Forklift state monitoring method under strong interference environment based on motor data

Abstract

The invention relates to the technical field of electrical testing, in particular to a forklift state monitoring method based on motor data in a strong interference environment, and the method comprises the steps: obtaining stator current and voltage signals of a forklift driving motor, recognizing waveform abrupt change, determining an interference section, calling a counter electromotive force signal to analyze flux linkage fluctuation, marking an unsynchronized time period, and generating flux linkage disturbance distribution. And monitoring controller output, comparing voltage and current wave crests to judge signal offset, monitoring torque power, analyzing power fluctuation division hierarchy, generating power interference distribution, joint power hierarchy, flux linkage disturbance and signal offset, judging stability, fluctuation or abnormity according to interference concentration and offset consistency, and generating a forklift operation monitoring result. According to the invention, through correlation analysis of current, voltage and counter electromotive force signal differences, a multi-layer coupling determination mechanism is constructed, synchronous identification tracking of signal abrupt change and energy disturbance is realized, transient interference and continuous offset are distinguished by using joint features, an abnormal trend is determined, and dynamic identification and evaluation reliability is enhanced.

Description

Technical Field

[0001] This invention relates to the field of electrical testing technology, and in particular to a method for monitoring the condition of a forklift under strong interference conditions based on motor data. Background Technology

[0002] The field of electrical testing technology encompasses the detection and assessment of the electrical parameters, performance characteristics, and operating status of motors, circuits, and related components. The core of this technology lies in identifying the operating status and potential anomalies of equipment by measuring physical quantities such as current, voltage, resistance, insulation performance, and signal waveforms. Electrical testing typically covers aspects such as electrical equipment condition assessment, performance verification, fault location, and operational safety monitoring. It relies on methods such as electrical parameter acquisition, electrical signal analysis, and condition identification to construct a systematic testing process. This field has a broad application foundation in scenarios such as industrial equipment operation monitoring, power system maintenance, and intelligent manufacturing equipment management.

[0003] Among them, the electrical signals such as current and voltage generated inside the forklift motor will change during operation in a strong interference environment. How to directly analyze the characteristic changes of the electrical signals inside the motor, establish the correspondence between electrical parameters and operating status, and complete the status identification and judgment based on the time domain and amplitude characteristics of the electrical signals to achieve effective monitoring of the forklift's operating status is currently a key research direction.

[0004] In existing technologies, motor status is determined solely by single-dimensional electrical parameters under strong interference conditions, lacking comprehensive analysis of changes in current, voltage, and flux coupling. This makes it difficult to distinguish the source of signal fluctuations. When waveforms undergo abrupt changes or phase drift, the system cannot identify internal response shifts in real time, resulting in lag in status detection and errors in locating interference sections. When interference persists or energy fluctuations accumulate, the identification results are easily affected by transient noise. The system lacks the ability to dynamically track changes in operating status, leading to a decrease in the reliability of equipment operation and the accuracy of fault warning under high load and multi-source interference environments. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a method for monitoring the status of forklifts under strong interference environments based on motor data. The technical solution is as follows: A method for monitoring the condition of a forklift under strong interference conditions based on motor data includes the following steps: S1: Continuously acquire the current and voltage signals of the stator of the forklift drive motor to form a power sequence. Obtain the waveform rate according to the sampling point direction. When the direction is reversed, it is determined to be a sudden change. Track the time range and determine the interval. Combine the fluctuation direction to identify discontinuous segments and generate interference segment identification results. S2: Based on the discontinuous time periods of current and voltage in the interference segment identification results, call the back EMF signal to record the changing trend and current and voltage directions, analyze the flux linkage fluctuation trajectory, mark the time periods of unsynchronized response, divide the disturbance distribution range, and generate flux linkage disturbance distribution information. S3: Based on the non-synchronized period of the flux linkage response in the flux linkage disturbance distribution information, monitor the voltage and current waveforms of the controller, compare the peak time difference, determine the offset starting point when the spacing continues to change, record the starting point and identify the direction of voltage lag or advance relative to current, and generate control signal offset record. S4: Based on the time offset characteristics of voltage and current in the control signal offset record, monitor the torque change of the drive motor, extract power data to form a power curve, divide the power fluctuation amplitude and continuous difference into levels, record the frequency, and generate a power interference intensity distribution record.

[0006] As a further aspect of the present invention, the interference segment identification result includes the interference start and end time, the range of waveform abrupt change interval, and the signal discontinuity characteristics; the flux linkage disturbance distribution information includes the location of the flux linkage disturbance period, the duration of the disturbance, and the amplitude distribution of the disturbance; the control signal offset record includes the offset start time, the offset direction, and the offset duration interval; and the power interference intensity distribution record includes the power fluctuation level location, the fluctuation amplitude difference, and the frequency of level occurrence.

[0007] As a further aspect of the present invention, the step of obtaining the interference segment identification result is as follows: S101: Acquire the stator current waveform and voltage raw signal of the forklift drive motor during operation, continuously sample in time sequence, record the time, instantaneous current and voltage of each sampling point, determine the waveform direction based on the change direction of current and voltage between adjacent sampling points, mark the sampling points where the change direction changes from rising to falling or from falling to rising as direction reversal points, count the number between consecutive reversal points, and generate waveform direction reversal distribution. S102: Based on the waveform direction reversal distribution, detect the time period in which the number of reversals continuously increases in the sampling sequence, calculate the waveform change rate by combining the current difference and voltage difference of adjacent sampling points, track the time of points where the change rate exceeds a set threshold, determine the duration of the over-limit point, and obtain the waveform change interval range. S103: Based on the waveform abrupt change range, compare the direction of change of current and voltage before and after the abrupt change, filter the time segment from the same direction to the opposite direction, calculate the corresponding start and end sampling time, mark the discontinuous intervals of current and voltage signals in the time series, and generate interference segment identification results.

[0008] As a further aspect of the present invention, the step of obtaining the magnetic flux disturbance distribution information is as follows: S201: Based on the start and end positions of the discontinuous segments of current and voltage signals in the interference segment identification results, extract the signal data of the corresponding time range, set sampling windows of the same time length, pair the current and voltage sampling points with corresponding times to form a continuous time series, record the start and end sampling times and sampling intervals of the time series, and establish the interference time window interval. S202: Based on the interference time window interval, extract the back EMF signal of the stator winding of the forklift drive motor, record the amplitude of the back EMF at each sampling point, determine the rising and falling rhythm based on the change of amplitude, and simultaneously record the changing direction of current and voltage. Compare the changing trends of current, voltage and back EMF to generate the back EMF changing trend. S203: Based on the back EMF change trend, mark the segments where the back EMF change is inconsistent with the direction of current and voltage, determine the duration and frequency of occurrence of the segments, divide the distribution location of the disturbance, and generate magnetic flux disturbance distribution information.

[0009] As a further aspect of the present invention, the step of obtaining the control signal offset record is as follows: S301: Based on the position of the asynchronous period of the flux response marked in the flux disturbance distribution information, monitor the voltage and current waveforms at the output of the forklift drive motor controller, record the peak arrival time of the voltage and current in the corresponding period, and compare the peak arrival time interval of the voltage and current to generate the peak time difference. S302: Based on the peak time difference, determine the changing trend of the voltage and current peak arrival time interval within the continuous waveform period, identify the expansion or contraction of the time interval, determine the start time of the offset, mark the time as the offset start point, and obtain the offset start point. S303: Based on the offset starting point, compare the voltage and current waveforms before and after the offset, analyze the alignment state of the waveforms, determine the lag or advance direction of the voltage signal relative to the current signal, record the direction and duration, and generate a control signal offset record.

[0010] As a further aspect of the present invention, the step of obtaining the power interference intensity distribution record is as follows: S401: Based on the time offset direction and duration interval of the voltage signal relative to the current signal in the control signal offset record, monitor the torque change of the forklift drive motor torque sensing unit, extract torque data, analyze the fluctuation trend over time, record the torque change amplitude and trend of each sampling point, and obtain the torque change trend. S402: Based on the torque change trend, obtain the power data of the motor output shaft power sensing unit within the same time range, connect the power data in time sequence to form a power curve, analyze the amplitude and duration of power fluctuation based on the rise and fall of power value in the power curve, calculate the range of change of each fluctuation segment, and obtain the power fluctuation trajectory. S403: Based on the power fluctuation trajectory, compare the amplitude difference and duration difference of power change in each fluctuation segment. Based on the combination result of amplitude difference and duration difference, divide the power fluctuation into different levels, record the occurrence frequency of each level, generate a power interference intensity distribution, and generate a power interference intensity distribution record.

[0011] As a further aspect of the present invention, the method further includes: S5: Based on the time distribution location and frequency of occurrence of power fluctuation levels in the power interference intensity distribution record, combined with the number of magnetic flux disturbances and the offset direction of voltage and current signals, and analyzing the temporal correspondence, determine the continuity of forklift operation status and interference concentration, determine the operation performance type based on the degree of interference level concentration and the consistency of signal offset direction, and generate forklift operation status monitoring results. The forklift operation status monitoring results include operation performance type, interference concentration level, and status continuity determination.

[0012] As a further aspect of the present invention, the steps for obtaining the forklift operating status monitoring results are as follows: S501: Based on the time distribution location and frequency of occurrence of the power fluctuation levels in the power interference intensity distribution record, combined with the number of flux linkage disturbances and the offset direction of the control signal, sort out the distribution of the power fluctuation levels, calculate the frequency of occurrence of each level, analyze the correlation between the change in the number of flux linkage disturbances and the offset direction of voltage and current, and obtain the interference concentration analysis results. S502: Based on the interference concentration analysis results, determine the state continuity during the forklift operation. By observing the consistency between the frequency of occurrence of each interference level and the signal offset direction, identify different performance types of the operating state. Based on the concentration of the interference levels, determine the fluctuation and abnormal state, and obtain the state continuity assessment results. S503: Based on the state continuity assessment results, combined with the degree of concentration of interference levels and the consistency of signal offset direction, determine the forklift operation performance type. When the power fluctuation levels are evenly distributed and the offset direction has no concentrated trend, it is determined to be a stable state. When the fluctuation levels are concentrated and the offset direction is consistent, it is determined to be a fluctuating state. When the fluctuation levels are highly concentrated and the offset direction is consistently consistent, it is determined to be an abnormal state. Generate forklift operation status monitoring results.

[0013] As a further aspect of the present invention, after obtaining the control signal offset record, cluster analysis is performed on the offset direction and offset duration data. Based on the consistency of offset direction and the distribution characteristics of duration, the control offset behavior is divided into different categories, and corresponding labels are assigned according to the offset mode characteristics of each category. In the subsequent motor operation characteristic evaluation process, different offset modes are distinguished and identified based on the classification labels.

[0014] As a further aspect of the present invention, after obtaining the power interference intensity distribution record, the amplitude difference and duration of the power fluctuation are clustered to divide samples with similar power fluctuation characteristics into different categories, and the power fluctuation pattern information corresponding to each category is determined based on the clustering results.

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this invention, by performing correlation analysis on the time series differences of current, voltage, and back EMF signals, a multi-layered coupled judgment mechanism for interference sections, flux disturbances, and power fluctuations is formed. This enables synchronous identification and time-series tracking of signal mutations and energy disturbances, improving the sensitivity and accuracy of state change capture. By utilizing the joint feature analysis of waveform direction reversal and power curve fluctuation rhythm, transient interference and continuous offset can be distinguished, and the time location and directional trend of energy transfer anomalies can be clearly identified. This allows for the identification of operating states to have both continuity and differential expression, maintaining stable signal analysis under complex electromagnetic interference conditions, and enhancing the ability to identify the internal dynamic response of the motor and the reliability of operating state assessment. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the interference segment identification results of the present invention. Figure 3 This is a flowchart illustrating the process of obtaining magnetic flux disturbance distribution information according to the present invention. Figure 4 This is a flowchart illustrating the acquisition process of the control signal offset record in this invention. Figure 5 This is a flowchart illustrating the acquisition process of power interference intensity distribution records according to the present invention. Figure 6 This is a flowchart illustrating the process of obtaining forklift operation status monitoring results according to the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1 This invention provides a technical solution: a method for monitoring the status of a forklift under strong interference conditions based on motor data, comprising the following steps: S1: Acquire the stator current waveform and voltage raw signal of the forklift drive motor during operation, continuously sample them in time sequence to form a sampling sequence, obtain the waveform change rate based on the difference in the direction of current and voltage change between adjacent sampling points, and determine the waveform change process when the change direction of multiple consecutive sampling points reverses. Track the duration of the change in the sampling sequence to determine the waveform change interval, compare the current and voltage fluctuation direction before and after the change, identify the discontinuous segments of current and voltage signals in the time sequence, mark the start and end positions of the interference, and generate interference segment identification results. S2: Based on the start and end positions of the discontinuous segments of current and voltage signals in the interference segment identification results, extract the corresponding time range to form an interference time window, call the back EMF signal of the stator winding of the forklift drive motor within the interference time window, and simultaneously record the rise and fall rhythm of the back EMF, and simultaneously record the direction of change of current and voltage within the interference time window. Based on the correspondence between the trend of back EMF change and the direction of current and voltage, analyze the flux linkage fluctuation trajectory, mark the time period when the flux linkage response is not synchronized with the changes of current and voltage, divide the disturbance distribution range, and generate flux linkage disturbance distribution information; S3: Based on the position of the asynchronous period of the flux response marked in the flux disturbance distribution information, monitor the voltage and current waveform response timing of the output terminal of the forklift drive motor controller. Within the corresponding period, compare the arrival time of the voltage and current peaks. When the interval between the arrival times of the voltage and current continuously expands or contracts compared with the previous waveform period, it is determined to be the start time of the offset. Record the start time as the starting point of the offset. Compare the alignment status of the voltage and current waveforms before and after the offset, identify the direction of the lag or advance of the voltage signal at the output terminal of the motor controller relative to the current signal in time, and generate a control signal offset record. S4: Based on the time offset direction and duration interval of the voltage signal relative to the current signal in the control signal offset record, monitor the torque change trend of the torque sensing unit of the forklift drive motor, and obtain the continuous power data output by the motor output shaft power sensing unit within the same time range. Connect the power data in time sequence to form a power curve. Based on the rising and falling trajectory of the power value in the power curve, analyze the fluctuation range and duration of the power fluctuation in time. Compare the amplitude difference and duration difference of the power change in each fluctuation segment. Based on the combination of amplitude difference and duration difference, divide the fluctuation into different levels and record the frequency of occurrence of each level to generate a power interference intensity distribution record. S5: Based on the time distribution location and frequency of power fluctuation levels in the power interference intensity distribution record, and combined with the number of flux disturbances and the time offset direction of the voltage signal at the motor controller output relative to the current signal, the correspondence between the power fluctuation level distribution, the change in the number of flux disturbances, and the offset direction of the control signal within the same time range is analyzed. The continuity of the forklift's operating state and the degree of interference concentration are determined. The operating performance type is determined based on the degree of interference level concentration and the consistency of the signal offset direction. When the power fluctuation level is evenly distributed and the offset direction has no concentrated trend, it is determined to be a stable state. When the fluctuation level shows a phased concentration and the offset direction is intermittently consistent, it is determined to be a fluctuating state. When the interference level is highly concentrated and the offset direction is continuously consistent, it is determined to be an abnormal state. The forklift operating status monitoring results are generated.

[0023] The interference segment identification results include the interference start and end time, waveform abrupt change range, and signal discontinuity characteristics. The flux linkage disturbance distribution information includes the location of the flux linkage disturbance period, the duration of the disturbance, and the amplitude distribution of the disturbance. The control signal offset record includes the offset start time, offset direction, and offset duration range. The power interference intensity distribution record includes the location of the power fluctuation level, the fluctuation amplitude difference, and the frequency of the level occurrence. The forklift operation status monitoring results include the operation performance type, the degree of interference concentration, and the determination of the continuity of the status.

[0024] Please see Figure 2 The steps for obtaining the interference segment identification results are as follows: S101: Acquire the stator current waveform and voltage raw signal of the forklift drive motor during operation, continuously sample in time sequence, record the time, instantaneous current and voltage of each sampling point, determine the waveform direction based on the change direction of current and voltage between adjacent sampling points, mark the sampling points where the change direction changes from rising to falling or from falling to rising as direction reversal points, count the number between consecutive reversal points, and generate waveform direction reversal distribution. To acquire the stator current waveform and voltage raw signal of the forklift drive motor during operation, the data acquisition module is started, the sampling frequency is set to 10kHz, that is, the instantaneous values ​​of stator A-phase current and Ua-phase voltage are synchronously acquired every 0.1ms, the time, instantaneous current and voltage of each sampling point are recorded, and the acquired data is stored in the sequence buffer to form the data structure shown in Table 1. Table 1: Sampling Sequence Data Table

[0025] As shown in Table 1, the waveform trend is determined based on the direction of change in current and voltage between adjacent sampling points. This determination is made by calculating the current sampling point. Compared with the previous sampling point The instantaneous difference; and This is implemented so that when the difference is positive, it is determined as "increasing" and when the difference is negative, it is determined as "decreasing". For example, at T=1.1ms; Current change (rise); voltage change (Rising), at T=1.3ms, the current changes (decline); voltage change (Declining), the sampling points where the direction of change changes from rising to falling or from falling to rising are marked as direction reversal points. At T=1.2ms, the current direction is rising. ), while at T=1.3ms, the current direction changes to decrease ( Therefore, T=1.3ms is marked as a point where the current waveform reverses direction; Similarly, T=1.6ms ( This is also a reversal point. The number of consecutive reversal points is counted, that is, between T=1.3ms and T=1.6ms, there are two sampling points with an interval of T=1.4ms and T=1.5ms. The number of records is 2, and the waveform direction reversal distribution is generated.

[0026] S102: Based on the waveform direction reversal distribution, detect the time period in the sampling sequence where the number of reversals increases continuously, calculate the waveform change rate by combining the current difference and voltage difference between adjacent sampling points, track the time of points where the change rate exceeds the set threshold, determine the duration of the over-limit point, and obtain the waveform change interval range. Based on the waveform direction reversal distribution, the sampling sequence is used to detect time periods where the number of reversals increases continuously. For example, through the statistics of S101, it is found that in the time period from T=5.0ms to T=10.0ms, the number of reversal points in each 1ms window increases from 2 / ms to 8 / ms, showing a continuous increasing trend. Therefore, this time period [5.0ms, 10.0ms] is locked for rate analysis. The waveform change rate is calculated by combining the current difference and voltage difference between adjacent sampling points. Specifically: ; ; For example, at T=7.1ms; I(7.1) = 12.5A, I(7.0) = 10.0A. but ; V(7.1) = 330V, V(7.0) = 305V; but The system tracks points where the rate of change exceeds a set threshold. This threshold is determined based on 1 million sets of baseline data collected from 10 minutes of stable operation of a forklift under no-load, constant speed (5 km / h) conditions. The rate of change of all sampling points is calculated from the baseline data, and the 99.5th percentile value is used as the threshold. The calculated current rate of change threshold is... The voltage change rate threshold is set to 20.0 A / ms. Set to 200V / ms, the calculated value at T=7.1ms is... ,and The system determines that T=7.1ms is the over-limit point, establishes the duration of the over-limit point, and continuously tracks the rates at T=7.2ms, T=7.3ms, and T=7.4ms. If T=7.2ms ( ),T=7.3ms( ),T=7.4ms( All exceeded the limit, while the rate at T=7.5ms The duration of the out-of-limit point is determined to be [7.1ms, 7.4ms], thus obtaining the range of waveform abrupt change.

[0027] S103: Based on the range of waveform abrupt change intervals, compare the direction of change of current and voltage before and after the abrupt change, filter the time segments that change from the same direction to the opposite direction, calculate the corresponding start and end sampling times, mark the discontinuous intervals of current and voltage signals in the time series, and generate interference segment identification results. Based on the waveform abrupt change range, i.e., [7.1ms, 7.4ms] determined in S102, the directions of current and voltage changes before and after the abrupt change are compared. Specifically, the preceding stable period immediately adjacent to the abrupt change range [7.1ms, 7.4ms] is extracted, for example, [6.8ms, 7.0ms], and the immediately following stable period immediately adjacent to it is extracted, for example, [7.5ms, 7.7ms]. Within the period before the abrupt change [6.8ms, 7.0ms], the directions of current and voltage changes recorded in S101 are analyzed, for example: When T=6.8ms (rise), (rise); When T=6.9ms (rise), (rise); When T=7.0ms (rise), (rise); It is determined that the current and voltage changes in the same direction (both increasing) during this period, within the period after the sudden change [7.5ms, 7.7ms]; When T=7.5ms (decline), (rise); When T=7.6ms (decline), (rise); When T=7.7ms (decline), (Rising), it is determined that the current and voltage changes in opposite directions during this period (current decreases, voltage increases). The time segment from the same direction to opposite direction is filtered. Since the time segment [6.8ms, 7.0ms] is in the same direction, while the time segment [7.5ms, 7.7ms] is in opposite direction and is separated by the abrupt change interval [7.1ms, 7.4ms], the abrupt change interval [7.1ms, 7.4ms] is determined to meet the filtering condition. The corresponding start and end sampling times are calculated, that is, the start and end times of the abrupt change interval [7.1ms, 7.4ms]. The discontinuous intervals of the current and voltage signals in the time series are marked, and [7.1ms, 7.4ms] is marked as the discontinuous interval, generating the interference segment identification result.

[0028] Please see Figure 3 The steps for obtaining magnetic flux disturbance distribution information are as follows: S201: Based on the start and end positions of the discontinuous segments of current and voltage signals in the interference segment identification results, extract the signal data of the corresponding time range, set sampling windows of the same time length, pair the current and voltage sampling points with corresponding times to form a continuous time series, record the start and end sampling times and sampling intervals of the time series, and establish the interference time window interval. Based on the start and end positions of the discontinuous segments of current and voltage signals in the interference segment identification results, i.e. [7.1ms, 7.4ms] identified by S103, the signal data in the corresponding time range is extracted, specifically from the original sampling sequence; The [time, current, voltage] data at four time points T=7.1ms, T=7.2ms, T=7.3ms, and T=7.4ms are sampled within a sampling window of the same length. This window length represents the duration of the discontinuous time segment. The window has four sampling points. Current and voltage sampling points are paired with corresponding time periods to form a continuous time series. Specifically, a data matrix is ​​created where each row represents a sampling point, containing a timestamp, current value, and voltage value. For example: {(7.1ms,I(7.1),V(7.1)),(7.2ms,I(7.2),V(7.2)),(7.3ms,I(7.3),V(7.3)),(7.4ms,I(7.4),V(7.4))}, records the start and end sampling times and sampling intervals of the time series, with the start time being... The recording time was 7.1ms, and the termination time was [not specified]. The recording time was 7.4ms, with a sampling interval of [missing information]. The time interval is recorded as 0.1ms, and an interference time window is established.

[0029] S202: Based on the interference time window interval, extract the back EMF signal of the stator winding of the forklift drive motor, record the amplitude of the back EMF at each sampling point, determine the rising and falling rhythm based on the change of amplitude, and simultaneously record the changing direction of current and voltage. Compare the changing trends of current, voltage and back EMF to generate the back EMF changing trend. Based on the interference time window interval, i.e. [7.1ms, 7.4ms], the back EMF signal of the stator winding of the forklift drive motor is extracted. Specifically, the output data of the back EMF estimation module or sensor synchronized with the time window [7.1ms, 7.4ms] is retrieved to obtain sampling points [E(7.1), E(7.2), E(7.3), E(7.4)], and the current and voltage values ​​during this period are retrieved simultaneously. The summary is shown in Table 2. Table 2: Interference Time Window Data Table

[0030] As shown in Table 2, the amplitude of the back electromotive force is recorded at each sampling point. The rise and fall rhythm is determined based on the changes in amplitude, and calculations are performed. accomplish; (rise); (decline); (decline); The back EMF rhythm sequence was obtained as [rising, falling, falling], and the directions of change of current and voltage were recorded simultaneously. According to the data in Table 2, the current direction sequence ( ) was observed within the time period [7.1ms, 7.4ms]. ) represents [rise, fall, fall] (e.g.) ), voltage direction sequence ( ) is [rise, rise, rise] (e.g.) By comparing the changing trends of current, voltage, and back EMF, the following trends are observed: in 7.1ms-7.2ms: E (increases), I (increases), V (increases); in 7.2ms-7.3ms: E (decreases), I (decreases), V (increases); in 7.3ms-7.4ms: E (decreases), I (decreases), V (increases), thus generating the trend of back EMF change.

[0031] S203: Based on the trend of back EMF change, mark the sections where the change of back EMF is inconsistent with the direction of current and voltage, determine the duration and frequency of occurrence of the sections, divide the distribution location of disturbances, and generate magnetic flux disturbance distribution information. Based on the trend of back electromotive force change, i.e., the comparison results of S202; [(7.1-7.2: E_increase, I_increase, V_increase), (7.2-7.3: E_decrease, I_decrease, V_increase), (7.3-7.4: E_decrease, I_decrease, V_increase)], marking the segments where the change in back EMF is inconsistent with the direction of current and voltage. Here, "consistent" is defined as all three moving in the same direction (both increasing or decreasing) or the back EMF being consistent with the direction of current. In the comparison results of S202, during the time period [7.1ms, 7.2ms]: E, I, and V are all increasing. The following segments are considered consistent: [7.2ms, 7.3ms]: E (decreasing), I (decreasing), V (increasing). Since V's direction is inconsistent with E and I, this segment is marked. [7.3ms, 7.4ms]: E (decreasing), I (decreasing), V (increasing). Since V's direction is inconsistent with E and I, this segment is marked. The duration and frequency of each segment are measured. The inconsistent segment is [7.2ms, 7.4ms], and its duration is... The interference occurred once within the time window [7.1ms, 7.4ms]. The distribution location of the disturbance was divided, and the time period [7.2ms, 7.4ms] was recorded as the time period when the magnetic flux response was not synchronized. The occurrence time of the disturbance was recorded as 7.2ms, and magnetic flux disturbance distribution information was generated.

[0032] Please see Figure 4 The steps for obtaining the control signal offset record are as follows: S301: Based on the position of the asynchronous period of the flux response marked in the flux disturbance distribution information, monitor the voltage and current waveforms at the output of the forklift drive motor controller, record the peak arrival time of the voltage and current in the corresponding period, and compare the peak arrival time interval of the voltage and current to generate the peak time difference. Based on the position of the asynchronous flux response period marked in the flux disturbance distribution information, i.e., [7.2ms, 7.4ms], the voltage and current waveforms at the output of the forklift drive motor controller are monitored. Since the flux disturbance occurs in [7.2ms, 7.4ms], this period belongs to the Nth waveform cycle (assuming this cycle is from T=5.0ms to T=15.0ms). The controller's output waveform within this cycle is acquired through the PWM monitoring module. The peak arrival times of voltage and current are recorded within the corresponding period. Peaks are captured through high-frequency sampling (e.g., 50kHz). In the Nth cycle, the peak of the controller's output voltage is detected. Appeared Controller output current peak Appeared By comparing the arrival time intervals of the voltage and current peaks, the peak time difference of the Nth cycle is calculated: The data from the previous stable period (the (N-1)th period, assuming T = -5.0ms to 5.0ms) is retrieved as the baseline; Its voltage peak Current peak ; The reference time difference was calculated. This generates the time difference between the peaks.

[0033] S302: Based on the peak time difference, determine the changing trend of the voltage and current peak arrival time interval within the continuous waveform period, identify the expansion or contraction of the time interval, determine the start time of the offset, mark the time as the offset start point, and obtain the offset start point. Based on the time difference between the peaks, i.e.: and Determine the changing trend of the voltage and current peak arrival time interval within the continuous waveform period, and calculate... and The difference: ; The expansion or contraction of the identification time interval is due to A positive value indicates that the time interval is expanding, and monitoring continues in subsequent cycles to obtain the time difference of the (N+1)th cycle (T=15.0ms to 25.0ms); ; Calculate changes Determine the start time of the offset, since it starts from the Nth cycle, the time difference Two consecutive cycles (N-cycle and N+1-cycle) change in the same direction. and (All are positive values), satisfying the continuous expansion condition, mark this moment as the starting point of the offset, determine the Nth period (starting time T=5.0ms) as the period when the offset begins, and record the starting time of this period, 5.0ms, as the starting point of the offset, thus obtaining the offset starting point.

[0034] S303: Based on the offset starting point, compare the voltage and current waveforms before and after the offset, analyze the alignment status of the waveforms, determine the lag or advance direction of the voltage signal relative to the current signal, record the direction and duration, and generate a control signal offset record. After obtaining the control signal offset record, cluster analysis is performed on the offset direction and offset duration data. Based on the consistency of offset direction and the distribution characteristics of duration, the control offset behavior is divided into different categories, and corresponding labels are assigned according to the offset mode characteristics of each category. In the subsequent motor operation characteristic evaluation process, different offset modes are distinguished and identified based on the classification labels. Based on the offset starting point, T=5.0ms, the voltage and current waveforms before and after the offset are compared. Specifically, the waveforms of the (N-1)th cycle (T=-5.0ms to 5.0ms), the Nth cycle (T=5.0ms to 15.0ms), and the (N+1)th cycle (T=15.0ms to 25.0ms) are compared to analyze the alignment of the waveforms. In the (N-1)th cycle, the peak time difference... In the Nth period, In the N+1th period, To determine the direction of lag or advance of the voltage signal relative to the current signal, due to the time difference... The value remains positive (+0.10ms, +0.20ms, +0.25ms), indicating a voltage peak. Always later than the current peak Therefore, it is determined that the voltage signal lags behind the current signal in time. The direction and duration of this lag are recorded. The offset direction is designated as "voltage lag," with the offset starting at T=5.0ms. After T=5.0ms, the lag continuously expands from 0.10ms to 0.25ms. This offset trend continues until the end of the N+1th cycle (T=25.0ms). Therefore, the offset duration is recorded as [5.0ms, 25.0ms], generating a control signal offset record. After obtaining the control signal offset record, cluster analysis is performed on the offset direction and offset duration data. Specifically, all control signal offset records within a set time window (e.g., 10 seconds) are collected, and the "offset direction" feature (quantified as a numerical value: voltage lag is recorded as +1, current lag as -1) and "duration" feature (in ms) of each record are extracted to form a feature vector set. For example, the records obtained in S303 are converted into vectors. Four preset cluster centers are set to represent typical offset behavior: (Transient voltage hysteresis: +1.5ms) (Steady-state voltage hysteresis: +1, 30ms) (Transient current hysteresis: -1.5ms) (Steady-state current hysteresis: -1, 30ms), calculate vector The Euclidean distances to each center point are obtained. The distance is ,arrive The distance is Based on the principle of minimum distance, this record is classified into Based on the consistency of the offset direction and the distribution characteristics of the duration, the control offset behavior is divided into different categories, that is... The categories are defined as those that are "consistent in direction and have a long duration," and corresponding labels are assigned based on the offset pattern characteristics of each category. The category is labeled "steady-state voltage hysteresis mode". In the subsequent evaluation of motor operating characteristics, different offset modes are distinguished and identified based on the classification label.

[0035] Please see Figure 5 The steps for obtaining the power interference intensity distribution record are as follows: S401: Based on the time offset direction and duration interval of the voltage signal relative to the current signal in the control signal offset record, monitor the torque change of the forklift drive motor torque sensing unit, extract torque data, analyze the fluctuation trend over time, record the torque change amplitude and trend of each sampling point, and obtain the torque change trend. Based on the time offset direction and duration interval of the voltage signal relative to the current signal in the control signal offset record, i.e. the offset direction is "voltage lag" and the duration interval is [5.0ms, 25.0ms], the torque change of the torque sensing unit of the forklift drive motor is monitored. That is, within the time range of T=5.0ms to T=25.0ms, torque data is collected at intervals of 1.0ms, the torque data is extracted, and time series data [T(ms), Torque(Nm)] is obtained. For example: [5.0,150.1],[6.0,150.5],[7.0,151.2],[8.0,148.0],[9.0,153.0],[10.0,153.5],[11.0,153.8],[12.0,145.0],…,[25.0,150.8], Analyze the fluctuation trend over time and calculate the torque variation between adjacent points: And the trend of change (rising / falling), record the torque change amplitude and trend at each sampling point, for example, when T=8.0ms, (decline); At T=9.0ms, (rise); At T=12.0ms, (Decrease), thus obtaining the torque change trend.

[0036] S402: Based on the torque change trend, obtain the power data of the motor output shaft power sensing unit within the same time range, connect the power data in time sequence to form a power curve, analyze the amplitude and duration of power fluctuations based on the rise and fall of power values ​​in the power curve, calculate the range of change of each fluctuation segment, and obtain the power fluctuation trajectory. Based on the torque variation trend, the power data of the motor output shaft power sensing unit is obtained within the same time range, that is, within the time window of [5.0ms, 25.0ms], the power value is synchronously collected at 1.0ms intervals. The power data are connected in chronological order to form a power curve, some examples of which are shown in Table 3: Table 3: Examples of Power Curve Data

[0037] As shown in Table 3, based on the rise and fall of power values ​​in the power curve, the amplitude and duration of power fluctuations are analyzed to identify continuous rising or falling segments. For example, Seg1=[5.0ms, 7.0ms] is the rising segment, Seg2=[7.0ms, 9.0ms] is the falling segment, and Seg3=[9.0ms, 11.0ms] is the rising segment. The range of change for each fluctuation segment is calculated. For Seg1[5.0ms, 7.0ms], the amplitude... Duration For Seg2[7.0ms, 9.0ms]; amplitude Duration For Seg3 [9.0ms, 11.0ms], the amplitude ; Duration The power fluctuation trajectory is obtained.

[0038] S403: Based on the power fluctuation trajectory, compare the amplitude difference and duration difference of power change in each fluctuation segment. Based on the combination of amplitude difference and duration difference, divide the power fluctuation into different levels, record the occurrence frequency of each level, generate power interference intensity distribution, and generate power interference intensity distribution record. After obtaining the power interference intensity distribution record, the amplitude difference and duration of power fluctuations are clustered to divide samples with similar power fluctuation characteristics into different categories, and the power fluctuation pattern information corresponding to each category is determined based on the clustering results. Based on the power fluctuation trajectory, i.e., the set of fluctuation segments identified in S402 {Seg1:A1=+0.4,D1=2.0}, {Seg2:A2=-1.0,D2=2.0}, {Seg3:A3=+2.3,D3=2.0},…, the amplitude difference and duration difference of power change in each fluctuation segment are compared. Specifically, the absolute amplitude difference between adjacent fluctuation segments (e.g., Seg(i) and Seg(i-1)) is calculated. and duration difference For Seg2 and Seg1, For Seg3 and Seg2, , Based on the combination of amplitude difference and duration difference, power fluctuations are divided into different levels. The level division criteria are preset (this criterion is based on the steady state of historical data). mean and Using the mean as the benchmark, with 2 and 5 times the standard deviation as the limits respectively: Level 1 (low volatility): and Level 2 (Medium Fluctuation):( )or( Level 3 (High Volatility): or Substituting the calculation results into the hierarchical standard, for Seg2 (compared to Seg1): ( ), satisfying level 1, for Seg3 (compared to Seg2): ( The system satisfies level 2 and records the frequency of occurrence of each level. Within a time window of [5.0ms, 25.0ms], assuming a total of 10 fluctuation segments are compared, level 1 occurs twice, level 2 occurs once, and level 3 occurs seven times. This generates a power interference intensity distribution record. After obtaining the power interference intensity distribution record, the amplitude difference and duration of the power fluctuations are clustered. Specifically, the amplitude difference of all fluctuation segment comparison data calculated in S403 is collected. and duration difference Construct a sample set For example, this includes Seg3 samples from S403. Samples with similar power fluctuation characteristics are divided into different categories, and three benchmark fluctuation mode centers based on historical fault data are set: Mode X (stable operation) center is... Mode Y (intense shaking) center is The center of mode Z (slow drift) is Calculate the weighted distance from the Seg3 sample to these three centers (with the magnitude difference weight set to 1 and the time difference weight set to 0.5 to balance the influence of dimensions), and the distance to mode Y: The distance to the center of the other two modes is much smaller than that of the Seg3 sample. Therefore, the Seg3 sample is classified into the mode Y category. Based on the clustering results, the power fluctuation mode information corresponding to each category is determined. Mode Y is identified as the "high amplitude rapid jitter" mode information for subsequent state assessment.

[0039] Please see Figure 6 The steps for obtaining forklift operating status monitoring results are as follows: S501: Based on the time distribution location and frequency of occurrence of power fluctuation levels in the power interference intensity distribution record, combined with the number of flux linkage disturbances and the offset direction of control signals, sort out the distribution of power fluctuation levels, calculate the frequency of occurrence of each level, analyze the correlation between the change in the number of flux linkage disturbances and the offset direction of voltage and current, and obtain the interference concentration analysis results. Based on the temporal distribution and frequency of power fluctuation levels in the power interference intensity distribution record, specifically, within the time window of [5.0ms, 25.0ms], level 1 (2 times), level 2 (1 time), and level 3 (7 times), combined with the number of flux linkage disturbances and the direction of control signal offset, the total number of flux linkage disturbances within the [5.0ms, 25.0ms] time window is calculated from the results of S203, for example, 15 times. The direction of control signal offset is obtained from the results of S303 as "voltage hysteresis." The distribution of power fluctuation levels is analyzed, and the frequency of occurrence of each level is calculated, i.e., level 3 accounts for 70% (7 / 10). The levels are then further analyzed. During the occurrence of level 3, it was found that all 7 instances of level 3 occurred within the period of [7.0ms-12.0ms] (corresponding to Seg3 and subsequent high fluctuations in S402). The correlation between the changes in the number of flux disturbances and the voltage and current offset directions was analyzed. The timestamps of the 15 flux disturbances were compared with the period of [7.0ms-12.0ms] of power fluctuation level 3. It was found that 13 of the 15 flux disturbances fell within this period of [7.0ms-12.0ms]. The control signal offset direction "voltage lag" remained unchanged throughout the time window of [5.0ms, 25.0ms], and the results of the interference concentration analysis were obtained.

[0040] S502: Based on the results of interference concentration analysis, determine the continuity of the forklift's operating state. By observing the consistency between the frequency of each interference level and the direction of signal offset, identify different types of operating state performance. Based on the degree of concentration of the interference levels, determine the fluctuation and abnormal state, and obtain the state continuity assessment results. Based on the interference concentration analysis results, the continuity of the forklift's operating state is determined. By observing the consistency between the frequency of occurrence of each interference level and the direction of signal offset, in the analysis of S501, the frequency of occurrence of power fluctuation level 3 (high fluctuation) (7 / 10) is absolutely dominant, and it and the flux disturbance (13 / 15) show a high concentration in time [7.0ms-12.0ms]. The direction of signal offset (voltage lag) remains consistent throughout the entire period [5.0ms, 25.0ms]. Different types of operating state are identified. Based on the high concentration of interference levels (level 3 accounts for 70% and is time-concentrated) and the continuous consistency of the direction of signal offset ("voltage lag" has not changed), fluctuation and abnormal states are judged, and the results of the state continuity assessment are obtained.

[0041] S503: Based on the status continuity assessment results, combined with the degree of concentration of interference levels and the consistency of signal offset direction, determine the forklift operation performance type. When the power fluctuation levels are evenly distributed and the offset direction has no concentrated trend, it is determined to be a stable state. When the fluctuation levels are concentrated and the offset direction is consistent, it is determined to be a fluctuating state. When the fluctuation levels are highly concentrated and the offset direction is consistently consistent, it is determined to be an abnormal state, and generate forklift operation status monitoring results. Based on the state continuity assessment results, combined with the concentration of interference levels and the consistency of signal offset direction, the forklift operation performance type is determined. In the S502 assessment, the interference level (level 3 accounting for 70%) is highly concentrated (concentrated in [7.0ms-12.0ms]), and the signal offset direction (voltage hysteresis) is consistently consistent (through [5.0ms, 25.0ms]). This assessment result is compared with the preset operation performance type standard: (1) Steady state: The power fluctuation hierarchy is evenly distributed and the offset direction has no tendency to concentrate; (2) Fluctuation state: The fluctuation level shows a phased concentration and the offset direction is intermittently consistent; (3) Abnormal state: The interference levels are highly concentrated and the offset direction is consistently consistent; The current evaluation results (highly concentrated + consistently consistent) match the definition of "abnormal state". When the interference level is highly concentrated and the offset direction is consistently consistent, it is judged as an abnormal state. The current state [5.0ms, 25.0ms] is judged as an abnormal state, and the forklift operation status monitoring results are generated.

[0042] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope 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 forklift state monitoring method based on motor data in a strong interference environment, characterized in that, The method comprises the following steps: S1: continuously acquiring current and voltage signals of a forklift driving motor stator to form a power sequence, acquiring waveform rate according to sampling point direction, determining mutation when direction reverses, tracking time range and determining interval, identifying discontinuous section in combination with fluctuation direction, and generating interference section identification result; S2: according to the current and voltage discontinuous time period in the interference section identification result, calling back electromotive force signal record change trend and current and voltage direction, analyzing flux linkage fluctuation trajectory, marking response unsynchronized period, dividing disturbance distribution range, and generating flux linkage disturbance distribution information; S3: based on the flux linkage response unsynchronized period in the flux linkage disturbance distribution information, monitoring voltage and current waveforms of a controller, comparing wave peak time difference, determining offset starting point when interval continuously changes, recording starting point and identifying voltage lag or advance direction relative to current, and generating control signal offset record; S4: according to the time offset characteristics of voltage and current in the control signal offset record, monitoring driving motor torque change, extracting power data to form a power curve, dividing levels according to power fluctuation amplitude and continuous difference, recording frequency, and generating power interference intensity distribution record.

2. The method for forklift state monitoring in strong interference environment based on motor data according to claim 1, characterized in that: The interference section identification result comprises interference start and end time, waveform mutation interval range and signal discontinuous characteristics, the flux linkage disturbance distribution information comprises flux linkage disturbance period position, disturbance duration and disturbance amplitude distribution, the control signal offset record comprises offset starting time, offset direction and offset continuous interval, and the power interference intensity distribution record comprises power fluctuation level position, fluctuation amplitude difference and level appearance frequency.

3. The method for forklift state monitoring in strong interference environment based on motor data according to claim 1, characterized in that: The interference section identification result acquisition step is: S101: acquiring stator current waveform and voltage original signal of the forklift driving motor during operation, continuously sampling in time sequence, recording time, instantaneous current and voltage of each sampling point, judging waveform trend according to current and voltage change direction between adjacent sampling points, marking sampling points where change direction changes from rising to falling or from falling to rising as direction reversal points, counting number of continuous reversal points, and generating waveform direction reversal distribution; S102: according to the waveform direction reversal distribution, detecting time period with continuously increasing reversal number in the sampling sequence, calculating waveform change rate in combination with current difference and voltage difference of adjacent sampling points, time tracking points with change rate exceeding a set threshold, determining continuous interval of the over-limit point, and obtaining waveform mutation interval range; S103: based on the waveform mutation interval range, comparing change directions of current and voltage before and after mutation, screening time sections where the same direction changes to reverse, calculating corresponding start and end sampling time, marking discontinuous interval of current and voltage signals in time sequence, and generating interference section identification result.

4. The method for forklift state monitoring in strong interference environment based on motor data according to claim 1, characterized in that: The flux linkage disturbance distribution information acquisition step is: S201: According to the time start and end positions of the current and voltage signal discontinuous paragraphs in the interference section identification result, signal data in the corresponding time range is extracted, a sampling window with the same time length is set, current and voltage sampling points are paired according to the corresponding time, a continuous time sequence is formed, the start and end sampling time and sampling interval of the time sequence are recorded, and an interference time window interval is established; S202: According to the interference time window interval, the back electromotive force signal of the forklift driving motor stator winding is extracted, the amplitude of the back electromotive force at each sampling point is recorded, the rising and falling rhythm is judged according to the amplitude change, the change direction of the current and voltage is recorded, the change trend of the current, voltage and back electromotive force is compared, and the back electromotive force change trend is generated; S203: According to the back electromotive force change trend, the section where the change of the back electromotive force is inconsistent with the direction of the current and voltage is marked, the duration and occurrence frequency of the section are determined, the distribution position of the disturbance is divided, and the magnetic chain disturbance distribution information is generated.

5. The method for forklift state monitoring in strong interference environment based on motor data according to claim 1, characterized in that: The acquisition step of the control signal offset record is: S301: According to the position of the magnetic chain response unsynchronized period marked in the magnetic chain disturbance distribution information, the voltage and current waveforms at the output end of the forklift driving motor controller are monitored, the voltage and current wave peak arrival time in the corresponding period is recorded, and the voltage and current wave peak arrival time interval is compared, and the wave peak time difference is generated; S302: According to the wave peak time difference, the change trend of the voltage and current wave peak arrival time interval in the continuous waveform period is judged, the expansion or contraction of the time interval is identified, the start time of the offset is determined, the start time of the offset is marked as the starting point of the offset, and the offset starting point is obtained; S303: According to the offset starting point, the voltage and current waveforms before and after the offset are compared, the alignment state of the waveforms is analyzed, the lag or advance direction of the voltage signal relative to the current signal is judged, the direction and duration are recorded, and the control signal offset record is generated.

6. The method for forklift state monitoring in strong interference environment based on motor data according to claim 1, characterized in that: The acquisition step of the power interference intensity distribution record is: S401: According to the time offset direction and duration interval of the voltage signal relative to the current signal in the control signal offset record, the torque change of the forklift driving motor torque sensing unit is monitored, torque data is extracted, the fluctuation trend with time is analyzed, the torque change amplitude and trend at each sampling point are recorded, and the torque change trend is obtained; S402: According to the torque change trend, the power data of the motor output shaft power sensing unit in the same time range is obtained, the power data is connected in time sequence to form a power curve, the rising and falling changes of the power value in the power curve are analyzed, the amplitude and duration of the power fluctuation are analyzed, the change range of each fluctuation section is calculated, and the power fluctuation trajectory is obtained; S403: Based on the power fluctuation trajectory, the amplitude difference and duration difference of the power change of each fluctuation section are compared, the power fluctuation is divided into different levels according to the combination results of the amplitude difference and duration difference, the occurrence frequency of each level is recorded, the power interference intensity distribution is generated, and the power interference intensity distribution record is generated.

7. The method for forklift state monitoring in strong interference environment based on motor data according to claim 1, characterized in that: The method further comprises: S5: Based on the time distribution position and occurrence frequency of the power fluctuation level in the power interference intensity distribution record, the number of flux linkage disturbances and the offset direction of the voltage and current signals are combined, and the corresponding relationship in time is analyzed to determine the continuity of the forklift running state and the interference concentration degree, determine the running performance type according to the interference level concentration degree and the consistency of the signal offset direction, and generate the forklift running state monitoring result; The forklift running state monitoring result includes running performance type, interference concentration degree and state continuity determination.

8. The method for forklift state monitoring in strong interference environment based on motor data according to claim 7, characterized in that: The acquisition step of the forklift running state monitoring result is: S501: According to the time distribution position and occurrence frequency of the power fluctuation level in the power interference intensity distribution record, the number of flux linkage disturbances and the offset direction of the control signal are combined, the distribution of the power fluctuation level is sorted out, the occurrence frequency of each level is calculated, the correlation between the number of flux linkage disturbances and the offset direction of the voltage and current is analyzed, and the interference concentration degree analysis result is obtained; S502: Based on the interference concentration degree analysis result, the state continuity in the forklift running process is determined, the consistency of the occurrence frequency of each interference level and the signal offset direction is observed to identify different performance types of the running state, and the fluctuation and abnormal state are determined according to the concentration degree of the interference level, and the state continuity evaluation result is obtained; S503: According to the state continuity evaluation result, the concentration degree of the interference level and the consistency of the signal offset direction are combined to determine the forklift running performance type, when the power fluctuation level distribution is uniform and the offset direction has no concentration trend, it is determined as a stable state, when the fluctuation level is concentrated and the offset direction is consistent, it is determined as a fluctuation state, and when it is highly concentrated and the offset direction is consistent, it is determined as an abnormal state, and the forklift running state monitoring result is generated.

9. The method for forklift state monitoring in strong interference environment based on motor data according to claim 1, characterized in that: After obtaining the control signal offset record, the offset direction and offset duration data are clustered and analyzed, the control offset behavior is divided into different categories according to the consistency of the offset direction and the duration distribution characteristics, and the corresponding labels are assigned according to the offset mode characteristics of each category. In the subsequent motor running characteristic evaluation process, different offset modes are distinguished and identified according to the classification labels.

10. The method for forklift state monitoring in strong interference environment based on motor data according to claim 1, characterized in that: After obtaining the power interference intensity distribution record, the amplitude difference and duration of the power fluctuation are clustered and processed, samples with similar power fluctuation characteristics are divided into different categories, and the power fluctuation mode information corresponding to each category is determined according to the clustering result.

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