High-stability forklift lifting method and system

Abstract

The invention discloses a high-stability forklift lifting method and system, and relates to the field of logistics carrying. The method is applied to a forklift control system and comprises the following steps: acquiring real-time sensor data of a forklift; the real-time sensor data are input into a plurality of physical field correlation models, a plurality of balance indexes are obtained, and one physical field correlation model corresponds to one balance index; comparing and analyzing the plurality of balance indexes, and determining whether an abnormal balance index exists in the plurality of balance indexes; if yes, correcting the real-time sensor data of the abnormal balance index to obtain corrected sensor data; and the corrected sensor data are sent to the self-adjusting system, so that the lifting mode adjusting accuracy of the self-adjusting system is improved. By implementing the technical scheme provided by the invention, the problem that the forklift shakes and is unbalanced due to errors of the detection precision of the sensor is solved.

Description

Technical Field

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

[0002] When handling goods in a warehouse, in order to reduce the manual burden, forklifts are often equipped as handling tools. Commonly used forklifts generally include stacking forklifts and knuckle boom forklifts. Among them, stacking forklifts are suitable for handling regular goods, while knuckle boom forklifts are suitable for handling irregular goods.

[0003] Currently, when a knuckle boom forklift is handling goods, a rotatable boom is used to grip the goods, and the goods will naturally sway in the air. At this time, there are relatively high requirements for the stability of the knuckle boom forklift. In order to maintain its stability, generally, various types of sensors are set to monitor the force conditions of the goods and the forklift itself in real time. When a serious force offset occurs, the forklift will automatically self-adjust the lifting method to prevent the forklift from tipping over.

[0004] However, because this kind of knuckle boom forklift highly depends on the real-time data of sensors, when there is a large error in the detection accuracy of a certain sensor, the adjustment effect of the self-adjustment system of the forklift is not ideal, resulting in the forklift swaying or losing balance when handling goods. Summary of the Invention

[0005] Aiming at the problem that the forklift sways and loses balance due to the detection accuracy error of the sensor, this application provides a forklift lifting method and system with high stability.

[0006] In a first aspect, this application provides a forklift lifting method with high stability, which is applied to a forklift control system. The method includes: Obtain the real-time sensor data of the forklift; Input the real-time sensor data into multiple physical field correlation models to obtain multiple balance indexes, where one physical field correlation model corresponds to one balance index; Conduct a comparative analysis on the multiple balance indexes to determine whether there is an abnormal balance index among the multiple balance indexes; If there is, correct the real-time sensor data of the abnormal balance index to obtain corrected sensor data; Send the corrected sensor data to the self-adjustment system to improve the adjustment accuracy of the self-adjustment system for the lifting method.

[0007] Optionally, the real-time sensor data is composed of various different types of sensor data. Before inputting the real-time sensor data into multiple physical field correlation models to obtain multiple balance indexes, it further includes: Calculate the data coupling degree among multiple types of the sensor data; Based on the data coupling degree among multiple types of the sensor data, construct multiple physical field correlation models, and the multiple physical field correlation models include a force field-temperature correlation model, a force field-sound field correlation model, and a temperature-sound field correlation model.

[0008] Optionally, inputting the real-time sensor data into multiple physical field correlation models to obtain multiple balance indexes, specifically: Divide the real-time sensor data into multiple data groups according to the data processing types corresponding to the multiple physical field correlation models, wherein one data group corresponds to one physical field correlation model; Determine the input data groups and output data groups corresponding to the multiple physical field correlation models according to the historical data corresponding to the multiple data groups; The multiple physical field correlation models process their respective input data groups to obtain multiple predicted data groups, wherein the data in the predicted data groups and the output data groups are of the same attribute; Perform residual analysis on the multiple output data groups and the corresponding predicted data groups to obtain the balance indexes of the multiple physical field correlation models.

[0009] Optionally, perform comparative analysis on the multiple balance indexes to determine whether there is an abnormal balance index among the multiple balance indexes, specifically: Obtain the historical data of the multiple balance indexes; Perform trend analysis on the historical data of the multiple balance indexes to obtain multiple trend indicators, and the multiple trend indicators include variance, mean, and standard deviation; Analyze the influence coefficients of the multiple balance indexes on their respective multiple trend indicators; Adopt a preset abnormal balance index determination condition to determine whether there is an abnormal balance index among the multiple balance indexes, and 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.

[0010] Optionally, correct the real-time sensor data of the abnormal balance index to obtain corrected sensor data, specifically including: Use the output data group corresponding to the abnormal balance index as the input data of the physical field correlation model corresponding to the abnormal balance index for calculation to obtain a first data group; Perform ratio calculation on the first data group and the input data group corresponding to the abnormal balance index to obtain a ratio sequence; According to the ratio sequence, scale and adjust the output data group corresponding to the abnormal balance index to obtain a second data group; Use the data in the second data group as the corrected sensor data.

[0011] Optionally, the sending the corrected sensor data to the self-regulating system specifically further includes: Send a fault message to the fault response system to remind the operator to perform maintenance.

[0012] In a second aspect, the present application provides a forklift lifting system with high stability. The system is a forklift control system, and the forklift control system includes an acquisition module, a processing module, and a sending module, where: The acquisition module is used to acquire real-time sensor data of the forklift; The processing module is used to input the real-time sensor data into a plurality of physical field correlation models to obtain a plurality of balance indexes. Among them, one physical field correlation model corresponds to one balance index; compare and analyze the plurality of balance indexes to determine whether there is an abnormal balance index among the plurality of balance indexes; if so, correct the real-time sensor data of the abnormal balance index to obtain corrected sensor data; The sending module is used to send the corrected sensor data to the self-regulating system to improve the accuracy of the self-regulating system in adjusting the lifting method.

[0013] In a third aspect, the present 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 so that the electronic device executes the method according to any one of the first aspects.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium. The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of the first aspects is executed.

[0015] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By obtaining the real-time sensor data of the forklift, comprehensively grasping various state information of the forklift during the lifting operation, and then inputting the real-time sensor data into multiple physical field correlation models to obtain multiple balance indices. The multiple physical field correlation models comprehensively consider the mutual relationships between different physical fields such as the force field, temperature field, and sound field, quantitatively evaluate the balance state of the forklift from multiple dimensions, and then conduct a comparative analysis of the multiple balance indices to determine whether there are abnormal balance indices. It uses the mutual verification method among the multiple balance indices to detect possible abnormal situations. If there are abnormal balance indices, the corresponding real-time sensor data is corrected to obtain corrected sensor data, thus effectively solving the problem of inaccurate adjustment caused by sensor detection accuracy errors and improving the reliability of the data. Finally, the corrected sensor data is sent to the self-regulating system, enabling the self-regulating system to adjust the lifting method based on accurate data, ensuring the stability of the forklift during the cargo handling process, reducing the risk of forklift shaking or imbalance, reducing the occurrence probability of safety accidents, and improving the efficiency and safety of the warehouse cargo handling operation.

[0016] 2. By obtaining the historical data of multiple balance indices, it provides a rich data basis for subsequent analysis. Then, trend analysis is performed on the historical data to obtain trend indicators such as variance, mean, and standard deviation. These indicators can clearly show the fluctuation law and central tendency of the balance indices. By analyzing the influence coefficients of multiple balance indices on their respective multiple trend indicators, we can deeply understand the role and influence degree of each balance index in the overall trend. Then, by comparing the difference in influence coefficients of multiple balance indicators, we can discover the relative change relationship between the balance indices, thereby determining the abnormal situations among the multiple balance indices. Finally, the preset abnormal balance index determination conditions are adopted, that is, the influence coefficient of the balance index is greater than or equal to the preset influence coefficient threshold, or the difference in influence coefficients of the balance index is greater than or equal to the preset influence coefficient difference threshold, to determine whether there are abnormal balance indices among the multiple balance indices. In this process, the multi-dimensional analysis abnormal determination method greatly improves the accuracy and reliability of abnormal detection, enabling the forklift control system to timely detect abnormal situations caused by factors such as sensor errors, providing a reliable basis for subsequent correction of the real-time sensor data corresponding to the abnormal balance index. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of a high-stability forklift lifting method provided by an embodiment of the present application.

[0018] Figure 2 is a structural diagram of a high-stability forklift lifting system provided by an embodiment of the present application.

[0019] Figure 3It is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0020] Explanation of reference numerals in the drawings: 1, acquisition module; 2, processing module; 3, sending module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed implementation manners

[0021] 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 accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0022] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0023] In the description of the embodiments of the present application, the meaning of the term "plurality" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0024] In the work of warehouse goods handling, in order to reduce the labor intensity and improve the operation efficiency, forklifts are commonly used handling tools. Common types of forklifts include stacking forklifts and jib cranes, and they are each suitable for different types of goods handling.

[0025] Stacking forklifts are mainly suitable for handling regular goods. Regular goods generally have regular shapes, uniform weight distributions and standard size specifications, such as neatly stacked pallet goods, boxes with regular shapes, etc. Stacking forklifts rely on their precise control performance and specially designed fork structures to be able to stack and handle regular goods efficiently and stably, and can complete operation tasks orderly between warehouse shelves.

[0026] The fly-arm forklift is suitable for handling irregular goods. Irregular goods usually have irregular shapes and uneven weight distributions, making it difficult to handle them in conventional ways, such as large mechanical equipment parts, building materials with special shapes, etc. The fly-arm forklift is equipped with a flexible rotating boom, which can be adjusted at multiple angles and in multiple directions according to the specific shape and position of the goods, and firmly clamp the goods through a special clamping device, thus realizing the effective handling of irregular goods.

[0027] However, the fly-arm forklift faces stability challenges during operation. When the fly-arm forklift uses the boom to lift the goods and move them in the air, due to the irregular shape of the goods themselves and the inertia generated during the lifting process, the goods will naturally sway. This sway will have a greater impact on the overall stability of the forklift, so the fly-arm forklift has high requirements for stability during operation. To ensure the stability of the fly-arm forklift when handling goods, various types of sensors are usually installed on the forklift. These sensors are distributed at key parts of the forklift and can monitor the force conditions of the goods and the forklift itself in real time. For example, the pressure sensor installed on the boom can accurately sense the weight and force distribution of the goods; the tilt sensor installed on the forklift chassis can monitor the tilt angle and attitude changes of the forklift in real time. When the sensor detects a serious force offset of the goods or the forklift, that is, when the forklift may be about to lose balance, the self-adjustment system of the forklift will quickly respond and automatically adjust 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 the forklift tipping over.

[0028] However, this method of ensuring stability that highly relies on real-time sensor data has obvious drawbacks. As a precision electronic device, the sensor is vulnerable to environmental factors (such as temperature and humidity changes), mechanical vibration impacts, and the aging of its own components during long-term use. Once the detection accuracy of a certain sensor has a large error, the wrong data it provides will mislead the self-adjustment system of the forklift. The adjustments made by the self-adjustment system based on the wrong information often cannot achieve the expected effect, and may even exacerbate the sway of the forklift when handling goods, resulting in the forklift losing balance, seriously affecting the safety and efficiency of the operation.

[0029] To solve the above problems, this application provides a forklift lifting method with high stability, which is applied to the forklift control system, such as Figure 1 shown, this method includes steps S101 to S105, and the above steps are as follows: S101. Obtain the real-time sensor data of the forklift.

[0030] In the above steps, real-time measurement data uploaded by sensors installed at various key positions is received in real time. Among them, 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 values.

[0031] S102. Input the real-time sensor data into multiple physical field correlation models to obtain multiple balance indices. Among them, one physical field correlation model corresponds to one balance index.

[0032] In the above steps, the physical field correlation model can be understood as a mapping model. By inputting sensor data in one physical field, it obtains sensor data in another physical field that is mapped to it. For example, if the physical field correlation model is a force field-temperature correlation model, at this time, input the force field sensor data of hydraulic pressure and output the temperature field sensor data of hydraulic oil temperature. The mapping relationship existing between hydraulic pressure and hydraulic oil temperature here is the physical field correlation model. Therefore, the types of multiple physical field correlation models proposed in this application are related to the types of sensors installed on the fly-boom forklift. Specifically: The real-time sensor data consists of data from various different types of sensors. For example, it may include stress sensor data, temperature sensor data, and vibration sensor data, etc.; and the stress sensor data may also include hydraulic pressure sensor data, boom strain sensor data, and sling tension sensor data, etc.; among these sensor data, some have an associated relationship, and some do not. This application constructs the sensor data with an associated relationship into a physical field correlation model to improve the accuracy of model prediction. Specifically: Perform correlation analysis on various different types of sensor data. Specifically, calculate the data coupling degree between various different types of sensor data. Since most of the various different types of sensor data are non-linearly related to each other, mutual information can be used for evaluation. Mutual information is an information parameter used to measure the dependence relationship between two random variables. It considers the non-linear relationship between variables. The greater the mutual information, the higher the degree of dependence between the two variables; at this time, statistically analyze the data coupling degree between various different types of sensor data and construct it into a mapping relationship between different physical fields. For example: By calculating the data coupling degree, it is found that when the cargo weight increases, the forklift hydraulic system needs to output more power to lift the cargo, resulting in an increase in hydraulic temperature, that is, the increase in hydraulic temperature depends on the increase in hydraulic oil temperature. In addition, when the hydraulic pressure increases, the motor temperature also increases, that is, the increase in motor temperature depends on the increase in hydraulic pressure. From these two dependence relationships, a physical field correlation model between the force field (hydraulic pressure) and the temperature field (hydraulic temperature, motor temperature) can be constructed.

[0033] For this form of mapping relationship, the present application constructs three physical field correlation models according to the three types of physical fields existing in the forklift operation process, namely, the force field-temperature correlation model, the force field-sound field correlation model, and the temperature-sound field correlation model. Then, curve fitting is performed on the mapping functions in the three physical field correlation models, and the data used for curve fitting all come from the historical sensor data of the forklift.

[0034] After obtaining the well-fitted physical field correlation models, multiple physical field correlation models are used to predict the real-time sensor data of the current forklift, and the predicted sensor data of multiple physical field correlation models are obtained. Then, based on the predicted sensor data and the real-time sensor data, multiple balance indices are calculated. The balance index characterizes the stability of the forklift crane, and multiple balance indices characterize the stability of the forklift crane from different evaluation angles. The specific calculation method of the balance index is as follows: First, according to the data processing types corresponding to the multiple physical field correlation models, the real-time sensor data uploaded by the sensor is divided into multiple data groups. For example, for the force field-temperature correlation model, its data processing type is temperature data and force field data. At this time, the corresponding data group contains both force field data and temperature field data. In each of the multiple data groups, there are unstable sensor data. At this time, if these unstable sensor data are input into the physical field correlation model, there is a great possibility that the predicted output result will deviate further, resulting in inaccurate balance indices calculated from the predicted output result. Therefore, before inputting the multiple data groups into their respective corresponding physical field correlation models, the input data of the physical field correlation model cannot be randomly determined. It is necessary to divide the multiple data groups into input data and output data to ensure that the input data is sensor data with high stability, thereby reducing the possibility of further deviation of the predicted output result. Among them, when judging the stability of sensor data, by obtaining the historical data of the sensor data, and then calculating the variance or standard deviation according to the historical data. If the variance or standard deviation is small, the sensor data can be determined as high-stability data.

[0035] After determining the input data groups and output data groups of the multiple physical field correlation models, the sensor data in the multiple input data groups are input into their respective corresponding physical field correlation models to obtain multiple predicted data groups. At this time, the sensor data types in the predicted data groups are the same as those in the output data groups divided before. For example, if the output data group contains hydraulic pressure sensor data, the predicted data group will also contain hydraulic pressure sensor prediction data.

[0036] The data in the prediction data set represents the likelihood data predicted by the sensor data under normal conditions. At this time, residual analysis is performed based on multiple prediction data sets and their respective corresponding output data sets to obtain the balance index characterized by multiple physical field correlation models. An example of residual analysis is as follows: For the force field-temperature field correlation model, the hydraulic pressure data actually measured by the hydraulic pressure sensor at different times is 、 、 . After inputting 、 、 into the force field-temperature field correlation model, the predicted hydraulic temperature is obtained. 、 . At this time, the actual hydraulic temperature is 、 、 . Then, calculate the residual between the predicted hydraulic temperature and the actual hydraulic temperature to obtain 、 、 . = . At this time, by calculating the root mean square error of the residuals, the balance index can be obtained. The specific calculation formula is: Wherein, is the balance index, is the number of residuals, is the residual.

[0037] 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, so as to consider the error of each data point, thereby reflecting the deviation degree between the model prediction result and the actual situation. And the model prediction result reflects the balance state of the forklift when the sensor is in a normal state. Therefore, if it is larger, the forklift is more unstable; if it is smaller, the forklift is more stable.

[0038] S103. Compare and analyze multiple balance indexes to determine whether there are abnormal balance indexes among the multiple balance indexes.

[0039] In the above steps, since the residual analysis result of the single physical field correlation model is not accurate, the present application further performs mutual verification on multiple physical field correlation models based on a mutual verification mechanism to identify abnormal balance indices among multiple balance indices. It can be understood that when a balance index is abnormal, there will also be abnormalities in the corresponding sensor data. Since the balance indices corresponding to multiple physical field correlation models all characterize the stability of the forklift, when the real-time sensor data are all normal, the values of multiple balance indices are the same or very close. Therefore, when the present application determines the abnormal balance index among multiple balance indices, if the value of a certain balance index is significantly different from other balance indices, it can be determined that this balance index is an abnormal balance index.

[0040] In a possible implementation manner, the above 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 all have abnormalities, the multiple balance indices calculated at this time have no rules to follow. For this situation, the present application obtains the historical data of multiple balance indices, and then performs trend analysis on the historical data of multiple balance indices to obtain multiple trend indicators. The multiple trend indicators include variance, mean, and standard deviation. Then, the current balance index is brought 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. At this time, if the influence coefficient of the balance index is greater than or equal to the preset influence coefficient, it is determined that this balance index is an abnormal balance index.

[0041] S104. If it exists, correct the real-time sensor data of the abnormal balance index to obtain corrected sensor data.

[0042] 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 the real-time sensor data may be deviated. Therefore, it is necessary to correct the sensor data corresponding to the abnormal balance index, providing a reliable data basis for the subsequent self-regulating system to adjust the lifting method. Specifically: First, input the output data group corresponding to the abnormal balance index into the physical field correlation model for calculation to obtain the first data group. Through this reverse input calculation, the mapped input data group of the current output data group is determined. Then, calculate the ratio of the first data group to the original input data group to obtain a ratio sequence, thereby quantifying the difference between the original input data group and the actual input data group and reflecting the deviation situation in each data dimension. For example, if the ratio of a certain dimension in the ratio sequence significantly deviates from 1, it indicates that the data in this dimension deviates greatly from the real data. At this time, based on the ratio of this dimension, the sensor data corresponding to this dimension in the output data group is scaled and adjusted to obtain the second data group, thereby correcting the output data that may have errors.

[0043] S105. Send the corrected sensor data to the self-regulating system to improve the accuracy of the self-regulating system in adjusting the lifting method.

[0044] In the above steps, after receiving the corrected sensor data, the self-regulating system replaces the original sensor data corresponding to the corrected sensor data, and then evaluates the stability of the forklift according to the corrected sensor data, thereby reducing the risk of the forklift shaking or losing balance. In addition, the forklift control system also sends a fault message to the fault response system to remind the operator to perform timely maintenance.

[0045] Refer to Figure 2 , this application also provides a forklift lifting system with high stability. This system is a forklift control system, and the forklift control system includes an acquisition module 1, a processing module 2, and a sending module 3, where: The acquisition module 1 is used to acquire the 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 balance indexes. Among them, one physical field correlation model corresponds to one balance index; conduct a comparative analysis on the multiple balance indexes to determine whether there is an abnormal balance index among the multiple balance indexes; if so, correct the real-time sensor data of the abnormal balance index to obtain the corrected sensor data; The sending module 3 is used to send the corrected sensor data to the self-regulating system to improve the accuracy of the self-regulating system in adjusting the lifting method.

[0046] It should be noted that: when the device provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is 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 belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be repeated here.

[0047] This application also discloses an electronic device. Referring to Figure 3 , Figure 3 is a schematic structural diagram 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.

[0048] Among them, the communication bus 302 is used to implement connection communication between these components.

[0049] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0050] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0051] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server using various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305, the processor 301 performs various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one of the following hardware forms: Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 301 may integrate one or several combinations of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0052] Among them, the memory 305 may include a Random Access Memory (RAM), or may also include a 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. Among them, 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 method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a high-stability forklift lifting method.

[0053] In Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user to obtain user input data; while the processor 301 can be used to call an application program for a high-stability forklift lifting method stored in the memory 305. When executed by one or more processors 301, the electronic device 300 is caused to execute 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 simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0054] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0055] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. 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 displayed or discussed coupling or direct coupling or communication connection between each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0056] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0057] In addition, the functional units in each embodiment of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0058] 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 memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this 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 to enable a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present application. And the aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks or optical discs that can store program codes.

[0059] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the specification and the disclosure of the practical truth.

[0060] The present application aims to cover any variations, uses or adaptive changes of the present disclosure. These variations, uses or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A high-stability forklift lifting method, characterized in that: Applied to a forklift control system, the method comprises: Get real-time sensor data from forklifts; Inputting the real-time sensor data into a plurality of physical field association models to obtain a plurality of balance indexes, wherein one physical field association model corresponds to one balance index; Comparatively analyzing the plurality of balance indexes to determine whether there is an abnormal balance index among the plurality of balance indexes; If so, 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 mode.

2. The method according to claim 1, characterized in that The real-time sensor data is composed of a plurality of different types of sensor data. Before the real-time sensor data is input into a plurality of physical field association models to obtain a plurality of balance indexes, the method further includes: Calculating data coupling degrees between a plurality of different types of sensor data; The data coupling degree between the multiple different types of sensor data is used to construct multiple physical field association models, and the multiple physical field association models include a force field-temperature association model, a force field-acoustic field association model and a temperature-acoustic field association model.

3. The method according to claim 1, characterized in that The real-time sensor data is input into a plurality of physical field association models to obtain a plurality of balance indexes, specifically: According to the data processing types corresponding to the multiple physical field association models, the real-time sensor data is divided into multiple data groups, wherein one data group corresponds to one physical field association model; Determine input data groups and output data groups corresponding to each of the plurality of physical field association models according to the historical data corresponding to the plurality of data groups; The plurality of physical field association models process respective input data groups to obtain a plurality of prediction data groups, wherein the data in the prediction data groups and the data in the output data groups are data of the same attributes; Residual analysis is performed on the plurality of output data groups and the corresponding prediction data groups to obtain balance indexes of the plurality of physical field association models.

4. The method according to claim 1, characterized in that Comparative analysis is performed on the multiple balance indexes to determine whether there is an abnormal balance index among the multiple balance indexes, specifically: Obtaining historical data of a plurality of said balance indices; Performing trend analysis on the historical data of the plurality of balance indexes to obtain a plurality of trend indicators, wherein the plurality of trend indicators include variance, mean and standard deviation; Analyze the influence coefficients of the plurality of balance indexes on the corresponding plurality of 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, and 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.

5. The method according to claim 3, characterized in that: The correcting the real-time sensor data of the abnormal balance index to obtain corrected sensor data specifically includes: The output data group corresponding to the abnormal balance index is used as input data of a physical field association model corresponding to the abnormal balance index to calculate and obtain a first data group; Calculate the ratio of the first data group to the input data group corresponding to the abnormal balance index to obtain a ratio sequence; According to the ratio sequence, scaling and adjusting the output data group corresponding to the abnormal balance index to obtain a second data group; The data in the second data group is used as the corrected sensor data.

6. The method according to claim 5, characterized in that The sending of the corrected sensor data to the self-regulating system specifically includes: Send fault information to the fault response system to alert operators to perform maintenance.

7. A high stability forklift lifting system, characterized in that: The system is a forklift control system, which comprises 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 a plurality of physical field association models to obtain a plurality of balance indexes, wherein one physical field association model corresponds to one balance index; compare and analyze the plurality of balance indexes to determine whether there is an abnormal balance index among the plurality of balance indexes; if there is, correct the real-time sensor data of the abnormal balance index to obtain corrected sensor data; The sending module (3) is used to send the corrected sensor data to the self-adjusting system, so as to improve the accuracy of the self-adjusting system in adjusting the lifting mode.

8. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 6 is performed.

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