adjustment and efficient flap system according to claim 1, wherein, The unit of the reference sheet weight is Kg; the unit of the total flipping duration of the sheets is h.

6. The intelligent adjustment and efficient flap system according to claim 1, characterized in that, The acquisition module is further configured to, if there is none, acquire the historical flipped sheet models from the starting working time to the current time.

7. An intelligent adjustment and efficient flap method, characterized in that, The intelligent adjustment high-efficiency turning plate method is used for turning plate alarm, including the following steps: S1. Respond to a calibration check signal and determine whether there is a historical calibration timestamp; S2. If there is one, acquire the historical flipped sheet models from the latest historical calibration timestamp to the current time; S3. Match in a database to obtain the reference sheet weights corresponding to the respective historical flipped sheet models. A model-weight mapping table is pre-stored in the database; S4. Count the total flipping duration of the sheets of each historical flipped sheet model from the latest historical calibration timestamp to the current time; S5. Use the reference sheet weight and the total flipping duration of the sheets of the same historical flipped sheet model as a calibration data array, and obtain a calibration data array set based on the calibration data arrays corresponding to the respective historical flipped sheet models; S6. Calculate a wear error degree R based on the calibration data array set; S7. If the wear error degree R and a preset value R1 satisfy a preset relationship, send a calibration alarm message; Wherein, the calculation of the wear error degree R based on the calibration data array set is obtained by using the following formula: ； Wherein, R is the wear error degree, and n is the number of elements in the calibration data array set. is the reference weight of the sheet for the i-th element in the calibration data array set. is the total flipping duration of the sheet for the i-th element in the calibration data array set. is the maximum reference weight of the sheet in the calibration data array set; a is the influence proportionality coefficient.

8. The intelligent adjustment and efficient flap method according to claim 7, wherein The satisfaction of the preset relationship means R + E * R / d ≥ R1; d is the number of flipping batches from the latest historical calibration timestamp to the current time, where E is a preset constant.

Images