Precise grain unloading system based on grain unloading motor multi-signal fusion control
By using a multi-signal fusion control system for the unloading motor, the problem of precise control of the unloading system under complex working conditions has been solved, achieving efficient and energy-saving unloading, improving the accuracy and stability of unloading operations, and reducing energy consumption and maintenance costs.
Patent Information
- Application Number
- CN202511397863.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-28
- Publication Date
- 2025-11-11
AI Technical Summary
Existing grain unloading systems cannot accurately and in real time adjust the power of unloading motors according to complex and ever-changing unloading conditions, resulting in long-term inefficient operation of the motors, increased energy costs, reduced equipment reliability, and vulnerability to environmental factors.
A precision grain unloading system based on multi-signal fusion control of unloading motors is adopted. The process generation module parses the operation instructions, the power benchmark calculation module calculates the power benchmark value of unloading operation, the control spectrum generation module generates a dynamic power control spectrum, and the benchmark deviation detection module monitors and controls in real time. Combined with a multi-dimensional adaptive calibration matrix, the motor power is optimized.
It achieves a precise match between motor power and unloading requirements, improves the accuracy and stability of unloading operations, reduces energy consumption, enhances system adaptability and reliability, extends equipment life, and reduces operation and maintenance costs.
Smart Images
Figure CN120928704A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of grain unloading technology, and in particular to a precision grain unloading system based on multi-signal fusion control of a grain unloading motor. Background Technology
[0002] In the process of modern agricultural production, the grain unloading process is a crucial node in ensuring that crops are safely stored, and its efficiency and accuracy have a profound impact on the overall agricultural production benefits. As unmanned farms gradually move from concept to reality, the development of combined harvesting operations towards intelligence and automation has become an inevitable trend. Under this trend, the intelligent upgrading of grain unloading systems is of paramount importance.
[0003] Currently, most grain unloading systems still face numerous problems during operation. Traditional grain unloading systems often rely on lagging and rigid control strategies, making it difficult to adjust the unloading motor power in real time and accurately according to complex and changing unloading conditions. They also lack refined management of motor energy consumption. Because the motor power cannot be precisely matched to actual unloading needs, the motor operates inefficiently for extended periods, resulting in significant energy waste and increasing agricultural production costs. Furthermore, excessive energy consumption can lead to motor overheating and other malfunctions, further reducing equipment reliability and lifespan. In addition, existing grain unloading systems exhibit significant vulnerability to complex environmental factors. Changes in ambient temperature and humidity affect the physical properties of grain, thus altering unloading resistance, but traditional systems struggle to effectively compensate for this. Summary of the Invention
[0004] This invention provides a precision grain unloading system based on multi-signal fusion control of the grain unloading motor, in order to solve the defects existing in the prior art.
[0005] This invention provides a precision grain unloading system based on multi-signal fusion control of a grain unloading motor, comprising: The process generation module is used to collect the operation instructions and basic equipment parameters of the grain unloading task, parse and identify the continuous operation links in the operation instructions, extract the unloading feature identifiers of each operation link in combination with the basic equipment parameters, and generate unloading process data.
[0006] The power benchmark calculation module is used to collect historical grain unloading operation datasets, extract the grain flow rate, motor energy consumption curves and time sequence correlations of operation links from the historical data, and calculate the power benchmark value of grain unloading operation based on the grain flow rate and motor energy consumption curves.
[0007] The control map generation module is used to set the allowable range of dynamic deviation based on the power benchmark value of grain unloading operation, and generate an initial power control map arranged in the time sequence of operation stages.
[0008] The dynamic power regulation module is used to collect current harmonic information of the unloading motor, grain flow pressure detection data and environmental temperature and humidity indicators, construct a multi-dimensional adaptive calibration matrix, and integrate it with the initial power regulation spectrum to obtain the dynamic power regulation spectrum of the unloading operation.
[0009] The benchmark deviation detection module is used to collect the actual power change curve of the motor in real time during the unloading process, compare the deviation between the average actual power in each operation stage and the benchmark value of the dynamic power control spectrum, calculate the morphological fit between the actual power change curve and the dynamic power control spectrum in the time window of each operation stage, and realize accurate monitoring of the operating status of the unloading system and dynamic control of the motor.
[0010] The precision grain unloading system based on multi-signal fusion control of the unloading motor provided by this invention includes the following operation instructions in the process generation module: total unloading target, grain category attribute parameters, unloading gate opening setting value, and conveyor belt running speed threshold. The grain category attribute parameters include moisture content and bulk density. Basic equipment parameters include the unloading motor's rated power, rated speed, torque characteristic curve, grain flow pressure detector measurement range, and unloading bin volume. Unloading characteristic identifiers include the motor starting power threshold, power range during the stable grain flow stage, real-time unloading gate opening, and calculated grain flow conveying rate.
[0011] The precision grain unloading system based on multi-signal fusion control of unloading motor provided by the present invention includes the following process in which the process generation module parses and identifies continuous operation links in the operation instructions: Lexical analysis was performed on the unloading control program file of the operation instructions to identify the core operation instructions, which include motor start / stop instructions, unloading gate opening adjustment instructions, and conveyor belt speed control instructions.
[0012] Based on the type of core operation instructions, continuous operation segments are divided to generate an initial grain unloading operation sequence, including the start-up phase, the grain flow stabilization phase, and the closing phase.
[0013] Extract the core control parameter set for each step in the initial grain unloading operation sequence. The core control parameter set includes the motor speed set value, the coordinates of the grain flow pressure detection point, and the cycle of the unloading gate opening.
[0014] Based on the core control parameter set, non-unloading auxiliary links in the initial unloading operation sequence are removed. These non-unloading auxiliary links include equipment preheating and empty silo debugging, resulting in the continuous operation links in the operation instruction.
[0015] According to the precision grain unloading system based on multi-signal fusion control of unloading motor provided by the present invention, the process of generating unloading process data by the process generation module includes: Based on the coordinates of the grain flow pressure detection point and the attribute parameters of the grain category, the grain flow pressure-density correspondence model is associated to calculate the real-time grain flow pressure value.
[0016] By combining the motor torque characteristic curve and rated speed, the maximum allowable power threshold of the motor in the current operation stage is derived.
[0017] By associating the unloading bin volume with the total unloading target, the number of unloading cycles per cycle is calculated.
[0018] The unique identifier of the packaging operation, the set of core control parameters, and the calculated value of grain flow rate are standardized data objects to obtain standardized grain unloading process data.
[0019] The precision grain unloading system based on multi-signal fusion control of unloading motor provided by the present invention includes the following process in which the power reference calculation module extracts the grain flow conveying rate, motor energy consumption curve, and time-series correlation of operation links from historical grain unloading operation data: Screen historical grain unloading operation records of the same type of grain and the same model of unloading equipment.
[0020] Analyze the calculated grain flow rate in the historical normalized data object, and statistically analyze the distribution characteristics of the grain flow rate, including the mean and standard deviation.
[0021] By analyzing historical grain unloading motor power detection records, a mapping model between motor power and grain flow conveying rate is constructed.
[0022] Extract the historical grain unloading operation chain sequence and the timestamp relationship of each stage. The timestamp relationship includes the duration of the start stage, the duration of the stable stage, and the duration of the finish stage.
[0023] The precision grain unloading system based on multi-signal fusion control of the unloading motor provided by the present invention includes the following process in which the power reference calculation module calculates the unloading operation power reference value based on the grain flow conveying rate and the motor energy consumption curve: Substitute the current grain flow transport rate setpoint into the mapping model to generate a basic power reference value.
[0024] The load correction factor is added to obtain the load correction power.
[0025] Introduce a grain unloading safety factor to generate a baseline value for grain unloading operation power.
[0026] According to the precision grain unloading system based on multi-signal fusion control of unloading motor provided by the present invention, the process of generating an initial power control spectrum arranged according to the time sequence of operation stages by the control spectrum generation module includes: A two-dimensional quantitative model of operation timing and motor power is constructed, with the horizontal axis representing the continuous operation time axis and the vertical axis representing the motor output power spectrum, and a dual-axis dynamic scaling system is established.
[0027] The baseline power curve is fitted according to the time sequence logic of the operation, and a continuous baseline trajectory is generated by the smooth transition algorithm of the baseline power value of each operation.
[0028] The allowable range of differential deviations is set based on the dynamic characteristics of the operation process.
[0029] The deviation tolerance domain is visualized and calibrated in a two-dimensional quantization model. Different color gradients are used to distinguish the deviation threshold range of each stage, forming an initial power control spectrum that includes the baseline trajectory, deviation boundary, and stage identifier.
[0030] Establish a mapping relationship between the graph and the characteristic parameters of the operation process, so that any time point in the graph can be traced back to the core control parameter set of the corresponding operation process.
[0031] The precision grain unloading system based on multi-signal fusion control of the unloading motor provided by the present invention includes the following process in which the dynamic optimization module constructs a multi-dimensional adaptive calibration matrix: The current signal of the unloading motor is collected, the current harmonics are decomposed by Fourier transform, the current harmonic distortion rate is calculated, and the motor health calibration coefficient is generated.
[0032] The thermal deformation calibration coefficient is calculated based on the temperature and humidity collected by the ambient temperature and humidity sensor.
[0033] Collect grain flow pressure detection data, calculate the grain flow pressure fluctuation coefficient, and generate the grain flow stability calibration coefficient.
[0034] The motor health calibration coefficient, thermal deformation calibration coefficient, and grain flow stability calibration coefficient are combined in column vector form to obtain a multi-dimensional adaptive calibration matrix.
[0035] According to the precision grain unloading system based on multi-signal fusion control of the unloading motor provided by the present invention, the process by which the dynamic power regulation spectrum of the unloading operation is obtained by the spectrum dynamic optimization module includes: Based on the adaptive calibration matrix, the power reference value of the initial spectrum is adjusted for each operational step.
[0036] The allowable deviation range is dynamically adjusted based on the grain flow stability calibration coefficient.
[0037] A calibration coefficient labeling layer is superimposed on the initial power regulation spectrum, and the values of motor health calibration coefficient, thermal deformation calibration coefficient and grain flow stability calibration coefficient for each operation stage are marked with different colors.
[0038] Establish a communication interface with the real-time data acquisition module of the grain unloading system, update the adaptive calibration matrix according to a preset cycle, and update the dynamic power control spectrum synchronously.
[0039] The precision grain unloading system based on multi-signal fusion control of the unloading motor provided by the present invention includes the following process in which the reference deviation detection module realizes precise monitoring of the operating status of the unloading system and dynamic control of the motor: The actual power change curve is collected using a motor power detector at a preset sampling frequency.
[0040] The actual power change curve is divided into time windows according to the operation stage. The average actual power of each stage is calculated and compared with the dynamic benchmark value. The deviation rate is calculated. When the deviation rate is greater than the allowable deviation range, a first-level warning is triggered.
[0041] The dynamic time warping algorithm is used to calculate the morphological fit between the actual power change curve and the dynamic reference curve.
[0042] When the morphological fit is less than a preset threshold, a level 2 warning is triggered, and the cause of the anomaly is analyzed based on the adaptive calibration matrix.
[0043] Based on the warning level and the cause of the anomaly, motor control commands are output to achieve closed-loop control of the grain unloading system.
[0044] This invention provides a precision grain unloading system based on multi-signal fusion control of the unloading motor. Through semantic parsing of the unloading process and extraction of unloading feature identifiers for each operational stage, it generates standardized unloading process data, providing a solid data foundation for precise control. The system calculates the baseline power value for unloading operations based on the grain flow rate and motor energy consumption curve, and dynamically optimizes the initial power control spectrum using a multi-dimensional adaptive calibration matrix, enabling precise matching of motor power with real-time unloading requirements. Regardless of different grain varieties, moisture contents, or complex and variable unloading conditions, it ensures a consistently stable and uniform grain flow, effectively avoiding problems such as grain flow blockage or insufficient flow, greatly improving the accuracy and stability of unloading operations, and significantly increasing unloading efficiency.
[0045] By conducting real-time analysis and adaptive calibration of multi-dimensional data such as motor load and environmental factors, the system ensures that the motor is always in a high-efficiency operating range, significantly reducing energy consumption, effectively reducing energy costs in agricultural production, and achieving the green development goal of energy conservation and emission reduction.
[0046] The system collects real-time data on the actual power variation curve of the motor during grain unloading, compares the deviation with the baseline value of the dynamic power control spectrum, and calculates the morphological fit. In the event of an anomaly, the system can quickly trigger an early warning mechanism and, based on an adaptive calibration matrix, deeply analyze the cause of the anomaly, accurately locating the fault type, such as potential motor malfunctions, environmental impact risks, or unstable grain flow. This not only helps to take timely and targeted measures to eliminate faults and avoid equipment damage and operational interruptions, but also provides a scientific basis for equipment maintenance, allowing for advance maintenance planning, extending equipment lifespan, and reducing equipment operation and maintenance costs.
[0047] By collecting environmental temperature and humidity data and calculating the thermal deformation calibration coefficient, the impact of environmental factors on motor power can be effectively compensated. Regardless of changes in the external environment, the grain unloading system can operate stably, greatly enhancing its adaptability and reliability in complex environments, ensuring that grain unloading operations are unaffected by environmental interference and continue to be carried out efficiently. Attached Figure Description
[0048] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0049] Figure 1 This is a schematic diagram of the structure of a precision grain unloading system based on multi-signal fusion control of the unloading motor provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the process of the process generation module parsing and identifying continuous operation steps in the operation instruction in an embodiment of the present invention. Figure 3 This is a schematic diagram of the process of generating unloading process data by the process generation module in an embodiment of the present invention. Detailed Implementation
[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0051] The following is combined Figures 1-3 This invention describes a precision grain unloading system based on multi-signal fusion control of a grain unloading motor.
[0052] Figure 1 This is a schematic diagram of the structure of a precision grain unloading system based on multi-signal fusion control of the unloading motor, provided in an embodiment of the present invention.
[0053] like Figure 1 As shown, the precision grain unloading system based on multi-signal fusion control of unloading motor provided in this embodiment of the invention includes a process generation module, a power reference calculation module, a control spectrum generation module, a spectrum dynamic optimization module, and a reference deviation detection module.
[0054] The process generation module is used to collect the operation instructions and basic equipment parameters of the grain unloading task, parse and identify the continuous operation links in the operation instructions, extract the unloading feature identifiers of each operation link in combination with the basic equipment parameters, and generate unloading process data.
[0055] The operational instructions include the total unloading target, grain category attribute parameters, unloading gate opening setting, and conveyor belt operating speed threshold. Grain category attribute parameters include moisture content and bulk density. Basic equipment parameters include the unloading motor's rated power, rated speed, torque characteristic curve, grain flow pressure detector measurement range, and unloading hopper volume. Unloading characteristic indicators include the motor starting power threshold, power range during stable grain flow stages, real-time unloading gate opening, and calculated grain flow conveying rate.
[0056] Figure 2 This is a flowchart illustrating the process of the process generation module parsing and identifying continuous operation steps in the operation instructions in an embodiment of the present invention.
[0057] like Figure 2 As shown, the process of parsing and identifying continuous work steps in a work instruction includes: Lexical analysis was performed on the unloading control program file of the operation instructions to identify the core operation instructions, which include motor start / stop instructions, unloading gate opening adjustment instructions, and conveyor belt speed control instructions.
[0058] Based on the type of core operation instructions, continuous operation segments are divided to generate an initial grain unloading operation sequence, which includes a start-up phase, a grain flow stabilization phase, and a closing phase.
[0059] Extract the core control parameter set for each step in the initial grain unloading operation sequence. The core control parameter set includes the motor speed set value, the coordinates of the grain flow pressure detection point, and the cycle of the unloading gate opening.
[0060] Based on the core control parameter set, non-unloading auxiliary links in the initial unloading operation sequence are removed. These non-unloading auxiliary links include equipment preheating and empty silo debugging, resulting in the continuous operation links in the operation instruction.
[0061] Figure 3 This is a schematic diagram of the process of generating unloading process data by the process generation module in an embodiment of the present invention.
[0062] like Figure 3 As shown, the process of generating unloading process data includes: Based on the coordinates of the grain flow pressure detection point and the grain category attribute parameters, and by associating the grain flow pressure-density correspondence model, the real-time grain flow pressure value is calculated. The calculation formula is as follows:
[0063] In the formula, F represents the real-time grain flow pressure value. Indicates bulk density. The value represents the calculated grain flow rate, S represents the cross-sectional area detected by the grain flow pressure detector, and g is the gravitational acceleration.
[0064] Based on the motor torque characteristic curve and rated speed, the maximum allowable power threshold of the motor in the current operating stage is derived using the following formula:
[0065] In the formula, This indicates the maximum allowable power threshold of the motor in the current operation phase. This represents the motor torque characteristic curve, where n is the actual motor speed during the current operation.
[0066] By associating the unloading silo volume with the total unloading volume target, the number of unloading cycles per cycle is calculated using the following formula:
[0067] In the formula, k represents the number of unloading cycles per operation. This indicates a rounding up operation, Q represents the total unloading target, and V represents the unloading bin volume. Indicates bulk density. This refers to the percentage of dry matter in grain.
[0068] The unique identifier of the packaging operation, the set of core control parameters, and the calculated value of grain flow rate are standardized data objects to obtain standardized grain unloading process data.
[0069] The power benchmark calculation module is used to collect historical grain unloading operation datasets, extract the grain flow rate, motor energy consumption curves and time sequence correlations of operation links from the historical data, and calculate the power benchmark value of grain unloading operation based on the grain flow rate and motor energy consumption curves.
[0070] The process of extracting grain flow rate, motor energy consumption curves, and time-series correlations of operational steps from historical grain unloading operation data includes: Screen historical grain unloading operation records of the same type of grain and the same model of unloading equipment.
[0071] Analyze the calculated grain flow rate in the historical normalized data object, and statistically analyze the distribution characteristics of the grain flow rate, including the mean and standard deviation.
[0072] By analyzing historical grain unloading motor power detection records, a mapping model between motor power and grain flow conveying rate is constructed, expressed by the formula:
[0073] In the formula, P represents the motor power, and c represents the power-grain flow rate coefficient. This represents the calculated value of the grain flow rate, where d is the no-load power of the motor.
[0074] Extract the historical grain unloading operation chain sequence and the timestamp relationship of each stage. The timestamp relationship includes the duration of the start stage, the duration of the stable stage, and the duration of the finish stage.
[0075] The process of calculating the benchmark value of unloading operation power based on the grain flow rate and motor energy consumption curve includes: Substituting the current grain flow rate setpoint into the mapping model, a basic power reference value is generated, expressed by the formula:
[0076] In the formula, This represents the baseline power reference value, where c represents the power-grain flow rate coefficient. This represents the set value of the grain flow conveying rate in the current operation stage, and d is the no-load power of the motor.
[0077] The load correction power is obtained by superimposing the motor load correction factor, as expressed by the formula:
[0078] In the formula, Indicates the load correction power. Indicates the base power reference value. This represents the motor load correction factor.
[0079] Introducing a grain unloading safety factor, a baseline value for grain unloading operation power is generated, expressed by the formula:
[0080] In the formula, This represents the baseline value for unloading power. Indicates the load correction power. This indicates the safety factor for unloading grain.
[0081] The control map generation module is used to set the allowable range of dynamic deviation based on the power benchmark value of grain unloading operation, and generate an initial power control map arranged in the time sequence of the operation. The process includes: A two-dimensional quantitative model of operation timing and motor power is constructed, with the horizontal axis representing the continuous operation time axis and the vertical axis representing the motor output power spectrum, and a dual-axis dynamic scaling system is established.
[0082] The baseline power curve is fitted according to the time sequence logic of the operation, and a continuous baseline trajectory is generated by the smooth transition algorithm of the baseline power value of each operation to eliminate the power abrupt change point when the operation switches.
[0083] Differentiated allowable deviation ranges are set based on the dynamic characteristics of each operational stage, including: For the starting stage, due to the fluctuations in motor starting torque, the allowable deviation range is set to a wide dynamic range. For the stabilizing stage, to maintain the stability of grain flow, the allowable deviation range is set to a narrow control range. For the finishing stage, because the grain flow shows a decreasing trend, the allowable deviation range is set to a gradual transition range.
[0084] The deviation tolerance domain is visualized and calibrated in a two-dimensional quantization model. Different color gradients are used to distinguish the deviation threshold range of each stage, forming an initial power control spectrum that includes the baseline trajectory, deviation boundary, and stage identifier.
[0085] Establish a mapping relationship between the graph and the characteristic parameters of the operation process, so that any time point in the graph can be traced back to the core control parameter set of the corresponding operation process.
[0086] The dynamic power regulation module is used to collect current harmonic information of the unloading motor, grain flow pressure detection data and environmental temperature and humidity indicators, construct a multi-dimensional adaptive calibration matrix, and integrate it with the initial power regulation spectrum to obtain the dynamic power regulation spectrum of the unloading operation.
[0087] The process of constructing a multi-dimensional adaptive calibration matrix includes: The current signal of the unloading motor is collected, and the current harmonics are decomposed by Fourier transform. The current harmonic distortion rate is calculated, and the motor health calibration coefficient is generated. The calculation formula is as follows:
[0088] In the formula, This indicates the motor health calibration coefficient. This represents the distortion rate coefficient. THD is the total harmonic distortion of the current. The higher the THD, the worse the motor's health condition. The smaller.
[0089] The thermal deformation calibration coefficient is calculated based on the temperature and humidity data collected by the environmental temperature and humidity sensor. The calculation formula is as follows:
[0090] In the formula, Indicates the thermal distortion calibration factor. This represents the temperature deviation coefficient, where T represents temperature. This represents the humidity deviation coefficient, where H represents humidity. 25℃ and 60%RH are standard environmental parameters. When the temperature is higher than 25℃ or the humidity is higher than 60%,... A value greater than 1 compensates for power losses in the motor caused by environmental factors.
[0091] Collect grain flow pressure detection data, calculate the grain flow pressure fluctuation coefficient, and generate the grain flow stability calibration coefficient. The calculation formula is as follows:
[0092]
[0093] In the formula, Indicates the calibration coefficient for grain flow stability. Indicates the coefficient of grain flow pressure fluctuation. Indicates the standard deviation of pressure. This represents the average pressure. The smaller the size, the more stable the grain flow. The closer it is to 1.
[0094] The motor health calibration coefficient, thermal deformation calibration coefficient, and grain flow stability calibration coefficient are combined in column vector form to obtain a multi-dimensional adaptive calibration matrix. .
[0095] The process of obtaining the dynamic power regulation spectrum of grain unloading operations includes: Based on the adaptive calibration matrix M, the power reference value of the initial spectrum is adjusted for each operational stage. The adjustment formula is as follows:
[0096] In the formula, Indicates the dynamic baseline value. This represents the baseline value for unloading power. To calibrate the geometric mean of the coefficients and avoid excessive influence of a single factor on the benchmark value.
[0097] The allowable deviation range is dynamically adjusted based on the grain flow stability calibration coefficient: when When this happens, the allowable deviation range is reduced by 10%. At this time, the allowable deviation range remains unchanged. At that time, the allowable deviation range is increased by 15%.
[0098] A calibration coefficient annotation layer is overlaid on the initial power regulation spectrum, and different colors are used to mark each operational stage. , and Numerical value.
[0099] Establish a communication interface with the real-time data acquisition module of the grain unloading system, update the adaptive calibration matrix M according to a preset cycle, and update the dynamic power control spectrum synchronously.
[0100] The benchmark deviation detection module is used to collect the actual power change curve of the motor in real time during the grain unloading process, compare the deviation between the average actual power in each operation stage and the benchmark value of the dynamic power control spectrum, and calculate the morphological fit between the actual power change curve and the dynamic power control spectrum within the time window of each operation stage. This enables precise monitoring of the unloading system's operating status and dynamic motor control. The process includes: The actual power change curve is collected using a motor power detector at a preset sampling frequency.
[0101] The actual power change curve is segmented by the time window of each operation stage. The average actual power of each stage is calculated, compared with the dynamic benchmark value, and the deviation rate is calculated. The formula is as follows:
[0102] In the formula, Indicates the deviation rate. This represents the average actual power. This represents the dynamic baseline value. A level one warning is triggered when the deviation rate exceeds the allowable deviation range.
[0103] The dynamic time warping algorithm is used to calculate the morphological fit between the actual power change curve and the dynamic reference curve. The calculation formula is as follows:
[0104] In the formula, S represents the morphological fit. For dynamic time-normalized distance, This represents the actual power change curve. Represents the dynamic baseline curve. S represents the duration of the work process. The value of S ranges from 0 to 1. S≥0.95 indicates excellent fit, 0.85≤S<0.95 indicates acceptable fit, and S<0.85 indicates unacceptable fit.
[0105] When the morphological fit S < 0.85, a secondary warning is triggered when the morphological fit is less than a preset threshold, and the cause of the anomaly is analyzed based on the adaptive calibration matrix M: if This was determined to be a potential motor malfunction. This was determined to be a potential environmental impact hazard. If This was determined to be a potential risk of unstable grain flow.
[0106] Based on the warning level and the cause of the anomaly, the system outputs motor control commands: For a Level 1 warning, the motor speed is fine-tuned. For a Level 2 warning, if the issue is a potential motor malfunction, the motor power is reduced and a maintenance reminder is issued. If the issue is a potential grain flow instability problem, the unloading gate opening is adjusted, thus achieving closed-loop control of the unloading system.
[0107] In summary, this embodiment provides a precision grain unloading system based on multi-signal fusion control of the unloading motor. Through semantic parsing of the unloading process and extraction of unloading feature identifiers for each operational stage, standardized unloading process data is generated, providing a solid data foundation for precise control. The system calculates the baseline power value for unloading operations based on the grain flow rate and motor energy consumption curve, and dynamically optimizes the initial power control spectrum using a multi-dimensional adaptive calibration matrix, enabling precise matching of motor power with real-time unloading requirements. Regardless of different grain varieties, moisture contents, or complex and variable unloading conditions, the system ensures a consistently stable and uniform grain flow, effectively avoiding problems such as grain flow blockage or insufficient flow, greatly improving the accuracy and stability of unloading operations, and significantly increasing unloading efficiency.
[0108] By conducting real-time analysis and adaptive calibration of multi-dimensional data such as motor load and environmental factors, the system ensures that the motor is always in a high-efficiency operating range, significantly reducing energy consumption, effectively reducing energy costs in agricultural production, and achieving the green development goal of energy conservation and emission reduction.
[0109] The system collects real-time data on the actual power variation curve of the motor during grain unloading, compares the deviation with the baseline value of the dynamic power control spectrum, and calculates the morphological fit. In the event of an anomaly, the system can quickly trigger an early warning mechanism and, based on an adaptive calibration matrix, deeply analyze the cause of the anomaly, accurately locating the fault type, such as potential motor malfunctions, environmental impact risks, or unstable grain flow. This not only helps to take timely and targeted measures to eliminate faults and avoid equipment damage and operational interruptions, but also provides a scientific basis for equipment maintenance, allowing for advance maintenance planning, extending equipment lifespan, and reducing equipment operation and maintenance costs.
[0110] By collecting environmental temperature and humidity data and calculating the thermal deformation calibration coefficient, the impact of environmental factors on motor power can be effectively compensated. Regardless of changes in the external environment, the grain unloading system can operate stably, greatly enhancing its adaptability and reliability in complex environments, ensuring that grain unloading operations are unaffected by environmental interference and continue to be carried out efficiently.
[0111] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A precision grain unloading system based on multi-signal fusion control of the grain unloading motor, characterized in that, include: The process generation module is used to collect the operation instructions and basic equipment parameters of the grain unloading task, parse and identify the continuous operation links in the operation instructions, extract the grain unloading feature identifiers of each operation link in combination with the basic equipment parameters, and generate grain unloading process data. The power benchmark calculation module is used to collect historical grain unloading operation datasets, extract the grain flow rate, motor energy consumption curve and operation sequence correlation from the historical data, and calculate the power benchmark value of grain unloading operation based on the grain flow rate and motor energy consumption curve. The control spectrum generation module is used to set the allowable range of dynamic deviation based on the power benchmark value of the unloading operation, and generate an initial power control spectrum arranged in the time sequence of the operation. The dynamic power regulation module is used to collect the current harmonic information of the unloading motor, the grain flow pressure detection data and the environmental temperature and humidity index, construct a multi-dimensional adaptive calibration matrix, and integrate it with the initial power regulation spectrum to obtain the dynamic power regulation spectrum of the unloading operation. The benchmark deviation detection module is used to collect the actual power change curve of the motor in real time during the unloading process, compare the deviation between the average actual power in each operation stage and the benchmark value of the dynamic power control spectrum, calculate the morphological fit between the actual power change curve and the dynamic power control spectrum in the time window of each operation stage, and realize accurate monitoring of the operating status of the unloading system and dynamic control of the motor.
2. The precision grain unloading system based on multi-signal fusion control of the grain unloading motor according to claim 1, characterized in that, The operation instructions in the process generation module include the total unloading target, grain category attribute parameters, unloading gate opening setting value, and conveyor belt running speed threshold. The grain category attribute parameters include moisture content and bulk density. The equipment basic parameters include the rated power, rated speed, torque characteristic curve of the unloading motor, measurement range of the grain flow pressure detector, and unloading bin volume. The unloading characteristic identifiers include the motor starting power threshold, power range during the stable grain flow stage, real-time unloading gate opening, and calculated grain flow conveying rate.
3. The precision grain unloading system based on multi-signal fusion control of the unloading motor according to claim 1, characterized in that, The process by which the process generation module parses and identifies consecutive work steps in the work instruction includes: Lexical analysis was performed on the unloading control program file of the operation instruction to identify the core operation instructions, which include motor start / stop instructions, unloading gate opening adjustment instructions, and conveyor belt speed control instructions. Based on the type of the core operation instructions, continuous operation segments are divided to generate an initial grain unloading operation sequence, including the start-up phase, the grain flow stabilization phase, and the closing phase. Extract the core control parameter set for each step in the initial grain unloading operation sequence. The core control parameter set includes the motor speed set value, the coordinates of the grain flow pressure detection point, and the grain unloading gate opening change period. Based on the core control parameter set, non-unloading auxiliary links in the initial unloading operation sequence are removed. These non-unloading auxiliary links include equipment preheating and empty silo debugging, resulting in the continuous operation links in the operation instruction.
4. The precision grain unloading system based on multi-signal fusion control of the grain unloading motor according to claim 1, characterized in that, The process of generating unloading process data by the process generation module includes: Based on the coordinates of the grain flow pressure detection point and the grain category attribute parameters, the grain flow pressure-density correspondence model is associated to calculate the real-time grain flow pressure value. By combining the motor torque characteristic curve and rated speed, the maximum allowable power threshold of the motor in the current operation stage is derived; By associating the unloading bin volume with the total unloading volume target, the number of unloading cycles per operation is calculated. The unique identifier of the packaging operation, the set of core control parameters, and the calculated value of grain flow rate are standardized data objects to obtain standardized grain unloading process data.
5. The precision grain unloading system based on multi-signal fusion control of the unloading motor according to claim 1, characterized in that, The process by which the power benchmark calculation module extracts grain flow rate, motor energy consumption curves, and time-series correlations of operational steps from historical grain unloading operation data includes: Screen historical grain unloading operation records of the same type of grain and the same model of unloading equipment; Analyze the calculated value of grain flow transport rate in historical normalized data objects, and statistically analyze the distribution characteristics of grain flow transport rate, including the mean and standard deviation. Analyze historical grain unloading motor power detection records and construct a mapping model between motor power and grain flow conveying rate; Extract the historical grain unloading operation chain sequence and the timestamp relationship of each stage. The timestamp relationship includes the duration of the start stage, the duration of the stable stage, and the duration of the finish stage.
6. The precision grain unloading system based on multi-signal fusion control of the unloading motor according to claim 5, characterized in that, The process by which the power benchmark calculation module calculates the power benchmark value for unloading operations based on the grain flow rate and motor energy consumption curve includes: Substitute the current grain flow conveying rate setpoint into the mapping model to generate a basic power reference value; The load correction factor is added to obtain the load correction power; Introduce a grain unloading safety factor to generate a baseline value for grain unloading operation power.
7. The precision grain unloading system based on multi-signal fusion control of the unloading motor according to claim 1, characterized in that, The process by which the regulation map generation module generates the initial power regulation map arranged according to the time sequence of the operation stages includes: A two-dimensional quantitative model of operation timing and motor power is constructed, with the horizontal axis representing the continuous operation time axis and the vertical axis representing the motor output power spectrum, and a dual-axis dynamic scaling system is established. The baseline power curve is fitted according to the time sequence logic of the operation, and a continuous baseline trajectory is generated by the smooth transition algorithm of the baseline power value of each operation. The allowable range of differential deviations is set based on the dynamic characteristics of the operation process; The deviation allowable domain is visualized and calibrated in a two-dimensional quantization model. Different color gradients are used to distinguish the deviation threshold range of each stage, forming an initial power control spectrum that includes the baseline trajectory, deviation boundary and stage identifier. Establish a mapping relationship between the graph and the characteristic parameters of the operation process, so that any time point in the graph can be traced back to the core control parameter set of the corresponding operation process.
8. The precision grain unloading system based on multi-signal fusion control of the unloading motor according to claim 1, characterized in that, The process of constructing a multi-dimensional adaptive calibration matrix by the dynamic optimization module of the graph includes: The current signal of the unloading motor is collected, the current harmonics are decomposed by Fourier transform, the current harmonic distortion rate is calculated, and the motor health calibration coefficient is generated. The thermal deformation calibration coefficient is calculated based on the temperature and humidity collected by the ambient temperature and humidity sensor. Collect grain flow pressure detection data, calculate grain flow pressure fluctuation coefficient, and generate grain flow stability calibration coefficient; The motor health calibration coefficient, thermal deformation calibration coefficient, and grain flow stability calibration coefficient are combined in column vector form to obtain a multi-dimensional adaptive calibration matrix.
9. The precision grain unloading system based on multi-signal fusion control of the unloading motor according to claim 1, characterized in that, The process by which the dynamic power regulation map for grain unloading operations is obtained by the dynamic map optimization module includes: Based on the adaptive calibration matrix, the power reference value of the initial spectrum is adjusted for each operational step; The allowable deviation range is dynamically adjusted based on the grain flow stability calibration coefficient; A calibration coefficient labeling layer is superimposed on the initial power regulation spectrum, and the values of motor health calibration coefficient, thermal deformation calibration coefficient and grain flow stability calibration coefficient for each operation stage are marked with different colors; Establish a communication interface with the real-time data acquisition module of the grain unloading system, update the adaptive calibration matrix according to a preset cycle, and update the dynamic power control spectrum synchronously.
10. The precision grain unloading system based on multi-signal fusion control of the grain unloading motor according to claim 1, characterized in that, The process by which the benchmark deviation detection module achieves precise monitoring of the grain unloading system's operating status and dynamic motor control includes: The actual power change curve is collected using a motor power detector at a preset sampling frequency; The actual power change curve is divided into time windows for each operation stage. The average actual power of each stage is calculated, compared with the dynamic benchmark value, and the deviation rate is calculated. When the deviation rate is greater than the allowable deviation range, a first-level warning is triggered. The dynamic time warping algorithm is used to calculate the morphological fit between the actual power change curve and the dynamic reference curve; When the morphological fit is less than a preset threshold, a level 2 warning is triggered, and the cause of the anomaly is analyzed based on the adaptive calibration matrix. Based on the warning level and the cause of the anomaly, motor control commands are output to achieve closed-loop control of the grain unloading system.