Forklift load distribution monitoring system and method, electronic equipment and storage medium

Abstract

The invention discloses a forklift load distribution monitoring system and method, electronic equipment and a storage medium. The system comprises a multi-point sensor array module which is used for monitoring the load distribution change on the forklift to obtain sensor acquisition data; the load distribution determining module is connected with the multi-point sensor array module, and the load distribution determining module is used for calculating and processing the data collected by the sensors to obtain a load distribution result of the forklift; the load distribution response module is connected with the load distribution determination module, the load distribution response module is used for responding to the load distribution result to obtain a load distribution execution result, and the load distribution execution result is used for representing interaction with a user through an acousto-optic signal based on the load distribution result. And / or the operation state of the forklift is adjusted based on the load distribution result. According to the invention, the technical problem that the efficiency of monitoring the load distribution of the forklift is low in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of forklift operation monitoring, and in particular to a forklift load distribution monitoring system, method, electronic equipment and storage medium. Background Art

[0002] In the field of power supply chain transportation, forklifts, as important handling equipment in industrial production, logistics and transportation, are widely used in warehousing, loading and unloading, handling and other operation scenarios. The main function of a forklift is to carry and move goods through the fork arm. The fork arm is an important load-bearing component of the forklift. Its structural design and operational safety directly affect the work efficiency and operational safety of the forklift. In routine operations, the load borne by the fork arm varies with the weight and placement of the goods. The forklift operator lifts, moves and places the goods by operating the fork arm.

[0003] In the related technologies, the load monitoring of forklifts and fork arms usually relies on the operator's experience or simple overload warning devices, especially when the load changes under dynamic changes in operation, such as acceleration, braking, turning, etc. At present, most forklift load monitoring technologies use a single load sensor to monitor the total weight. This system only issues an alarm when the overall weight is overloaded. Especially in complex operating environments, the center of gravity changes of the load during the dynamic operation of the forklift, such as turning, climbing, and sudden braking, may cause instantaneous imbalance, increasing the operational risk. In addition, the traditional load monitoring system has limited data processing capabilities, resulting in incomplete load distribution information, making it difficult to provide early warning of impending dangers, resulting in low efficiency in monitoring the load distribution of forklifts.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] Embodiments of the present invention provide a forklift load distribution monitoring system, method, electronic device and storage medium to at least solve the technical problem of low efficiency in monitoring forklift load distribution in the related art.

[0006] According to one aspect of an embodiment of the present application, a forklift load distribution monitoring system is provided, including: a multi-point sensor array module, including multiple sensors arranged on the forklift, the multi-point sensor array module is used to monitor the load distribution changes on the forklift and obtain sensor collected data; a load distribution determination module, connected to the multi-point sensor array module, the load distribution determination module is used to calculate and process the sensor collected data to obtain the load distribution result of the forklift; a load distribution response module, connected to the load distribution determination module, the load distribution response module is used to respond to the load distribution result and obtain the load distribution execution result, wherein the load distribution execution result is used to indicate interaction with the user based on the load distribution result with sound and light signals, and / or adjust the operating state of the forklift based on the load distribution result.

[0007] Optionally, the sensor acquisition data includes at least: stress data; a multi-point sensor array module, including: a stress sensor module, including multiple stress sensors arranged on the forklift arm of the forklift, the stress sensor module is used to monitor stress changes at different parts of the forklift arm to obtain stress data.

[0008] Optionally, the sensor collected data also includes at least one of the following: pressure data, acceleration data and inclination data; the multi-point sensor array module also includes at least one of the following: a pressure sensor module, including at least one pressure sensor arranged on the forklift arm, the pressure sensor module is used to monitor the pressure changes on the forklift arm and obtain pressure data; an acceleration sensor module, including an acceleration sensor arranged on the forklift, the acceleration sensor module is used to monitor the acceleration changes of the forklift during operation and obtain acceleration data; an inclination sensor module, including an inclination sensor arranged on the forklift, the inclination sensor module is used to monitor the inclination changes of the forklift during operation and obtain inclination data.

[0009] Optionally, the load distribution determination module includes: a data acquisition and edge computing module, which is used to collect sensor data in parallel and perform edge computing on the sensor data to obtain an initial load distribution result, wherein the edge computing at least includes a filtering operation; a data fusion and processing module, which is connected to the data acquisition and edge computing module, and the data fusion and processing module is used to fuse the initial load distribution results and obtain the load distribution results.

[0010] Optionally, the load distribution result includes at least: a load distribution diagram and a real-time center of gravity position; a data acquisition and edge computing module, including: a load distribution diagram calculation module, used to determine the load distribution diagram based on the initial load distribution result, wherein the load distribution diagram is used to characterize the load distribution conditions at different positions on the forklift arm of the forklift; a center of gravity position calculation module, used to determine the real-time center of gravity position based on the initial load distribution result, wherein the real-time center of gravity position is used to characterize the real-time center of gravity position of the forklift.

[0011] Optionally, the load distribution response module includes: a deep learning load prediction module, which is used to predict the load distribution change of the forklift within a preset future time period based on the load distribution results, and obtain the load change trend; an audio-visual display module, which is connected to the deep learning load prediction module, and the audio-visual display module is used to interact with the user with audio-visual signals based on the load distribution results and the load change trend, wherein the audio-visual display module includes at least a display screen and an alarm; a response execution module, which is connected to the deep learning load prediction module, and the response execution module is used to adjust the operating state of the forklift based on the load distribution results and the load change trend, wherein the response execution module is at least used to adjust the operating state of the track wheels on the forklift.

[0012] According to another aspect of an embodiment of the present application, a method for monitoring the load distribution of a forklift is also provided, including: based on a multi-point sensor array module in a monitoring system for the load distribution of the forklift, monitoring the load distribution changes on the forklift to obtain sensor collection data, wherein the multi-point sensor array module includes multiple sensors arranged on the forklift; based on a load distribution determination module, calculating and processing the sensor collection data to obtain a load distribution result of the forklift, wherein the load distribution determination module is connected to the multi-point sensor array module; based on a load distribution response module, responding to the load distribution result to obtain a load distribution execution result, wherein the load distribution response module is connected to the load distribution determination module, and the load distribution execution result is used to indicate interaction with a user based on the load distribution result using sound and light signals, and / or adjusting the operating status of the forklift based on the load distribution result.

[0013] According to another aspect of the embodiments of the present application, an electronic device is further provided, including: a memory storing an executable program; and a processor for running the program, wherein the method of each embodiment of the present application is executed when the program is running.

[0014] According to another aspect of an embodiment of the present application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in each embodiment of the present application.

[0015] According to another aspect of the embodiments of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present application is implemented.

[0016] According to another aspect of an embodiment of the present application, a computer program product is also provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present application is implemented.

[0017] According to another aspect of the embodiments of the present application, a computer program is also provided, and when the computer program is executed by a processor, the methods in the various embodiments of the present application are implemented.

[0018] In an embodiment of the present invention, a monitoring system for load distribution of a forklift is provided, comprising: a multi-point sensor array module, comprising a plurality of sensors arranged on a forklift, the multi-point sensor array module being used to monitor changes in load distribution on the forklift and obtain sensor collected data; a load distribution determination module, connected to the multi-point sensor array module, the load distribution determination module being used to perform calculations and processing on the sensor collected data and obtain a load distribution result of the forklift; a load distribution response module, connected to the load distribution determination module, the load distribution response module being used to respond to the load distribution result and obtain a load distribution execution result, wherein the load distribution execution result is used to indicate interaction with a user based on the load distribution result using an acoustic and optical signal, and / or to adjust the operating state of the forklift based on the load distribution result. It is easy to notice that the application adopts a multi-point sensor array module to monitor the load distribution changes on the forklift in real time, obtain accurate sensor collection data, improve the accuracy and real-time performance of monitoring, and obtain the load distribution results of the forklift by calculating and processing the sensor collection data, which can help users understand the real-time working status and load conditions of the forklift, improve the analysis and processing efficiency of the monitoring data, and can perform corresponding processing and feedback according to the load distribution results, interact with the user through sound and light signals, adjust the operating status of the forklift, and improve the operating efficiency and safety of the forklift. Through the real-time monitoring of the multi-point sensor array module and the calculation and processing of the load distribution determination module, the load distribution of the forklift can be quickly and accurately obtained. The feedback and adjustment functions of the load distribution response module can help users deal with problems in the forklift work in a timely manner, and improve the operating efficiency and safety of the forklift. This solves the technical problem of low efficiency in monitoring the load distribution of forklifts in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 is a schematic diagram of a forklift load distribution monitoring system according to an embodiment of the present invention;

[0021] Figure 2 is a flow chart of a method for monitoring forklift load distribution according to an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of an optional forklift equipped with a load distribution monitoring system according to an embodiment of the present invention;

[0023] Figure 4 is a schematic diagram of a forklift equipped with a load distribution monitoring system from another optional perspective according to an embodiment of the present invention;

[0024] Figure 5 is a schematic diagram of a forklift arm structure of a forklift equipped with a load distribution monitoring system according to an optional embodiment of the present invention;

[0025] Figure 6 is a schematic diagram of an optional Internet of Things expansion and cloud data analysis module on a forklift equipped with a load distribution monitoring system according to an embodiment of the present invention;

[0026] Figure 7 is a schematic diagram of an optional sound and light display module on a forklift equipped with a load distribution monitoring system according to an embodiment of the present invention;

[0027] Figure 8 is a schematic diagram of an optional load distribution monitoring system according to an embodiment of the present invention.

[0028] Among them, 1. Forklift body; 2. Forklift arm; 3. Multi-point sensor array module; 301. Stress sensor; 302. Pressure sensor; 303. Acceleration sensor; 304. Tilt sensor; 4. Data acquisition and edge computing module; 5. Data fusion and processing module; 6. Sound and light display module; 601. Fixed plate; 602. Display screen; 603. Alarm; 7. Adaptive calibration and machine learning module; 8. Internet of Things expansion and cloud data analysis module; 9. Track wheel. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] According to an embodiment of the present application, a forklift load distribution monitoring system is provided. Figure 1 is a schematic diagram of a forklift load distribution monitoring system according to the present application, such as Figure 1 As shown, the forklift load distribution monitoring system includes: a multi-point sensor array module 102 , a load distribution determination module 104 and a load distribution response module 106 .

[0032] The multi-point sensor array module includes a plurality of sensors arranged on the forklift. The multi-point sensor array module is used to monitor the load distribution change on the forklift and obtain sensor collection data.

[0033] In an optional embodiment, in the monitoring system of the load distribution of the forklift, the multi-point sensor array module may include a plurality of sensors arranged on the forklift, and these sensors may be distributed in different parts of the forklift, such as the fork arm, fork head, body and other positions. Such a setting can realize the comprehensive monitoring of the load distribution on the forklift, and can more accurately obtain the load condition of the forklift during the working process. The multi-point sensor array module can obtain the data collected by the sensor by real-time monitoring the load distribution changes on the forklift, which can reflect the load distribution of the forklift under different working conditions, including the force conditions at different positions, the load size and other information. By analyzing these data, it is possible to timely find problems that may exist in the forklift at work, such as overload, imbalance and other situations, so as to take corresponding measures to adjust or deal with them. The multi-point sensor array module can monitor the load distribution changes of the forklift in real time, and timely feedback the working status of the forklift, which is conducive to preventing potential safety risks in advance. Through the layout of multiple sensors, the load distribution data on the forklift can be obtained more comprehensively and accurately, and the accuracy and reliability of monitoring can be improved.

[0034] The load distribution determination module is connected to the multi-point sensor array module. The load distribution determination module is used to calculate and process the sensor collected data to obtain the load distribution result of the forklift.

[0035] In an optional embodiment, in a monitoring system for the load distribution of a forklift, a load distribution determination module can be connected to a multi-point sensor array module, and the sensor will collect load data from different parts of the forklift and transmit the data to the load distribution determination module. The load distribution determination module will calculate and process the data collected by the sensor, and determine the load distribution on the forklift through algorithms and models, so that the load distribution results of the forklift at different parts can be obtained, including information such as the center of gravity position and load size. The present application can achieve accurate monitoring of the load distribution on the forklift through real-time monitoring of the multi-point sensor array module and calculation and processing of the load distribution determination module, thereby ensuring the stability and safety of the forklift at work. The system can store and analyze the collected data, provide a basis for subsequent maintenance and improvement, and achieve a comprehensive understanding and optimized management of the working status of the forklift.

[0036] The load distribution response module is connected to the load distribution determination module. The load distribution response module is used to respond to the load distribution result and obtain the load distribution execution result, wherein the load distribution execution result is used to indicate interaction with the user based on the load distribution result with sound and light signals, and / or adjust the operating state of the forklift based on the load distribution result.

[0037] In an optional embodiment, in a monitoring system for the load distribution of a forklift, a load distribution response module can be connected to a load distribution determination module to help users understand the load condition of the forklift by monitoring the load distribution on the forklift. When the load distribution response module obtains the load distribution result, it can respond accordingly according to the result to obtain the load distribution execution result. The execution result can interact with the user through sound and light signals, or adjust the operating status of the forklift. Monitoring the load distribution of the forklift can help users understand the load condition of the forklift in a timely manner and avoid safety hazards caused by overloading or uneven load. When the load distribution of the forklift is unreasonable, the system can remind the user through sound and light signals or automatically adjust the operating status of the forklift to ensure the safe operation of the forklift. Interacting with the user through sound and light signals can allow the user to understand the operating status of the forklift more intuitively and improve the user experience. At the same time, the system automatically adjusts the operating status of the forklift according to the load distribution result, reduces the user's operating burden, and improves work efficiency.

[0038] In an embodiment of the present invention, a monitoring system for load distribution of a forklift is provided, comprising: a multi-point sensor array module, comprising a plurality of sensors arranged on a forklift, the multi-point sensor array module being used to monitor changes in load distribution on the forklift and obtain sensor collected data; a load distribution determination module, connected to the multi-point sensor array module, the load distribution determination module being used to perform calculations and processing on the sensor collected data and obtain a load distribution result of the forklift; a load distribution response module, connected to the load distribution determination module, the load distribution response module being used to respond to the load distribution result and obtain a load distribution execution result, wherein the load distribution execution result is used to indicate interaction with a user based on the load distribution result using an acoustic and optical signal, and / or to adjust the operating state of the forklift based on the load distribution result. It is easy to notice that the application adopts a multi-point sensor array module to monitor the load distribution changes on the forklift in real time, obtain accurate sensor collection data, improve the accuracy and real-time performance of monitoring, and obtain the load distribution results of the forklift by calculating and processing the sensor collection data, which can help users understand the real-time working status and load conditions of the forklift, improve the analysis and processing efficiency of the monitoring data, and can perform corresponding processing and feedback according to the load distribution results, interact with the user through sound and light signals, adjust the operating status of the forklift, and improve the operating efficiency and safety of the forklift. Through the real-time monitoring of the multi-point sensor array module and the calculation and processing of the load distribution determination module, the load distribution of the forklift can be quickly and accurately obtained. The feedback and adjustment functions of the load distribution response module can help users deal with problems in the forklift work in a timely manner, and improve the operating efficiency and safety of the forklift. This solves the technical problem of low efficiency in monitoring the load distribution of forklifts in related technologies.

[0039] Optionally, the sensor acquisition data includes at least: stress data; a multi-point sensor array module, including: a stress sensor module, including multiple stress sensors arranged on the forklift arm of the forklift, the stress sensor module is used to monitor stress changes at different parts of the forklift arm to obtain stress data.

[0040] In an optional embodiment, in a monitoring system for forklift load distribution, a multi-point sensor array module can be used to achieve a more comprehensive monitoring of the stress distribution on the forklift arm. The module includes multiple stress sensors arranged on the forklift arm, which can monitor the stress changes of different parts of the forklift arm in real time, thereby obtaining more comprehensive stress data. The multi-point sensor array module can monitor the stress changes of various parts of the forklift arm in real time, and promptly detect uneven load distribution or overload conditions to avoid safety accidents caused by them. By arranging multiple stress sensors on the forklift arm, stress changes in different parts can be measured more accurately, and more accurate data can be obtained, providing engineers with more accurate reference information. The use of a multi-point sensor array module can comprehensively monitor the load distribution of the forklift, realize real-time monitoring, accurate measurement and data analysis, and improve the safety and work efficiency of the forklift.

[0041] Optionally, the sensor collected data also includes at least one of the following: pressure data, acceleration data and inclination data; the multi-point sensor array module also includes at least one of the following: a pressure sensor module, including at least one pressure sensor arranged on the forklift arm, the pressure sensor module is used to monitor the pressure changes on the forklift arm and obtain pressure data; an acceleration sensor module, including an acceleration sensor arranged on the forklift, the acceleration sensor module is used to monitor the acceleration changes of the forklift during operation and obtain acceleration data; an inclination sensor module, including an inclination sensor arranged on the forklift, the inclination sensor module is used to monitor the inclination changes of the forklift during operation and obtain inclination data.

[0042] In an optional embodiment, in the monitoring system of forklift load distribution, the multi-point sensor array module also includes a pressure sensor module, an acceleration sensor module and an inclination sensor module. Among them, the pressure sensor module can monitor the pressure change of the forklift arm through the pressure sensor arranged on the forklift arm, so as to obtain accurate pressure data. By real-time monitoring of the pressure change of the forklift arm, it is possible to timely discover the situation that the forklift load is too heavy or unbalanced, and avoid damage or accidents caused by excessive load. The acceleration sensor module monitors the acceleration change of the forklift during operation through the acceleration sensor arranged on the forklift to obtain accurate acceleration data. By real-time monitoring of the acceleration change of the forklift, the dynamic situation of the forklift during operation can be understood, which helps to improve the operation efficiency and safety of the forklift. The inclination sensor module monitors the inclination change of the forklift during operation through the inclination sensor arranged on the forklift to obtain accurate inclination data. By real-time monitoring of the inclination change of the forklift, it is possible to ensure that the forklift remains stable during operation, and avoid the dumping of loaded items or instability of the forklift due to unstable inclination. Through the monitoring of various sensor modules, accurate data can be obtained to ensure accurate assessment of the forklift load distribution. By monitoring the forklift load distribution, the forklift's working efficiency can be effectively improved and the reduction in transportation efficiency caused by unbalanced load can be reduced. By monitoring the forklift's inclination and acceleration changes, the forklift's operating safety can be improved and accidents caused by unstable conditions can be avoided.

[0043] Optionally, the load distribution determination module includes: a data acquisition and edge computing module, which is used to collect sensor data in parallel and perform edge computing on the sensor data to obtain an initial load distribution result, wherein the edge computing at least includes a filtering operation; a data fusion and processing module, which is connected to the data acquisition and edge computing module, and the data fusion and processing module is used to fuse the initial load distribution results and obtain the load distribution results.

[0044] In an optional embodiment, in the monitoring system of forklift load distribution, the data acquisition and edge computing module in the load distribution determination module is responsible for collecting sensor data and performing real-time edge computing on these data to obtain the initial load distribution result. The edge computing process includes at least filtering operations, which can effectively remove noise and interference in the data and improve the accuracy and reliability of the analysis. The data fusion and processing module can be responsible for fusing the data collected by different sensors to obtain the final load distribution result. Edge computing and data fusion processing can improve the analysis accuracy of the system, reduce errors, and ensure that the monitoring results of forklift load distribution are accurate and reliable. Filtering operations and data fusion processing can improve the stability of the system, reduce the impact of interference and noise on the monitoring results, and ensure that the system can operate normally in various environments. The system can adaptively adjust the parameters of data acquisition and processing according to actual conditions, adapt to the monitoring needs under different working conditions, and improve the flexibility and applicability of the system.

[0045] Optionally, the load distribution result includes at least: a load distribution diagram and a real-time center of gravity position; a data acquisition and edge computing module, including: a load distribution diagram calculation module, used to determine the load distribution diagram based on the initial load distribution result, wherein the load distribution diagram is used to characterize the load distribution conditions at different positions on the forklift arm of the forklift; a center of gravity position calculation module, used to determine the real-time center of gravity position based on the initial load distribution result, wherein the real-time center of gravity position is used to characterize the real-time center of gravity position of the forklift.

[0046] In an optional embodiment, in the monitoring system of forklift load distribution, the data acquisition and edge computing module collects the load data on the forklift arm in real time through sensors, and performs edge computing, that is, performs data processing and analysis locally, which reduces the time of data transmission and processing, and improves the real-time performance and response speed of the system. The load distribution diagram calculation module uses the collected load data to calculate the load distribution diagram, that is, the load distribution status at different positions on the forklift arm through an algorithm. The load distribution diagram can intuitively display the distribution of items carried by the forklift, helping the operator to better understand the load status of the forklift, thereby avoiding safety hazards caused by overloading or uneven loads. The center of gravity position calculation module can determine the real-time center of gravity position based on the initial load distribution results. The real-time center of gravity position can accurately reflect the center of gravity position of the forklift, helping the operator to better control the balance state of the forklift, and avoid dangerous situations such as rollover caused by center of gravity offset. Through the above settings, the forklift load distribution monitoring system can realize accurate monitoring and real-time feedback of load distribution and center of gravity position, help operators to promptly discover and solve potential safety problems, ensure the safety of forklifts and personnel, effectively improve the working efficiency and safety of forklifts, and provide more reliable protection for industrial production.

[0047] Optionally, the load distribution response module includes: a deep learning load prediction module, which is used to predict the load distribution change of the forklift within a preset future time period based on the load distribution results, and obtain the load change trend; an audio-visual display module, which is connected to the deep learning load prediction module, and the audio-visual display module is used to interact with the user with audio-visual signals based on the load distribution results and the load change trend, wherein the audio-visual display module includes at least a display screen and an alarm; a response execution module, which is connected to the deep learning load prediction module, and the response execution module is used to adjust the operating state of the forklift based on the load distribution results and the load change trend, wherein the response execution module is at least used to adjust the operating state of the track wheels on the forklift.

[0048] In an optional embodiment, in the monitoring system of forklift load distribution, the load distribution response module mainly includes a deep learning load prediction module, an acoustic and light display module and a response execution module. The deep learning load prediction module can predict the load distribution change trend of the forklift in the future by analyzing and learning the current load distribution of the forklift, which can help users understand the working status of the forklift in advance and help make corresponding adjustments and decisions. The acoustic and light display module is connected to the deep learning load prediction module, and can interact with the user through sound and light signals, presenting the predicted load change trend to the user in an intuitive way. Devices such as display screens and alarms can promptly transmit information to the user to remind the user to pay attention to the load distribution of the forklift. The response execution module is connected to the deep learning load prediction module, and can adjust the operating state of the forklift according to the predicted load change trend, such as adjusting the operating state of the track wheels on the forklift to ensure the stability and safety of the forklift. The forklift load distribution monitoring system set up in this way can help users monitor the load distribution of the forklift in real time, predict future load change trends, and make timely response adjustments, thereby improving the working efficiency and safety of the forklift, reducing the possibility of accidents, and providing users with a more convenient and safe working environment.

[0049] Figure 2 is a flow chart of a method for monitoring forklift load distribution according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0050] Step S202 : Based on the multi-point sensor array module in the forklift load distribution monitoring system, the load distribution change on the forklift is monitored to obtain sensor collection data.

[0051] The multi-point sensor array module includes a plurality of sensors arranged on the forklift.

[0052] Step S204: Calculate and process the data collected by the sensor based on the load distribution determination module to obtain the load distribution result of the forklift.

[0053] Wherein, the load distribution determination module is connected with the multi-point sensor array module.

[0054] Step S206, obtaining a load distribution execution result based on the load distribution response module responding to the load distribution result.

[0055] The load distribution response module is connected to the load distribution determination module, and the load distribution execution result is used to indicate interaction with the user through sound and light signals based on the load distribution result, and / or to adjust the operation state of the forklift based on the load distribution result.

[0056] Figure 3 is a schematic diagram of a forklift equipped with a load distribution monitoring system according to an embodiment of the present invention. Figure 3 As shown, it includes, 1. forklift body; 2. forklift arm; 3. multi-point sensor array module; 304. inclination sensor; 4. data acquisition and edge computing module; 5. data fusion and processing module; 6. sound and light display module; 7. adaptive calibration and machine learning module; 9. track wheel.

[0057] Figure 4 is a schematic diagram of a forklift equipped with a load distribution monitoring system from another optional perspective according to an embodiment of the present invention, such as Figure 3 As shown, it includes, 1. forklift body; 2. forklift arm; 304. inclination sensor; 6. sound and light display module; 601. fixing plate; 8. Internet of Things expansion and cloud data analysis module; 9. crawler wheel.

[0058] Figure 5 is a schematic diagram of a forklift arm structure of a forklift equipped with a load distribution monitoring system according to an embodiment of the present invention. Figure 3 As shown, it includes: 1. forklift body; 301. stress sensor; 302. pressure sensor; 303. acceleration sensor.

[0059] Figure 6 is a schematic diagram of an optional Internet of Things expansion and cloud data analysis module on a forklift equipped with a load distribution monitoring system according to an embodiment of the present invention, such as Figure 3 As shown, it includes, 1. forklift body; 4. data acquisition and edge computing module; 5. data fusion and processing module; 602. display screen; 603. alarm; 7. adaptive calibration and machine learning module; 8. Internet of Things expansion and cloud data analysis module.

[0060] Figure 7 is a schematic diagram of an optional sound and light display module on a forklift equipped with a load distribution monitoring system according to an embodiment of the present invention, such as Figure 3As shown, it includes, 601, a fixing plate; 602, a display screen; 603, an alarm; 8, an Internet of Things expansion and cloud data analysis module.

[0061] Figure 8 is a schematic diagram of an optional load distribution monitoring system according to an embodiment of the present invention, such as Figure 8 As shown, it includes: intelligent sensing and display system, multi-point sensor array module, data acquisition and edge computing module, data fusion and processing module, intelligent display and early warning module, adaptive calibration and machine learning module, Internet of Things expansion and cloud data analysis module.

[0062] The above-mentioned intelligent display and warning module can be an audio-visual display module.

[0063] The technical solution proposed in the present application is described below in combination with an optional application scenario. The present application provides an intelligent sensing and display system for forklift load distribution, which includes: a forklift body; a forklift arm, which is connected to the forklift body; a multi-point sensor array module, which includes multiple sensors arranged on the forklift arm, and is used to monitor the load distribution on the forklift arm; a data acquisition and edge computing module, which is used to collect sensor data from the multi-point sensor array module in real time and perform preliminary calculation and processing; a data fusion and processing module, which is used to fuse the data processed by the data acquisition and edge computing module and calculate the load distribution; an intelligent display and early warning module, which is used to display real-time load distribution information according to the calculation results of the data fusion and processing module, and provide an early warning when the load is abnormal; an adaptive calibration and machine learning module, which is used to automatically calibrate the sensors of the multi-point sensor array module according to the operation data, and optimize the system's early warning threshold and operation suggestions; an Internet of Things extension and cloud data analysis module, which is used for remote monitoring and data analysis, and to generate equipment maintenance reports and operation optimization suggestions; crawler wheels, which are installed at the bottom of the forklift body, are used to provide stable ground movement support.

[0064] The multi-point sensor array module includes: multiple stress sensors arranged inside the forklift arm, which are used to monitor the stress at multiple parts of the forklift arm; a pressure sensor arranged at the root of the forklift arm, which is used to detect the overall pressure change of the forklift arm; an acceleration sensor arranged in the forklift body, which is used to monitor the load changes during dynamic operation, especially during acceleration, steering and braking.

[0065] Specifically, multiple stress sensors are arranged inside the forklift arm. These stress sensors are precisely arranged at different positions of the forkarm to monitor the stress distribution of key parts of the forkarm in real time, especially under conditions with large load changes, to provide all-round monitoring of different stress areas; the pressure sensor arranged at the root of the forklift arm is positioned after optimization and can accurately detect the overall pressure change of the forkarm, thereby providing feedback on load uniformity and overall stress conditions; the acceleration sensor arranged inside the forklift body is used to sense the load changes of the forklift during dynamic operation, especially under conditions such as acceleration, steering and braking. By detecting the acceleration changes of the vehicle body, the transient change characteristics of the load distribution are further analyzed to ensure the safety and stability of the operation.

[0066] The data acquisition and edge computing module includes: a data collector, which is used to collect data from multiple sensors in parallel; and an edge computing unit, which is used to filter and perform preliminary data processing on the data collected by the data collector.

[0067] Specifically, the data collector is used to efficiently and in parallel collect data from multiple sensors, ensuring that multi-dimensional data from sensors such as stress, pressure, acceleration, etc. can be obtained in real time and stably under complex working conditions, and has anti-interference capabilities to maintain the accuracy of data collection in a noisy environment; the edge computing unit is used to perform advanced filtering on the data collected by the data collector to remove irrelevant noise and interference signals, and perform preliminary calculations and analysis on the data, including real-time stress distribution calculations and abnormal data detection, to ensure the integrity and accuracy of the data, and reduce the computational burden of back-end processing, thereby achieving rapid response and preliminary intelligent analysis of the system.

[0068] The data fusion and processing module can use the Kalman filter algorithm to fuse the data from the stress sensor, acceleration sensor, and tilt sensor of the multi-point sensor array module to generate a load distribution diagram of the forklift arm.

[0069] Specifically, the data fusion and processing module uses the Kalman filter algorithm to perform high-precision fusion processing on the multi-source data obtained from the stress sensor, acceleration sensor and tilt sensor of the multi-point sensor array module. Through dynamic estimation and updating, the algorithm can not only effectively filter the noise in the sensor data, but also continuously optimize the accuracy of the data under real-time changing working conditions. Through the intelligent fusion of these sensor data, the system can quickly generate a real-time load distribution map of the forklift arm, provide accurate load distribution analysis, and ensure a comprehensive grasp of the load status during forklift operation, especially under complex dynamic operations, to ensure the timeliness and stability of the data.

[0070] The intelligent display and warning module includes: a fixed plate for installing the display unit and the warning unit, which is fixed on the forklift body; a display screen for displaying the load distribution diagram and real-time center of gravity position of the forklift arm; an alarm, including a multi-level warning function, which triggers different levels of sound and light alarm prompts when the load reaches different thresholds.

[0071] Specifically, the fixing plate is designed to stably install the display unit and the warning unit, and is fixed on the forklift body to ensure that the system has high reliability and stability during operation, and can effectively support the operation of the display and warning equipment even under harsh working conditions; the display screen is used to intuitively and dynamically present the load distribution diagram and real-time center of gravity position of the forklift arm. The display screen provides a high-resolution graphical interface, allowing the operator to accurately grasp the current load conditions and center of gravity changes, thereby optimizing operations; the alarm has a multi-level warning function. Through the combination of sound and visual signals, when the load is detected to reach different preset thresholds, the system will automatically trigger the corresponding level of sound and light alarm prompts to ensure that the operator can take corrective measures in time to prevent potential safety risks caused by overloading or unbalanced loads.

[0072] The alarm's multi-level warnings include: when the load exceeds 80% of the preset threshold, a yellow warning prompt is triggered; when the load exceeds 90% of the preset threshold, sound and light signal prompts are triggered; when the load exceeds the preset safety value, the system automatically limits the forklift's operating speed or stops the forklift's operation.

[0073] Specifically, when the load exceeds 80% of the preset threshold, the system will automatically trigger a yellow warning prompt, reminding the operator that the load is close to the safety limit, thereby attracting attention and making operational adjustments; when the load exceeds 90% of the preset threshold, the system will issue sound and light signal prompts in a more prominent way to ensure that the operator can quickly identify potential overload risks and immediately take necessary preventive measures; when the load exceeds the preset safety critical value, the system will automatically take protective measures to limit the operating speed of the forklift or directly stop the operation of the forklift to avoid safety accidents caused by overload or unbalanced load, thereby fully ensuring the safety and stability of the operation.

[0074] The adaptive calibration and machine learning module is used to automatically calibrate the sensor baseline value at each startup to eliminate drift caused by long-term use; by learning from historical operation data, the load warning threshold is optimized to reduce false alarms and missed alarms.

[0075] Specifically, the sensor reference value calibration is automatically performed each time the system is started to ensure that the accuracy of stress, pressure and acceleration sensors is always maintained at a good state, eliminating signal drift caused by long-term use of sensors or environmental changes, thereby ensuring data stability and accuracy; at the same time, the module also continuously optimizes the load warning threshold through deep learning of historical operation data, dynamically adjusts the system's warning strategy to adapt to different working conditions, significantly reduces the possibility of false alarms and missed alarms, and improves the reliability of warnings and the safety of operations. Through the adaptive ability of machine learning, the system can continuously accumulate operating experience, gradually improve its intelligence level, and make forklift operations more efficient and safe.

[0076] The Internet of Things extension and cloud data analysis module can be connected to the cloud platform through a wireless network to achieve remote data monitoring and operation analysis. The Internet of Things extension and cloud data analysis module is fixed on a fixed plate.

[0077] Specifically, the IoT extension and cloud data analysis module is seamlessly connected to the cloud platform through a wireless network, supporting real-time remote data monitoring and in-depth operation analysis, ensuring that managers can access the real-time operation data and load status of the forklift through the cloud platform at any location. This module can not only monitor the current operation status, but also provide comprehensive equipment health reports and operation optimization suggestions through big data analysis, further improving the operation efficiency and safety of the forklift. In addition, the IoT extension and cloud data analysis module is firmly installed on the fixed plate to ensure its high stability in long-term operation, and the structural design of the module is easy to maintain and upgrade to meet the expansion needs of more functions in the future.

[0078] The IoT extension and cloud data analysis module can generate equipment health reports and preventive maintenance recommendations, and generate operation optimization recommendations based on historical analysis of load data from multiple forklifts.

[0079] Specifically, the IoT extension and cloud data analysis module can automatically generate equipment health reports and evaluate the working status of the forklift and the performance of key components in real time by performing comprehensive cloud processing and analysis on the received forklift operation data. Based on the long-term accumulated multi-forklift load data, the module can deeply analyze various trends and changes in equipment use, and then generate preventive maintenance suggestions to help operators and managers predict potential failure risks in advance and reduce unexpected downtime. At the same time, the module can also generate optimization suggestions for different operating scenarios through the mining and comparison of historical data, helping to improve overall operational efficiency and safety, and achieving continuous improvement and intelligent management of operating processes.

[0080] The track wheels are designed to be anti-slip, suitable for stable driving in complex terrain environments, and are linked with the system's intelligent display and early warning modules to ensure timely adjustment of the forklift's operating mode in dangerous environments.

[0081] The sensor layout and installation steps in this application, multi-point stress sensor layout, sensor type selection, high-precision strain gauge sensors can be used as the main stress monitoring equipment to measure stress changes in different parts of the fork arm. Additional sensor types include pressure sensors, acceleration sensors and tilt sensors. The layout position can install stress sensors at the root of the fork arm to detect the stress condition of the load-bearing root. Stress sensors are laid out in the middle of the fork arm to monitor the uniformity of the overall force of the fork arm. Stress sensors are installed at the front end of the fork arm to monitor whether the front end load is too heavy to prevent the front end from tipping over. Optionally installed on the side wall, the side wall-mounted sensor is used to monitor the lateral force of the fork arm. Installation method, the stress sensor can be embedded in the fork arm or installed on the side wall of the fork arm, and the appropriate installation method is selected according to the forklift use environment. Other sensor installations, pressure sensors can be installed at the root of the fork arm or on the body of the forklift to detect overall pressure changes. Acceleration sensors are installed on the body structure to monitor the impact of dynamic loads. Tilt sensors are installed on the forklift chassis or fork arm to monitor the tilt angle during forklift operation to prevent instability caused by center of gravity shift.

[0082] Data collection and transmission steps: Multi-channel data collector: Use a collector with multiple channels to collect stress, pressure, acceleration and tilt data at the same time. Data collection frequency: The system default collection frequency is set to 1kHz. When encountering dynamic operation scenarios, such as sudden braking or turning, the collection frequency can be automatically increased to 5kHz to ensure the timeliness and accuracy of the collection. Noise filtering: Perform signal preprocessing at the data collection end, including filtering and noise elimination, to ensure the reliability of sensor data.

[0083] The data transmission method can be wired bus (CAN) or wireless communication, such as low-power Bluetooth data transmission. Wireless transmission is suitable for flexible installation requirements, but attention should be paid to signal interference and data stability. Edge computing, the edge computing module is built into the data collector end to complete basic data preprocessing, such as preliminary filtering and anomaly detection, to reduce the burden on the central processing unit.

[0084] Data fusion and processing steps,Multi-sensor fusion can integrate the data from stress sensors, pressure sensors, acceleration sensors and tilt sensors through fusion algorithms.Weighted average and Kalman filter algorithms are used to fuse data from different sensors to eliminate noise and improve the detection accuracy of load distribution.

[0085] Load distribution calculation, real-time load calculation can be based on the fork arm geometry and sensor data, the system calculates the real-time load of each point of the fork arm. The stress data is used to deduce the stress distribution of each point, and the distribution matrix of the load on the fork arm is generated to reflect the load status of each point.

[0086] Center of gravity position calculation, center of gravity analysis can be done through the load data of each point, the system calculates the load center of gravity in real time, and the calculation formula of the center of gravity position can be as follows:

[0087]

[0088] Among them, W i is the weight of each point, X i ,Y i is the coordinate of the sensor, and Ftotal is the total load.

[0089] Deep learning load prediction, dynamic prediction model, the system uses the long short-term memory network (LSTM) deep learning model to predict the changes in load distribution in the short term based on historical load data, especially in dynamic operation scenarios such as acceleration and turning, predict future load trends, and provide early warnings.

[0090] Intelligent display and warning steps, real-time display, graphical display interface, the system installs a high-definition display screen in the cockpit to display the real-time load distribution diagram. The three-dimensional (3D) model of the fork arm reflects the load status of different parts through color gradient, red indicates overload, and green indicates safety. The center of gravity position display includes real-time display of the fork arm center of gravity position on the display screen to help the operator determine whether the load is balanced and avoid tipping caused by uneven load.

[0091] The multi-level warning mechanism can include: Level 1 warning, which can be mild: when the load approaches 80% of the preset threshold, the system reminds the operator through a yellow prompt and sound. Level 2 warning, which can be moderate: when the load approaches 90% of the threshold, the system issues a stronger sound and light signal reminder and advises the operator to adjust the cargo. Level 3 warning, which can be severe: when the load exceeds the threshold or the center of gravity is seriously offset, the system automatically limits the forklift function and may force deceleration or stop operation to prevent accidents.

[0092] Adaptive calibration and machine learning steps can include dynamic self-calibration and real-time calibration. The system automatically calibrates sensor data at each startup to ensure the accuracy of sensor signals, especially automatically adjusting the baseline value when no-load status is detected to eliminate sensor drift. Machine learning optimization and historical data learning. The system records and analyzes operation history data through machine learning modules, continuously optimizes warning thresholds and load prediction algorithms, reduces false alarms and missed alarms, and provides more intelligent operation suggestions.

[0093] IoT remote monitoring and data management steps: real-time remote monitoring, data cloud storage. The system uploads real-time load data and operation status to the cloud. Managers can monitor the working status of forklifts at any time through remote devices such as mobile phones and tablets.

[0094] Cloud big data analysis, equipment health analysis, the cloud big data analysis module can generate equipment health status reports based on the historical data of multiple forklifts, predict the loss of fork arms or sensors, and provide preventive maintenance suggestions. Operation optimization suggestions, based on historical load data and operation modes, the cloud platform generates operation optimization solutions to improve the overall efficiency and safety of forklifts.

[0095] The present application relates to the technical field of forklifts, and discloses an intelligent sensing and display system for forklift load distribution, including: a forklift body; a forklift arm, the forklift arm is connected to the forklift body; a multi-point sensor array module, including a plurality of sensors arranged on the forklift arm, for monitoring the load distribution on the forklift arm; a data acquisition and edge computing module, for real-time acquisition of sensor data from the multi-point sensor array module, and preliminary calculation and processing; a data fusion and processing module, for fusion and calculation of load distribution of data acquired and processed by the edge computing module. Through the multi-point sensor array module and high-precision data fusion processing, the system can monitor the load distribution of the forklift fork arm in real time and accurately, and with the intelligent display and early warning module, the operator can grasp the dynamic changes of the load and the position of the center of gravity at any time.

[0096] The present application provides an intelligent sensing and display system for forklift load distribution. It has the following beneficial effects: Through multi-point sensor array modules and high-precision data fusion processing, the present application can monitor the load distribution of forklift forks in real time and accurately. With the intelligent display and early warning module, the operator can grasp the dynamic changes of the load and the position of the center of gravity at any time. At the same time, the multi-level early warning mechanism will issue early warnings or take restrictive measures in time when the load approaches or exceeds the safety threshold, effectively preventing safety hazards such as overloading and center of gravity shift, thereby significantly improving the safety and work efficiency of operations.

[0097] The adaptive calibration and machine learning module built into the system of this application solves the problem of sensor drift caused by long-term use by automatically calibrating the baseline value of the sensor, ensuring the continuous accuracy of the sensor data. At the same time, the system conducts deep learning based on historical operation data, intelligently optimizes load warning thresholds and operation strategies, and continuously reduces the possibility of false alarms and missed alarms, ensuring the efficient operation of the system under various working conditions, making the operation process more intelligent and personalized.

[0098] Through the expansion of the Internet of Things and the cloud data analysis module, this application not only realizes the remote monitoring of the forklift's real-time operating data, but also can provide equipment health reports and preventive maintenance recommendations based on the big data analysis of the cloud platform. This historical data analysis capability based on multiple forklifts enables managers to identify potential equipment failures in advance, optimize maintenance plans, and reduce unplanned downtime. At the same time, it provides optimization suggestions for different operating scenarios, further improving the operational efficiency and work safety of the forklift.

[0099] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method in each embodiment of the present application is executed when the program is running.

[0100] The above-mentioned memory may refer to a device inside a computer for storing data and programs, and may include memory, hard disk, etc., wherein the memory may be used to temporarily store running programs and data, the hard disk may be used to store programs and data for a long time, and the memory may be used to enable the computer to read and write data, and execute programs; the above-mentioned processor may be responsible for executing instructions in computer programs and performing data processing, and may be responsible for controlling and executing various operations, including arithmetic operations, logical operations, data transmission, etc.

[0101] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in each embodiment of the present application.

[0102] The above-mentioned computer storage medium may refer to a medium in a computer memory used to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, laser disks, etc.; the stored program included in the computer-readable storage medium may be a set of instructions that can be recognized and executed by a computer, running on an electronic computer, and is an information tool that meets certain needs of people.

[0103] The embodiments of the present application also provide a computer program product, including a computer program, which implements the methods in the various embodiments of the present application when executed by a processor.

[0104] The above-mentioned computer program product may refer to a software program that has been written, tested and released and can be run on a computer or other device. The computer program product may include an application, an operating system, tool software, etc., which is used to implement specific functions or solve specific problems.

[0105] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present application is implemented.

[0106] The above-mentioned non-volatile computer-readable storage medium may refer to a medium for storing data. The non-volatile computer-readable storage medium can keep the data from being lost when the power is off, and can be used to store long-term data, such as operating systems, applications, and user files. The non-volatile storage medium may include hard disk drives, solid-state drives, optical disks, and flash memory storage devices, etc.

[0107] The embodiments of the present application also provide a computer program, which implements the methods in the above-mentioned embodiments of the present application when executed by a processor.

[0108] The above-mentioned computer program may refer to a collection of instructions used to tell a computer to perform a specific task or operation. A computer program may be written by a programmer using a specific programming language and may include algorithms, data structures, logic, and control flows. A computer program may be used for a variety of purposes, including application software, operating systems, and the like.

[0109] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0111] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0112] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0113] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0114] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A forklift load distribution monitoring system, characterized in that: include: A multi-point sensor array module, comprising a plurality of sensors arranged on the forklift, wherein the multi-point sensor array module is used to monitor changes in load distribution on the forklift and obtain sensor collection data; A load distribution determination module connected to the multi-point sensor array module, the load distribution determination module is used to calculate and process the sensor collected data to obtain the load distribution result of the forklift; A load distribution response module is connected to the load distribution determination module, and the load distribution response module is used to respond to the load distribution result to obtain a load distribution execution result, wherein the load distribution execution result is used to indicate interaction with a user based on the load distribution result with an acoustic and optical signal, and / or to adjust the operating state of the forklift based on the load distribution result.

2. The forklift load distribution monitoring system according to claim 1, characterized in that: The sensor acquisition data at least includes stress data; the multi-point sensor array module includes: The stress sensor module comprises a plurality of stress sensors arranged on the forklift arm of the forklift, and the stress sensor module is used to monitor stress changes at different positions on the forklift arm to obtain the stress data.

3. The forklift load distribution monitoring system according to claim 2, characterized in that: The sensor acquisition data also includes at least one of the following: pressure data, acceleration data and inclination data; the multi-point sensor array module also includes at least one of the following: A pressure sensor module, comprising at least one pressure sensor arranged on the forklift arm, wherein the pressure sensor module is used to monitor the pressure change on the forklift arm and obtain the pressure data; An acceleration sensor module, comprising an acceleration sensor arranged on the forklift, wherein the acceleration sensor module is used to monitor the acceleration change of the forklift during operation to obtain the acceleration data; The inclination sensor module comprises an inclination sensor arranged on the forklift, and the inclination sensor module is used to monitor the inclination change of the forklift during operation to obtain the inclination data.

4. The forklift load distribution monitoring system according to claim 1, characterized in that: The load distribution determination module comprises: A data acquisition and edge computing module, used for acquiring the sensor data in parallel and performing edge computing on the sensor data to obtain an initial load distribution result, wherein the edge computing at least includes a filtering operation; A data fusion and processing module is connected to the data acquisition and edge computing module, and the data fusion and processing module is used to fuse the initial load distribution result and obtain the load distribution result.

5. The forklift load distribution monitoring system according to claim 4, characterized in that: The load distribution result includes at least: a load distribution diagram and a real-time center of gravity position; the data acquisition and edge computing module includes: A load distribution diagram calculation module, used to determine the load distribution diagram based on the initial load distribution result, wherein the load distribution diagram is used to characterize the load distribution conditions at different positions on the forklift arm of the forklift; A center of gravity position calculation module is used to determine the real-time center of gravity position based on the initial load distribution result, wherein the real-time center of gravity position is used to represent the real-time center of gravity position of the forklift.

6. The forklift load distribution monitoring system according to claim 1, characterized in that: The load distribution response module comprises: A deep learning load prediction module, used to predict the load distribution change of the forklift within a preset future time period based on the load distribution result, and obtain the load change trend; An acoustic and optical display module, connected to the deep learning load prediction module, and used to interact with the user with the acoustic and optical signals based on the load distribution result and the load change trend, wherein the acoustic and optical display module at least includes a display screen and an alarm; A response execution module is connected to the deep learning load prediction module, and the response execution module is used to adjust the operating state of the forklift based on the load distribution result and the load change trend, wherein the response execution module is at least used to adjust the operating state of the track wheels on the forklift.

7. A method for monitoring forklift load distribution, characterized in that: include: A multi-point sensor array module in a monitoring system based on forklift load distribution monitors changes in load distribution on the forklift to obtain sensor collection data, wherein the multi-point sensor array module includes a plurality of sensors arranged on the forklift; The sensor collected data is calculated and processed based on a load distribution determination module to obtain a load distribution result of the forklift, wherein the load distribution determination module is connected to the multi-point sensor array module; Based on the load distribution response module responding to the load distribution result, a load distribution execution result is obtained, wherein the load distribution response module is connected to the load distribution determination module, and the load distribution execution result is used to indicate interaction with the user based on the load distribution result with sound and light signals, and / or adjust the operating state of the forklift based on the load distribution result.

8. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program executes the forklift load distribution monitoring method described in claim 7 when running.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the storage medium is located is controlled to execute the forklift load distribution monitoring method described in claim 7.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method for monitoring the load distribution of a forklift according to claim 7.

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