Method and device for correcting a metering module of a feeding apparatus, storage medium, computer program product and feeding apparatus

Abstract

The application relates to the technical field of precise metering control of feeding equipment, in particular to a metering module correction method and device of feeding equipment, a storage medium, a computer program product and feeding equipment. The method comprises the following steps: acquiring the state of a mounting platform under the action of external disturbance, weighing a preset reference mass set by a reference weighing channel under the corresponding disturbance condition, and obtaining a reference weighing data set; generating a metering instruction set based on the reference mass set, controlling the metering module to execute material metering output under the disturbance condition, and acquiring a material weighing data set corresponding to the metering instruction set; establishing an interference correction model based on the reference mass set, the reference weighing data set and the material weighing data set; acquiring real-time reference weighing data in subsequent operation and inputting the model to obtain correction parameters, correcting the metering output data of the metering module according to the correction parameters, and obtaining target material metering data.

Description

Technical Field

[0001] This application relates to the field of precise metering and control technology for feeding equipment, and in particular to a method, device, storage medium, computer program product, and feeding equipment for correcting the metering module of a feeding equipment. Background Technology

[0002] The aquaculture industry has high requirements for the metering and control of feed feeding, and feed costs account for a large proportion of the total aquaculture costs. Therefore, weight is usually the primary metering method during feeding to control the amount of feed and manage the feeding process. Existing feeding machines can generally be divided into two categories according to their application environment: land-based feeding equipment and marine feeding equipment. For land-based feeding equipment, due to the relatively stable fixed platform, weighing is mostly used for metering. Although the vibration of the equipment operation will have some impact on the weighing, this impact can usually be suppressed or eliminated through appropriate structural or control measures, thereby achieving relatively stable weight measurement. However, for various aquaculture scenarios, some existing feeding equipment uses flow rate or volumetric methods for measurement to avoid weighing stability issues, requiring conversion to weight based on parameters such as feed density. This measurement method itself suffers from insufficient accuracy and inconvenience in adapting to changes in feed type and feeding amount. Furthermore, feeding equipment is typically deployed on ships, where factors such as wind, waves, and currents can cause tilting, swaying, and vibration of the ship and its platform, leading to deviations in the weighing data output by the feeding equipment's metering module and affecting the reliability and consistency of material measurement. Therefore, improving the reliability and consistency of feeding material measurement has become an urgent technical problem to be solved. Summary of the Invention

[0003] The main objective of this application is to provide a method, apparatus, storage medium, computer program product, and feeding equipment for correcting the metering module of a feeding device, aiming to solve the technical problem of how to improve the reliability and consistency of feeding material metering.

[0004] To achieve the above objectives, this application provides a method for correcting the metering module of a feeding device, the method comprising: The state of the mounting platform under external disturbance is obtained, and under the disturbance conditions corresponding to the state of the mounting platform, the reference weighing channel weighs the preset set of reference mass parts to obtain the corresponding reference weighing dataset. A set of metering instructions is generated based on the preset set of reference mass components, and a material weighing dataset is obtained by controlling the metering module of the feeding device to perform material metering output based on the set of metering instructions under the disturbance conditions. The material weighing dataset corresponds one-to-one with the set of metering instructions. An interference correction model is established based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset. The interference correction model is used to characterize the correlation between the measurement deviation under the disturbance condition and the reference weighing dataset. In subsequent operations, the real-time benchmark weighing data output by the benchmark weighing channel is acquired, and the real-time benchmark weighing data is input into the interference correction model to obtain correction parameters; The metering output data of the metering module is corrected based on the correction parameters to obtain the metering data of the target material.

[0005] In one embodiment, the step of acquiring the state of the mounting platform under external disturbance, and under the disturbance conditions corresponding to the state of the mounting platform, causing the reference weighing channel to weigh a preset set of reference mass components to obtain the corresponding reference weighing dataset includes: Acquire attitude change information and / or motion characteristic information of the mounting platform to characterize the influence of external disturbances, and determine the state parameters of the mounting platform based on the attitude change information and / or the motion characteristic information; The sampling configuration of the reference weighing channel is matched with the disturbance condition, and the reference weighing channel is triggered to weigh and collect data on a preset set of reference mass components to output the original weighing sequence. The original weighing sequence is processed to generate a set of weighing results that corresponds one-to-one with the preset set of reference mass parts, and the set of weighing results is used as the corresponding reference weighing dataset.

[0006] In one embodiment, the step of generating a metering instruction set based on the preset reference mass component set and obtaining a material weighing dataset obtained by controlling the metering module of the feeding device to perform material metering output based on the metering instruction set under the disturbance conditions includes: The mass parameters of each reference mass component in the preset reference mass component set are read, and the mass parameters are encapsulated according to a preset instruction format to form a set of measurement instructions that corresponds one-to-one with the preset reference mass component set. The set of metering instructions is sent to the metering module in a preset order, and the metering module is driven to complete the corresponding material metering output process based on each metering instruction, so that the material enters the preset bearing structure. Under the disturbance conditions, material weighing data corresponding to each metering command is acquired, and the material weighing data is correlated and organized to obtain a material weighing dataset.

[0007] In one embodiment, the step of establishing an interference correction model based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset includes: The mass parameters in the preset set of reference mass parts are matched and associated with the corresponding weighing results in the reference weighing dataset to obtain reference deviation data; The reference deviation data is matched and associated with the corresponding material weighing results in the material weighing dataset to form a modeling sample set for characterizing the relationship between the reference channel and the output of the metering module under the same disturbance condition. The input feature quantities characterizing the weighing state of the reference channel are extracted from the modeling sample set, and the output calibration quantity characterizing the measurement deviation of the measurement module is extracted. The mapping structure of the interference correction model is determined according to the input feature quantities and the output calibration quantity, and the parameters of the mapping structure are solved based on the preset fitting strategy to obtain the model parameters. The mapping structure and the model parameters are encapsulated and stored as an interference correction model.

[0008] In one embodiment, the step of acquiring real-time benchmark weighing data output by the benchmark weighing channel during subsequent operations and inputting the real-time benchmark weighing data into the interference correction model to obtain correction parameters includes: During the operation period when the feeding equipment performs material metering output, the reference weighing channel is triggered to collect real-time weighing data of the preset reference mass component, and the collected weighing results are time-stamped and cached to form the real-time reference weighing data. The real-time benchmark weighing data is subjected to data integrity checks and format unification processing, and the real-time benchmark weighing data is feature-assembled based on the input definition corresponding to the interference correction model to obtain model input data; The model input data is input into the disturbance correction model, and the correction parameters corresponding to the current disturbance conditions are output.

[0009] In one embodiment, the step of correcting the metering output data of the metering module based on the correction parameters to obtain the target material metering data includes: The correction parameters are parsed to determine the correction amount definition corresponding to the metering output data of the metering module, and the correction amount definition is converted into a correction configuration. The original measurement data output by the measurement module is obtained, and the correction parameter is associated and matched at the time of generation of the original measurement data. Based on the correction configuration, the original measurement data is subjected to deviation compensation processing to obtain the corrected measurement data. The corrected measurement data is subjected to data consistency verification and result encapsulation, and the corrected measurement data that has passed the verification and encapsulation is output as the target material measurement data.

[0010] Furthermore, to achieve the above objectives, this application also proposes a metering module correction device for a feeding device, the metering module correction device for the feeding device comprising: The benchmark weighing module is used to acquire the state of the mounting platform under external disturbance, and under the disturbance conditions corresponding to the state of the mounting platform, the benchmark weighing channel weighs the preset set of benchmark mass parts to obtain the corresponding benchmark weighing dataset. The material weighing module is used to generate a set of metering instructions based on the preset set of reference mass components, and to obtain a material weighing dataset obtained by the metering module controlling the feeding device to perform material metering output based on the set of metering instructions under the disturbance conditions. The material weighing dataset corresponds one-to-one with the set of metering instructions. The model building module is used to build a disturbance correction model based on the preset set of reference mass parts, the reference weighing dataset, and the material weighing dataset. The disturbance correction model is used to characterize the correlation between the measurement deviation under the disturbance conditions and the reference weighing dataset. The correction parameter module is used to acquire the real-time benchmark weighing data output by the benchmark weighing channel during subsequent operations, and input the real-time benchmark weighing data into the interference correction model to obtain correction parameters; The target module is used to correct the metering output data of the metering module based on the correction parameters to obtain the target material metering data.

[0011] In addition, to achieve the above objectives, this application also proposes a feeding device, which includes: a memory, a processor, and a metering module correction program for the feeding device stored in the memory and executable on the processor. The metering module correction program for the feeding device is configured to implement the steps of the metering module correction method for the feeding device as described in any of the above embodiments.

[0012] In addition, to achieve the above objectives, this application also proposes a storage medium storing a metering module correction program for a feeding device, wherein when the metering module correction program for the feeding device is executed by a processor, it implements the steps of the metering module correction method for the feeding device as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the metering module correction method for the feeding device as described above.

[0014] This application obtains the state of the mounting platform under external disturbance, and under the disturbance conditions corresponding to the mounting platform state, enables the reference weighing channel to weigh a preset set of reference mass components to obtain the corresponding reference weighing dataset; generates a set of metering instructions based on the preset set of reference mass components, and obtains a material weighing dataset obtained by controlling the metering module of the feeding device to perform material metering output based on the set of metering instructions under disturbance conditions, with a one-to-one correspondence between the material weighing dataset and the set of metering instructions; establishes a disturbance correction model based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset, which is used to characterize the correlation between the metering deviation under disturbance conditions and the reference weighing dataset; in subsequent operations, obtains the real-time reference weighing data output by the reference weighing channel, and inputs the real-time reference weighing data into the disturbance correction model to obtain correction parameters; and corrects the metering output data of the metering module based on the correction parameters to obtain the target material metering data. This application first obtains the platform status and, under the corresponding disturbance conditions, weighs a preset set of reference mass components using a reference weighing channel to obtain a reference weighing dataset, enabling the reference weighing dataset to characterize the reference weighing output features under the current disturbance conditions. Subsequently, a set of metering instructions is generated based on the same preset set of reference mass components, and the metering module is controlled to output material metering under the same disturbance conditions to obtain a material weighing dataset that corresponds one-to-one with the set of metering instructions, thereby establishing an alignable sample correspondence between the reference weighing dataset and the material weighing dataset. A disturbance correction model is established based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset to characterize the correlation between the metering deviation and the reference weighing dataset under disturbance conditions. In subsequent operations, real-time reference weighing data is input into this model to obtain correction parameters, which are then used to correct the metering output data of the metering module to obtain the target material metering data. Thus, the metering output can be synchronously corrected as disturbance conditions change, reducing the impact of disturbances on the metering results and improving the reliability and consistency of the feeding material metering. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart illustrating the first embodiment of the metering module correction method for the feeding device of this application. Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the method for correcting the metering module of the feeding device in this application; Figure 3This is a schematic diagram of a sub-process in the third embodiment of the method for correcting the metering module of the feeding device in this application; Figure 4 This is a schematic diagram of a real-time calibration system for the metering module of a feeding device in one embodiment of the method for correcting the metering module of the feeding device according to this application. Figure 5 This is a schematic diagram of the module structure of the metering module correction device for the feeding equipment in an embodiment of this application; and Figure 6 This is a schematic diagram of the hardware operating environment involved in the metering module correction method of the feeding device in the embodiments of this application.

[0016] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0018] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0019] It should be noted that the aquaculture industry has high requirements for the metering and control of feed feeding, and feed costs account for a large proportion of the total aquaculture costs. Therefore, weight is usually the primary metering method during feeding to control the amount of feed and manage the feeding process. Existing feeding machines can generally be divided into two categories according to their application environment: land-based feeding equipment and other feeding equipment. For land-based feeding equipment, due to its relatively stable fixed platform, weighing is mostly used. Although the vibration of the equipment operation will have some impact on the weighing, this impact can usually be suppressed or eliminated through appropriate structural or control measures, thereby achieving relatively stable weight measurement. However, in aquaculture scenarios, some existing feeding equipment uses flow rate or volumetric methods for measurement to circumvent weighing stability issues, requiring conversion to weight based on parameters such as feed density. This method suffers from insufficient accuracy and inconvenience in adapting to changes in feed type and feeding amount. Furthermore, feeding equipment is typically deployed on ships, where factors like wind, waves, and currents can cause tilting, swaying, and vibration of the vessel and its platform, leading to deviations in the weighing data output by the equipment's metering module and affecting the reliability and consistency of material measurement. Therefore, improving the reliability and consistency of feeding material measurement has become a pressing technical problem.

[0020] The main solution of this application is as follows: First, obtain the state of the mounting platform under external disturbance. Second, under the disturbance conditions corresponding to the mounting platform state, enable the reference weighing channel to weigh a preset set of reference mass components to obtain the corresponding reference weighing dataset. Third, generate a set of metering instructions based on the preset set of reference mass components, and obtain the material weighing dataset obtained by controlling the metering module of the feeding device to perform material metering output under the disturbance conditions based on the set of metering instructions. The material weighing dataset corresponds one-to-one with the set of metering instructions. Fourth, establish a disturbance correction model based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset. The disturbance correction model is used to characterize the correlation between the metering deviation under disturbance conditions and the reference weighing dataset. Fifth, in subsequent operations, obtain the real-time reference weighing data output by the reference weighing channel and input the real-time reference weighing data into the disturbance correction model to obtain correction parameters. Sixth, perform correction processing on the metering output data of the metering module based on the correction parameters to obtain the target material metering data.

[0021] This application first obtains the platform status and, under the corresponding disturbance conditions, weighs a preset set of reference mass components using a reference weighing channel to obtain a reference weighing dataset, enabling the reference weighing dataset to characterize the reference weighing output features under the current disturbance conditions. Subsequently, a set of metering instructions is generated based on the same preset set of reference mass components, and the metering module is controlled to output material metering under the same disturbance conditions to obtain a material weighing dataset that corresponds one-to-one with the set of metering instructions, thereby establishing an alignable sample correspondence between the reference weighing dataset and the material weighing dataset. A disturbance correction model is established based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset to characterize the correlation between the metering deviation and the reference weighing dataset under disturbance conditions. In subsequent operations, real-time reference weighing data is input into this model to obtain correction parameters, which are then used to correct the metering output data of the metering module to obtain the target material metering data. Thus, the metering output can be synchronously corrected as disturbance conditions change, reducing the impact of disturbances on the metering results and improving the reliability and consistency of the feeding material metering.

[0022] It should be noted that the executing entity of the method in this embodiment can be a computing service device with data processing, network communication, and program execution functions, or it can be the aforementioned feeding device with the same or similar functions. This embodiment and the following embodiments will be described using a feeding device as an example.

[0023] Based on this, a first embodiment of the metering module correction method for the feeding device of this application is proposed. Please refer to [link / reference]. Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the metering module correction method for the feeding device of this application.

[0024] In this embodiment, the metering module correction method of the feeding device includes the following steps: S1: Obtain the state of the mounting platform under external disturbance, and under the disturbance conditions corresponding to the state of the mounting platform, make the reference weighing channel weigh the preset reference mass component set to obtain the corresponding reference weighing dataset. It should be noted that external disturbances refer to non-steady-state influencing factors acting on the feeding equipment and its mounting platform in the working environment. Mounting platform state refers to the state characterization information of the mounting platform under the influence of external disturbances. Disturbance conditions refer to the current disturbance situation jointly characterized by the working environment and platform motion characteristics corresponding to the mounting platform state. The reference weighing channel refers to the weighing measurement path used to weigh a preset set of reference mass components. The preset set of reference mass components refers to a set of mass reference objects pre-set and used for weighing reference.

[0025] Specifically, when the feeding equipment is in operation, state information characterizing the impact of external disturbances on the platform is collected to obtain the platform's state. This state information can come from the platform's own state acquisition unit or the equipment operation monitoring unit, and is represented by state characterization data of platform attitude changes and / or motion changes. Based on the platform's state, the platform's interference background at the current operating moment is identified or characterized to determine the disturbance conditions corresponding to the platform's state. These disturbance conditions are then used as constraints for subsequent weighing data collection to ensure that the weighing process occurs under these disturbance conditions.

[0026] Furthermore, under the aforementioned disturbance conditions, the reference weighing channel performs weighing acquisition on a preset set of reference mass components. Specifically, this may include: placing each reference mass component into its weighing position in the reference weighing channel according to a preset order, triggering the reference weighing channel to output the corresponding weighing result, and recording and organizing the weighing results; during the recording process, each weighing result can be associated with a corresponding reference mass component identifier and acquisition time information to establish a "reference mass component—weighing result" correspondence. Finally, the sets of weighing results obtained under the aforementioned disturbance conditions, each corresponding to one of the preset set of reference mass components, are summarized to obtain the corresponding reference weighing dataset.

[0027] By first acquiring the platform status under external disturbance and defining the disturbance conditions accordingly, and then weighing a preset set of reference mass components under these disturbance conditions using a reference weighing channel to form a reference weighing dataset, the reference weighing dataset can be collected under a disturbance background consistent with actual operations and maintain its correspondence with the reference mass components. Since the set of reference mass components provides a stable reference input, the reference weighing dataset can be used to characterize the deviation characteristics of the weighing output under the current disturbance conditions, thereby providing a reliable data foundation for subsequent metrological deviation correlation modeling and parameter correction based on this reference data, and thus supporting more stable metrological result output under disturbance environments.

[0028] S2: Generate a set of metering instructions based on the preset set of reference mass components, and obtain a material weighing dataset obtained by controlling the metering module of the feeding device to perform material metering output based on the set of metering instructions under the disturbance conditions. The material weighing dataset corresponds one-to-one with the set of metering instructions. S3: Establish a disturbance correction model based on the preset set of reference mass parts, the reference weighing dataset, and the material weighing dataset. The disturbance correction model is used to characterize the correlation between the measurement deviation under the disturbance condition and the reference weighing dataset. It should be noted that the preset reference mass set is a pre-set set of mass reference objects. The metering instruction set is a set of control instructions generated by the preset reference mass set. The metering module is a functional module in the feeding equipment used to execute material metering output. Material metering output is the material output process completed by the metering module under the drive of metering instructions. The material weighing dataset is the data set obtained by weighing and collecting the material metering output results under the disturbance conditions. The reference weighing dataset is the corresponding data set obtained by the reference weighing channel weighing the preset reference mass set under the disturbance conditions. The disturbance correction model is a model established based on the preset reference mass set, the reference weighing dataset, and the material weighing dataset. The metering deviation is the deviation between the metering output of the metering module under disturbance conditions and the reference quantity corresponding to the preset reference mass set.

[0029] Specifically, based on a preset set of reference mass components, the mass parameters corresponding to each reference mass component are read, and these mass parameters are mapped into control commands recognizable by the metering module according to preset rules, thereby forming a metering command set. The metering command set maintains a one-to-one correspondence with the preset set of reference mass components. Subsequently, under the disturbance conditions corresponding to the platform state, each metering command in the metering command set is sequentially issued to control the metering module of the feeding equipment to execute material metering output, allowing the material to enter the preset bearing structure. Weighing data is collected from the bearing structure after each material metering output, recording the material weighing result corresponding to that metering command. Data is then correlated and organized based on the metering command identifier and the collection time to obtain a material weighing dataset that corresponds one-to-one with the metering command set.

[0030] Furthermore, after obtaining the benchmark weighing dataset and the material weighing dataset, the mass parameters in the preset benchmark mass component set are used as reference benchmarks, and combined with the corresponding weighing results in the benchmark weighing dataset, benchmark samples are constructed to characterize the output characteristics of the benchmark channel under disturbance conditions. Further, the benchmark samples are paired with the corresponding material weighing results in the material weighing dataset to form a modeling sample set reflecting the relationship between the benchmark weighing output and the material metering output under the same disturbance condition. Based on the modeling sample set, a preset fitting strategy is used to determine the model structure and model parameters of the disturbance correction model, enabling the disturbance correction model to use the output characteristics characterized by the benchmark weighing dataset as input and output correlation results characterizing the metering deviation.

[0031] By generating a set of measurement instructions that corresponds one-to-one with a preset set of reference mass components, and obtaining a set of material weighing data that corresponds one-to-one with the set of measurement instructions under the same disturbance conditions, the material measurement output results can form an alignable data correspondence with the reference benchmark at the sample level. Furthermore, a disturbance correction model is established by combining the benchmark weighing dataset and the material weighing dataset, and this model is used to characterize the correlation between measurement deviation and benchmark weighing data under disturbance conditions. This transforms the measurement deviation under the influence of external disturbances into a correlation representation that can be driven by benchmark weighing data, providing a basis for generating correction parameters based on real-time benchmark weighing data and correcting the measurement output, thereby supporting the acquisition of more stable and consistent measurement results under disturbance conditions.

[0032] S4: In subsequent operations, acquire the real-time benchmark weighing data output by the benchmark weighing channel, and input the real-time benchmark weighing data into the interference correction model to obtain correction parameters; S5: Based on the correction parameters, the metering output data of the metering module is corrected to obtain the target material metering data.

[0033] It should be noted that the subsequent operation process refers to the operational phase where the feeding equipment enters actual feeding operation after the disturbance correction model is established. Real-time benchmark weighing data refers to the data collected in real-time by the benchmark weighing channel on the preset benchmark mass component during the subsequent operation process. Correction parameters refer to the parameters calculated from the real-time benchmark weighing data using the disturbance correction model. Metering module output data refers to the material metering-related data output by the metering module during the subsequent operation process. Correction processing refers to the process of compensating for deviations in the metering output data based on the correction parameters. Target material metering data refers to the metering data obtained after completing the correction processing on the metering module's metering output data.

[0034] Specifically, during subsequent operations, the benchmark weighing channel is triggered to weigh and collect data on a preset benchmark mass component to output real-time benchmark weighing data. This real-time benchmark weighing data is recorded and managed, for example, by adding acquisition time information and caching it to ensure its availability for subsequent processing at the corresponding operation time. Subsequently, the real-time benchmark weighing data is processed and formatted according to the input requirements of the interference correction model to meet the data structure constraints of the model input. The processed real-time benchmark weighing data is then input into the interference correction model, which outputs correction parameters corresponding to the current operation time.

[0035] Furthermore, the metering output data output by the metering module during subsequent operations is acquired, and the metering output data is associated and matched with the correction parameters to establish a consistent processing object relationship between the correction parameters and the corresponding metering output data. Correction processing is performed on the metering output data according to the correction parameters to obtain corrected metering data. After the correction processing is completed, the corrected metering data is encapsulated and output as a result, and the corrected metering data determined through output is output as the target material metering data.

[0036] By continuously acquiring real-time benchmark weighing data from the benchmark weighing channel during subsequent operations, and inputting this real-time benchmark weighing data into the disturbance correction model to obtain correction parameters, the correction parameters can be dynamically updated according to the benchmark weighing output status changes at the time of operation. Furthermore, the correction parameters are used to correct the metering output data of the metering module and output the target material metering data. This implements the "correlation between benchmark weighing data and metering deviation" represented by the disturbance correction model into the deviation compensation process of the actual metering output data, enabling the output of the metering module to be synchronously corrected according to the current disturbance background, thereby reducing the metering output fluctuation caused by external disturbances, and making the obtained target material metering data more stable and consistent during continuous operations.

[0037] This embodiment acquires the state of the mounting platform under external disturbances, and under the disturbance conditions corresponding to the mounting platform state, enables the reference weighing channel to weigh a preset set of reference mass components to obtain the corresponding reference weighing dataset. A set of metering instructions is generated based on the preset set of reference mass components, and a material weighing dataset is obtained by controlling the metering module of the feeding device to perform material metering output under disturbance conditions based on the metering instruction set. The material weighing dataset corresponds one-to-one with the metering instruction set. An interference correction model is established based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset. The interference correction model characterizes the correlation between the metering deviation under disturbance conditions and the reference weighing dataset. In subsequent operations, real-time reference weighing data output by the reference weighing channel is acquired and input into the interference correction model to obtain correction parameters. Based on the correction parameters, the metering output data of the metering module is corrected to obtain the target material metering data. This embodiment first acquires the platform status and, under the corresponding disturbance conditions, weighs a preset set of reference mass components using a reference weighing channel to obtain a reference weighing dataset. This dataset characterizes the reference weighing output features under the current disturbance conditions. Subsequently, a set of metering instructions is generated based on the same preset set of reference mass components, and the metering module is controlled to output material metering under the same disturbance conditions to obtain a material weighing dataset that corresponds one-to-one with the set of metering instructions. This establishes an alignable sample correspondence between the reference weighing dataset and the material weighing dataset. A disturbance correction model is established based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset to characterize the correlation between metering deviation and the reference weighing dataset under disturbance conditions. In subsequent operations, real-time reference weighing data is input into this model to obtain correction parameters, which are then used to correct the metering output data of the metering module to obtain the target material metering data. Thus, the metering output can be synchronously corrected as disturbance conditions change, reducing the impact of disturbances on the metering results and improving the reliability and consistency of the feeding material metering.

[0038] Based on the first embodiment described above, a second embodiment of the metering module correction method for the feeding device of this application is proposed. Please refer to... Figure 2 , Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the metering module correction method for the feeding equipment of this application.

[0039] like Figure 2 As shown, in this embodiment, step S1 includes: S11: Obtain attitude change information and / or motion characteristic information to characterize the impact of external disturbances on the platform, and determine the state parameters of the platform based on the attitude change information and / or the motion characteristic information; S12: Match the sampling configuration of the reference weighing channel with the disturbance conditions, and trigger the reference weighing channel to weigh and collect the preset reference mass set to output the original weighing sequence; S13: Organize the original weighing sequence to generate a set of weighing results that corresponds one-to-one with the preset set of reference mass parts, and use the set of weighing results as the corresponding reference weighing dataset.

[0040] It should be noted that attitude change information is collected to characterize changes in the attitude state of the platform. Motion characteristic information is collected to characterize changes in the motion state of the platform. Sampling configuration refers to the data acquisition settings used by the reference weighing channel during weighing. The raw weighing sequence is the unprocessed weighing data sequence output by the reference weighing channel during the weighing process. The weighing result set is a set of weighing results obtained by data processing, corresponding one-to-one with a preset set of reference mass components. The reference weighing dataset is a set of reference weighing data containing the weighing result set and used for subsequent processing.

[0041] Specifically, in the operational scenario, to obtain a state representation of the platform under external disturbances, attitude change information and / or motion characteristic information of the platform affected by external disturbances are collected. This attitude change information and / or motion characteristic information can be obtained by the platform's state acquisition unit, equipment operation monitoring unit, or acquisition components related to platform motion. Based on the collected attitude change information and / or motion characteristic information, platform state parameters are generated according to preset state determination rules to form a parameterized description of the current disturbed state of the platform, which serves as the state basis for subsequent weighing acquisition under "disturbance conditions."

[0042] Furthermore, based on the disturbance conditions corresponding to the platform state parameters, the sampling configuration of the benchmark weighing channel is matched and set so that the benchmark weighing channel outputs weighing data under the disturbance conditions using a preset sampling strategy. Subsequently, the benchmark weighing channel is triggered to collect weighing data from a preset set of benchmark mass components, obtaining the original weighing sequence. The original weighing sequence is then processed to establish a correspondence between "benchmark mass components and weighing data" at the data level, and a set of weighing results corresponding one-to-one with the preset set of benchmark mass components is generated. The set of weighing results is then output as the corresponding benchmark weighing dataset.

[0043] By collecting attitude change information and / or motion characteristic information and determining the state parameters of the platform accordingly, the state of the platform under external disturbances can be parameterized, thus providing a state basis for subsequent weighing data collection under corresponding disturbance conditions. Furthermore, the sampling configuration of the benchmark weighing channel is matched with the disturbance conditions, triggering the weighing data collection of a preset set of benchmark mass components, so that the original weighing sequence can reflect the benchmark weighing output under the disturbance background. Then, by organizing the original weighing sequence, a set of weighing results corresponding one-to-one with the set of benchmark mass components is generated, forming a benchmark weighing dataset. This gives the benchmark weighing data a clear object correspondence and a usable data organization form, thus providing a consistent data foundation for subsequent correlation modeling and parameter correction generation using the benchmark weighing data.

[0044] Based on the first embodiment described above, in this embodiment, step S2 includes: S21: Read the mass parameters of each reference mass component in the preset reference mass component set, and encapsulate the mass parameters according to a preset instruction format to form a set of measurement instructions that corresponds one-to-one with the preset reference mass component set. S22: The set of metering instructions is sent to the metering module in a preset order, and the metering module is driven to complete the corresponding material metering output process based on each metering instruction, so that the material enters the preset bearing structure; S23: Under the disturbance conditions, acquire the material weighing data corresponding to each metering command, and associate and organize the material weighing data to obtain a material weighing dataset.

[0045] It should be noted that quality parameters are parameter information used to characterize the mass size of the reference mass component. The preset instruction format is a pre-defined instruction organization rule recognizable by the metering module. The metering instruction set is a set of control instructions encapsulated from the quality parameters of each reference mass component according to the preset instruction format. The material metering output process is the material output action process executed by the metering module under the drive of the metering instructions. The preset support structure is a pre-defined structure or container used to receive the metered output material. Material weighing data is the data obtained by weighing and collecting materials in the preset support structure. Association and organization is the process of establishing a correspondence between the material weighing data and the corresponding metering instructions and organizing and summarizing them. The material weighing dataset is a set of material weighing data formed through association and organization, maintaining a one-to-one correspondence with the metering instruction set.

[0046] Specifically, the mass parameters of each reference mass component in the preset reference mass component set are read to obtain the mass parameter information corresponding to each reference mass component. Then, the mass parameters are encapsulated according to a preset instruction format to form a set of metering instructions that corresponds one-to-one with the preset reference mass component set. The set of metering instructions is then sent to the metering module in a preset order, enabling the metering module to receive each metering instruction sequentially and drive the material metering output process based on the metering control information carried in each instruction. This causes the material to enter a preset bearing structure, thus forming multiple material metering output tasks executed according to the instruction sequence.

[0047] Furthermore, under the aforementioned disturbance conditions, weighing data is collected from the preset support structure after each metering command is triggered to obtain material weighing data corresponding to each metering command. During the collection process, metering command identifiers and collection time information can be added to the material weighing data, and multiple collected material weighing data are summarized and managed. Further, the material weighing data is correlated and organized to establish a one-to-one correspondence between each material weighing data and its corresponding metering command, and a material weighing dataset is generated and output according to a preset organization method.

[0048] By reading the mass parameters of a preset set of reference mass components and encapsulating them according to a preset instruction format, a set of metering instructions corresponding one-to-one with the set of reference mass components is formed. This ensures that subsequent metering outputs have clear reference inputs and a consistent instruction organization method. The set of metering instructions is then issued in a preset order to drive the metering module to execute the corresponding material metering output, allowing the material to enter the preset bearing structure. This establishes a traceable execution link between "instruction and output material". Simultaneously, under the same disturbance conditions, the material weighing data corresponding to each metering instruction is acquired and correlated to form a material weighing dataset corresponding one-to-one with the set of metering instructions. This ensures that the material metering output results have a clear data correspondence and a data organization form that can be used for subsequent modeling processing, thus providing a consistent data foundation for subsequently establishing an interference correction model using reference weighing data and material weighing data.

[0049] This embodiment acquires the state of the mounting platform under external disturbances, and under the disturbance conditions corresponding to the mounting platform state, enables the reference weighing channel to weigh a preset set of reference mass components to obtain the corresponding reference weighing dataset. A set of metering instructions is generated based on the preset set of reference mass components, and a material weighing dataset is obtained by controlling the metering module of the feeding device to perform material metering output under disturbance conditions based on the metering instruction set. The material weighing dataset corresponds one-to-one with the metering instruction set. An interference correction model is established based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset. The interference correction model characterizes the correlation between the metering deviation under disturbance conditions and the reference weighing dataset. In subsequent operations, real-time reference weighing data output by the reference weighing channel is acquired and input into the interference correction model to obtain correction parameters. Based on the correction parameters, the metering output data of the metering module is corrected to obtain the target material metering data. This embodiment first acquires the platform status and, under the corresponding disturbance conditions, weighs a preset set of reference mass components using a reference weighing channel to obtain a reference weighing dataset. This dataset characterizes the reference weighing output features under the current disturbance conditions. Subsequently, a set of metering instructions is generated based on the same preset set of reference mass components, and the metering module is controlled to output material metering under the same disturbance conditions to obtain a material weighing dataset that corresponds one-to-one with the set of metering instructions. This establishes an alignable sample correspondence between the reference weighing dataset and the material weighing dataset. A disturbance correction model is established based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset to characterize the correlation between metering deviation and the reference weighing dataset under disturbance conditions. In subsequent operations, real-time reference weighing data is input into this model to obtain correction parameters, which are then used to correct the metering output data of the metering module to obtain the target material metering data. Thus, the metering output can be synchronously corrected as disturbance conditions change, reducing the impact of disturbances on the metering results and improving the reliability and consistency of the feeding material metering.

[0050] Based on the second embodiment described above, a third embodiment of the metering module correction method for the feeding device of this application is proposed. Please refer to... Figure 3 , Figure 3 This is a schematic diagram of a sub-process in the third embodiment of the method for correcting the metering module of the feeding equipment in this application.

[0051] In this embodiment, step S3 includes: S31: Match and associate the mass parameters in the preset reference mass component set with the corresponding weighing results in the reference weighing dataset to obtain reference deviation data; S32: Match and associate the reference deviation data with the corresponding material weighing results in the material weighing dataset to form a modeling sample set for characterizing the relationship between the reference channel and the output of the metering module under the same disturbance condition; S33: Extract the input feature quantity representing the weighing state of the benchmark channel from the modeling sample set, and extract the output calibration quantity representing the measurement deviation of the measurement module. Determine the mapping structure of the interference correction model based on the input feature quantity and the output calibration quantity, and solve the parameters of the mapping structure based on the preset fitting strategy to obtain the model parameters. S34: Encapsulate the mapping structure and the model parameters and store them as an interference correction model.

[0052] It should be noted that the weighing result is the single weighing data output from the benchmark weighing channel or the material weighing stage. Matching and association involves binding data from different data sources corresponding to the same object or task based on association keys such as object identifier, instruction identifier, acquisition sequence, and / or time stamp. Benchmark deviation data is deviation characterization data obtained by matching mass parameters with corresponding weighing results in the benchmark weighing dataset. Material weighing results are weighing data results in the material weighing dataset corresponding to a specific measurement instruction. Input feature quantities are data elements extracted from the modeling sample set to characterize the weighing state of the benchmark channel. Output calibration quantities are data elements extracted from the modeling sample set to characterize the measurement deviation of the measurement module. The preset fitting strategy is a fitting training rule or optimization process used to solve the parameters of the mapping structure. Model parameters are the set of parameters obtained by solving the mapping structure based on the preset fitting strategy.

[0053] Specifically, firstly, the mass parameters in the preset set of reference mass components are matched and associated with the corresponding weighing results in the reference weighing dataset. This matching and association can be completed based on information such as the reference mass component identifier, weighing task identifier, acquisition sequence, or time stamp, ensuring that each mass parameter is correctly bound to its corresponding reference weighing result. After binding, reference deviation data is generated to characterize the correspondence between the two. Subsequently, the reference deviation data is matched and associated with the corresponding material weighing results in the material weighing dataset. This alignment can also be completed based on the metering instruction identifier, acquisition sequence, and / or time stamp, ensuring that each reference deviation data point corresponds to a material weighing result under the same disturbance condition. This constructs a modeling sample set characterizing the relationship between the reference channel and the metering module output under the same disturbance condition.

[0054] Furthermore, after obtaining the modeling sample set, input feature quantities characterizing the weighing state of the reference channel are extracted from the modeling sample set, and output calibration quantities characterizing the measurement deviation of the measurement module are extracted, so that the modeling sample set can be organized into an "input-output" sample format. Based on the input feature quantities and the output calibration quantities, the mapping structure of the interference correction model is determined, and the parameters of the mapping structure are solved according to a preset fitting strategy to obtain the model parameters. Finally, the mapping structure and the model parameters are encapsulated and stored to form a callable interference correction model.

[0055] By matching and associating the mass parameters of a preset set of reference mass components with the weighing results in the reference weighing dataset to obtain reference deviation data, the deviation of the weighing output of the reference channel under disturbance conditions is explicitly represented in data form. Then, the reference deviation data is matched and associated with the corresponding material weighing results in the material weighing dataset to form a modeling sample set, establishing an alignable sample correspondence between the weighing state of the reference channel and the output results of the metering module under the same disturbance conditions. Further, the input feature quantity and output calibration quantity are extracted from the modeling sample set, and the mapping structure is determined and the parameters are solved accordingly. Finally, the mapping structure and model parameters are encapsulated and stored as a disturbance correction model, so that the correlation between "the state represented by the reference weighing data" and "the metering deviation of the metering module" under disturbance conditions is solidified into a callable model, providing a basis for subsequent generation of correction parameters and correction of metering output based on real-time reference weighing data.

[0056] Based on the second embodiment described above, in this embodiment, step S4 includes: S41: During the operation period when the feeding equipment performs material metering output, the reference weighing channel is triggered to collect real-time weighing data of the preset reference mass component, and the collected weighing results are time-marked and cached to form the real-time reference weighing data. S42: Perform data integrity checks and format unification processing on the real-time benchmark weighing data, and perform feature assembly on the real-time benchmark weighing data based on the input definition corresponding to the interference correction model to obtain model input data; S43: Input the model input data into the disturbance correction model and output the correction parameters corresponding to the current disturbance conditions.

[0057] It should be noted that time stamping is a process that adds corresponding acquisition time information to the weighing results. Real-time benchmark weighing data is data acquired through real-time weighing and formed by time stamping and caching. Data integrity checking is a process that checks for missing, abnormal, or discontinuous real-time benchmark weighing data. The input definition corresponding to the interference correction model is the requirement of the interference correction model for the fields, organization, and data elements of the input data. Feature assembly is the process of concatenating, aligning, or combining fields of the real-time benchmark weighing data according to the input definition to generate data objects that meet the model input requirements.

[0058] Specifically, during the material metering output period of the feeding equipment, the reference weighing channel is triggered to collect real-time weighing data of a preset reference mass component, causing the reference weighing channel to output the corresponding weighing result. The collected weighing results are time-stamped to establish a correlation between the weighing result and its collection time; at the same time, the time-stamped weighing results are cached, so that subsequent processing steps can read the weighing results in chronological order and form real-time reference weighing data accordingly.

[0059] Furthermore, data integrity checks and format standardization processing are performed on the real-time benchmark weighing data to ensure that the real-time benchmark weighing data meets the preset data field and data structure requirements. Subsequently, the real-time benchmark weighing data is feature-assembled according to the input definition corresponding to the disturbance correction model to form model input data; the feature assembly may include the alignment, combination, and organization of data fields to meet the model's requirements for the organization of input data. The model input data is then input into the disturbance correction model, which calculates the model input data and outputs correction parameters corresponding to the current disturbance conditions.

[0060] By triggering the benchmark weighing channel during the operation period to collect real-time weighing data of preset benchmark mass parts, and by time-marking and caching the weighing results to form real-time benchmark weighing data, the benchmark weighing output has a traceable data organization form in the time dimension. Further, the real-time benchmark weighing data undergoes integrity checks and format standardization, and feature assembly is completed according to the input definition of the interference correction model to obtain model input data, ensuring that the real-time data can be stably input in a form that meets the model requirements. Finally, the model input data is input into the interference correction model to output correction parameters, making the correction parameters correspond to the benchmark weighing output state at the current operation time, thus providing direct data and parameter basis for subsequent correction processing of the metering module output based on the correction parameters.

[0061] Based on the second embodiment described above, in this embodiment, step S5 includes: S51: Parse the correction parameters to determine the correction amount definition corresponding to the metering output data of the metering module, and convert the correction amount definition into a correction configuration; S52: Obtain the raw measurement data output by the measurement module, and associate and match the correction parameter at the time the raw measurement data is generated. Perform deviation compensation processing on the raw measurement data based on the correction configuration to obtain the corrected measurement data. S53: Perform data consistency verification and result encapsulation on the corrected measurement data, and output the corrected measurement data that has passed the verification and encapsulation as the target material measurement data.

[0062] It should be noted that the raw measurement data is the measurement data directly output by the measurement module and has not yet undergone correction processing. Deviation compensation processing is a process that performs correction calculations on the raw measurement data according to the correction configuration. Data consistency verification is a process that verifies the corrected measurement data in terms of structure, fields, range, timing, or integrity. Result encapsulation is the process of organizing the verified corrected measurement data according to the preset output structure and generating the output result.

[0063] Specifically, the correction parameters are parsed to identify the key fields used for correction processing and their corresponding meanings, and based on this, the correction amount definition corresponding to the metering output data of the metering module is determined. The correction amount definition is used to clarify the data object targeted by the correction processing and its correction method. Subsequently, the correction amount definition is converted into an executable correction configuration, so that the correction configuration can be directly called by the subsequent deviation compensation processing flow and used to guide the correction calculation of the original metering data.

[0064] Furthermore, the raw metering data output by the metering module is acquired, and at the moment the raw metering data is generated, it is correlated and matched with the correction parameters to determine the correction parameters corresponding to the raw metering data. After the matching is completed, deviation compensation processing is performed on the raw metering data based on the correction configuration to obtain the corrected metering data. Subsequently, the corrected metering data is subjected to data consistency verification. After the verification is passed, the corrected metering data is encapsulated, and the verified and encapsulated corrected metering data is output as the target material metering data.

[0065] By parsing the correction parameters to determine the correction amount definition and converting it into a correction configuration, the correction parameters can establish a clear processing correspondence with the metering output data of the metering module in a configurable form. Furthermore, at the time of original metering data generation, the correction parameters are associated and matched, and deviation compensation processing is performed on the original metering data based on the correction configuration to obtain corrected metering data. This ensures that each piece of original metering data receives the correction processing corresponding to its generation time. Finally, the corrected metering data undergoes consistency verification, and the results are encapsulated and output as target material metering data. This ensures that the output data has a usable data structure and a consistent output format, thereby implementing the correction parameters output by the model into the correction process of the metering module's output data and forming a directly usable target metering result.

[0066] This embodiment acquires the state of the mounting platform under external disturbances, and under the disturbance conditions corresponding to the mounting platform state, enables the reference weighing channel to weigh a preset set of reference mass components to obtain the corresponding reference weighing dataset. A set of metering instructions is generated based on the preset set of reference mass components, and a material weighing dataset is obtained by controlling the metering module of the feeding device to perform material metering output under disturbance conditions based on the metering instruction set. The material weighing dataset corresponds one-to-one with the metering instruction set. An interference correction model is established based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset. The interference correction model characterizes the correlation between the metering deviation under disturbance conditions and the reference weighing dataset. In subsequent operations, real-time reference weighing data output by the reference weighing channel is acquired and input into the interference correction model to obtain correction parameters. Based on the correction parameters, the metering output data of the metering module is corrected to obtain the target material metering data. This embodiment first acquires the platform status and, under the corresponding disturbance conditions, weighs a preset set of reference mass components using a reference weighing channel to obtain a reference weighing dataset. This dataset characterizes the reference weighing output features under the current disturbance conditions. Subsequently, a set of metering instructions is generated based on the same preset set of reference mass components, and the metering module is controlled to output material metering under the same disturbance conditions to obtain a material weighing dataset that corresponds one-to-one with the set of metering instructions. This establishes an alignable sample correspondence between the reference weighing dataset and the material weighing dataset. A disturbance correction model is established based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset to characterize the correlation between metering deviation and the reference weighing dataset under disturbance conditions. In subsequent operations, real-time reference weighing data is input into this model to obtain correction parameters, which are then used to correct the metering output data of the metering module to obtain the target material metering data. Thus, the metering output can be synchronously corrected as disturbance conditions change, reducing the impact of disturbances on the metering results and improving the reliability and consistency of the feeding material metering.

[0067] Please see Figure 4 , Figure 4 This is a schematic diagram of a real-time calibration system for the metering module of a feeding device, as shown in one embodiment of the method for correcting the metering module of the feeding device according to this application. Figure 4 As shown, in one embodiment, the metering module real-time calibration system is located inside the feeding equipment. The platform is affected by factors such as wind, waves, and currents, which can cause vibrations and tilting, leading to deviations in feed metering data. The computer system collects distorted data from both systems in real time. By comparing and processing the distorted data from the reference weighing system with the weight values ​​of standard weights, an interference mathematical model is identified. This interference mathematical model is then used to process the distorted material metering data, thereby obtaining the correct material metering value.

[0068] Step 1: Under test conditions, provide an external disturbance to the mounting platform, such as irregular vibration, reciprocating tilting, or reciprocating shaking. Step 2: Weigh the standard weights Weighti (i = 1, 2, 3... n) in sequence, and record the weighing results mi (i = 1, 2, 3... n) output by the reference sensor using the data processing system; Step 3: Simultaneously, using the standard weight Weighti (i = 1, 2, 3... n) as input instructions, control the weighing module of the feeder to measure the corresponding mass of feed and spray it into the measuring bucket. Weigh the feed weight m'i (i = 1, 2, 3... n) in the measuring bucket and record the results using the data processing system. Step 4: Based on the actual weight of the standard weights, and referring to the measurement results mi (i = 1, 2, 3... n) from the sensor and the measurement results m'i (i = 1, 2, 3... n) from the weighing module, establish a mathematical model to correct the data; Step 5: Correct the established mathematical model under real aquaculture operating conditions to improve the accuracy of the correction.

[0069] This application also provides a metering module correction device for a feeding device. Please refer to... Figure 5 , Figure 5 This is a schematic diagram of the module structure of the metering module correction device for the feeding equipment according to an embodiment of this application. The metering module correction device for the feeding equipment includes: The reference weighing module 501 is used to obtain the state of the mounting platform under external disturbance, and under the disturbance conditions corresponding to the state of the mounting platform, to make the reference weighing channel weigh a preset set of reference mass parts to obtain the corresponding reference weighing dataset. The material weighing module 502 is used to generate a set of metering instructions based on the preset set of reference mass components, and to obtain a material weighing dataset obtained by the metering module of the feeding device controlling the material metering output based on the set of metering instructions under the disturbance conditions. The material weighing dataset corresponds one-to-one with the set of metering instructions. The model building module 503 is used to build a disturbance correction model based on the preset set of reference mass parts, the reference weighing dataset and the material weighing dataset. The disturbance correction model is used to characterize the correlation between the measurement deviation under the disturbance condition and the reference weighing dataset. The correction parameter module 504 is used to acquire the real-time benchmark weighing data output by the benchmark weighing channel during subsequent operations, and input the real-time benchmark weighing data into the interference correction model to obtain correction parameters; The target module 505 is used to correct the metering output data of the metering module based on the correction parameters to obtain the target material metering data.

[0070] The metering module correction device for feeding equipment provided in this application adopts the metering module correction method for feeding equipment in the above embodiments, which can solve the technical problem of how to improve the reliability and consistency of feeding material metering. Compared with the prior art, the beneficial effects of the metering module correction device for feeding equipment provided in this application are the same as the beneficial effects of the metering module correction method for feeding equipment provided in the above embodiments, and other technical features in the metering module correction device for feeding equipment are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0071] This application provides a feeding device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the metering module correction method of the feeding device in the above embodiments.

[0072] The following is for reference. Figure 6 , Figure 6 This is a schematic diagram of the hardware operating environment involved in the metering module correction method of the feeding device in the embodiments of this application, which shows a schematic diagram of the feeding device suitable for implementing the embodiments of this application. Figure 6 The feeding device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0073] like Figure 6As shown, the feeding device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the feeding device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the feeding device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show feeding devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0074] In particular, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. When the computer program is executed by the processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0075] The feeding device provided in this application, employing the metering module correction method of the feeding device in the above embodiments, can solve the technical problem of how to improve the reliability and consistency of feeding material metering. Compared with the prior art, the beneficial effects of the feeding device provided in this application are the same as those of the metering module correction method of the feeding device provided in the above embodiments, and other technical features of this feeding device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0076] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0077] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0078] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the metering module correction method of the feeding device in the above embodiments.

[0079] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the feeding device, the feeding device: acquires the state of the mounting platform under external disturbance, and under the disturbance conditions corresponding to the mounting platform state, causes the reference weighing channel to weigh a preset set of reference mass components to obtain a corresponding reference weighing dataset; generates a set of metering instructions based on the preset set of reference mass components, and acquires a material weighing dataset obtained by controlling the metering module of the feeding device to perform material metering output under the disturbance conditions based on the set of metering instructions, wherein the material weighing dataset corresponds one-to-one with the set of metering instructions; establishes a disturbance correction model based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset, wherein the disturbance correction model is used to characterize the correlation between the metering deviation under disturbance conditions and the reference weighing dataset; in subsequent operations, acquires real-time reference weighing data output by the reference weighing channel, and inputs the real-time reference weighing data into the disturbance correction model to obtain correction parameters; and performs correction processing on the metering output data of the metering module based on the correction parameters to obtain the target material metering data. Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0081] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0082] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the metering module correction method of the above-described feeding device, which can solve the technical problem of how to improve the reliability and consistency of feeding material metering. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the metering module correction method of the feeding device provided in the above embodiments, and will not be repeated here.

[0083] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the metering module correction method for the feeding device as described above.

[0084] The computer program product provided in this application can solve the technical problem of how to improve the reliability and consistency of feeding material metering. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the metering module correction method of the feeding equipment provided in the above embodiments, and will not be repeated here.

[0085] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A method for correcting the metering module of a feeding device, characterized in that, The method includes: The state of the mounting platform under external disturbance is obtained, and under the disturbance conditions corresponding to the state of the mounting platform, the reference weighing channel weighs the preset set of reference mass parts to obtain the corresponding reference weighing dataset. A set of metering instructions is generated based on the preset set of reference mass components, and a material weighing dataset is obtained by controlling the metering module of the feeding device to perform material metering output based on the set of metering instructions under the disturbance conditions. The material weighing dataset corresponds one-to-one with the set of metering instructions. An interference correction model is established based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset. The interference correction model is used to characterize the correlation between the measurement deviation under the disturbance condition and the reference weighing dataset. In subsequent operations, the real-time benchmark weighing data output by the benchmark weighing channel is acquired, and the real-time benchmark weighing data is input into the interference correction model to obtain correction parameters; The metering output data of the metering module is corrected based on the correction parameters to obtain the metering data of the target material.

2. The method as described in claim 1, characterized in that, The step of acquiring the state of the mounting platform under external disturbance, and under the disturbance conditions corresponding to the state of the mounting platform, having the reference weighing channel weigh a preset set of reference mass components to obtain the corresponding reference weighing dataset includes: Acquire attitude change information and / or motion characteristic information of the mounting platform to characterize the influence of external disturbances, and determine the state parameters of the mounting platform based on the attitude change information and / or the motion characteristic information; The sampling configuration of the reference weighing channel is matched with the disturbance condition, and the reference weighing channel is triggered to weigh and collect data on a preset set of reference mass components to output the original weighing sequence. The original weighing sequence is processed to generate a set of weighing results that corresponds one-to-one with the preset set of reference mass parts, and the set of weighing results is used as the corresponding reference weighing dataset.

3. The method as described in claim 1, characterized in that, The step of generating a metering instruction set based on the preset reference mass component set, and obtaining a material weighing dataset obtained by controlling the metering module of the feeding device to perform material metering output based on the metering instruction set under the disturbance conditions, includes: The mass parameters of each reference mass component in the preset reference mass component set are read, and the mass parameters are encapsulated according to a preset instruction format to form a set of measurement instructions that corresponds one-to-one with the preset reference mass component set. The set of metering instructions is sent to the metering module in a preset order, and the metering module is driven to complete the corresponding material metering output process based on each metering instruction, so that the material enters the preset bearing structure. Under the disturbance conditions, material weighing data corresponding to each metering command is acquired, and the material weighing data is correlated and organized to obtain a material weighing dataset.

4. The method as described in claim 1, characterized in that, The step of establishing an interference correction model based on the preset set of reference mass components, the reference weighing dataset, and the material weighing dataset includes: The mass parameters in the preset set of reference mass parts are matched and associated with the corresponding weighing results in the reference weighing dataset to obtain reference deviation data; The reference deviation data is matched and associated with the corresponding material weighing results in the material weighing dataset to form a modeling sample set for characterizing the relationship between the reference channel and the output of the metering module under the same disturbance condition. The input feature quantities characterizing the weighing state of the reference channel are extracted from the modeling sample set, and the output calibration quantity characterizing the measurement deviation of the measurement module is extracted. The mapping structure of the interference correction model is determined according to the input feature quantities and the output calibration quantity, and the parameters of the mapping structure are solved based on the preset fitting strategy to obtain the model parameters. The mapping structure and the model parameters are encapsulated and stored as an interference correction model.

5. The method as described in claim 1, characterized in that, The step of acquiring real-time benchmark weighing data output from the benchmark weighing channel and inputting the real-time benchmark weighing data into the interference correction model to obtain correction parameters during subsequent operations includes: During the operation period when the feeding equipment performs material metering output, the reference weighing channel is triggered to collect real-time weighing data of the preset reference mass component, and the collected weighing results are time-stamped and cached to form the real-time reference weighing data. The real-time benchmark weighing data is subjected to data integrity checks and format unification processing, and the real-time benchmark weighing data is feature-assembled based on the input definition corresponding to the interference correction model to obtain model input data; The model input data is input into the disturbance correction model, and the correction parameters corresponding to the current disturbance conditions are output.

6. The method as described in claim 1, characterized in that, The step of correcting the metering output data of the metering module based on the correction parameters to obtain the target material metering data includes: The correction parameters are parsed to determine the correction amount definition corresponding to the metering output data of the metering module, and the correction amount definition is converted into a correction configuration. The original measurement data output by the measurement module is obtained, and the correction parameter is associated and matched at the time of generation of the original measurement data. Based on the correction configuration, the original measurement data is subjected to deviation compensation processing to obtain the corrected measurement data. The corrected measurement data is subjected to data consistency verification and result encapsulation, and the corrected measurement data that has passed the verification and encapsulation is output as the target material measurement data.

7. A metering module correction device for a feeding device, characterized in that, The device includes: The benchmark weighing module is used to acquire the state of the mounting platform under external disturbance, and under the disturbance conditions corresponding to the state of the mounting platform, the benchmark weighing channel weighs the preset set of benchmark mass parts to obtain the corresponding benchmark weighing dataset. The material weighing module is used to generate a set of metering instructions based on the preset set of reference mass components, and to obtain a material weighing dataset obtained by the metering module controlling the feeding device to perform material metering output based on the set of metering instructions under the disturbance conditions. The material weighing dataset corresponds one-to-one with the set of metering instructions. The model building module is used to build a disturbance correction model based on the preset set of reference mass parts, the reference weighing dataset, and the material weighing dataset. The disturbance correction model is used to characterize the correlation between the measurement deviation under the disturbance conditions and the reference weighing dataset. The correction parameter module is used to acquire the real-time benchmark weighing data output by the benchmark weighing channel during subsequent operations, and input the real-time benchmark weighing data into the interference correction model to obtain correction parameters; The target module is used to correct the metering output data of the metering module based on the correction parameters to obtain the target material metering data.

8. A feeding device, characterized in that, The feeding device includes: a memory, a processor, and a metering module correction program for the feeding device stored in the memory and executable on the processor, the metering module correction program for the feeding device being configured to implement the method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a metering module correction program for the feeding device, which, when executed by a processor, implements the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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