Electronic belt scale dynamic deviation rectification control system based on AI algorithm

Abstract

The invention discloses an electronic belt scale dynamic deviation rectification control system based on an AI algorithm, relates to the technical field of belt deviation rectification control, and solves the technical problems of low overall data transmission efficiency and low deviation rectification efficiency caused by lack of effective analysis and adjustment in the aspects of communication protocol optimization and mechanism response delay. According to the method, the data transmission quantity and the communication interaction times are reduced, the communication efficiency is improved, the requirement of the system for computing resources is reduced, the stability of the system in a complex industrial environment is enhanced, and the system is suitable for industrial application. Belt deviation misjudgment and deviation correction abnormity caused by the communication problem are reduced, and a model can be established through the execution response optimization module by obtaining historical data and related data of an execution mechanism to analyze the correlation between the belt deviation amount and the response of the execution mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of belt deviation correction control, and specifically to a dynamic deviation correction control system for an electronic belt scale based on an AI algorithm. Background Art

[0002] During the operation of an electronic belt scale, the belt often runs off track, which will seriously affect the weighing accuracy and weighing stability of the electronic belt scale.

[0003] The patent application with the publication number CN209106302U discloses a new type of automatic deviation correction control system for tobacco feeding, including a complete feedback control system. The feedback control system includes a detection unit, a control unit, and an execution unit: the control unit consists of a PLC control system, and the PLC control system is installed in an industrial control cabinet at the production site; the detection unit consists of an E+H flowmeter and an electronic belt scale. The E+H flowmeter is installed on the main body of the feeding machine, and the electronic belt scale is installed at the inlet end of the feeding machine; the execution unit consists of a gear pump, a motor, and a frequency converter, and the gear pump, motor, and frequency converter are integrally installed on the main body of the feeding machine.

[0004] Existing technologies usually adopt some conventional deviation correction means, but lack systematic and refined optimization schemes for links such as sensor data acquisition, deviation analysis, deviation correction instruction transmission, and actuator response. In terms of communication protocols, there are redundant information and complex processes, resulting in low data transmission efficiency and high resource consumption. In terms of optimizing the actuator response delay, there are also no effective analysis and adjustment methods, making it difficult to achieve a high level of system stability and deviation correction efficiency. Summary of the Invention

[0005] In view of the deficiencies of the existing technologies, the present invention provides a dynamic deviation correction control system for an electronic belt scale based on an AI algorithm, which solves the problems of lack of effective analysis and adjustment in communication protocol optimization and mechanism response delay, resulting in low overall data transmission efficiency and low deviation correction efficiency.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A dynamic deviation correction control system for an electronic belt scale based on an AI algorithm, including:

[0007] A deviation analysis module, configured to match the sensor data transmitted by the sensor data acquisition module with standard data, generate a normal monitoring signal or an offset analysis signal, and at the same time analyze the offset analysis signal to determine the offset difference and the offset mode to generate deviation correction information, and transmit it to the instruction transmission optimization module and the execution response optimization module respectively;

[0008] The instruction transmission optimization module eliminates redundant information and character information in the protocol format to obtain a preprocessed protocol, and simultaneously performs merging and omission analysis on the protocol process corresponding to the preprocessed protocol;

[0009] Determine the corresponding merging steps according to the data dependency relationship between the protocol process steps, and perform merging processing. At the same time, based on the data flow and usage in the protocol process steps, draw a data dependency graph and determine isolated processes, and perform omission at the same time. Generate optimization information based on the merging and omission of the protocol process, and then transmit it to the deviation correction control information display module;

[0010] The execution response optimization module is used to optimize and adjust the response delay of the actuator. By analyzing and sorting historical data, analyze the correlation between the belt deviation amount and the actuator response, and obtain the response delay situation for analysis;

[0011] Analyze the large response delay situation, calculate the adjustment sampling period according to the current acquisition period and the maximum burden of the system, compare it with the maximum and minimum periods of the system to determine the sampling period, and generate period adjustment information;

[0012] Analyze the small amplitude less than the delay situation, and increase the proportional gain by an equal amplitude in combination with the system stability to generate gain adjustment information, and optimize the actuator.

[0013] As a further solution of the present invention, it further includes a sensor data acquisition module for transmitting the acquired sensor data to the deviation analysis module;

[0014] It further includes a deviation correction control information display module for transmitting the acquired deviation correction information to the corresponding actuator.

[0015] As a further solution of the present invention, the specific method for the deviation analysis module to determine the deviation difference and the deviation method to generate deviation correction information is as follows:

[0016] Compare the sensor data with the standard data, and the standard data represents the distance between the edge of the belt and both sides during rotation. If they match, it indicates no deviation and a normal monitoring signal is generated. If they do not match, it indicates a deviation and a deviation analysis signal is generated. Subsequently, obtain the sensor data, determine the deviation difference and direction, and generate deviation correction information.

[0017] As a further solution of the present invention, the specific method for the instruction transmission optimization module to obtain the preprocessed protocol is as follows:

[0018] Obtain the current communication protocol and the corresponding protocol format, study the standard document of the current communication protocol in detail, clarify each field, data type and function, identify the redundant information and character information in the protocol, and simultaneously eliminate the two, and retain the remaining protocol information to obtain the preprocessed protocol.

[0019] As a further solution of the present invention, the specific manner of combining and analyzing the protocol processes corresponding to the preprocessing protocol is as follows:

[0020] Obtain all protocol processes and label them as i, where i = 1, 2, …, j, and j represents the steps of the protocol process. Take the protocol process with i = 1 as the analysis object, and then sequentially analyze the data dependency relationships between the remaining protocol processes and the analysis object. If the data dependency relationships between two or more steps are weak and their execution order has little impact on the final result, then the steps can be combined.

[0021] As a further solution of the present invention, the specific manner of omitting and analyzing the protocol processes corresponding to the preprocessing protocol is as follows:

[0022] Understand each process step of the current communication protocol, clarify the specific functions and execution order of each step, determine the data flow direction between each step and the generation and usage situations, analyze the generation and usage situations of data in each step, and draw a data dependency graph. Further determine the protocol processes with dependencies, and at the same time screen out isolated processes, and omit the obtained isolated processes.

[0023] As a further solution of the present invention, the specific manner in which the execution response optimization module optimizes and adjusts the response delay of the actuator is as follows:

[0024] By obtaining historical data, and the historical data includes the time of deviation, the direction of deviation, and the degree of deviation. At the same time, collect relevant data of the actuator, and its relevant data includes the rotation speed of the motor, the output signal of the driver, the action time and position of the deviation correction device;

[0025] Sort out the collected data in chronological order and ensure the accuracy and integrity of the data. Establish a module to analyze the correlation between the belt deviation amount and the actuator response, and at the same time obtain the actuator response delay situation, and classify it into large response delay and small response delay.

[0026] As a further solution of the present invention, the specific manner in which the execution response optimization module analyzes the large response delay situation is as follows:

[0027] Obtain the current sampling period T0, and at the same time obtain the CPU usage threshold and CPU usage rate corresponding to the system, denoted as T c and T current , then calculate the resource margin ratio R = (T c - T current ) / T c , and calculate the adjusted sampling period T1 according to the formula T1 = T0 × (1 - k × R), where k is the adjustment coefficient;

[0028] At the same time, compare the obtained adjusted sampling period T1 with the minimum period T and the maximum period T allowed by the system. If T1 is less than T, then take T1 = T as the standard to adjust the current sampling period. On the contrary, if T1 is greater than T, then take T1 = T as the standard to adjust the current sampling period and generate period adjustment information. min and the maximum period T max for comparison. If T1 is less than T min , then take T1 = T min as the standard to adjust the current sampling period. Conversely, if T1 is greater than T max , then take T1 = T max as the standard to adjust the current sampling period and generate period adjustment information.

[0029] As a further solution of the present invention, the specific way for the execution response optimization module to analyze the small amplitude less than the delay situation is as follows:

[0030] Obtain the proportional gain corresponding to the actuator, perform an equal-amplitude increase adjustment on the proportional gain, judge the stability of the system at the same time, and select the proportional gain that satisfies the conditions at the same time to generate gain adjustment information.

[0031] The present invention provides a dynamic deviation correction control system for an electronic belt scale based on an AI algorithm. Compared with the prior art, it has the following beneficial effects:

[0032] By deeply processing the communication protocol, eliminating redundant information, merging process steps with low correlation, reducing the data transmission volume and the number of communication interactions, improving the communication efficiency, reducing the system's demand for computing resources, enhancing the stability of the system in a complex industrial environment, and reducing the misjudgment of belt deviation and deviation correction anomalies caused by communication problems;

[0033] It can also establish a model through the execution response optimization module to analyze the correlation between the belt deviation amount and the actuator response by obtaining historical data and actuator-related data. For large-amplitude response delays, optimize by calculating and adjusting the sampling period; for small-amplitude response delays, optimize by reasonably adjusting the proportional gain and combining the system stability judgment. Enable the actuator to respond more promptly to belt deviation, improve the system stability and deviation correction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flowchart of the method steps of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0035] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0036] Example 1

[0037] Please refer to Figure 1 , this application provides a dynamic deviation correction control system for an electronic belt scale based on an AI algorithm, including a sensor data acquisition module, a deviation analysis module, an execution response optimization module, an instruction transmission optimization module, and a deviation correction control information display module, and combined with Figure 1 it can be known that the above functional modules are unidirectionally electrically connected to each other.

[0038] The sensor data acquisition module is used to obtain sensor data and transmit it to the deviation analysis module at the same time. The sensors are installed on both sides of the belt or at specific positions to detect the position of the belt edge or specific marks in real time.

[0039] The deviation analysis module is used to compare and match the obtained sensor data with the standard data. When the sensor data highly coincides with the standard data, it indicates that the belt is running in good condition and there is no deviation problem. At this time, the module will immediately generate a normal monitoring signal to provide a feedback basis for the continuous and stable operation of the system. For example, in an ideal working condition, the standard data is set that the distance from the left edge of the belt to the fixed mark is 50 mm, and the distance from the right edge of the belt to the fixed mark is 52 mm. If the sensor real-time acquisition data shows that the distances from the left and right edges of the belt to the corresponding marks are 50.1 mm and 52.05 mm respectively, and after comparison within the allowable error range, the deviation analysis module determines a match and then generates a normal monitoring signal, indicating that the belt is running normally.

[0040] On the contrary, once there is an obvious deviation between the sensor data and the standard data, it is determined that the belt is deviated. The module quickly generates a deviation analysis signal. Subsequently, the module deeply processes the deviation analysis signal, which can not only accurately obtain the sensor data, but also quickly determine the specific deviation difference and the deviation direction. For example, if the sensor feedback data shows that the distance from the left edge of the belt to the fixed mark becomes 45 mm, and the distance from the right edge of the belt to the fixed mark becomes 58 mm, after comparison with the standard data, the module can calculate that the left deviation difference is -5 mm (the negative sign indicates a left deviation), and the right deviation difference is +6 mm (the positive sign indicates a right deviation), so as to determine that the belt is deviated to the right as a whole. At the same time, the module generates corresponding deviation correction information based on the deviation information.

[0041] Process the obtained offset analysis signal, acquire sensor data, determine the specific offset difference and offset direction, generate corresponding deviation correction information, and perform optimization processing on the deviation correction information. Specifically, transmit it to the instruction transmission optimization module and the execution response optimization module respectively. The instruction transmission optimization module is responsible for ensuring that the deviation correction instruction can be efficiently and accurately conveyed to the execution end, reducing transmission delay and interference; the execution response optimization module focuses on improving the response speed and accuracy of the actuator to the deviation correction instruction, ensuring that the deviation correction action is executed quickly and accurately.

[0042] Embodiment 2

[0043] As Embodiment 2 of the present invention, it is implemented on the basis of Embodiment 1, and the difference from Embodiment 1 is that the instruction transmission optimization module performs transmission optimization analysis on the obtained deviation correction information.

[0044] Obtain the current communication protocol and its corresponding protocol format, study the standard document of the used communication protocol in detail, clarify each field, data type and their functions, identify the redundant information and character information in the protocol, and remove both of them at the same time, retaining the remaining protocol information to obtain the preprocessed protocol; for example, in some protocols, there may be multiple fields representing the same information, or there are some reserved fields for compatibility but not used in actual applications, and these can be considered removed.

[0045] Suppose in a custom communication protocol, there are two fields "StatusFlag1" and "StatusFlag2", both of which are used to represent the operating status of the device (such as normal, faulty, standby, etc.). In actual applications, only one field is needed to transmit this information, so one of the fields can be removed. For example, retain "StatusFlag1" and modify the relevant program logic and data processing based on this field;

[0046] Some communication protocols set some reserved fields for subsequent expansion or compatibility with other systems. For example, in a data frame of a certain protocol, there is an 8-bit reserved field "ReservedField", which has no actual use in the current dynamic deviation correction system of the electronic belt scale. At this time, this reserved field can be removed from the protocol to reduce the data transmission volume.

[0047] Then obtain the protocol process corresponding to the preprocessed protocol, and perform merging and omission analysis on the protocol process. The specific method for performing merging analysis on the protocol process is as follows:

[0048] Obtain all protocol processes and label them as i, where i = 1, 2, …, j, and j represents the steps of the protocol process. Taking the protocol process with i = 1 as the analysis object, determine the data dependency relationships between each interaction step. If the data dependency relationships between two or more steps are weak and their execution order has little impact on the final result, then these steps can be considered for merging. For example, in the two steps of obtaining belt deviation data and obtaining belt speed data, there is no direct dependency between these two data, and the acquisition order does not affect the subsequent deviation correction control calculation, so these two steps can be merged into an operation of obtaining deviation data and speed data simultaneously to reduce the number of communication interactions;

[0049] The specific method of omitting the protocol process is to understand in detail each process step of the protocol, clarify the specific functions and execution order of each step, and determine the flow direction of data between each step, that is, where the data is generated, which steps it passes through, and finally where it reaches. For example, in a database query protocol, the client first sends query request data to the server, the server processes it after receiving the request, and then returns the query result data to the client, which forms a data flow direction from the client to the server and then back to the client. Analyze the generation and usage of data in each step, determine which steps will generate new data, and in which subsequent steps these data will be used. For example, in an authentication protocol, the client sends login request data containing the username and password. After receiving the request, the server will generate a verification result data based on the user information in the database. If the verification is successful, it may also generate a session token data, and this session token data will be used by the client in subsequent requests to prove that it has passed the authentication, and draw a data dependency graph to further determine the protocol processes with dependencies, and at the same time filter out the isolated processes, and omit the obtained isolated processes;

[0050] Based on the merging and omission of the protocol process, obtain the corresponding optimization information, and at the same time transmit the deviation correction information according to the optimization information and transmit it to the deviation correction control information display module.

[0051] By eliminating redundant information in the protocol and merging process steps with low correlation, the data transmission volume and the number of communication interactions are effectively reduced, thereby improving the communication efficiency. The streamlined protocol and optimized process reduce the complexity of data processing and lower the system's requirements for computing resources (such as CPU, memory). This helps to ensure the stable operation of the system under limited hardware resources, and at the same time reduces the energy consumption and maintenance costs of the device.

[0052] Reducing unnecessary information transmission and process steps decreases the probability of errors and failures during communication, thereby enhancing the stability of the system. When facing electromagnetic interference, network fluctuations, etc. in a complex industrial environment, the optimized system can maintain a more reliable communication connection, reducing misjudgments of belt deviation and abnormal deviation correction caused by communication problems.

[0053] The deviation correction control information display module is used to perform deviation correction processing on the electronic belt scale according to the obtained deviation correction information.

[0054] Embodiment III

[0055] As Embodiment III of the present invention, it is implemented on the basis of Embodiment I, and the difference from Embodiment I is that there is an execution response optimization module for performing response optimization analysis on the deviation correction information.

[0056] The execution response optimization module is used to optimize and adjust the response delay of the actuator. By obtaining historical data, and the historical data includes information such as the time of deviation, the direction of deviation (left or right), the degree of deviation (represented by the deviation amount or the readings of relevant sensors), etc. At the same time, relevant data of the actuator are collected, such as the rotation speed of the motor, the output signal of the driver, the action time and position of the deviation correction device, etc. The collected data are sorted and arranged in chronological order, and the accuracy and integrity of the data are ensured. The correlation between the belt deviation amount and the actuator response is analyzed, and the analysis of the correlation here is judged by establishing a corresponding model. At the same time, the actuator response delay situation is obtained, and the response delay situation specifically includes large-scale response delay and small-scale response delay, and further analysis is performed on different situations respectively;

[0057] Through the optimized adjustment of the actuator response delay, the actuator can respond more promptly to the belt deviation. When the belt deviates, the actuator can act quickly, control the deviation amount within a smaller range, and avoid the further deterioration of the deviation situation, thereby improving the stability of the dynamic deviation correction system of the electronic belt scale.

[0058] The analysis of the large-scale response delay situation is as follows: Obtain the current sampling period T0, and at the same time obtain the maximum burden corresponding to the system, and here the maximum burden is represented by the maximum values of CPU usage rate and memory usage rate, and obtain the CPU usage threshold and CPU usage rate respectively denoted as T c and T current , then calculate the resource margin ratio R=(T c -T current ) / T c , calculate the adjusted sampling period T1 according to the formula T1 = T0×(1 - k×R), where k is the adjustment coefficient, and at the same time compare the obtained adjusted sampling period T1 with the minimum period T allowed by the systemmin and the maximum period T max Compare with it. If T1 is less than T min , then take T1 = T min as the standard to adjust the current sampling period. Conversely, if T1 is greater than T max , then take T1 = T max as the standard to adjust the current sampling period, and at the same time generate period adjustment information;

[0059] Suppose a belt deviation control system with an initial sampling period T0 = 50 milliseconds and a CPU usage threshold T c corresponding to the maximum load of the system = 80%. After monitoring, the current CPU usage rate T current = 60%. Further, according to the formula, the resource margin ratio is calculated to be 0.25. Assuming the adjustment coefficient k = 1, the adjusted sampling period can be calculated to be 37.5 milliseconds according to the formula;

[0060] If the minimum sampling period T min allowed by the system = 20 milliseconds and the maximum sampling period T max = 100 milliseconds, since 20 < 37.5 < 100, the adjusted sampling period is 37.5 milliseconds. If the calculated T1 value is not between T min and T max , then corresponding boundary processing is required. For example, if it is calculated that T1 = 120 milliseconds, which is greater than T max = 100 milliseconds, then the adjusted sampling period should take T1 = 100 milliseconds.

[0061] The analysis of the small - amplitude response delay situation is as follows: Obtain the proportional gain corresponding to the actuator, and adjust the proportional gain by an equal amplitude increase. Here, the equal amplitude increase means cumulative increase by 0.1. At the same time, judge the stability of the system. The judgment of the system stability is specifically analyzed through the operating state of the system, and the stability of the system is evaluated through theoretical analysis, simulation or actual operation test, and select the proportional gain that meets the conditions simultaneously to generate gain adjustment information;

[0062] Transmit the obtained period adjustment information and gain adjustment information to the deviation correction control information display module.

[0063] Embodiment 4

[0064] As Embodiment 4 of the present invention, the key lies in combining the implementation processes of Embodiment 1, Embodiment 2 and Embodiment 3 for implementation.

[0065] For some data in the above formula, only their numerical values are taken for calculation, and the parameter units are not substituted for calculation. At the same time, the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0066] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An electronic belt scale dynamic deviation rectification control system based on an AI algorithm, characterized in that, Including: A deviation analysis module, which is used to match the sensor data transmitted by the sensor data acquisition module with the standard data, generate a normal monitoring signal or an offset analysis signal, analyze the offset analysis signal at the same time, determine the offset difference and the offset method to generate correction information, and transmit them to the instruction transmission optimization module and the execution response optimization module respectively; An instruction transmission optimization module, which eliminates redundant information and character information in the protocol format to obtain a preprocessed protocol, and analyzes the merging and omission of the protocol process corresponding to the preprocessed protocol at the same time; Determine the corresponding merging steps according to the data dependency between the protocol process steps, perform merging processing, draw a data dependency graph and determine the isolated process based on the data flow and usage in the protocol process steps, perform omission at the same time, generate optimization information based on the merging and omission of the protocol process, and then transmit it to the correction control information display module; An execution response optimization module, which is used to optimize and adjust the response delay of the actuator, analyze and sort out the historical data, analyze the correlation between the belt deviation amount and the actuator response, and obtain the response delay situation for analysis; Analyze the situation of large response delay, calculate the adjustment sampling period according to the current acquisition period and the maximum burden of the system, compare it with the maximum and minimum periods of the system to determine the sampling period, and generate period adjustment information; Analyze the situation of small response delay less than the delay, increase the proportional gain by an equal amplitude in combination with the system stability, generate gain adjustment information, and optimize the actuator.

2. The dynamic deviation correction control system of the electronic belt scale based on the AI algorithm according to claim 1, wherein, It also includes a sensor data acquisition module, which is used to transmit the acquired sensor data to the deviation analysis module; It also includes a correction control information display module, which is used to transmit the acquired correction information to the corresponding actuator.

3. The dynamic deviation correction control system of the electronic belt scale based on the AI algorithm according to claim 1, wherein The specific method for the deviation analysis module to determine the offset difference and the offset method to generate correction information is as follows: Compare the sensor data with the standard data, and the standard data represents the distance between the edge and both sides when the belt rotates. If they match, it indicates no offset and a normal monitoring signal is generated. If they do not match, it indicates an offset and an offset analysis signal is generated. Subsequently, obtain the sensor data, determine the offset difference and direction, and generate correction information.

4. The dynamic deviation correction control system of the electronic belt scale based on the AI algorithm according to claim 1, characterized in that, The specific method for the instruction transmission optimization module to obtain the preprocessed protocol is as follows: Obtain the current communication protocol and the corresponding protocol format, study the standard document of the current communication protocol in detail, clarify each field, data type and function, identify the redundant information and character information in the protocol, and eliminate both at the same time, and retain the remaining protocol information to obtain the preprocessed protocol.

5. The dynamic deviation correction control system of the electronic belt scale based on the AI algorithm according to claim 1, characterized in that, The specific method for analyzing the merging of the protocol process corresponding to the preprocessed protocol is as follows: Obtain all protocol processes and label them as i, and i = 1, 2,..., j, where j represents the steps of the protocol process. Take the protocol process with i = 1 as the analysis object, and then analyze the data dependency between the remaining protocol processes and the analysis object in turn. If the data dependency between two or more steps is weak and their execution order has little impact on the final result, the steps can be merged.

6. The dynamic deviation correction control system of the electronic belt scale based on the AI algorithm according to claim 1, characterized in that, The specific method for analyzing the omission of the protocol process corresponding to the preprocessed protocol is as follows: Understand each process step of the current communication protocol, clarify the specific functions and execution order of each step, determine the data flow between each step as well as the generation and usage situations, analyze the generation and usage of data in each step, draw a data dependency graph, further determine the protocol processes with dependencies, and at the same time screen out isolated processes, and omit the obtained isolated processes.

7. The dynamic deviation rectification control system of the electronic belt scale based on the AI algorithm according to claim 1, wherein The specific method for the execution response optimization module to optimize and adjust the response delay of the actuator is as follows: By obtaining historical data, and the historical data includes the time of deviation, the direction of deviation, and the degree of deviation. At the same time, collect relevant data of the actuator, and its relevant data includes the rotation speed of the motor, the output signal of the driver, the action time and position of the deviation correction device; Sort the collected data in chronological order, ensure the accuracy and integrity of the data, establish a module to analyze the correlation between the belt deviation amount and the actuator response, and at the same time obtain the actuator response delay situation, and classify it into large-scale response delay and small-scale response delay.

8. The dynamic deviation correction control system of the electronic belt scale based on the AI algorithm according to claim 1, wherein, The specific method for the execution response optimization module to analyze the large-scale response delay situation is as follows: Obtain the current sampling period T0, and at the same time obtain the corresponding CPU usage threshold and CPU usage rate of the system, denoted as T c and T current , then calculate the resource margin ratio R = (T c - T current ) / T c , and calculate the adjusted sampling period T1 according to the formula T1 = T0 × (1 - k × R), where k is the adjustment coefficient; At the same time, compare the obtained adjusted sampling period T1 with the minimum period T and the maximum period T allowed by the system. If T1 is less than T, then take T1 = T as the standard to adjust the current sampling period. On the contrary, if T1 is greater than T, then take T1 = T as the standard to adjust the current sampling period and generate period adjustment information at the same time. min and the maximum period T max for comparison. If T1 is less than T min , then take T1 = T min as the standard to adjust the current sampling period. Conversely, if T1 is greater than T max , then take T1 = T max as the standard to adjust the current sampling period and generate period adjustment information at the same time.

9. The dynamic deviation correction control system of the electronic belt scale based on the AI algorithm according to claim 1, characterized in that, The specific method for the execution response optimization module to analyze the small-scale less-than-delay situation is as follows: Obtain the proportional gain corresponding to the actuator, increase the proportional gain in equal amplitude, judge the stability of the system at the same time, and select the proportional gain that meets the conditions at the same time to generate gain adjustment information.

Images