Intelligent forklift and control method thereof

Abstract

The invention discloses an intelligent forklift and a control method thereof, and belongs to the field of forklift control, according to the scheme, the dominant frequency offset rate and the total harmonic distortion rate are analyzed according to the physical characteristics of strong coupling of the rigidity and the vibration mode of a cargo stacking body, and therefore the hidden risks of interlayer looseness and slippage of cargoes are judged in advance; the lag limitation of traditional dominant tilt detection is broken through; in addition, through median filtering, low-pass filtering and self-adaptive noise cancellation, forklift bumping and motor interference can be accurately separated, a pure cargo vibration signal is extracted, misjudgment inducements are eliminated, and the judgment accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of forklift control, and in particular to a control method for unmanned forklifts. Background Technology

[0002] As autonomous driving technology matures, its applications extend from daily life to the industrial sector. Autonomous forklifts are also becoming more mature. Compared to AGVs, forklifts can not only transport goods but also pick up and place them, making them more versatile. However, because they are unmanned, it is difficult to monitor the condition of goods during transport. If the goods are stacked unstablely, they can easily cause a collapse, resulting in damage to the goods or personal injury. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides a solution for adjusting the forklift's operating status by assessing the risk of cargo tilting, thereby preventing cargo from tipping over.

[0004] To achieve the above objectives, the present invention provides a control method for an intelligent forklift, comprising the following steps:

[0005] Step 1: Collect vibration signals from the forklift, including vibration signals from the cargo and vibration signals from the forklift's posture.

[0006] Step 2: Preprocess the collected vibration signals by using a median filtering algorithm to remove pulse interference signals and a low-pass filtering algorithm to remove high-frequency interference signals; and use an adaptive noise cancellation algorithm to separate the forklift attitude vibration coupled to the cargo vibration signal from the vehicle body vibration signal collected by the auxiliary IMU.

[0007] Step 3: Use Discrete Fourier Transform and Fast Fourier Transform to convert the preprocessed vibration signal into a frequency domain signal, and extract the main frequency signal and harmonic signal;

[0008] Step 4: Calibrate the reference frequency under steady-state conditions to ensure that the reference mode matches the current operating conditions;

[0009] Step 5: Calculate the relative offset rate between the main frequency signal and the reference main frequency, as well as the total harmonic distortion rate, from Step 3;

[0010] Step 6: Determine whether the cargo is stable based on the relative offset rate and total harmonic distortion rate from Step 5;

[0011] Step 7: Adjust the forklift's driving status and the fork tilt angle based on the stability determined in Step 6.

[0012] Optionally, in step 2:

[0013] Use formula (1) to remove pulse interference signals;

[0014] (1);

[0015] in, This represents the vibration signal after removing pulse interference signals; Indicates the median operation; This represents the vertical vibration acceleration of the cargo; M is an odd number, representing the size of the filter window; Indicates time;

[0016] Use formula (2) to remove high-frequency interference signals;

[0017] (2);

[0018] in, This represents the vibration signal after removing high-frequency interference signals. , representing the filter coefficient; , represents the time constant; Indicates the cutoff frequency; , indicating the sampling frequency;

[0019] Formula (3) is used to separate the coupled forklift posture vibration from the cargo vibration signal;

[0020] (3);

[0021] in, This represents the vibration signal that couples the forklift posture vibration to the separated cargo vibration signal; Indicates the adaptive filter weights. This indicates the vibration acceleration of the vehicle frame.

[0022] Optionally, the vibration signal after removing interference can be normalized using formula (4);

[0023] (4);

[0024] in, This represents the normalized vibration signal; , These represent the minimum and maximum values ​​of the vibration signal after the interference has been separated.

[0025] Optionally, in step 3, the time-domain signal is converted into a frequency-domain signal using formula (5);

[0026] (5);

[0027] in, represents the complex copy of the k-th frequency point; j represents the imaginary unit; n represents the index of the time-domain sampling point; This represents the total number of points in a discrete-time signal. This represents the actual frequency of the k-th frequency point.

[0028] Optionally, in step 3, the energy at each frequency point is calculated using formula (6);

[0029] (6);

[0030] in, This represents the vibrational energy at the k-th frequency point;

[0031] The main frequency signal is extracted using formula (7); This represents the absolute value of the frequency domain amplitude.

[0032] (7);

[0033] in, Indicates the main frequency; Indicates the index of the point with the highest energy frequency;

[0034] The first harmonic signal and the second harmonic signal are extracted using formula (8);

[0035] (m=2,3)(8);

[0036] in, This represents the energy of the m-th harmonic; This represents the frequency of the m-th harmonic. , which represents frequency resolution.

[0037] Optionally, step 4 includes:

[0038] After the forklift picks up the goods, the main frequency signal is collected multiple times to reflect the inherent mode while the forklift is moving at a constant speed or stationary.

[0039] The reference frequency is determined by calculating the arithmetic mean of multiple acquisitions using formula (9);

[0040] (9);

[0041] Where K represents the total number of samples; This represents the dominant frequency of the k-th sample. Indicates the reference clock frequency.

[0042] Optionally, in step 5, the relative offset rate between the main frequency signal and the reference main frequency is calculated using formula (10);

[0043] (10);

[0044] in, Indicates the relative offset rate;

[0045] The total harmonic distortion rate is calculated using formula (11);

[0046] (11);

[0047] in, Indicates the total harmonic distortion rate; Indicates the main frequency energy; , These represent the second harmonic energy and the third harmonic energy, respectively.

[0048] Optionally, step 6 includes:

[0049] When the relative offset rate of the main frequency Less than or equal to the first threshold and total harmonic distortion rate When the value is less than or equal to the second threshold, it indicates that the goods are in a stable state;

[0050] When the relative offset rate of the main frequency is greater than the first threshold or the total harmonic distortion rate If the value exceeds the second threshold, it indicates that the goods are in an unstable state.

[0051] Optionally, in step 7:

[0052] When the goods are in an unstable state, adjust the speed of the forklift according to formula (12);

[0053] (12);

[0054] in, Indicates the current speed of the forklift; , As weight, The first threshold, The second threshold;

[0055] When the goods are in an unstable state, adjust the tilt angle of the forks according to formula (13);

[0056] (13);

[0057] in, This is the maximum angle that the forks are allowed to adjust.

[0058] On the other hand, the present invention also provides an intelligent forklift, comprising:

[0059] The acquisition unit is used to acquire vibration signals of the forklift, including vibration signals of the goods and vibration signals of the forklift's posture vibration.

[0060] The preprocessing unit is used to preprocess the acquired vibration signals, using a median filtering algorithm to remove pulse interference signals; a low-pass filtering algorithm to remove high-frequency interference signals; and an adaptive noise cancellation algorithm to separate the forklift attitude vibration coupled in the cargo vibration signal from the vehicle body vibration signal acquired by the auxiliary IMU.

[0061] The extraction unit is used to convert the preprocessed vibration signal into a frequency domain signal using discrete Fourier transform and fast Fourier transform, and to extract the main frequency signal and harmonic signal.

[0062] The calibration unit is used to calibrate the reference frequency under steady-state conditions to ensure that the reference mode matches the current operating conditions.

[0063] The calculation unit is used to calculate the relative offset rate between the main frequency signal and the reference main frequency, as well as the total harmonic distortion rate.

[0064] The judgment unit is used to determine whether the state of the cargo is stable based on the relative offset rate and the total harmonic distortion rate.

[0065] The adjustment unit is used to adjust the forklift's driving status and the fork tilt angle based on stability.

[0066] The advantages of this invention over the prior art are as follows: Based on the physical characteristics of strong coupling between the stiffness of the stacked goods and the vibration modes, this invention analyzes the main frequency offset rate and total harmonic distortion rate, thereby predicting the hidden risks of interlayer loosening and slippage of goods in advance, breaking through the lag limitation of traditional explicit tilt detection; in addition, through median filtering, low-pass filtering and adaptive noise cancellation, it can accurately separate forklift bumps and motor interference, extract pure goods vibration signals, eliminate the causes of misjudgment, and improve the accuracy of judgment. Attached Figure Description

[0067] Figure 1 This is a flowchart of a control method for an intelligent forklift provided by the present invention;

[0068] Figure 2 This is a simulation result diagram of a control method for an intelligent forklift provided by the present invention;

[0069] Figure 3 This is a structural diagram of an intelligent forklift provided by the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0071] Reference Figure 1 This embodiment provides a control method for an intelligent forklift, including the following steps:

[0072] Step 1: Collect vibration signals from the forklift, including vibration signals from the cargo and vibration signals from the forklift's posture.

[0073] Specifically, accelerometers are installed on the forklift forks to collect the vertical acceleration signals of the goods. In addition, auxiliary IMU sensors are installed on the forklift frame to collect the vibration acceleration and angular velocity of the forklift body, which is used for adaptive noise cancellation to separate body interference from goods vibration.

[0074] Step 2: Preprocess the collected vibration signals by using a median filtering algorithm to remove pulse interference signals and a low-pass filtering algorithm to remove high-frequency interference signals; and use an adaptive noise cancellation algorithm to separate the forklift attitude vibration coupled in the cargo vibration signal from the vehicle body vibration signal collected by the auxiliary IMU.

[0075] Specifically, this step is divided into three parts: first, removing pulse interference signals; second, removing high-frequency interference signals; and finally, removing forklift posture vibration signals.

[0076] In this embodiment, the pulse interference signal is removed by the median filtering algorithm. The principle of median filtering is to remove pulse noise (such as ground bumps and hydraulic shocks) by sorting the data in the window and taking the median value, and retain the continuous signal trend. Specifically, the pulse interference signal is removed by using formula (1).

[0077] (1);

[0078] in, This represents the vibration signal after removing pulse interference signals; Indicates the median operation; This represents the vertical vibration acceleration of the cargo; M is an odd number, representing the size of the filter window; Indicates time.

[0079] It should be noted that in this embodiment, the filtering window M is 7, which can completely cover longer pulse interference without causing signal lag and affecting real-time performance.

[0080] In this embodiment, high-frequency signals are removed using a low-pass filtering algorithm. Since the capacitor has low impedance to high-frequency signals and high impedance to low-frequency signals, high-frequency noise filtering can be achieved. Therefore, high-frequency interference signals are removed using formula (2).

[0081] (2);

[0082] in, This represents the vibration signal after removing high-frequency interference signals. , representing the filter coefficient; , represents the time constant; Indicates the cutoff frequency; , which represents the sampling frequency.

[0083] In this embodiment, an adaptive noise cancellation algorithm is used to separate the forklift posture vibration coupled to the cargo vibration signal. The adaptive noise cancellation uses the gradient descent method to minimize the mean square error of the output signal by dynamically adjusting the weights. Specifically, formula (3) is used to separate the forklift posture vibration coupled to the cargo vibration signal;

[0084] (3);

[0085] in, This represents the vibration signal that couples the forklift posture vibration to the separated cargo vibration signal; Indicates the adaptive filter weights. This indicates the vibration acceleration of the vehicle frame.

[0086] Finally, in order to eliminate the signal amplitude differences in different operating stages of the forklift and ensure that the modal characteristics are comparable under different working conditions, all parameters need to be normalized. The normalization formula is shown in equation (4):

[0087] (4);

[0088] in, This represents the normalized vibration signal, ranging from [0,1]. , These represent the minimum and maximum values ​​of the vibration signal after the interference has been separated.

[0089] Step 3: Use Discrete Fourier Transform and Fast Fourier Transform to convert the preprocessed vibration signal into a frequency domain signal, and extract the main frequency signal and harmonic signals.

[0090] To improve the accuracy of the judgment, the preprocessed time-domain vibration signal needs to be converted into a frequency-domain signal, mainly targeting the inherent vibration characteristics of the stacked goods, and avoiding dynamic time-domain interference from forklift operations.

[0091] Specifically, the time-domain signal is converted into a frequency-domain signal using formula (5);

[0092] (5);

[0093] in, represents the complex copy of the k-th frequency point; j represents the imaginary unit; n represents the index of the time-domain sampling point; This represents the total number of points in a discrete-time signal. This represents the actual frequency of the k-th frequency point.

[0094] In the process of converting a complete food and drink stack, the first step is to extract the main frequency signal. The main frequency of the stacked goods is determined by its equivalent stiffness and total mass. The support method of the forklift forks and the position of the forklift pick-up point will affect the equivalent stiffness distribution of the goods. Therefore, the main frequency with the largest energy proportion in the frequency domain can be extracted. Specifically, the energy at each frequency point is calculated using formula (6).

[0095] (6);

[0096] in, This represents the vibrational energy at the k-th frequency point;

[0097] The main frequency signal is extracted using formula (7); This represents the absolute value of the frequency domain amplitude.

[0098] (7);

[0099] in, Indicates the main frequency; This represents the index of the point with the highest energy frequency.

[0100] Stable cargo stack vibration is dominated by the dominant frequency, with extremely low harmonic energy. When the layers are loose or slip, the contact state between the cargo and the forks changes, and the vibration system exhibits nonlinear characteristics, exciting harmonic components. Therefore, the energy distribution of the dominant frequency and each harmonic is extracted as a supplementary feature for judging the stack stability. Therefore, it is only necessary to extract the first and second harmonics. In this embodiment, the first and second harmonic signals are extracted using formula (8).

[0101] (m=2,3)(8);

[0102] in, This represents the energy of the m-th harmonic; This represents the frequency of the m-th harmonic. , which represents frequency resolution.

[0103] Step 4: Calibrate the reference frequency under steady-state conditions to ensure that the reference mode matches the current operating conditions.

[0104] In this embodiment, the reference frequency is the main parameter for determining whether the goods are at risk of tilting. Therefore, it is necessary to collect the frequency signal multiple times after the forklift picks up the goods, either while the forklift is moving at a constant speed or stationary, to reflect the inherent mode. Specifically, the reference frequency is determined by calculating the arithmetic mean of the multiple collections using formula (9).

[0105] (9);

[0106] Where K represents the total number of samples; This represents the dominant frequency of the k-th sample. Indicates the reference clock frequency.

[0107] Step 5: Calculate the relative offset rate between the main frequency signal and the reference main frequency in Step 3, as well as the total harmonic distortion rate.

[0108] Specifically, during forklift operation, loosening and slippage between layers of goods can lead to changes in equivalent stiffness, which in turn causes a shift in the main frequency. By calculating the relative offset rate between the current main frequency and the reference main frequency, the stiffness change of the stacked structure can be quantified. The larger the offset rate, the more unstable the coupling state between the goods and the forks. In this embodiment, the relative offset rate between the main frequency signal and the reference main frequency is calculated using formula (10).

[0109] (10);

[0110] in, This represents the relative offset rate.

[0111] In addition, changes in the contact state between the goods and the forks will exacerbate the nonlinear characteristics of the vibration and significantly increase the harmonic energy. By calculating the total harmonic distortion rate, the degree of nonlinear distortion of the vibration signal is quantified. The larger the distortion rate, the more significant the nonlinear characteristics of the goods stacking and the higher the instability risk. Therefore, the total harmonic distortion rate is calculated by formula (11).

[0112] (11);

[0113] in, Indicates the total harmonic distortion rate; Indicates the main frequency energy; , These represent the second harmonic energy and the third harmonic energy, respectively.

[0114] Step 6: Determine whether the cargo is stable based on the relative offset rate and total harmonic distortion rate from Step 5.

[0115] Specifically, when the relative offset rate of the main frequency Less than or equal to the first threshold and total harmonic distortion rate When the value is less than or equal to the second threshold, it indicates that the goods are in a stable state.

[0116] When the relative offset rate of the main frequency is greater than the first threshold or the total harmonic distortion rate If the value exceeds the second threshold, it indicates that the goods are in an unstable state.

[0117] When any distortion indicator exceeds the threshold, it indicates that the cargo stacking structure has changed significantly (such as obvious slippage) and is about to become unbalanced, requiring emergency control intervention.

[0118] In other embodiments, appropriate threshold values ​​for main frequency offset and harmonic distortion can be set based on the forklift's operating scenario, such as no-load / heavy-load, low-speed / high-speed travel, flat-ground / ramp-road operation, and cargo type, to avoid the shortcomings of a single threshold being unable to adapt to diverse working conditions.

[0119] Step 7: Adjust the forklift's driving status and the fork tilt angle based on the stability determined in Step 6.

[0120] In this embodiment, when the goods are found to be in an unstable state, it is necessary to intervene in the driving state of the forklift, including adjusting the speed and the tilt angle of the forks.

[0121] Specifically, when the goods are in an unstable state, the speed of the forklift is adjusted according to formula (12);

[0122] (12);

[0123] in, Indicates the current speed of the forklift; , As weight, The first threshold, This is the second threshold.

[0124] When the goods are in an unstable state, adjust the tilt angle of the forks according to formula (13);

[0125] (13);

[0126] in, This is the maximum angle that the forks are allowed to adjust.

[0127] To verify the technical effects of the present invention, experimental data is provided in this embodiment for illustration:

[0128] The cargo parameters for this experiment are as follows: cargo weight 500KG, cargo center of gravity height 0.8m, operation scenario: narrow warehouse aisle with a turning radius R=2.5m, initial turning speed V0=2km / h. The first threshold is 30%, and the second threshold is 40%. The data acquisition time is T=20s, the number of signal points is 20000, and the frequency resolution is 1Hz.

[0129] Base frequency =31Hz, the current main frequency collected is 39Hz.

[0130] The calculation shows that the main frequency offset is... The frequency is 39Hz, and the total harmonic distortion (THD) is 43.5%.

[0131] Therefore, it is necessary to adjust the forklift speed or the fork tilt angle. In this embodiment, the weight... It is 0.4. It is 0.6. The angle is 10°. Calculations show that the vehicle speed is adjusted to 0.62 km / h, and the tilt angle is adjusted to 2.58°. Figure 2 As can be seen, the goods returned to a stable state after adjusting the vehicle speed and tilt angle. This demonstrates that the present invention, through forklift-based signal decoupling and modal analysis, accurately separates the vibration of the goods themselves from the interference of forklift operations, and can capture unstable risks such as interlayer loosening and hidden slippage that do not manifest as overt tilting, thus avoiding collapse accidents from the source.

[0132] In another embodiment, reference is made to... Figure 3 The present invention also provides an intelligent forklift, comprising:

[0133] The acquisition unit 100 is used to acquire vibration signals of the forklift, including vibration signals of the goods and vibration signals of the forklift's posture vibration. It should be noted that since the specific acquisition process has been described in detail in step 1 of the above embodiment, it will not be repeated here.

[0134] The preprocessing unit 200 is used to preprocess the collected vibration signals, using a median filtering algorithm to remove pulse interference signals; using a low-pass filtering algorithm to remove high-frequency interference signals; and using an adaptive noise cancellation algorithm to separate the forklift attitude vibration coupled in the cargo vibration signal from the vehicle body vibration signal collected by the auxiliary IMU. It should be noted that since the specific preprocessing process has been described in detail in step 2 of the above embodiment, it will not be repeated here.

[0135] Extraction unit 300 is used to convert the preprocessed vibration signal into a frequency domain signal using discrete Fourier transform and fast Fourier transform, and to extract the main frequency signal and harmonic signal. It should be noted that since the specific extraction process has been described in detail in step 3 of the above embodiment, it will not be repeated here.

[0136] The calibration unit 400 is used to calibrate the reference main frequency under stable conditions to ensure that the reference mode matches the current operating conditions. It should be noted that since the specific calibration process has been described in detail in step 4 of the above embodiment, it will not be repeated here.

[0137] The calculation unit 500 is used to calculate the relative offset rate between the main frequency signal and the reference main frequency and the total harmonic distortion rate. It should be noted that since the specific calculation process has been described in detail in step 5 of the above embodiment, it will not be repeated here.

[0138] The judgment unit 600 is used to determine whether the state of the cargo is stable based on the relative offset rate and the total harmonic distortion rate. It should be noted that since the specific judgment process has been described in detail in step 6 of the above embodiment, it will not be repeated here.

[0139] The adjustment unit 700 is used to adjust the driving state of the forklift and the tilt angle of the forks according to the stability. It should be noted that the specific adjustment process has been described in detail in step 7 of the above embodiment, so it will not be repeated here.

[0140] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] The above description is merely a specific implementation measure of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations, substitutions, or heterogeneities that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A control method for an intelligent forklift, characterized in that, Includes the following steps: Step 1: Collect vibration signals from the forklift, including vibration signals from the cargo and vibration signals from the forklift's posture. Step 2: Preprocess the collected vibration signals by using a median filtering algorithm to remove pulse interference signals and a low-pass filtering algorithm to remove high-frequency interference signals; and use an adaptive noise cancellation algorithm to separate the forklift attitude vibration coupled to the cargo vibration signal from the vehicle body vibration signal collected by the auxiliary IMU. Step 3: Use Discrete Fourier Transform and Fast Fourier Transform to convert the preprocessed vibration signal into a frequency domain signal, and extract the main frequency signal and harmonic signal; Step 4: Calibrate the reference frequency under steady-state conditions to ensure that the reference mode matches the current operating conditions; Step 5: Calculate the relative offset rate between the main frequency signal and the reference main frequency, as well as the total harmonic distortion rate, from Step 3; Step 6: Determine whether the cargo is stable based on the relative offset rate and total harmonic distortion rate from Step 5; Step 7: Adjust the forklift's driving status and the fork tilt angle based on the stability determined in Step 6.

2. The control method according to claim 1, characterized in that, In step 2: Use formula (1) to remove pulse interference signals; （1）； in, This represents the vibration signal after removing pulse interference signals; Indicates the median operation; This represents the vertical vibration acceleration of the cargo; M is an odd number, representing the size of the filter window; Indicates time; Use formula (2) to remove high-frequency interference signals; （2）； in, This represents the vibration signal after removing high-frequency interference signals. , representing the filter coefficient; , represents the time constant; Indicates the cutoff frequency; , indicating the sampling frequency; Formula (3) is used to separate the coupled forklift posture vibration from the cargo vibration signal; （3）； in, This represents the vibration signal that couples the forklift posture vibration to the separated cargo vibration signal; Indicates the adaptive filter weights. This indicates the vibration acceleration of the vehicle frame.

3. The control method according to claim 2, characterized in that, The vibration signal after removing interference is normalized using formula (4); （4）； in, This represents the normalized vibration signal; , These represent the minimum and maximum values ​​of the vibration signal after the interference has been separated.

4. The control method according to claim 1, characterized in that, In step 3, the time-domain signal is converted into a frequency-domain signal using formula (5); （5）； in, represents the complex copy of the k-th frequency point; j represents the imaginary unit; n represents the index of the time-domain sampling point; This represents the total number of points in a discrete-time signal. This represents the actual frequency of the k-th frequency point.

5. The control method according to claim 1, characterized in that, In step 3, the energy at each frequency point is calculated using formula (6); （6）； in, This represents the vibrational energy at the k-th frequency point; The main frequency signal is extracted using formula (7); This represents the absolute value of the frequency domain amplitude. （7）； in, Indicates the main frequency; Indicates the index of the point with the highest energy frequency; The first harmonic signal and the second harmonic signal are extracted using formula (8); （m=2,3）（8）； in, This represents the energy of the m-th harmonic; This represents the frequency of the m-th harmonic. , which represents frequency resolution.

6. The control method according to claim 1, characterized in that: Step 4 includes: After the forklift picks up the goods, the main frequency signal is collected multiple times to reflect the inherent mode while the forklift is moving at a constant speed or stationary. The reference frequency is determined by calculating the arithmetic mean of multiple acquisitions using formula (9); （9）； Where K represents the total number of samples; This represents the dominant frequency of the k-th sample. Indicates the reference clock frequency.

7. The control method according to claim 1, characterized in that, In step 5, the relative offset rate between the main frequency signal and the reference main frequency is calculated using formula (10); （10）； in, Indicates the relative offset rate; The total harmonic distortion rate is calculated using formula (11); （11）； in, Indicates the total harmonic distortion rate; Indicates the main frequency energy; , These represent the second harmonic energy and the third harmonic energy, respectively.

8. The control method according to claim 1, characterized in that, Step 6 includes: When the relative offset rate of the main frequency Less than or equal to the first threshold and total harmonic distortion rate When the value is less than or equal to the second threshold, it indicates that the goods are in a stable state; When the relative offset rate of the main frequency is greater than the first threshold or the total harmonic distortion rate If the value exceeds the second threshold, it indicates that the goods are in an unstable state.

9. The control method according to claim 8, characterized in that, In step 7: When the goods are in an unstable state, adjust the speed of the forklift according to formula (12); （12）； in, Indicates the current speed of the forklift; , As weight, The first threshold, The second threshold; When the goods are in an unstable state, adjust the tilt angle of the forks according to formula (13); （13）； in, This is the maximum angle that the forks are allowed to adjust.

10. An intelligent forklift, characterized in that, include: The acquisition unit is used to acquire vibration signals of the forklift, including vibration signals of the goods and vibration signals of the forklift's posture vibration. The preprocessing unit is used to preprocess the acquired vibration signals, using a median filtering algorithm to remove pulse interference signals; a low-pass filtering algorithm to remove high-frequency interference signals; and an adaptive noise cancellation algorithm to separate the forklift attitude vibration coupled in the cargo vibration signal from the vehicle body vibration signal acquired by the auxiliary IMU. The extraction unit is used to convert the preprocessed vibration signal into a frequency domain signal using discrete Fourier transform and fast Fourier transform, and to extract the main frequency signal and harmonic signal. The calibration unit is used to calibrate the reference frequency under steady-state conditions to ensure that the reference mode matches the current operating conditions. The calculation unit is used to calculate the relative offset rate between the main frequency signal and the reference main frequency, as well as the total harmonic distortion rate. The judgment unit is used to determine whether the state of the cargo is stable based on the relative offset rate and the total harmonic distortion rate. The adjustment unit is used to adjust the forklift's driving status and the fork tilt angle based on stability.

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