Electric fork truck remote control method and system based on single-chip microcomputer

Abstract

The application relates to the technical field of remote control, in particular to an electric forklift remote control method and system based on a single-chip microcomputer, which comprises the following steps: adjusting data compression configuration by judging signal quality, adjusting communication resource allocation in combination with a load state, calculating a current rising rate difference, constructing a mutual exclusion control set, adjusting a driving amplitude, monitoring battery voltage, environmental temperature and task load in real time, and adjusting a task priority, in the application, signal strength is monitored in real time, and signal quality is optimized, the stability and transmission efficiency in wireless communication are improved, load fluctuation analysis and dynamic communication resource allocation are utilized, communication congestion under a high load condition is avoided, the efficiency and stability of task execution are improved, resource occupation of a driving unit is judged in combination, cooperation between multiple driving units is optimized, resource conflicts are reduced, data consistency analysis is adopted to adjust a control signal amplitude, and the stability and precision of forklift action are improved.

Description

Technical Field

[0001] This invention relates to the field of remote control technology, and in particular to a remote control method and system for electric forklifts based on a microcontroller. Background Technology

[0002] The field of remote control technology encompasses technologies related to non-contact operation and management of target equipment or systems. Through wired or wireless communication, commands issued by the control terminal are transmitted to the controlled terminal, enabling real-time interaction regarding equipment operating status, action execution, and feedback information. This involves multiple stages, including signal acquisition, communication transmission, control command parsing, actuator driving, and safety management, forming a complete remote control closed-loop system. In scenarios such as industrial automation, intelligent manufacturing, and warehousing and logistics, remote control technology has become a key component for improving operational flexibility and environmental adaptability. Combined with the development of microcontrollers, wireless communication, and edge computing technologies, remote control systems are gradually acquiring higher processing capabilities and response speeds, achieving efficient data interaction and control commands in multi-terminal, multi-device collaborative environments. The remote control method for electric forklifts based on a microcontroller refers to a method that uses a microcontroller as the core control unit and integrates Bluetooth communication and edge computing devices to remotely control the operating status and execution components of the electric forklift. This method addresses technical aspects such as drive control, steering adjustment, fork lifting, and energy consumption monitoring of the electric forklift. Specifically, it involves using the microcontroller's internal timer and interrupt system for action logic control, relying on Bluetooth for wireless reception and parsing of control commands, driving the motor to perform mechanical actions through pulse width modulation signals, and using edge computing nodes to perform local rapid analysis and transmission of load, current, voltage, and position data collected by sensors. Internally, data interaction is achieved through bus communication to ensure real-time synchronization of control commands and status information, thus forming a remote control system for electric forklifts.

[0003] Traditional remote control technology for electric forklifts lacks intelligent coping strategies when facing factors such as load fluctuations, signal strength changes, and insufficient battery power in complex environments. When the battery power gradually decreases or the task load fluctuates drastically, it is difficult to adjust task priorities and communication resource allocation in a timely manner, resulting in task execution conflicts, signal delays, or low execution efficiency. It fails to effectively combine real-time battery power and load status to dynamically optimize resource management, and there is insufficient signal quality monitoring, which leads to system crashes or task interruptions when the signal is weak or the load is too high, affecting the stability and operational efficiency of the forklift. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a microcontroller-based remote control method for electric forklifts, comprising the following steps:

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a remote control method for electric forklifts based on a microcontroller, comprising the following steps:

[0006] S1: Call the Bluetooth communication sampling buffer, extract the signal strength data sequence, arrange it in time order to form a fluctuation sample, compare the maximum amplitude of the current signal strength with the offset between the previous period sampling points, determine the signal quality, construct the compression channel trigger flag, and generate the compression state switching flag.

[0007] S2: Based on the compression state switching identifier, analyze load fluctuations, read cargo weight, current feedback and vehicle speed data, form a joint trend sequence, determine the load state, adjust communication resource allocation, and generate communication structure adjustment results;

[0008] S3: Based on the communication structure adjustment results, extract the drive unit startup record and current change rate, calculate the difference in current rise rate, determine the resource occupancy status and energy consumption concentration, mark the main control action path, construct a mutual exclusion control set, adjust the drive unit control path, and generate mutual exclusion control group information.

[0009] S4: Based on the mutual exclusion control group information, extract the displacement, acceleration and current data during the lifting process of the forks, align them according to the cycle, determine the consistency of data growth, identify unstable actions, adjust the amplitude of the control signal, and generate drive amplitude adjustment parameters;

[0010] S5: Based on the drive amplitude adjustment parameters, monitor the battery voltage, ambient temperature, and forklift task load in real time, align the data according to the time axis and form a joint state sequence, determine the load status, combine the real-time battery power, adjust the task execution priority, and generate task scheduling adjustment results.

[0011] As a further embodiment of the present invention, the compression state switching identifier includes Bluetooth link quality change trend, abnormal fluctuation state identification information, and compression channel trigger identifier; the communication structure adjustment result includes load fluctuation analysis result, load state judgment information, and communication resource adjustment configuration; the mutual exclusion control group information includes dominant execution unit identifier, control priority order adjustment information, and execution path blocking parameters; the drive amplitude adjustment parameters include data sequence growth direction consistency, control signal amplitude adjustment result, and action stability evaluation information; and the task scheduling adjustment result includes battery voltage drop trend, real-time load state, and task execution priority.

[0012] As a further aspect of the present invention, the step of obtaining the compression state switching identifier specifically includes:

[0013] S101: Based on the Bluetooth communication sampling buffer, obtain the signal strength data sequence of continuous communication cycles, arrange the signal data in time order to form fluctuation samples, and generate signal strength change analysis results by comparing the maximum amplitude of the current signal strength with the offset of the middle sampling point of the previous cycle.

[0014] S102: Based on the signal strength change analysis results, by analyzing the offset change, determine the signal quality change trend of the Bluetooth link and identify abnormal fluctuation status, and generate a Bluetooth link fluctuation identifier.

[0015] S103: Based on the Bluetooth link fluctuation identifier, adjust the signal processing path and construct a compression channel trigger identifier, including assigning real-time control data to the direct path, assigning sensor data to the compression path, and generating a compression state switching identifier.

[0016] As a further aspect of the present invention, the step of obtaining the communication structure adjustment result specifically includes:

[0017] S201: Based on the compression state switching indicator, obtain the cargo weight output by the forklift load sensor, the drive current fed back by the current detection terminal, and the moving speed recorded by the vehicle speed sampler, and arrange the three data items according to the time axis to form a joint trend sequence.

[0018] S202: Based on the joint trend sequence, by comparing the consistency of the growth direction of each data item, the data change trend is determined, the consistency of the growth direction of each data item on the time axis is analyzed, the load status is identified, and the load status change trend is generated.

[0019] S203: Based on the load status change trend, adjust the communication resource allocation configuration in real time, reallocate the bandwidth usage structure and data transmission path, and generate communication structure adjustment results.

[0020] As a further aspect of the present invention, the step of obtaining the mutual exclusion control group information specifically includes:

[0021] S301: Based on the communication structure adjustment results, extract the start-up record and current change rate of each drive unit in the current control cycle, calculate the difference ratio between the current rise rates of multiple units, combine the start-up time interval of each unit, compare the current growth order, identify the resource occupancy status of the drive unit, and generate the drive unit occupancy status value.

[0022] S302: Based on the occupancy status value of the drive unit, by judging the degree of energy consumption concentration, the dominant execution unit is selected and the main control action path is marked, and a main control action path identifier is generated;

[0023] S303: Based on the master control action path identifier, construct a mutual exclusion control set, adjust the control paths between multiple drive units, including setting control priority order and scheduling blocking relationship, and generating mutual exclusion control group information.

[0024] As a further aspect of the present invention, the step of obtaining the driving amplitude adjustment parameter specifically includes:

[0025] S401: Based on the mutually exclusive control grouping information, extract the displacement feedback sequence, drive end acceleration data sequence and current sampling value sequence during the fork lifting process, align them according to the control cycle, obtain the correspondence of each data item under multiple cycles, and generate cycle data alignment value;

[0026] S402: Based on the periodic data alignment value, determine the consistency of the growth direction of each data sequence within the same control cycle, calculate the consistency degree of the sequence change of the cycle, evaluate the stable state of the forklift action, and generate the action stability coefficient.

[0027] S403: Based on the motion stability coefficient, identify the degree of instability of the forklift motion, combine the fluctuation characteristics of the data sequence corresponding to the period, adjust the amplitude of the output control signal, obtain the adjustment amplitude parameter range, and generate the drive amplitude adjustment parameter.

[0028] As a further aspect of the present invention, the step of obtaining the task scheduling adjustment result specifically includes:

[0029] S501: Based on the drive amplitude adjustment parameters, monitor the battery voltage, ambient temperature, and forklift task load in real time, extract the battery voltage change sequence, ambient temperature data, and load status data, align the data according to the time axis, and obtain the joint status data sequence.

[0030] S502: Based on the joint state data sequence, by analyzing the battery voltage drop trend, ambient temperature fluctuation and load change, the load state of each time period is determined, and the load state determination result is obtained;

[0031] S503: Based on the load status judgment result, combined with the real-time battery power and task type, dynamically adjust the execution priority of multiple tasks, set the task hierarchy order, and generate task scheduling adjustment results.

[0032] As a further aspect of the present invention, the specific process of dynamically adjusting the priority is as follows:

[0033] By monitoring real-time battery power and task load status, the power requirements and actual power consumption ratio of each task are obtained. The remaining battery percentage is compared with a low power threshold. When the remaining battery power is lower than the threshold, the system enters a low power mode.

[0034] In low power mode, tasks are prioritized based on the forklift's current load status and task type. High-power tasks, including driving and fork lifting, are assigned high priority based on battery power consumption, while low-power tasks, including environmental monitoring and data uploading, are assigned low priority.

[0035] The low battery threshold is set by statistically analyzing the rate of change of battery power over multiple control cycles, combining various tasks with the rate of power decrease per unit time, determining the safe remaining power required to maintain core functions under continuous task execution, and calculating and setting the low battery threshold for early warning based on the target percentage.

[0036] A microcontroller-based remote control system for electric forklifts includes:

[0037] The signal quality analysis module calls the Bluetooth communication sampling buffer, extracts the signal strength data sequence, arranges it in time order to form fluctuation samples, compares the maximum amplitude of the current signal strength with the offset between the previous period sampling points, judges the signal quality, constructs a compression channel trigger flag, and generates a compression state switching flag.

[0038] The load status analysis module analyzes load fluctuations based on the compression status switching identifier, reads cargo weight, current feedback and vehicle speed data, forms a joint trend sequence, judges the load status, adjusts communication resource allocation, and generates communication structure adjustment results.

[0039] Based on the communication structure adjustment results, the resource contention control module extracts the drive unit startup record and current change rate, calculates the difference in current rise rate, determines the resource occupancy status and energy consumption concentration, marks the main control action path, constructs a mutual exclusion control set, adjusts the drive unit control path, and generates mutual exclusion control group information.

[0040] The motion stability adjustment module extracts displacement, acceleration, and current data during the lifting and lowering of the forks based on the mutually exclusive control group information, aligns them by period, judges the consistency of data growth, identifies motion instability, adjusts the amplitude of the control signal, and generates drive amplitude adjustment parameters.

[0041] The task scheduling adjustment module monitors battery voltage, ambient temperature, and forklift task load in real time according to the drive amplitude adjustment parameters, aligns the data along the time axis to form a joint state sequence, judges the load status, and adjusts the task execution priority based on the real-time battery power, generating task scheduling adjustment results.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] In this invention, by real-time monitoring of signal strength and optimization of signal quality, the stability and transmission efficiency of wireless communication are improved. By utilizing load fluctuation analysis and dynamic communication resource allocation, communication congestion under high load conditions is avoided, thereby improving the efficiency and stability of task execution. Combined with the judgment of drive unit resource occupancy, the cooperation between multiple drive units is optimized, reducing resource conflicts. Data consistency analysis is used to adjust the amplitude of control signals, thereby improving the smoothness and accuracy of forklift movements. By ensuring the priority execution of high-priority tasks under low power conditions, the execution time of core tasks is extended, improving stability and response speed under complex working conditions, and enhancing the efficiency and reliability of the remote control process. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the steps of the present invention;

[0046] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0047] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0048] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0049] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0050] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0051] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] Please see Figure 1 This invention provides a remote control method for electric forklifts based on a microcontroller, comprising the following steps:

[0058] S1: Call the Bluetooth communication sampling buffer, extract the signal strength data sequence, arrange it in time order to form a fluctuation sample, compare the maximum amplitude of the current signal strength with the offset between the previous period sampling points, determine the signal quality, construct the compression channel trigger flag, and generate the compression state switching flag.

[0059] S2: Based on the compression state switching indicator, analyze load fluctuations, read cargo weight, current feedback and vehicle speed data, form a joint trend sequence, determine the load state, adjust communication resource allocation, and generate communication structure adjustment results;

[0060] S3: Based on the communication structure adjustment results, extract the drive unit startup record and current change rate, calculate the difference in current rise rate, determine the resource occupancy status and energy consumption concentration, mark the main control action path, construct the mutual exclusion control set, adjust the drive unit control path, and generate mutual exclusion control group information.

[0061] S4: Based on the mutual exclusion control grouping information, extract the displacement, acceleration and current data during the lifting process of the forks, align them by period, judge the consistency of data growth, identify unstable actions, adjust the amplitude of the control signal, and generate drive amplitude adjustment parameters;

[0062] S5: Based on the drive amplitude adjustment parameters, monitor the battery voltage, ambient temperature, and forklift task load in real time, align the data according to the time axis and form a joint state sequence, determine the load status, combine the real-time battery power, adjust the task execution priority, and generate task scheduling adjustment results.

[0063] Compression state switching identifiers include Bluetooth link quality change trends, abnormal fluctuation status identification information, and compression channel trigger identifiers. Communication structure adjustment results include load fluctuation analysis results, load status judgment information, and communication resource adjustment configuration. Mutual exclusion control group information includes the dominant execution unit identifier, control priority order adjustment information, and execution path blocking parameters. Drive amplitude adjustment parameters include data sequence growth direction consistency, control signal amplitude adjustment results, and action stability evaluation information. Task scheduling adjustment results include battery voltage drop trends, real-time load status, and task execution priority.

[0064] Please see Figure 2 The specific steps for obtaining the compression state switching indicator are as follows:

[0065] S101: Based on the Bluetooth communication sampling buffer, obtain the signal strength data sequence of continuous communication cycles, arrange the signal data in time order to form fluctuation samples, and generate signal strength change analysis results by comparing the maximum amplitude of the current signal strength with the offset of the middle sampling point of the previous cycle.

[0066] The system retrieves raw signal strength data for five consecutive communication cycles (each cycle is set to 100 milliseconds) using a Bluetooth communication sampling buffer. This buffer is a first-in-first-out (FIFO) queue, storing the 50 most recent sampling points (sampled every 10 milliseconds). One communication cycle contains 10 sampling points. These 50 data points are arranged in chronological order to form a fluctuation sample sequence. For example, the 10 sampling points for cycle T-1 are: [-72, -70, -71, -73, -70, -72, -71, -69, -70, -71] dBm. The 10 sampling points for cycle T are: [-68, -69, -67, -68, -85, -88, -70, -69, -67, -68] dBm. A comparison operation is performed, first extracting the "middle sampling point of the previous cycle," that is, the 5th sampling point of cycle T-1, whose value is -70 dBm. Next, extract the "maximum amplitude in the current signal strength," which is the strongest sampling point of the signal in period T (maximum value, i.e., least negative), with a value of -67dBm. Calculate the offset: The system performs this operation continuously. For example, if the maximum amplitude of period T+1 is -80dBm, and the intermediate sampling point of period T is -85dBm, then the offset of period T+1 is... The system combines these continuously calculated offset values ​​[…,3,5,…] to generate signal strength change analysis results.

[0067] S102: Based on the signal strength change analysis results, by analyzing the offset change, determine the signal quality change trend of the Bluetooth link and identify abnormal fluctuation status, and generate Bluetooth link fluctuation identifier;

[0068] Based on the signal strength change analysis results, i.e., the offset sequence […,3,5,…], offset change analysis is performed. This analysis process calculates the first-order difference between consecutive offsets. For example, the offset change between period T and period T+1 is... An "abnormal fluctuation threshold" is set for identifying "abnormal fluctuation states." This threshold is set based on calibration experiments under actual operating conditions.

[0069] Table 1. Experimental data on Bluetooth signal offset variation.

[0070]

[0071] As shown in Table 1, in Experiment 2 (simulating an operator bypassing a metal shelf), the signal quality experienced a sudden and drastic deterioration, with the minimum offset change reaching -18 dBm. The maximum change under steady-state conditions did not exceed 5 dBm. To cover interference and retain margin, the "abnormal fluctuation threshold" was set to 10 dBm. The system continuously monitored the offset change value. When the offset calculated for cycle T+2 was -12 dBm, the offset change between cycle T+1 and cycle T+2 was... The system determines this change: If this condition is met, the system determines that the signal quality change trend of the Bluetooth link is "unstable" and identifies an "abnormal fluctuation state." A Bluetooth link fluctuation flag is then generated and set to 1 (indicating an abnormality). If the absolute value of the change (e.g., 2dBm) does not exceed 10dBm, the flag remains at 0 (indicating normality).

[0072] S103: Based on the Bluetooth link fluctuation identifier, adjust the signal processing path and construct the compression channel trigger identifier, including assigning real-time control data to the direct path, assigning sensor data to the compression path, and generating a compression state switching identifier.

[0073] Based on the Bluetooth link fluctuation flag (currently valued at 1), the system performs signal processing path adjustment. First, data types are categorized: "Real-time control data" is defined as data packets containing [steering angle commands, drive speed commands, fork lifting commands, horn switch]; "Sensor data" is defined as data packets containing [battery voltage, motor temperature, hydraulic oil temperature, load weight]. Next, a "compression channel trigger flag" is constructed and set to 1. The system performs path allocation based on this flag: "Real-time control data" is assigned to the "straight-through path," which has high priority and performs no compression, ensuring that each control command (e.g., a 100-millisecond cycle) is sent immediately. Simultaneously, "Sensor data" is assigned to the "compression path," which has low priority and enables data bundling strategies. For example, sensor data packets originally sent every 500 milliseconds are changed to be sent every 2000 milliseconds, and the data from these four cycles is bundled into a single data packet. Through this path allocation, a compression state switching flag is generated, with a value of 1.

[0074] Please see Figure 3 The specific steps for obtaining the communication structure adjustment results are as follows:

[0075] S201: Based on the compression state switching indicator, obtain the cargo weight output by the forklift load sensor, the drive current fed back by the current detection terminal, and the moving speed recorded by the vehicle speed sampler. Arrange the three data items according to the time axis to form a joint trend sequence.

[0076] Based on the compression state switching flag (currently 1), the system begins monitoring the forklift's physical status. Through the CAN bus interface, it reads cargo weight data from the load sensor (installed in the hydraulic system), drive current feedback from the motor controller's current detection terminal, and the travel speed recorded by the speed sampler from the travel motor encoder. The sampling period is set to 100 milliseconds, continuously acquiring data for three periods: T1: [Cargo weight: 1500kg, Drive current: 120A, Travel speed: 1.5m / s]. T2: [Cargo weight: 1500kg, Drive current: 150A, Travel speed: 1.8m / s]. T3: [Cargo weight: 1500kg, Drive current: 180A, Travel speed: 2.1m / s]. Arrange these three data points in the corresponding time axis order of T1, T2, T3 to form a joint trend sequence, which is represented as {T1:[1500,120,1.5],T2:[1500,150,1.8],T3:[1500,180,2.1]}.

[0077] S202: Based on the joint trend sequence, by comparing the consistency of the growth direction of each data item, the data change trend is determined, the consistency of the growth direction of each data item on the time axis is analyzed, the load status is identified, and the load status change trend is generated.

[0078] Based on the joint trend sequence {T1:[1500,120,1.5],T2:[1500,150,1.8],T3:[1500,180,2.1]}, a data growth direction consistency comparison was performed. To quantify the "growth direction," "stability thresholds" were set for each data item: the cargo weight stability threshold (based on sensor noise) was set to 10kg; the drive current stability threshold was set to 3A; and the movement speed stability threshold was set to 0.05m / s. First, the data changes from T1 to T2 were compared: weight change . The direction is marked as 0 (stable). Current change. . The direction is denoted as +1 (increase). Speed ​​change. . The direction is denoted as +1 (increase). The trend vector from T1 to T2 is [0, +1, +1]. Next, compare the data changes from T2 to T3: weight change. Direction is marked as 0. Current change. Direction is denoted as +1. Velocity change. The direction is denoted as +1. The trend vector from T2 to T3 is [0, +1, +1]. The system identifies this state as "heavy load acceleration" by analyzing the consistency of the growth direction of each data point on the time axis (the trend vector for two consecutive periods is [0, +1, +1]). If the trend vector is [0, +1, 0] (e.g., during lifting), it is identified as "heavy load lifting". The system outputs the load state change trend: "heavy load acceleration".

[0079] S203: Based on the load status change trend, adjust the communication resource allocation configuration in real time, reallocate the bandwidth usage structure and data transmission path, and generate communication structure adjustment results;

[0080] Based on the load status change trend ("Heavy Load Acceleration") and combined with the compression state switching flag (value 1), the system performs real-time adjustments to the communication resource allocation configuration. The current communication link is in compression state, with a total available bandwidth set to 64kbps. The default configuration is 32kbps for the "Direct Path" (control) and 32kbps for the "Compressed Path" (sensing). The system performs a judgment: if (compressed state switching flag == 1) and (load status change trend == "Heavy Load Acceleration" or "Heavy Load Lift"), it is determined to be a high-risk condition. The system performs a bandwidth usage structure reallocation: increasing the bandwidth of the "Direct Path" to 56kbps and compressing the bandwidth of the "Compressed Path" to 8kbps. Simultaneously, the data transmission path strategy is adjusted, extending the transmission interval of sensor data (such as battery voltage and temperature) in the "Compressed Path" from 2000 milliseconds to 5000 milliseconds. The resulting communication structure adjustment is: {Direct Path Bandwidth: 56kbps, Compressed Path Bandwidth: 8kbps, Sensor Data Interval: 5000ms}.

[0081] Please see Figure 4 The specific steps for obtaining mutual exclusion control group information are as follows:

[0082] S301: Based on the communication structure adjustment results, extract the start-up record and current change rate of each drive unit in the current control cycle, calculate the difference ratio between the current rise rates of multiple units, combine the start-up time interval of each unit, compare the current growth order, identify the resource occupancy status of the drive unit, and generate the drive unit occupancy status value.

[0083] Based on the communication structure adjustment results, the system begins monitoring the internal drive units. Unit A is defined as the travel drive motor, and Unit B as the forklift motor. At T=100ms, Unit A receives a start-up record; at T=150ms, Unit B receives a start-up record. The system monitors its current change rate: Unit A's current is 5A (standby) at T=100ms and 125A at T=200ms. Its current change rate is calculated as follows: Unit B has a current of 8A (standby) at T=150ms and a current of 208A at T=250ms. The rate of change of its current is calculated as follows: Calculate the ratio of the differences in the current rise rates among multiple units: Combining the startup time interval of each unit: The current growth order is compared: UnitB (2000A / s) > UnitA (1200A / s). The system uses this to identify the resource occupancy status of the drive units. To determine concentrated energy consumption, the "concurrent start threshold" is set to 200ms (based on experiments, motors starting within 200ms are considered concurrent). This is considered concurrent. The "Total Current Rate Threshold" is set to 2500 A / s (based on a 48V battery pack's maximum instantaneous discharge test; exceeding 2500 A / s will cause the voltage sag to exceed the safety threshold of 6.0V). The total rate is calculated as follows: . The condition is determined to be "concentrated energy consumption". The generated drive unit occupancy status values ​​are: {UnitA_Rate:1200,UnitB_Rate:2000,Concurrent:True,Concentration:True}.

[0084] S302: Based on the drive unit occupancy status value, by judging the degree of energy consumption concentration, the dominant execution unit is selected and the main control action path is marked, and the main control action path identifier is generated;

[0085] Based on the drive unit occupancy status value, which displays "Concentration: True", the system performs a selection process to determine the degree of energy concentration (which has been determined to be True). The selection logic is as follows: among the units determined to have "energy concentration", the unit with the highest current change rate is selected as the dominant execution unit. Comparison: Unit B rate (2000A / s) > Unit A rate (1200A / s). The system selects Unit B (forklift motor) as the dominant execution unit. The system then marks the control logic path associated with Unit B (e.g., the PWM output channel controlling the oil pump motor) as the main control action path. A main control action path identifier is generated: 'Path_B_Main'.

[0086] S303: Based on the master control action path identifier, construct a mutual exclusion control set, adjust the control paths between multiple drive units, including setting the control priority order and scheduling blocking relationship, and generating mutual exclusion control grouping information;

[0087] Based on the master control action path identifier ('Path_B_Main'), the system constructs a mutual exclusion control set containing all concurrent units that cause concentrated energy consumption, i.e., Mutex_Set={UnitA,UnitB}. The system adjusts the control paths between multiple drive units within this set. First, the control priority is set: the priority of the dominant execution unit UnitB is set to 1 (highest), and the priority of UnitA is set to 2. Next, the scheduling blocking relationship is set: the system needs to reduce the total current rate from 3200A / s to the threshold of 2500A / s, and the required rate reduction is... The system applies this reduction to the non-dominant execution unit (Unit A). The new target rate for Unit A is calculated: The system immediately adjusts the control signal to UnitA (e.g., limiting the growth slope of its PWM duty cycle to a specific value) to lock its current rise rate at 500A / s. Mutual exclusion control group information is generated: {Priority:[B=1,A=2],Lock:[UnitA_Throttle_Rate=500A / s]}.

[0088] Please see Figure 5 The specific steps for obtaining the drive amplitude adjustment parameters are as follows:

[0089] S401: Based on the mutual exclusion control grouping information, extract the displacement feedback sequence, drive-end acceleration data sequence and current sampling value sequence during the fork lifting process, align them according to the control cycle, obtain the correspondence of each data item under multiple cycles, and generate cycle data alignment value;

[0090] Based on the mutual exclusion control grouping information, the current system is executing UnitB (fork lifting) with the highest priority. The system extracts the displacement feedback sequence from the displacement encoder on the fork mast, the drive-end acceleration data sequence from the MEMS sensor on the fork carriage, and the current sampling value sequence from the UnitB controller. These three sets of sequences are aligned according to the control cycle (set to 50 milliseconds) to obtain the correspondence between each data item under multiple cycles.

[0091] Table 2 Alignment of Key Parameters for Fork Lifting

[0092]

[0093] As shown in Table 2, the system acquired alignment data for five periods, from T1 to T5. This tabulated sequence represents the generated period data alignment values.

[0094] S402: Based on the periodic data alignment value, determine the consistency of the growth direction of each data sequence within the same control cycle, calculate the consistency degree of the sequence change of the cycle, evaluate the stable state of the forklift action, and generate the action stability coefficient.

[0095] Based on the periodic data alignment values ​​(see Table 2), the system determines the consistency of the growth direction of each data sequence within the same control period. To perform this determination, a "stable change threshold" is set for each sequence: displacement change < 0.005m is recorded as 0; acceleration change < 0.05m / s² is recorded as 0; current change < 5A is recorded as 0. Growth is recorded as +1, and decrease as -1. The change from T2 to T3 is calculated: displacement... (+1); acceleration (0); Current (0). The trend vector from T2 to T3 is [+1, 0, 0]. Calculate the change from T3 to T4: displacement. (+1); acceleration (-1); Current (-1). The trend vector from T3 to T4 is [+1, -1, -1]. Calculate the change from T4 to T5: displacement. (+1); acceleration (-1); Current (+1). The trend vector from T4 to T5 is [+1, -1, +1]. The system calculates the "consistency of the periodic sequence change": under the standard rise and fall model, all three directions should be +1 or 0. The vector from T2 to T3 is [+1, 0, 0], all three terms meet the requirement, so the consistency is = The vector T3->T4 [+1,-1,-1] has only one term (displacement) that matches the expression; the consistency score is 1 / 3. The vector T4->T5 [+1,-1,+1] has two terms (displacement and current) that match, so the consistency degree is = The system uses this to evaluate the stability of the forklift's movements. The latest movement stability coefficient is generated: 0.67.

[0096] S403: Based on the motion stability coefficient, identify the degree of instability of the forklift's motion, combine the fluctuation characteristics of the data sequence corresponding to the cycle, adjust the amplitude of the output control signal, obtain the adjustment amplitude parameter range, and generate the drive amplitude adjustment parameters.

[0097] Based on the motion stability coefficient (0.67), the system identifies the instability of the forklift's motion. First, it sets the "stability coefficient range": [0.8, 1.0] is defined as "stable"; [0.5, 0.8) is defined as "critically stable"; [0, 0.5) is defined as "unstable". The current coefficient of 0.67 falls within the "critically stable" range. The system combines the fluctuation characteristics of the data sequence corresponding to the cycle (T4->T5: acceleration is negative, but current is increasing, indicating "load stagnation") and adjusts the amplitude of the output control signal. The system queries the "adjustment amplitude parameter range": stable state, parameter = 1.0 (no adjustment); critically stable, parameter = 0.8 (amplitude attenuation 20%); unstable, parameter = 0.5 (amplitude attenuation 50%). The current state matches "critically stable", so the adjustment parameter is selected as 0.8. The system applies this parameter to the control signal of UnitB (lifting motor). For example, if the current PWM duty cycle instruction is 70%, the new instruction is adjusted to... Generate drive amplitude adjustment parameter: 0.8.

[0098] Please see Figure 6 The specific steps for obtaining the task scheduling adjustment results are as follows:

[0099] S501: Based on the drive amplitude adjustment parameters, monitor the battery voltage, ambient temperature, and forklift task load in real time, extract the battery voltage change sequence, ambient temperature data, and load status data, align the data according to the time axis, and obtain the joint status data sequence.

[0100] Based on the drive amplitude adjustment parameter (0.8), the system enters a higher level of status monitoring. Real-time monitoring includes battery voltage feedback from the BMS, ambient temperature sensor data mounted on the MCU, and forklift task load (load status). Data is extracted for T1 (0 minutes): [Voltage: 47.5V, Temperature: 30°C, Load Status: "Heavy Load Lifting"]. T2 (1 minute): [Voltage: 47.1V, Temperature: 31°C, Load Status: "Heavy Load Lifting"]. T3 (2 minutes): [Voltage: 46.6V, Temperature: 32°C, Load Status: "Heavy Load Lifting"]. For alignment, load status quantization is performed: "No Load" = 1, "Light Load" = 2, "Medium Load" = 3, "Heavy Load" = 4. The quantization value for the "Heavy Load Lifting" status is 4. Align the data along the time axis to obtain the joint state data sequence: {T1:[47.5,30,4],T2:[47.1,31,4],T3:[46.6,32,4]}.

[0101] S502: Based on the joint state data sequence, by analyzing the battery voltage drop trend, ambient temperature fluctuation and load change, the load state of each time period is determined, and the load state judgment result is obtained.

[0102] Based on the joint state data sequence, the system performs trend analysis. The analysis examines the battery voltage decrease trend: the rate from T1 to T2 is... The T2->T3 rate is The voltage drop trend is accelerating. Analysis of ambient temperature fluctuations: the rate from T1 to T3 is... The temperature is continuously rising. Analyzing the load changes: the sequence is [4,4,4], which is determined to be "continuous heavy load". The system uses the above analysis to determine the load status for each time period. The criteria for determining a "high consumption" state are set as follows: (voltage drop trend < -0.3V / min) and (temperature rise trend > 0.5°C / min) and (load quantization value)... 3). The latest state of the current T3 period: (-0.5 < -0.3) and (1.0 > 0.5) and (4 3) All conditions are met. The load status judgment result is: "High consumption".

[0103] S503: Based on the load status judgment result, combined with the real-time battery level and task type, dynamically adjust the execution priority of multiple tasks, set the task hierarchy order, and generate task scheduling adjustment results;

[0104] Based on the load status assessment ("High Consumption"), the system combines the real-time battery charge (SOC) obtained from the BMS and retrieves the current task type list [Task A: Fork Lifting, Task B: Travel Drive, Task C: Data Upload]. First, it executes the "Low Battery Threshold" setting process: by statistically analyzing historical data, it confirms that the safe remaining battery charge required to "maintain core functions" (such as safely lowering the forks) is 5% of the total battery capacity. To provide an early warning, the "Low Battery Threshold" is set to 20%. The system obtains the real-time battery charge as 18%. Then, it performs a judgment: The system enters "low power mode". In low power mode, tasks are prioritized based on the forklift's current load status (high consumption) and task type.

[0105] Table 3 Low Power Mode Task Priority Table

[0106]

[0107] As shown in Table 3, the system dynamically adjusts the execution priority of multiple tasks. High-power core tasks (TaskA and TaskB) are assigned high priority (Priority=1), while low-power non-core tasks (TaskC) are assigned low priority (Priority=3). This task priority order is set. The task scheduling adjustment result is generated as: {LowBatMode:True,Priority:[TaskA=1,TaskB=1,TaskC=3]}.

[0108] Please see Figure 7 A microcontroller-based remote control system for electric forklifts includes:

[0109] The signal quality analysis module calls the Bluetooth communication sampling buffer, extracts the signal strength data sequence, arranges it in time order to form fluctuation samples, compares the maximum amplitude of the current signal strength with the offset between the previous period sampling points, judges the signal quality, constructs a compression channel trigger flag, and generates a compression state switching flag.

[0110] The load status analysis module analyzes load fluctuations based on compression status switching indicators, reads cargo weight, current feedback and vehicle speed data, forms a joint trend sequence, judges the load status, adjusts communication resource allocation, and generates communication structure adjustment results.

[0111] Based on the communication structure adjustment results, the resource contention control module extracts the drive unit startup record and current change rate, calculates the difference in current rise rate, determines the resource occupancy status and energy consumption concentration, marks the main control action path, constructs a mutual exclusion control set, adjusts the drive unit control path, and generates mutual exclusion control group information.

[0112] The motion stability adjustment module extracts displacement, acceleration, and current data during the lifting and lowering of the forks based on the mutually exclusive control group information, aligns them by period, judges the consistency of data growth, identifies motion instability, adjusts the amplitude of the control signal, and generates drive amplitude adjustment parameters.

[0113] The task scheduling adjustment module adjusts parameters based on the drive amplitude, monitors battery voltage, ambient temperature, and forklift task load in real time, aligns the data along the time axis to form a joint state sequence, judges the load status, and adjusts the task execution priority based on the real-time battery power, generating task scheduling adjustment results.

[0114] 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 remote control method for electric forklifts based on a microcontroller, characterized in that, Includes the following steps: S1: Call the Bluetooth communication sampling buffer, extract the signal strength data sequence, arrange it in time order to form a fluctuation sample, compare the maximum amplitude of the current signal strength with the offset between the sampling points of the previous period, determine the signal quality, construct a compression channel trigger flag, assign real-time control data to the direct path, which is assigned high priority and does not perform any compression operation, send each real-time control data immediately according to each period, assign sensor data to the compression path, which is assigned low priority, bundle multiple consecutive periods of sensor data into a data packet and send it, and generate a compression state switching flag; S2: Based on the compression state switching identifier, analyze load fluctuations, read cargo weight, current feedback and vehicle speed data, form a joint trend sequence, determine the load state, adjust communication resource allocation, and generate communication structure adjustment results; S3: Based on the communication structure adjustment results, extract the drive unit startup record and current change rate, calculate the difference in current rise rate, determine the resource occupancy status and energy consumption concentration, mark the main control action path, construct a mutual exclusion control set, adjust the drive unit control path, and generate mutual exclusion control group information. The specific steps for obtaining the mutual exclusion control group information are as follows: S301: Based on the communication structure adjustment results, extract the start-up record and current change rate of each drive unit in the current control cycle, calculate the difference ratio between the current rise rates of multiple units, combine the start-up time interval of each unit, compare the current growth order, identify the resource occupancy status of the drive unit, and generate the drive unit occupancy status value. S302: Based on the occupancy status value of the drive unit, by judging the degree of energy consumption concentration, the dominant execution unit is selected and the main control action path is marked, and a main control action path identifier is generated; S303: Based on the master control action path identifier, construct a mutual exclusion control set, adjust the control paths between multiple drive units, including setting control priority order and scheduling blocking relationship, and generating mutual exclusion control grouping information; S4: Based on the mutually exclusive control group information, extract the displacement, acceleration and current data during the lifting process of the forks, align them according to the cycle, determine the consistency of data growth, identify unstable actions, adjust the amplitude of the control signal, and generate drive amplitude adjustment parameters.

2. The remote control method for electric forklifts based on a microcontroller according to claim 1, characterized in that, The compression state switching identifier includes Bluetooth link quality change trend, abnormal fluctuation state identification information, and compression channel trigger identifier. The communication structure adjustment result includes load fluctuation analysis result, load state judgment information, and communication resource adjustment configuration. The mutual exclusion control group information includes dominant execution unit identifier, control priority order adjustment information, and execution path blocking parameters. The drive amplitude adjustment parameters include data sequence growth direction consistency, control signal amplitude adjustment result, and action stability evaluation information.

3. The remote control method for electric forklifts based on a microcontroller according to claim 1, characterized in that, The specific steps for obtaining the compression state switching identifier are as follows: S101: Based on the Bluetooth communication sampling buffer, obtain the signal strength data sequence of continuous communication cycles, arrange the signal data in time order to form fluctuation samples, and generate signal strength change analysis results by comparing the maximum amplitude of the current signal strength with the offset of the middle sampling point of the previous cycle. S102: Based on the signal strength change analysis results, by analyzing the offset change, determine the signal quality change trend of the Bluetooth link and identify abnormal fluctuation status, and generate a Bluetooth link fluctuation identifier. S103: Based on the Bluetooth link fluctuation identifier, adjust the signal processing path and construct a compression channel trigger identifier, including assigning real-time control data to the direct path, assigning sensor data to the compression path, and generating a compression state switching identifier.

4. The remote control method for electric forklifts based on a microcontroller according to claim 3, characterized in that, The specific steps for obtaining the communication structure adjustment result are as follows: S201: Based on the compression state switching indicator, obtain the cargo weight output by the forklift load sensor, the drive current fed back by the current detection terminal, and the moving speed recorded by the vehicle speed sampler, and arrange the three data items according to the time axis to form a joint trend sequence. S202: Based on the joint trend sequence, by comparing the consistency of the growth direction of each data item, the data change trend is determined, the consistency of the growth direction of each data item on the time axis is analyzed, the load status is identified, and the load status change trend is generated. S203: Based on the load status change trend, adjust the communication resource allocation configuration in real time, reallocate the bandwidth usage structure and data transmission path, and generate communication structure adjustment results.

5. The remote control method for electric forklifts based on a microcontroller according to claim 1, characterized in that, The specific steps for obtaining the drive amplitude adjustment parameters are as follows: S401: Based on the mutually exclusive control grouping information, extract the displacement feedback sequence, drive end acceleration data sequence and current sampling value sequence during the fork lifting process, align them according to the control cycle, obtain the correspondence of each data item under multiple cycles, and generate cycle data alignment value; S402: Based on the periodic data alignment value, determine the consistency of the growth direction of each data sequence within the same control cycle, calculate the consistency degree of the sequence change of the cycle, evaluate the stable state of the forklift action, and generate the action stability coefficient. S403: Based on the motion stability coefficient, identify the degree of instability of the forklift motion, combine the fluctuation characteristics of the data sequence corresponding to the period, adjust the amplitude of the output control signal, obtain the adjustment amplitude parameter range, and generate the drive amplitude adjustment parameter.

6. The remote control method for electric forklifts based on a microcontroller according to claim 1, characterized in that, The method further includes: S5: Based on the drive amplitude adjustment parameters, monitor the battery voltage, ambient temperature, and forklift task load in real time, align the data according to the time axis and form a joint state sequence, determine the load status, combine the real-time battery power, adjust the task execution priority, and generate task scheduling adjustment results. The task scheduling adjustment results include the battery voltage drop trend, real-time load status, and task execution priority.

7. The remote control method for electric forklifts based on a microcontroller according to claim 6, characterized in that, The specific steps for obtaining the task scheduling adjustment results are as follows: S501: Based on the drive amplitude adjustment parameters, monitor the battery voltage, ambient temperature, and forklift task load in real time, extract the battery voltage change sequence, ambient temperature data, and load status data, align the data according to the time axis, and obtain the joint status data sequence. S502: Based on the joint state data sequence, by analyzing the battery voltage drop trend, ambient temperature fluctuation and load change, the load state of each time period is determined, and the load state determination result is obtained; S503: Based on the load status judgment result, combined with the real-time battery power and task type, dynamically adjust the execution priority of multiple tasks, set the task hierarchy order, and generate task scheduling adjustment results.

8. The remote control method for electric forklifts based on a microcontroller according to claim 7, characterized in that, The specific process of dynamically adjusting the priority is as follows: By monitoring real-time battery power and task load status, the power requirements and actual power consumption ratio of each task are obtained. The remaining battery percentage is compared with a low power threshold. When the remaining battery power is lower than the low power threshold, the system enters low power mode. In low power mode, tasks are prioritized based on the forklift's current load status and task type. High-power tasks, including driving and fork lifting, are assigned high priority based on battery power consumption, while low-power tasks, including environmental monitoring and data uploading, are assigned low priority. The low battery threshold is set by statistically analyzing the rate of change of battery power over multiple control cycles, combining various tasks with the rate of power decrease per unit time, determining the safe remaining power required to maintain core functions under continuous task execution, and calculating and setting the low battery threshold for early warning based on the target percentage.

9. A remote control system for electric forklifts based on a microcontroller, characterized in that: The system is used to implement the microcontroller-based remote control method for electric forklifts as described in any one of claims 1-8, and the system includes: The signal quality analysis module calls the Bluetooth communication sampling buffer, extracts the signal strength data sequence, arranges it in time order to form fluctuation samples, compares the maximum amplitude of the current signal strength with the offset between the previous period sampling points, judges the signal quality, constructs a compression channel trigger flag, and generates a compression state switching flag. The load status analysis module analyzes load fluctuations based on the compression status switching identifier, reads cargo weight, current feedback and vehicle speed data, forms a joint trend sequence, judges the load status, adjusts communication resource allocation, and generates communication structure adjustment results. Based on the communication structure adjustment results, the resource contention control module extracts the drive unit startup record and current change rate, calculates the difference in current rise rate, determines the resource occupancy status and energy consumption concentration, marks the main control action path, constructs a mutual exclusion control set, adjusts the drive unit control path, and generates mutual exclusion control group information. The motion stability adjustment module extracts displacement, acceleration, and current data during the lifting and lowering of the forks based on the mutually exclusive control group information, aligns them by period, judges the consistency of data growth, identifies motion instability, adjusts the amplitude of the control signal, and generates drive amplitude adjustment parameters. The task scheduling adjustment module monitors battery voltage, ambient temperature, and forklift task load in real time according to the drive amplitude adjustment parameters, aligns the data along the time axis to form a joint state sequence, judges the load status, and adjusts the task execution priority based on the real-time battery power, generating task scheduling adjustment results.

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