A highly stable forklift lifting method and system

Abstract

A highly stable forklift lifting method and system is disclosed, relating to the field of logistics handling. This method, applied to a forklift control system, includes: acquiring real-time sensor data of the forklift; inputting the real-time sensor data into multiple physical field correlation models to obtain multiple balance indices, wherein each physical field correlation model corresponds to one balance index; comparing and analyzing the multiple balance indices to determine whether there are any abnormal balance indices; if so, correcting the real-time sensor data of the abnormal balance index to obtain corrected sensor data; and sending the corrected sensor data to a self-adjusting system to improve the accuracy of the self-adjusting system in adjusting the lifting method. Implementing the technical solution provided in this application solves the problem of forklift swaying and imbalance caused by errors in sensor detection accuracy.

Description

Technical Field

[0001] This application relates to the technical field of logistics handling, specifically to a highly stable forklift lifting method and system. Background Art

[0002] When moving goods in a warehouse, forklifts are often used as handling tools to reduce the burden on manpower. Commonly used forklifts include stacker forklifts and boom forklifts. Stacker forklifts are suitable for handling regular goods, while boom forklifts are suitable for handling irregular goods.

[0003] Currently, jib cranes use a flexible, rotating boom to grip goods during transport. The goods naturally sway in the air, placing high demands on the jib crane's stability. To maintain this stability, various types of sensors are typically installed to monitor the forces acting on the goods and the forklift itself in real time. When a significant force shift occurs, the forklift automatically adjusts its lifting method to prevent tipping.

[0004] However, because this type of boom crane forklift is highly dependent on real-time sensor data, when the detection accuracy of a certain sensor has a large error, the adjustment effect of the forklift's self-adjustment system is not ideal, which causes the forklift to shake or become unbalanced when handling goods. Summary of the Invention

[0005] To address the problem of forklifts swaying and becoming unbalanced due to errors in sensor detection accuracy, this application provides a highly stable forklift lifting method and system.

[0006] In a first aspect, this application provides a highly stable forklift lifting method, applied to a forklift control system, the method comprising:

[0007] Acquire real-time sensor data from the forklift;

[0008] The real-time sensor data is input into multiple physical field correlation models to obtain multiple balance indices, wherein one physical field correlation model corresponds to one balance index;

[0009] A comparative analysis of multiple balance indices is conducted to determine whether there are any abnormal balance indices among the multiple balance indices.

[0010] If it exists, the real-time sensor data of the abnormal balance index is corrected to obtain corrected sensor data;

[0011] The corrected sensor data is sent to the self-adjusting system to improve the accuracy of the self-adjusting system in adjusting the lifting method.

[0012] Optionally, the real-time sensor data consists of various types of sensor data. Before inputting the real-time sensor data into multiple physical field correlation models to obtain multiple equilibrium indices, the method further includes:

[0013] Calculate the data coupling degree between the various types of sensor data;

[0014] The data coupling between various types of sensor data is used to construct multiple physical field correlation models, including force field-temperature correlation model, force field-sound field correlation model and temperature-sound field correlation model.

[0015] Optionally, the step of inputting the real-time sensor data into multiple physical field correlation models to obtain multiple equilibrium indices specifically involves:

[0016] Based on the data processing type corresponding to each of the multiple physical field correlation models, the real-time sensor data is divided into multiple data groups, wherein each data group corresponds to one physical field correlation model.

[0017] Based on the historical data corresponding to each of the multiple data sets, determine the input data set and output data set corresponding to each of the multiple physical field correlation models;

[0018] Multiple physical field correlation models process their respective input data sets to obtain multiple prediction data sets, wherein the data in the prediction data sets and the data in the output data sets are data with the same attribute;

[0019] Residual analysis is performed on multiple output data sets and their corresponding prediction data sets to obtain the balance index of multiple physical field correlation models.

[0020] Optionally, a comparative analysis is performed on multiple balance indices to determine whether there are any abnormal balance indices among them, specifically:

[0021] Obtain historical data for multiple balance indices;

[0022] Trend analysis is performed on historical data of multiple balance indices to obtain multiple trend indicators, including variance, mean, and standard deviation.

[0023] Analyze the influence coefficients of the multiple balance indices on their respective multiple trend indicators;

[0024] A preset abnormal balance index determination condition is adopted to determine whether there is an abnormal balance index among the multiple balance indices. The preset abnormal balance index determination condition is that the influence coefficient of the balance index is greater than or equal to a preset influence coefficient threshold.

[0025] Optionally, the step of correcting the real-time sensor data of the abnormal balance index to obtain corrected sensor data specifically includes:

[0026] The output data set corresponding to the abnormal balance index is used as the input data of the physical field correlation model corresponding to the abnormal balance index to calculate the first data set.

[0027] The ratio of the first data set to the input data set corresponding to the abnormal balance index is calculated to obtain a ratio sequence.

[0028] Based on the ratio sequence, the output data group corresponding to the abnormal balance index is scaled and adjusted to obtain the second data group;

[0029] The data in the second data set is used as the corrected sensor data.

[0030] Optionally, sending the corrected sensor data to the self-adjusting system further includes:

[0031] Send fault information to the fault response system to remind operators to perform maintenance.

[0032] Secondly, this application provides a highly stable forklift lifting system, which is a forklift control system. The forklift control system includes an acquisition module, a processing module, and a sending module, wherein:

[0033] The acquisition module is used to acquire real-time sensor data of the forklift;

[0034] The processing module is used to input the real-time sensor data into multiple physical field correlation models to obtain multiple balance indices, wherein one physical field correlation model corresponds to one balance index; to perform comparative analysis on the multiple balance indices to determine whether there is an abnormal balance index among the multiple balance indices; if there is, to correct the real-time sensor data of the abnormal balance index to obtain corrected sensor data.

[0035] The sending module is used to send the correction sensor data to the self-adjusting system to improve the accuracy of the self-adjusting system in adjusting the lifting mode.

[0036] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0037] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0038] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0039] 1. By acquiring real-time sensor data from the forklift, a comprehensive understanding of its various states during lifting operations is achieved. This real-time sensor data is then input into multiple physical field correlation models to obtain multiple balance indices. These models comprehensively consider the interrelationships between different physical fields such as force, temperature, and sound fields, quantitatively evaluating the forklift's balance state from multiple dimensions. The multiple balance indices are then compared and analyzed to determine if any abnormal balance indices exist. This method utilizes the mutual verification between multiple balance indices to detect potential anomalies. If an abnormal balance index is found, its corresponding real-time sensor data is corrected to obtain corrected sensor data. This effectively solves the problem of inaccurate adjustment caused by sensor detection errors, improving data reliability. Finally, the corrected sensor data is sent to the self-adjusting system, enabling it to adjust the lifting method based on accurate data. This ensures the stability of the forklift during cargo handling, reduces the risk of forklift swaying or imbalance, decreases the probability of safety accidents, and improves the efficiency and safety of warehouse cargo handling operations.

[0040] 2. By acquiring historical data for multiple balance indices, a rich data foundation is provided for subsequent analysis. Trend analysis of the historical data yields trend indicators such as variance, mean, and standard deviation. These indicators clearly demonstrate the fluctuation patterns and central tendency of the balance indices. Further analysis of the influence coefficients of multiple balance indices on their corresponding trend indicators provides a deeper understanding of the role and degree of influence of each balance index within the overall trend. Then, comparing the differences in the influence coefficients of multiple balance indices reveals the relative changes between them, thereby identifying anomalies. Finally, a preset anomaly judgment condition is used: either the influence coefficient of a balance index is greater than or equal to a preset influence coefficient threshold, or the difference in the influence coefficients of balance indices is greater than or equal to a preset influence coefficient difference threshold, to determine whether an abnormal balance index exists among the multiple balance indices. In this process, the multi-dimensional analysis anomaly judgment method significantly improves the accuracy and reliability of anomaly detection, enabling the forklift control system to promptly detect anomalies caused by factors such as sensor errors, providing a reliable basis for subsequent correction of real-time sensor data corresponding to abnormal balance indices. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating a highly stable forklift lifting method provided in an embodiment of this application.

[0042] Figure 2 This is a structural schematic diagram of a highly stable forklift lifting system provided in an embodiment of this application.

[0043] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0044] Explanation of reference numerals in the attached drawings: 1. Acquisition module; 2. Processing module; 3. Transmission module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0045] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0046] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0047] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0048] In warehouse cargo handling, forklifts are commonly used tools to reduce manual labor intensity and improve operational efficiency. Common forklift types include stacker forklifts and boom lift forklifts, each suitable for handling different types of goods.

[0049] Stacker forklifts are primarily suitable for handling regularly shaped goods. Regularly shaped goods typically have a regular shape, uniform weight distribution, and standard dimensions, such as neatly stacked pallets or regularly shaped boxes. Relying on their precise handling and specially designed fork structure, stacker forklifts can efficiently and stably stack and handle regularly shaped goods, completing tasks systematically between warehouse shelves.

[0050] Flying jib forklifts are suitable for handling irregularly shaped goods. Irregularly shaped goods are usually irregular in shape and uneven in weight distribution, making them difficult to handle using conventional methods, such as large machinery parts and oddly shaped building materials. Flying jib forklifts are equipped with a flexible rotating boom that can be adjusted at multiple angles and directions according to the specific shape and position of the goods. A specialized clamping device securely holds the goods, thus enabling the efficient handling of irregularly shaped goods.

[0051] However, jib cranes face stability challenges during operation. When a jib crane lifts goods and moves them in the air, the goods will naturally sway due to their irregular shape and the inertia generated during lifting. This swaying significantly affects the overall stability of the forklift, thus requiring high stability during operation. To ensure the stability of jib cranes when handling goods, various types of sensors are typically installed on the forklift. These sensors are distributed across key parts of the forklift, monitoring the forces acting on the goods and the forklift itself in real time. For example, pressure sensors mounted on the boom can accurately sense the weight and force distribution of the goods; tilt sensors mounted on the forklift chassis can monitor the tilt angle and attitude changes of the forklift in real time. When the sensors detect a significant force shift in the goods or forklift, indicating that the forklift may be about to lose balance, the forklift's self-adjusting system responds quickly, automatically adjusting the lifting method, such as adjusting the boom angle, changing the lifting speed, or redistributing the center of gravity of the goods, to prevent dangerous situations such as forklift tipping over.

[0052] However, this method of ensuring stability by heavily relying on real-time sensor data has significant drawbacks. As precision electronic devices, sensors are susceptible to environmental factors (such as changes in temperature and humidity), mechanical vibration and shock, and the aging of their own components during long-term use. If the detection accuracy of a sensor deteriorates significantly, the erroneous data it provides can mislead the forklift's self-adjusting system. Adjustments made by the self-adjusting system based on erroneous information often fail to achieve the desired effect and may even exacerbate the forklift's swaying during cargo handling, causing it to lose balance and severely impacting operational safety and efficiency.

[0053] To address the aforementioned problems, this application provides a highly stable forklift lifting method, which is applied in a forklift control system, such as... Figure 1As shown, the method includes steps S101 to S105, which are as follows:

[0054] S101. Obtain real-time sensor data from the forklift.

[0055] In the above steps, sensors installed at various key locations receive real-time measurement data uploaded by the sensors. The real-time sensor data includes data from various types of sensors such as hydraulic pressure, boom strain, hydraulic oil temperature, sling tension, vibration acceleration, sound pressure level, ultrasonic signal, motor temperature, hydraulic oil temperature, and sound velocity measurement.

[0056] S102. Input the real-time sensor data into multiple physical field correlation models to obtain multiple equilibrium indices, where one physical field correlation model corresponds to one equilibrium index.

[0057] In the above steps, the physics-field correlation model can be understood as a mapping model. It obtains sensor data in another physical field by inputting sensor data from one physical field, which is then mapped to that data. For example, if the physics-field correlation model is a force-temperature correlation model, then the input force field sensor data (hydraulic pressure) outputs the temperature field sensor data (hydraulic oil temperature). The mapping relationship between hydraulic pressure and hydraulic oil temperature in this case constitutes the physics-field correlation model. Therefore, the various types of physics-field correlation models proposed in this application are related to the types of sensors installed on the boom crane. Specifically:

[0058] Real-time sensor data consists of various types of sensor data, such as stress sensor data, temperature sensor data, and vibration sensor data; stress sensor data may also include hydraulic pressure sensor data, boom strain sensor data, and sling tension sensor data; some of these sensor data are correlated, while others are not. This application constructs a physical field correlation model from the correlated sensor data to improve the accuracy of model predictions. Specifically:

[0059] Correlation analysis is performed on various types of sensor data, specifically calculating the data coupling degree between them. Since most of the correlations between these different types of sensor data are nonlinear, mutual information can be used for evaluation. Mutual information is an information parameter used to measure the dependency between two random variables. It takes into account the nonlinear relationship between variables; the larger the mutual information, the higher the degree of dependency between the two variables. Then, the data coupling degree between these different types of sensor data is statistically analyzed to construct a mapping relationship between different physical fields. For example, by calculating the data coupling degree, it is found that when the weight of the goods increases, the forklift hydraulic system needs to output more power to lift the goods, resulting in an increase in hydraulic temperature. That is, the increase in hydraulic temperature depends on the increase in hydraulic oil temperature. In addition, the increase in hydraulic pressure also leads to an increase in motor temperature; that is, the increase in motor temperature depends on the increase in hydraulic pressure. Based on these two dependencies, a physical field correlation model between the force field (hydraulic pressure) and the temperature field (hydraulic temperature, motor temperature) can be constructed.

[0060] To address this type of mapping relationship, this application constructs three physical field correlation models based on the three types of physical fields present during forklift operation: force field-temperature correlation model, force field-sound field correlation model, and temperature-sound field correlation model. Then, the mapping functions in the three physical field correlation models are curve fitted, and the data used for curve fitting are all from the historical sensor data of the forklift.

[0061] After obtaining a well-fitted physical field correlation model, multiple physical field correlation models are used to predict the real-time sensor data of the forklift, resulting in predicted sensor data from multiple physical field correlation models. Based on the predicted sensor data and the real-time sensor data, multiple balance indices are calculated. These balance indices characterize the stability of the forklift crane, and each index represents the stability of the forklift crane under different evaluation perspectives. The specific calculation method for the balance indices is as follows:

[0062] First, based on the data processing type corresponding to each of the multiple physical field association models, the real-time sensor data uploaded by the sensor is divided into multiple data groups. For example, for the force field-temperature association model, its data processing type is temperature data and force field data. At this time, its corresponding data group contains both force field data and temperature field data.

[0063] In multiple data sets, each contains unstable sensor data. If this unstable sensor data is input into the physics correlation model, there is a high probability that the predicted output will deviate further, resulting in inaccurate equilibrium indices calculated from the predicted output. Therefore, before inputting multiple data sets into their respective physics correlation models, the input data cannot be randomly determined. The data sets need to be divided into input and output data to ensure that the input data consists of highly stable sensor data, thereby reducing the possibility of further deviations in the predicted output. Specifically, when assessing the stability of the sensor data, historical data is obtained, and the variance or standard deviation is calculated based on this data. If the variance or standard deviation is small, the sensor data can be considered highly stable.

[0064] After determining the input and output data sets of multiple physical field association models, the sensor data from the multiple input data sets are input into their respective physical field association models to obtain multiple prediction data sets. At this time, the sensor data types in the prediction data sets are consistent with the sensor data types in the previously divided output data sets. For example, if the output data set contains hydraulic pressure sensor data, then the prediction data set will also contain hydraulic pressure sensor prediction data.

[0065] The data in the predicted data set represents the probable data predicted under normal sensor conditions. Then, residual analysis is performed on multiple predicted data sets and their corresponding output data sets to obtain the equilibrium index represented by multiple physical field correlation models. An example of residual analysis is illustrated below:

[0066] For the force-temperature field correlation model, the hydraulic pressure data actually measured by the hydraulic pressure sensor at different times are as follows: , , ,Will , , After inputting the force field-temperature field correlation model, the predicted hydraulic temperature is obtained. , , At this time, the actual hydraulic temperature is , , Then, the residual between the predicted hydraulic temperature and the actual hydraulic temperature is calculated to obtain... , , , = At this point, the balance index can be obtained by calculating the root mean square error of the residuals; the specific calculation formula is as follows:

[0067]

[0068] in, As a balance index, The number of residuals. It represents the residual.

[0069] In the above formula, the difference between the predicted value and the actual value is measured by calculating the square root of the sum of the squares of the differences between the predicted value and the actual value. This takes into account the error of each data point, thereby reflecting the degree of deviation between the model prediction result and the actual situation. The model prediction result reflects the balance state of the forklift when the sensor is in normal condition. Therefore, if the value is larger, the forklift is more unstable, and if the value is smaller, the forklift is more stable.

[0070] S103. Compare and analyze multiple balance indices to determine whether there are any abnormal balance indices among them.

[0071] In the above steps, since the residual analysis results of a single physical field correlation model are not accurate, this application further verifies multiple physical field correlation models based on a mutual verification mechanism to identify abnormal balance indices among multiple balance indices. It is understood that when a balance index is abnormal, its corresponding sensor data will also be abnormal. Since the balance indices corresponding to multiple physical field correlation models all characterize the stability of the forklift, if the real-time sensor data are all normal, the values ​​of multiple balance indices will be the same or very similar. Therefore, when judging abnormal balance indices among multiple balance indices, if the value of a certain balance index is significantly different from the other balance indices, then the balance index can be determined to be an abnormal balance index.

[0072] In one possible implementation, the above-mentioned discrimination method is only applicable to the case where a single balance index is abnormal. When the sensor data corresponding to multiple physical field correlation models are all abnormal, the calculated multiple balance indices will have no discernible pattern. This application addresses this situation by acquiring historical data of multiple balance indices, then performing trend analysis on the historical data of multiple balance indices to obtain multiple trend indicators, including variance, mean, and standard deviation. Then, the current balance index is input into the historical data and trend analysis is performed again to obtain multiple real-time trend indicators. By comparing the multiple real-time trend indicators with the multiple trend indicators calculated from the historical data, the influence coefficient of the current balance index on the multiple trend indicators is determined. If the influence coefficient of the balance index is greater than or equal to the preset influence coefficient, then the balance index is determined to be an abnormal balance index.

[0073] S104. If it exists, the real-time sensor data of the abnormal balance index is corrected to obtain corrected sensor data.

[0074] In the above steps, when a certain balance index is determined to be abnormal, it indicates that the result calculated by the physical field correlation model based on real-time sensor data may be biased. Therefore, it is necessary to correct the sensor data corresponding to the abnormal balance index, providing a reliable data basis for the subsequent adjustment of the lifting method by the self-adjusting system. Specifically:

[0075] First, the output data set corresponding to the abnormal balance index is input into the physical field correlation model for calculation to obtain the first data set. Through this reverse input calculation, the mapping input data set of the current output data set is determined. Then, the ratio of the first data set to the original input data set is calculated to obtain a ratio sequence, which quantifies the degree of difference between the original input data set and the actual input data set, reflecting the deviation in each data dimension. For example, if the ratio of a certain dimension in the ratio sequence deviates significantly from 1, it indicates that the data in that dimension deviates significantly from the true data. At this time, the sensor data corresponding to that dimension in the output data set is scaled and adjusted based on the ratio of that dimension to obtain the second data set, thereby correcting the output data that may have errors.

[0076] S105. Send the correction sensor data to the self-adjustment system to improve the accuracy of the self-adjustment system in adjusting the lifting method.

[0077] In the above steps, after receiving the corrected sensor data, the self-adjusting system replaces the original sensor data corresponding to the corrected sensor data, and then evaluates the stability of the forklift based on the corrected sensor data, thereby reducing the risk of forklift swaying or becoming unbalanced; in addition, the forklift control system also sends fault information to the fault response system to remind the operator to perform timely maintenance.

[0078] Reference Figure 2 This application also provides a highly stable forklift lifting system, which is a forklift control system. The forklift control system includes an acquisition module 1, a processing module 2, and a sending module 3, wherein:

[0079] Acquisition module 1 is used to acquire real-time sensor data of the forklift;

[0080] Processing module 2 is used to input real-time sensor data into multiple physical field association models to obtain multiple equilibrium indices, wherein one physical field association model corresponds to one equilibrium index; to compare and analyze the multiple equilibrium indices to determine whether there are any abnormal equilibrium indices; if so, to correct the real-time sensor data of the abnormal equilibrium indices to obtain corrected sensor data.

[0081] The sending module 3 is used to send the correction sensor data to the self-adjusting system to improve the accuracy of the self-adjusting system in adjusting the lifting mode.

[0082] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0083] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0084] The communication bus 302 is used to implement the connection and communication between these components.

[0085] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0086] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0087] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0088] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also optionally be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a highly stable forklift lifting method.

[0089] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a highly stable forklift lifting method. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0090] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as 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 service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0092] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0093] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0094] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0095] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0096] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A highly stable forklift lifting method, characterized in that, Applied to a forklift control system, the method includes: Acquire real-time sensor data from the forklift; The real-time sensor data is input into multiple physical field correlation models to obtain multiple balance indices, wherein one physical field correlation model corresponds to one balance index; A comparative analysis of multiple balance indices is conducted to determine whether there are any abnormal balance indices among the multiple balance indices. If it exists, the real-time sensor data of the abnormal balance index is corrected to obtain corrected sensor data; The corrected sensor data is sent to the self-adjusting system to improve the accuracy of the self-adjusting system in adjusting the lifting method, wherein... The real-time sensor data consists of various types of sensor data. Before inputting the real-time sensor data into multiple physical field correlation models to obtain multiple equilibrium indices, the process also includes: Calculate the data coupling degree between the various types of sensor data; The data coupling between various types of sensor data is used to construct multiple physical field correlation models, including force field-temperature correlation model, force field-sound field correlation model and temperature-sound field correlation model. The process of inputting the real-time sensor data into multiple physical field correlation models to obtain multiple equilibrium indices is as follows: Based on the data processing type corresponding to each of the multiple physical field correlation models, the real-time sensor data is divided into multiple data groups, wherein each data group corresponds to one physical field correlation model. Based on the historical data corresponding to each of the multiple data groups, determine the input data group and output data group corresponding to each of the multiple physical field correlation models; Multiple physical field correlation models process their respective input data sets to obtain multiple prediction data sets, wherein the data in the prediction data sets and the data in the output data sets are data with the same attribute. Residual analysis is performed on multiple output data sets and their corresponding prediction data sets to obtain the balance index of multiple physical field correlation models.

2. The method according to claim 1, characterized in that, A comparative analysis is performed on multiple balance indices to determine whether any abnormal balance indices exist among them. Specifically: Obtain historical data for multiple balance indices; Trend analysis is performed on historical data of multiple balance indices to obtain multiple trend indicators, including variance, mean, and standard deviation. Analyze the influence coefficients of the multiple balance indices on their respective multiple trend indicators; A preset abnormal balance index determination condition is adopted to determine whether there is an abnormal balance index among the multiple balance indices. The preset abnormal balance index determination condition is that the influence coefficient of the balance index is greater than or equal to a preset influence coefficient threshold.

3. The method according to claim 1, characterized in that, The process of correcting the real-time sensor data of the abnormal balance index to obtain corrected sensor data specifically includes: The output data set corresponding to the abnormal balance index is used as the input data of the physical field correlation model corresponding to the abnormal balance index to calculate the first data set. The ratio of the first data set to the input data set corresponding to the abnormal balance index is calculated to obtain a ratio sequence. Based on the ratio sequence, the output data group corresponding to the abnormal balance index is scaled and adjusted to obtain the second data group; The data in the second data set is used as the corrected sensor data.

4. The method according to claim 3, characterized in that, The step of sending the corrected sensor data to the self-adjustment system further includes: Send fault information to the fault response system to remind operators to perform maintenance.

5. A highly stable forklift lifting system, characterized in that, The system is a forklift control system, which includes an acquisition module (1), a processing module (2), and a sending module (3), wherein: The acquisition module (1) is used to acquire real-time sensor data of the forklift; The processing module (2) is used to input the real-time sensor data into multiple physical field correlation models to obtain multiple equilibrium indices, wherein one physical field correlation model corresponds to one equilibrium index; to perform comparative analysis on the multiple equilibrium indices to determine whether there is an abnormal equilibrium index among the multiple equilibrium indices; if there is, to correct the real-time sensor data of the abnormal equilibrium index to obtain corrected sensor data, wherein the real-time sensor data consists of multiple different types of sensor data, and before inputting the real-time sensor data into multiple physical field correlation models to obtain multiple equilibrium indices, it further includes: Calculate the data coupling degree between the various types of sensor data; The data coupling between various types of sensor data is used to construct multiple physical field correlation models, including force field-temperature correlation model, force field-sound field correlation model and temperature-sound field correlation model. The process of inputting the real-time sensor data into multiple physical field correlation models to obtain multiple equilibrium indices is as follows: Based on the data processing type corresponding to each of the multiple physical field correlation models, the real-time sensor data is divided into multiple data groups, wherein each data group corresponds to one physical field correlation model. Based on the historical data corresponding to each of the multiple data groups, determine the input data group and output data group corresponding to each of the multiple physical field correlation models; Multiple physical field correlation models process their respective input data sets to obtain multiple prediction data sets, wherein the data in the prediction data sets and the data in the output data sets are data with the same attribute. Residual analysis is performed on multiple output data sets and their corresponding prediction data sets to obtain the balance index of multiple physical field correlation models; The sending module (3) is used to send the correction sensor data to the self-adjusting system to improve the accuracy of the self-adjusting system in adjusting the lifting mode.

6. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 4.

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